Getting Personal: Omics of the Heart

Jane Ferguson: Hi there. Welcome to Getting Personal: Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson, and this is Episode 36 from February 2020. First up, we have "Identification of Circulating Proteins Associated with Blood Pressure Using Mendelian Randomization" from Sébastien Thériault, Guillaume Paré, and colleagues from McMaster University in Ontario. They set out to assess whether they could identify protein biomarkers of hypertension using a Mendelian randomization approach. They analyzed data from a genome-wide association study of 227 biomarkers which were profiled on a custom Luminex-based platform in over 4,000 diabetic or prediabetic participants of the origin trial. They constructed genetic predictors of each protein and then used these as instruments for Mendelian randomization. They obtained systolic and diastolic blood pressure measurements in almost 70,000 individuals, in addition to mean arterial pressure and pulse pressure in over 74,000 individuals, all European ancestry with GWAS data, as part of the International Consortium for Blood Pressure. Out of the 227 biomarkers tested, six of them were significantly associated with blood pressure traits by Mendelian randomization after correction for multiple testing. These included known biomarkers such as NT-proBNP, but also novel associations including urokinase-type plasminogen activator, adrenomedullin, interleukin-16, cellular fibronectin and insulin-like growth factor binding protein-3. They validated all of the associations apart from IL-16 in over 300,000 participants in UK Biobank. They probed associations with other cardiovascular risk markers and found that NT-proBNP associated with large artery atherosclerotic stroke, IGFBP3 associated with diabetes, and CFN associated with body mass index. This study identified novel biomarkers of blood pressure, which may be causal in hypertension. Further study of the underlying mechanisms is required to understand whether these could be useful therapeutic targets in hypertensive disease. The next paper comes from Sony Tuteja, Dan Rader, Jay Giri and colleagues from the University of Pennsylvania and it's entitled, "Prospective CYP2C19 Genotyping to Guide Antiplatelet Therapy Following Percutaneous Coronary Intervention: A Pragmatic Randomized Clinical Trial". They designed a pharmacode genomic trial to assess effects of CYP2C19 genotyping on antiplatelet therapy following PCI. Because loss of function alleles in CYP2C19 impair the effectiveness of clopidogrel, the team were interested in understanding whether knowledge of genotype status would affect prescribing in a clinical setting. They randomized 504 participants to genotype guided or usual care groups and assessed the rate of prasugrel or ticagrelor prescribing in place of clopidogrel within each arm. As a secondary outcome, they assessed whether prescribers adhere to genotype guided recommendations. Of genotyped individuals, 28% carried loss of function alleles. Within the genotype guided group overall, there was higher use of prasugrel or ticagrelor with these being prescribed to 30% of patients compared with only 21% in the usual care group. Within genotype individuals carrying loss of function alleles, 53% were started on prasugrel or ticagrelor, demonstrating some adherence to genotype guided recommendations. However, this also meant that 47% of people whose genotype suggested reduced effectiveness were nevertheless prescribed clopidogrel. This study highlights that even when genotype information is available, interventional cardiologists consider clinical factors such as disease presentation and may weight these more highly than genotype information when selecting antiplatelet therapy following PCI. The next paper is about "Deep Mutational Scan of an SCN5A Voltage Sensor and comes to us from Andrew Glazer, Dan Roden and colleagues from Vanderbilt University Medical Center. In this paper, the team aim to characterize the functional consequences of variants and the S4 voltage sensor of domain IV and the SCN5A gene using a high throughput method that they developed. SCN5A encodes the major voltage gated sodium channel in the heart and variants in SCN5A can cause multiple distinct genetic arrhythmia syndromes, including Brugada syndrome, long QT syndrome, atrial fibrillation, and dilated cardiomyopathy, and have been linked to sudden cardiac death. Because of this, there's considerable interest in understanding the functional and clinical consequences of different variants, but previous approaches were time consuming and results were often inconclusive with many variants being classified as uncertain significance. This newly developed deep mutational scanning approach allows for simultaneous assessment of the function of thousands of variants, making it much more efficient than low throughput patch clamping. The team assessed the function of 248 variants using a triple drug assay in HEK293T cells expressing each variant and they identified 40 putative gain of function and 33 putative loss of function variants. They successfully validated eight of nine of these by patch clamping data. Their study highlights the effectiveness of this deep mutational scanning approach for investigating variants in the cardiac sodium channel SCN5A gene and suggests that this may also be an effective approach for investigating putative disease variants and other ion channels. The next article is a research letter from Connor Emdin, Amit Khera, and colleagues from Mass General Hospital in the Broad Institute entitled, "Genome-Wide Polygenic Score and Cardiovascular Outcomes with Evacetrapib in Patients with High-Risk Vascular Disease: A Nested Case-Control Study". In this study, the team set out to probe the utility of using polygenic risk scores to predict the risk of major adverse cardiovascular events within individuals already known to be at high cardiovascular risk and to assess whether genetic scores can identify individuals who would benefit from the use of a CETP inhibitor such as Evacetrapib. They analyze data from the ACCELERATE trial which had tested Evacetrapib in a high risk population, and they found no effect on the incidents of major adverse cardiovascular events overall. Within a nested case-control sample of individuals experiencing major CVD events versus no events, they applied a polygenic risk score and found that the score predicted major cardiovascular events. Patients in the highest quintile of the risk score were at 60% higher risk of a major cardiovascular event than patients in the lowest quintile. There was no evidence of any interaction between the genetic risk score and Evacetrapib. These data suggest that genetic risk scores may have utility in identifying individuals at high risk events but may not have utility in identifying individuals who may derive more benefit from CETP inhibition. The next letter concerns "Epigenome-Wide Association Study Identifies a Novel DNA Methylation in Patients with Severe Aortic Valve Stenosis" and comes from Takahito Nasu, Mamoru Satoh, Makoto Sasaki and colleagues from Iwate Medical University in Japan. They were interested in understanding whether differences in DNA methylation could underlie the risk of aortic valve stenosis. They conducted an EWAS or epigenome-wide association study of peripheral blood mononuclear cells or PBMCs from 44 individuals with aortic stenosis and 44 disease free controls. They collected samples at baseline before a surgical intervention in the individuals with aortic stenosis and collected a follow-up sample one year later. They found that DNA methylation at a site on chromosome eight mapping to the TRIB1, or tribbles homolog one gene, was lower in the aortic stenosis group than in the controls at baseline. They replicated the association in an independent sample of 50 cases and 50 controls. TRIB1 MRNA levels were higher in the aortic stenosis group than the controls. When they looked at methylation status one year after aortic valve replacement or a transcatheter aortic valve implantation in patients with stenosis, they found that DNA methylation had increased in the cases while TRIB1 MRNA decreased. These data suggests that methylation status of TRIB1 and expression of TRIB1 may relate to the disease processes in aortic stenosis such as hemodynamic dysregulation and they can be reversed through surgical intervention. Changes in the methylation status of TRIB1 could be a novel biomarker of response to aortic valve replacement. The next letter comes from Niels Grote Beverborg, Pim van der Harst, and colleagues from University Medical Center Groningen and is entitled, "Genetically Determined High Levels of Iron Parameters Are Protective for Coronary Artery Disease". Their study addresses the conflicting hypotheses that high iron status is either deleterious or protective against cardiovascular disease. The team constructed genetic predictors of serum iron status using 11 previously identified snips and tested the genetic association with CAD in UK Biobank data from over 408,000 white participants. Overall, the genetic score for higher iron status was associated with protection against CAD. Ten of the snips suggested individual neutral or protective effects of higher iron status on CAD, while one iron increasing snip was associated with increased risk of disease but this was thought to be likely through an iron independent mechanism. Overall, these data suggest that a genetic predisposition to higher iron status does not increase risk of CAD and is actually protective against disease. The final letter is entitled, "Confidence Weighting for Robust Automated Measurements of Popliteal Vessel Wall MRI" and comes from Daniel Hippe, Jenq-Neng Hwang, and colleagues from the University of Washington. They were interested in assessing whether images of popliteal artery wall incidentally obtained during knee MRI as part of an osteoarthritis study could be used to study the development and progression of atherosclerosis. They developed an automated deep learning based algorithm to segment and quantify the popliteal artery wall in images obtained over 10 years in over 4,700 individuals. Their approach, which they named FRAPPE, or fully automated and robust analysis technique for popliteal artery evaluation, was able to reduce the average time required for segmentation analysis from four hours to eight minutes per image. They applied weights based on confidence for each segment to automatically improve the accuracy of aggregate measurements such as mean wall thickness or mean lumen area. Their data suggest that this automated method can rapidly generate useful information on atherosclerosis from MRI images obtained as part of other studies. When combined with other data. This approach may facilitate novel discovery in secondary analyses of existing studies in an efficient and cost effective way. And that's all for issue one of 2020. Come back next time for more of the latest papers from Circulation: Genomic and Precision Medicine. Speaker 2: This podcast is copyright American Heart Association 2020.

Jane Ferguson: Hi there. Welcome to the November 2019 issue of Getting Personal: Omics of the Heart. I'm Jane Ferguson. This is your podcast from Circulation: Genomic and Precision Medicine. Let's get started. First up from Eric Curruth, Christopher Haggerty and colleagues from Geisinger, we have a paper entitled, "Prevalence and Electronic Health Record-based Phenotype of Loss-of-function Genetic Variance in Arrhythmogenic Right Ventricular Cardiomyopathy-associated Genes". In this study, the team set out to understand the phenotypic consequences of variants and desmosome genes which has been associated with a arrhythmogenic right ventricular cardiomyopathy or ARVC. In clinical genetic testing, secondary findings of pathogenic or likely pathogenic variants in desmosome genes are recommended for clinical reporting. However, relatively little is known about the phenotypic consequences of these variants in a general clinical population. The team obtained whole exome sequencing data for over 61,000 individuals from the DiscovEHR cohort, part of the Geisinger MyCode Community Health Initiative. They then screened individuals for a putative loss of function variants in PKP2, DSC2, DSG2, and DSP. They evaluated ARVC diagnostic criteria using previously conducted ECG and echocardiograms and performed a phenom-wide association study or PHeWAS using EHR derived phenotypes. They found 140 people with an ARVC variant in one of the four genes, none of whom had an existing diagnosis of ARVC in the EHR. Further, there were no measurable differences in their ECG or echocardiogram findings compared with matched controls. There were also no associations with any heart disease phenotypes as assessed by PHeWAS. Overall, they report a prevalence of ARVC loss of function variants of around one in 435 in a general clinical population of predominantly European descent, but they did not find evidence that these variants associated with specific phenotypes. Thus, the clinical relevance of putative loss of function variants in desmosome genes still remains to be determined. The next paper is titled, "MRAS Variants Cause Cardiomyocyte Hypertrophy in Patients-specific iPSC-derived Cardiomyocytes". Additional evidence for MRS as a definitive Noonan syndrome susceptibility gene. This comes from Erin Higgins, Michael Ackerman, and colleagues from the Mayo Clinic. They were interested in understanding whether a recently identified Noonan syndrome variant in the MRS gene was necessary and sufficient to cause Noonan syndrome with cardiac hypertrophy. They generated induced pluripotent STEM cell or IPS C lines from patient derived cells carrying the glycine 23 veiling variant and MRS. In addition to isogenic control cells where the pathogenic variant was corrected back to wild-type using CRISPR CAS nine gene editing, they also created a disease model cell line by introducing the MRS variant into unrelated control cells. They then comprehensively characterized the phenotypes of the three cell lines using a variety of approaches including microscopy, immunofluorescence, single cell RNA seek, Western blot, qPCR, and live cell calcium imaging. Both the patient derived and the disease model IPS cardiomyocytes were larger than control cells and demonstrated changes in gene expression and intracellular pathway signaling characteristic of cardiac hypertrophy. The patient and disease model cells also displayed impaired calcium handling. Through in-vitro phenotyping, the team was able to demonstrate that the glycine 23 veiling MRS variant elicits a cardiac hypertrophy phenotype and IPSC cardiomyocytes, that strongly suggests that this variant is responsible for the observed Noonan syndrome associated cardiac hypertrophy in the effected patients. Next up is a review from Christopher Lee, Iftikhar Kullo, and colleagues also from the Mayo Clinic on "New Case Detection by Cascade Testing in Familial Hypercholesterolemia: A Systematic Review of the Literature". In this review they set out to systematically assess cascade testing programs for familial hypercholesterolemia, a disease which has a prevalence of about one and 250 but is estimated to be diagnosed in under 10% of patients. They identified published studies across the world which had conducted cascade testing and had reported the number of index cases and number of relatives tested and had also specified their methods of contacting relatives and testing. Using these criteria, they identified 10 studies for inclusion spanning several European countries, South Africa, New Zealand, Australia, and Brazil. The team calculated the proportion of relatives testing positive and the number of new cases per index case to facilitate comparison between studies. The mean number of programs was 242 with an average of 826 relatives per study. The average yield was 45%, ranging from 30 to 60%. the mean new cases per index case was 1.65 with a range of 0.22 to 8.0. Studies that use direct contact versus indirect contact for relatives and those that tested beyond first degree relatives had a greater yield. Further, active sample collection versus collection at clinic and using genetic testing versus biochemical testing was similarly associated with a higher yield. Despite differences between the United States and other countries, applying these strategies when establishing new cascade testing programs in the US may help promote success of these programs. Our next paper concerns "Randomization of Left-right Asymmetry and Congenital Heart Defects: The Role of DNAH5 in Humans and Mice". And this was conducted by Tabea Nöthe-Menchen, Heymut Omran, and colleagues from University Children's Hospital Muenster and the PCD study group. They were interested in understanding the relationship between congenital heart defects and laterality defects where internal organs are atypically positioned, such as in a mirror image as occurs in situs inversus. Ciliary dyskinesia is thought to play a role in situs inversus and the most frequently mutated gene in primary ciliary dyskinesia is DNAH5. The team does hypothesize that DNAH5 mutations may play a role in congenital heart disease. They characterized phenotypes in 132 patients with primary ciliary dyskinesia carrying disease causing DNAH5 mutations and also studied left right access establishment using a DNAH5 mutant mouse model. 66% of patients in their study had laterality defects, 88% of whom presented with situs inversus totalis and 6% presented with congenital heart disease. In the mass model, they observed immotile cilia, impaired flow with the left right organizer and randomization of nodal signaling with normal reversed or bilateral expression of key molecules. Their study thus demonstrates that mutation of DNAH5 is associated with congenital heart defects and they further highlight the ciliary mechanisms underlying defects and development of left right positioning during embryogenesis. Consideration of celiopathy related symptoms may be warranted when examining patients with congenital heart defects. Next up, we have a research letter from William Goodyear, Marco Perez and colleagues from Stanford University on "Broad Genetic Testing in a Clinical Setting Uncovers a High Prevalence of Titan Loss-of-Function Variants in Very Early-Onset Atrial Fibrillation". They were interested in understanding genetic determinants of atrial fibrillation and hypothesized that causal genetic variants would be enriched in individuals with very early onset AF, who are diagnosed with AF under the age of 45 with no other significant comorbidities. They identified 25 families comprising 23 unrelated patients with very early onset AF who had been evaluated and received genetic counseling at Stanford between 2014 and 2018. The mean age of AF diagnosis was 27.2 years and 76% of patients were male. 40% of patients had a first or second degree relative with very early onset AF, while 36% at first or second degree relatives with either early onset idiopathic cardiomyopathy, unexplained sudden death or strokes. 85% of patients were identified as having at least one rare variant in a cardiomyopathy associated gene. Six patients carried actionable pathogenic or likely pathogenic variants, four of which were in the titan gene. A subset of individuals were further evaluated by MRI or computed tomography on average 817 days after their first presentation and this revealed high rates of cardiac abnormalities including reduced ventricular function, chamber enlargement, borderline LV non compaction, or late gadolinium enhancement. These were not noted on echocardiogram at presentation, suggesting there may have been subsequent disease development or progression. Overall, this study highlights a high rate of familial disease and implicates an association between very early onset AF and rare variants in titan before the clinical onset of cardiomyopathy. The final letter this month comes from Yu Xia, Shaoxian Chen, Ping Li, Jian Zhuang and colleagues from Guangdong Academy of Medical Sciences and is entitled, "A Novel Mutation in MYH6 in Two Unrelated Chinese Han Families with Familial Atrial Septal Defect". They report on two unrelated families who presented with secundum atrial septal defect or ASD2. Whole exome sequencing revealed a novel variant and the MYH6 gene in both families, with the same variant present in all effected individuals but not in unaffected family members or unrelated controls. Because other variants in MYH6 have been reported to effect myofibril formation. The team studied the effect of the novel variant on the myofibrillar organization through transient transfection of CTC 12 cells. The MYH6 E526K variant was associated with a reduced striated I pattern and increased non-striated patterning. There was no effect on ATPase activity. Protein modeling suggested a variant of the effective position would reduce hydrogen bonding between alpha helices in the actin interface two region, increasing the volume of the cavity between the alpha helices and promoting the exposure of the alkaline side chain in the actin binding region. This could impair the interaction between the myosin motor head and actin. What these data suggests are that this novel MYH6 heterozygous variant may underlie ASD2 in two unrelated Chinese Han families by impairing myofibrillar organization. That's all for November 2019. Thank you for listening and I look forward to being back in December for the final episode of 2019. This podcast was brought to you by Circulation: Genomic and Precision Medicine and the American Heart Association council on genomic and precision medicine. This program is copyright American Heart Association 2019.

Jane Ferguson: Hi, everyone. Welcome to Getting Personal: Omics of the Heart, the monthly podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson, an assistant professor of medicine at Vanderbilt University Medical Center and an associate editor at CircGen. This is episode 32 from September 2019. Starting off this month, we have a paper on Genetic Mosaicism in Calmodulinopathy brought to us by Lisa Wren, Alfred George and colleagues from Northwestern University. They were interested in exploring the disease phenotypes that result from variation in the calmodulin genes, CALM1, 2 and 3. Mutations in calmodulin are known to associate with congenital arrhythmia, but the group hypothesized that there may be a broader range of phenotypes associated with calmodulin mutations. They report on four unrelated families all with pro bands exhibiting symptoms of prolonged QTC interval and documented ventricular arrhythmia. They conducted targeted exome sequencing in these individuals and in their families and identified mutations in calmodulin genes, including two novel mutations. In one family with multiple occurrences of intrauterine fetal demise, there was evidence for sematic mosaicism in both parents. The team studied the two novel mutations and found that the variants led to alterations in a calcium binding site resulting in impaired calcium binding. In human induced pluripotent stem cell derived cardiomyocytes, the team showed that the mutations impaired calcium dependent inactivation of L-type calcium channels and prolonged action potential duration. Their study not only demonstrates that mutations in calmodulins can cause dysregulation of L-type calcium channels, but that parental mosaicism maybe a factor in families with unexplained fetal arrhythmia or fetal demise. Our next paper come from Wan G Pang, Christiana Kartsonaki, Michael Holmes and Zing Min Chen from the University of Oxford and Peking University Health Science Center and is entitled Physical Activity, Sedentary Leisure Time, Circulating Metabolic Markers, and Risk of Major Vascular Diseases. In this study, the authors were interested in finding out whether circulating metabolites are associated with the relationship between physical inactivity or sedentary behavior and increased risk of cardiovascular disease. They identified over 3000 cases of incident CVD from the China Kadoorie Biobank and included over 1400 controls without CVD. They measured 225 different metabolites and baseline plasma samples using NMR. They used measures of self-reported physical activity and sedentary leisure time to associate physical activity with circulating metabolites, and then they ran analysis to relate the metabolites to CVD. Physical activity and sedentary leisure time were associated with over 100 metabolic markers. In general, the patterns of associations were similar using either activity measure. Physical activity was inversely related to very low and low density HDL particles, but positively related to large and very large HDL particle concentrations. Physical activity was also inversely associated with alanine, glucose, lactate, acetoacetate, and glycoprotein acetyls. When they examined the associations of these same metabolites with CVD, the directions were generally consistent with expectation, going on the premise that physical activity is protective, and that sedentary behavior is a risk factor for CVD. Their analyses suggests that metabolite markers could explain about 70% of the protective associations of physical activity and around 50% of the risk associations of sedentary leisure time with cardiovascular disease. Next up, we have a paper on Biallelic Variants in ASNA1, Encoding a Cytosolic Targeting Factor of Tail-Anchored Proteins, Cause Rapidly Progressive Pediatric Cardiomyopathy, coming from Judith Verhagen, Ingrid van de Laar and colleagues from University Medical Center Rotterdam. Their focus was on pediatric cardiomyopathies, which are both clinically and genetically heterogeneous. They had identified a family where two siblings had died during early infancy of rapidly progressive dilated cardiomyopathy. Through exome sequencing, they identified variants in the ASNA-1 gene and established that the children were compound heterozygotes for the variants. This highly conserved gene encodes an ATPase, which is required for post-translational membrane insertion of tail-anchored proteins. The team looked at expression of this protein in patient samples and then followed this up with functional analyses using cells and zebrafish. They found that one of the variants was predicted to result in a premature stop codon. In support of this, they observed decreased protein expression in myocardial tissue and skin fibroblasts. The other variant caused a missense mutation, and the team found that this resulted in protein misfolding, as well as less effective tail-anchored protein insertion. In zebrafish, knock out of the ASNA1 gene resulted in reduced cardiac contractility and early lethality, which could not be rescued by either version of the variant mRNA. This translational study highlights the importance of the ASNA1 gene as a cardiomyopathy susceptibility gene and further reveals the importance of tail-anchored membrane protein insertion pathways in cardiac function. The next paper from Karni Moshal, Gideon Koren and colleagues from Brown University is entitled LITAF Regulates Cardiac L-Type Calcium Channels by Modulating NEDD 4-1 Ubiquitin Ligase. In this paper, the authors report on the role of ubiquitination as a crucial component in cardiac ion channel turnover and action potential duration. Previous genome wide association studies of QT interval had identified snips in or near genes regulating protein ubiquitination, particularly the LITAF or lipopolysaccharide-induced tumor necrosis factor gene. Using zebrafish, the team performed optical mapping in hearts to identify calcium and found that knocked down of LITAF resulted in an increase in calcium transients. They studied intracellular calcium handling and rapid derived cardiomyocytes and found that over expression of LITAF caused a decrease in L-type calcium channel current and abundance of the L-type calcium channel alpha1c sub unit or Cava1c, whereas LITAF knocked down increased calcium channel current and Cava1c protein. LITAF downregulated total and surface pools of Cava1c via increased Cava1c ubiquitination and lysosomal degradation in tsA201 kidney cells. There was evidence of colocalization between LITAF and L-type calcium channel, or LTCC, in the tsA201 kidney cells and in cardiomyocytes. In the tsA201 cells, NEDD4-1 protein increased Cava1c ubiquitination, but a catalytically inactive form of NEDD4-1 had no effect. Cava1c ubiquitination was further increased by co-expressed LITAF NEDD4-1, but not the inactive version of NeNEDD4-1. NEDD4-1 knockdown abolished the negative effect of LITAF on L-type calcium channel current and Cava1c levels in three week old rapid cardiomyocytes. Taken together, these data show that LITAF acts as an adapter protein promoting NEDD4-1 mediated ubiquitination and subsequent degradation of LTCC, highlighting LITAF as a novel regulator of cardiac excitation. Rounding out this issue is a review on the Gut Microbiome and Response to Cardiovascular Drugs from Sony Tuteja and Jane Ferguson from the University of Pennsylvania and Vanderbilt University Medical Center. Since that last author is me, I'm sure I have a biased view of the importance of the topic, but the increasing awareness of the microbiome in every aspect of health has also led to increased awareness of the role of commensal microbiota in drug metabolism, including in the metabolism of drugs used to treat cardiovascular diseases. In this article, we aim to review what is currently known about how the gut microbiome interacts with cardiovascular drugs and to summarize some of the mechanisms whereby gut microbiota might affect drug metabolism. Early evidence suggests that the gut microbiome modulates response to statins and antihypertensive medications, but there may be many other drugs that are susceptible to interaction with microbiota. Drug metabolism by the gut microbiome can result in altered drug pharmacokinetics and pharmacodynamics or in the formation of toxic metabolites which can interfere with drug response. While we are still in a relatively early stage in this field, we suggest that a better understanding of the complex interactions of the gut microbiome, host factors and response to medications will be important for the development of novel precision therapeutics in cardiovascular disease prevention and treatment. That's all for the September issue of Circulation: Genomic and Precision Medicine. Come back next month for the next installment. Thanks for listening. This podcast was brought to you by Circulation: Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association 2019.

