From molecules to populations: appreciating and estimating recombination rate variation

Peñalba, Joshua V.; Wolf, Jochen B. W.

doi:10.1038/s41576-020-0240-1

Cited by 112 publications

(119 citation statements)

References 236 publications

Supporting

Mentioning

117

Contrasting

Order By: Relevance

“…A major challenge to modeling the evolution of linked genomic architectures in diverse taxa has been the availability of relevant estimates for LD. LD depends on the distance and epistasis among loci, chromosome size, position of loci along a chromosome, local sequence content, other sources of recombination rate and gene conversion variation (e.g., genic modifiers and structural genomic polymorphisms), and demographic factors such as effective population size ( Figure 1 ; Kong et al 2002 ; Butlin 2005 ; Peñalba and Wolf 2020 ). LD can be estimated from individual-level population-scale genomic data ( Gianola et al 2013 ; Bilton et al 2018 ; Ragsdale and Gravel 2020 ).…”

Section: Eco-evolutionary Responses Hinge On Genetic and Genomic Archmentioning

confidence: 99%

“…LD can be estimated from individual-level population-scale genomic data ( Gianola et al 2013 ; Bilton et al 2018 ; Ragsdale and Gravel 2020 ). For example, population-based inference of LD can be obtained by using multiple genome alignments to calculate the statistical association of alleles, ideally accounting for demographic history ( Peñalba and Wolf 2020 ). One advantage of this approach is that the genomic data required are already available for many systems, which also explains the frequent use of such LD estimates as proxies for inferring population recombination rates ( Peñalba and Wolf 2020 ).…”

Section: Eco-evolutionary Responses Hinge On Genetic and Genomic Archmentioning

confidence: 99%

“…More direct estimates of recombination and gene conversion rates can be obtained using pedigree-based and gamete-based approaches (e.g., Korunes and Noor 2019 ; Rowan et al 2019 ; reviewed by Peñalba and Wolf 2020 ). Though they can be challenging to implement in some systems (e.g., wild populations lacking pedigree information and species without external fertilization for pedigree-based and gamete-based methods, respectively), recent technological advances have made them feasible for many non-model species ( Peñalba and Wolf 2020 ). However, biological variation in recombination rates is abundant within and among chromosomes, individuals, sexes, populations, and species, the cataloging of which is still in its infancy ( Peñalba and Wolf 2020 ).…”

Section: Eco-evolutionary Responses Hinge On Genetic and Genomic Archmentioning

confidence: 99%

See 2 more Smart Citations

Consequences of Single-Locus and Tightly Linked Genomic Architectures for Evolutionary Responses to Environmental Change

Oomen

Kuparinen

Hutchings

2020

Journal of Heredity

View full text Add to dashboard Cite

Abstract Genetic and genomic architectures of traits under selection are key factors influencing evolutionary responses. Yet, knowledge of their impacts has been limited by a widespread assumption that most traits are controlled by unlinked polygenic architectures. Recent advances in genome sequencing and eco-evolutionary modelling are unlocking the potential for integrating genomic information into predictions of population responses to environmental change. Using eco-evolutionary simulations, we demonstrate that hypothetical single-locus control of a life history trait produces highly variable and unpredictable harvesting-induced evolution relative to the classically applied multi-locus model. Single-locus control of complex traits is thought to be uncommon, yet blocks of linked genes, such as those associated with some types of structural genomic variation, have emerged as taxonomically widespread phenomena. Inheritance of linked architectures resembles that of single loci, thus enabling single-locus-like modeling of polygenic adaptation. Yet, the number of loci, their effect sizes, and the degree of linkage among them all occur along a continuum. We review how linked architectures are often associated, directly or indirectly, with traits expected to be under selection from anthropogenic stressors and are likely to play a large role in adaptation to environmental disturbance. We suggest using single-locus models to explore evolutionary extremes and uncertainties when the trait architecture is unknown, refining parameters as genomic information becomes available, and explicitly incorporating linkage among loci when possible. By overestimating the complexity (e.g., number of independent loci) of the genomic architecture of traits under selection, we risk underestimating the complexity (e.g., nonlinearity) of their evolutionary dynamics.

