RFMix: A Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference
RFMix: A Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference
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DOI：
10.1016/j.ajhg.2013.06.020
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发表时间：
2013-08-08
影响因子：
9.8
通讯作者：
Bustamante, Carlos D.
中科院分区：
文献类型：
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作者：
Maples, Brian K.;Gravel, Simon;Bustamante, Carlos D.
地方祖先推断是全序列人类基因组遗传分析的重要步骤。目前的方法只能检测大陆级祖先（即，欧洲人对非洲人对亚洲人），即使使用数百万个标记也是准确的。在这里，我们提出了RFMix，一种功能强大的判别建模方法，比现有方法更快（类似于30倍），更准确。我们通过使用由在参考面板上训练的随机森林参数化的条件随机场来实现这一点。RFMix能够从混合样本本身学习，以提高性能并自动纠正相位误差。RFMix在模拟的西班牙裔/拉丁裔和非裔美国人以及混合的欧洲人、非洲人和亚洲人中显示出高灵敏度和特异性。最后，我们证明了HapMap中的非洲裔美国人包含适度（但非零）的美洲原住民血统（类似于0.4%）。
Local-ancestry inference is an important step in the genetic analysis of fully sequenced human genomes. Current methods can only detect continental-level ancestry (i.e., European versus African versus Asian) accurately even when using millions of markers. Here, we present RFMix, a powerful discriminative modeling approach that is faster (similar to 30x) and more accurate than existing methods. We accomplish this by using a conditional random field parameterized by random forests trained on reference panels. RFMix is capable of learning from the admixed samples themselves to boost performance and autocorrect phasing errors. RFMix shows high sensitivity and specificity in simulated Hispanics/Latinos and African Americans and admixed Europeans, Africans, and Asians. Finally, we demonstrate that African Americans in HapMap contain modest (but nonzero) levels of Native American ancestry (similar to 0.4%).