Jane Ferguson: Hi everyone. Welcome to Getting Personal: Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson and this is episode 30 from July 2019. First up we have a paper, the Subtype Specificity of Genetic Loci Associated With Stroke in 16664 cases and 32792 Controls, from Matthew Trailer and colleagues on behalf of the NINDS Stroke Genetics Network and the International Stroke Genetics Consortium. They were interested in understanding whether genetic loci previously found to be associated with stroke have distinct associations with stroke subtypes, specifically ischemic and hemorrhagic stroke. They compiled data sets through an international consortium to analyze 16664 stroke cases and 32792 controls, all of European ancestry. The cases were subtyped using two different stroke classification systems: the Trial of ORG 10172 in Acute Stroke Treatment, or TOAST system, and the Causative Classification of Stroke, or CCS system. They selected genetic loci for consideration based on previous association with stroke in general or stroke subtypes in the MEGASTROKE consortium, which had included a large number of the subjects included in the present study. They used a Bayesian multinomial logistic regression approach to evaluate the association of snips at each locus with stroke subtypes identified under the TOAST and CCS classifications, giving five different case groups compared with a set of controls. 16 loci were taken forward for further analysis. There were seven loci which associated with both ischemic and hemorrhagic strokes subtypes, four which clearly associated with either ischemic or hemorrhagic stroke, with the rest showing less consistent effects. One locus, EDNRA, showed opposite affects for ischemic and hemorrhagic stroke. Overall, the findings indicate a large degree of genetic heterogeneity, but some overlap, suggesting common underlying pathophysiological pathways in different stroke subtypes, potentially related to small vessel disease. More detailed phenotyping and further analysis in large samples is required to fully understand genetic mechanisms underlying the risk of different stroke subtypes. And, just to add, this paper was previously submitted to the pre-print server Bio Archive. We support open science and are always happy to consider papers that have been submitted to pre-print servers. So, if you have a particularly cool paper on Bio Archive that fits our scope, do feel free to send it our way. Next up, we have a paper from Fabiola del Greco, Cristian Pattaro, Peter Pramstaller, Alessandera Rossini, and colleagues, from Eurac Research Institute for Biomedicine. This paper, entitled Lipidomics, Atrial Conduction, and Body Mass Index, Evidence from Association, Mediation, and Mendelian Randomization Models, aims to investigate the mechanisms underlying associations between circulating lipids and atrial conduction. They used mass spectrometry measurement of 151 sphingo- and phospholipids in plasma or serum from individuals who had undergone electrocardiogram measurements to ascertain P-wave duration. They first looked for associations in 839 individuals from the micro islets in South Tyrol, or MICROS study, based in Italy, and replicated in 951 participants of the Orkney Complex Disease Study, ORCADES, based in Scotland. They identified and replicated an association between levels of phosphatidylcholine 38-3 and P-wave duration, which was independent of cholesterol, triglycerides, and glucose levels. However, the association was mediated by BMI, and suggested that increased BMI may cause both increased levels of PC38-3 and longer P-wave duration, suggesting a role for body mass in altered lipids in atrial electrical activity. The next paper is a research letter from Hana Bangash, Iftikhar Kullo, and colleagues from the Mayo Clinic on Use of Twitter to Promote Awareness of Familial Hypercholesterolemia. Scientists and health professionals are increasingly using Twitter to communicate. This team wondered whether organized awareness campaigns, including Twitter events like Tweetathons, really make a different. They analyzed Twitter activity related to familial hypercholesterolemia in September 2018, during national cholesterol education month, which included an international familial hypercholesterolemia awareness day and Tweetathon. They also analyzed tweets from August and October 2018, where there was no formal awareness campaign and compared the FH Twitter activity with that of colorectal cancer, which did not have any formal awareness campaigns at that time. In September, FH-related tweets increased by 152.9% compared to August, and then declined by over 58% in October. The topic reach for familial hypercholesterolemia was 11.1 million in August, and increased over 250% in September to 37.7 million. The reach declined by over 71% in October to just over 10 million. In comparison, the reach for colorectal cancer declined from 453 million in August to 300 million in September and then increased to 677 million in October, which happened to be breast cancer awareness month. These data suggest that awareness campaigns like national cholesterol education month do lead to an increase in Twitter activity. However, this increase isn't necessarily sustained during the following month, and it remains unclear whether Twitter activity actually translates into a wider awareness amongst providers or patients, which could translate into clinical benefits. Nonetheless, as the use of Twitter increases, this may be a promising avenue to promote awareness and to disseminate knowledge. And, of course, I have to take this opportunity to mention that Circulation: Genomic and Precision Medicine is on Twitter and you can follow us @Circ_Gen to keep up with what's going on at the journal. Next up, we have a letter entitled B-iallelic Mutations in NUP205 and NUP210 Are Associated with Abnormal Cardiac Left-Right Patterning from WeiCheng Chen, Yuan Zhang, Sunhu Yang, Xiangyu Zhou, and colleagues from Tongji University. They set out to understand the genetic underpinnings of cardiac left-right patterning and to probe why individuals with situs inversus totalis, or SIT, where the chest organs are in a complete mirror image to typical, have almost no symptoms or complications, while individuals with heterotaxy, who have abnormal organ arrangement that is not a mirror image, typically have severe phenotypes including congenital heart disease. They performed whole exome and whole genome sequencing in 61 family trios with SIT or heterotaxy and identified ballielic missense mutations in nucleoporins NUP205 and NUP210. Nucleoporins comprise the main components of the nuclear pore complex in eukaryotic cells. The team generated induced pluripotent sense cells from peripheral blood cells of an affected patient and a healthy control, and found that there were impairments in protein interactions in the variant cells, particularly interactions with another crucial nucleoporin, NUP93. In zebra fish, NUP205 knockdown resulted in left-right assymetry and defects in heart looping formation in a subset of fish embryos. Knockdown of both NUP205 and NUP93 resulted in impairments in cilia and human retinal pigment epithelial cells. Gene expression analysis revealed affects in known cilia genes NEC2 and NEC3. Overall, this study provides evidence that mutations in nucleoporins NUP205 and NUP210 may cause defects in cardiac left/right patterning, potentially through effects on ciliary function. This issue closes with a letter and response conversation around a recent article on missense mutations in the FLNC gene, causing familial restrictive cardiomyopathy. Hisham Ahamed and Muthiah Subramanian from Amrita Institute of Medical Scientists write to share a case of a woman presenting with features of heart failure and muscular weakness consistent with distal myopathy who was found to carry a deletion in exome 37 of the FLNC gene. This case adds to the previous evidence published by Alvaro Roldan Sofia and Julian Palomino-Doza in March 2019 in our journal, Highlighting Mutations in the FLNC Gene in Cardiomyopathy. That's all for this month. Come back in August for your roundup of the next issue. Thanks for listening! This podcast was brought to you by Circulation: Genomic and Precision Medicine, and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association, 2019.

Jane Ferguson: Hi, everyone. Welcome to Getting Personal: Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. It's May 2019, and this is episode 28. So let's see what papers we have in the journal this month. First up, a paper from Mengyao Yu, Nabila Bouatia-Naji and colleagues from the Inserm Cardiovascular Research Center in Paris, entitled GWAS-Driven Gene-set Analyses, Genetic and Functional Follow-Up Suggest Glis1 as a Susceptibility Gene for Mitral Valve Prolapse. In this paper, they set out to characterize the genetic contributions to mitral valve prolapse, or MVP, to better understand the biological mechanisms underlying disease. They applied the gene-set enrichment analysis for QWAS tool and the pathway enrichment tool DEPICT to existing GWAS for MVP in a French sample to identify gene sets associated with MVP. They find significant enrichment of genes involved in pathways of relevance to valve biology and enrichment for gene expression in tissues of relevance to cardiovascular disease. They zeroed in a Glis family zinc finger gene Glis1 with consistently strong pattern of evidence across the GWAS enrichment and transcription analyses. They replicated the association between Glis1 and MVP in a UK biobank sample. They found that Glis1 is expressed in valvular cells during embryonic development in mice, but is mostly absent at later times. They targeted two Glis1 orthologs in zebrafish and found that knockdown of Glis1 B was associated with a significant increase in the incidence of severe atrioventricular regurgitation. These data highlight Glis1 as a potential regulator of cardiac valve development with relevance for risk of mitral valve prolapse. Next up is a paper from Gina Peloso, Akihiro Namuro, Sek Kathiresan and colleagues from Boston University, Kanazawa University, and Mass General Hospital. In their paper, Rare Protein Truncating Variance in APOB, Lower LDL-C, and Protection Against Coronary Heart Disease, the team was interested in understanding whether protein truncating variance in APOB underlying familial hypobetalipoproteinemia confer any protection against coronary heart disease. They sequenced the APOB gene in 29 Japanese families with hypobetalipoproteinemia as well as in over 57,000 individuals, some with early onset CHD and some without CHD. They found that presence of an APOB truncating variant was associated with lower LDL cholesterol and lower triglycerides, and also with significantly lower risk for coronary heart disease. This study confirms that variance in APOB, leading to reduced LDL and triglycerides are also protective against coronary heart disease. : The next paper entitled Mortality Risk Associated with Truncating Founder Mutations in Titin comes to us from Mark Jansen, Dennis Dooijes, and colleagues from University Medical Center Utrecht. They analyzed the effect of titin truncating variance on mortality in Dutch families. Titin truncating variants are associated with dilated cardiomyopathy, but have a very variable penetrance. In this study, the authors looked at three titin truncating variants, established to be founder mutations, and traced the pedigrees back to 18th century ancestors. They looked at 61 individuals on the transmission line and 360 of their first-degree relatives. They find no evidence for excess mortality in variant carriers overall. However, when they restrict it to individuals over 60 years of age, they did find a significant difference in mortality, which was also observed in individuals born after 1965. What these data tell us is that these titin truncating variants have a relatively mild phenotype with effects on mortality only manifesting later in life in many carriers. Given increases in life expectancy over the past several decades, the prevalence of morbidity and mortality attributable to titin truncating variants may increase. Genetic screening may identify genotype-positive, phenotype-negative individuals who would benefit from preventative interventions. Continuing on the theme of genetic variance, we have a paper from John Giudicessi, Michael Ackerman, and colleagues from the Mayo Clinic, Assessment and Validation of a Phenotype-Enhanced Variant Classification Framework to Promote or Demote RYR2 Missense Variants of Uncertain Significance. In this paper, they aim to find a better way to classify variants of unknown significance, of VUS, in the RYR2 gene. Variants in this gene are commonly associated with catecholaminergic polymorphic ventricular tachycardia, or CPVT. They examined 72 distinct variants in 84 Mayo Clinic cases and find that 48% were classified as VUS under ACMG guidelines. The rate was similar in a second sample from the Netherlands, with 42% of variants originally classified as VUS. They developed a diagnostic scorecard to incorporate a pretest clinical probability of CPVT, which included various clinical criteria, including symptoms and stress test results. Application of the phenotype enhanced ACMG criteria brought the VUS rate down to 7% in Mayo Clinic and 9% in the Dutch samples. The majority of VUS were reclassified as likely pathogenic. This study highlights how incorporation of disease-specific phenotype information can help to improve variant classification and reduce the ambiguity of reporting variants of unknown significance. We also have a number of research letters in the journal this month. From Karine Ngoyen, Gilbert Habib, and coauthors from Marseilles, we have a paper entitled Whole Exome Sequencing Reveals a Large Genetic Heterogeneity and Revisits the Causes of Hypertrophic Cardiomyopathy, Experience of a Multicentric study of 200 French Patients. In this study, they examined the genetic contributions to hypertrophic cardiomyopathy, or HCM, in 200 individuals as part of the HYPERGEN study and compared the benefits of whole exome sequencing compared with targeted sequencing of candidates' sarcomeric genes. All subjects had HCM documented by echocardiography. In the whole exome sequencing data, they first looked for mutations within 167 genes known to be involved in cardiomyopathies or other hereditary diseases. Of these 167 virtual panel genes, they find variants in 101 genes. Following whole exome sequencing, over 87% of the patients had an identified pathogenic, or likely pathogenic, mutation compared with only 35% of patients who only had targeted sequencing of sarcomeric genes. This highlights the generic heterogeneity of HCM and suggests that whole exome sequencing has utility in identifying variants not covered by sarcomeric gene panels. The next letter is from Wouter Te Rijdt, Martin [Vandenberg] and colleagues from University Medical Center Groningen and states that [dissynchronopathy] can be a manifestation of heritable cardiomyopathy. They hypothesized that left bundle branch block, also designated as dissynchronopathy, may be a manifestation of familial cardiomyopathy. They analyzed patients from a database of cardiac resynchronization therapy and identified super-responders whose left ventricular dysfunction was normalized by therapy. They carried out targeted sequencing in 60 known cardiomyopathy genes in 16 of these super-responder individuals and identified several variants, including a pathogenic variant in troponin T in one individual and variants of unknown significance in nine individuals. Pedigree analysis identified multiple family members with dilated cardiomyopathy. This study highlights that dissynchronopathy can be a manifestation of DCM, but that affected individuals may still benefit from cardiac resynchronization therapy. The next letter entitled Targeted Long-Read RNA Sequencing Demonstrates Transcriptional Diversity Driven by Splice-Site Variation in MYBPC3 comes from Alexandra Dainis, Euan Ashley, and colleagues from Stanford University. They set out to understand whether transcriptome sequencing could improve the diagnostic yield over genome sequencing in patients with hypertrophic cardiomyopathy. In particular, they hypothesized that long-read sequencing would allow for identification of alternative splicing linked to disease variance. They used long-read RNA and DNA sequencing to target the MYBPC3 gene in an individual with severe HCM who carried a putative splice-site altering variant in the gene. They were able to obtain heart tissue for sequencing and included several HCM and control subjects in addition to the patient with the MYBPC3 variant. They identified several novel isoforms that were only present in the patient sample, as well as some additional isoforms, including retained introns, extended exons, and an additional cryptic exon, which would not have been predicted based on the DNA variant. While the effects on protein function is not known, the transcripts are predicted to be translated. This analysis highlights the effect of a rare variant on transcription of MYBPC3 and provides additional evidence to link the variant to disease. This is a really nice approach, which could be used to probe causality and mechanisms, not only for cardiovascular disease, but for other rare variants in many disease settings. We finish with a perspective piece from Nosheen Reza, Anjali Owens, and coauthors from the University of Pennsylvania entitled Good Intentions Gone Bad, The Dangers of Sponsored Personalized Genomics. They present a case of a 23-year-old woman who presented for genetic counseling and evaluation after discovering she carried a likely pathogenic MYH7 variant associated with cardiomyopathy. She had no significant medical history, but had participated in employer-sponsored genetic testing motivated to identify potential variants related to cancer given a family history of cancer. After receiving her results, she experienced considerable anxiety and stopped exercising out of fear of cardiac complications. She visited an ER after experiencing chest pain, something she had not experienced previously. There was no appropriate counseling available at her institution for her genetic test results, leading her to seek out the additional counseling. Thus, while she was initially motivated to complete genetic testing because her employer offered it free of change, she ended up incurring costs related to the followup evaluation and counseling. Ultimately, she had no significant clinical findings. Although the variant had been listed as likely pathogenic, other sources consider it to be of unknown significance. This story highlights the psychological and financial impact that genetic testing can have on individuals, particularly when carried out without any pretest counseling or accessible post-test support when variants are identified. Despite the considerable promise of personalized medicine, there are many complexities to be considered, particularly with direct-to-consumer testing and employer-sponsored testing. This perspective highlights the ethical considerations and urges caution to maintain the best interests of patients. That's all for this month. Thanks for listening. I look forward to bringing you more next month. This podcast was brought to you by Circulation Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association 2019.

Jane Ferguson: Hello, and welcome to episode 26 of Getting Personal: Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson. It's March 2019, and I'm ready to spring into this month's papers, and apparently make really bad seasonal related jokes. Sorry all. Okay, let's get started. First up, is a paper from Oren Akerborg, Rapolas Spalinskas, Sailendra Pradhananga, Pelin Sahlén and colleagues from the Royal Institute of Technology in Solna, Sweden entitled "High Resolution Regulatory Maps Connect Vascular Risk Variants to Disease Related Pathways." Their goal was to identify non-coding variants associated with coronary artery disease, particularly those with putative enhancers and to map these to changes in gene function. They generated genomic interaction maps using Hi-C chromosome confirmation capture, coupled with sequence capture in several cell types, including aortic and ethelial cells, smooth muscle cells and LPS stimulated THP-1 macrophages. They captured over 25,000 features and they additionally sequenced the cellular transcriptomes and looked at epigenetic signatures using chromatin immunoprecipitation. They looked at regions interacting with gene promoters and found significant enrichment for enhancer elements. Looking at variants previously implicated in genome-wide associated studies, they identified 727 variants with promoter interactions and they were able to assign potential target genes for 398 GWAS variants. In many cases, the gene associated with a particular variant was not the closest neighbor, highlighting the importance of considering chromatin lupane when assigning intergenic variants to a gene. They identified several variants that interacted with multiple promoters, influencing expression of several genes simultaneously. Overall, this paper is a great resource for the community and takes many of these GWAS hits to the next level in starting to understand their biological relevance. They have a lot of supplemental material available online so it's definitely worth checking that out and taking a look for your favorite non-coding variant or chromosomal region to see if you can get some more information on it. Next up, Pierrick Henneton, Michael Frank and colleagues from the Hopital Europeen Georges-Pompidou in Paris bring us "Accuracy of Clinical Diagnostic Criteria For Patients with Vascular Ehlers-Danlos Syndrome in a Tertiary Referral Center." The authors were interested in determining the accuracy of the diagnostic criteria used to select patients for genetic testing for suspected vascular Ehlers-Danlos syndrome. This is because, despite the Villefrench criteria being recommended for diagnosis, the accuracy of the diagnostic criteria was never formally tested. They selected 519 subjects, including 384 probands and 135 relatives who had been seen between 2001 and 2016. They assessed the sensitivity and specificity of the Villefrench classification. Almost 32% of tested individuals carried a pathogenic COL3A1 variant. The sensitivity of the Villefrench criteria was 79% with a negative predictor value of 87%. Symptomatic probands had the highest accuracy at 92% sensitivity and 95% negative predictive value. However, the specificity was just 60%. Applying revised diagnostic criteria from 2017, it was actually less accurate because even though there was an increase in specificity, the sensitivity was reduced. Overall diagnostic performance was worst in individuals under 25 and neither set of diagnostic classifications allowed for early clinical diagnosis in individuals without a family history. Our next paper is a Mendelian randomization analysis from Susanna Larsson, Stephen Burgess and colleagues from Uppsala University and the University of Cambridge. This paper entitled "Thyroid Function And Dysfunction In Relation to Sixteen Cardiovascular Diseases: A Mendelian Randomization Study" aims to understand how subclinical thyroid dysfunction relates to risk of cardiovascular diseases. They generated genetic predictors for thyroid stimulating hormone, or TSH, through a GWAS meta-analysis in over 72,000 individuals. They then analyzed the association of genetically predicted TSH with cardiovascular outcomes in large GWAS studies of atrial fibrillation, coronary artery disease, and ischemic stroke, and further assessed associations with phenotypes in the UK Biobank. They found genetically decreased TSH levels and hyperthyroidism were associated with increased risk of atrial fibrillation but not other tested phenotypes. Overall, these data support a causal role for TSH and thyroid dysfunction in atrial fibrillation but not in other cardiovascular diseases. The next paper is also a Mendelian randomization analysis from members of the same group, Susanna Larsson, Stephen Burgess and colleagues published "Resting Heart Rate and Cardiovascular Diseases: A Mendelian Randomization Analysis." In this letter, they describe a study of the relationship between genetically increased resting heart rate and cardiovascular diseases. They constructed genetic predictors of resting heart rate and similarly to the previous study, used that as an instrument to test for associations with coronary artery disease, atrial fibrillation, and ischemic stroke in the cardiogram, atrial fibrillation, and mega stroke consortia respectively. They also looked at 13 CVD outcomes in the UK Biobank. They found that genetically predicted heart rate was inversely associated with atrial fibrillation with suggestive evidence for an inverse association with ischemic, cardioembolic, and large artery stroke. The inverse association with AF was replicated in the UK Biobank, supporting previous reports linking resting heart rate to atrial fibrillation. Next up, we have a letter from Robyn Hylind, Dominic Abrams, and colleagues from Boston Children's Hospital. This study entitled "Phenotypic Characterization of Individuals with Variants in Cardiovascular Genes in the Absence of a Primary Cardiovascular Indication For Testing" describes their work to probe incidental findings for potential cardiovascular disease variants in individuals undergoing clinical genomic sequencing for non-cardiac indications. They included 33 individuals who had been referred as carrying variants that were indicated as being associated with cardiovascular disease in primary or secondary findings. The variants were reclassified using the 2015 ACMG guidelines, and then were compared to the original classification report obtained at the time of sequencing. Of 10 pathogenic or likely pathogenic variants, only four of these were actually considered pathogenic or likely pathogenic after reclassification under the 2015 ACMG criteria, and none of these were associated with a cardiac phenotype. None of the variants could be definitively linked to any cardiac phenotype. The costs ranged from $75 to over $3700 per subject with a cost per clinical cardiac finding estimated at almost $14,000. This study highlights the relatively high cost and low yield of investigating potential cardiovascular variants and prompts consideration of how to implement strategies to ensure that variant reporting maximizes clinical return but minimizes the financial, time, and psychological burdens inherent in lengthy follow-ups. The next paper is a clinical letter from Serwet Demirdas, Gerben Schaaf and colleagues from Erasmus University Rotterdam entitled "Delayed Diagnosis of Danon Disease in Patients Presenting with Isolated Cardiomyopathy." They report on a clinical case of a 14-year-old boy presenting with cardiac arrest due to ventricular fibrillation during exercise. Echocardiography and MRI showed cardiac concentric hypertrophy, particularly in the left ventricle. The boy's mother had died at age 31 after being diagnosed with peripartum dilated cardiomyopathy. Sequencing in the boy revealed a variant in the LAMP2 gene, known to be responsible for Danon disease, which typically presents as cardiomyopathy, skeletal myopathy, and intellectual disability. This same LAMP2 variant was found in preserved maternal tissue, but not in other family members. In this case, there was no evidence of muscle or intellectual abnormalities. However, sequencing had allowed for this diagnosis of Danon disease in the child and posthumously in his mother. This study demonstrates a utility of using extended gene panels in clinical sequencing to aid in diagnosis and to inform management of patients. The next letter is from Alvaro Roldan, Julian Palomino-Doza, Fernando Arribas and colleagues from University Hospital of the 12th of October in Madrid and is entitled "Missense Mutations in the FLNC Causing Familial Restrictive Cardiomyopathy: Growing Evidence." This report also highlights clinical cases. In this case, two individuals with variants in the filamin C, or FLNC gene. Two unrelated individuals presenting with restricting cardiomyopathy were sequenced and found to carry two different variants in the FLNC gene, one of which had not been previously reported. This expands the number of reported cases of filamin C mutations in restrictive cardiomyopathy and highlights the need for further study of the pathophysiology linking filamin C to cardiac function. Finally, we have some correspondence related to a previously published article. In the letter, Christopher Chung, Briana Davies, and Andrew Krahn comment on the recently published article from Jody Ingles on concealed arrhythmogenic right ventricular cardiomyopathy in sudden unexplained cardiac death events. In that paper earlier this year, they had reported on four cases of individuals presenting with cardiac arrest or sudden cardiac death, attributable to concealed arrhythmogenic right ventricular cardiomyopathy with underlying mutations in the plakophilin-2 gene. In the letter from Chung et al, they report similar findings where individuals may first experience electrical phenotypes before manifesting structurally detectable disease. Indeed, in their response to this letter, Ingles et al report identification of an additional case since publication of their original article. Taken together, this further strengthens the case for development of additional strategies to identify at risk individuals and predict and prevent disease events. That's all for the papers for March 2019. Go online to check them out and follow us on Twitter @Circ_Gen to see new papers as they are published online. Thanks for listening. Until next month everyone. This podcast was brought to you by Circulation Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association 2019.

