Detection and exploitation of expression QTL in drug discovery and development - Grace, C (2015)
Expression quantitative trait loci (eQTLs) are genetic markers associated with transcription of Ribonucleic Acid (RNA). eQTLs are detected using association analysis to detect correlations between RNA expression data (microarray or RNA-SEQ) and the genotypes of individuals within a study. Trans-ethnic meta-analysis can increase power to detect genetic variants for eQTLs and improve fine-mapping resolution because of differential patterns of linkage disequilibrium (LD) between diverse populations. Lymphoblastoid cell lines (LCLs) from samples in the Phase II and III HapMap populations have been used to detect cis eQTLs using association analysis followed by meta-analysis. Phase III HapMap samples have also been imputed using the 1000 Genomes March 2012 "all ancestries" panel. The goals of this thesis are to perform meta-analysis on multi-ethnic association summary statistics in order to: Increase the power to detect eQTLs, leverage differences in LD between ancestry groups to fine map eQTL variants and investigate and characterize heterogeneity in allelic effect sizes on expression between diverse populations. In addition to this, eQTLs identified are used to perform integration with signals from genome-wide association studies (GWAS) of complex human traits. A pipeline has been developed where eSNPs from the eQTL datasets are integrated with disease SNPs (dSNPs) from the NHGRI GWAS catalog using reciprocal conditional analysis to determine whether eSNP and dSNP tag or are the same causal variant. Also, eQTLs which are also "absorption, distribution, metabolism, and excretion" (ADME) genes are studied in more detail, specifically looking for heterogeneity and enrichment in this dataset. The analysis shows that combining association analysis summary statistics using meta-analysis leads to an increase in power to detect eQTLs. Differences in LD between ancestry groups can be used to improve fine mapping resolution, as measured by "credible sets" of variants most likely to drive the eQTL signal, when all ancestry groups are combined. Considerable heterogeneity between ancestry groups has been detected, much of which is due to differing LD between tag SNP and causal variants across ancestry groups. Furthermore, the GWAS integration has led to the identification of several dSNP – eSNP pairs for disease such as Ulcerative Colitis, Inflammatory Bowel Disease, Bechet's Disease, Sarcoidosis, Crohn's Disease, Grave’s Disease and Primary Biliary Cirrhosis, and have provided potential novel insights of genes through which these disease association signals are mediated. Several eQTLs for genes within the ADME dataset have also been identified some of which have significant heterogeneity.