TY - JOUR T1 - An integrated platform to systematically identify causal variants and genes for polygenic human traits JF - bioRxiv DO - 10.1101/813618 SP - 813618 AU - Damien J. Downes AU - Ron Schwessinger AU - Stephanie J. Hill AU - Lea Nussbaum AU - Caroline Scott AU - Matthew E. Gosden AU - Priscila P. Hirschfeld AU - Jelena M. Telenius AU - Chris Q. Eijsbouts AU - Simon J. McGowan AU - Antony J. Cutler AU - Jon Kerry AU - Jessica L. Davies AU - Calliope A. Dendrou AU - Jamie R.J. Inshaw AU - Martin S.C. Larke AU - A. Marieke Oudelaar AU - Yavor Bozhilov AU - Andrew J. King AU - Richard C. Brown AU - Maria C. Suciu AU - James O.J. Davies AU - Philip Hublitz AU - Chris Fisher AU - Ryo Kurita AU - Yukio Nakamura AU - Gerton Lunter AU - Stephen Taylor AU - Veronica J. Buckle AU - John A. Todd AU - Douglas R. Higgs AU - Jim R. Hughes Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/01/15/813618.abstract N2 - Genome-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. ER -