Bioinformatics Approaches for Investigating Missing Heritability in Complex Traits

Abstract

Advancements in high-throughput technologies and high-performance computing have enabled the discovery of tens of thousands of genetic associations for complex traits and diseases. However, these associations explain only part of the heritability, defined as the portion of the trait variability that is accounted for by genetic factors. The overarching goal of my dissertation is to investigate the missing heritability in order to improve our understanding of the underlying genomic factors. First, we tested the hypothesis that many of the associated markers for complex traits are non-causal but are in linkage disequilibrium (LD) with causal variants. We identified 27 potentially functional variants in LD with previously associated markers for drug response outcomes, which could account in part for the missing heritability of pharmacogenomic traits. Next, we examined the main effects of genetic variations as well as interaction effects with environmental exposures in determining risk of complex diseases. In our study of childhood asthma, we computed the genetic risk score (GRS) by integrating the weighed effects of multiple genetic variants and determined that early-life modifiable exposures interact with genetic risk to determine respiratory outcomes. Finally, we applied a weighted network model and machine learning algorithm to investigate biological networks associated with chemotherapy response among ovarian cancer patients and identified potentially regulatory variants (expression quantitative loci: eQTL) associated with gene expression. In summary, my thesis work demonstrates that the missing heritability of complex traits may be explained in part by accounting for polygenic effects, gene-gene interactions, gene-environment interactions, linkage disequilibrium, and integrative analysis of 'omics datasets.