Complex genetic admixture histories reconstructed with Approximate Bayesian Computation

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Fortes-Lima, Cesar A.,PhD,1985-Uppsala universitet,Människans evolution (author)

Complex genetic admixture histories reconstructed with Approximate Bayesian Computation

Article/chapterEnglish2021

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2021-02-26
John Wiley & Sons,2021
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LIBRIS-ID:oai:DiVA.org:uu-442640
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-442640URI
https://doi.org/10.1111/1755-0998.13325DOI

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Language:English
Summary in:English

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Admixture is a fundamental evolutionary process that has influenced genetic patterns in numerous species. Maximum-likelihood approaches based on allele frequencies and linkage-disequilibrium have been extensively used to infer admixture processes from genome-wide data sets, mostly in human populations. Nevertheless, complex admixture histories, beyond one or two pulses of admixture, remain methodologically challenging to reconstruct. We developed an Approximate Bayesian Computation (ABC) framework to reconstruct highly complex admixture histories from independent genetic markers. We built the software package MetHis to simulate independent SNPs or microsatellites in a two-way admixed population for scenarios with multiple admixture pulses, monotonically decreasing or increasing recurring admixture, or combinations of these scenarios. MetHis allows users to draw model-parameter values from prior distributions set by the user, and, for each simulation, MetHis can calculate numerous summary statistics describing genetic diversity patterns and moments of the distribution of individual admixture fractions. We coupled MetHis with existing machine-learning ABC algorithms and investigated the admixture history of admixed populations. Results showed that random forest ABC scenario-choice could accurately distinguish among most complex admixture scenarios, and errors were mainly found in regions of the parameter space where scenarios were highly nested, and, thus, biologically similar. We focused on African American and Barbadian populations as two study-cases. We found that neural network ABC posterior parameter estimation was accurate and reasonably conservative under complex admixture scenarios. For both admixed populations, we found that monotonically decreasing contributions over time, from Europe and Africa, explained the observed data more accurately than multiple admixture pulses. This approach will allow for reconstructing detailed admixture histories when maximum-likelihood methods are intractable.

Subject headings and genre

NATURVETENSKAP Biologi Genetik hsv//swe
NATURAL SCIENCES Biological Sciences Genetics hsv//eng
Approximate Bayesian Computation
admixture
inference
machine-learning
population genetics
Biologi med inriktning mot människans evolution och genetik
Biology with Specialisation in Human Evolution and Genetics

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Laurent, RomainUMR7206 Eco‐anthropologie, CNRS, Muséum National d’Histoire Naturelle, Université de Paris, Paris, France (author)
Thouzeau, ValentinUMR7534 Centre de Recherche en Mathématiques de la Décision, CNRS, Université Paris‐Dauphine, PSL University, Paris, France; Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes Cognitives, ENS, PSL University, EHESS, CNRS, Paris, France (author)
Toupance, BrunoUMR7206 Eco‐anthropologie, CNRS, Muséum National d’Histoire Naturelle, Université de Paris, Paris, France (author)
Verdu, PaulUMR7206 Eco‐anthropologie, CNRS, Muséum National d’Histoire Naturelle, Université de Paris, Paris, France (author)
Uppsala universitetMänniskans evolution (creator_code:org_t)

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In:Molecular Ecology Resources: John Wiley & Sons21:4, s. 1098-11171755-098X1755-0998

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