Seminar SERIES - Zhang Zhou, PhD

"Statistical methods for transcriptome-wide association studies"

Integrating genome-wide association studies (GWASs) and gene expression studies through transcriptome-wide association study (TWAS) has the potential to shed light on the causal molecular mechanisms underlying disease etiology. Here, I will discuss a few new statistical methods that our group has recently developed for TWAS. Specifically, I will first talk about PMR, a probabilistic Mendelian randomization method for TWAS applications. PMR relies on a MR likelihood framework that unifies many existing TWAS and MR methods, accommodates multiple correlated instruments, and tests the causal effect of gene on trait in the presence of horizontal pleiotropy. I will talk about multiple extensions of PMR, including moPMR for analyzing multiple outcome traits, METRO for leveraging gene expression data across multiple genetic ancestries, and HMAT for aggregating multiple gene expression prediction models, all further enhancing TWAS power. Additionally, I will talk about GIFT for conditional TWAS analysis and TWAS fine-mapping, explicitly controlling for the genetic regulated components of multiple genes residing in a local region to fine-map causal genes. Finally, if time allows, I will talk about VINTAGE, which explicitly quantifies and tests the proportion of genetic effects on a trait that are mediated through gene expression using a local genetic correlation test, and further leverages such information to guide the integration of gene expression mapping study towards GWAS for gene association mapping through a genetic variance test.