Jane Ferguson: Hello, everyone. Welcome to Episode 23 of Getting Personal, Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. It's December 2018. I'm Jane Ferguson. So let's get started. This month I talked to Dr. Merlin Butler from Kansas University Medical Center about an interesting clinical case he described recently in the Journal of Pediatric Genetics, concerning cardiac presentations in a case of classic Ehlers-Danlos syndrome with COL5A1 mutations. Keep listening for that interview, but first, let's talk about the papers in this month's issue of the Journal. Our first paper, entitled "Effects of Genetic Variance Associated With Familial Hypercholesterolemia on LDL Cholesterol Levels and Cardiovascular Outcomes in the Million Veteran Program." Comes from Yan Sun, Peter Wilson and co-authors on behalf of the V.A. Million Veterans Program. They were interested in the relatively between variants in LDLR, APOB and PCSK9, and LDL cholesterol in the general population. Low-frequency variants in these genes have been identified to underlie the greatly elevated LDL cholesterol seen in cases of familial hypercholesterolemia, but the effects of the population level are unknown. Using data from the Million Veterans Program, the team analyzed the associations between putatively pathogenic variants and the maximum recorded LDL cholesterol level, as measured repeatedly over a 15-year period, in over 330,000 participants. They restricted analysis to variants that were present in at least 30 people and found that eight of the 16 variants tested were associated with significantly higher LDL cholesterol. Through phenome-wide association analysis, they found that carriers had a higher likelihood of a diagnosis of hypercholesterolemia or coronary heart disease, but not of other diagnoses. Even though individuals carrying risk variants generally reduce their LDL cholesterol through statin treatment, they still had residual risk, suggesting that even earlier initiation of treatment may be required in individuals with genetic risk of high HDL. Continuing the theme, the next paper comes from Laurens Reeskamp, Merel Hartgers, Kees Hovingh and colleagues from the University of Amsterdam, and is entitled, "A Deep Intronic Variant in LDLR in Familial Hypercholesterolemia: Time to Widen the Scope?" This team had encountered a family with familial hypercholesterolemia, who did not carry a coding mutation in LDLR, APOB or PCSK9, and they wanted to figure out what was causing the elevated LDL cholesterol in this family. They conducted whole-genome sequencing in nine family members, five affected and four unaffected. They found a variant in an intron in LDLR, which resulted in an insertion of 97 nucleotides, leading to a frame shift in premature stop codon in exon 15 of LDLR. They confirmed the disease segregation in a second family, and found a frequency of over 0.2% in additional FH cases without a confirmed mutation. This study highlights the need to consider more than just exons when looking for causal variants, particularly in families where no coding mutations are identified. Next up, from Kathryn Siewert and Ben Voight from University of Pennsylvania, a paper reporting that "Bivariate Genome-Wide Association Scan Identified 6 Novel Loci Associated With Lipid Levels and Coronary Artery Disease." This paper started with a premise that, because heritable plasma lipids are genetically linked to coronary artery disease, we would have greater power to detect variants contributing to both traits by conducting joint GWAS analysis, rather than independent analyses for lipids or coronary disease, as has been done traditionally. Using data from over 500,000 individuals for CAD and over 180,000 individuals from the Global Lipid Genetics Consortium, they conducted a bivariate GWAS and identified six previously unreported loci that associated with CAD and either triglycerides, LDL cholesterol or total cholesterol. Many of these loci also had signals for effects on gene expression of genes in the region, suggesting that these novel loci may affect lipid levels and CAD risk through modulation of gene expression. Interestingly, for some of the newly-identified loci, there were multiple potential regulatory targets, suggesting that these loci may affect lipids and CAD through separate mechanisms. Overall, for closely-linked traits such as lipids and CAD, this joint GWAS approach gives additional power to detect novel variants. The next article comes from Terry Solomon, John-Bjarne Hansen and colleagues from University of California-San Diego and the Arctic University of Norway. Their paper concerns the "Identification of Common and Rare Genetic Variation Associated With Plasma Protein Levels Using Whole-Exome Sequencing and Mass Spectrometry." They were interested in identifying genetic variants that associate with plasma protein levels, both to understand genetic regulation and to identify potential sources of bias, where a genetic variant affects the assay used to quantify the protein, without necessarily altering biological components of the protein. Using data from 165 participants of the Tromsø Study, they quantified 664 proteins in plasma by tandem mass tag mass spectrometry and genotypes by whole-exome sequencing. They identified 109 proteins or peptides associated with genotype, and of these identified 49 that appeared to be technical artifacts based on genotype data. Of the rest, many of the genetic variants affected protein level by modulation of RNA, but some appeared to directly affect protein metabolism. Their method of quantifying multiple peptides from each protein and sequencing exons allowed them to identify spurious associations that would often be missed, and highlights the large number of artifacts that could be present in protein quantitative trait locus studies. At the same time, they show that over half of the pQTLs are real, with genetic variants affecting circulating proteins through diverse mechanisms. Our last of the full-length original research articles also applied proteomics. "Proteomic Analysis of the Myocardium in Hypertrophic Obstructive Cardiomyopathy" comes from Caroline Coats, Perry Elliott and coauthors from University College, London. They obtained myocardial samples from 11 patients with hypertrophic cardiomyopathy and measured over 1500 proteins using label-free proteomic analysis. They compared protein expression to six control samples from healthy hearts. They identified 151 proteins that were differentially expressed in HCM hearts, compared with control, and they validated a subset of these using an additional 65 myocardial samples from healthy and diseased subjects. Of eight validated differentially expressed proteins, they represented pathways in metabolism, muscle contraction, calcium regulation and oxidative stress. Of particular interest, they highlighted lumican as a novel disease protein, and showed the potential of proteomics to identify mechanisms underlying HCM. We have two research letters this month, the first from Hisato Suzuki, Kenjiro Kosaki and coauthors from Keio University School of Medicine at Tokyo. It's titled, "Genomic Comparison With Supercentenarians Identifies RNF213 as a Risk Gene for Pulmonary Arterial Hypertension." In this letter, they were interested in identifying genetic variants underlying pulmonary arterial hypertension. They hypothesized that individuals with extremely long lifespan would be less likely to carry potentially pathogenic variants. They performed whole-exome sequencing in 76 individuals with PAH and compared them to 79 supercentenarians who had lived for over 110 years. They report variants in RNF213 and TMEM8A that were present in PAH but not in the controls, suggesting these genes may be important in the pathophysiology of PAH. The second research letter comes from Tessa Barrett, Jeffrey Berger and colleagues from New York University School of Medicine, and is entitled, "Whole-Blood Transcriptome Profiling Identifies Women With Myocardial Infarction With Nonobstructive Coronary Artery Disease: Findings From the American Heart Association Go Red for Women Strategically Focused Research Network." Most of the 750,000 acute MIs occurring in the U.S. each year are caused by obstructive coronary artery disease, but around 15% of the acute MIs occur in individuals whose arteries have less than 50% stenosis and are defined as unobstructed. These individuals are more likely to be female and of higher morbidity and mortality. In this AHSAFRM-funded project, the team sequenced whole-blood RNA from 32 women who presented with an MI with or without CAD, or controls. They report several thousand transcripts differing between groups on conducted pathway analysis, which highlighted several pathways, most notably estrogen signaling. This suggests that estrogen may be a novel component in MIs occurring in the absence of obstructive disease. We also have two clinical letters this month. The first, "Desmoplakin Variant-Associated Arrhythmogenic Cardiomyopathy Presenting as Acute Myocarditis," is brought to us by Kaitlyn Reichl, Chetan Shenoy and colleagues from University of Minnesota Medical School. They report a case of a 24-year-old man presenting with acute myocarditis, who was found to have a pathogenic variant in desmoplakin underlying arrhythmogenic cardiomyopathy, also present in his father and one brother. This case highlights myocarditis as a possible initial presentation of arrhythmogenic cardiomyopathy, which requires cardiac MRI and genetic testing for full evaluation. The second clinical letter comes from Judith Verhagen, Marja Wessels and co-authors from University Medical Center, Rotterdam, and is entitled, "Homozygous Truncating Variant in PKP2 Causes Hypoplastic Left Heart Syndrome." They report on a family with consanguineous parents, where two children were affected with left ventricular hypoplasia, leading to intrauterine death in one child and death at day 19 of life in a second child. Sequencing identified a variant in PKP2, which encodes plakophilin 2. Both parents were heterozygous for the mutation, and their affected children were homozygous for the mutation. This mutation resulted in disorganization of the sarcomere and affected localization of other proteins affecting gap junctions. The case highlights PKP2 variants as causal in hypoplastic left heart syndrome. Dr. Merlin Butler is a professor at Kansas University Medical Center and Director of their Division of Research and Genetics. Dr. Butler joined me to discuss an interesting case of Ehlers-Danlos Syndrome in a father and son, with heart failure in the father. This case is in press in the Journal of Pediatric Genetics, and the prepublication version is available online, published on the 13th of October 2018. We'll tweet out a link to that paper, if you're interested in viewing the full case, but here's Dr. Butler, who joined me to discuss it now. Dr. Butler: ... I'm a clinical geneticist here at University of Kansas Medical Center, and I see both adult and pediatric patients, but one of the more common reasons for referral to my adult side clinical genetic services is connective tissue disorders. And that's how we were involved with this particular family, a son and father, that led to my interest in looking at the question about genetics of cardiac transplantation of those patients that present for cardiology services because of heart failure and worked up and ultimately end up as a candidate for transplantation. And that transpired in this particular family, which the patient was a 13-year-old boy who was referred into the clinic because of connective tissue disorder. Actually the primary care wanted to rule out Ehlers-Danlos Syndrome. And so we evaluated the 13-year-old boy in the clinic setting, and then we ordered comprehensive connective tissue and next-generation DNA sequencing panel, and lo and behold, he had a mutation of the classical gene that causes classic Ehlers-Danlos, the collagen 5A1 gene. The gene variant was classified as unknown clinical significance, which is often the case as we know with this technology, next-generation sequencing. Regardless of the condition we're looking at, we find about 10% of time, the panel of tests, the panel of genes that come back that are tested. 10% of the time we find no variants, no spelling errors, no mutations. 10% of the time the results come back from the commercial laboratory ... these are clinic patients, so it's done in commercially-approved laboratories, clinically-approved laboratories ... and we find that about 10% is pathogenic, which means it's disease-causing. The gene variant or mutation has been reported before. There is information in the literature that we know that it causes disease, Ehlers-Danlos, whatever type. About 80% of the time, the results come back as unknown clinical significance, and this is related to connective tissue. You probably order a test in cardiology or any other service and you'll find the same area. Most of the variants come back as unknown. What is meant by that is they haven't been reported previously in the literature, and therefore we don't know ... They may be disease-causing, that particular change, but we don't know that. We as geneticists, we have to then figure out whether that gene variant is a mutation or background noise. So we go through a process by where we try to characterize that particular gene finding to see whether it could be causative in that particular patient we see, or if it looks like it's probably tolerated and is just background noise, and it has really probably no apparent phenotypic change resulting from that particular gene variant. So this particular gene variant that we found, the collagen 5A1, did meet the criteria. We looked for computer programs and silica prediction to see if it was tolerated or damaging. We looked at how common that gene variant is seen in the general population, looking at exact various types of genome databases at the laboratories used to search for that variant in the population that's been serviced by genetic services, to see how rare it is or how common it is. We also check to see if it's a missense change, missense variant that is, one amino acid got switched for a different amino acid. There are five classes of amino acids, so if they stay within the same class, that change one amino acid to the next probably doesn't have much meaning, but if it changes to an entirely different class, like positive to negative, hydrophilic to hydrophobic, that could make a big change at the protein translation level, and therefore impact on protein development and function. And then we looked to see if it's conserved in evolution. The laboratories that we use, they look at approximately 80 different animals, mammals, vertebrates, primates, non-mammal vertebrates, to see if that particular spelling change is conserved throughout evolution. If it is, if C is always that position 205 in the coding sequence of that gene throughout evolution, that means you need to have C at that position, not A, G or T, because that would be conserved and impact that we don't want to change that, because it's conserved through evolution. So those kind of criteria, how common it is in the population, how conserved it is, what the amino acid change might be and what the computer programs predict that change might relate to the function of the protein. So we used those criteria, found this gene variant, although it hadn't been reported before ... well, it hasn't been characterized as pathogenic. In this particular family, 13-year-old son and 55-year-old father, they both had the classical features of classic Ehlers-Danlos, so that gene variant, we know at this point is informative. Dr. Ferguson: That's a really helpful introduction to how you go about looking at variants and screening them and picking the ones of most importance. So you had this 13-year-old patient who came in and then you tested the patient, and then did you also test both parents? Other family members? Dr. Butler: Well, the mother was no longer in the loop, so the primary care, the pediatrician, referred this 13-year-old boy because of joint laxity. He had experienced multiple spontaneous knee dislocations, beginning around nine years of age. He was 13 when I saw him in clinic. He had a history of knee pain, generalized joint hypermobility, loose skin, excessive bruising and poor scarring. And he had that history coming in, and we certainly could identify those findings on this patient. In fact, we reported this patient in the literature. The title of the paper is "Classic Ehlers-Danlos Syndrome in a Son and Father with a Heart Transplant Performed in the Father," published in Journal of Pediatric Genetics, but during a genetics clinic visit, we assessed a hypermobility Beighton scale, that we used to determine the degree of hypermobility, hyperflexibility, and we recorded a score of eight out of nine. Nine is the maximum number. And what we use as kind of a cut-off, this score is five or more, five out of nine or more, then that would indicate that probably there is some kind of joint issues, connective tissue disorder in the way. He had no heart murmur detected, normal rate and rhythm, but a previous echocardiogram showing he had no valvular problems but he had aortic root dilation. He also had skin marbling, atrophic scars, particularly on the lower leg, and increased pigment secondary to easy bruising. He had asymmetry of the anterior body wall, pretty classical findings that we recognize in Ehlers-Danlos. Dr. Ferguson: So the reason we're talking to you about this is actually less related to the son, right? And then related to what you found in the father. Dr. Butler: The father, right. So the father was 55 years old when we saw him. So we did testing on the son, based on his examinations, and then we obtained DNA and we found out, had the sequencing. We found he had a gene variant of the collagen 5A1 gene. And the collagen 5A1 codes for collagen, low fibrils protein changes, and that's a classical finding we see in Ehlers-Danlos. So we then, on follow-up, we looked more closely at the father, based on what we found in the child, and the father is 55 years of age and he exhibited similar clinical features seen in his son, including stretchable, thin skin, poor scarring, hypermobile joints, with pain and easy bruising. He had a Beighton score of six out of nine, but due to multiple knee surgeries, we were really not able to able to assess his knee findings. And he had strabismus repair when he was like 12 years of age. He had surgery on his right knee due to frequent dislocations, and had bilateral foot surgeries due to flat feet, pes planus. He had a stroke at 37 years of age, but without hypertension. At 43 years of age he underwent a heart transplant because of heart failure with no known cause, such as infections or anatomical defects or metabolic problems seen. And at 54 years of age he had fusion of the lower vertebrae, correct complications, nerve compression, impacting ambulation. So he had multiple, multiple problems, and we did DNA testing on him. He also had the same gene variant of the collagen 5A1 gene, which causes classic Ehlers-Danlos Syndrome. Dr. Ferguson: Yeah, so he essentially had been undiagnosed his entire life, I guess. Dr. Butler: In his entire life, he just kind of lived with it. Obviously no one really picked it up because he had multiple, multiple orthopedic surgeries. Of course he had the cardiac transplant because of a very large heart size. They didn't really find out what had taken place with that. They didn't find any reason why he had heart failure. So, because of this connective tissue issue, I began to think more closely about this. Could somehow his cardiac transplantation due to no determined reason why he had heart failure, could that somehow be related to a connective tissue problem, such as classic Ehlers-Danlos? And classic Ehlers-Danlos is fairly common, about one in 20,000 people. As far as our concern in the field of genetics, one in 20,000 is common, because we see rare diseases. So one in 20,000 is common. There's like six different categories of Ehlers-Danlos in classic and hypermobile form, vascular form, but he had the clinical findings, he and his son, and he had mutation of a gene that causes classic Ehlers-Danlos. So the thrust of this communication is, could it be that there may be a group of individuals that are on a heart transplantation service, waiting to be transplanted, that might have a connective tissue disorder, such as Ehlers-Danlos or one of the other connective tissue disorders, that could be an issue and a causation of their cardiac issues? We know that there are around 70 genes being recognized that cause connective tissue, and these numbers increase all the time as we learn more about genetics and the capabilities of testing. There are over 130 recognized genes that are thought to play a role in hereditary cardiomyopathies and there are now thought to be over 230 genes that are commercially available in a comprehensive cardiovascular next-generation DNA panels, and several of those genes are collagen genes. So we know there are hundreds of genes that play a role with cardiac health, I guess. Disturbance of those genes, several of those could be connective tissue. Obviously there's others involved, too ... myopathies and conduction issues. But the question I would have, the focus is, could there be a group that would have a connective tissue? And why is that important? Well, not only do they have issues when it comes to these multiple surgical concerns, but they may have, obviously, concerns that might be related to complications of surgery. We know that connective tissue disorders, they have poor wound healing, scarring and other tissue involvement such as vascular anomalies, aneurysms. So they become ... whether it's for cardiac procedures or whether it's orthopedic, whatever ... they become poor candidates for surgical intervention, for surgical operation procedures, because of the complications of surgery. Connective tissue, poor wound healing, scarring. And because connective tissue is involved in not only the skin, but involves internal organs such as the vessels, where you're concerned about aneurysms and vascular anomalies, that could be playing a role. So there may be more complications related to the surgical procedures than your typical patient who undergoes heart transplantation. So I think that would be important to know, so I would encourage, for the cardiology services, for patients that are on these transplant care and services, to consider a comprehensive genetic DNA analysis to look at connective tissues, as well as other causations of cardiac disease. As I mentioned, there's over 200 different genes been recognized now on comprehensive DNA testing panels related to cardiac and connective tissue problems. So, I would encourage that patients that are on the transplant list, they should undergo a next-generation detailed comprehensive connective and cardiovascular panel ... they're certainly available in several laboratory settings ... that might help lead to not only the diagnosis of the cardiac issues, but might help in medical management and monitoring and the surveillance, as well as the surgical interventions and care following the surgical procedures might be taking place. Frequently have an arterial wall might be a little fragile and obviously clamping during surgical procedures for an extended period of time might cause some trauma, even to a normal artery, let alone an artery that might be disorganized because of connective tissue problem. Dr. Ferguson: Yeah. Dr. Butler: So those complications might occur as well, too, during the procedure or following the procedure. Even there may not be any aneurysms going in, there might be a weakness of the arterial wall at the clamp site that could lead to an aneurysm following the procedure, so it needs to be monitored. So I'm just bringing these to the medical attention that may or may not be out there, but I want to bring this to ... You know, there have been over 90,000 heart transplants been done since 1983, at least that many, and there's 23 million people worldwide that are affected with congestive heart failure, and that's about 7.5 million people in North America. Dr. Ferguson: Yeah. Dr. Butler: So it's out there. Some of these genetic conditions are rare, but collectively they're common. Ehlers-Danlos, one in 20,000, is probably considered rare, but yet it still is not rare to the person that has it. Dr. Ferguson: Right, right, and maybe enriched in these patient populations. So is this something you think that could be sort of found with more careful physical exams, or do you think that [crosstalk] genomic sequencing is sort of the best way to get at this? Dr. Butler: Well, I think that Beighton scale we just mentioned, the hypermobility scale, just to see if there's, you know, if it's pretty common. Most adults can't put their palms on the floor when they're standing up. Dr. Ferguson: I certainly can't. Dr. Butler: That is usually not gonna happen for multiple reasons. But maybe some of the cardiologists are, but those that aren't, maybe they should consider, just check for hyperflexibility in their adult patient. [crosstalk] Dr. Ferguson: Yeah, that seems like an easy [crosstalk] click-and-check, right? Dr. Butler: Right. There being loose skins, poor scarring. You can ask the patient, obviously. Easy bruisability and poor scars, and it's pretty obvious in these conditions. I mean, on a physical exam it jumps out at you, particularly the multiple scars and bruising on the lower extremities with the pigmented because of iron deposits. You'll see that pretty clear in the scarring issues. And they'll tell you, too. I mean, the patients, they know. "Oh, yeah. I'm very hyperflexible." So you just ask the question and the patients will tell you. They say yes, and then it might need further testing physically; that is, actually do the exam and see if they have, on this Beighton scale, what the hyperflexibility score looks like. And if it is positive ... what we consider positive is five and above, five out of nine ... then those would candidates for a comprehensive DNA testing, whether it's related to cardiomyopathies, but I think connective tissue collection genes. Like I say, there's roughly 70 of these genes out there now that we test for in the commercial clinical laboratory setting. That should be monitored, as well as adding other genes if need be. So I'd encourage that. Physical examination number one. If it's positive, then check into a DNA panel for these types of disorders. It could help long-term for the care and outcome of the patient. Dr. Ferguson: Yeah. I do think that's really important from the patient perspective and then, if more of these cases start being reported, I think it's very interesting also from the research perspective to find out what are the mechanisms that are potentially linking these mutations to cardiac disorders which have- [crosstalk] Dr. Butler: That's true, and also realize that a lot of these patients have hypotension, and that can lead to some complications before, during and after surgical intervention, too. Dr. Ferguson: Yeah. Dr. Butler: So that's important to realize. Dr. Ferguson: Very important. Yeah. So thank you for telling us about this interesting case and for raising this. I think it's an important issue and I'm sure a lot of the cardiologists and clinicians listening will start to look out for connective tissue disorders in their own patients. Dr. Butler: I think, first thing is just ask questions. Are you hyperflexible? And they'll tell you. It's something that is very obvious to the patient. It will be obvious to the physician once he or she puts their hands on the patient, examine the patient, they realize, "Oh, this patient really is quite hyperflexible, digits and arms and knees and elbows," et cetera, et cetera. But, just ask the question, are they hyperflexible? If they say no, then the connective tissue is lower. It still could be. There could still be some aneurysms, those kind of things going on because there's, like I say, there's 70 genes, and there's six types of Ehlers-Danlos, so there's many other conditions out there that kind of look like an Ehlers-Danlos, but they're not. They may have another gene involving protein that's related to connective tissue, but not in the Ehlers-Danlos group of disorders or genes. Still could play a role. Could be similar. They may [inaudible] aneurysms, and that's important to know before they get into the procedures, too. Dr. Ferguson: Yeah, really important, really interesting. Thank you so much for joining us. Thanks, everyone, for listening. And I wish you all the best for the holiday season, and a very happy new year. We're looking forward to bringing you lots more in 2019. This podcast was brought to you by Circulation: Genomic and Precision Medicine, and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association, 2018.

