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无相位局部祖先推断减轻了切换错误对基于相位的方法的影响。

Phase-free local ancestry inference mitigates the impact of switch errors on phase-based methods.

作者信息

Avadhanam Siddharth, Williams Amy L

机构信息

Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA.

Department of Computer Science, Brigham Young University, Provo, UT 84602, USA.

出版信息

G3 (Bethesda). 2025 Aug 6;15(8). doi: 10.1093/g3journal/jkaf122.

Abstract

Local ancestry inference is an indispensable component of a variety of analyses in medical and population genetics, from admixture mapping to characterizing demographic history. However, the accuracy of local ancestry inference depends on a number of factors such as phase quality (for phase-based local ancestry inference methods) and time since admixture. Here, we present an empirical analysis of four local ancestry inference methods using simulated individuals of mixed African and European ancestry, examining the impact of variable phase quality and a range of demographic scenarios. We find that regardless of phasing options, calls from local ancestry inference methods that operate on unphased genotypes (phase-free local ancestry inference) have 2.6-4.6% higher Pearson correlation with the ground truth than methods that operate on phased genotypes (phase-based local ancestry inference). Applying the TRACTOR phase correction algorithm led to modest improvements in phase-based local ancestry inference, but despite this, the Pearson correlation of phase-free local ancestry inference remains 2.4-3.8% higher than phase-corrected phase-based approaches (considering the best-performing methods in each category). Further, analyzing perfectly phased data yields accuracies for the phase-based local ancestry inference methods that are only slightly inferior to those of HAPMIX. Phase-free and phase-based local ancestry inference accuracy differences can dramatically impact downstream analyses: estimates of the time since admixture using phase-based local ancestry inference tracts are upwardly biased by ≈10 generations using our highest quality statistically phased data but have virtually no bias using phase-free local ancestry inference calls. This study underscores the strong dependence of phase-based local ancestry inference accuracy on phase quality and highlights the merits of local ancestry inference approaches that analyze unphased genetic data.

摘要

本地祖先推断是医学和群体遗传学中各种分析不可或缺的组成部分,从混合映射到描述人口历史。然而,本地祖先推断的准确性取决于许多因素,如相位质量(对于基于相位的本地祖先推断方法)和混合后的时间。在这里,我们使用模拟的非洲和欧洲混合血统个体对四种本地祖先推断方法进行了实证分析,研究了可变相位质量和一系列人口统计场景的影响。我们发现,无论相位选项如何,对未分型基因型进行操作的本地祖先推断方法(无相位本地祖先推断)的调用与真实情况的皮尔逊相关性比基于分型基因型的方法(基于相位的本地祖先推断)高2.6%-4.6%。应用TRACTOR相位校正算法在基于相位的本地祖先推断中带来了适度的改进,但尽管如此,无相位本地祖先推断的皮尔逊相关性仍比经相位校正的基于相位的方法高2.4%-3.8%(考虑每个类别中表现最佳的方法)。此外,分析完全分型的数据会得出基于相位的本地祖先推断方法的准确性,这些准确性仅略低于HAPMIX。无相位和基于相位的本地祖先推断准确性差异会极大地影响下游分析:使用我们最高质量的统计分型数据,基于相位的本地祖先推断片段对混合后时间的估计向上偏差约10代,但使用无相位本地祖先推断调用几乎没有偏差。这项研究强调了基于相位的本地祖先推断准确性对相位质量的强烈依赖性,并突出了分析未分型遗传数据的本地祖先推断方法的优点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c07/12341942/0046bec32754/jkaf122f1.jpg

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