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

Downes, D. J.; Schwessinger, R.; Hill, S. J.; Nussbaum, L.; Scott, C.; Gosden, M. E.; Hirschfeld, P. P.; Telenius, J. M.; Eijsbouts, C. E.; McGowan, S. J.; Cutler, A. J.; Kerry, J.; Davies, J. L.; Dendrou, C. A.; Inshaw, J. R. J.; Larke, M. S. C.; Oudelaar, A. M.; Bozhilov, Y.; King, A.; Brown, R. C.; Suciu, M. C.; Davies, J. O. J.; Hublitz, P.; Fisher, C.; Kurita, R.; Nakamura, Y.; Taylor, S.; Buckle, V. J.; Todd, J. A.; Higgs, D. R.; Hughes, J. R.

doi:10.1101/813618

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bioRxiv · 10.1101/813618

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

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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.

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Downes, D. J., Schwessinger, R., Hill, S. J., Nussbaum, L., Scott, C., Gosden, M. E., Hirschfeld, P. P., Telenius, J. M., Eijsbouts, C. E., McGowan, S. J., Cutler, A. J., Kerry, J., Davies, J. L., Dendrou, C. A., Inshaw, J. R. J., Larke, M. S. C., Oudelaar, A. M., Bozhilov, Y., King, A., Brown, R. C., Suciu, M. C., Davies, J. O. J., Hublitz, P., Fisher, C., Kurita, R., Nakamura, Y., Taylor, S., Buckle, V. J., Todd, J. A., Higgs, D. R., Hughes, J. R.. 2019-10-24. An integrated platform to systematically identify causal variants and genes for polygenic human traits.. https://doi.org/10.1101/813618

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