Using computational simulations to quantify genetic load and predict extinction risk

Kyriazis, Christopher C.; Robinson, Jacqueline; Lohmueller, Kirk E.

doi:10.1101/2022.08.12.503792

Cited by 17 publications

(22 citation statements)

References 127 publications

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“…Simulations of genome-wide variation help us better understand the behaviour of both neutral and non-neutral genetic variation in response to demographic change. Furthermore, when informed by species' ecological histories 105 , such simulations will help us assess the present and future impacts of genomic erosion 9,25,99,106,107,108,103,109,110,111 . These methods are advancing rapidly but they need to become more standardised 112 .…”

Section: Species Recovery Plans and Modellingmentioning

confidence: 99%

Genomic erosion in the assessment of species extinction risk and recovery potential

Oosterhout

Speak

Birley

et al. 2022

Preprint

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Many species are facing unprecedented population size declines and deterioration of their environment. This exposes species to genomic erosion, which we define here as the damage inflicted to a species' genome or gene pool due to a loss of genetic diversity, an increase in expressed genetic load, maladaptation, and/or genetic introgression. The International Union for Conservation of Nature (IUCN) bases its extinction risk assessments on direct threats to population size and habitat. However, it does not assess the long-term impacts of genomic erosion, and hence, it is likely to underestimate the extinction risk of many species. High-quality whole genome sequence data that is currently being generated could help improve extinction risk assessments. Genomic data contains information about a species' past demography, its genome-wide genetic diversity, the incidence of genetic introgression, as well as the genetic load of deleterious mutations. Computer modelling of these data enables forecasting of population trajectories under different management scenarios. In this Perspective, we discuss the threats posed by genomic erosion. Using evolutionary genomic simulations, we argue that whole-genome sequence data provides critical information for assessing species extinction risk and recovery potential. Genomics-informed assessments of the extinction risk complement the IUCN Red List, and such genomics-informed conservation is invaluable in guiding species recovery programs in the UN's Decade on Ecosystem Restoration and beyond.

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Section: Species Recovery Plans and Modellingmentioning

confidence: 99%

Genomic erosion in the assessment of species extinction risk and recovery potential

Oosterhout

Speak

Birley

et al. 2022

Preprint

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“…Empirical measures of deleterious variation are often challenging to interpret given that the functional impact and dominance of mutations are uncertain ( Cooper and Shendure 2011 ; Pedersen et al 2017 ; Kyriazis et al 2022 ). Given these limitations, we conducted forward-in-time genetic simulations to assess the impact of bottlenecks on deleterious genetic variation in North American moose using SLiM3 ( Haller and Messer 2019 ).…”

Section: Resultsmentioning

confidence: 99%

Genomic Underpinnings of Population Persistence in Isle Royale Moose

Kyriazis

Beichman

Brzeski

et al. 2023

Molecular Biology and Evolution

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Island ecosystems provide natural laboratories to assess the impacts of isolation on population persistence. However, most studies of persistence have focused on a single species, without comparisons to other organisms they interact with in the ecosystem. The case study of moose and gray wolves on Isle Royale allows for a direct contrast of genetic variation in isolated populations that have experienced dramatically differing population trajectories over the past decade. Whereas the Isle Royale wolf population recently declined nearly to extinction due to severe inbreeding depression, the moose population has thrived and continues to persist, despite having low genetic diversity and being isolated for ∼120 years. Here, we examine the patterns of genomic variation underlying the continued persistence of the Isle Royale moose population. We document high levels of inbreeding in the population, roughly as high as the wolf population at the time of its decline. However, inbreeding in the moose population manifests in the form of intermediate-length runs of homozygosity suggestive of historical inbreeding and purging, contrasting with the long runs of homozygosity observed in the smaller wolf population. Using simulations, we confirm that substantial purging has likely occurred in the moose population. However, we also document notable increases in genetic load, which could eventually threaten population viability over the long term. Overall, our results demonstrate a complex relationship between inbreeding, genetic diversity, and population viability that highlights the use of genomic datasets and computational simulation tools for understanding the factors enabling persistence in isolated populations.

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“…They can also serve as baselines for further analyses: for example, simulations incorporating demographic history serve as null models when detecting selection (Hsieh et al, 2016) or seed downstream breeding program simulations (Gaynor et al, 2020). More recently, population genomic simulations have been used to help guide conservation decisions for threatened species (Teixeira and Huber, 2021; Kyriazis et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations

Lauterbur

Cavassim

Gladstein³

et al. 2022

Preprint

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Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

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Using computational simulations to quantify genetic load and predict extinction risk

Cited by 17 publications

References 127 publications

Genomic erosion in the assessment of species extinction risk and recovery potential

Genomic erosion in the assessment of species extinction risk and recovery potential

Genomic Underpinnings of Population Persistence in Isle Royale Moose

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations

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