Recent demographic history inferred by high-resolution analysis of linkage disequilibrium

Santiago, Enrique, Novo, Irene, Pardinas, Antonio F. ORCID: https://orcid.org/0000-0001-6845-7590, Saura, María, Wang, Jinliang and Caballero, Armando 2020. Recent demographic history inferred by high-resolution analysis of linkage disequilibrium. Molecular Biology and Evolution 37 (12) , pp. 3642-3653. 10.1093/molbev/msaa169

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Abstract

Inferring changes in effective population size (Ne) in the recent past is of special interest for conservation of endangered species and for human history research. Current methods for estimating the very recent historical Ne are unable to detect complex demographic trajectories involving multiple episodes of bottlenecks, drops and expansions. We develop a theoretical and computational framework to infer the demographic history of a population within the past 100 generations from the observed spectrum of linkage disequilibrium (LD) of pairs of loci over a wide range of recombination rates in a sample of contemporary individuals. The cumulative contributions of all of the previous generations to the observed LD are included in our model, and a genetic algorithm is used to search for the sequence of historical Ne values that best explains the observed LD spectrum. The method can be applied from large samples to samples of fewer than 10 individuals using a variety of genotyping and DNA sequencing data: haploid, diploid with phased or unphased genotypes and pseudo-haploid data from low-coverage sequencing. The method was tested by computer simulation for sensitivity to genotyping errors, temporal heterogeneity of samples, population admixture and structural division into subpopulations, showing high tolerance to deviations from the assumptions of the model. Computer simulations also show that the proposed method outperforms other leading approaches when the inference concerns recent timeframes. Analysis of data from a variety of human and animal populations gave results in agreement with previous estimations by other methods or with records of historical events.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Publisher:	Oxford University Press
ISSN:	0737-4038
Date of First Compliant Deposit:	17 July 2020
Date of Acceptance:	29 June 2020
Last Modified:	06 Nov 2023 17:46
URI:	https://orca.cardiff.ac.uk/id/eprint/133551

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