Response to spatiotemporal variation in selection gradients resulted in signatures of polygenic adaptation in human genomes. We introduce RAISING, a two-stage deep learning framework that optimizes neural network architecture through hyperparameter tuning before performing feature selection and prediction tasks. We tested RAISING on published and newly designed simulations that incorporate the complex interplay between demographic history and selection gradients. RAISING outperformed Phylogenetic Generalized Least Squares (PGLS), ridge regression and DeepGenomeScan, with significantly higher true positive rates (TPR) in detecting genetic adaptation. It reduced computational time by 60-fold and increased TPR by up to 28% compared to DeepGenomeScan on published data. In more complex demographic simulations, RAISING showed lower false discoveries and significantly higher TPR, up to 17-fold, compared to other methods. RAISING demonstrated robustness with least sensitivity to demographic history, selection gradient and their interactions. We developed a sliding window method for genome-wide implementation of RAISING to overcome the computational challenges of high-dimensional genomic data. Applied to African, European, South Asian and East Asian populations, we identified multiple genomic regions undergoing polygenic selection. Notably, ∼70% of the regions identified in Africans are unique, with broad patterns distinguishing them from non-Africans, corroborating the Out of Africa dispersal model.

中文翻译：

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对人类群体中多基因适应的不同模式的深度学习洞察


对选择梯度时空变化的响应导致人类基因组中多基因适应的特征。我们介绍了 RAISING，这是一个两阶段深度学习框架，在执行特征选择和预测任务之前，通过超参数调整来优化神经网络架构。我们在已发布和新设计的模拟上测试了 RIRAISE，这些模拟结合了人口统计历史和选择梯度之间的复杂相互作用。RAISING 优于系统发育广义最小二乘法 （PGLS） 、岭回归和 DeepGenomeScan，在检测遗传适应方面具有显着更高的真阳性率 （TPR）。与 DeepGenomeScan 相比，它将已发表数据的计算时间缩短了 60 倍，并将 TPR 提高了 28%。在更复杂的人口统计模拟中，与其他方法相比，RAISING 显示出更低的错误发现和显着更高的 TPR，高达 17 倍。RAISING 表现出稳健性，对人口统计学历史、选择梯度及其交互作用的敏感性最低。我们开发了一种滑动窗口方法，用于 RAISING 的全基因组实施，以克服高维基因组数据的计算挑战。应用于非洲、欧洲、南亚和东亚人群，我们确定了多个经历多基因选择的基因组区域。值得注意的是，在非洲人中发现的地区中，约有 70% 是独特的，他们与非非洲人区分开来的广泛模式证实了走出非洲的扩散模型。