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

Abstract

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, ATP2B4, and provide experimental evidence of a regulatory role for variants discovered using this framework.

Fig 1. Overall heuristic for variant identification, genotype determination, initial annotation and downstream prioritization.

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

Fig 2. STMP identifies a likely functional regulatory variant in a novel candidate disease gene for neonatal ventricular arrhythmia.

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 cis with a nonsense variant in ATP2B4, 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 ATP2B4, 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.