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<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>

Original publication

DOI

10.1101/813618

Type

Journal article

Publisher

Cold Spring Harbor Laboratory

Publication Date

24/10/2019