Improved Ancestry Estimation for both Genotyping and Sequencing Data using Projection Procrustes Analysis and Genotype Imputation
Improved Ancestry Estimation for both Genotyping and Sequencing Data using Projection Procrustes Analysis and Genotype Imputation
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DOI：
10.1016/j.ajhg.2015.04.018
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发表时间：
2015-06-04
影响因子：
9.8
通讯作者：
Lin, Xihong
中科院分区：
文献类型：
--
作者：
Wang, Chaolong;Zhan, Xiaowei;Lin, Xihong
准确估计个体血统在遗传关联研究中非常重要，特别是当从多个来源收集大量样本时。然而，针对全基因组 SNP 数据开发的现有方法不能很好地处理少量的遗传数据，例如在靶向测序或外显子组芯片基因分型实验中。我们提出了一个统计框架来估计由个体参考集生成的主成分祖先地图中的个体祖先。该框架扩展并改进了我们之前使用低覆盖率序列读取（LASER 1.0）来分析基因分型或测序数据来估计祖先的方法。特别是，我们引入了一种投影 Procrustes 分析方法，该方法使用高维主成分来估计低维参考空间中的祖先。使用广泛的模拟和经验数据示例，我们表明我们的新方法 (LASER 2.0) 与参考个体的基因型插补相结合，在估计精细遗传祖先方面可以大大优于 LASER 1.0。具体来说，LASER 2.0 可以使用外显子组芯片基因型或靶向测序数据准确估计欧洲境内的精细血统，脱靶覆盖率低至 0.05 倍。在 LASER 2.0 的框架下，我们可以在共享参考空间中估计不同位点或通过不同技术检测的样本的个体祖先。因此，我们的祖先估计方法不仅可以帮助在个体研究中建立祖先模型，而且还可以促进对多个来源的遗传数据的组合分析，从而加速疾病关联研究的发现。
Accurate estimation of individual ancestry is important in genetic association studies, especially when a large number of samples are collected from multiple sources. However, existing approaches developed for genome-wide SNP data do not work well with modest amounts of genetic data, such as in targeted sequencing or exome chip genotyping experiments. We propose a statistical framework to estimate individual ancestry in a principal component ancestry map generated by a reference set of individuals. This framework extends and improves upon our previous method for estimating ancestry using low-coverage sequence reads (LASER 1.0) to analyze either genotyping or sequencing data. In particular, we introduce a projection Procrustes analysis approach that uses high-dimensional principal components to estimate ancestry in a low-dimensional reference space. Using extensive simulations and empirical data examples, we show that our new method (LASER 2.0), combined with genotype imputation on the reference individuals, can substantially outperform LASER 1.0 in estimating fine-scale genetic ancestry. Specifically, LASER 2.0 can accurately estimate fine-scale ancestry within Europe using either exome chip genotypes or targeted sequencing data with off-target coverage as low as 0.05x. Under the framework of LASER 2.0, we can estimate individual ancestry in a shared reference space for samples assayed at different loci or by different techniques. Therefore, our ancestry estimation method will accelerate discovery in disease association studies not only by helping model ancestry within individual studies but also by facilitating combined analysis of genetic data from multiple sources.