Jane Ferguson: Hello, welcome to Getting Personal: Omics of the Heart, Episode 22. This is a podcast from Circulation: Genomic and Precision Medicine, and the AHA Council on Genomic and Precision Medicine. I am Jane Ferguson and it's November 2018. Our first article comes from Carlos Vanoye, Alfred George and colleagues from Northwestern University Feinberg School of Medicine and is entitled, High Throughput Functional Evaluation of KCNQ1 Decrypts Variance of Unknown Significance. So a major growing problem in clinical genomics is that following the identification of a variant that is potentially linked to a disease phenotype, without further interrogation, it's really hard to make sense of the functional significance of that variant. Right now, the large number of variants of unknown significance lead to confusion for patients and clinicians alike. To allow for accurate diagnoses and the best treatment plans, we need a way to be able to screen variants to assess their function in a fast and cost-effective manner. In this paper, the authors decided to focus in the KCNQ1 gene, a cardiac ion channel, which can affect arrhythmias. They aim to assess whether a novel high-throughput functional evaluation strategy could identify functional mutations, as well as an in vitro electrophysiological approach. Which is effective, but expensive and time-consuming. Their approach capitalized on an existing automated electrophysiological recording platform that had originally had been developed for drug discovery essays. They selected 78 variants in KCNQ1 and assessed their function using the High-Throughput platform, which coupled high efficiency, cell electroporation with automated plain or patch clamp recording. They compared the results to traditional electrophysiological essays and find a high rate of concordance between the two methods. Overall, they were able to reclassify over 65% of the variants tested, with far greater efficiency than traditional methods. While this method will not work for all genes and phenotypes, the authors have demonstrated an efficient method for functional interrogation of variants. Which may greatly accelerate discovery and conditions such as Long QT or other congenital arrhythmias. The next paper, Nocturnal Atrial Fibrillation Caused by Mutations in KCND2 Encoding Poor Forming Alpha Subunit of the Cardiac KV 4.2 Potassium Channel, comes from Max Drabkin, Ohad Birk, and colleagues at Soroka University Medical Center in Israel. This paper also focuses on cardiac ion channels and the role of mutations in atrial fibrillation. In a family with early-onset peroxisomal AF across three generations, whole XM sequencing revealed a variant in KCND2 encoding the KV 4.2 Potassium Channel, which segregated consistent with autosomal dominant heredity. This variant resulted in a replacement of a conserved [inaudible] residue with an arginine. To investigate functional consequences of this novel variant, they conducted experiments in xenopos laevis oocytes and found that there is decreased voltage depended channel and activation and impaired formation of the KV 4.2 Homotetramer and the KV 4.2, KV 4.3 Heterotetramer. Overall, this study shows that a novel mutation in a conserved Protein kinase C Phosphorylation site within the KV 4.2 Potassium Channel underlies the phenotypes observed in a family of peroxisomal atrial fibrillation. The targeting Atrial KV 4.2 might be an effective therapeutic avenue. Next up, Michael Levin and Scott Damrauer and colleagues from the University of Pennsylvania published an article entitled, Genomic Risks Stratification Predicts All-Cause Mortality After Cardiac Catheterization. They were interested in understanding the utility of polygenic risk scores for disease prediction. They constructed a genome Y genetic risk score for CAD and applied it to individuals from the Penn Medicine Bio-bank who had undergone Coronary angiography and genotyping. They included over 139,000 variants for the 1,500 ancestry subjects who were included and classified them as high or low polygenic risk. Individuals who were classified as high polygenic risk were shown to have higher risk of All-Cause mortality than low polygenic risk individuals despite no differences in traditional risk factor profiles. This was particularly evident in individuals with high genetic risk but no evidence of angiographic CAD. Adding the polygenic risk score to a traditional risk assessment model was able to improve prediction of five year All-Cause mortality. Highlighting the utility of a polygenic score and underscoring traditional risk factors do not yet fully capture mortality risk. The next article entitled, "Bio-marker Glycoprotein Acetyls is Associated with the Risk of A Wide Spectrum of Incident Diseases and Stratifies Mortality Risk in Angiography Patients" comes from Johannes Kettunen, Scott Ritchie, Peter Würtz and colleagues from the University of Oulu Finland. GlycA is a circulating biomarker that reflects the amount of Glycated proteins in the circulation. It has been associated with cardiovascular disease, Type 2 Diabetes, and all-cause mortality. In this paper, the authors used electronic health record data from over 11,000 adults from the finish general population previously included in the "FINRISK" and "Dilgom" studies and they tested for a associations between GlycA and 468 different health outcomes over an 8-12 year follow up. They report new associations between GlycA and multiple conditions including incident alcoholic liver disease, chronic renal failure, glomerular diseases, chronic obstructive pulmonary disease, inflammatory polyarthric disease and hypertension. These associations held true even after adjusting for CRP suggesting that GlycA represents an independent biological contributor to inflammation and disease. Their findings highlight potential utility for GlycA as a biomarker of many diseases and underscore the importance future functional and mechanistic studies to understand how GlycA is linked to disease risk. Our last original research article entitled, "Tissue Specific Differential Expression of Novel Jeans and Long Intergenic Non-coding RNAs in Humans with Extreme Response to Endotoxic glycemia comes from Jane Ferguson, Murdock Riley, and colleagues from Vanderbilt University, Columbia University, and the University of Pennsylvania. That first author is none other than me, so I'm not unbiased reader of this particular manuscript, but I'd like to tell you a little bit about it anyway. We were interested in understanding the transcriptional changes that occur in tissues during acute inflammation. As part of the genetics of evoked responses to Niacin and Endotoxemia, or gene study, we recruited healthy individuals and performed an inpatient endotoxin challenge where we administered a low dose of LPS and looked at the systemic inflammatory response. Individuals vary greatly in the degree of their inflammatory response to LPS and we identified high and low responders, men and women, of African and European ancestry, who had responses in the top or bottom 10% for cytokines and fever. We conducted RNA seek and adipose tissue in 25 individuals and CD-14 positive monosites for 15 individuals in pre and two or four hours post LPS samples. We found that the differences in transcriptional response between high or low responders are mostly explained by magnitude rather than discrete sets of genes. So some core genes were altered similarly, in both groups, but overall the high responders mounted a large transcription of response to LPS or low responders rather than mounting an anti-inflammatory response actually just barely responded on the transcription level. We saw clear tissue specificity between manosites and adipose tissue we identified several long non-coding RNAs that were up or down regulated in response to LPS and validated these independent samples one of these link RNAs which we have now named Monosite LPs induced link RNA regulator vile six or Mahler Isle six, with highly regulated by LPs and monosites but not in adipose tissue. We [inaudible] THP-1 monosites and find a significant effect on iOS six expression suggesting that this is a novel link RNA that regulates Isle six expression in manosites potentially through a cd-86 dependent pathway. Overall our data revealed tissue specific transcriptional of changes that correlate with clinical inflammatory responses and highlight the role of specifically incarnate and inflammatory response. Next up is a research letter entitled "Reduced Sodium Current in Native Cardiomyocytes of a Regatta Syndrome Patient Associated with Beta Two Central Mutation" published by Constance Schmidt, Felix Wiedmann, Ibrahim El-Battrawy, Dierk Thomas, and co-authors from University Hospital Heidelberg. They obtained cardiomyocytes from a patient with Regatta Syndrome previous whole XM sequencing had implicated a variant in the Beta Two Syntrophin or "SNTB2" gene as potentially causal in this individual. Expression analysis showed lower SNTB2 expression and atrial tissue of the affected individual compared with controls. They performed electrophysiology on the Microcytes and found reduced peak sodium density and reduced late sodium current. They co-express wild type or mutant SNTB2 in heck 293 T cells and [inaudible] with the cardiac sodium channel NAV-1.5 and found a significant effect on binding which adversely affected sodium currents. This study nicely demonstrates the functional effect of this SNTB2 mutation underlying Regatta Syndrome in this patient. A second research letter comes from A.T. van den Hoven and Jolien Roos- Hesselink and colleagues from Erasmus University Medical Center in the Netherlands and is entitled "Aortic Dimensions and Clinical Outcome in Patients with SMAD three mutations, they were interested in understanding how the Aortic dilation comment individuals with SMAD three mutations compared to individuals with other syndrome and causes of Aortic dilation. In 28 patients with SMAD three mutations, there were significant growth in the Sinotubular Junction the ascending Aorta on the diaphragm over an average of 10 years of follow up at reads far higher population averages but lower than might be seen in other syndromes, such as [inaudible]. Intensive management and preventive surgery and many of the patients prevented any mortality in this group. Rounding out this issue is a clinical letter entitled "Concealed Arrhythmogenic Right Ventricular Cardiomyopathy in Sudden unexplained Cardiac Death events from Jodie Ingles, Chris Semsarian, and colleagues from the University of Sydney, Australia. They report on for clinical cases where individuals presented in early adulthood with unexplained cardiac arrest, which was later found to be attributable to mutations in the PKP2 gene. PKP2 or, Plakophilin 2, encodes an integral component of the Desmosome, which is important and Cell-Cell adhesion. Further PKP2 is involved in transcriptional activation of genes controlling intracellular calcium cycling. This gene has been implicated arrhythmogenic right ventricular cardiomyopathy in individuals with cardiac structural abnormalities. These four cases where unrelated individuals were all fans to have loss of function variants and PKP2 underlying sudden cardiac death or events, despite structurally normal hearts. This prompts questions on the clinical management of such cases of concealed ARVC. That's all from us for November, thanks to all of you out there listening. We'll be back in December for the final episode of 2018. This podcast was brought to you by Circulation Genomic and Precision Medicine and the American Heart Association Council on Genomic and Precision Medicine. This program is copyright American Heart Association 2018.

Jane Ferguson: Hi everyone. Welcome to episode 20 of Getting Personal Omics of the Heart, the podcast brought to you by the Circulation: Genomic and Precision Medicine Journal and the American Heart Association Council on Genomic and Precision Medicine. I'm Jane Ferguson from Vanderbilt University. It's September 2018 and let's dive straight into the papers from this month's issue of Circulation: Genomic and Precision Medicine. We're starting off with some pharmacogenomics. Bruce Peyser, Deepak Voora and colleagues from Duke University published an article entitled, "Effects of Delivering SLCO1B1 Pharmacogenetic Information in Randomized Trial and Observational Settings." Although statins are generally well tolerated, 5-15% of patients taking statins for LDL lowering and cardiovascular protection end up developing statin associated muscular symptoms. Because onset of muscular symptoms associated with discontinuing statin use, as well as increased cardiovascular morbidity, there is a clear need to identify ways to prevent or reduce symptoms in these people. Variants affecting statin related myopathy have previously been discovered through GWAS, including a variant in the SLCO1B1 gene, which also has been shown to relate to statin myalgia and discontinuation of statin use. The risks appear to be greatest with simvastatin, indicating the people at risk of muscle complications may do better on either low-dose Simvastatin or another statin. However, there's still some uncertainty surrounding the risks and benefits of various statins as they pertain to risk of muscular symptoms. The authors have previously shown that pharmacogenetics testing led to increased number of people reporting statin use, but effects of pharmacogenetic testing on adherence, prescribing, and LDL cholesterol had never been tested in a randomized control trial. In this study, they randomized 159 participants to either genotype informed statin therapy or usual care, and then followed them for months to eight months. 25% of participants were carriers of the SLCO1B1 star five genotype. The authors found that statin adherence was similar in both groups, but gene type guided therapy resulted in more new statin prescriptions and significantly lower LDL cholesterol at three months, and levels that were lower but no longer significantly different at eight months. In individual's randomized to usual care who then crossed over to genotype informed therapy after the trial period ended, there was an additional decrease in LDL cholesterol. Overall, genotype informed statin therapy led to an increase in re-initiation of statins and decreases in LDL cholesterol, but did not appear to affect adherence. The authors also examined the effects of commercial genetic testing for SLCO1B1 variants in an observational setting by looking at over 92000 individuals with data available in the EHR. They found the people who receive genetic testing results had a larger drop in LDL cholesterol compared to untested controls. Overall, the study indicates that carriers of the SLCO1B1 risk variant may benefit from genotype informed statin therapy, while for non-carriers receiving their results may has limited effects. If you want to read more on this, Sony Tuteja and Dan Rader from UPenn wrote an editorial to accompany this article, which was published in the same issue. We're staying on the topic of statins and LDL for our next paper. This article comes from Akinyemi Oni-Orisan, and Neil Risch and colleagues from the University of California and is entitled, "Characterization of Statin Low-Density Lipoprotein Cholesterol Dose-Response Utilizing Electronic Health Records in a Large Population-Based Cohort." They were interested in understanding what determines variation in statin induced LDL reduction, particularly the genetic component, and they used a large EHR derived data set, the Kaiser Permanente Genetic Epidemiology research on adult health and aging cohort to address this important question. An EHR dataset does have intrinsic limitations, but also has some clear strengths, not only as a readily available and cost-effective data source for large sample sizes, but also because it reflects real world clinical care in diverse individuals, which is not always well represented within the selective constraints of a randomized trial. There were over 33000 individuals who met their inclusion criteria. To account for differences in potency between different statins and doses, the authors generated a defined daily dose value, with one defined daily dose equal to 40 milligrams per day of Lovastatin. The slope of the dose response was similar across statin types and across different sex and race or ethnicity groups. But there were differences by statin type in the response independent of dose, as well as differences in absolute responses by sex, age, race, smoking, and diabetes. Based on these differences, the authors revised the defined daily doses and they highlight how previously defined equivalencies between different statins may not be accurate. They found that individuals with East Asian ancestry had an enhanced response to therapy compared with individuals of European ancestry. The authors identified related individuals within the data set and the estimated heritability of statin response using parent-offspring and sibling pairs. They found only modest heritability, indicating that non-genetic factors may be more important in determining variability in statin response. Overall, this large single cohort study adds to our knowledge on determinants of statin response and raises further questions on the relative effects of different statins and doses within patient subgroups. Okay, so now let's talk about GWAS and Athero. Sander van der Lann, Paul de Bakker, Gerard Pasterkamp and coauthors from University Medical Center Utrecht published a paper entitled, "Genetic Susceptibility Loci for Cardiovascular Disease and Their Impact on Atherosclerotic Plaques." Over the past decade, genome-wide association studies in large cohorts have been very successful in identifying cardiovascular risk loci. However, relating these to subclinical disease or two mechanisms has been more challenging. The authors were interested in understanding whether established GWAS loci for stroke and coronary disease are associated with characteristics of atherosclerotic plaque, the idea being that some of the risk loci may alter disease risk by determining the development and stability of plaque. They identified seven plaque characteristics to study and histological samples, including intraplaque fat, collagen content, smooth muscle cell percentage, macrophage percentage, calcification, intraplaque hemorrhage, and intraplaque vessel density. They selected 61 known loci and examined association of those SNiPA with black phenotypes in over 1400 specimens from the athero express biobank study. Out of the 61 loci, 21 were associated with some black phenotype compared with zero of five negative control loci, which were chosen as established GWAS loci for bipolar disorder, which, presumably, should share limited mechanistic etiology with plaque. They used the software package VEGAS to run gene-based analyses. They also assessed SNiPA relationships with gene expression and methylation in multiple tissues derived from two independence Swedish biobanks, which included atherosclerotic arterial wall, internal mammary artery, liver, subcutaneous fat, skeletal muscle, visceral fat, and fasting whole blood. One CAD locus on chromosome 7q22 that survived correction for multiple testing was associated with intraplaque fat, and was also an EQTL for expression of several genes across multiple tissues. In addition, it was also a methylation QTL. The authors focused on this locus and looked at correlation of expression within the LDL receptor and noted associations with HDL and LDL cholesterol in the global lipids genetics contortion data, which suggests that this locus may have a role in the metabolism. At this locus, the HBP1 gene expressed foam cells may be an interesting candidate as a causal gene in determining plaque-lipid accumulation and cardiovascular risk. So next up, we have a paper that is also about athero and is coauthored by many of the same group as did that previous study. So yeah, this group's productivity is kind of making the rest of us look bad this month. So Martin Siemelink, Sander van de Lann, and Gerard Pasterkamp and their colleagues published, "Smoking is Associated to DNA Methylation in Atherosclerotic Carotid Lesions." Okay. So I think one of the few things we can all definitely agree on is that smoking is bad. But, does smoking exert any of its cardiovascular damage by altering within atherosclerotic plaques? That's the question this group set out to answer. They carried out a two-stage epigenome-wide association study, or EWAS, with discovery and replication of differentially methylated loci with tobacco smoking within carotid arteriosclerotic plaques of a total of 664 patients undergoing carotid endarterectomy and enrolled in the arthero-expressed biobanks study. In discovery, they found 10 CpG loci within six genes that associated with smoking. Four of the CpG loci replicated. These four loci mapping within six genes showed reduced methylation in current smokers compared with former or never smokers. However, there was no difference in specific plaque characteristics based on methylation at any of the four loci. There was also no significant difference in plaque gene expression at these loci based on smoking status. However, a SNiPA at a nearby locus located in the 3' UTR of the PLEKHGB4 Gene was associated with methylation at AHRR, and was a [inaudible 00:09:58] QTL for PLEKHGB4 of expression but not a AHRR expression. The authors speculate that PLEKHGB4 may co-regulate AHRR expression. The authors also examined blood methylation in a subset of the same subjects, and they were able to replicate previously identified CPG sites associated with smoking. This is a really complex area, and it's hard to identify mechanisms and causality from these multiple layers of data, but the authors demonstrate the importance of using disease relevant tissues to start to understand how environmental factors interact with genetics and other underlying physiology to modify methylation and function within the vasculature. Our final full-length research paper this issue from Brian Byrd and colleagues Michigan, is actually the subject of our interview today. So I won't go into too much detail on it right now, but keep listening for an interview with Brian about their paper, "Human Urinary mRNA as a Biomarker of Cardiovascular Disease: A Proof-of-Principle Study of Sodium-Loading in Prehypertension." Our review article this month is about the "Dawn of Epitranscriptomic Medicine" from Konstantinos Stellos from Newcastle University and Aikaterini Gatsiou from Goethe-Universität Frankfurt. In this paper, they're taking us to the next level beyond just RNA, but towards RNA epigenetics. Given the large number of possible modifications that can and are made to RNA during RNA name metabolism, there's huge potential to gain a new biological and mechanistic understanding by studying the RNA epitranscriptome. I think we'll ignore this at our peril. So if you need to catch up on this new field, this comprehensive review will get you right up to speed. Moving on, our research letters are short format papers that allow authors to present focused results. These are also a great avenue to submit findings from replication studies that might not necessitate a full-length paper. So if you have some data from a replication study that you've been procrastinating writing up, a short research letter is a great format to consider. This month, Bertrand Favre, Luca Borradori and coauthors from Bern University Hospital published a letter entitled, "Desmoplakin Gene Variants and Risk for Arrhythmogenic Cardiomyopathy: Usefulness of a Functional Biochemical Assay." The desmoplakin is essential for the cell-cell adhesion complex's desmosomes. Mutations in this gene have been associated with a wide range of phenotypes, including some in skin and hair, but also in heart, which can manifest arrhythmogenic or dilated cardiomyopathy. This protein anchors intermediate filaments, so mutations that alter binding to intermediate filaments may pathogenicity. The author selected seven reported amino acid altering mutations in desmoplakin, and they screened for effects on binding using a novel fluorescence binding assay. They found that three of the seven mutations had a clear impact on binding. This assay is a novel way to assess functional impact of desmoplakin variants, and may be useful to inform the severity of future phenotypes in individuals carrying a desmoplakin mutation. Finally, if you want to stay up-to-date on the genetics of aortic disease and Marfan syndrome, you can find a letter from Christian Groth and colleagues and an author response from Norifumi Takeda and colleagues regarding their previously published paper on impact of pathogenic FBN1 variant types on the progression of aortic disease in patients with Marfan syndrome. I am joined today by Dr. Brian Byrd from the University of Michigan, who is the senior author on a Manuscript published in this month's issue, entitled, "Human Urinary mRNA as a Biomarker of Cardiovascular Disease: A Proof-of-Principle Study of Sodium-Loading in Prehypertension." So welcome Brian. Thanks so much for coming on the podcast. Brian Byrd: Thank you for having me. Jane Ferguson: So before we get started, could you give a brief introduction of yourself to the listeners and maybe tell us a little bit about how you got into the field? Brian Byrd: Absolutely. So I am a cardiologist and a physician scientist. I'm an assistant professor at the University of Michigan, where I have a laboratory engaged in clinical investigation. My background is that I did my Internal Medicine Residency at Vanderbilt University. And after I finished residency, I entered Nancy Brown's lab. She's the Chair of Medicine at Vanderbilt, as I know you're aware. And she had a laboratory focused, and still does have a laboratory focused, on the investigation of high blood pressure, with a lot of focus on understanding high blood pressure as it occurs in humans. And I got a Master of Science degree in clinical investigation while I was in her lab, and we did some work on a number of topics related to the renin-angiotensin-aldosterone system, which has been a long-standing interest of mine ever since then. So, at the same time, I was learning how to take care of patients with very complex blood pressure problems, who required three, or four, or five, or six blood pressure medications, in some cases, to control. And it's with that background that I became very interested in the science that underlies treatment-resistant high blood pressure in people and what we might be able to do about that. Jane Ferguson: Wow. Nice. Yeah and I think that background of sort of the combination of clinical and experimental is really nice and really important. I think your paper actually exemplifies that really nicely, so using humans but also some basic science techniques and combining them to really have a very patient focused instead of mechanistic interrogation. So as I mentioned, you just published this really nice manuscript using urine as a source of mRNA biomarkers, which has relevance to hypertension and potentially also to other diseases. But before we get sort of too much into the weeds on the specific details, for any of our listeners who didn't get a chance to read your paper yet, maybe you could briefly summarize what you did? Brian Byrd: Okay, so the general overview of what we were interested in was that the patients who have treatment resistant high blood pressure tend to have a lot of activation of a receptor in the kidney called the mineralocorticoid receptor. And this receptor helps control salt in bladder in the body. Obviously the amount of salt in the blood stays very, very homeostatic, but we if eat more salt one day then the next and there needs to be a system to help regulate the homeostasis. And so, you waste more or less salt in the urine depending upon how much sodium you're taking in. And one of the functions of the mineralocorticoid receptor is to control this salt and bladder regulation or to fine tune it anyway. And the reason we know that that's an important receptor in patients with treatment-resistant high blood pressure is because of a series of studies done by David Calhoun and Brian Williams and others, showing that mineralocorticoid receptor blockers, or antagonists, are very effective in the treatment of tough to control high blood pressure. And so, we had some insight that there would be something interesting to study there, and one of the things that we knew was that the mineralocorticoid receptor is a ligand activated transcription factor. So when it gets activated by it's ligan which canonically is a steroid hormone from the adrenal gland aldosterone, the receptor, which is in the cytoplasm, ordinarily dimerizes and translocates to the nucleus, where it controls the regulation of a number of genes. We also were aware that cells secrete RNA, and others had the idea that it might be inside vesicles because there's a lot of ribonuclease and biofluids. And you would think it might be difficult to pass the RNA if it were sort of naked as it were. And it turns out that that's right. If you, for example, introduced synthetic RNAs into biofluids, the RNAs will be gone very quickly in a matter of seconds. So, we had this idea that we might be able to look at RNA that was being secreted by cells probably in vesicles, and assay the activity of the receptor potentially. We weren't sure if that was going to be possible or not. One of the things we did was we used part of the available data to look at the transcriptome of vesicles in the urine that had been isolated from 3300 milliliters of urine by ultracentrifugation [inaudible 00:18:57]. Jane Ferguson: So it's a big centrifusion. Brian Byrd: Exactly. Jane Ferguson: Like you [inaudible 00:19:00] Brian Byrd: It must have been some project. So that was the work of Kevin Miranda and colleagues, and we were able to compare that transcriptome to the transcriptome of human kidney cortex samples from the GTEx project, which a large consortium focused on human transcriptomics. And that was sort of the first part of what we presented in this paper, and the second thing that we did was we looked within a crossover study in a collaboration with Scott Hummel, one of my close collaborators here at the University of Michigan. We looked at individuals who had been put on a low salt diet activating renin-angiotensin-aldosterone system and causing more activation of the mineralocorticoid receptor. And then, those same individuals underwent saline infusion, so salt loading, and we knew that that would suppress the renin-angiotensin-aldosterone system. And we measured the [inaudible 00:20:02] measures of the renin-angiotensin-aldosterone system, but we also took the urine samples that had been recently banked from that experiment and we centrifuged them to try to palette the cells. We took the supernatant and we extracted RNA after trying to enrich for extracellular vesicles. And with that approach, we measure targets that we thought would be regulated my the mineralocorticoid receptor, as well as some things that we did not think would be regulated by mineralocorticoid receptor. So that's the general overview of what we undertook. Jane Ferguson: Great. Right. So it's very cool. I guess we can break it down into sort of the two different parts, because I think it was a really nice examples of using public data to sort of start addressing your question and then actually doing a human experiment. But so for the GTEx data and the urinary data, you looked at few different tissues, right? And was kidney the one that you were thinking upfront would sort of most likely to correlate, or were you also looking at bladder and other sort of tissues that could potentially be of relevance to urine? But what sort of the ... I guess sort of tell me more about those different tissues that you looked at and what you found and what surprised you or not. Brian Byrd: Great question. So, the kidney was on our minds from the outset. We knew that Mark Knepper at the National Institute of Health had published in the [inaudible 00:21:25] National Academy of Sciences back in 2004 that there are urinary extracellular vesicles. And he had found proteins that are very characteristic of the aldosterone sensitive distal nephron, that part of the kidney that we're interested in, embedded in the vesicles. So we became quite interested in the idea that it seemed that there was likely a population of vesicles in the urine that is of kidney origin. And that's not to say that there weren't also plenty of vesicles from other origins as well, and there could very well be RNA that is not vesicle enclosed, but is rather ribonucleic protein bound or even bound to other carriers potentially. That could be there as well, and it's possible that the origin of those things could be any number of tissues. I don't really think that we know yet where the possible tissue origins are. But we were curious to know ... I guess the direct answer to your question is we thought from the outset that we probably would find some sort of signal related to the kidney. But we wanted to also consider the possibility that our findings were not very specific to the kidney. And so we thought that the brain would be an interesting negative control. If we say very high correlation with the brain, it would suggest that maybe what we're looking for is a signal that's not really coming from the kidney. And we also wanted to look at the bladder just to try to understand whether or not the signals that we're detecting could be coming from the bladder. It's certainly true that some aspects of the system that we're interested in are present in the bladder, so I wondered whether that might even serve as a signal amplifier for what we were looking for since there's, presumably, quite a bit of bladder tissue right around the urine. It might be contributing vesicles. So that's sort of the rationale for why we looked at those things. Jane Ferguson: And you found mostly enrichment for kidneys. So sort of I guess what you were hoping to find came true? That actually there was sort of evidence that even though there may be contribution from other tissues, that really kidney seem to be the predominant contributor to the expression of the genes in the urine. Brian Byrd: I think there's a lot of truth to that. One of the things I would say is we found high correlation looking across all genes. But it occurred to us ... As soon as we thought that, we realized, wait a second, that could be driven by ubiquitously expressed genes. Housekeeping genes. So we really wanted to stratify our analysis by things we thought would be expressed in the kidney as well as things that we thought would be ubiquitously expressed to make sure that we could see signals that correlate ... That the transcriptome of the kidney, per se, had a good correlation with those same in terms of the abundance of the gene counts or recounts. They said it was similar to what was in the vesicles. And so, we looked in the literature, and we found that a group had already established a number, 55 genes actually, that were highly kidney enriched as well as over 8000 genes that were ubiquitously expressed. And so we started the analysis from this perspective of the stratification. We thought that was a very important aspect of the analysis. And it's definitely true that if you look at our findings with respect to the kidney enriched genes, as you might expect, they correlate quite well with what is in the urinary extracellular vesicles compared to the kidney cortex. You look at the brain as you might expect the expression of those kidney enriched genes is not well correlated with what's happening in the urine. And then, with respect to the bladder, it's sort of somewhere in between. Jane Ferguson: Okay. Interesting. So I know that some people look at small non-coding RNAs in urine, but you were mostly focused on mRNAs. Is that right? Brian Byrd: That's right. I thought of this as sort of frontier, something that I knew from some early publications was probably measurable. But I didn't know what it would signify, if anything, with respect to physiology. And I knew that there were quite a few papers about micro RNAs and I wanted to do something a little bit innovated, partly. But the main reason that I was interested in the RNAs was because I could relatively easily tie those to the existing literature from animal models. Preclinical