Quantifying the fraction of new mutations that are recessive lethal

Wade, Emma E.; Kyriazis, Christopher C.; Cavassim, Maria Izabel A.; Lohmueller, Kirk E.

doi:10.1093/evolut/qpad061

Cited by 9 publications

(8 citation statements)

References 60 publications

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“…3), consistent with previous work demonstrating that such mutations are likely to be highly recessive [14,15,17,25,37,38]. For instance, the impacts of recessive lethal mutations in humans are well documented [37, 43,44], though evidence from Drosophila suggests that such lethal mutations may in fact have a small fitness consequence in the heterozygote state [17], consistent with our findings (Figs. 2 & 3) Dominance is an essential determinant of the influence of demography on patterns of deleterious variation [1,2,4,5].…”

Section: Discussionsupporting

confidence: 93%

“…However, we note that a major limitation of our study is that we are unable to finely estimate selection and dominance parameters for strongly deleterious mutations, which as defined here encompass a wide range of |s| from 0.01 to 1. This limitation is due to SFS-based methods being underpowered for estimating the strongly deleterious tail of the DFE [44], due to the fact that such mutations tend not to be segregating in genetic variation datasets [48][49][50]. Thus, an important area for future work is to further refine selection and dominance parameters for strongly deleterious mutations and determine whether dominance parameters may differ between strongly deleterious mutations (|s| on the order of ~0.01) and lethal mutations (|s|=1).…”

Section: Discussionmentioning

confidence: 99%

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Constraining models of dominance for nonsynonymous mutations in the human genome

Kyriazis,

Lohmueller

2024

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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: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Constraining models of dominance for nonsynonymous mutations in the human genome

Kyriazis,

Lohmueller

2024

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“…Most DFE inference methods assume that new mutations are semidominant and thus provide estimates of the joint distribution of the selection and dominance coefficients, “ s d ℎ”. A recent study by Wade et al . (2023) suggests that, if there are recessive lethal mutations, current DFE inference approaches can lead to underestimating the true proportion of strongly deleterious mutations, as more strongly deleterious mutations are likely to persist in the population if they are recessive.…”

Section: Resultsmentioning

confidence: 96%

Hill-Robertson interference may bias the inference of fitness effects of new mutations in highly selfing species

Daigle,

Johri

2024

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The accurate estimation of the distribution of fitness effects (DFE) of new mutations is critical for population genetic inference but remains a challenging task. While various methods have been developed for DFE inference using the site frequency spectrum of putatively neutral and selected sites, their applicability in species with diverse life history traits and complex demographic scenarios is not well understood. Selfing is common among eukaryotic species and can lead to decreased effective recombination rates in such populations, increasing the effects of selection at linked sites, including interference between selected alleles. We employ forward simulations to investigate the limitations of current DFE estimation approaches in the presence of selfing and linked effects of selection. We find that distortions of the site frequency spectrum due to Hill-Robertson interference in highly selfing populations lead to mis-inference of the deleterious DFE of new mutations. While accounting for the decrease in the effective population size due to linked effects of selection largely accounts for the observed bias in populations with moderate levels of selfing, this correction is unable to accurately estimate the DFE in highly selfing populations. In addition, the presence of cryptic population structure and uneven sampling across subpopulations leads to the false inference of a deleterious DFE skewed towards effectively neutral/mildly deleterious mutations. Finally, the proportion of adaptive substitutions estimated at high rates of selfing is substantially overestimated. Our results clarify the parameter space where current DFE methods might be problematic and where they remain robust in the presence of selfing and other model violations, such as departures from semidominance, population structure, and uneven sampling. Our observations apply broadly to species and genomic regions with little/no recombination.

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“…We only modeled two very simple DFEs for synonymous mutations: a uniform DFE across all synonymous mutations and a partial DFE, where 22% of synonymous mutations have the same selection coefficient and the rest are neutral. While it has been established that the DFE of non-synonymous sites is well represented by a gamma distribution (Eyre-Walker et al 2007; Kim et al 2017; Wade et al 2023), the shape of the DFE of synonymous sites has not been well characterized. However, some studies have shown evidence that at least a portion of synonymous sites are under moderate ( Ns >1) to strong ( Ns >10) selection in both Drosophila (Lawrie et al 2013) and humans (Keightley and Halligan, 2011).…”

Section: Discussionmentioning

confidence: 99%

The impact of non-neutral synonymous mutations when inferring selection on non-synonymous mutations

Martinez i Zurita,

Kyriazis,

Lohmueller

2024

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The distribution of fitness effects (DFE) describes the proportions of new mutations that have different effects on reproductive fitness. Accurate measurements of the DFE are important because the DFE is a fundamental parameter in evolutionary genetics and has implications for our understanding of other phenomena like complex disease or inbreeding depression. Current computational methods to infer the DFE for nonsynonymous mutations from natural variation first estimate demographic parameters from synonymous variants to control for the effects of demography and background selection. Then, conditional on these parameters, the DFE is then inferred for nonsynonymous mutations. This approach relies on the assumption that synonymous variants are neutrally evolving. However, some evidence points toward synonymous mutations having measurable effects on fitness. To test whether selection on synonymous mutations affects inference of the DFE of nonsynonymous mutations, we simulated several possible models of selection on synonymous mutations using SLiM and attempted to recover the DFE of nonsynonymous mutations using Fit∂a∂i, a common method for DFE inference. Our results show that the presence of selection on synonymous variants leads to incorrect inferences of recent population growth. Furthermore, under certain parameter combinations, inferences of the DFE can have an inflated proportion of highly deleterious nonsynonymous mutations. However, this bias can be eliminated if the correct demographic parameters are used for DFE inference instead of the biased ones inferred from synonymous variants. Our work demonstrates how unmodeled selection on synonymous mutations may affect downstream inferences of the DFE.

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Quantifying the fraction of new mutations that are recessive lethal

Cited by 9 publications

References 60 publications

Constraining models of dominance for nonsynonymous mutations in the human genome

Constraining models of dominance for nonsynonymous mutations in the human genome

Hill-Robertson interference may bias the inference of fitness effects of new mutations in highly selfing species

The impact of non-neutral synonymous mutations when inferring selection on non-synonymous mutations

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