bioRxivGeneticsPreprint
An integrated platform to systematically identify causal variants and genes for polygenic human traits.
Damien J. DownesiD, Ron SchweßingeriD, Stephanie Hill, Lea NußbaumiD, Caroline ScottiD, Matthew E. GosdeniD, Priscila P. Hirschfeld, Jelena M. TeleniusiD, Chris Q. EijsboutsiD, Simon J. McGowaniD, Antony J. CutleriD, Jon Kerry, Jessica L. DaviesiD, Calliope Athena DendrouiD, Jamie R. J. InshawiD, Martin LarkeiD, A. Marieke OudelaariD, Yavor K. BozhiloviD, Andrew John KingiD, Richard C. Brown, Maria SuciuiD, James O. J. DaviesiD, Philip HublitziD, Christopher A. Fisher, Ryo Kurita, , Jim R. HughesiD
Abstract
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.
- organism: human
- assay: ATAC-seq
- research focus: variant effect prediction
- approach: machine learning (classical)
- Citations
- 19
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