animal models and cell culture studies showing what happens when the mineralocorticoid receptor's activated. That was really the driving reason that I was interested in the RNA. Because if you think about what is the approximate event that might be a readout for activation of a new growth hormone receptor like the mineralocorticoid receptor, it's really the transcriptional events that happen when the receptors translocates to the nucleus and serves its ligan activated transcription factor role. Jane Ferguson: Right. So, [inaudible 00:26:43] sort of the first part of analysis, you saw these really nice correlations between expression and kidney and in urine. And then, a lot of the times when you tried to publish that kind of thing, people are like, "Okay, so what? So you didn't do any intervention. We don't really know what that means." But I like that you took it to the next step and you did sort of a human intervention experimental model. So tell me more about that model and how that worked. Brian Byrd: Right. Well, I'll just mention also that the work that was done in terms of RNA [inaudible 00:27:14] was done in collaboration with Mark Bertini in Italy as well as Dr. [inaudible 00:27:19]. They were fundamental to getting that work done. With respect to the collaboration with Scott Hummel, one of my colleagues here at the University of Michigan, what we did in that setting was to look at whether or not we could identify within these urinary mRNA signals that are in the supernatant in the urine, whether we could identify changes in physiology. That was the question that was of greatest interest scientifically. And for a very practical or blind perspective, the question was could we detect the activation of the receptor that might determine whether or not people should get a certain medication. Of course, we're not saying that that's an established fact yet, but this is sort of concept, that there's something here to explore further. And so, what we found was that a number of genes that are regulated by the mineralocorticoid receptor, including genes encoding the subunits of the amiloride-sensitive epithelial sodium channel that regulates the salt that I was talking about earlier. We found that those genes changed with sodium loading in terms of their abundance in the expected direction. We also found that several of the assays that we made changed ... I'm sorry. That they correlated with the serum aldosterone concentration. So the concentration of the ligan for the receptor whose readout we were looking for. And we also noticed an inverse correlation with urinary sodium excretion, which is what we would expect if we really identified a readout of the mineralocorticoid receptor's activity. So this study supported the idea that we have identified a way to measure this nuclear hormone receptors activity in living humans. Jane Ferguson: Right. Which is really nice. So there's probably a huge amount of extra things you could do with this, some sort of different ways you could look at it. So how did you pick the time point? So, I suppose when you think about it, I mean the genes, they're transcribed and then that takes a little bit of time, and then it takes a little bit of time for that to sort of make its way into the urine and to be excreted. So how did you decide on sort of what time points to use, and do you think you would see the same things or different things [inaudible 00:29:39] if you did repeated sampling or if you looked at different time points? Brian Byrd: That's a fantastic question. So this was a study that had already been completed, and I had mentioned to Scott what we were working on. And he said, "You know, we have these samples from this study and it might be possible for us to collaborate." So, we didn't get to pick the timeframes. Jane Ferguson: Right. Brian Byrd: So, that's a great point. And what I would say is that, as you can imagine, we're very focused on exactly the questions you're asking now. What about sort of signal refinement? What about the chrono-biology of these signals, and how do we understand when we see what in the urine? So, I'm actively pursuing those questions. Jane Ferguson: Right. So, I know as well, there was quite a lot of sort of technical challenges I think to doing this work. Sort of getting to be even able to amplify and get a signal from these RNAs that are really present, sort of pretty low abundance in urine compared to tissues or biofluids that we're used to working with. So tell me maybe a little bit about that process and sort of how much optimization was required to get these essays to work? Brian Byrd: Great question. So, I had known [inaudible 00:30:58] since 2014 when I took a course on isolation of extracellular vesicles in Heidelberg, Germany. And I had talked to him at a meeting in Washington DC, and I had mentioned what we were trying to do. And he said, "You know, if you were trying to do that, you might want to consider preamplification." You know, using something like 15 cycles of preamplification. And he was willing to share that protocol that he had with me, because they were interested in similar issues. So, I was able to use that protocol to evaluate these gene targets in the urine. And so that was immensely helpful. And the other thing that we did was we used locked nucleic acid probes to try to increase the sensitivity and specificity of our assays. Finally, we just tried to use good logic in the design of the assays. So we were concerned that the RNA might be fragmented, so where it was possible to do so within the design constraints that I'll mention in a second, we made multiple assays per gene target just in case this was fragmented. Which makes the analysis a little more complicated, but I think it was probably the right thing to do, given the state of knowledge that we had then. And one of the other things we did was we made sure that the primers either ... Within a primer, there was an intron or between the primers there was an intron, so that if we actually did try to amplify DNA, abundant amounts of DNA, with those primers just to make sure that our theorizing about the inability to amplify things was actually factual. And that turned out that we couldn't amplify anything at 40 cycles with those. So, we spent a lot of time thinking about how not to get fooled, but also to have adequate signal detection. And have included in the supplement quite a bit of information about the technical merits of the assays and showing how close the technical replicates were. They tended to be very, very similar to one another. We didn't see a signal in every urine sample for every participant at both time points, and I think that was interesting to me about that there tended to be a very binary result, so that you'd either see three technical replicants for the QPCR assays, our QPCR assays that were extremely similar to each other, or you would see no CT value detected. [inaudible 00:33:47] That these were valid assessments of very low copy numbers. Jane Ferguson: Right. And that's probably related to up front of what happens to urine right after it's collected and stored, or during that RNA extraction. But it seems like once you've got RNA, then downstream assays were sort of ... They held through, but I guess ... I mean, and you obviously didn't have necessarily a huge amount of control over how these urine samples were collected. So it's kind of nice that you were able to see something even though these were collected possibly in a way that was not optimized for preserving RNAs. But do you think those ... Are there ways that you could make this even sort of more streamlined and better as far from the get go of how you collect the urine, whether you could be extracting stuff right away? Is that anything you sort of looked into of how this could be improved? Brian Byrd: That's really been the focus of the labs work since we completed that project, is sort of understanding how would we do this in a prospective study in the best possible way so that the results are highly repeatable, that we get a CT value in everybody so that we're really ... I mean, as you can imagine, that actually has something to do with the input volume of urine that you use. So if you have too little input volume, then you won't be able to detect the targets that you might be interested in every person. However, if you have more, then you can do more with that. But then you have to think about how you're going to deal with the larger volumes of urine. There are lots of questions that we've been interested in related to extract the RNA and the stability of the RNA. And so we have done some experiments of that type, and we continue to work in that area. And I do think that those questions you're asking are the right questions with respect to next steps. Jane Ferguson: All right. So you looked at sort of specific targets, which I think made a lot of sense. Sort of this proof of principle. But do you think this would work on a transcriptome wide level? I mean, could you look at all the genes, or do you think that's just sort beyond the possibility right now given sort of the RNA fragmentation and how you have to sort of amplify it before being able to detect anything? Brian Byrd: I think it's possible. So the group that had preceded our work with 3300 mils of urine, isolating the vesicles from there, eight have showed that that's something that can be done. The question that's of interest to me is does it actually require such large volumes of urine? And I think the answer to that question is going to be no from what we're overseeing so far. And so, we're thinking along exactly the lines that you are. And certainly some of the feedback we've gotten as we've discussed this project with people is, "Hey, could you look at everything rather than picking targets at [inaudible 00:36:41]." I think there's advantages and disadvantages. I think we chose based on prior knowledge in a way that was rational. But at the same time, it may turn out that there are many things about activation of the mineralocorticoid receptor in humans, especially in the living in-tact human, that don't exactly mirror what's found in rabbits, rats, mice or cells, which are really the systems that have been evaluated the most thoroughly in the past. So I'm very interested in exactly what you're proposing. Jane Ferguson: Yeah. I mean, I think it's exciting because it's obviously relevant for hypertension, but potentially a lot of other conditions, to be able to look at that sort of dynamic change. So I think it's really exciting. It's very cool. Brian Byrd: And I appreciate your asking about this study. We were excited to do this work and very, very excited to see where we can in the future with this. And I agree with the point you were making, that here we've gone from a rather specific application driven question and we've, I think, made some insights that are probably useful outside the application that we had in mind. And it may turn out that the application where this is the most important is not even the one that we considered in the first place at all. And so I'm pleased by that. I'm pleased by the fact that I think in a sense we're working in what Donald Stokes described as pasture's quadrant, which is a sense that the work is driven both by curiosity and by an intent to use the results. Jane Ferguson: Right. Brian Byrd: And so that's really what gets me out of bed in the morning, is working that exact space. So that's what we were glad to have done and continue to do. Jane Ferguson: Yeah. No, I think it's grea.t and I feel like a lot of people will read this paper and be like, "Hey, I have urine stored in the freezer. What can I do with this now?" Brian Byrd: Contact me. Let's talk. We'll see what we can do. But we certainly tried to describe the methods in such a way that people could easily follow in our footsteps if they want to apply these methods. Jane Ferguson: Yeah. Now having read through them, I think that ... Really thorough. I really liked the sort of attention to detail. It was definitely one of those ones where I was like, "Oh yeah. I can see exactly how I could do this if I wanted to. So I think that was great. Brian Byrd: Thank you. Jane Ferguson: So yeah. Congratulations on the paper. Really nice work and thanks so much for talking to me. Brian Byrd: Thank you. It was a delight. Jane Ferguson: That's it from me for September. If you haven't had enough yet, you can access all the papers online and you can choose to digest the papers in video format. Available on our website or the Circulation YouTube channel. Thank you for listening and subscribing. I look forward to bringing you more next month.

Jane: Hi, everyone. Welcome to Episode 18 of Getting Personal: Omics of the Heart. I'm Jane Ferguson, and this podcast is brought to you by the Circulation: Genomic and Precision Medicine Journal and the American Heart Association Counsel on Genomic and Precision Medicine. It is July 2018, which means that the best possible place to be listening to this episode is at the beach, but failing that I can also recommend listening on planes, during your commute, while exercising or while drinking a nice cup of tea. So before I get into the papers we published this month, I want to ask for your help. If you're listening to this right now, hi, that means you, we're a year and a half into podcasting and I would love to know what content you like and where we could improve things. We have a poll up on Twitter this week, and I would really appreciate your input. If you're listening to this a little bit later and miss the active voting part of the poll, you can still leave suggestions. Okay, so what I would like you to do right now is to go to Twitter. You can find us as Circ_Gen and locate the poll. If you don't already follow us on Twitter, go do that now too. We want you to let us know what content we should focus on and what is most useful to you, so go ahead and pick your favorites from the options and also please reply or tweet at us with other thoughts and suggestions. Options include giving summaries of the recent articles like I'm about to do later this episode, conducting interviews with authors of recently published papers, interviews with people working in cardiovascular genomics, broader topics. For example, to get their insight on career paths and lessons learned along the way. And something we have not done yet on the podcast but are considering, would be to record podcasts that focus on particular topics in genomics and precision medicine. These could give some background on an emerging field or technology and we could talk to experts who are leading particular innovations in the field. So, if that sounds good to you, let me know! If you're not on Twitter, I don't want to exclude you, so you can email me at jane.f.ferguson@vanderbilt.edu and give me your thoughts that way. I'm looking forward to hearing from you. Okay, so on to the July 2018 issue of Circ.: Genomic and Precision Medicine. First up is a PhWAS from Abrahim Rao, Eric Ingelsson, and colleagues from Stanford. The discovery of the PCSK9 gene as a regulator of cholesterol levels has led to a new avenue of LDL lowering therapies through PCSK9 inhibition. However, some studies suggest that long term use of PCSK9 inhibitors could have adverse consequences. Because of the long follow-up time required, it will take many more years to address this question through clinical studies. However, genetic approaches offer a fast and convenient alternative to address the issue. In this paper, entitled: "Large Scale Phenome-Wide Association Study of PCSK9 Variants Demonstrates Protection Against Ischemic Stroke," the authors use genetic and phenotype data from over 300,000 individuals in the UK BioBank to address whether genetic loss of function variants in PCSK9 are associated with phenotypes including coronary heart disease, stroke, type II diabetes, cataracts, heart failure, atrial fibrillation, epilepsy, and cognitive function. The missense variant RS11591147 was associated with protection against coronary heart disease and ischemic stroke. This SNP also associated with type II diabetes after adjustment for lipid medication status. Overall, this study recapitulated the associations between PCSK9 and coronary disease, and revealed an association with stroke. Previous studies suggested use of LDL lowering therapies may increase risk of cataracts, epilepsy, and cognitive dysfunction, but there was no evidence of association in this study. Overall, this study provides some reassurance that the primary effect of PCSK9 is on lipids and lipid related diseases, and that any effects on other phenotypes appear to be modest at best. While a PhWAS can't recapitulate a clinical trial, what this study indicates is that PCSK9 inhibition is an effective strategy for CVD prevention, which may confer protection against ischemic stroke and does not appear to convey increased risk for cognitive side effects. Next up we have a manuscript form Jason Cowan, Ray Hershberger, and colleagues from Ohio State University College of Medicine. Their paper, "Multigenic Disease and Bilineal Inheritance in Dilated Cardiomyopathy Is Illustrated in Non-segregating LMNA Pedigrees," explored pedigrees of apparent LMNA related cardiomyopathy identifying family members who manifested disease, despite not carrying the purported causal LMNA variant. Of 19 pedigrees studies, six of them had family members with dilated cardiomyopathy who did not carry the family's LMNA mutation. In five of those six pedigrees, the authors identified at least one additional rare variant in a known DCM gene that was a plausible candidate for disease causation. Presence of additional variants was associated with more severe disease phenotype in those individuals. Overall, what this study tells us is that in DCM, there is evidence for multi-gene causality and bilineal inheritance may be more common than previously suspected. Future larger studies should consider multi-genic causes and will be required to fully understand the genetic architecture of DCM. Yukiko Nakano, Yasuki Kihara, and colleagues from Hiroshima University published a manuscript detailing how HCN4 gene polymorphisms are associated with tachycardia inducted cardiomyopathy in patients with atrial fibrillation. Tachycardia induced cardiomyopathy is common in subjects with atrial fibrillation, but the pathophysiology is poorly understood. Recent studies have implicated the cardiac hyperpolarization activated cyclic nucleotide gated channel gene, or HCN4, in atrial fibrillation and ventricular function. In this paper, the authors enrolled almost 3,000 Japanese subjects with atrial fibrillation, both with and without tachycardia-induced cardiomyopathy, as well as non-AF controls. They compared frequency of variants in HCN4 in AF subjects with or without tachycardia-induced cardiomyopathy, and found a SNP, RS7164883, that may be a novel marker of tachycardia-induced cardiomyopathy in atrial fibrillation. Xinyu Yang, Fuli Yu, and coauthors from Tianjin University were interested in finding causal genes for intracranial aneurysms, and report their results in a manuscript entitled, "Rho Guanine Nucleotide Exchange Factor ARHGEF17 Is a Risk Gene for Intracranial Aneurysms." They sequenced the genomes of 20 Chinese intracranial aneurysm patients to search for potentially deleterious, rare, and low frequency variants. They found a coding variant in the ARHGEF17 gene which was associated with associated with increased risk in the discovery sample, and which they replicated in a sample of Japanese IA and in a larger Chinese sample. They expanded this to other published studies, including individuals of European-American and French-Canadian origin and found a significantly increased mutation burden in ARHGEF17 in IA patients across all samples. They were interested in further functional characterization of this gene and found that Zebra fish ARHGEF17 was highly expressed in blood vessels in the brain. They used morpholinos to knock down ARHGEF17 in Zebra fish, and found that ARHGEF17 deficient Zebra fish developed endothelial lesions on cerebral blood vessels, and showed evidence of bleeding consistent with defects in the vessel. This study implicates ARHGEF17 as a cerebro-vascular disease gene which may impact disease risk through effects on endothelial function and blood vessel stability. Sumeet Khetarpal, Paul Babb, Dan Rader, Ben Voight, and colleagues from the University of Pennsylvania used targeted resequencing to look at determinants of extreme HDL cholesterol in their aptly titled manuscript, "Multiplexed Targeted Resequencing Identifies Coding and Regulatory Variation Underlying Phenotypic Extremes of HDL Cholesterol in Humans." Stay tuned because we're gonna hear more about this paper from the first author Dr. Sumeet Khetarpal later this episode. Rounding out this issue we have a Perspective article from Chris Haggerty, Cynthia James, and coauthors from Geisinger and Johns Hopkins Medical Center entitled, "Managing Secondary Genomic Findings Associated With Arrhythmogenic Right Ventricular Cardiomyopathy: Case Studies and Proposal for Clinical Surveillance." In this paper the authors discuss the challenges for returning findings from clinical sequencing for arrhythmogenic right ventricular cardiomyopathy, presenting case studies exemplifying these challenges. They also propose a management approach for returning clinical genomic findings, and discuss new innovations in the light of precision medicine. We also published a review article by Pradeep Natarajan, Siddhartha Jaiswal, and Sekar Kathiresan from MGH on "Clonal Hematopoiesis Somatic Mutations in Blood Cells and Atherosclerosis", which discusses recent advances in our knowledge on the role of somatic mutations in cardiovascular disease risk. Finally, we have an update on some pharmacogenomics research into CYP2C19 Genotype-Guided Antiplatelet Therapy by Craig Lee and colleagues which we published a few months ago. Dr. Lee was also featured on Podcast episode 15 in April of this year. Jernice Aw and colleagues from Khoo Teck Puat Hospital, Singapore shared from complimentary data from their sample of 247 Asian subjects which found the risk for major adverse cardiovascular events was over 30-fold greater for poor metabolizers, as defined by CYP2C19 genotype on clopidogrel, as compared to those with no loss of function allele. You can read that letter and the response from Dr. Lee and colleagues online now. And, as usual, all of the original research articles come with an editorial to help give some more background and perspective to each paper. Go to circgenetics.ahajournals.org to find all the papers and to access video summaries and more. Our interview is with Dr. Sumeet Khetarpal who recently completed his MD-PhD training at the University of Pennsylvania, and is currently a resident in Internal Medicine at Massachusets General Hospital. Sumeet kindly took some time out from his busy residency schedule to talk to me about his recently published paper, and to explain how molecular inversion probe target capture actually works. So I am here with Dr. Sumeet Khetarpal who is co-first author on a manuscript entitled, "Multiplexed Targeted Resequencing Identifies Coding and Regulatory Variation Underlying Phenotypic Extremes of High-Density Lipoprotein Cholesterol in Humans." Welcome Sumeet, thanks for taking the time to talk to me. Dr. Khetarpal: Thank you so much Dr. Ferguson, it's really a pleasure to talk to you today. Jane: Before we get started, maybe you could give a brief introduction on yourself and then how you started working on this paper. Dr. Khetarpal: Sure, so this work actually was a collaboration that came out at the University of Pennsylvania that I was involved with through my PhD thesis lab, my mentor was Dan Rader, and also a lab that is a somewhat newer lab at Penn, Benjamin Voight's lab which is a strong sort of computational genomic lab. This work actually highlights the fun of collaborating within your institution. We had, for some time, been interested in developing a way to sequence candidate genes. Both known genes and also new genes that have come out of genome-wide association studies that underlie the extremes of HDL cholesterol, namely very high cholesterol versus low HDL cholesterol. We've been looking for a cost-effective and scalable way to do this. Independently, Ben, who is very interested in capturing the non-coding genome, was interested in developing a method to better understand the non-coding variation, both common and rare variation that may be present at all of these new loci that have come out for complex traits such as HDL. We, at some Penn event several years ago, were talking about our common interest and Ben had actually identified this work that had come out of J. Shendure's lab at the University of Washington. A paper by the first author, Brian O'Rouke, in Science in 2012 in which they had developed an approach that involved molecular inversion probes, or MIPs, to capture regions of the genome related to target the gene that they were interested in studying for autism-spectrum disorders. They had applied this largely to coding regions of, I think, almost 50 genes and almost 2,500 patients with the feedback to do deep, targeted sequencing. So our thought was, well, we could try to apply this approach and adapt it to capture non-coding regions, and also see if we can expand the utility of this approach to study the phenotypic extremes of a complex trait such as HDL cholesterol. Jane: Yeah, that's really cool. I love how you saw this method in a totally different application and then realized that there was expertise at Penn that you could bring together to apply this in a different way. I'd love to hear more about this MIP, the molecular inversion probe. How does it work? How difficult is it to actually do? Is it very different from normal library preparation for sequencing or is it something that's actually relatively easy to apply? Dr. Khetarpal: These MIP probes are oligonucleotide probes that capture your region of interest by flanking them and capturing by gap filling. There's a method to capture parts of the genome in a library-free way. They do ultimately involve barcoding the way traditional library-based target capture does and then deep sequencing. But the most impressive feature about them is just that they're very scalable. I think in the original paper by O'Rouke and colleagues they were able to sequence their set of genes and their set of samples at about a sample preparation cost of $1 per sample, and we were actually able to do about the same for our study. The main utility of the approach is just the economic scalability, and the ability to customize your panel to capture several regions of the genome that are adjacent to each other. Jane: Right, so how many genes or regions can you multiplex at the same time? Is it just one prep, like you just design all of your oligos, you put them all together in one reaction, or are you doing separate reactions for each region? Dr. Khetarpal: We're actually doing all of our oligos together. In our case, I think it ended up being around the order of almost 600 oligos together to capture our ultimately 50kB of genomic territory that we wanted to capture. Really, our study was kind of a pilot experiment where we picked a few genes or regions of high interest to us, both known genes that effect HDL and also those that have been implicated in genome-wide association studies that were of high interest to our labs. I think that this approach could actually be expanded to capture much more genomic territory in a single capture reaction. We sort of touched the surface probably of what we could do. Jane: Wow, that's cool! And then for sequencing it, I guess it's really just a function of how many samples you wanna multiplex and how much you want to sequence from each region. So I suppose the way you did it, you had about 50kB and then you had over 1,500 participants and you were able to do those on a single HiSeq run, right? Dr. Khetarpal: Right. Jane: So I suppose if you'd done more genetic regions, you would've had fewer people and vice versa so you can balance that out depending on if you're having more samples or more genomic regions to sequence. Dr. Khetarpal: Exactly, in certain ways the design of our experiment we had a limited sample size that did afford us some luxury in terms of knowing that we would have deep coverage of the region that we were targeting. I think that's always a critical question in sort of targeted or just sequencing in general. The balance between the number of regions that you want to sequence and the number of samples you want to sequence is going to dictate what your sequencing depth with be. Jane: Right, okay so I guess if we go on to what you actually found, how'd you pick this? You picked seven regions which encompasses eight candidate genes for HDL, so how did you select those? Dr. Khetarpal: The population that we were studying, the samples we were looking to sequence were largely individuals which fall into two bins if you will. One was extremely high HDL cholesterol which we're defining as the greater than the 95th percentile, but really there was a range within that population that spanned individuals with probably greater than the 99th percentile of HDL. We were hoping as a proof of principle effort to identify variation in genes that were known causes of high HDL cholesterol in prior studies of Mendelian genes for HDL. So genes such as LIP gene which encodes endothelial lipase or CETP or SCARB1, these 3 genes are, at this point, well-known genes that loss of function mutations are associated with extremely high HDL. We thought that capturing some of those genes would potentially both provide a level of validation for the approach, hypothesizing that individuals with high HDL would be enriched with these genes, but also may allow us to find new variants in these genes or also non-coding variants which has not previously been studied before. Some of the genes came out from that line of thinking, then some of the other genes happened to be genes that in the Rader laboratory we had a vested interest in understanding the genetic variation that might link the genes to HDL, which may not have necessarily come out before. For example, the gene GALNT2 is one of the first g-loss implicated novel genes for HDL, novel as in the earliest g-loss study for plasma lipids had identified that gene as associated with HDL but it never had come out before as being so. Our laboratory was very interested in better understanding the genetic relationship between genes such as GALNT2 and several of the others such as CCDC92 and ZNF664 with HDL. It ended up being a hodge-podge or a sampling of genes that had at some level been implicated with HDL, but really it's just a proof of principle that this method could work for both identifying variation in known genes and also less studied ones. Jane: You validated the MIP genotyping by exome genotyping, and then saw concordance of over 90%, is that lower than you were expecting? Was it about what you were expecting based on these two different methods of genotyping? Dr. Khetarpal: Yes, I think we were expecting somewhere on the order of 90 plus percent. It's hard to know why we just hit that, we likely would've benefited from being able to genotype all of the individuals by the exome chip that we had sequenced as well, where we were able to validate in about two-thirds of those individuals. It's hard to know exactly what the cause of the about 10% discordance rate might be, whether it's just in certain samples the genotyping quality was perhaps on the border of being valid or the sequencing quality. Jane: Right, I'm wondering sort of with the MIP, what's the gold standard? Is the XM chip genotyping still the gold standard and the MIP maybe is more error-prone, or perhaps the other way around? Or is it you can't tell at this point which is the true genotype and which is an error potentially for those discordant ones? Dr. Khetarpal: Certainly whenever there's a new sequencing methodology that is proposed I think it's critical to have some sort of validation. We happened to cover regions that would span the genome enough that we had XM chip genotyping in a large subset, that that might be the best approach. But if you had a limited number of regions or variance that you were interested in one could imagine also doing Sanger sequencing as the tried and tested validation approach. Of course it becomes not so scalable at a certain point. Certainly we would say that the MIPs, while the method has been developed and expanded by the Shendure lab, our hope is that through our studies maybe it will be applied further. It's still very much a new approach and so validation is key. Jane: Very important. What do you think was the most exciting finding that came out of this, after you analyzed the data, what were you most excited about seeing? Dr. Khetarpal: The critical finding for us, which I think implies the utility of the approach, was just the validation of four of the loci that we had studied. Validation in our cohort of known genome-wide significant associations for HDL that had been published previously in almost 200,000 individuals in terms of sample size, in our experiment involving just about 1,500 people we were able to find consistent associations of those same variants that segregated with low versus high HDL. Directionally consistent with the large genome-wide association studies. I think the value of this finding is really just to emphasize the utility of the case control design in these phenotypic extremes, in addition to the overarching goal of our study, which was in a way that perhaps provides the most validation of the approach in terms of concordance with prior known studies. Jane: So if somebody was listening to this and was trying to decide should they use MIP for a study they have in mind, should they use another technique? Based on your experience, what would you recommend? Dr. Khetarpal: I think in our current stage it's a very exciting time because we're just seeing whole genome sequencing really take off and being used at scale to ask critical questions about non-coding variation as it relates to both disease and complex traits. I don't think we're quite there yet with being able to apply that approach in a cost effective manner. The ability to annotate and analyze that data is still at it's infancy. The utility of the MIPs is that it provides a very cheap alternative. I can say from my experiences actually doing the capture and preparation from sample to sequencer stage that it's a very easy to use methodology that is very fast and cheap. That if one is really interested in a handful, or more than a handful, of candidate genes and their non-coding regions as it relates to a trait or disease of interest, it may not be the era for going full on with whole genome sequencing, especially at the current cost. That's where I think the MIPs really come in to be very useful. Jane: It sounds great, is there anything else that you'd like to mention? Dr. Khetarpal: Just to say that we recognize it's a relatively small study as our pioneer approach with this method but that the Rader lab and Voight labs are actively pursuing larger applications of this to study, not only HDL, but other complex traits, such as diabetes, in much larger populations. I can't overemphasize how easy of a method it is to apply, but also that I think a bigger take home of this study for me as a very recent graduate student working in a very collaborative institution the ability of two laboratories to come together with different sets of expertise to try to tackle a problem that I think goes beyond the individual science. For any human geneticist how to find the variation you're interested in and not break the bank is kind of at the core of what we do, and so I think it was very fun to be part of this collaboration and our hope is that the outcome of it is a method that can be useful for many people, both in our field and beyond. Jane: I think it's great and I'm hoping this will inspire a lot of other people to try this method and see if it can work for them. So, congratulations on the study, it's really nice work. Dr. Khetarpal: Thank you so much! Jane: That's all I have for you for July, thanks for listening. Send me your thoughts on the podcast via Twitter or email, or leave us a review in Itunes. I look forward to talking to you next month.

