Evolutionary genetics: Inversions — Do not quail but go big!

Ishigohoka, Jun; Liedvogel, Miriam

doi:10.1016/j.cub.2021.12.016

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…The first point is not expected to play any role in the observed differences since the true population size is constant. As a control, we observe that MSMC recovered the simulated demography with fairly good accuracy with the exception of the largest recombination rate, where it inferred an initial slight decline followed by a re-growth ( Ishigohoka and Liedvogel 2024 ). The variance between replicates was typically larger in the most recent and most ancient time intervals ( Supplementary Fig.…”

Section: Resultsmentioning

confidence: 74%

On the estimation of genome-average recombination rates

Dutheil

2024

Genetics

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The rate at which recombination events occur in a population is an indicator of its effective population size and the organism’s reproduction mode. It determines the extent of linkage disequilibrium along the genome and, thereby, the efficacy of both purifying and positive selection. The population recombination rate can be inferred using models of genome evolution in populations. Classic methods based on the patterns of linkage-disequilibrium provide the most accurate estimates, providing large sample sizes are used and the demography of the population is properly accounted for. Here, the capacity of approaches based on the sequentially Markov coalescent (SMC) to infer the genome-average recombination rate from as little as a single diploid genome is examined. SMC approaches provide highly accurate estimates even in the presence of changing population sizes, providing that (1) within genome heterogeneity is accounted for and (2) classic maximum-likelihood optimization algorithms are employed to fit the model. SMC-based estimates proved sensitive to gene conversion, leading to an overestimation of the recombination rate if conversion events are frequent. Conversely, methods based on the correlation of heterozygosity succeed in disentangling the rate of crossing over from that of gene conversion events, but only when the population size is constant and the recombination landscape homogeneous. These results call for a convergence of these two methods to obtain accurate and comparable estimates of recombination rates between populations.

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Section: Resultsmentioning

confidence: 74%

On the estimation of genome-average recombination rates

Dutheil

2024

Genetics

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“…However, Upadhya and Steinrücken (2022) suggested that demographic inference performance with Relate improves when recombination rates are variable due to the presence of low recombination rate regions from which exact local genealogies can be estimated, which could in part explain the moderate improvement provided by modelling mutation and recombination rate heterogeneity in D. melanogaster simulated populations (Figure S3). It is unclear whether recombination and mutation rate heterogeneity may have more substantial effects in different species, as recent reports suggest masking low-recombination regions in songbird species may improve demographic inference with Relate (Ishigohoka and Liedvogel 2024).…”

Section: Discussionmentioning

confidence: 99%

“…Among recently developed scalable ARG approximation tools, Relate has emerged as the most accessible and widely used for demographic inference as of yet ( e.g. Speidel et al 2019; Almarri et al 2021; Minadakis et al 2023; Moorjani and Hellenthal 2023; Teterina et al 2023; Ishigohoka and Liedvogel 2024). To approximate an ARG, Relate estimates topological features of local genealogies (trees) based on position-specific distance matrices calculated between haplotypes.…”

Section: Introductionmentioning

confidence: 99%

Biases in ARG-based inference of historical population size in populations experiencing selection

Marsh,

Johri

2024

Preprint

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Inferring the demographic history of populations provides fundamental insights into species dynamics and is essential for developing a null model to accurately study selective processes. However, background selection and selective sweeps can produce genomic signatures at linked sites that mimic or mask signals associated with historical population size change. While the theoretical biases introduced by the linked effects of selection have been well established, it is unclear whether ARG-based approaches to demographic inference in typical empirical analyses are susceptible to mis-inference due to these effects. To address this, we developed highly realistic forward simulations of human and Drosophila melanogaster populations, including empirically estimated variability of gene density, mutation rates, recombination rates, purifying and positive selection, across different historical demographic scenarios, to broadly assess the impact of selection on demographic inference using a genealogy-based approach. Our results indicate that the linked effects of selection minimally impact demographic inference for human populations, though it could cause mis-inference in populations with similar genome architecture and population parameters experiencing more frequent recurrent sweeps. We found that accurate demographic inference of D. melanogaster populations by ARG-based methods is compromised by the presence of pervasive background selection alone, leading to spurious inferences of recent population expansion which may be further worsened by recurrent sweeps, depending on the proportion and strength of beneficial mutations. Caution and additional testing with species-specific simulations are needed when inferring population history with non-human populations using ARG-based approaches to avoid mis-inference due to the linked effects of selection.

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Evolutionary genetics: Inversions — Do not quail but go big!

Cited by 2 publications

References 19 publications

On the estimation of genome-average recombination rates

On the estimation of genome-average recombination rates

Biases in ARG-based inference of historical population size in populations experiencing selection

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