PQG Seminar

Genome-wide association studies (GWAS) have identified many SNPs associated with common diseases and disease-relevant complex traits. However, the precise underlying causal signals and molecular mechanisms underlying these associations remain largely unknown. Here, I will discuss a few statistical methods that our group has recently developed for fine-mapping putatively causal SNPs and genes in GWAS. Specifically, I will first talk about MESuSiE, a probabilistic multi-ancestry fine-mapping method, to improve the accuracy and resolution of fine-mapping by leveraging association information across ancestries. I will talk about PMR, a probabilistic Mendelian randomization (MR) likelihood-based framework that unifies many existing TWAS (transcriptome-wide association study) and MR methods, accommodates multiple correlated instruments, while testing the causal effect of gene on trait in the presence of horizontal pleiotropy. I will also talk about GIFT, a frequentist method that performs conditional TWAS analysis by controlling for other genes residing in a local region to fine-map putatively causal genes, while explicitly accounting for gene expression correlation, cis-SNP linkage disequilibrium, as well as the uncertainty associated with gene expression predictions.