Comprehensive Multiple eQTL Detection and Its Application to GWAS InterpretationZeng, B., Lloyd-Jones, L. R., Montgomery, G. W., Metspalu, A., Esko, T., Franke, L., Vosa, U., Claringbould, A., Brigham, K. L., Quyyumi, A. A., Idaghdour, Y., Yang, J., Visscher, P. M., Powell, J. E. & Gibson, G., Jul-2019, In : Genetics. 212, 3, p. 905-918 14 p.
Research output: Contribution to journal › Article › Academic › peer-review
Expression QTL (eQTL) detection has emerged as an important tool for unraveling the relationship between genetic risk factors and disease or clinical phenotypes. Most studies are predicated on the assumption that only a single causal variant explains the association signal in each interval. This greatly simplifies the statistical modeling, but is liable to biases in scenarios where multiple local causal-variants are responsible. Here, our primary goal was to address the prevalence of secondary cis-eQTL signals regulating peripheral blood gene expression locally, utilizing two large human cohort studies, each >2500 samples with accompanying whole genome genotypes. The CAGE (Consortium for the Architecture of Gene Expression) dataset is a compendium of Illumina microarray studies, and the Framingham Heart Study is a two-generation Affymetrix dataset. We also describe Bayesian colocalization analysis of the extent of sharing of cis-eQTL detected in both studies as well as with the BIOS RNAseq dataset. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for similar to 40% of over 3500 eGenes in both microarray datasets, and that the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. Although
|Number of pages||14|
|Publication status||Published - Jul-2019|
- fine mapping, linkage disequilibrium, conditional association, colocalization, PolyQTL, gene regulation, GENOME-WIDE ASSOCIATION, INFLAMMATORY-BOWEL-DISEASE, GENE-EXPRESSION, ANNOTATION DATA, LOCI, ARCHITECTURE, SNPS, IDENTIFICATION, HERITABILITY, ALIGNMENT