Jane Ferguson: Hi, everyone. Welcome to Getting Personal: Omics of the Heart. This is podcast episode 16 from May 2018. I'm Jane Ferguson from Vanderbilt University Medical Center and this podcast is brought to you by Circulation Genomic and Precision Medicine and the AHA Council on Genomic and Precision Medicine. Jane Ferguson: This month we talked to Dr. Caitrin McDonough from the University of Florida. We briefly mentioned her paper in last month's episode Genetic Variants Influencing Plasma Renin Activity in Hypertensive Patients From the PEAR Study, but we wanted to go into it in more depth this month. Caitrin shared with us that this manuscript actually resulted from student course work and was a collaborative effort between students and instructors. The manuscript highlights has successful as approach can be both in increasing student engagement and as an effective way to do high quality research. You can hear her talk more about her innovative approach to student learning and the study findings later in this episode. Jane Ferguson: Of course, we have a great lineup of papers in Circulation Genomic and Precision Medicine this month. First up, a paper entitled, "SCN5A Variant Functional Perturbation and Clinical Presentation Variants of a Certain Significance" by Brett Kroncke, Andrew Glazer, and Dan M. Roden and colleagues from Vanderbilt University Medical Center. They were interested in investigating the functional significance of variants in the cardiac sodium channel in particular to see if they could explain why some variant carriers present with cardiac arrhythmias while others remain asymptomatic. Through a comprehensive literature search, they identified 1712 SCN5A variants and characterized the carriers by disease presentation. Variants associated with disease were more likely to fall in transmembrane domains consistent with the importance of these domains for channel function. Jane Ferguson: Using American College of Medical Genetics Criteria for variant classification, they found that variants classified as more pathogenic were also more penetrant. Penetrance was also associated with electrophysiological parameters. This approach highlights how modeling the penetrance of different variants can help define disease risk for individuals who carry potentially pathogenic variants. Jane Ferguson: Next we have a paper from Vincenzo Macri, Jennifer Brody, Patrick Ellinor, Nona Sotoodehnia and colleagues from the University of Washington and Massachusets General Hospital. This is also related to sodium channels and the paper is entitled, "Common Coding Variants in SCN10A Are Associated With the Nav1.8 Late Current and Cardiac Conduction". They were interested in SCN10A and sequenced this gene in over 3600 individuals from the CHARGE consortium to identify variants associated with cardiac conduction. They were able to replicate associations between variants and PR and the QRS intervals in a sample of almost 21,000 individuals from the CHARGE Exome sample. They identified several missense variants have clustered into distinct haplotypes and they showed that these haplotypes were associated with late sodium current. Jane Ferguson: Continuing the cardiac conduction theme, Honghuang Lin, Aaron Isaacs and colleagues published a manuscript entitled, "Common and Rare Coding Genetic Variation Underlying the Electrocardiographic PR Interval". They conducted a meta-analyses of PR interval in over 93000 individuals which included over 9000 individuals of African ancestry. They identified 31 loci, 11 of which have not been reported before. We see SCN5A come up again as a gene of interest in this study but their analyses also implicated a novel locus, MYH6. Jane Ferguson: Next up moving from the heart to the vasculature, Janne Pott, Markus Scholz and colleagues from the University of Leipzig published a manuscript entitled, "Genetic Regulation of PCSK9 Plasma Levels and Its Impact on Atherosclerotic Vascular Disease Phenotypes". They were interested in whether circulating PCSK9 can be used as a diagnostic or predictive biomarker. To address this, they conducted a GWAS of plasma PCSK9 in over 3000 individuals from the LIFE-Heart study. They found that several independent variants within the PCSK9 gene were associated with plasma PCSK9 as well as some suggestive variants in another gene locus FBXL18. They used Mendelian randomization to probe causality and the data suggest that PCSK9 variants have a causal role in the presence and severity of atherosclerosis. Jane Ferguson: Moving on to another biomarker, Lisanne Blauw, Ko Willems van Dijk and colleagues from the Einthoven Laboratory for Experimental Vascular Medicine report on CETP in their manuscript Cholesteryl Ester Transfer Protein Concentration A Genome-Wide Association Study Followed by Mendelian Randomization on Coronary Artery Disease. They aimed to assess potential causal effects of circulating CDP on cardiovascular disease through GWAS and Mendelian randomization. Jane Ferguson: In a study of over 4000 individuals from the Netherlands Epidemiology of Obesity Study, they identified three variants in CTP that associated with plasma levels of CETP and explained over 16% in the total variation in CDP levels. Genetically predicted in CETP was associated with reduced HDL and LDL cholesterol suggesting that CETP may be causally associated with coronary disease. Jane Ferguson: Rounding out the table of contents we also have a clinical case perspective from Nosheen Reza, Anjali The Importance of Timely Genetic Evaluation in family members in cases of cardiac disfunction and cardiomyopathy. We have a report from Adrianna Vlachos, Jeffrey Lipton and colleagues on the Diamond Blackfan Anemia Registry and we have a clinical case from Yukihiro Saito, Hiroshi Ito and colleagues on TRP and poor mutations in patients with ventricular non-compaction and cardiac conduction disease. Jane Ferguson: To read all of these papers and the accompanying commentaries, log on to circgenetics.aha.journals.org and if you're a visual learner or you need a work related excuse to spend time on YouTube, you can also access video summaries of all our articles from the CircGen website or directly from our YouTube channel Circulation Journal. Lastly, follow us on Twitter at circ_gen or on Facebook to get new content directly in your feed. Jane Ferguson: I'm joined today by Caitrin McDonough from the University of Florida and Caitrin is an Assistant Professor in the Department of Pharmacotherapy and Translational research in the College of Pharmacy and she's the first author on a recently published manuscript entitled, "Genetic Variants Influencing Plasma Renin Activity in Hypertensive Patients From the PEAR Study". This was published in the April 2018 issue of Circulation Genomic and Precision Medicine. Welcome, Caitrin. Caitrin M.: Thank you. Jane Ferguson: For listeners who haven't had a chance to read the paper yet, I wonder could you give us a brief overview of what prompted you to do this study? Caitrin M.: Sure so this looks at plasma renin activity and just initially a GWAS but it was done in a hypertensive population from the pharmogenomic evaluation of antihypertensive responses study. Particularly, since our group here at the University of Florida is more interested in pharmacogenomics we wanted to address plasma renin since it can influence blood pressure response to antihypertensive medication particularly if you use it as something to predict but also to correlate it with that as there have been also prior data from our group that shows if you have different levels of plasma renin that would predict if you would respond better to certain types of antihypertensive medications such as a beta-blocker or a diuretic. Caitrin M.: We used both a GWAS approach as well as a prioritization through blood pressure response to focus in on signals and then furthered by using prioritization using data from RNA seq and looking at eQTLs and then finally looking at more of just a traditional net replication of the original plasma renin activity signal. Caitrin M.: Overall, one of the interesting things and why we were initially doing this study was really in connection with a graduate course that myself and another faculty member here who's also an author on the paper, Yan Gong [inaudible 00:09:12]. We often have the students analyze data from the PEAR study as we have a lot of data from that study and it helped us analyze additional papers but we didn't necessarily know if this was going to be an interesting phenotype but through that course work which turned out that it really did have some interesting signals so we wanted to follow up more on. Jane Ferguson: Yeah, I love that approach so I think that's a really smart way to do it. To actually get your students to analyze your data and get them really involved in the process. How much then did the students ... how much were then they able to get involved when it started transpiring that their results would actually be something that could be put together for a manuscript? Caitrin M.: Overall, they are fairly involved. During the course work, what we usually do is give them just directly types data since a lot of them have not done this type of genetic analysis before and we split it up where each student gets about four to five chromosomes of data and then different phenotypes in the different race groups as we have both whites and African Americans. They get a certain race group, certain number of chromosomes and so they're able to conduct the analysis just using the Uplink software which is fairly user-friendly and straightforward. Then they get experience making Manhattan plots and using LocusZoom. Caitrin M.: After they have the basic techniques, then we teach them how to start following up top signals and determine what is a good signal. They're looking at the LD or SNP function or possibly gene function or looking at their genotype, phenotype relationships and making sure that it's not just one person who's driving the whole signal. Then selecting what top reasons and top SNPs may need a follow up. That part they all do there in the class and learn more of the basics. Caitrin M.: After the class, the students who want to continue to participate we get together and redistribute data where they would then move on to working on the imputed data sets and we teach them how to do that. Then we give them ... operate it somewhat similar to a consortia level meta-analysis type thing. I write up an analysis plan, each student does some part of the analysis. They have to bring it all back to me. I sort through it. We meet and go through it. Then we set our next steps to follow up. Then different students get different SNPs to investigate the function of or different subanalyses to do. Caitrin M.: One of our graduate students who is on this particular project, her dissertation project was very focused on our RNA seq data so that was how we were able to bring in the eQTL analysis using the RNA seq data as she had done a lot of the groundwork with that already. In one of our discussions that was one of the ways that we were able to incorporate the prioritization since she was intimately familiar with that data set. Jane Ferguson: Yeah and I think that's great. I can imagine that, that's a much more compelling way for students to learn about how to analyze data when they see the natural follow through. Do you find that some of the students maybe get more excited about research or are more likely to pursue future research opportunities by having had this hands on experience with the publication process and completing a project really did to this very end? Caitrin M.: They do, yeah. I see some of the students that end up sticking with it more are the students who I work more closely with and see more closely some of the students who are from other departments still stay involved but sometimes don't stay quite as involved. But, all of them really do continue to follow up and ask if they can still help or if there's anything they need to do until we get it to publication which is really nice. Jane Ferguson: Yeah, right. I think that's fantastic and I'm sure every study has its challenges. I'm interested what were the challenges you encountered in doing this study and which one of them may be unique to the way you have a lot of different people analyzing different aspects of the data versus the regular challenges that would come up in a study like this. Caitrin M.: Yeah so some of it I think is just keeping everyone on track and keeping it organized, making sure I think some of our challenges with this study was just me making I think on a lot of other studies, while I had certainly hands on the data it was more of an oversight rule for some pieces of it and just making sure everything looked the way that I thought it did, double checking. Some of it I think the teaching aspect of it just making sure everything was also done correctly and then keeping everything organized made the study a little bit more challenging. Caitrin M.: I think part of it too was with the PEAR study, it is a very rich data set. Determining what we wanted for our prioritization scheme and how to work through the different types of data sets that we had and put it all together as initially we just assign each student a different piece and we had a vague plan but it was a little bit more tricky as to work through how it was all coming together then when everyone came back together since a lot of people were doing as opposed to just one person doing it. Jane Ferguson: Right, so yeah and I think you're touching on the part that all of us have when we're writing papers that you sometimes end up with a lot of data at the beginning, you're trying to sift through it and then sometimes at a certain point you see something and you're like, "Okay, yes. This is interesting." Then you start following it up. Jane Ferguson: I wonder at what point did that happen? I suppose you probably ... You ran the GWAS for plasma renin activity and then find a number of suggested SNPs that were significant you associated but then ... Describe your strategy and you did so the second screening stuff to look at the pharmacological aspect defining [crosstalk 00:15:12]? Caitrin M.: Yeah, our initial plan going in was the first two steps, to do the GWAS for plasma renin activity and then to do the prioritization through blood pressure responses. I was very familiar with what our lab was familiar with but then after we got there, I think we were then troubled with what we did next and where to go. When we decided to bring in the RNA seq data, I think that was when it really started coming together as our top signal, the SNN-TXNDC11 gene region really stuck out then and it showed up. That seemed like a much stronger signal and it gave us a little bit more focus and also brought it much more of a functional aspect where we would maybe start to believe that signal more. That I think was really when we did that more of a turning point for the study and helped us focus more on where then to go with the results. Jane Ferguson: As far as the data you had I think over 700 people for your GWAS. Then you had a pretty large number of ... Was it the same subjects or different subjects where you also had the RNA seq data to do the QTL analysis? Caitrin M.: The same subject so not everyone has RNA seq. We have RNA seq data on 50 individuals and they were selected from whites and at the extremes of the blood pressure response so that it has a slightly interesting selection process. It's the main data analysis there was a best responder, worse responder to thiazide diuretics. Caitrin M.: When we do the eQTL analysis, we aren't always sure what we're going to get since we're missing the middle of blood pressure response. But, when we're just looking strictly at eQTL analysis sometimes we get lucky and sometimes it looks weird. Jane Ferguson: In your case as well you had the added issue of subjects were randomized to drug treatment so it was some where responders were ... I guess some people got the drug that worked for them and some people did not get the drug that worked for them. Caitrin M.: Yeah. Jane Ferguson: Did you I guess were incorporating both groups so good responders to either and some of that was because of their gene. They got the right drug for their genotype. Caitrin M.: Exactly, yeah. Jane Ferguson: It's good and then you were able to replicate this. After you were able to prioritize your gene region based on the GWAS and the drug response and the eQTL data, you actually ended up being able to go to a second sample to replicate the association right? Caitrin M.: Yes, it was in a lot of the same investigators, we have a second study PEAR-2, which has a very similar design to the PEAR study but used different drugs but also collected baseline plasma renin activity. We were able to use that phenotype again. We did have slight differences in GWAS to imputation panels at that point in time for when we were conducting this study so we ended up using a proxy to replicate but we did see the same signal in the second population which was very nice to see. Jane Ferguson: What is known about this gene region or either of these two genes? Caitrin M.: Overall, that was I think one of our harder points when we started trying to make the connections back to our phenotype. This was one of the areas where we did also have help from our students and the students as that was part of their initial training where they really looked to see what function was of various different genes and how to follow them up. That was one area where they came in, was to help look up some of the function of these ... there have been some connections with the various genes, the other phenotypes and with SNN and to atherosclerosis and other inflammatory cytokines such as TNF-alpha. Then there have been data also from [inaudible 00:19:37] that really show that there is an eQTL in this region that which supports what we saw in our own data. However, there really wasn't any direct connections with renin and the renin angiotensin aldosterone system and blood pressure regulation that we could find in the literature. Caitrin M.: We're not exactly sure how it connects but based off of our functional data and levels of evidence and then we saw some of that in publicly available data, we're still very interested in the region. Jane Ferguson: I think the data is compelling enough that it looks like you've identified the new region that probably is the mechanistically related that will require a whole bunch of basic mechanistic research to figure out what exactly the genes in this region are doing and how this ultimately connect back to blood pressure and response to drug therapy. Caitrin M.: Exactly. Jane Ferguson: I could see this ... I mean obviously there's a whole lot of potential functional work there and then probably also the clinical work, I wonder what you think about how this would affect any pharmacogenetic therapeutic ... You know at present I think you can look at plasma renin activity and use that as a predictor of drug response to help guide therapies. Would you think that a genotype guided therapy may end up being more effective than the plasma renin activity measurement in this case? Caitrin M.: In this case since this is so connected with a phenotype that you could use with plasma renin, I think if you're able to draw a plasma renin you may just want to do that. I think our overall goal would be if someone had preexisting genetic data and you weren't wanting to do an additional test or if you're contemplating response to a lot of different drugs that perhaps you could use a genetic data. One of the issues that was brought up on review and that are a lot of group considers quite a lot is that we have a lot of signals and that our group has certainly published a lot in this area and there's a lot of signals that we have to a lot of different drugs and how do you incorporate all of them together, is there overlap between them or where do they all fall? Caitrin M.: That is certainly something that we're still working on as more I think ultimate goal would be more to delve more of a SNP score or gene score and some type of risk score that would help you determine what drug you would best respond to. We've done that a little bit in some of our prior publications but we haven't yet taken all of our data together and help to build something that would if you had a lot of data on an individual and various different alleles at various different genes, how that would respond. Caitrin M.: Overall, when we look at blood pressure response as a pharmacogenetic signal, certainly we see larger affect sizes than you would in disease genetics but we're not seeing affect sizes like you would with more of an adverse drug event. We're in between there and we're often times it's not necessarily just going to be one SNP or one gene that would tell your whole story but a combination of quite a few of them. Jane Ferguson: I wonder are there more similar stories like this from the same data set? You know you've been through this process from start to finish and building in the functional work and do you think that next year's class will be able to do this again with the same data set? That maybe pick one of the next priority candidate down the list and maybe find another interesting story like this? Caitrin M.: Yeah, so we actually just finished our class this year and they looked at potassium. We just got done grading final papers and submitting grades so we will over the summer be working with them a little bit more. I think some of our new graduate students too are starting to work on trying to make more connections between a lot of our different phenotypes and as you start to layer those together what it exactly means for a patient or implementation perspective. Jane Ferguson: Yeah, interesting. We'll have to look for that story whenever you guys get done with it. Otherwise, are you planning on following up this specific SNP region in any other way or any other studies? Where's next for you guys overall? Caitrin M.: I think one of the things we would like to do is look at this more in PEAR-2. We really just brought the PEAR-2 data set in here as replication of the top region in that last stage but we have that data set and we can certainly look at that data set. Caitrin M.: The other thing that I would like to do is as we started this project in conjunction with the class that was a couple of years ago at this point in time, we used [TAP/MAP 3 00:24:35] imputed data since that was what we had in the lab and what we were using at that point in time. At this point in time, we have now imputed both PEAR and PEAR-2 2000 genomes phase three data. It'd be interesting to see if we are able to see any additional signals or if these regions become stronger or exactly what would happen using a more imputation panel that has more coverage and where we would have the same panel between both PEAR and PEAR-2. Jane Ferguson: Right because you may or may not have identified the causal SNPs in the previous access but- Caitrin M.: Yeah. Jane Ferguson: -yeah so it'd be nice to see if you can actually get that out. That potentially could end being a drugable target maybe suitable for something more specific but who knows. Is there anything else that we haven't covered yet that you'd like to mention? Caitrin M.: Overall, I think that just this type of model of utilizing more of a real world analysis and data in a class project really certainly engages our students a lot and I think they all enjoy actually being able to work with data that came out of this study and have a lot more hands-on experience and really project-based analysis experience. We've been very happy with this model and have used it multiple times. We have an HDL paper, the renin paper, our glucose response paper and now we're working on the potassium project. It's been a good model for us here with our pharmacogenomics class. Jane Ferguson: Yeah, I mean I think it's a really smart and intuitive way to think about education. It's mutually beneficial it sounds like, so it's helping you guys get your data analyzed. It's really helping the students learn so I think it's a win-win situation. I think it's a model that a lot of other people would really be interested in adopting. Caitrin M.: Yeah. Jane Ferguson: Okay well thanks so much for talking to me and talking about your model and your research. It's been great. Caitrin M.: Yes, thank you very much for having me. Jane Ferguson: That's it from us for May. Thank you for listening and come back for more next month.

