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bioRxiv · 10.1101/203380

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

Hormozdiari, F.·Gazal, S.·Geijn, B. v. d.·Finucane, H.·Ju, C. J.- T.·Loh, P.-R.·Schoech, A.·Reshef, Y.·Liu, X.·O'Connor, L.·Gusev, A.·Eskin, E.·Price, A.
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Abstract

There is increasing evidence that many GWAS risk loci are molecular QTL for gene ex-pression (eQTL), histone modification (hQTL), splicing (sQTL), and/or DNA methylation (meQTL). Here, we introduce a new set of functional annotations based on causal posterior prob-abilities (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 stan-dard 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 dis-eases and blood cell traits and tissue-specific brain eQTL annotations produced stronger enrich-ments 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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Hormozdiari, F., Gazal, S., Geijn, B. v. d., Finucane, H., Ju, C. J.- T., Loh, P.-R., Schoech, A., Reshef, Y., Liu, X., O'Connor, L., Gusev, A., Eskin, E., Price, A.. 2017-10-15. Leveraging molecular QTL to understand the genetic architecture of diseases and complex traits. https://doi.org/10.1101/203380

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