RFMix: A Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference
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 discrimin...
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| Veröffentlicht in: | American journal of human genetics 2013-08, Vol.93 (2), p.278-288 |
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| creator | Maples, Brian K. Gravel, Simon Kenny, Eimear E. Bustamante, Carlos D. |
| description | 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 (∼30×) 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 (∼0.4%). |
| doi_str_mv | 10.1016/j.ajhg.2013.06.020 |
| format | Article |
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Published by Elsevier Inc. All rights reserved.</rights><rights>Copyright Cell Press Aug 8, 2013</rights><rights>2013 The American Society of Human Genetics. Published by Elsevier Ltd. 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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 (∼30×) 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. 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| subjects | Asian People - genetics Black or African American Black People - genetics Computer Simulation Discriminant analysis Ethnicity Genealogy Genetic Testing Genome, Human Genomes Haplotypes Humans Indians, North American - genetics Mathematical models Models, Genetic Polymorphism, Single Nucleotide Simulation White People - genetics |
| title | RFMix: A Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference |
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