Public Health Sciences, Henry Ford Health System, Detroit, Department of Psychiatry, University of Michigan, Ann Arbor and Center for Health Services Research, Henry Ford Health System, Detroit, MI, USA.
Bioinformatics. 2013 Nov 1;29(21):2750-6. doi: 10.1093/bioinformatics/btt488. Epub 2013 Aug 19.
The inference of local ancestry of admixed individuals at every locus provides the basis for admixture mapping. Local ancestry information has been used to identify genetic susceptibility loci.
In this study, we developed a statistical method, efficient inference of local ancestry (EILA), which uses fused quantile regression and k-means classifier to infer the local ancestry for admixed individuals. We also conducted a simulation study using HapMap data to evaluate the performance of EILA in comparison with two competing methods, HAPMIX and LAMP. In general, the performance declined as the ancestral distance decreased and the time since admixture increased. EILA performed as well as the other two methods in terms of computational efficiency. In the case of closely related ancestral populations, all the three methods performed poorly. Most importantly, when the ancestral distance was large or moderate, EILA had higher accuracy and lower variation in comparison with the other two methods.
EILA is implemented as an R package, which is freely available from the Comprehensive R Archive Network (http://cran.r-project.org/).
推断混合个体在每个基因座的局部祖源为混合映射提供了基础。局部祖源信息已被用于识别遗传易感基因座。
本研究开发了一种统计方法，有效推断局部祖源（EILA），它使用融合分位数回归和 k-均值分类器来推断混合个体的局部祖源。我们还使用 HapMap 数据进行了模拟研究，以评估 EILA 在与两种竞争方法（HAPMIX 和 LAMP）的比较中的性能。一般来说，随着祖先距离的减小和混合时间的增加，性能下降。在计算效率方面，EILA 与其他两种方法一样好。在亲缘关系密切的祖先群体的情况下，所有三种方法的性能都很差。最重要的是，当祖先距离较大或中等时，EILA 与其他两种方法相比，准确性更高，变化更小。
EILA 实现为一个 R 包，可从 Comprehensive R Archive Network（http://cran.r-project.org/）免费获得。