show abstract

Section: Eco-evolutionary Responses Hinge On Genetic and Genomic Archmentioning

confidence: 99%

Section: Eco-evolutionary Responses Hinge On Genetic and Genomic Archmentioning

confidence: 99%

Section: Eco-evolutionary Responses Hinge On Genetic and Genomic Archmentioning

confidence: 99%

See 1 more Smart Citation

Consequences of Single-Locus and Tightly Linked Genomic Architectures for Evolutionary Responses to Environmental Change

Oomen

Kuparinen

Hutchings

2020

Journal of Heredity

View full text Add to dashboard Cite

show abstract

“…Although population‐averaged recombination rates can be inferred with whole genome sequences from 10 or so individuals, these estimates can carry the signatures of past selection and demography, rendering their use in understanding selection circular (Lotterhos, 2019). Fine‐scale, directly estimated recombination maps remain costly and are limited to a few species, and even fewer studies assay recombination rate variability within an ecotype or species (Peñalba & Wolf, 2020). Notably, Booker et al.…”

Section: Figurementioning

confidence: 99%

“…Despite discontinuation of the Linked Reads product line by 10X Genomics, other companies (e.g., Universal Sequencing Technology) and methods (TELL‐seq, single cell sequencing, haplotagging, single‐tube long‐fragment read sequencing) are filling the niche for direct identification of crossovers, especially in gametes. Thus, as estimating recombination rates becomes less of an obstacle (Peñalba & Wolf, 2020), we hope to see a dramatic expansion of studies that incorporate recombination rate variation into estimation of outliers.…”

Section: Figurementioning

confidence: 99%

It’s time to stop sweeping recombination rate under the genome scan rug

Stevison

McGaugh

2020

Molecular Ecology

View full text Add to dashboard Cite

Different parts of the genome can vary widely in their evolutionary histories and sequence divergence from other species. Indeed, some of the most interesting biology (e.g., hybridization, horizontal gene transfer, variable mutation rates across the genome) is revealed by the discordant relationships between taxa across the genome. The goal for much of evolutionary genetics is centred on understanding the evolutionary processes by which such varied signatures arise and are maintained. Many evolutionary genetics studies seek to identify signatures of positive selection between two closely related ecotypes or taxa by delineating regions with particularly high divergence relative to a genome‐wide average, often termed “divergence outliers.” In a From the Cover article in this issue of Molecular Ecology, Booker et al. take a major step forward in showing that recombination rate differences are sufficient to create false positive divergence outliers, even under neutrality. They demonstrate that the variance of genome scan metrics is especially high in regions with low recombination rates, consistent with previous work. Furthermore, they show that both relative and absolute measures of divergence (FST and DXY, respectively) as well as other commonly used statistics in genome scans (e.g., πW, Tajima's D and H12) all have similar covariance between variance and local recombination rate. Finally, Booker et al. show that low recombination regions will tend to produce more outliers if genome‐wide averages are used as cut‐offs to define genomic outliers. Booker et al.’s results suggest that recombination rate variation, even under neutral conditions, can shape genome scans for selection, and this important variable can no longer be ignored.

show abstract

Network architecture and sex chromosome turnovers

2020

View full text Add to dashboard Cite

Recent studies have revealed an astonishing diversity of sex chromosomes in many vertebrate lineages, prompting questions about the mechanisms of sex chromosome turnover. While there is considerable population genetic theory about the evolutionary forces promoting sex chromosome replacement, this theory has not yet been integrated with our understanding of the molecular and developmental genetics of sex determination. Here, we review recent data to examine four questions about how the structure of gene networks influences the evolution of sex determination. We argue that patterns of epistasis, arising from the structure of genetic networks, may play an important role in regulating the rates and patterns of sex chromosome replacement.

show abstract

From molecules to populations: appreciating and estimating recombination rate variation

Cited by 112 publications

References 236 publications

Consequences of Single-Locus and Tightly Linked Genomic Architectures for Evolutionary Responses to Environmental Change

Consequences of Single-Locus and Tightly Linked Genomic Architectures for Evolutionary Responses to Environmental Change

It’s time to stop sweeping recombination rate under the genome scan rug

Network architecture and sex chromosome turnovers

Contact Info

Product

Resources

About