Using Computational Simulations to Model Deleterious Variation and Genetic Load in Natural Populations

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

doi:10.1086/726736

Cited by 9 publications

(21 citation statements)

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“…Our work also has implications for studies of deleterious variation in non-human taxa. In particular, there has been a great deal of recent interest in modelling the impact of recessive deleterious variation on extinction risk in small and isolated populations [2,39,[45][46][47]. Our analysis can help guide such studies by informing the parameterization of dominance and selection models, as many of these studies are focused on endangered species of mammals that may have similar population genetic parameters to humans [39].…”

Section: Discussionmentioning

confidence: 99%

“…In particular, there has been a great deal of recent interest in modelling the impact of recessive deleterious variation on extinction risk in small and isolated populations [2,39,[45][46][47]. Our analysis can help guide such studies by informing the parameterization of dominance and selection models, as many of these studies are focused on endangered species of mammals that may have similar population genetic parameters to humans [39]. For instance, it has been previously suggested that the historical population size of a species may greatly influence risk of extinction due to inbreeding depression, particularly in cases where strongly deleterious alleles (1e-2<|s|=<1) are highly recessive (h<0.05) [1][2][3].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, the selection coefficient bins for these SLiM simulations were defined as: neutral (0 ≤ |𝑠| < 2 * 10 For each DFE and dominance model, we ran 100 simulation replicates and, at the conclusion of the simulation, outputted the mean genetic load, mean inbreeding load, and average number of derived deleterious alleles per individual in each population, taken from a sample of 100 individuals. Here, we define genetic load as the reduction in fitness due to segregating and fixed deleterious alleles, where fitness is multiplicative across sites [4,39,67]. We measure the inbreeding load as the "number of haploid lethal equivalents", which quantifies the summed selective effects of heterozygous recessive deleterious alleles in a population [3,39,40].…”

Section: Simulation Methodsmentioning

confidence: 99%

“…Here, we define genetic load as the reduction in fitness due to segregating and fixed deleterious alleles, where fitness is multiplicative across sites [4,39,67]. We measure the inbreeding load as the "number of haploid lethal equivalents", which quantifies the summed selective effects of heterozygous recessive deleterious alleles in a population [3,39,40]. Due to the high computational load of these simulations, we ran simulations for only two autosomes and projected results to a full genome of 22 autosomes.…”

Section: Simulation Methodsmentioning

confidence: 99%

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Constraining models of dominance for nonsynonymous mutations in the human genome

Kyriazis,

Lohmueller

2024

Preprint

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Dominance is a fundamental parameter in genetics, determining the dynamics of natural selection on deleterious and beneficial mutations, the patterns of genetic variation in natural populations, and the severity of inbreeding depression in a population. Despite this importance, dominance parameters remain poorly known, particularly in humans or other non-model organisms. A key reason for this lack of information about dominance is that it is extremely challenging to disentangle the selection coefficient (s) of a mutation from its dominance coefficient (h). Here, we explore dominance and selection parameters in humans by fitting models to the site frequency spectrum (SFS) for nonsynonymous mutations. When assuming a single dominance coefficient for all nonsynonymous mutations, we find that numeroushvalues can fit the data, so long ashis greater than ∼0.15. Moreover, we also observe that theoretically-predicted models with a negative relationship betweenhandscan also fit the data well, including models withh=0.05 for strongly deleterious mutations. Finally, we use our estimated dominance and selection parameters to inform simulations revisiting the question of whether the out-of-Africa bottleneck has led to differences in genetic load between African and non-African human populations. These simulations suggest that the relative burden of genetic load in non-African populations depends on the dominance model assumed, with slight increases for more weakly recessive models and slight decreases shown for more strongly recessive models. Moreover, these results also demonstrate that models of partially recessive nonsynonymous mutations can explain the observed severity of inbreeding depression in humans, bridging the gap between molecular population genetics and direct measures of fitness in humans. Our work represents a comprehensive assessment of dominance and deleterious variation in humans, with implications for parameterizing models of deleterious variation in humans and other mammalian species.Author SummaryThe dominance coefficient (h) of a mutation determines its impact on organismal fitness when heterozygous. For instance, fully recessive mutations (h=0) have no effects on fitness when heterozygous whereas additive mutations (h=0.5) have an effect that is intermediate to the two heterozygous mutations. The extent to which deleterious mutations may be recessive, additive, or dominant is a key area of study in evolutionary genetics. However, dominance parameters remain poorly known in humans and most other organisms due to a variety of technical challenges. In this study, we aim to constrain the possible set of dominance and selection parameters for amino acid changing mutations in humans. We find that a wide range of models are possible, including models with a theoretically-predicted relationship betweenhands. We then use a range of plausible selection and dominance models to explore how deleterious variation may have been shaped by the out-of-Africa bottleneck in humans. Our results highlight the subtle influence of dominance on patterns of genetic load in humans and demonstrate that models of partially recessive mutations at amino-acid-changing sites can explain the observed effects of inbreeding on mortality in humans.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Simulation Methodsmentioning

