Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness.
Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness.
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DOI：
10.1002/gepi.21896
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发表时间：
2015-05
影响因子：
2.1
通讯作者：
Thornton TA
中科院分区：
文献类型：
--
作者：
Conomos MP;Miller MB;Thornton TA
利用遗传数据进行群体结构推断在群体遗传学和遗传关联研究中有着广泛的应用。已经提出了几种方法来识别样本中的遗传祖先差异，其中研究参与者被假设为不相关，包括主成分分析（PCA），多维尺度（MDS）和基于模型的比例祖先估计方法。然而，许多遗传学研究包括具有一定程度相关性的个体，并且现有的推断遗传祖先的方法在相关样本中失败。我们提出了一种方法PC-AiR，用于在存在已知或神秘相关性的情况下进行稳健的群体结构推断。PC-AiR利用基因组筛选数据和有效的算法来识别代表样本中所有祖先的不相关个体的不同子集。PC-AiR方法直接对所识别的祖先代表性子集执行PCA，然后基于遗传相似性预测所有剩余个体的变异分量。在模拟研究和应用程序的真实的数据从第三阶段的HapMap项目中，我们证明了PC-AiR提供了一个显着的改善现有的方法，人口结构推断相关的样本。我们还证明了显着的效率增益，其中一个单一的轴的变化从PC-AIR提供更好的预测祖先在各种结构设置比使用10个（或更多）的组件的变化从广泛使用的PCA和MDS方法。最后，我们说明了PC-AIR可以提供改进的人口分层校正现有的方法在遗传关联研究与人口结构和相关性。
Population structure inference with genetic data has been motivated by a variety of applications in population genetics and genetic association studies. Several approaches have been proposed for the identification of genetic ancestry differences in samples where study participants are assumed to be unrelated, including principal components analysis (PCA), multi-dimensional scaling (MDS), and model-based methods for proportional ancestry estimation. Many genetic studies, however, include individuals with some degree of relatedness, and existing methods for inferring genetic ancestry fail in related samples. We present a method, PC-AiR, for robust population structure inference in the presence of known or cryptic relatedness. PC-AiR utilizes genome-screen data and an efficient algorithm to identify a diverse subset of unrelated individuals that is representative of all ancestries in the sample. The PC-AiR method directly performs PCA on the identified ancestry representative subset and then predicts components of variation for all remaining individuals based on genetic similarities. In simulation studies and in applications to real data from Phase III of the HapMap Project, we demonstrate that PC-AiR provides a substantial improvement over existing approaches for population structure inference in related samples. We also demonstrate significant efficiency gains, where a single axis of variation from PC-AiR provides better prediction of ancestry in a variety of structure settings than using ten (or more) components of variation from widely used PCA and MDS approaches. Finally, we illustrate that PC-AiR can provide improved population stratification correction over existing methods in genetic association studies with population structure and relatedness.