Efficient Moment-Based Inference of Admixture Parameters and Sources of Gene Flow
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Berger_Efficient moment.pdf
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Author(s)
Lipson, Mark
Loh, Po-Ru
Levin, Alexander
Reich, David
Patterson, Nick
Berger, Bonnie
Date Issued
May 2013
Journal
Molecular Biology and Evolution
Publisher
Oxford University Press
Citation
Lipson, M., P.-R. Loh, A. Levin, D. Reich, N. Patterson, and B. Berger. “Efficient Moment-Based Inference of Admixture Parameters and Sources of Gene Flow.” Molecular Biology and Evolution 30, no. 8 (July 11, 2013): 1788-1802.
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Final published version
Abstract
The recent explosion in available genetic data has led to significant advances in understanding the demographic histories of and relationships among human populations. It is still a challenge, however, to infer reliable parameter values for complicated models involving many populations. Here, we present MixMapper, an efficient, interactive method for constructing phylogenetic trees including admixture events using single nucleotide polymorphism (SNP) genotype data. MixMapper implements a novel two-phase approach to admixture inference using moment statistics, first building an unadmixed scaffold tree and then adding admixed populations by solving systems of equations that express allele frequency divergences in terms of mixture parameters. Importantly, all features of the model, including topology, sources of gene flow, branch lengths, and mixture proportions, are optimized automatically from the data and include estimates of statistical uncertainty. MixMapper also uses a new method to express branch lengths in easily interpretable drift units. We apply MixMapper to recently published data for Human Genome Diversity Cell Line Panel individuals genotyped on a SNP array designed especially for use in population genetics studies, obtaining confident results for 30 populations, 20 of them admixed. Notably, we confirm a signal of ancient admixture in European populations—including previously undetected admixture in Sardinians and Basques—involving a proportion of 20–40% ancient northern Eurasian ancestry.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mathematics
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1093/molbev/mst099
