Mapping Susceptibility to Complex Disease with Genome-Wide Association Studies

Abstract: Genome-wide association studies (GWAS) have identified hundreds of thousands of genomic loci where germline genetic variation is correlated with human disease or complex traits. 90% of GWAS variants reside in noncoding sequences that are predominantly gene regulatory regions, but it is challenging to identify the target genes of these regulatory regions. Knowing which genetic variants cause disease is often not sufficient for clinical intervention, whereas identifying disease genes can accelerate the development of therapeutics. Therefore, much effort has been dedicated to variant-to-function research to identify important disease genes regulated by noncoding genetic variation via analysis of transcriptomic and epigenetic data. The statistical association between genotype and gene expression via expression quantitative trait loci (eQTL) studies can provide valuable insight into the mechanism of disease-associated variants. For example, a transcriptome-wide association study, a multivariate type of eQTL analysis, found MAPK3 to be associated with schizophrenia and neurodevelopmental phenotypes via a key role in neuronal proliferation.

Innovative experimental and statistical approaches are needed to discover new eQTL, which is necessary for the functional characterization of GWAS variants. Progress in variant-to-gene mapping is currently limited by lack of ancestral diversity in gene expression cohorts, low power to detect distal effects on gene expression, and difficulty in studying rare cell types. Our central hypothesis is that undetected disease-critical genetic variation is concentrated in ancestry-specific, distal, and cell-type-specific regulatory elements, offering new opportunities to implicate disease-critical genes. Recently, the integration of genetic data across different populations has improved polygenic risk score accuracy and fine-mapping of causal variants, high-dimensional feature selection algorithms have predicted complex disease, and heritability estimation methods have advanced, but none have not been explored for genetic analysis of gene expression. In this presentation I will discuss our ongoing work to overcome limitations of variant-to-gene mapping to reveal disease-critical genes regulated by GWAS variants.