Estimating local ancestry in admixed populations
Estimating local ancestry in admixed populations
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DOI：
10.1016/j.ajhg.2007.09.022
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发表时间：
2008-02-01
影响因子：
9.8
通讯作者：
Halperin, Eran
中科院分区：
文献类型：
--
作者：
Sankararaman, Sriram;Sridhar, Srinath;Halperin, Eran
SNP 的大规模基因分型在识别可能与疾病相关的标记方面显示出了巨大的前景。进行这些研究的主要障碍之一是潜在的人口亚结构可能会产生虚假关联。种群亚结构可能是由两个不同的亚种群或单个混合个体池的存在引起的。在这项工作中，我们重点关注后者，这在实践中很难检测到。这一研究方向的新进展预计将在识别不同人群之间存在差异且仍与疾病相关的基因座方面发挥关键作用。我们评估了在这种情况下推断人口子结构的当前方法，并表明即使在相对简单的情况下，它们也可能相当不准确。因此，我们引入了一种新方法，LAMP（混合群体中的本地祖先），它可以根据每个单核苷酸多态性（SNP）推断每个个体的祖先。 LAMP 计算连续 SNP 重叠窗口的祖先结构，并将结果与多数投票相结合。我们的实证结果表明，LAMP 比现有的推断基因座特异性祖先的方法更加准确和高效，使其能够处理大规模数据集。我们进一步表明，LAMP 可用于估计每个个体的个体混合。我们的实验评估表明，与 STRUCTURE 或 EIGENSTRAT 等最先进的方法相比，这种扩展对个体混合的估计要准确得多，这些方法经常用于校正关联研究中的群体分层。
Large-scale genotyping of SNPs has shown a great promise in identifying markers that could be linked to diseases. One of the major obstacles involved in performing these studies is that the underlying population substructure could produce spurious associations. Population substructure can be caused by the presence of two distinct subpopulations or a single pool of admixed individuals. In this work, we focus on the latter, which is significantly harder to detect in practice. New advances in this research direction are expected to play a key role in identifying loci that are different among different populations and are still associated with a disease. We evaluated current methods for inference of population substructure in such cases and show that they might be quite inaccurate even in relatively simple scenarios. We therefore introduce a new method, LAMP (Local Ancestry in adMixed Populations), which infers the ancestry of each individual at every single-nucleotide polymorphism (SNP). LAMP computes the ancestry structure for overlapping windows of contiguous SNPs and combines the results with a majority vote. Our empirical results show that LAMP is significantly more accurate and more efficient than existing methods for inferrring locus-specific ancestries, enabling it to handle large-scale datasets. We further show that LAMP can be used to estimate the individual admixture of each individual. Our experimental evaluation indicates that this extension yields a considerably more accurate estimate of individual admixture than state-of-the-art methods such as STRUCTURE or EIGENSTRAT, which are frequently used for the correction of population stratification in association studies.