Jane Ferguson: Hello, welcome to Getting Personal: Omics of the Heart, episode 14 from March, 2018. I'm Jane Ferguson, and this podcast is brought to you by Circulation Genomic and Precision Medicine, and the AHA Council on Genomic and Precision Medicine. This month I talk to Deirdre Tobias about her research on branch gene amino acids and incident cardiovascular disease in women, and I got the chance to talk to Kiran Musunuru, the new editor in chief of Circulation Genomic and Precision Medicine, about his take of the publishing process and new directions the journal has been taking. I'm joined today by Dr. Deirdre Tobias who is an instructor in medicine at the Brigham and Women's Hospital in Boston. Dr. Tobias is the first author of a paper entitled Circulating Branch Chain Amino Acids and CBD Risk in Women that is published this month in Circulation Genomic and Precision Medicine. Deirdre is also presenting this work at the lifestyle sessions in New Orleans this month, where she is receiving the Scott Grundy fellowship award for excellence in metabolism research. Deirdre, thank you so much for joining us, and congratulations on your manuscript and your upcoming award. Deirdre Tobias: You're very welcome, and thank you. Jane Ferguson: Could you tell us a little bit more about your background, and what led you to this specific research focus? Deirdre Tobias: Sure. I am an epidemiologist, and I investigate risk factors for type two diabetes and other obesity related chronic diseases. Most recently I've been interested in looking at the mechanisms linking obesity with many of it's associated risk factors through metabolomics. Metabolomics is a field that's relatively new, and it identifies, or seeks to identify small circulating molecules throughout the blood, or urine, or other specimen samples, and then relate those levels to risk of disease. Type two diabetes has had large successes with finding markers that are novel, and these are often consistently identified across studies, which is very reassuring, and not always the case for many risk factors of chronic diseases. Branch chain amino acids in particular have been consistently associated with diabetes risk across study populations, and tissue type samples, and methodologies for measuring metabolites. This is reassuring that these metabolite markers might really be picking up on an important signal in diabetes risk. Jane Ferguson: For any of our listeners who haven't had a chance to read your paper yet, I wondered. Can you give us sort of a brief overview of what you did, and a summary of your findings? Deirdre Tobias: Sure. Metabolomic studies, as I mentioned, have been successful in the field of type two diabetes, but less so for cardiovascular disease endpoints. With branch chain amino acids being a risk factor for type two diabetes we then thought to establish, or to examine whether they were also associated with incidents of cardiovascular disease risk. In our study we had branch chain amino acids measured on over 27,000 women from their baseline blood samples, and these women were participants in the women's health study in the US. This cohort already has over 18 years of mean follow up with a number of CBD and diabetes cases already accrued, so we used these baseline measures of branch chain amino acids measured from plasma samples to then relate these to incident CBD risk. We had over 22,000 MI, stroke, and revascularization events in these women. What we then did was analyze the relationship between branch chain amino acids levels with incident in CBD. Jane Ferguson: That's a really amazing data set. I mean, that's a huge number of subject, so that's really, really powerful. Deirdre Tobias: Yeah. For metabolomics most of the prior evidence has come from smaller case control studies, so this is really an unprecedented number of women participants in general that have had metabolomics with this much follow up. It was definitely a rich resource to be able to address this question and with a substantial amount of statistical power. Jane Ferguson: Right. I know you ran a large number of different models, and you looked at a lot of different covariances, so what were the primary findings? Deirdre Tobias: The main results indicated that even adjusting for the traditional CBD risk factors, including behavioral, and lifestyle, smoking status, family history, race and ethnicity, even body mass index which is a strong predictor of branch chain amino acids levels, we observed a striking association indicating that higher levels of branch chain amino acids were associated with a greater risk of MI stroke or revascularization during follow up. Jane Ferguson: Yeah, and I thought it was really interesting that you saw this in the whole sample, and then you also saw this sort of same association, in sort of like attenuated strengths in individuals without type two diabetes. Then, when I was looking I suppose you had adjusted for a number of different biomarkers, and then when you adjusted for the biomarkers as pre diabetic risks, so hemoglobin A1C or insulin resistance, the association went away. I wonder, even in your subjects who didn't have diabetes yet, were the association between the branch chain amino acids and CBD events was significant, but do you think that this was primarily driven by the pre-diabetes level, where these women are probably on track to developing diabetes, and just haven't hit that threshold for diagnosis yet? Deirdre Tobias: I suspect that could be the case. We do know that diabetes is a risk factor for CBD, and as I mentioned with the strong, consistent evidence indicating branch chain amino acids as a risk factor for type two diabetes we wanted to disentangle any association that we would see with branch chain and CBD to be able to show if that was independent of intermediate type two diabetes or not. When we did stratify by diabetes, clearly the majority of that risk seemed to be driven through type two diabetes. The women who had diabetes prior to their CBD event, clearly we saw the relationship much stronger for them. The residual is still there among those without type two diabetes, could be some clinical diabetes or pre-diabetes. It's hard to really say for sure, but we do have additional models where we adjusted for biomarkers that were measured in the same baseline blood samples as the branch chain amino acids. When we adjust for these markers that are more related to insulin resistance and glycemic control we see that the relationship between branch chain amino acids and CBD becomes attenuated. We can interpret this as being that the branch chain amino acids may be mediating the relationship with CBD through these markers of insulin resistance and glucose metabolism. If we similarly adjust for cholesterol, LDL or HDL, the relationship between branch chain amino acids and CBD didn't budge. That would indicate that perhaps branch chain amino acids and CBD risk is being mediated largely through this diabetic pathway rather than other more traditional CB pathways such dyslipidemia or inflammation. Jane Ferguson: Right, so it's an entirely independent pathway, I guess, that probably adds to the risk rather than being complimentary with traditional risk factors like LDL cholesterol. Deirdre Tobias: Yeah. The common soil hypothesis which has been around for many years suggests that there's a common set of risk factors, or cardio metabolic dysfunction, that leads to both diabetes and CBD. This suggests that branch chain amino acids and impaired branch chain amino acid metabolism could be one pattern or marker of this common predisposition to both type two diabetes and CBD. Jane Ferguson: Right. It's really interesting stuff, and I think this field, it's so challenging following up these markers when you're trying to find out, is a biomarker, when it's associated with a disease is it actually causal, is it a bystander? Are branch chain amino acids increased by insulin resistance, but don't actually themselves contribute to disease, or are they also individually contributing to disease. I know there's been some papers trying to address this through Mandelian randomization approach. It's indicated that the genetic predictors of branch chain amino acids aren't necessarily causal for diabetes, but that's sort of a consequence for insulin resistance. I wonder how that fits into your findings with, then, the additional associations with cardiovascular disease where we think of this pathway where we get this increased diabetic risk, and then perhaps as a consequence of the increased diabetic risk you have increased branch chain amino acids. Are these, then, themselves increasing oxidative stress or some other mechanism that's then leading to pro-atherogenic, pro-cardiovascular risk, or is it still just, well, it's a bystander, and they're there? They're a biomarker, but they're maybe not great targets for therapeutic interventions. I don't know what you think of this. Deirdre Tobias: Yeah. No, that's a completely fair interpretation of the data, and the literature overall. I think with observational epi, disentangling not only the causality but the temporality, so which biomarker is really, if it is causal, occurring first on this pathophysiology from say obesity or lifestyle to ultimately CBD? I think that it's difficult to really determine from one single study what that directionality is. Looking to the other literature we do see that there is the Mandelian Randomization. I think there have been more than one now, but one did indicate that genetic predictors of circulating branch chain amino acid levels were associated with type two diabetes risk, which supports there being a causal relationship between branch chain and diabetes. I haven't seen that also done for CBD, but that could be a next step. I think another next step for branch chain amino acids and metabolomics in general is to establish whether or not these are modifiable. If they're just along for the ride, or they're even just a strong biomarker, what does that mean clinically? I think it's still a very open question for many of these metabolomic studies, and even when we have these metabolites that really rise to the top as strong biomarkers, what do they mean? It could even be that we know they're causal, but that they're not entirely modifiable. Maybe they are just very strongly genetically determined. I think the next step might be to then identify what modifies levels of branch chain amino acids, whether it's lifestyle, or pharmacologic therapies, I think is still unknown, but if we can identify what can modify branch chain amino acids and then answer that next step of, "Well, does the modifying branch chain amino acids then confer a lower risk in disease," that would be an ultimate question to answer. I think these large scale epidemiological studies are very important as that first step in telling us whether we're even going in the right direction, and then subsequent studies will only strengthen these results, especially when it comes to the causality point. Jane Ferguson: Right. Right. I know in your current study, you're limited somewhat by the data, but I wonder. Were you able to look at whether dietary factors, or physical activity and exercise levels were even associated with branch chain amino acids. Obviously protein intake, or carbohydrate, sort of a prudent versus a western diet, were you able to look at that, or is that something that you may be able to look at in the future? Deirdre Tobias: Right. In the future we are hoping to disentangle what the determinants are for branch chain amino acids in this cohort, so looking at the role of physical activity, or dietary patterns, or specific foods and nutrients might play. But for this analysis here, we adjusted for all of those factors, and when we do compare, the first model where we only adjust for age, with the second model where we include adjusting for all the more traditional CBD factors as well as the behavioral, lifestyle, dietary patterns or et cetera, we see that there is quite an attenuation. This does suggest that there is some room for modifiability, because these other potential confounders are impacting the association that we see. If there was no effect in our results when we adjusted for all of these behavioral factors then they might not be that strong determinant of branch chain amino acids, but we do see some attenuation. It is possible that many of these factors, or maybe collectively they impact branch chain amino acid levels. But that's another next step, and I think that's something that we can address very well with the data that we have in our current data set, the women's health study. Jane Ferguson: Right. I mean, given the large number of subjects, even with messy dietary or exercise data, I think you'll have a lot of power to at least get you in the right direction to then define interventional trials that could specifically address that issue. Deirdre Tobias: Right. Another thing to keep in mind about branch chain amino acids is that they're essential amino acids, meaning that they're derived from diet and not synthesized within our bodies, but surprisingly the correlation between dietary intake and circulating levels is quite low. This, to me, tells the message that metabolism of branch chain amino acids, more so than their dietary intake, is what's driving these elevated levels, and what leads to this impaired ability to break down branch chain amino acids leaving these higher circulating levels, I think that's the risk factor that might predispose to these other cardio metabolic conditions down the road. Really I think the next steps would be to determine why certain people have more impaired metabolism of branch chain than others. Body mass index is highly correlated with branch chain amino acid levels, that's, you know, an obvious next direction would then be to look at lifestyle factors and anthropometrics to see where those lead us. Jane Ferguson: Right. Right, and I wonder, is there some genetic [inaudible 00:16:05] where you could maybe find the people who don't fit the profile, like say somebody who has really dysregulated glucose and insulin homeostasis but actually has very low branch chain amino acids, or the converse, where they have very high branch chain amino acids but actually their insulin levels seem fine, and see. Is there something special about those groups of people, if they even exist? I'm sure there's probably some disconnect sometimes. It's not obviously perfectly predicted. Deirdre Tobias: Right. Sometimes looking at those discordant phenotypes can be really informative, and I think that could be interesting to look at as well. I think the genetic predictors, the few snips that have been associated with circulating branch chain amino acid levels from some of the Mandelian Randomization studies indicate that heritability might not be that strong, but certainly there are genetic factors that influence levels. But a lot of it is likely to be more of the modifiable, or environmental risk factors. Jane Ferguson: Right. Right. Deirdre Tobias: Which I think is an important message for prevention, or motivation towards prevention, because if we can modify this important risk factor, then we can ultimately reduce risk. It's certainly a lead worth investigating. Jane Ferguson: Yes, absolutely. Yeah, what's next for you. Are you going to keep working on some branch chain amino acids? Are there other metabolites that you're interested in as well? Deirdre Tobias: The overall metabolome is something I'm still very interested in. Branch chain amino acids have risen pretty quickly to be among my favorite metabolites, if I'm allowed to have a favorite metabolite, but I think that although an overall metabolomic pattern identifying what the overall pattern or profile of metabolites looks like for individuals who go on to develop certain diseases, and using those patterns as predictors, or again looking to see what we can modify about them I think is also really interesting. But branch chain amino acids clearly have an important role either as a predictor, a bystander along for the ride, or possibly even causal, yet to be determined. Yeah, next steps would be to, like I said, identify, what are the determinants of higher levels, and whether or not these can then be modified to reduce risk. Jane Ferguson: Important work. Is there anything else you want to add that I haven't asked you about yet? Deirdre Tobias: The other findings in the paper were that it seemed fairly consistent across the three branch chain amino acids that we investigated, so isoleucine, leucine, and valine, which share a similar catabolic pathway. Again, maybe the overall breakdown of this metabolite type rather than any one metabolite might be what's relevant to [inaudible 00:19:01]. Jane Ferguson: Right. Yeah, so a defect, if there is one, is a stream of all of the branch chain amino acid processing. Deirdre Tobias: Mm-hmm (affirmative). Yeah. Jane Ferguson: Yeah, really interesting. Well, thank you again for joining this, and for talking about your work. Congratulations again on it. It's a really interesting paper, and your presentation and award at EPI Lifestyle, it's great stuff. Deirdre Tobias: Great. Well, thank you very much. I appreciated talking to you. Jane Ferguson: Thanks. As many of you may know, Dr. Kiran Musunuru is an associate professor of medicine at The University of Pennsylvania, Perelman School of Medicine. More pertinent for this conversation, he assumed the role of editor in chief of Circulation Genomic and Precision Medicine this year. Welcome Kiran, and congratulations on your appointment as editor in chief. That's a fantastic achievement. Kiran Musunuru: Thank you. Jane Ferguson: With great power comes great responsibility. I know you've been making a lot of changes to the types of content available on the journal website, and finding new ways to ensure the journal is publishing best science and keeping up with changing technologies and capabilities. I'd love to hear about some of the initiatives that you've been putting in place and what your vision is for the journal going forward. Kiran Musunuru: Sure. I think it's not just about publishing the best science, although I think that's obviously the core mission, but I think also publicizing the good science that we're publishing. With that in mind, the name change of the journal to Circulation Genomic and Precision Medicine really indicates our desire to go beyond what can seem like the very limited scope of cardiovascular genetics, and really take advantage of the rich work, and the energy that's now being infused into whatever you want to call it, Omics, or precision medicine, or so forth. It really is much more than just cardiovascular genetics. We're casting a wider net. We want to publish a more diverse group of articles. In a sense, we want to be a player in the field. We have a nice opportunity where there is this thing called precision medicine, but everyone is hard pressed to define exactly what that means. I think what that signifies is that it's just so new, and still in the process of early evolution, that the journal I think will have a part to play in helping to define the field over the next few years. I think that's very exciting. It's a very exciting opportunity for any journal, or any journal editor, to be able to contribute in that way. But as I said, it's not just about publishing the best science. It's also about really trying to make maximum possible impact with that science. With that in mind we're working very hard to introduce elements in the publication process to really expand the reach of the work that is published. The way I like to think of it is, traditional journals a paper is published on the printed page. It gets mailed out to it's subscribers. Some people read it. Some people don't, but within a few weeks after the issue is published basically that's it. No one really, certainly is not going to casually pick up an issue and look at it, and only a very limited number of people are going to come across the paper on PubMed or whatnot, and actually go track the paper down. I think it should not be the end of it. I think that should just be the beginning. I think the day that it's published online, or in the formal journal, is really just the beginning of the opportunity to make that work known, and really try to make an impact on the field. Some of the things that we are doing in that spirit include this podcast series, that chain. You've taken the leadership, and have been doing a wonderful job with in terms of promoting some of the science that we're publishing, as well as just general issues in the field that I think that will be of broad interest to the same people who are interested in the content of the journal, and interested in the science that's encompassed by the Genomic and Precision Medicine Council, The American Heart Association. One element beyond that is what we call our calling, I think of as video summaries. Some journals have been experimenting with this in a sort of exploratory way inviting authors to make their own videos and submit them so that they can be placed on the journal website. Of course that demands a lot of authors 'cause there's a lot of inconsistency in the tone and the quality of those videos because they are coming from very diverse people rather than being centrally produced with the journal. Some of the very high profile journals have a substantial enough budget that they can actually internally produce, with high production values, very nice videos, but again, they tend to focus on just one or two key articles in each issue. Our ambition is to have a video summary for every original research article that's published in the journal bar none. We think that plays a very useful purpose. We think that getting someone to actually download a PDF, and read through it carefully, in this day and age is a harder sell than it has been in the past, and if we want to take full advantage of social media, and the fact that everything is out there on the internet, and can be accessed through devices, through smart phones, et cetera ... We felt the best way to introduce a paper and the content of that paper to the casual viewer, or listener, or reader, if you will, maybe not someone who is super specialized in the topic that's covered by a given paper, but still nevertheless has something the learn from the paper, can get something useful out of it. We feel that generating a video summary that goes through the high points of the paper, that can actually within the video show the key figures of the paper, and then in, not exactly layman's terms, or lay people's terms, but rather, in a non-specialist sense, so still speaking to the science, but without a lot of jargon, without a lot of specialized vocabulary that's really specific to the specialists in the area that's covered by the paper, but really more broadly speaking to scientists, and even more than that, clinical practitioners, really explain in a very straightforward and simple way exactly what the key message of the paper is and why it is important for genomic and precision medicine. We have started doing this internally. Starting with the January issue we have had a video accompany every single original research article as well as a scientific statement that we published in the January issue. They are linkable, or linked to, from the website, but they are physically, well, not that anything digital is really physical, but let me put it this way, they're hosted by a YouTube channel under the umbrella of the Circulation Journal. They can be found there because they are on a YouTube channel they are essentially a permanent part of YouTube, so anyone searching YouTube can find them. Then something we've started doing with the transition internal leadership and the new name of the journal is using Twitter to make people aware of these articles that we are publishing, but one very nice thing we can do since we have video summaries available on YouTube is we can actually link within a Tweet directly to that video, and people can actually watch the video directly within Twitter on their devices, on their smartphones, on their computers, wherever they are, however they're accessing it. We've been finding that people are watching these videos, regardless of what channel they use to get to the videos. They are watching. I think that's a nice thing, because it is expanding the reach of the journal to an audience that might not necessarily or naturally go to the journal website to see what the latest articles that have been published are. It's a different way of reaching the intended audience. Jane Ferguson: Yeah, I think it's fantastic. I mean, it's much easier to watch a video for a few minutes when you're, I don't know, waiting for the bus or something, rather than take out your PDF and try to read through these dense method sections. It's really fantastic, I think, even for people who are, if it's our field, or if it's something outside of our field, or people who don't typically read scientific articles but are interested in science [inaudible 00:28:24] and learning more. I think it's really nice to offer that sort of alternative format for people to discover a new science. I wonder, what do you think is the value of commentating an interactivity in scientific publications? I know in some places they allow commenting, whether anonymous, or named. For a lot of people they get a lot of their science on Twitter, and they have Twitter conversations. Do you think that's an important role that needs to grow more in our dissemination, our discussion of science? Do you think there's an ideal forum for this kind of discussion, or is it still something that's evolving, and we'll find our place, whether it's the comments section of the YouTube videos, or on Twitter, or elsewhere? Kiran Musunuru: Sure. No, that's a great question. It strikes me that a lot of groups, a lot of organizations, have been hitting upon the same question, and attempting to answer it by generating a forum that's, if not exactly proprietary, is very much under their thumbs, with the intent of fostering conversations about papers published in journals, about content from publishers and so forth. Because everyone is trying to do their own thing it ends up being quite fragmented. There's not a lot of uptake for any one particular publisher's website, or any one particular mechanism. What has sort of naturally, spontaneously occurred, is a lot of those conversations have just happened on Twitter, which is not intended specifically for scientific discourse, but has cast such a wide net, and has so prevalent a footprint in our society that it serves that purpose just as well as it serves 100 other purposes. I think it's becoming clear that, increasingly, conversations, discussions, debates, controversies even, are unfolding on Twitter. That's not to say that everyone's on Twitter, or even a large segment of our intended audience is on Twitter, but I think a growing proportion of our audience is on Twitter, and Twitter I think skews younger. But we're a young discipline, and I feel like a lot of the practitioners, a lot of the investigators in our field are of a younger generation who is more ready to embrace Twitter, and is already out on Twitter, on Facebook, on other social media outlets. I think this is going to be absolutely critical going forward. Twitter, it's not happenstance of course, but it turns out that it lends itself nicely to communication about scientific articles because you can link directly to a paper through Twitter right there in the Tweet, so people don't have to look very far. You can send out your Tweet describing the paper in very brief terms, but then within that Tweet you actually see a box with a description of the paper, with a link to the paper. You can link on it. It takes you directly to the journal's website. You don't have to actually work to find the paper. The same is true of other elements that we've already discussed. If you want to listen to this month's podcast for Circulation Genomic and Precision Medicine, spearheaded by Jane Ferguson, then the Tweet that goes out, and then is retweeted among our network, allows you to click right there and go to the podcast. Again, you don't have to track it down. You don't have to find it. It's just kind of put there, out there, and people will immediately be notified about it because it will be on their feeds, and then they'll see it. Then, as you say, when you're waiting for the elevator, or you have a little bit of down time, you can go ahead and just click on it and immediately start consuming it, if you will. The same, of course, is true of our videos. Our videos are five to ten minutes, ten minutes maximum. They're intended to be bite sized pieces that you can kind of catch on the run as you described. It's not a matter of you having to download a PDF and read through very dense language and through method sections and try to figure out what's going on. Then people can access these on Twitter, through other channels. Then a lot of the conversation can happen on Twitter, and on the other channels. I think we're seeing more and more of this happening, among at least that portion of the scientific community that is Twitter savvy. Jane Ferguson: Yes. And I will use this opportunity to plug everybody to follow us on Twitter. You can follow us at @circ_gen, and you can tweet at us and tell us what you think of the journal, of the council, of the podcast, of science in general. We would love to hear your thoughts on that. As we're talking about this, how we're able to access so much information so quickly at our fingertips, the publication process has in some ways still lagged behind, where you submit your manuscript, and it goes out for review, and sometimes you don't hear back for a very long time. I'm wondering how you at the journal are dealing with that aspect of the publication process, and the turnaround time? Kiran Musunuru: Sure. I mean, I think it's becoming increasingly less acceptable for what has happened in the past, and it has been highly prevalent in the past to continue to occur. What I mean by that is, a very long, drawn out, peer reviewed process and time to publication of the final project. I mean, it's so unacceptable these days when there's so many avenues through which you can communicate your science. We're seeing things like pre-print servers that are really getting messages out. Then to have to go through a formal review process, and take many months, even years, to actually get your work actually formally published in a journal with the imprimatur of high quality peer reviewed process is just too long. It's impeding communication among scientists. That's a disservice to the scientific process, especially in a day and age where there is such a rapid evolution of technologies. There's such rapid advances in a field, to think that the journals are having to play catch up with what's actually going on on the ground is really quite frustrating. It's part and parcel of the publication process as it is, and it's not going to change overnight, but in my own role as editor of a journal I'm doing what I can to remedy it. I've committed, and the journal's editorial staff has committed to really trying to streamline the review process. Now, we're not going to work miracles, but I'm happy to say that, whereas in prior years it would have taken on the order of three, four, five weeks for a paper to be reviewed, we've really worked hard to streamline that to make it much more quick, nimble, and we hope less cumbersome to authors. For example, I will say in the month of January our average time from submission to first decision was under one week. Part of that is, we've accelerated the review process, engaged high quality reviewers who we know are responsive, and who will turn around papers relatively quickly. We've engineered things so that there's not much delay to any step in the process. Even those papers that are sent out for review we've accelerated the process and decreased the time on that basis alone, but something else we've introduced is a very rapid triage process. Rather than have a paper sit around, sit around, and then be assigned to somebody to look at, and then have to wait for a weekly conference call among the editors to decide the disposition of that paper, you can see how things can get drawn out. Even before the decision is made to send something for review you might have a paper sitting around for a week or two. That happens, I know from my own personal experience at quite a few journals. We're committed to making the decision, ideally within 24 hours, but certainly within 48 hours. Our editorial staff is extremely responsive. When a paper is submitted, there's a whole lot of eyeballs among the editorial staff that look at that manuscript from the get go, quite a few opinions, not just one, or two, or three opinions, but more like six or seven opinions within 24 to 48 hours. That's useful in two ways: One, it allows us to make a very informed but rapid decision as to the disposition of a paper. I think the optimal scenario is where we can forecast whether a paper is not going to fare well in the review process, and is ultimately going to get rejected, without actually having to go through the full length peer review process. If we can predict that outcome occurring, but be able to do that within the first 24 to 48 hours, then we can just make that decision and spare the authors a long delay for what ultimately is likely to be a negative outcome. Conversely, if we are fairly confident that a paper will ultimately get accepted, we'll need some revisions. Every paper can be improved, and that's what the peer review process at it's best is all about is improving the quality of a paper. But if we can forecast very early, this paper is very likely to be accepted. This is what we should send out for review. Then that allows us to really save everybody time. It saves author's time, 'cause they're not going through a long, drawn out process before ultimately having a negative outcome, but it also spares reviewers' time. We can focus reviewers' efforts on those papers that are extremely likely to ultimately be accepted, and that's the most constructive use of reviewers' time, I think. There's a secondary purpose, however, and that is when we do reject a paper without review because we've garnered so many opinions about the paper in a very short time, we're actually in a position where we can offer useful feedback to the authors as to why the paper was felt not to be of high enough priority for the journal. Most journals, and I'm speaking from my own experience as a scientist, as an investigator, as an author, you send the paper, and then it takes them how many days, or weeks to make a decision. Then often they will decide that it's just not for them, and they will reject without review. That's okay, but it can be very frustrating when the decision is made, and then you get the letter back saying, essentially in effect, "We don't like this paper," without any explanation why. What we have been doing, which I think has been well received by authors, is offering fairly detailed feedback. If you want to think of it as a triage review you can think of it that way, but corporate feedback from not just one or two peer reviewers, but actually six or seven editors who have looked through the paper. We've actually gotten feedback from a few of the authors, that even though we are not publishing the paper, it's been useful in helping them to revise the paper and then go on to the next journal for submission. Jane Ferguson: Yeah. I think that's fantastic on all fronts. I think making the process faster is better for everybody, and also making it constructive, so that yes, you're not just sending out form rejection letters, but you're actually helping scientists and authors to identify what could be improved in their papers and what sort of things they might not have thought about. I think that's really fantastic. Is there anything else new with the journal, or anything else that we can look to in the next few months coming out? Kiran Musunuru: Well, I think we've hit the high points. I think the idea that we want to be friendly to authors, that we want to streamline the review process, that we want to work hard to publicize their work and maximize it's impact in the community. I think by broadening the scope of the journal we're hoping to get substantially more submissions, and more diverse papers. We're very interested in publishing in the area that broadly falls within genomic and precision medicine. We're working hard to generate high value, non-original research articles. What I mean by that is scientific statements from The American Heart Association, working closely with the Council on Genomic and Precision Medicine to generate scientific statements that are of value to the community. Of course, we would continue to want to publish state of the art reviews, but also partner with other organizations, and publish white papers. For example, we have a couple of white papers on which we're working with The National Heart, Lung, and Blood Institute. That should be coming out in calendar year 2018. Jane Ferguson: Okay. Kiran Musunuru: Beyond things you would normally associate with publishers, we're also working hard to increase our presence at meetings. Part of that is what you might expect editors of journals to do, which is being there in person, and attending talks, and attending poster sessions, and pressing the flesh, and inviting promising presentations to be submissions to the journal. Of course, that's part and parcel of what any good, proactive journal is going to do. But I think going beyond that, and doing more, and actually being involved in contributing educational activities at conferences. As you well know, the Council on Genomic and Precision Medicine in partnership with the journal has been organizing boot camps and workshops at various American Heart Association meetings that have been extremely well received. And we've now started to organize similar sorts of boot camps and workshops at non American Heart Association meetings. We'll be doing quite a lot of that, at least three or four national or international meetings in calendar year 2018. I think this is all part of a big strategy to not just be a place where papers are published, read for a few weeks, and then never really thought much of again, but really to make it more of a living piece of work that can inform the community, that has a reach beyond the printed page, whether it's through videos, or podcasts, or forming the basis for educational activities, workshops, boot camps, maybe eventually journal clubs, things of that sort. That's what we would really like to do with the journal going forward. Jane Ferguson: That's fantastic. I think it's really great. You're doing some great new initiatives. Congratulations again on your appointment as editor, and on all of the wonderful work you're doing. Kiran Musunuru: Thank you, Jane. Jane Ferguson: That's all for this month. As a reminder, you can follow us on Twitter @circ_gen, and you can also now connect with us on Facebook. 