Dataset Information

RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference.

ABSTRACT: 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%).

SUBMITTER: Maples BK

PROVIDER: S-EPMC3738819 | biostudies-other | 2013 Aug

REPOSITORIES: biostudies-other

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RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference.

Maples Brian K BK Gravel Simon S Kenny Eimear E EE Bustamante Carlos D CD

American journal of human genetics 20130801 2

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 panel ...[more]

PMID: 23910464

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Project description:MotivationLocal ancestry inference (LAI) is the high resolution prediction of ancestry labels along a DNA sequence. LAI is important in the study of human history and migrations, and it is beginning to play a role in precision medicine applications including ancestry-adjusted genome-wide association studies (GWASs) and polygenic risk scores (PRSs). Existing LAI models do not generalize well between species, chromosomes or even ancestry groups, requiring re-training for each different setting. Furthermore, such methods can lack interpretability, which is an important element in each of these applications.ResultsWe present SALAI-Net, a portable statistical LAI method that can be applied on any set of species and ancestries (species-agnostic), requiring only haplotype data and no other biological parameters. Inspired by identity by descent methods, SALAI-Net estimates population labels for each segment of DNA by performing a reference matching approach, which leads to an interpretable and fast technique. We benchmark our models on whole-genome data of humans and we test these models' ability to generalize to dog breeds when trained on human data. SALAI-Net outperforms previous methods in terms of balanced accuracy, while generalizing between different settings, species and datasets. Moreover, it is up to two orders of magnitude faster and uses considerably less RAM memory than competing methods.Availability and implementationWe provide an open source implementation and links to publicly available data at github.com/AI-sandbox/SALAI-Net. Data is publicly available as follows: https://www.internationalgenome.org (1000 Genomes), https://www.simonsfoundation.org/simons-genome-diversity-project (Simons Genome Diversity Project), https://www.sanger.ac.uk/resources/downloads/human/hapmap3.html (HapMap), ftp://ngs.sanger.ac.uk/production/hgdp/hgdp_wgs.20190516 (Human Genome Diversity Project) and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA448733 (Canid genomes).Supplementary informationSupplementary data are available from Bioinformatics online.

Project description:BACKGROUND:Accurate determination of genetic ancestry is of high interest for many areas such as biomedical research, personal genomics and forensics. It remains an important topic in genetic association studies, as it has been shown that population stratification, if not appropriately considered, can lead to false-positive and -negative results. While large association studies typically extract ancestry information from available genome-wide SNP genotypes, many important clinical data sets on rare phenotypes and historical collections assembled before the GWAS area are in need of a feasible method (i.e., ease of genotyping, small number of markers) to infer the geographic origin and potential admixture of the study subjects. Here we report on the development, application and limitations of a small, multiplexable ancestry informative marker (AIM) panel of SNPs (or AISNP) developed specifically for this purpose. RESULTS:Based on worldwide populations from the HGDP, a 41-AIM AISNP panel for multiplex application with the ABI SNPlex and a subset with 31 AIMs for the Sequenome iPLEX system were selected and found to be highly informative for inferring ancestry among the seven continental regions Africa, the Middle East, Europe, Central/South Asia, East Asia, the Americas and Oceania. The panel was found to be least informative for Eurasian populations, and additional AIMs for a higher resolution are suggested. A large reference set including over 4,000 subjects collected from 120 global populations was assembled to facilitate accurate ancestry determination. We show practical applications of this AIM panel, discuss its limitations for admixed individuals and suggest ways to incorporate ancestry information into genetic association studies. CONCLUSION:We demonstrated the utility of a small AISNP panel specifically developed to discern global ancestry. We believe that it will find wide application because of its feasibility and potential for a wide range of applications.

Dataset Information

RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference.

Publications

RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference.

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