Biology subjects

Beaumont, M. A.

Publications and source records attributed to Beaumont, M. A..

5 recordsLinked to original sources

bioRxiv2023

To unscramble an egg: admixed captive breeding populations can be rescued using local ancestry information

This paper asks the question: can genomic information recover a species that is already on the pathway to extinction due to genetic swamping from a related and more numerous population? We show that whole genome sequencing can be used to identify and remove hybrid segments of DNA, when used as part of the breeding policy in a captive breeding program. The proposed policy uses a generalised measure of kinship or heterozygosity accounting for local ancestry, that is, whether a specific genetic location was inherited from from the target of conservation. We then show that optimising these measures would minimise undesired ancestry whilst also controlling undesired kinship or heterozygosity respectively, in a simulated breeding population. The process is applied to real data representing the hybridized Scottish wildcat breeding population, with the result that it should be possible to breed out the domestic cat ancestry. The ability to reverse introgression is a powerful new tool brought about from both sequencing and computational advances in ancestry estimation. Since it works best when applied early in the process, important decisions need to be made about which genetically distinct populations should benefit from it and which should be left to reform into a single population.

genetics↗

bioRxiv2022

The evolution of novel biotic interactions at ecological margins in response to climate change involves alleles from across the geographical range of the UK Brown Argus butterfly

Understanding the rate and extent to which populations can adapt to novel environments at their ecological margins is fundamental to predicting the persistence of biological communities during ongoing and rapid global change. Recent range expansion in response to climate change in the UK butterfly Aricia agestis is associated with the evolution of novel interactions with a larval food plant, and the loss of its ability to use its ancestral larval host species. Using ddRAD analysis of 61210 variable SNPs from 261 females from throughout the UK range of this species, we identify genomic regions at multiple chromosomes that are associated with these evolutionary responses, and their association with demographic history and ecological variation. Gene flow appears widespread throughout the range, despite the apparently fragmented nature of the habitats used by this species. Patterns of haplotype variation between selected and neutral genomic regions suggest that evolution associated with climate adaptation is polygenic, resulting from the independent spread of existing alleles throughout the established range of this species, rather than the colonisation of pre-adapted genotypes from coastal populations. These data suggest that rapid responses to climate change do not depend on the availability of pre-adapted genotypes. Instead, the evolution of novel forms of biotic interaction in Aricia agestis has occurred during range expansion, through the assembly of novel genotypes from alleles from multiple localities.

evolutionary biology↗

bioRxiv2021

Moment-based approximations for the Wright-Fisher model of population dynamics under natural selection at two linked loci

Properly modelling genetic recombination and local linkage has been shown to bring significant improvement to the inference of natural selection from time series data of allele frequencies under a Wright-Fisher model. Existing approaches that can account for genetic recombination and local linkage are built on either the diffusion approximation or a moment-based approximation of the Wright-Fisher model. However, methods based on the diffusion approximation are likely to require much higher computational cost, whereas moment-based approximations may suffer from the distribution support issue: for example, the normal approximation can seriously affect computational accuracy. In the present work, we introduce two novel moment-based approximations of the Wright-Fisher model on a pair of linked loci, both subject to natural selection. Our key innovation is to extend existing methods to account for both the mean and (co)variance of the two-locus Wright-Fisher model with selection. We devise two approximation schemes, using a logistic normal distribution and a hierarchical beta distribution, respectively, by matching the first two moments of the Wright-Fisher model and the approximating model. As compared with the diffusion approximation, our approximations enable the approximate computation of the transition probabilities of the Wright-Fisher model at a far smaller computational cost. We can also avoid the distribution support issue found in the normal approximation.

genetics↗

bioRxiv2020

Numerical simulation of the two-locus Wright-Fisher stochastic differential equation with application to approximating transition probability densities

Over the past decade there has been an increasing focus on the application of the Wright-Fisher diffusion to the inference of natural selection from genetic time series. A key ingredient for modelling the trajectory of gene frequencies through the Wright-Fisher diffusion is its transition probability density function. Recent advances in DNA sequencing techniques have made it possible to monitor genomes in great detail over time, which presents opportunities for investigating natural selection while accounting for genetic recombination and local linkage. However, most existing methods for computing the transition probability density function of the Wright-Fisher diffusion are only applicable to one-locus problems. To address two-locus problems, in this work we propose a novel numerical scheme for the Wright-Fisher stochastic differential equation of population dynamics under natural selection at two linked loci. Our key innovation is that we reformulate the stochastic differential equation in a closed form that is amenable to simulation, which enables us to avoid boundary issues and reduce computational costs. We also propose an adaptive importance sampling approach based on the proposal introduced by Fearnhead (2008) for computing the transition probability density of the Wright-Fisher diffusion between any two observed states. We show through extensive simulation studies that our approach can achieve comparable performance to the method of Fearnhead (2008) but can avoid manually tuning the parameter{rho} to deliver superior performance for different observed states.

genetics↗

bioRxiv2019

Maximum likelihood estimation of natural selection and allele age from time series data of allele frequencies

Temporally spaced genetic data allow for more accurate inference of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel likelihood-based method for jointly estimating selection coefficient and allele age from time series data of allele frequencies. Our approach is based on a hidden Markov model where the underlying process is a Wright-Fisher diffusion conditioned to survive until the time of the most recent sample. This formulation circumvents the assumption required in existing methods that the allele is created by mutation at a certain low frequency. We calculate the likelihood by numerically solving the resulting Kolmogorov backward equation backwards in time while re-weighting the solution with the emission probabilities of the observation at each sampling time point. This procedure reduces the two-dimensional numerical search for the maximum of the likelihood surface for both the selection coefficient and the allele age to a one-dimensional search over the selection coefficient only. We illustrate through extensive simulations that our method can produce accurate estimates of the selection coefficient and the allele age under both constant and non-constant demographic histories. We apply our approach to re-analyse ancient DNA data associated with horse base coat colours. We find that ignoring demographic histories or grouping raw samples can significantly bias the inference results.

genetics↗