Table1_Polygenic risk score predicts all-cause death in East Asian patients with prior coronary artery disease.docx

IntroductionCoronary artery disease (CAD) is a highly heritable and multifactorial disease. Numerous genome-wide association studies (GWAS) facilitated the construction of polygenic risk scores (PRS) for predicting future incidence of CAD, however, exclusively in European populations. Furthermore, identifying CAD patients with elevated risks of all-cause death presents a critical challenge in secondary prevention, which will contribute largely to reducing the burden for public healthcare.MethodsWe recruited a cohort of 1,776 Chinese CAD patients and performed medical follow-up for up to 11 years. A pruning and thresholding method was used to calculate PRS of CAD and its 14 risk factors. Their correlations with all-cause death were computed via Cox regression.Results and discussionWe found that the PRS for CAD and its seven risk factors, namely myocardial infarction, ischemic stroke, angina, heart failure, low-density lipoprotein cholesterol, total cholesterol and C-reaction protein, were significantly associated with death (P ≤ 0.05), whereas the PRS of body mass index displayed moderate association (P < 0.1). Elastic-net Cox regression with 5-fold cross-validation was used to integrate these nine PRS models into a meta score, metaPRS, which performed well in stratifying patients at different risks for death (P < 0.0001). Combining metaPRS with clinical risk factors further increased the discerning power and a 4% increase in sensitivity. The metaPRS generated from the genetic susceptibility to CAD and its risk factors can well stratify CAD patients by their risks of death. Integrating metaPRS and clinical risk factors may contribute to identifying patients at higher risk of poor prognosis.</p

Sequence to Medical Phenotypes: A Framework for Interpretation of Human Whole Genome DNA Sequence Data

<div><p>Abstract</p><p>High throughput sequencing has facilitated a precipitous drop in the cost of genomic sequencing, prompting predictions of a revolution in medicine via genetic personalization of diagnostic and therapeutic strategies. There are significant barriers to realizing this goal that are related to the difficult task of interpreting personal genetic variation. A comprehensive, widely accessible application for interpretation of whole genome sequence data is needed. Here, we present a series of methods for identification of genetic variants and genotypes with clinical associations, phasing genetic data and using Mendelian inheritance for quality control, and providing predictive genetic information about risk for rare disease phenotypes and response to pharmacological therapy in single individuals and father-mother-child trios. We demonstrate application of these methods for disease and drug response prognostication in whole genome sequence data from twelve unrelated adults, and for disease gene discovery in one father-mother-child trio with apparently simplex congenital ventricular arrhythmia. In doing so we identify clinically actionable inherited disease risk and drug response genotypes in pre-symptomatic individuals. We also nominate a new candidate gene in congenital arrhythmia, <i>ATP2B4</i>, and provide experimental evidence of a regulatory role for variants discovered using this framework.</p></div

Overall heuristic for variant identification, genotype determination, initial annotation and downstream prioritization.

<p>Abbreviations: CPIC, clinical pharmacogenomics implementation consortium; LOF, loss of function; MAF, minor allele frequency; Pgx, pharmacogenomics; SNVs, single nucleotide variants; SVs, structural variants.</p

STMP identifies a likely functional regulatory variant in a novel candidate disease gene for neonatal ventricular arrhythmia.

<p>A) Pedigree (left) and representative neonatal ECG from a proband with ventricular fibrillation (right). B) UCSC Genome Browser screenshot showing ENCODE regulatory tracks surrounding a novel variant in the 5’ UTR, rs4600103 (red box), found in <i>cis</i> with a nonsense variant in <i>ATP2B4</i>, as well as linked variant (r² = 0.87) rs4951276 (green box). Tracks for chromatin accessibility, including DNaseI hypersensitivity, and promoter histone modification (H3K4M3) ChIP-seq data are shown for human cardiac myocytes (HCM), human cardiac fibroblasts (HCF) and heart tissue. DNaseI hypersensitivity clusters, transcription factor ChIP-seq and active histone modification (H3K27Ac) ChIP-seq data are shown for multiple ENCODE cell lines. C) Functional validation of common variants using allele-specific reporter assays. Common variants at <i>ATP2B4</i>, rs4600103 and rs4951276 were evaluated in luciferase reporter assays in HEK293 and H9c2. Values are expressed as relative fold change versus empty vector (pLuc) and represent mean ± SEM of triplicates from three independent experiments.</p

Summary of STMP tiers 1–3 in whole genome sequence data from twelve unrelated adults recruited from primary care clinics.

<p>*As defined by allele frequency < 0.01.</p><p>Abbreviations: LOF, loss of function.</p><p>Summary of STMP tiers 1–3 in whole genome sequence data from twelve unrelated adults recruited from primary care clinics.</p