Random Field Methods for integrative genomic analysis and high-dimensional risk prediction of congenital heart defects

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Project Summary Congenital heart defects (CHDs) are the most prevalent type of birth defects and the leading cause of infant mortality attributable to birth defects. Most CHDs are thought to result from a complex interaction between maternal exposures and genetic susceptibilities. In the past decade, large-scale genome-wide association studies and whole-genome sequencing studies have been conducted to comprehensively investigate the role of a deep catalog of genetic variants in CHDs. Although substantial progress has been made in the identification of individual genetic variants associated with CHDs, the complex interactions during embryogenesis have not been well understood. There has been even less success to identify rare genetic variants with heterogenous effect and the regulatory pathways among genetic variants, epigenetic modifications, and transcriptional changes leading to the disease development. To date, the etiology of CHDs has remained largely unknown, and the disease risk prediction models generally have low clinical utility. The proposed research aims to address the heterogeneous, interactive, and multi-omic etiology of CHDs by leveraging existing resources from multiple federal entities relevant to CHD research. We will conduct integrative genomic analysis using samples from the National Birth Defect Prevention Studies (NBDPS), the Pediatric Cardiac Genomics Consortium (PCGC) and the Trans-Omics for Precision Medicine (TOPMed) CHD Biobank. We will address the complex etiology of CHDs by applying novel random field methods developed by our research team. The planned specific aims are: 1) Identify and replicate genomic regions associated with CHD risk in the presence of disease heterogeneity and genetic interactions, 2) Detect methylation and expression quantitative trait loci and discover biological pathways among multi-omic factors contributing to CHD risk, and 3) Build a high-dimensional risk prediction model of CHDs based on genetic and maternal lifestyle profiles. The proposed research will be led by an early-stage new investigator, who has assembled a research team with complementary expertise in statistical genetics, bioinformatics, causal inference, CHD epidemiology, reproductive genetics, clinical cardiovascular genetics, and pediatric clinical practice. The successful completion of this research will lead to the discovery of novel disease- susceptible genes and biological pathways resulting in CHDs. The establishment of an accurate strategy for genetic risk prediction will provide new directions for precise preconceptional consultation, standardize genetic diagnosis after birth, and inform subsequent pregnancies.