Comparing local ancestry inference models in populations of two- and three-way admixture.
Comparing local ancestry inference models in populations of two- and three-way admixture.
复制标题
作者：
Schubert R;Andaleon A;Wheeler HE
地方血统估计是利用参考群体和多种统计模型推断混合群体中染色体片段的区域祖先起源。将当地血统整合到复杂的性状遗传学中，有可能增加对遗传关联的检测，并改善研究不足的混合人群（包括非洲裔美国人和西班牙裔美国人）的遗传预测模型。用于人类复杂性状遗传学的五种地方祖先估计方法是LAMP-LD（2012），RFMix（2013），ELAI（2014），Loter（2018）和MOSAIC（2019）。作为用户而不是开发人员，我们试图对这些软件工具的准确性、运行时间、内存使用和可用性进行直接比较，以确定哪一种最适合纳入关联研究管道。我们发现，在大多数情况下，RFMix具有最高的中位精度，其余软件的排名取决于测试人群的祖先架构。此外，我们估计了每个软件的内存和运行时间的O（n），并发现对于时间和内存，大多数软件相对于样本大小线性增加。唯一的例外是RFMix，它相对于运行时间呈二次方增长，相对于内存呈线性增长。有效的地方祖先估计工具是必要的，以增加多样性和防止人类遗传学研究中的人口差异。RFMix在所有方法中表现最好，但是，根据应用程序的不同，其他方法的表现也一样好，运行时更短。用于格式化数据、运行软件和估计精度的工具可以在https://github.com/WheelerLab/LAI_benchmarking上找到。
Local ancestry estimation infers the regional ancestral origin of chromosomal segments in admixed populations using reference populations and a variety of statistical models. Integrating local ancestry into complex trait genetics has the potential to increase detection of genetic associations and improve genetic prediction models in understudied admixed populations, including African Americans and Hispanics. Five methods for local ancestry estimation that have been used in human complex trait genetics are LAMP-LD (2012), RFMix (2013), ELAI (2014), Loter (2018), and MOSAIC (2019). As users rather than developers, we sought to perform direct comparisons of accuracy, runtime, memory usage, and usability of these software tools to determine which is best for incorporation into association study pipelines. We find that in the majority of cases RFMix has the highest median accuracy with the ranking of the remaining software dependent on the ancestral architecture of the population tested. Additionally, we estimate the O(n) of both memory and runtime for each software and find that for both time and memory most software increase linearly with respect to sample size. The only exception is RFMix, which increases quadratically with respect to runtime and linearly with respect to memory. Effective local ancestry estimation tools are necessary to increase diversity and prevent population disparities in human genetics studies. RFMix performs the best across methods, however, depending on application, other methods perform just as well with the benefit of shorter runtimes. Scripts used to format data, run software, and estimate accuracy can be found at https://github.com/WheelerLab/LAI_benchmarking.
影响因子：
2.9
作者：
Hui D;Fang Z;Lin J;Duan Q;Li Y;Hu M;Chen W
通讯作者：
Chen W