A hidden Markov model for investigating recent positive selection through haplotype structure
A hidden Markov model for investigating recent positive selection through haplotype structure
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DOI：
10.1016/j.tpb.2014.11.001
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发表时间：
2015-02-01
影响因子：
1.4
通讯作者：
Slatkin, Montgomery
中科院分区：
文献类型：
--
作者：
Chen, Hua;Hey, Jody;Slatkin, Montgomery
最近的积极选择可以增加频率的一个有利的突变体足够迅速，一个相对较长的祖先单倍型将保持完整的周围。我们提出了一个隐马尔可夫模型（HMM），以确定这样的单倍型结构。利用HMM识别的单倍型结构，建立祖先单倍型分布的群体遗传模型，对选择强度和等位基因年龄进行参数推断。仿真结果表明，该方法能在较宽的条件下检测出选择，且比现有的基于频谱的方法具有更高的功率。此外，它提供了良好的估计的选择系数和等位基因年龄的强选择。该方法在合理的运行时间内分析大型数据集。将该方法应用于HapMap III数据进行基因组扫描，并确定了最近正选择下的候选区域列表。它也适用于几个已知的基因是最近的积极选择，包括LCT，KITLG和TYRP 1基因在北方欧洲人，和OCA 2在东亚人，估计他们的等位基因年龄和选择系数。(C)2014 Elsevier Inc. All rights reserved.
Recent positive selection can increase the frequency of an advantageous mutant rapidly enough that a relatively long ancestral haplotype will be remained intact around it. We present a hidden Markov model (HMM) to identify such haplotype structures. With HMM identified haplotype structures, a population genetic model for the extent of ancestral haplotypes is then adopted for parameter inference of the selection intensity and the allele age. Simulations show that this method can detect selection under a wide range of conditions and has higher power than the existing frequency spectrum-based method. In addition, it provides good estimate of the selection coefficients and allele ages for strong selection. The method analyzes large data sets in a reasonable amount of running time. This method is applied to HapMap III data for a genome scan, and identifies a list of candidate regions putatively under recent positive selection. It is also applied to several genes known to be under recent positive selection, including the LCT, KITLG and TYRP1 genes in Northern Europeans, and OCA2 in East Asians, to estimate their allele ages and selection coefficients. (C) 2014 Elsevier Inc. All rights reserved.