An integrated platform to systematically identify causal variants and genes for polygenic human traits
Downes DJ., Schwessinger R., Hill SJ., Nussbaum L., Scott C., Gosden ME., Hirschfeld PP., Telenius JM., Eijsbouts CQ., McGowan SJ., Cutler AJ., Kerry J., Davies JL., Dendrou CA., Inshaw JRJ., Larke MSC., Marieke Oudelaar A., Bozhilov Y., King AJ., Brown RC., Suciu MC., Davies JOJ., Hublitz P., Fisher C., Kurita R., Nakamura Y., Lunter G., Taylor S., Buckle VJ., Todd JA., Higgs DR., Hughes JR.
<jats:title>ABSTRACT</jats:title><jats:p>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 <jats:italic>in vivo</jats:italic> modelling.</jats:p>