Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data

By Ryan N. Gutenkunst, Ryan D. Hernandez, S. Williamson, Carlos D. Bustamante. PLoS Genetics, 2009

1,857 citations recorded by the source corpus.

Demographic models built from genetic data play important roles in illuminating prehistorical events and serving as null models in genome scans for selection. We introduce an inference method based on the joint frequency spectrum of genetic variants within and between populations. For candidate models we numerically compute the expected spectrum using a diffusion approximation to the one-locus, two-allele Wright-Fisher process, involving up to three simultaneous populations. Our approach is a composite likelihood scheme, since linkage between neutral loci alters the variance but not the expectation of the frequency spectrum. We thus use bootstraps incorporating linkage to estimate uncertainties for parameters and significance values for hypothesis tests. Our method can also incorporate selection on single sites, predicting the joint distribution of selected alleles among populations experiencing a bevy of evolutionary forces, including expansions, contractions, migrations, and admixture. We model human expansion out of Africa and the settlement of the New World, using 5 Mb of noncoding DNA resequenced in 68 individuals from 4 populations (YRI, CHB, CEU, and MXL) by the Environmental Genome Project. We infer divergence between West African and Eurasian populations 140 thousand years ago (95% confidence interval: 40–270 kya). This is earlier than other genetic studies, in part because we incorporate migration. We estimate the European (CEU) and East Asian (CHB) divergence time to be 23 kya (95% c.i.: 17–43 kya), long after archeological evidence places modern humans in Europe. Finally, we estimate divergence between East Asians (CHB) and Mexican-Americans (MXL) of 22 kya (95% c.i.: 16.3–26.9 kya), and our analysis yields no evidence for subsequent migration. Furthermore, combining our demographic model with a previously estimated distribution of selective effects among newly arising amino acid mutations accurately predicts the frequency spectrum of nonsynonymous variants across three continental populations (YRI, CHB, CEU).

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/adcfba569de90334cbb8aeecbc7f17aa8f1f743c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

Citations in the corpus

Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes (2015, methodology): “Researchers are seeking to understand fine scale population structure using methods based on principal component analysis [112], admixture fractions [113–115], length of haplotype blocks [116–118] and allele frequencies [119].”
Supervised Machine Learning for Population Genetics: A New Paradigm (2018, methodology): “Indeed, much attention in the field has been placed on developing methods for the inference of population size histories and patterns of population splitting and migration [54–58].”
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…”
Spatial Population Genetics: It's About Time (2019, background): “Methods for inferring the distant history of populations using a few samples are the most well-developed (e.g.,Gutenkunst et al. 2009, Li &Durbin 2011,Kamm et al. 2017), but these can incorporate very 442 Bradburd • Ralph A nn u.”
A genomic history of Aboriginal Australia (2016, result): “Under our best model, Australo-Papuans began to diverge from Eurasians ~58 kya (95% CI 51-72 kya, Figure5c, S07), whereas Europeans and East Asians diverged from each other ~42 kya (95% CI 29-55 kya, Figure5c, S07) in agreement with previous estimates 19,39,60,61 .”
Demographic history and rare allele sharing among human populations (2011, methodology): “When inferring demographic parameters by maximum likelihood of a Poisson Random Field (6), we therefore incorporate the error model in our demographic model rather than attempt to correct the SFS. Prediction of the Rate of Variant Discovery.”
Robust and scalable inference of population history from hundreds of unphased whole-genomes (2016, background): “Next, we compared SMC++ with MSMC and the SFS-based inference method ∂ a ∂ i 18 .”
Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data (2012, background): “In application to dogs, we show that the boxer and basenji breeds have a considerable component of ancestry from grey wolves subsequent to domestication.”
How to fail at species delimitation (2013, background): “1998), population divergence (Hey & Nielsen 2007) or combinations of these processes (Gutenkunst et al. 2009) are widely applied to phylogeographical data.”
Using bioinformatics to predict the functional impact of SNVs (2011, methodology): “Here, we describe the essential components of bionformatics tools that predict functional SNVs. Results: Bioinformatics tools have great potential to identify functional SNVs, but the black box nature of many tools can be a pitfall for researchers.”
Robust Demographic Inference from Genomic and SNP Data (2013, methodology): “The use of an outgroup species with fixed divergence time is a standard way to calibrate mutation rates (as e.g. in [21]), but we note it could also be used within species for DNA sequence data when some uncertainty exist on mutation rates, which is currently the case in humans [67,68].”
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.,”
A framework for incorporating evolutionary genomics into biodiversity conservation and management (2015, background): “See [84-86] [WGS], [RRS], [DE] with [IG] or [WGS], [DE] with [PPS] Bayesian skyline p /coalescent simulations or like od-based diffusion modellin om SNP data Genomics can provide a comprehensive assessment; however, a similar outcome might be achieved using non-genomic tools (e.”