An integrated platform to systematically identify causal variants and genes for polygenic human traits
Keyword(s):
ABSTRACTGenome-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.
2019 ◽
Vol 25
(10)
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pp. 2455-2467
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2020 ◽
Vol 38
(15_suppl)
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pp. 1528-1528
2020 ◽
2012 ◽
Vol 111
(7)
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pp. 1148-1155
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2019 ◽
2009 ◽
Vol 40
(7)
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pp. 1063-1077
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