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Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits
There is increasing evidence that many GWAS risk loci are molecular QTL (eQTL, hQTL, sQTL, and/or meQTL). Here, we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTL, using data from the GTEx and BLUEPRINT consortia. We show that th...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030458/ https://www.ncbi.nlm.nih.gov/pubmed/29942083 http://dx.doi.org/10.1038/s41588-018-0148-2 |
Sumario: | There is increasing evidence that many GWAS risk loci are molecular QTL (eQTL, hQTL, sQTL, and/or meQTL). Here, we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTL, using data from the GTEx and BLUEPRINT consortia. We show that these annotations are far more strongly enriched for heritability (e.g. 5.84x for eQTL; P=1.19×10(−31)) across 41 independent diseases and complex traits than annotations containing all significant molecular QTL (1.80x for eQTL). eQTL annotations that were obtained by meta-analyzing all GTEx tissues generally performed best, but tissue-specific eQTL annotations produced stronger enrichments for blood- and brain-related diseases and traits. Notably, eQTL annotations restricted to loss-of-function intolerant genes from ExAC were even more strongly enriched for heritability (17.06x; P=1.20×10(−35)). All molecular QTL except sQTL remained significantly enriched in a joint analysis, implying that each of these annotations is uniquely informative for disease and complex trait architectures. |
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