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Selecting SNPs to Identify Ancestry

机译：选择SNP来确定祖先

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An individual's genotypes at a group of single-nucleotide polymorphisms (SNPs) can be used to predict that individual's ethnicity or ancestry. In medical studies, knowledge of a subject's ancestry can minimize possible confounding, and in forensic applications, such knowledge can help direct investigations. Our goal is to select a small subset of SNPs, from the millions already identified in the human genome, that can predict ancestry with a minimal error rate. The general form for this variable selection procedure is to estimate the expected error rates for sets of SNPs using a training dataset and consider those sets with the lowest error rates given their size. The quality of the estimate for the error rate determines the quality of the resulting SNPs. As the apparent error rate performs poorly when either the number of SNPs or the number of populations is large; we propose a new estimate, the Improved Bayesian Estimate. We demonstrate that selection procedures based on this estimate produce small sets of SNPs that can accurately predict ancestry. We also provide a list of the 100 optimal SNPs for identifying ancestry.
机译：一组单核苷酸多态性（SNP）的个体基因型可用于预测该个体的种族或血统。在医学研究中，对受试者祖先的了解可以使可能的混淆降至最低，而在法医学应用中，此类知识可以帮助进行直接调查。我们的目标是从人类基因组中已经鉴定的数百万个中选择一小部分SNP，以最小的错误率预测祖先。此变量选择过程的一般形式是使用训练数据集估计SNP集的预期错误率，并考虑给定大小的SNP集的最低错误率。错误率估算的质量决定了生成的SNP的质量。当SNP数量或人口数量很大时，表观错误率表现不佳;我们提出了一个新的估计值，即改进的贝叶斯估计值。我们证明基于此估计值的选择程序会产生少量SNP，可以准确地预测祖先。我们还提供了用于识别祖先的100个最佳SNP的列表。

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