Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia
Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia
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DOI：
10.1016/j.ajhg.2015.12.022
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发表时间：
2016-03-03
影响因子：
9.8
通讯作者：
Price, Alkes L.
中科院分区：
文献类型：
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作者：
Galinsky, Kevin J.;Bhatia, Gaurav;Price, Alkes L.
寻找亚群之间具有异常分化的遗传变异是识别自然选择信号的既定方法。然而，现有的方法通常需要离散的亚群。我们介绍了一种方法，推断选择使用主成分（PC）通过识别变异的分化沿着顶部PC显着大于零分布的遗传漂变。为了将这种方法应用于大型数据集，我们开发了FastPCA软件，该软件采用随机矩阵理论的最新进展来精确地近似顶级PC，同时将时间和内存成本从二次减少到个体数量的线性，这是许多数量级的计算改进。我们将FastPCA应用于54，734名欧洲裔美国人的队列，确定了5个不同的亚群，涵盖前4个PC。使用基于PC的自然选择测试，我们复制了以前已知的选择位点，并确定了三个新的全基因组选择的重要信号，包括欧洲人在ADH 1B的选择。编码变异rs1229984*T先前已与酗酒的风险降低，并显示在东亚人的选择下，我们表明，这是一个罕见的例子，独立的进化在两大洲。我们还检测了IGFBP3和IGH的选择信号，这些信号以前也与人类疾病有关。
Searching for genetic variants with unusual differentiation between subpopulations is an established approach for identifying signals of natural selection. However, existing methods generally require discrete subpopulations. We introduce a method that infers selection using principal components (PCs) by identifying variants whose differentiation along top PCs is significantly greater than the null distribution of genetic drift. To enable the application of this method to large datasets, we developed the FastPCA software, which employs recent advances in random matrix theory to accurately approximate top PCs while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude. We apply FastPCA to a cohort of 54,734 European Americans, identifying 5 distinct subpopulations spanning the top 4 PCs. Using the PC-based test for natural selection, we replicate previously known selected loci and identify three new genome-wide significant signals of selection, including selection in Europeans at ADH1B. The coding variant rs1229984*T has previously been associated to a decreased risk of alcoholism and shown to be under selection in East Asians; we show that it is a rare example of independent evolution on two continents. We also detect selection signals at IGFBP3 and IGH, which have also previously been associated to human disease.