An integrated platform to systematically identify causal variants and genes for polygenic human traits-Reference-Cited by-同舟云学术

An integrated platform to systematically identify causal variants and genes for polygenic human traits

Published:2019-10-24 Issue: Volume: Page:
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Downes Damien J.^ORCID,Schwessinger Ron^ORCID,Hill Stephanie J.,Nussbaum Lea^ORCID,Scott Caroline^ORCID,Gosden Matthew E.^ORCID,Hirschfeld Priscila P.,Telenius Jelena M.^ORCID,Eijsbouts Chris Q.,McGowan Simon J.^ORCID,Cutler Antony J.^ORCID,Kerry Jon,Davies Jessica L.,Dendrou Calliope A.^ORCID,Inshaw Jamie R.J.^ORCID,Larke Martin S.C.,Marieke Oudelaar A.^ORCID,Bozhilov Yavor^ORCID,King Andrew J.,Brown Richard C.,Suciu Maria C.^ORCID,Davies James O.J.^ORCID,Hublitz Philip^ORCID,Fisher Chris,Kurita Ryo,Nakamura Yukio,Lunter Gerton,Taylor Stephen^ORCID,Buckle Veronica J.^ORCID,Todd John A.^ORCID,Higgs Douglas R.^ORCID,Hughes Jim R.^ORCID

Abstract

ABSTRACTGenome-wide association studies (GWAS) have identified over 150,000 links between common genetic variants and human traits or complex diseases. Over 80% of these associations map to polymorphisms in non-coding DNA. Therefore, the challenge is to identify disease-causing variants, the genes they affect, and the cells in which these effects occur. We have developed a platform using ATAC-seq, DNaseI footprints, NG Capture-C and machine learning to address this challenge. Applying this approach to red blood cell traits identifies a significant proportion of known causative variants and their effector genes, which we show can be validated by direct in vivo modelling.

Publisher

Cold Spring Harbor Laboratory

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