Deciphering signatures of natural selection via deep learning

• Published in: Briefings in bioinformatics Vol. 23; no. 5
• Main Authors: Qin, Xinghu, Chiang, Charleston W K, Gaggiotti, Oscar E
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 20.09.2022; Oxford Publishing Limited (England)
• Subjects: Datasets; Deep learning; Genes; Natural selection; Principal components analysis; Problem Solving Protocol; Redundancy; Signatures; deep learning; signatures of natural selection; genome-wide association studies; genome scan
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbac354

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.