Robust Demographic Inference from Genomic and SNP Data

By Laurent Excoffier, Isabelle Dupanloup, E. Huerta-Sánchez, Vitor C. Sousa, Matthieu Foll. PLoS Genetics, 2013

1,368 citations recorded by the source corpus.

We introduce a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic datasets. We show that our composite-likelihood approach allows one to study evolutionary models of arbitrary complexity, which cannot be tackled by other current likelihood-based methods. For simple scenarios, our approach compares favorably in terms of accuracy and speed with , the current reference in the field, while showing better convergence properties for complex models. We first apply our methodology to non-coding genomic SNP data from four human populations. To infer their demographic history, we compare neutral evolutionary models of increasing complexity, including unsampled populations. We further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. Whereas previous ways of handling ascertained SNPs were either restricted to a single population or only allowed the inference of divergence time between a pair of populations, our framework can correctly infer parameters of more complex models including the divergence of several populations, bottlenecks and migration. We apply this approach to the reconstruction of African demography using two distinct ascertained human SNP panels studied under two evolutionary models. The two SNP panels lead to globally very similar estimates and confidence intervals, and suggest an ancient divergence (>110 Ky) between Yoruba and San populations. Our methodology appears well suited to the study of complex scenarios from large genomic data sets.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/09fe5f9084c4e5d3cc0487c72093c44a6471b723

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Citations in the corpus

Dsuite - fast D-statistics and related admixture evidence from VCF files (2020, methodology): “Finally, methods based on detailed population genetic models, such as δ a δ i (Gutenkunst et al., 2009), fastsimcoal2 (Excoffier et al., 2013), or IMa2 (Hey, 2010) can be used to infer demographic history that includes events such as population size changes, population splits and joins, and…”
What is Speciation Genomics? The roles of ecology, gene flow, and genomic architecture in the formation of species (2018, methodology): “• First, several methods can now be used to estimate parameters of IM models from the site frequency spectrum (SFS), that is, the distribution of allele frequencies aggregated across the available sequencing data specific to populations (Gutenkunst et al., 2009; Excoffier et al., 2013; Lohse et al.,”
Reliable ABC model choice via random forests (2014, methodology): “The dataset, first analyzed in Lombaert et al. (2011) and Estoup et al.”
Robust and scalable inference of population history from hundreds of unphased whole-genomes (2016, positive): “SFS/PRF methods 18,19 employ results from diffusion or coalescent theory to characterize the sampling distribution of unlinked segregating sites in a random sample of DNA sequences.”