Sampling strategies for frequency spectrum-based population genomic inference

Abstract

BackgroundThe allele frequency spectrum (AFS) consists of counts of the number of single nucleotide polymorphism (SNP) loci with derived variants present at each given frequency in a sample. Multiple approaches have recently been developed for parameter estimation and calculation of model likelihoods based on the joint AFS from two or more populations. We conducted a simulation study of one of these approaches, implemented in the Python module δaδi, to compare parameter estimation and model selection accuracy given different sample sizes under one- and two-population models.ResultsOur simulations included a variety of demographic models and two parameterizations that differed in the timing of events (divergence or size change). Using a number of SNPs reasonably obtained through next-generation sequencing approaches (10,000 - 50,000), accurate parameter estimates and model selection were possible for models with more ancient demographic events, even given relatively small numbers of sampled individuals. However, for recent events, larger numbers of individuals were required to achieve accuracy and precision in parameter estimates similar to that seen for models with older divergence or population size changes. We quantify i) the uncertainty in model selection, using tools from information theory, and ii) the accuracy and precision of parameter estimates, using the root mean squared error, as a function of the timing of demographic events, sample sizes used in the analysis, and complexity of the simulated models.ConclusionsHere, we illustrate the utility of the genome-wide AFS for estimating demographic history and provide recommendations to guide sampling in population genomics studies that seek to draw inference from the AFS. Our results indicate that larger samples of individuals (and thus larger AFS) provide greater power for model selection and parameter estimation for more recent demographic events.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-014-0254-4) contains supplementary material, which is available to authorized users.

Whole-genome sequence analyses of Western Central African Pygmy hunter-gatherers reveal a complex demographic history and identify candidate genes under positive natural selection

“…Two of these studies (Patin et al 2009;Batini et al 2011) inferred recent population contraction in the Pygmy groups. Our small sample size limits our power to detect such recent events, but the simulation study of Robinson et al (2014) suggests that ∂a∂i can confidently infer ancient events in models of similar complexity to those we infer here.…”

“…: 4.7-5.7 kya). Because our small sample size limits power to infer recent demographic events (Robinson et al 2014), we assumed that the Baka-Biaka divergence did not change the rates of gene flow with the Yoruba, and our model includes no Baka-Biaka gene flow.…”

The evolution of phylogeographic data sets

“…at or near the Last Glacial Maximum), it is clear that the number of individuals sampled is also important (Robinson et al . ) and that the ideal ratio of loci to individuals depends on the question at hand (Maddison & Knowles ). Furthermore, given that phylogeographic studies often focus on organisms for which few or no genomic resources exist (e.g.…”

Examining Phylogenetic Relationships Among Gibbon Genera Using Whole Genome Sequence Data Using an Approximate Bayesian Computation Approach

“…These methods are likely to be particularly effective for estimating additional parameters such as recent size changes and migration when many individuals are sampled from multiple species compared to our approach, which uses only a summary of the AFS, though demographic events on the order of millions of years ago may still not be tractable regardless of sample size (Robinson et al 2014). These approaches can also take advantage of recent methods that allow relatively unbiased estimation of the AFS from even low coverage second generation sequencing data (though this currently must be done for each population separately).…”