Jane Ferguson: Hi, everyone. Welcome to episode 35 of Getting Personal: Omics of the Heart, the 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 Circulation: Genomic and Precision Medicine. This episode is first airing in December 2019. Let's see what we published this month. ...
Jane Ferguson: Hi, everyone. Welcome to episode 35 of Getting Personal: Omics of the Heart, the 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 Circulation: Genomic and Precision Medicine. This episode is first airing in December 2019. Let's see what we published this month.
Our first paper is an “Integrated Multiomics Approach to Identify Genetic Underpinnings of Heart Failure and Its Echocardiographic Precursors: The Framingham Heart Study” from Charlotte Anderson, Ramachandran Vasan and colleagues from Herlev and Gentofte Hospital, Denmark and Boston University.
In this paper, the team investigated the genomics of heart failure, combining GWAS with methylation and gene expression data, to prioritize candidate genes. They analyzed four heart failure related and eight echocardiography related phenotypes in several thousand individuals, and then identified SNPs, methylation markers, and differential gene expression associated with those phenotypes. They then created scores for each gene, based on the rank of statistical significance, aggregated across the different omics analysis.
They examined the top ranked genes for evidence of pathway enrichment, and also looked up top SNPs for PheWAS associations in UK Biobank, and examined tissue specific expression in public data. While their data cannot definitively identify causal genes, they highlight several genes of potential relevance to heart failure pathogenesis, which may be promising candidates for future mechanistic studies.
The next paper is “Genetic Determinants of Lipids and Cardiovascular Disease Outcomes: A Wide-Angled Mendelian Randomization Investigation” and comes from Elias Allara, Stephen Burgess and colleagues, from the University of Cambridge and the INVENT consortium. While it has been established, therapies to lower LDL cholesterol and triglycerides lead to lower risk of coronary artery disease, it remains less clear whether these lipid lowering efforts can also reduce risk for other cardiovascular outcomes. The team set out to address this question using Mendelian randomization. They generated genetic predictors of LDL cholesterol and triglycerides using data from the Global Lipids Genetics Consortium, and then assessed whether genetically predicted increased LDL and triglycerides associated with risk of cardiovascular phenotypes using UK Biobank data. Beyond CAD, they found that higher LDL was associated with abdominal aortic aneurysm and aortic valve stenosis. High triglyceride levels were positively associated with aortic valve stenosis and hypertension, but inversely associated with venous thromboembolism and hemorrhagic stroke.
High LDL cholesterol and triglycerides were also associated with heart failure, which appeared to be mediated by CAD. Their data suggests that LDL lowering may have additional cardiovascular benefits in reducing aortic aneurism and aortic stenosis, while efforts to lower triglycerides may reduce the risk of aortic valve stenosis, but could result in increased thromboembolic risk.
Next up is a paper from Steven Joffe, G.L. Splansky and colleagues, from the University of Pennsylvania and Boston University, on “Preferences for Return of Genetic Results Among Participants in the Jackson Heart Study and Framingham Heart Study”. There has been increasing discussion and concern about how to handle genetic data, and whether genetic results should be returned to participants, and under which circumstances. In this study, the teams that had to assess what participants themselves think. They query participants in the Jackson Heart Study, the Framingham Heart Study and the FHS Omni cohort, presenting them with potential scenarios that varied by five factors including phenotype severity, actionability, reproductive significance and relative of the absolute risk of the phenotype.
Across all scenarios, 88 to 92% of respondents said that they would definitely or probably want to learn their result. In Jackson Heart Study respondents, factors increasing the desire for results included a positive attitude towards genetic testing, lower education, higher subjective numeracy, and younger age. The five pre-identified factors did not affect desire to receive results in Jackson Heart Study. Among Framingham Heart Study respondents, desire for results was associated with higher absolute risk, presentability, reproductive risk and positive attitudes towards genetic testing. Among FHS Omni respondents, desire for results was associated with positive attitudes towards genetic testing and younger age. Overall, these data show that across a variety of studies, there a high level of interest in receiving genetic results and that these are not necessarily linked to the phenotype or clinical significance of the results themselves.
The next paper concerns “Peripheral Blood RNA Levels of QSOX1 and PLBD1 Are New Independent Predictors of Left Ventricular Dysfunction after Acute Myocardial Infarction” and this comes from Martin Vanhaverbeke, Peter Sinnaeve and colleagues, from University Hospital Leuven. They were interested in understanding whether they could identify subsequent left ventricular dysfunction in patients who suffered an acute myocardial infarction. They obtained blood and performed RNA-Seq at multiple time points in 143 individuals, following acute MI, to identify transcripts that were associated with subsequent LV dysfunction. They validated candidate gene transcripts in a validation sample of 449 individuals, confirming that expression of QSOX1 and PLBD1 at admission, were associated with LV dysfunction at follow-up. Adding QSOX1 to a model, consisting of clinical variables and cardiac biomarkers, including NT proBNP, had an incremental predictive value. They took their findings to a pig model and found that whole blood expression of both genes was associated with neutrophil infiltration in these ischemic myocardium. This study suggests that expression of QSOX1 and PLBD1 following MI, may have utility in predicting development of LV dysfunction and may be markers of cardiac inflammation.
The next paper is a research letter from Hanna Hanania, Denver Sallee and Dianna Milewicz, from the University of Texas Health Science Center, and Emory University School of Medicine. Who set out to answer the question, “Do HCN4 Variants Predisposed to Thoracic Aortic Aneurysms and Dissections?” Previous work has suggested that rare variants in HCN4 associated with thoracic aortic disease, including ascending aortic dilation, left ventricular noncompaction cardiomyopathy, and sinus bradycardia. However, the evidence for disease segregation was relatively weak. The team set out to explore these potential associations using exome sequencing data from 521 individuals, from 347 unrelated families with heritable thoracic aortic disease, as well as 355 individuals with early onset sporadic aortic dissections, but no family history of disease. They identified a missense variant G482R, which segregated with disease in four unrelated families, was absent from the nomad database and was predicted to disrupt protein function and have deleterious effects. Their data support the evidence that HCN4 rare variants can cause heritable thoracic aortic disease with left ventricular noncompaction cardiomyopathy and bradycardia.
Our final paper is a white paper from H. Li, X. J. Luo and colleagues, from the National Heart, Lung and Blood Institute at the NIH, and will likely interest anybody who applies for NIH grants, which I'm assuming is most of you listening to this podcast. Their paper on, “Portfolio Analysis of Research Grants in Data Science Funded by the National Heart, Lung, and Blood Institute”, delves into the type of data science research funded by NHLBI between fiscal year 2008 and fiscal year 2017. They identified 630 data science focused grants, funded by NHLBI, using keywords for bioinformatics and computational biology. They then analyzed the distribution of these grants across different disease areas and compared the results to data science grants funded by other NIH institutes or centers. Around 64% of funded grants were for cardiovascular disease with 22% in lung and airway disease, 12% in blood disease and 2% in sleep.
NHLBI's investment in data science research grants averaged about 1% of its overall research grant investment, and this remained constant over the 10-year period. However, this proportion does not include other large scale investment by NHLBI in building data science platforms through other mechanisms. Of relevance to our listeners across all institutes, most funded data science research grants were related to genomics and other omics data. In this paper they include lots of graphs breaking down grant distributions across different categories, so it's worth a look as you plan your next grant application.
That's all for December and the final episode of 2019. Thanks for listening and happy holidays to all who celebrate. I'm excited to be back in 2020, to kick off the next decade of exciting advances in genomic and precision cardiovascular medicine.
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.
