MPI Campus Seminar: Statistical methods to discover regulatory SNPs in the human genome

Over the last decade Genome-Wide Association Studies (GWAS) have discovered many disease associated SNPs and unveiled unexpected disease mechanisms. However, these SNPs only explain a small proportion of the genetic contribution to these diseases and it is not clear what is the underlying biology affecting disease risk. An important class of SNPs, termed expression quantitative trait loci (eQTLs), influence the expression level of genes. Genome wide eQTL studies have provided valuable information that have helped to understand how GWAS SNPs affect disease pathways. Yet most eQTLs discovered so far are short-range eQTLs and can only explain ~30% of gene expression variation.
We focus on the discovery of long-range acting eQTLs, or trans-eQTLs, variants that influence the expression level of distant genes, even in other chromosomes. Several studies estimate that trans-eQTLs are responsible for the remaining ~70% of gene expression variation and hold the key to discover causative disease pathways. However trans-eQTLs are challenging to find because they have small effect sizes, can target dozens to hundred of genes and can be tissue specific. We describe an approach to predict trans-eQTLs and apply it to transcriptomic data from 49 different tissues, where we discover trans-eQTLs associated with various complex traits.