Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China.
Clinical Research Institute, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China.
Mol Ecol Resour. 2021 Jul;21(5):1732-1744. doi: 10.1111/1755-0998.13370. Epub 2021 Mar 17.
Detecting genetic regions under selection in structured populations is of great importance in ecology, evolutionary biology and breeding programmes. We recently proposed EigenGWAS, an unsupervised genomic scanning approach that is similar to but does not require grouping information of the population, for detection of genomic regions under selection. The original EigenGWAS is designed for the random mating population, and here we extend its use to inbred populations. We also show in theory and simulation that eigenvalues, the previous corrector for genetic drift in EigenGWAS, are overcorrected for genetic drift, and the genomic inflation factor is a better option for this adjustment. Applying the updated algorithm, we introduce the new EigenGWAS online platform with highly efficient core implementation. Our online computational tool accepts plink data in a standard binary format that can be easily converted from the original sequencing data, provides the users with graphical results via the R-Shiny user-friendly interface. We applied the proposed method and tool to various data sets, and biologically interpretable results as well as caveats that may lead to an unsatisfactory outcome are given. The EigenGWAS online platform is available at www.eigengwas.com, and can be localized and scaled up via R (recommended) or docker.
在结构群体中检测受选择影响的遗传区域在生态学、进化生物学和育种计划中具有重要意义。我们最近提出了 EigenGWAS，这是一种无监督的基因组扫描方法，类似于但不需要群体分组信息，用于检测受选择影响的基因组区域。原始的 EigenGWAS 是为随机交配群体设计的，而在这里我们将其扩展应用于近交群体。我们还从理论和模拟两方面表明，特征值是 EigenGWAS 中之前用于修正遗传漂变的修正因子，但在修正遗传漂变时会过度修正，而基因组膨胀因子是更好的选择。应用更新后的算法，我们引入了新的 EigenGWAS 在线平台，其具有高效的核心实现。我们的在线计算工具接受 plink 数据的标准二进制格式，这些数据可以很容易地从原始测序数据转换而来，通过 R-Shiny 用户友好的界面为用户提供图形化结果。我们将提出的方法和工具应用于各种数据集，并给出了具有生物学意义的结果以及可能导致不满意结果的注意事项。EigenGWAS 在线平台可在 www.eigengwas.com 上获取，可通过 R（推荐）或 docker 进行本地化和扩展。
