Efficient Moment-Based Inference of Admixture Parameters and Sources of Gene Flow

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.

Admixture, Population Structure, and F-Statistics

“…This relationship provides ample opportunities for interaction between these currently diverged fields: Theory (Huson and Bryant 2006;Huson et al 2010) and algorithms for finding phylogenetic networks such as Neighbor-Net (Bryant and Moulton 2004) may provide a useful alternative to tools specifically developed for allele frequencies and F-statistics Pickrell and Pritchard 2012;Lipson et al 2013), particularly in complex cases. On the other hand, the tests and different interpretations described here may be useful to test for treeness in other phylogenetic applications, and the complex history of humans may provide motivation to further develop the theory of phylogenetic networks and stress its usefulness for within-species demographic analyses.…”

“…I then review some basic properties of distance-based phylogenetic trees, show how the admixture tests are interpreted in this context, and evaluate their behavior. Many of the results that are highlighted here are implicit in classical (Wahlund 1928;Wright 1931;Cavalli-Sforza and Edwards 1967;Felsenstein 1973Felsenstein , 1981Cavalli-Sforza and Piazza 1975;Slatkin 1991;Excoffier et al 1992) and more recent work Pickrell and Pritchard 2012;Lipson et al 2013), but often not explicitly stated or given in a different context.…”

“…A similar argument was used by Lipson et al (2013) to estimate ancestral heterozygosities and to linearize F 2 . These equations can be rearranged to make the connection between other measures of genetic drift and F 2 more explicit:…”

“…However, while the estimators are identical, the underlying modeling assumptions are different: The original definition considered only loci that were segregating in an ancestral population; loci not segregating there were discarded. Since ancestral populations are usually unsampled, this is often replaced by ascertainment in an outgroup Lipson et al 2013). In contrast, Equation 17 assumes that all markers are used, which is the more natural interpretation for sequence data.…”

“…OR humans, whole-genome genotype data are now available for individuals from hundreds of populations (Lazaridis et al 2014;Yunusbayev et al 2015), opening up the possibility to ask more detailed and complex questions about our history (Pickrell and Reich 2014;Schraiber and Akey 2015) and stimulating the development of new tools for the analysis of the joint history of these populations (Reich et al 2009;Patterson et al 2012;Pickrell and Pritchard 2012;Lipson et al 2013;Ralph and Coop 2013;Hellenthal et al A simple and intuitive approach that has quickly gained in popularity are the F-statistics, introduced by Reich et al (2009) and summarized in Patterson et al (2012). In that framework, inference is based on "shared genetic drift" between sets of populations, under the premise that shared drift implies a shared evolutionary history.…”