Integrating summarized imaging and genomic data with GWAS for powerful endophenotype association testing in Alzheimer’s Disease

Abstract

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits. However, for most diseases, individual risk variants have small effects which impact disease indirectly through upstream endophenotypes. To improve on the power and interpretability of GWAS, a number of approaches have been developed which aggregate contributions from one or multiple genetic variants to investigate the role of genetically regulated endophenotypes in complex traits. These methods include Mendelian Randomization (MR) and the Transcriptome/Imaging Wide Association Study (TWAS/IWAS, which test for associated gene expression and imaging phenotypes, respectively). In this dissertation, I will compare the performance of these approaches for detecting brain imaging derived phenotypes (IDPs) associated with Alzheimer’s Disease. I will present novel extensions to the TWAS/IWAS framework to account for key biological factors which may impact their performance in practice, namely 1) genetic pleiotropy and 2) population substructure. The first of these factors, genetic pleiotropy, describes the phenomenon in which genetic loci affect multiple intermediate risk phenotypes. The presence of pervasive pleiotropy can result in inconsistent IWAS estimates. I will present a novel extension to the IWAS model (namely, MV-IWAS) which provides consistent causal estimates of endophenotype-trait associations by directly and indirectly accounting for pleiotropic pathways. The second of these factors, population substructure, describes ancestral variation in the underlying genetic architecture of endophenotypes. This variation can lead to ancestry-specific effects of gene expression in TWAS, which go undetected in the standard TWAS framework. Here, I will present a score test to detect heterogeneity in the effects of genetically-regulated gene expression which are correlated with ancestry. By jointly analyzing samples from multiple populations, our multi-ancestry TWAS framework can improve power for detecting genes with shared expression-trait associations across populations through increased sample sizes, as compared to existing stratified TWAS approaches.