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Transcript for January 2018 Podcast Circulation: Genomic and Precision Medicine Jane Ferguson: Hi, everyone. Happy New Year. You are listening to "Getting Personable: Omics of the Heart". I'm Jane Ferguson and this is episode twelve from January 2018. This month I have some exciting announcements to make. The journal formerly known as "Circulation: Cardiovascular Genetics" has a new name. As of this month, the podcast is brought to you by "Circulation: Genomic and Precision Medicine". We're still publishing papers focused on cardiovascular genetics but as genomics and other omics have expanded our scope has grown to so much more than just genetics. The new name, "Genomic and Precision Medicine" signifies the journals focus not only on genetics, but also genomics and all the other omic technologies and the feel of precision medicine. Along with the new name we have a new editing team. Dr. Kiran Musunuru, an associate professor of cardiovascular medicine and genetics at the Perelman School of Medicine at the University of Pennsylvania has officially taken over as editor-in-chief. He has already been implementing new initiatives to allow the journal to serve authors and readers even better. Along with create original research articles you can find accompanying editorials, videos and interviews with authors, including the interview we're featuring in this month's podcast. Finally, while "Circulation: Cardiovascular Genetics" was published every two months, "Circulation: Genomic and Precision Medicine" will now be published monthly. So, you can look forward to a new issue every month and even less time waiting for the newest research to be published. Check out the latest issue and all of the new material at circgenetics.ahajournals.org and follow us on Twitter at Circ_Gen. Now, along with the name change for the journal, we have another name change in the pipeline. Our AHA Council, Functional Genomics and Translational Biology, is also being renamed to "The Council on Genomic and Precision Medicine". As with the journal name change this better reflects the evolution in our scope and focus. This name change will be formalized in the coming months. So, if you are one of the many people who could never remember what the acronym FGTB stood for or what order all those letters came in, your struggles will soon be over. We have a number of interesting papers published this month, including an article by George Hindy and colleagues on how smoking modifies the relationship between a genetic risk score and coronary heart disease; a mendelian randomization study from Jie Zhao and Mary Schooling on coagulation factors and ischemic heart disease; an exome wide association study of QT interfolds from Nathan Bihlmeyer and colleagues; a study on genetic testing of cardiac ion-channelopathies and still births from Patricia Munroe and colleagues; and a genetic study of cardiac disfunction in Duchenne Muscular Dystrophy from Tetsushi Yamamoto and colleagues. You can also catch up on the genetic cardi-oncology literature with a review by Marijke Linschoten and colleagues on chemotherapy related cardiac disfunction. And read a clinical case on left-ventricular non-compaction by Vi Tang and colleagues. Finally, we also have a scientific statement on the use of induced pluripotent stem cells for cardiovascular disease modeling in precision medicine by Kiran Musunuru and colleagues. Moving on to our feature article, Andrew Landstrom, an early career member of the Genomic and Precision Medicine Council, formerly FGTB, talk to Guillaume Paré and Sébastien Thériault about their article published this month entitled, "Polygenic Contribution in Individuals with Early Onset Coronary Artery Disease". In this paper, Dr. Thériault and colleagues report the use of the genetic risk score which improves on our ability to predict very early onset CAD. Listen on to the authors talk more about the background to this study and what they learned along the way. Andrew: Welcome. My name is Andrew Landstrom, an assistant professor in the Department of Pediatrics, Section of Cardiology at Baylor College of Medicine. I am a member of the early career committee of the American Heart Association Council on Genomic and Precision Medicine, previously the Council Functional Genomics and Translational Biology. I'm joined today by Sebastien Theriault, assistant professor in the Department of Molecular Biology Medical Biochemistry and Pathology at Laval University, and Guillaume Pare, the Canada Research Chair in genetic and molecular epidemiology, assistant professor in integrative health bio-systems and associate professor of medicine at McMaster University. Guillaume: Hi. Good morning. Andrew: Well, I'm wondering if we could just start by introducing ourselves maybe a little bit more thoroughly than I just did and talking a bit about your research paper and what brought you to this as a research question. Guillaume: Absolutely. So, this … [inaudible] and thank you for having us. My name is Guillaume Pare, and as stated, I'm an associate professor at McMaster University, and I would say like my longstanding clinical interest is about individuals and families with very early coronary artery disease and heart disease. And this really was the basis for this project and to try to understand why do some people in family are afflicted by this disease when we cannot find any of the conventional risk factors. And as Sebastien came to join me and this endeavor, and spent two years with us here at McMaster and was instrumental in getting this project off the ground. Sebastien: Yes, exactly. So, I was a physician trained in Quebec City and I went to McMaster University as a research and clinical fellowship. And that's where I did some cardiovascular clinics with Dr. Pare and that's when we noted that some patients with early coronary artery disease didn't have much explanation for their disease. So, that's how this project stem, that we wanted to understand what was going on and we thought that really genetic factors could be involved. Andrew: And speaking of these genetic factors, in fact, you established a genetic risk score as sort of a way of aggregating a large number of genetic variants into a single prognostic risk indicator. How did you come up with the score, and where did these genetic variants that you aggregated come from? Sebastien: So, the results of many of our studies looking at the association between common genetic variants and coronary artery disease have recently been released. For this study, we use the variants identified in the latest CARDIoGRAM for C4D consortium meter analysis, which includes more than 60000 individuals with coronary artery disease and 120000 individuals without coronary artery disease from a total of 48 studies. Most of the participants in these studies were European. And so we decided to use the independent variants that were associated with the disease in that very study and look if we could predict early coronary artery disease in some patients. Guillaume: Andrew, maybe I'll backtrack a little bit. The initial idea about the gene score, first of all came from the observation that a lot of the patients who we're seeing do not have any traditional risk factor. The second observation is that we already knew that genetic risk scores are predictive of coronary artery disease. But the key question is, is it possible that there are people at the extreme of severity of a cardiovascular or genetic risk score that could be at much, much higher risk of having the disease. And this is what the hypotheses really that we wanted to test is whether these genes scores they could identify people that clearly have outlying risk, outlying genetic risk of having the disease. And to explain, the patients that we were seeing a deflation in the clinic will clearly have an outlying risk of disease because they have a First Earth attack or multi vessel disease in their 30s or 40s, and we thought that this cannot be just like bad luck, there had to be some ... and this something is really most likely genetics. We cannot put a finger on it because all the known mutations that we know could cause this, well, we're just not finding them. Andrew: Sure, sure. And there's certainly having a large number of genome association studies, which have implicated a number of common variants and not so common variants in coronary artery disease. So, is this where some of this idea behind the genetic risk or was initially thought of? Guillaume: Absolutely. And I think you know ... and this is where Sebastian really came in and to really like look at this literature, to feel like the variants that went in into the score. Andrew: And certainly to go to your earlier point, it seemed like you were saying early on that coronary artery disease would be a great phenotypic model to explore this question in, mainly because it would seem that at that age, with that severe disease, that it must be something innate to that person, and genetics would certainly play a role. Guillaume: Absolutely. And to me, it's more than simply scientific because we see these patients at our clinic and we've got a lot of ref roles for these patients, and we really feel for them because they're really young people, and I think like when we think about genomic and like preventative medicine having an impact, I cannot see a greater impact than preventing a first heart attack in the 30s or early 40s. So, this is a ... it's a very vulnerable patient population. It's also a patient population that has a lot of questions about why this might be happening to them, and often what we see is that, I think everyone feels that clearly there's a genetic component, and one, a loved one has first attack in his or her 30s, this raises questions for the whole family really, and it clearly sends a shock wave in the family, and everyone, I think rightfully, is quite scared of having the disease and the fact that there is no answer for these people, to me is a huge unmet clinical need. And it's just for the sake of providing people with answers. Andrew: Yeah. Absolutely, I think it's certainly a clinically relevant question that you attempted to answer. And to try to get to this a little bit, and you utilized a large UK-based biobank as your primary study population to establish this risk score. Can you tell me more about this biobank and what sort of data you were able to obtain from it? Sebastien: Sure, I can speak a bit about it. So, the UK biobank is a large prospective cohort of about 500000 individuals between the age of 40 and 69, with an average of 58 years, and they were recruited from 2006 to 2010 in several centers in the United Kingdom, and the general objective is to study the effect on the environment and genetics on health. And what's interesting is that the data is made available to the research community worldwide following registration process. And the data in that includes a very vast amount of information, from questionnaires, specific evaluations, such as height, and weight, and aging data, and the diagnosis from the participants, medical charts, in addition to the genetic data of course. And for this study we used the first release of the genetic data, which included information on about 40 million variants in about 150000 individuals, and selected the individuals who had a diagnosis of early coronary artery disease, so aged 40 or less for men, 45 or less for women, and then it underwent a reversed relation procedure in order to identify patients with obstruction in coronary artery disease, and we used all the other participants as controls. And that's basically leveraging this huge amount of data that we were able to confirm the fact that patients with early coronary artery disease, some of them very high and pathogenic components of their disease. Andrew: That certainly sounds like a really amazing, both biobank and cohort of information that could be utilized. Such a huge sample population with so many clinical variables as well as genetic variables and collected prospectively. What a great resource. Sebastien: Yes indeed. Guillaume: It's a fantastic resource and to me, this type of initiative it's a game changer to accelerate research, because with these data being made available, then it's really up to testing new bold ideas to try to improve our understanding of this disease. So, I think you know we have to say kudos to United Kingdom for financing this this great cohort and making it available to researcher worldwide. Andrew: And you didn't just stop there. You also utilized a local cohort as a foundation cohort for your study. Could you speak a little more about that? Guillaume: So, that's interesting because this cohort really stems from the patients that we've seen at the clinic. And essentially, we felt this was this huge unmet clinical need. To better address causes of disease, and these roles that's barely a disease. And then we said, well, if we were to do this, let's do this formal, and let's do this properly and collect the information and samples and everything, and we had a very enthusiastic response from our cardiologist, and international cardiologist colleagues that really helped us identify these early cases and send them to us and in our study. And so these are local patients. These are people that we care deeply about, and that's really want to make a difference. And again, you know, when Sebastian was with us at McMaster, we were seeing these patients together, and maybe he can add some of the details there if you want. Sebastien: Yeah. Just to specify again, these were patients at the very early coronary artery disease, for age 40 or less for men, and age 45 or less for women. And these were patients without the clear secondary cause of their disease. Most of them were clueless about what were the factors that caused the disease outside a few risk factors such as smoking or hypertension, there wasn't clear explanation as to why they had such early disease, and we could see that it was a struggle to try to understand and then see if there is a risk for their family also. So yeah, it was really interesting to find an explanation for some of them, and we did report the findings to a few of them who seemed to have polygenic contribution to their disease, and it did make a difference. They were quite happy to at least have some kind of an explanation to what was happening to them. Guillaume: And I think that one thing that I think was striking to me when doing this is that when we started to formally collect family history in these individuals, we just realized that and in many, if not most of them, the family history is really striking. And these are folks that clearly has a very severe individual disease, but when we start asking about their brothers, and sisters, and parents, and uncles, you just realized that coronary artery disease was just all over the place and was very aggressive and early. And I think to us, this gave us purpose in this project to say that, 'Yes, we have to do something about this,' but also, I think it also reassures us that our primary hypothesis was right in thinking that there has to be a genetic component that goes beyond just having bad luck, and this genetic component was expressing itself by the family history that we saw. And a further clue that I think we might be on the right track is that the pattern of inheritance didn't shift one of the single mutations that aggregates in a family and that can explain the disease. So, the disease was more diffuse and oftentimes it was both from the paternal and maternal branch of the family without a clear genetic pattern that would be more in line with the so-called mendelian disease, where a single gene mutation causes the disease. And I think really that puts to us in the mind that we might be looking at the different modes of inheritance, and this is partly how we came with this idea of looking at gene scores in these individuals and families. Andrew: So certainly a close clinical connection to the patients and their families that you're trying to risk stratify and certainly, it sounds like clinical suggestion that you were dealing with something genetic and inheritable, but not necessarily mendelian, where one gene defect leads to say an autosomal balanoid express disease, more of a polygenic family history exactly. Guillaume: Exactly. Andrew: And so with these two scores and this genetic risk score, what exactly did you all find? Sebastien: So first we found that participants from the UK biobank who had this early coronary artery disease had a very significantly higher number of common genetic risk variants. So the score was very significantly higher in these patients. And what was interesting too is that the increase in risk that was associated with the score was independent from traditional risk factors such as smoking and high blood pressure. And when we looked in the local cohort with early coronary artery disease, out of 30 participants that were involved, we found seven with a significant polygenic contribution, which we define as, a two-fold increase in risk, and one of the participants actually more than six-fold estimated increase in risk. So we really did identify an explanation for some of these participants with the early coronary artery disease. Guillaume: And I think this was maybe a bit of a eureka moment to see that some of these individuals actually had a much, much increased risk of disease based on the polygenic risk score, and this really was the primary hypothesis that when looking at extreme of disease, which is what we're looking at, we might find extreme of genetic predisposition. But the one thing I thought that's quite striking is then we went back to think all that. And to try to put this in perspective with what we would usually do in these patients that we've done already, and to look for mutations that cause familial hypercholesterolemia. Familial hypercholesterolemia is a disease of cholesterol metabolism that leads to a much increased concentration of cholesterol and early coronary artery disease, and a discovery that led to a Nobel Prize for Goldstein and Brown, back in the day, and really like, up to this point, when we see people with early disease clinically, this is what we will be looking for. And certainly, there's a lot of these individuals that have very high cholesterol and a lot of them is due to familial hypercholesterolemia. But it's a minority of patients really. It looks like we're having an association and this gene score concept is really panning out. But I wouldn't compare to familial hypercholesterolemia, and I guess that the results were kind of surprising to us and I think we had to take a step back and think about the implications. And I don't know, Sebastien do you want to describe these results or ... Sebastien: Yeah, sure of course. So we've looked at how frequent this polygenic contribution to coronary artery disease could be. So we look at the prevalence of high genetic risk or that would cause a risk similar to familial hypercholesterolemia see the ratio about 3.7, and we realized that one in 53 individuals had an increasing risk that was similar. So that's almost 2% of the population, and that is way more frequent than the actual prevalence of familial hypercholesterolemia, which is one in 250. So in other words, the polygenic contribution could be almost five times more frequent than familial hypercholesterolemia. Andrew: But yet not all of those individuals manifest as disease, which sort of hits as something that's a common thread in genetic association studies where we're trying to describe sort of multifactorial disease en points with finite genetic and a whole spectrum of acquired disease, required lifestyle modifications and things. So no model is 100% perfect, and so where do you think that additional variation lies, either in the reduced penetrance of some of these disease phenotypes, or are there other genetic loci, or are these all secondary to acquired changes that happen, or where does some of that variation lie? Guillaume: Well, to me I think there's two parts to this question. The first one is that I see the cells study as in some sense, proof of concept, to look for the concept of very high burden of polygenic risk as a mendelian equivalent really. But the fact is that, especially with the new discoveries and the genetics of coronary artery disease, the gene scores that we've been using for this study could be much improved. And I think the concept is there, but the gene score could be improved, and I think they will be improved and I think in three, four, five, ten, years from now, they're going to be even better because we will have many more variants that we know are preceded with coronary artery disease and that might be upwards to 1000 variance, for example will have much better gene score I think we'll have much more predictive gene scores. So I think the concept is there, but I think it's going to improve, with the years is only going to get better. And I think part of this missing risk, if I may, is due to the fact that we're missing a lot of genetic variants associated with coronary artery disease, and I'm very confident that the community will find them in the years to come. I think the second part of the study is that, that being said, I think genetic risk is obviously important but we shouldn't neglect also classical risk factors. And a lot of [inaudible] … they did have the classic risk factors and that was a fairly high proportion of smokers, and a few cases of diabetes, and I think that individually, this risk factor wouldn't be enough to explain the aggressiveness of this disease. But I think the fact that we do find an enrichment for these factors also give us ... I think it feeds the idea that it's not only genetics and that even in these individuals classic risk factors do matter and trying our best to decrease the burden of these risk factors on a community and its role family level is probably also very important. Sebastien: I'd also want to know that there's an environmental part that's involved even in these individuals with high genetic risk. And as he just mentioned, we did notice a high proportion of traditional risk factors in patients with early coronary artery disease even in some of them with high polygenic score, some of the environmental factors seem to be also involved in their disease. Guillaume: And to some extent I think that's going to be an interesting research question, in these individuals with very high polygenic burden, do traditional risk factor, do they at the time, are they stronger or weaker, is there a synergistic effect between, for example, smoking and being at this extreme of the polygenic risk? And these are kind of open questions that we couldn't address in the current study but I think will be interesting to see in the years to come. Andrew: Absolutely. I think there's definitely a road ahead of us but this is definitely a step in the right direction. What are some of the practical applications of this genetic risk scores, either from your study or from others in the identification of individuals? Is it something that could be used for primary production? I mean, in theory, this could be done at birth. You could be screened for these genetic variants and the risks will be calculated within the first days of life. What do you think are the practical applications of this and where is this fit into a rapidly expanding world of clinical genetics? Guillaume: Well, I think you know what you've just described is exactly how I see the future, and I think that if we want to be consistent, and we consider folks with a familial [inaudible] mutation to be at higher risk, I think that someone with a predicted polygenic risk of twofold, threefold, or fourfold increase risk of coronary artery disease should definitely be put in a higher risk category when it comes to primary prevention irrespective of other risk factors, or maybe like in combination with these other risk factors, and I think should be treated accordingly. And as we see, these are people are very aggressively affected by the disease, and I think the sooner we could identify these individuals at high risk and try to intervene to lessen as much as possible this risk, I think we will do these individuals and families a great service. So I think it's definitely a case for primary prevention and especially in a world where genomics is more clinically prevalent and used, also we see a role for this and the role that's already affected. And to me personally, I see great value in providing people with answer on why they've had an event and probably providing an answer not only to them, but also to their families. Andrew: And so if something like this were to be able to be applied broadly in the clinical arena, what sort of steps do you think need to happen from this point forward to make this sort of testing ready for prime time? Guillaume: This is a great question and I have to say that my passion I would say is to bring genomics to the clinic. I think there's a long road ahead to make this happen. But I think there's two main obstacles. The first one is that I think there's a knowledge gap between people that do this 24/7 like me, and I think you know the rest of the community and that there's been so much rapid progress in the field of genomics in the last few years that I think there's a lot of education to be done for people to catch up and just the concept of polygenic risk. I think only a minority of clinicians will know about this and very rightfully, because right now it's in the realm of research papers. So I think to make this happen, there's a huge role in education and awareness. I also think that our hospitals ... or maybe it's a Canadian thing, are not prepared just for the flow of information and how to derive the routines commercially, and probably how to handle these highly multi-dimensional data and to be able to take the right information out of them and I think in this world, I would think that probably the best way to do it is to do it in a way that these gene scores can be updated, the science progress. But we're so far away. Sometimes I feel that our hospital system is struggling to provide [inaudible] time to clinicians. And I'm just thinking without the prevention or how to handle something as complex as polygenic score, in this case we barely had like all the plugs in 200 variants, but you could clearly imagine like genetic risk scores being done with hundreds of thousands, if not millions of variants and will bring a whole new set of challenges. Andrew: And Sebastian, do you have a perspective on this? Sebastien: Yeah. I would just add that this knowledge is in the research community but to really put that into the clinic there's old setting, you have first to interpret the results and also to disclose the results to patients in a way that they can understand and that wouldn't create unnecessary anxiety, but more give them informed and an informed view of their health. So there's this also translation to the patient that needs to be evaluated and developed for it to be used to mainstream I would say. Guillaume: And I think the classic tools like publications also presentations and meeting and even reaching out to the cardiology community to start discussing these concepts will be important. And clearly it's a big shift from just classic genetics and even familial hypercholesterolemia, I think there isn't a lot of awareness, I don't think there's enough awareness as far as I'm concerned. And then we're bringing new concepts that might be even further remote from what people have been taught about genetics and score, it's going to be a huge challenge, but we have to. And I think the great thing about the medical community as far as I'm concerned, is that every time that there's been something that was worthwhile to do clinically, the community has always come around and making sure that these things are implemented and made available and everything. So I'm also very confident, but I think there's a great challenge ahead as well. Andrew: It sounds like the challenge has a potential for great benefit and if proper partnerships between the clinicians, and the geneticists, the scientists, and the patients and their families can all sort of come together to establish a path forward for this type of information to be applied clinically. Guillaume: Yeah, absolutely. And I really like that to add there that you've put the clinicians, geneticists, and patients as well. I think it's very important, patient advocates are a very important part of the equation here. Andrew: Going forward, are other disease processes besides early onset coronary artery disease that you all feel might benefit from a similar polygenic risk? Sebastien: The recent studies show that a lot of complex traits seem to have polygenic origin. So traits like hypertension, diabetes, obesity, atrial fibrillation, for example, they show a similar genetic architecture where there seem to be combinations of a very large number of common variants that explain the genetic risk. So it's a big number of variants with smaller effects that seem to be responsible for the appearance of these complex traits. So this concept could potentially be applied to a lot of different diseases. Guillaume: I think I would maybe just go even one step further, but I really have the feeling that most late onset disease actually has a polygenic architecture, which means that similar polygenic risk score could be done targeting the extreme of distribution to look into this. I mean obviously, I think metabolic traits, diabetes, hypertension as Sebastien mentioned, but probably why not some cancers, or [inaudible] or any of the large number of disease where a polygenic inheritance either has been proven or is highly suspected. So I think that we will hear a lot of polygenic risk score in the future, and I might be biased here, but I think it might become a staple of clinical practice that people will be looking at polygenic risk for a number of disease. And I think the great thing is that now that we've got genome-wide genotyping that is really affordable and we can type with statistical imputation and tens of millions of variants, then I think one concept is that we only have to genotype once and then we can derive these polygenic risk scores for ... why not a dozen diseases that are important and are actionable and really like turbo charge primary prevention by using this information. I might be getting ahead of myself, but I really think that this is something that we might see and that for us, we should see. Andrew: And certainly that seems to be the way that at least the literature is trending, definitely towards more, and more data and more, and more exploration into a number of diseases that may have mendelian inheritance pattern but may also have a significant component that's polygenic, particularly like you were saying in those individuals that present at the extremes of severity. So I think it's certainly where we're heading. Is there anything else that either of you would like to share about the study that you feel be important? Guillaume: I think we've covered a lot of ground here, but perhaps the one thing is just to reiterate that this is a proof of concept, but I really think that the act of polygenic risk score will continue to improve for quite a while, and as it improves, it will only get better. So we can only move forward with this in terms of the accuracy of the prediction, and I think that that's a great thing and hopefully with this we'll be able to better predict risk. And the other thing as well is that, I would say that at this point we can identify people at risk. And I think it's great because it provides answers, we can target known risk factors. But I think a big part that's still open is, can we use this risk to derive like more individualized treatment, or to actually choose what should be the best way to prevent events in these individuals. And again, I don't think we're there yet but this is something that I think it's worthwhile investigating in the future and maybe trying to dissect this polygenic risk and to see maybe it falls in one or two categories or maybe it's a global risk, and these are all open questions that I think are important, but that are still very much of a mystery right now. Andrew: Sebastian? Sebastien: I think we've covered a lot of ground like you said and I don't have too much to add. Otherwise, I think we'll see a lot of these polygenic risk scores in the future and for risk improvement even to understand better the physiology of disease. These are very important concepts. Guillaume: And I think you know the common approach of physiology is good because these gene scores they don't seem to be associated with classical risk factor. In our study, rather weak association with blood pressure and families history. Now, family history is kind of logical. Blood pressure suggests that perhaps there's an overlap between the two pathways, but clearly adjusting for blood pressure like that only slightly attenuated the predictiveness. So basically what this is telling us is that this polygenic risk score seems to be acting through pathways that we don't know of, that we're not measuring clinically, and I think that's a big part of the future would be to say, 'well, what are these pathways, and can we actually assess them? Are there other cholesterols out there?' Cholesterol is great because it's causal, we've got synthetic pharmachemicals, you've got tools to decrease it, and we've got fantastic evidence that decreasing cholesterol decrease risk. Is it possible that there's other pathways that are there and that we could do to sign, and I think all of this gives us great clues that this might be so. I think as happens quite often in science, we start with an hypothesis and we try to address it the best we can, and at the end of the day, here I guess we've been lucky because it kind of panned out, but it also opens so many more questions about; So what are these other pathways that these genetic risk scores are capturing that we're not capturing clinically right now. And how could this lead to better treatment, and how to implement this and everything, and I think this is really what's so exciting about doing research, and as far as I'm concerned, doing research that has an impact on people's lives and trying to improve people and provide answers to people. Andrew: Sounds like a great summary of the rationale for doing this. Thank you very much for joining me and for sharing your work. Guillaume: My pleasure. Sebastien: Thanks. Jane Ferguson: Thanks for listening to "Getting Personal: Omics of the Heart." You can subscribe on iTunes to get each new episode delivered straight to you. And we'll be back with more next month.