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Leveraging molecular QTL to understand the genetic architecture of diseases and complex traits

By Farhad Hormozdiari, Steven Gazal, Bryce van de Geijn, Hilary Finucane, Chelsea J.-T. Ju, Po-Ru Loh, Armin Schoech, Yakir Reshef, Xuanyao Liu, Luke O’Connor, Alexander Gusev, Eleazar Eskin, Alkes L. Price

Posted 15 Oct 2017
bioRxiv DOI: 10.1101/203380 (published DOI: 10.1038/s41588-018-0148-2)

There is increasing evidence that many GWAS risk loci are molecular QTL for gene expression (eQTL), histone modification (hQTL), splicing (sQTL), and/or DNA methylation (meQTL). Here, we introduce a new set of functional annotations based on causal posterior probabilities (CPP) of fine-mapped molecular cis-QTL, using data from the GTEx and BLUEPRINT consortia. We show that these annotations are very strongly enriched for disease heritability across 41 independent diseases and complex traits (average $N$=320K): 5.84x for GTEx eQTL, and 5.44x for eQTL, 4.27-4.28x for hQTL (H3K27ac and H3K4me1), 3.61x for sQTL and 2.81x for meQTL in BLUEPRINT (all P < 1.39e-10), far higher than enrichments obtained using standard functional annotations that include all significant molecular cis-QTL (1.17-1.80x). eQTL annotations that were obtained by meta-analyzing all 44 GTEx tissues generally performed best, but tissue-specific blood eQTL annotations produced stronger enrichments for autoimmune diseases and blood cell traits and tissue-specific brain eQTL annotations produced stronger enrichments for brain-related diseases and traits, despite high cis-genetic correlations of eQTL effect sizes across tissues. Notably, eQTL annotations restricted to loss-of-function intolerant genes from ExAC were even more strongly enriched for disease heritability (17.09x; vs. 5.84x for all genes; P = 4.90e-17 for difference). All molecular QTL except sQTL remained significantly enriched for disease heritability in a joint analysis conditioned on each other and on a broad set of functional annotations from previous studies, implying that each of these annotations is uniquely informative for disease and complex trait architectures.

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