BACKGROUND Global and local ancestry inference in admixed human populations can be performed using computational tools implementing distinct algorithms. The development and resulting accuracy of these tools has been tested largely on populations with relatively straightforward admixture histories but little is known about how well they perform in more complex admixture scenarios. RESULTS Using simulations, we show that RFMix outperforms ADMIXTURE in determining global ancestry proportions even in a complex 5-way admixed population, in addition to assigning local ancestry with an accuracy of 89%. The ability of RFMix to determine global and local ancestry to a high degree of accuracy, particularly in admixed populations provides the opportunity for more accurate association analyses. CONCLUSION This study highlights the utility of the extension of computational tools to become more compatible to genetically structured populations, as well as the need to expand the sampling of diverse world-wide populations. This is particularly noteworthy as modern-day societies are becoming increasingly genetically complex and some genetic tools and commonly used ancestral populations are less appropriate. Based on these caveats and the results presented here, we suggest that RFMix be used for both global and local ancestry estimation in world-wide complex admixture scenarios particularly when including these estimates in association studies.

中文翻译：

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在复杂的混合人群中测试RFMix和ADMIXTURE。

背景技术可以使用实现不同算法的计算工具来执行混合人口中的全局和局部祖先推断。这些工具的开发和由此产生的准确性已在具有相对简单的混合历史的总体上进行了测试，但是对于它们在更复杂的混合场景中的性能知之甚少。结果使用模拟结果表明，RFMix在确定全局祖先比例方面甚至在复杂的5向混合人口中都比ADMIXTURE优越，并且可以分配89％的精度。RFMix能够高度准确地确定全球和本地血统的能力，尤其是在混合人群中，为更准确的关联分析提供了机会。结论这项研究强调了扩展计算工具以与遗传结构种群更加兼容的实用性，以及扩大对世界各地不同种群抽样的需求。随着现代社会在遗传上的日益复杂以及某些遗传工具和常用祖传种群的不适当，这尤其值得注意。基于这些注意事项和此处给出的结果，我们建议在全球复杂的混合物场景中，尤其在将这些估计包括在关联研究中时，将RFMix用于全球和本地祖先估计。随着现代社会在遗传上变得越来越复杂，并且某些遗传工具和常用祖传种群越来越不适合，这尤其值得注意。基于这些注意事项和此处给出的结果，我们建议在全球复杂的混合物场景中，尤其在将这些估计包括在关联研究中时，将RFMix用于全球和本地祖先估计。随着现代社会在遗传上变得越来越复杂，并且某些遗传工具和常用祖传种群越来越不适合，这尤其值得注意。基于这些注意事项和此处给出的结果，我们建议在全球复杂的混合物场景中，尤其在将这些估计包括在关联研究中时，将RFMix用于全球和本地祖先估计。