Pasaniuc Bogdan, Sankararaman Sriram, Kimmel Gad, Halperin Eran
International Computer Science Institute, Computer Science Division, University of California, Berkeley, CA, USA.
Bioinformatics. 2009 Jun 15;25(12):i213-21. doi: 10.1093/bioinformatics/btp197.
A characterization of the genetic variation of recently admixed populations may reveal historical population events, and is useful for the detection of single nucleotide polymorphisms (SNPs) associated with diseases through association studies and admixture mapping. Inference of locus-specific ancestry is key to our understanding of the genetic variation of such populations. While a number of methods for the inference of locus-specific ancestry are accurate when the ancestral populations are quite distant (e.g. African-Americans), current methods incur a large error rate when inferring the locus-specific ancestry in admixed populations where the ancestral populations are closely related (e.g. Americans of European descent).
In this work, we extend previous methods for the inference of locus-specific ancestry by the incorporation of a refined model of recombination events. We present an efficient dynamic programming algorithm to infer the locus-specific ancestries in this model, resulting in a method that attains improved accuracies; the improvement is most significant when the ancestral populations are closely related. An evaluation on a wide range of scenarios, including admixtures of the 52 population groups from the Human Genome Diversity Project demonstrates that locus-specific ancestry can indeed be accurately inferred in these admixtures using our method. Finally, we demonstrate that imputation methods can be improved by the incorporation of locus-specific ancestry, when applied to admixed populations.
The implementation of the WINPOP model is available as part of the LAMP package at http://lamp.icsi.berkeley.edu/lamp.
对近期混合人群的遗传变异进行特征描述可能会揭示历史人群事件，并且对于通过关联研究和混合映射检测与疾病相关的单核苷酸多态性（SNP）很有用。推断位点特异性祖先对于我们理解此类人群的遗传变异至关重要。虽然当祖先人群差异较大时（例如非裔美国人），许多推断位点特异性祖先的方法是准确的，但在推断祖先人群关系密切的混合人群（例如欧洲裔美国人）的位点特异性祖先时，当前方法会产生较高的错误率。
在这项工作中，我们通过纳入改进的重组事件模型扩展了先前推断位点特异性祖先的方法。我们提出了一种有效的动态规划算法来推断该模型中的位点特异性祖先，从而得到一种具有更高准确性的方法；当祖先人群关系密切时，这种改进最为显著。对包括人类基因组多样性项目的52个种群组混合情况在内的广泛场景进行评估表明，使用我们的方法确实可以准确推断这些混合情况中的位点特异性祖先。最后，我们证明，当应用于混合人群时，通过纳入位点特异性祖先可以改进归因方法。
WINPOP模型的实现可作为LAMP软件包的一部分在http://lamp.icsi.berkeley.edu/lamp获取。
