Centre for Biological Diversity, Sir Harold Mitchell Building, University of St Andrews, Fife, KY16 9TF, UK.
Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine & Department of Quantitative and Computational Biology, University of Southern California, USA.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac354.
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.
鉴定受自然选择影响的基因组区域为局部适应的遗传基础提供了基本的见解。然而，检测复杂空间变化选择下的基因座仍然具有挑战性。我们提出了一种基于深度学习的框架 DeepGenomeScan，它可以检测空间变化选择的特征。我们证明，DeepGenomeScan在识别受复杂空间选择模式影响的数量性状的基因座方面，优于基于主成分分析和冗余分析的基因组扫描。值得注意的是，DeepGenomeScan在非线性环境选择模式下可将统计能力提高高达 47.25%。我们将 DeepGenomeScan 应用于欧洲人类遗传数据集，并鉴定了一些受选择的已知基因，以及大量在应用于同一数据集时 SPA、iHS、Fst 和 Bayenv 未鉴定出的具有临床重要性的基因。
