The molecular basis and genetic control of local gene co-expression and its impact in human disease

ended

The genetic makeup intrinsic to each person shapes their particular traits, disease susceptibilities and treatment effectiveness, making understanding the functional impact of genetic variants one of the most pursued challenges in genetics research. Genome-wide association studies (GWAS) have so far associated >10,000 genetic variants with disease, with expression quantitative trait loci (eQTL) studies adamant in linking some of these disease variants to causal genes. Yet, understanding a variant’s molecular link to disease is still a major challenge, given that most are found in the genome’s non-coding regions, act only in specific tissues and may affect several genes. Recent studies revealed that neighbouring genes are often co-expressed – forming co-expression domains (CODs) – potentially regulated by shared regulatory variants, yet, this has been ignored in eQTL and GWAS studies. This project aims to investigate how local gene co-expression is achieved and regulated by genetic variants, and their impact on human disease. For this, I propose a novel genome-wide framework to detect human CODs and their regulatory variants (cod-QTLs) using transcriptomic profiles across hundreds of genotyped individuals. The mechanisms through which variants affect the expression of several genes will be discovered through causality inference and molecular characterisation using state-of-the-art datasets (e.g. Hi-C, promoter-enhancer maps). Notably, CODs’ tissue-specificity will be studied using gene expression across 53 human tissues and the co-expression variation across 120 individuals will be assessed using a cutting-edge dataset of single-cell RNA-seq. The impact of cod-QTLs on dozens of societal-relevant diseases will be determined by colocalization analysis with GWAS hits from the UK Biobank. This project promises to clarify fundamental aspects of gene (co-)regulation and provide functional interpretation of eQTLs and GWAS findings, including revealing novel disease genes.