Selective sweeps influence diversity over large regions of the mouse genome

Booker, Tom R; Bc, Jackson; Rj, Craig; Pd, Keightley; Charlesworth, Brian

doi:10.1101/2021.06.10.447924

Cited by 3 publications

(6 citation statements)

References 76 publications

(137 reference statements)

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“…How do our estimates in M. m. castaneus compare to those obtained using LDHelmet and a CO-only recombination model? Coestimated under a model of GC, our genome-wide average of the CO rate per-base (0.00186) is approximately 5 times lower than the ρ estimates obtained by Booker et al (2017 , 2021) using LDhelmet (0.00924 and 0.0100, respectively, averaged across autosomes). If we instead compare the total recombination rate between any 2 adjacent bases, which corresponds to the upper bound of the recombination rate in our model (for d = 1 eq.…”

Section: Discussioncontrasting

confidence: 62%

“…The data—originally described in Halligan et al (2010) (ENA accession number PRJEB2176)—consists of Illumina (PE) resequence data for 10 individuals sampled from a wild M. m. castaneus population in India. Variant calling is described in Booker et al (2021) .…”

Section: Methodsmentioning

confidence: 99%

“…heRho obtains maximum composite likelihood (MCL) estimates for ρ were obtained using the Python library NLopt . In addition to heRho ( https://github.com/samebdon/heRho ), we estimated the weighted mean of ρ across autosomes using the LDhelmet estimates of Booker et al (2017) and Booker et al (2021) .…”

Section: Methodsmentioning

confidence: 99%

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Estimating the rates of crossover and gene conversion from individual genomes

et al. 2022

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Recombination can occur either as a result of crossover or gene conversion events. Population genetic methods for inferring the rate of recombination from patterns of linkage disequilibrium generally assume a simple model of recombination that only involves crossover events and ignore gene conversion. However, distinguishing the two processes is not only necessary for a complete description of recombination, but also essential for understanding the evolutionary consequences of inversions and other genomic partitions in which crossover (but not gene conversion) is reduced. We present heRho, a simple composite likelihood scheme for co-estimating the rate of crossover and gene conversion from individual diploid genomes. The method is based on analytic results for the distance-dependent probability of heterozygous and homozygous states at two loci. We apply heRho to simulations and data from the house mouse Mus musculus castaneus, a well studied model. Our analyses show i) that the rates of crossover and gene conversion can be accurately co-estimated at the level of individual chromosomes and ii) that previous estimates of the population scaled rate of recombination ρ = 4 Ner under a pure crossover model are likely biased.

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Section: Discussioncontrasting

confidence: 62%

Section: Methodsmentioning

confidence: 99%

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Estimating the rates of crossover and gene conversion from individual genomes

et al. 2022

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“…The ability of house mice to rapidly adapt to these new conditions was paramount to their successful global colonization. Although prior studies have identified genome-wide targets of positive selection in house mice (Staubach, et al 2012; Phifer-Rixey, et al 2018; Booker, et al 2021; Lawal, et al 2021), focus has been mostly restricted to the discovery of selection on new mutations, rather than selection on standing variation. The waiting time to new adaptive mutations may be substantial, implying that adaptation may often be driven by selection on standing variants (Duvaux, et al 2011; Reid, et al 2016; Lai, et al 2019).…”

Section: Discussionmentioning

confidence: 99%

Ancestral variation and its impact on wild house mouse genomes

Lawal,

Dumont

2023

Preprint

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Ancestral alleles are important contributors to adaptation and disease risk in populations. House mice emerged in and/or around the Indian subcontinent, but the genetic composition of this ancestral population and the extent to which ancestral variants contribute to contemporary global mouse diversity are poorly understood. To address these knowledge gaps, we assessed the origins and demographic patterning of global mouse diversity using a set of 169 wild mouse genome sequences from across the world. This dataset includes 37 mouse genomes from the broadly designated ancestral regions, providing crucial resources needed to evaluate the contributions and the impact of ancestral diversity on the genomic scale. We show that house mice emerged in the Indo-Pakistan region around 700 kya, withM. m. castaneusat the root of theM. musculusspecies. Migration out of the Indo-Pakistan homeland led to the subsequent emergence of theM. m. domesticusandM. m. musculussubspecies ∼360 and 260 kya, respectively. A modest fraction of ancestral alleles have persisted long-term across mouse populations through balancing selection, and we demonstrate that such regions are strongly enriched for genes with immune-related functions. Finally, we find widespread allele-sharing acrossMus musculuslineages and show that this trend is largely due to incomplete lineage sorting, an interpretation contrary to some recent claims of pervasive subspecies introgression. Together, our work underscores the contributions of ancestral variants to contemporary house mouse diversity and adaptation, and refines our understanding of the natural evolutionary history of this important model species.

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“…In particular, the impact of positive selection, in which beneficial alleles are favored by selection and thus increase in frequency in the population, is hotly debated [1][2][3]. This controversy extends to the role of selective sweeps [4], in which positively selected mutations rapidly sweep through a population thereby drastically reducing and altering patterns of genetic diversity in the vicinity of the selected locus [5,6], with some studies finding that signatures of selective sweeps alter genome-wide patterns of polymorphism [7][8][9][10][11] , and others purporting that these signatures may be largely or even entirely false positives [12].…”

Section: Introductionmentioning

confidence: 99%

Timesweeper: Accurately Identifying Selective Sweeps Using Population Genomic Time Series

Whitehouse

Schrider

2022

Preprint

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Despite decades of research, identifying selective sweeps, the genomic footprints of positive selection, remains a core problem in population genetics. Of the myriad methods that have been made towards this goal, few are designed to leverage the potential of genomic time-series data. This is because in most population genetic studies only a single, brief period of time can be sampled for a study. Recent advancements in sequencing technology, including improvements in extracting and sequencing ancient DNA, have made repeated samplings of a population possible, allowing for more direct analysis of recent evolutionary dynamics. With these advances in mind, here we present Timesweeper, a fast and accurate convolutional neural network-based tool for identifying selective sweeps in data consisting of multiple genomic samplings of a population over time. Timesweeper utilizes this serialized sampling of a population by first simulating data under a demographic model appropriate for the data of interest, training a 1D Convolutional Neural Network on said model, and inferring which polymorphisms in this serialized dataset were the direct target of a completed or ongoing selective sweep. We show that Timesweeper is accurate under multiple simulated demographic and sampling scenarios, and identifies selected variants with impressive resolution. In sum, we show that more accurate inferences about natural selection are possible when genomic time-series data are available; such data will continue to proliferate in coming years due to both the sequencing of ancient samples and repeated samplings of extant populations with faster generation times. Methodological advances such as Timesweeper thus have the potential to help resolve the controversy over the role of positive selection in the genome. We provide Timesweeper both as a Python package and Snakemake workflow for use by the community.

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Selective sweeps influence diversity over large regions of the mouse genome

Cited by 3 publications

References 76 publications

Estimating the rates of crossover and gene conversion from individual genomes

Estimating the rates of crossover and gene conversion from individual genomes

Ancestral variation and its impact on wild house mouse genomes

Timesweeper: Accurately Identifying Selective Sweeps Using Population Genomic Time Series

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