Deciphering signatures of natural selection via deep learning

Xinghu Qin*, Charleston Chiang, Oscar Eduardo Gaggiotti*

*Corresponding author for this work

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Abstract

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.
Original languageEnglish
Article numberbbac354
Number of pages10
JournalBriefings in Bioinformatics
Volume23
Issue number5
Early online date2 Sept 2022
DOIs
Publication statusPublished - Sept 2022

Keywords

  • Deep learning
  • Genome scan
  • Genome-wide association studies
  • Signatures of natural selection

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