Deciphering signatures of natural selection via deep learning
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Identifying genomic regions influenced by natural selection provides fundamental insights into the genetic basis of local adaptation. However, it remains challenging to detect loci under complex spatially varying selection. We propose a deep learning-based framework, DeepGenomeScan, which can detect signatures of spatially varying selection. We demonstrate that DeepGenomeScan outperformed principal component analysis- and redundancy analysis-based genome scans in identifying loci underlying quantitative traits subject to complex spatial patterns of selection. Noticeably, DeepGenomeScan increases statistical power by up to 47.25% under nonlinear environmental selection patterns. We applied DeepGenomeScan to a European human genetic dataset and identified some well-known genes under selection and a substantial number of clinically important genes that were not identified by SPA, iHS, Fst and Bayenv when applied to the same dataset.
Qin , X , Chiang , C & Gaggiotti , O E 2022 , ' Deciphering signatures of natural selection via deep learning ' , Briefings in Bioinformatics , vol. 23 , no. 5 , bbac354 . https://doi.org/10.1093/bib/bbac354
Briefings in Bioinformatics
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DescriptionXQ was supported by a PhD scholarship from the China Scholarship Council and now is supported by International Postdoctoral Exchange Fellowship Program (Talent-Introduction Program) from China Postdoc Council. CWKC is supported in part by National Institute of General Medical Sciences (NIGMS) of the National Institute of Health (award number R35GM142783). Computation for this work is supported in part by USC’s Center for Advanced Research Computing (https://www.carc.usc.edu/).
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