confidence: 99%

Section: Simulation Methodsmentioning

confidence: 99%

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Constraining models of dominance for nonsynonymous mutations in the human genome

Kyriazis,

Lohmueller

2024

Preprint

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“…Specifically, we assumed that 99.5% of mutations were drawn from a gamma distribution with a mean s = −0.0131 and shape parameter = 0.186, with the other 0.5% of mutations being recessive lethal with s = −1.0, as informed by Wade et al (2023). Dominance coefficients (h) for deleterious mutations were set following Kyriazis et al (2023) as h = 0.45 for weakly deleterious mutations (s > −0.001), h = 0.2 for moderately deleterious mutations (−0.001 > =s > −0.01), h = 0.05 for strongly deleterious mutations (−0.1 > s ≥ −0.01) and h = 0.0 for lethal and semi-lethal mutations (−1.0 ≤ s < −0.1). Note that the mean h of new deleterious mutations in this model is 0.28.…”

Section: Genomic Parametersmentioning

confidence: 99%

The influence of gene flow on population viability in an isolated urban caracal population

Kyriazis,

Serieys,

Bishop

et al. 2024

Molecular Ecology

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Wildlife populations are becoming increasingly fragmented by anthropogenic development. Small and isolated populations often face an elevated risk of extinction, in part due to inbreeding depression. Here, we examine the genomic consequences of urbanization in a caracal (Caracal caracal) population that has become isolated in the Cape Peninsula region of the City of Cape Town, South Africa, and is thought to number ~50 individuals. We document low levels of migration into the population over the past ~75 years, with an estimated rate of 1.3 effective migrants per generation. As a consequence of this isolation and small population size, levels of inbreeding are elevated in the contemporary Cape Peninsula population (mean FROH = 0.20). Inbreeding primarily manifests as long runs of homozygosity >10 Mb, consistent with the effects of isolation due to the rapid recent growth of Cape Town. To explore how reduced migration and elevated inbreeding may impact future population dynamics, we parameterized an eco‐evolutionary simulation model. We find that if migration rates do not change in the future, the population is expected to decline, though with a low projected risk of extinction. However, if migration rates decline or anthropogenic mortality rates increase, the potential risk of extinction is greatly elevated. To avert a population decline, we suggest that translocating migrants into the Cape Peninsula to initiate a genetic rescue may be warranted in the near future. Our analysis highlights the utility of genomic datasets coupled with computational simulation models for investigating the influence of gene flow on population viability.

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AI-informed conservation genomics

van Oosterhout

2023

Heredity

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Using Computational Simulations to Model Deleterious Variation and Genetic Load in Natural Populations

Cited by 9 publications

References 133 publications

Constraining models of dominance for nonsynonymous mutations in the human genome

Constraining models of dominance for nonsynonymous mutations in the human genome

The influence of gene flow on population viability in an isolated urban caracal population

AI-informed conservation genomics

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