Inferring Demographic History Using Two-Locus Statistics

Ragsdale, Aaron P.; Gutenkunst, Ryan N.

doi:10.1534/genetics.117.201251

Cited by 30 publications

(24 citation statements)

References 74 publications

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“…While classical approaches for computing Ψ n (Golding, 1984;Hudson, 2001) were limited to neutrality and steady-state demography, recent coalescent and diffusion developments allow for Ψ n to be computed under non-equilibrium demography and selection (Kamm et al, 2016;Ragsdale and Gutenkunst, 2017). These approaches are computationally expensive and limited to one population, as Ψ n has size (n+1)(n+2)(n+3) 6 , and the P -population distribution grows asymptotically as n 3P .…”

Section: Generalizing To Arbitrary Two-locus Haplotype Distributionmentioning

confidence: 99%

“…Using the same closure strategy for selection and recombination, however, we can approximate the entries of Ψ n+1 and Ψ n+2 as linear combinations of entries in Ψ n and obtain a closed equation. This approach provides accurate approximation for moderate n under recombination and selection (Appendix S1.3.5) that represent a 10 to 100-fold speedup over the numerical PDE implementation in Ragsdale and Gutenkunst (2017) (Table S1). However, closure is inaccurate for small n.…”

Section: Generalizing To Arbitrary Two-locus Haplotype Distributionmentioning

confidence: 99%

“…All figures showing σ 2 d -type statistics are normalized using π 2 (YRI), the joint heterozygosity in the Yoruba from Ibidan, Nigeria (YRI). This normalization removes all dependence of the statistics on the overall mutation rate, so that estimates of split times and population sizes are calibrated by the recombination rate per generation instead of the mutation rate (Ragsdale and Gutenkunst, 2017). This is convenient given that genome-wide estimates of the recombination rate tend to be more consistent across experimental approaches than estimates of the mutation rate.…”

Section: Genome Project Datamentioning

confidence: 99%

“…While two-locus statistics have been extensively studied Robertson, 1966, 1968;Karlin and Mc-Gregor, 1968;Ohta and Kimura, 1969a,b;Golding, 1984;Ethier and Griffiths, 1990;Hudson, 2001;McVean, 2002;Song and Song, 2007), most of this work focused on a single population at equilibrium demography, precluding their application to realistic demographic scenarios. Recently, approaches for computing the full two-locus sampling distribution for a single population with non-equilibrium demography were developed via the coalescent (Kamm et al, 2016) or a numerical solution to the diffusion approximation (Ragsdale and Gutenkunst, 2017), allowing for more robust inference of fine-scale recombination rates and single population demographic history. However, there remain significant limitations.…”

Section: Introductionmentioning

confidence: 99%

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Models of archaic admixture and recent history from two-locus statistics

Ragsdale

Gravel

2018

Preprint

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We learn about population history and underlying evolutionary biology through patterns of genetic polymorphism. Many approaches to reconstruct evolutionary histories focus on a limited number of informative statistics describing distributions of allele frequencies or patterns of linkage disequilibrium. We show that many commonly used statistics are part of a broad family of two-locus moments whose expectation can be computed jointly and rapidly under a wide range of scenarios, including complex multi-population demographies with continuous migration and admixture events. A full inspection of these statistics reveals that widely used models of human history fail to predict simple patterns of linkage disequilibrium. To jointly capture the information contained in classical and novel statistics, we implemented a tractable likelihood-based inference framework for demographic history. Using this approach, we show that human evolutionary models that include archaic admixture in Africa, Asia, and Europe provide a much better description of patterns of genetic diversity across the human genome. We estimate that an unidentified, deeply diverged population admixed with modern humans within Africa both before and after the split of African and Eurasian populations, contributing 4 − 8% genetic ancestry to individuals in world-wide populations. Author SummaryThroughout human history, populations have expanded and contracted, split and merged, and exchanged migrants. Because these events affected genetic diversity, we can learn about human history by comparing predictions from evolutionary models to genetic data. Here, we show how to rapidly compute such predictions for a wide range of diversity measures within and across populations under complex demographic scenarios. While widely used models of human history accurately predict common measures of diversity, we show that they strongly underestimate the co-occurence of low frequency mutations within human populations in Asia, Europe, and Africa. Models allowing for archaic admixture, the relatively recent mixing of human populations with deeply diverged human lineages, resolve this discrepancy. We use such models to infer demographic models that include both recent and ancient features of human history. We recover the well-characterized admixture of Neanderthals in Eurasian populations, as well as admixture from an as-yet unknown diverged human population within Africa, further suggesting that admixture with deeply diverged lineages occurred multiple times in human history. By simultaneously testing model predictions for a broad range of diversity statistics, we can assess the robustness of common evolutionary models, identify missing historical events, and build more informed models of human demography.

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Section: Generalizing To Arbitrary Two-locus Haplotype Distributionmentioning

confidence: 99%

Section: Generalizing To Arbitrary Two-locus Haplotype Distributionmentioning

confidence: 99%

Section: Genome Project Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Models of archaic admixture and recent history from two-locus statistics

Ragsdale

Gravel

2018

Preprint

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“…We should expect the same behavior in the case of multiple populations, which requires estimating the joint AFS. This problem can be solved by using some additional information about observed populations, for example, twolocus statistics (Ragsdale and Gutenkunst, 2017).…”

Section: Existing Optimizations Implemented In Eithermentioning

confidence: 99%

GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data

Noskova

Ulyantsev

Koepfli

et al. 2018

Preprint

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The demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum or AFS, the distribution of allele frequencies in populations. The joint allele frequency spectrum is commonly used to reconstruct the demographic history of multiple populations and several methods based on diffusion approximation (e.g., ∂a∂i) and ordinary differential equations (e.g., moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS.Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint allele frequency spectrum data.Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Analysis, https://github.com/ctlab/GADMA). We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer demographic models at different local optima close to the global one, making it is possible to detect more biology corrected model during further research.

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Inference of population history using coalescent HMMs: review and outlook

Spence

Steinrücken

Terhorst

et al. 2018

Current Opinion in Genetics & Development

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Studying how diverse human populations are related is of historical and anthropological interest, in addition to providing a realistic null model for testing for signatures of natural selection or disease associations. Furthermore, understanding the demographic histories of other species is playing an increasingly important role in conservation genetics. A number of statistical methods have been developed to infer population demographic histories using whole-genome sequence data, with recent advances focusing on allowing for more flexible modeling choices, scaling to larger data sets, and increasing statistical power. Here we review coalescent hidden Markov models, a powerful class of population genetic inference methods that can utilize linkage disequilibrium information effectively. We highlight recent advances, give advice for practitioners, point out potential pitfalls, and present possible future research directions.

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Inferring Demographic History Using Two-Locus Statistics

Cited by 30 publications

References 74 publications

Models of archaic admixture and recent history from two-locus statistics

Models of archaic admixture and recent history from two-locus statistics

GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data

Inference of population history using coalescent HMMs: review and outlook

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