MaLAdapt Reveals Novel Targets of Adaptive Introgression From Neanderthals and Denisovans in Worldwide Human Populations.
MaLAdapt Reveals Novel Targets of Adaptive Introgression From Neanderthals and Denisovans in Worldwide Human Populations.
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DOI：
10.1093/molbev/msad001
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发表时间：
2023-01-04
影响因子：
10.7
通讯作者：
Lohmueller, Kirk E.
中科院分区：
文献类型：
--
作者：
Zhang, Xinjun;Kim, Bernard;Singh, Armaan;Sankararaman, Sriram;Durvasula, Arun;Lohmueller, Kirk E.
适应性渐渗（AI）促进了广泛物种的局部适应。许多先进的人工智能检测方法都是通过特别的方法来识别汇总统计异常值，或者通过扫描基因渗入区域来交叉扫描正选择。虽然被广泛使用，但交叉异常值的方法容易受到高假阴性率的影响，因为不同方法的能力不同，特别是对于复杂的渗入事件。此外，与人工智能无关的群体遗传过程，如背景选择或杂种优势，可能会产生与人工智能相似的基因组信号，从而影响依赖中性零分布的方法的可靠性。近年来，机器学习（ML）方法越来越多地应用于群体遗传问题。在这里，我们提出了一种基于ml的方法，称为MaLAdapt，用于从全基因组测序数据中识别AI位点。使用Extra-Trees分类器算法，我们的方法结合了来自大量具有生物学意义的汇总统计数据的信息，以捕获整个基因组中强大的人工智能复合特征。与现有方法相比，MaLAdapt在检测具有轻微有益影响的人工智能（包括对古老变异的选择）方面表现得特别出色，并且对非人工智能选择性扫描、有害突变的杂种优势和人口统计错误规范具有鲁棒性。此外，MaLAdapt优于现有的基于模拟数据分析和通过单倍型模式视觉检查经验信号验证的人工智能检测方法。我们将MaLAdapt应用于1000基因组计划人类基因组数据，并在非非洲人群中发现新的人工智能候选区域，包括富含调节代谢和免疫反应的重要功能生物学途径的基因。
Adaptive introgression (AI) facilitates local adaptation in a wide range of species. Many state-of-the-art methods detect AI with ad-hoc approaches that identify summary statistic outliers or intersect scans for positive selection with scans for introgressed genomic regions. Although widely used, approaches intersecting outliers are vulnerable to a high false-negative rate as the power of different methods varies, especially for complex introgression events. Moreover, population genetic processes unrelated to AI, such as background selection or heterosis, may create similar genomic signals to AI, compromising the reliability of methods that rely on neutral null distributions. In recent years, machine learning (ML) methods have been increasingly applied to population genetic questions. Here, we present a ML-based method called MaLAdapt for identifying AI loci from genome-wide sequencing data. Using an Extra-Trees Classifier algorithm, our method combines information from a large number of biologically meaningful summary statistics to capture a powerful composite signature of AI across the genome. In contrast to existing methods, MaLAdapt is especially well-powered to detect AI with mild beneficial effects, including selection on standing archaic variation, and is robust to non-AI selective sweeps, heterosis from deleterious mutations, and demographic misspecification. Furthermore, MaLAdapt outperforms existing methods for detecting AI based on the analysis of simulated data and the validation of empirical signals through visual inspection of haplotype patterns. We apply MaLAdapt to the 1000 Genomes Project human genomic data and discover novel AI candidate regions in non-African populations, including genes that are enriched in functionally important biological pathways regulating metabolism and immune responses.
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影响因子：
9.8
作者：
Dannemann M;Kelso J
通讯作者：
Kelso J
影响因子：
10.7
作者：
Adrion, Jeffrey R.;Galloway, Jared G.;Kern, Andrew D.
通讯作者：
Kern, Andrew D.
影响因子：
64.5
作者：
Browning SR;Browning BL;Zhou Y;Tucci S;Akey JM
通讯作者：
Akey JM
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
64.8
作者：
Choin, Jeremy;Mendoza-Revilla, Javier;Quintana-Murci, Lluis
通讯作者：
Quintana-Murci, Lluis