Relating pathogenic loss-of-function mutations in humans to their evolutionary fitness costs

Agarwal, Ipsita; Fuller, Zachary L.; Myers, Simon; Przeworski, Molly

doi:10.7554/elife.83172

Cited by 22 publications

(54 citation statements)

References 98 publications

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“…We summarize variant effect predictions by taking the tail of deleterious scores across each gene and investigate how well each method can classify essential versus non-essential genes among the cell lines. GPN-MSA outperforms other genome-wide variant effect predictors, as well as methods (pLI [33] and hs [34]) specifically designed for gene essentiality prediction (Figure 2g).…”

Section: Resultsmentioning

confidence: 99%

GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction

Benegas,

Albors,

et al. 2023

Preprint

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Whereas protein language models have demonstrated remarkable efficacy in predicting the effects of missense variants, DNA counterparts have not yet achieved a similar competitive edge for genome-wide variant effect predictions, especially in complex genomes such as that of humans. To address this challenge, we here introduce GPN-MSA, a novel framework for DNA language models that leverages whole-genome sequence alignments across multiple species and takes only a few hours to train. Across several benchmarks on clinical databases (ClinVar, COSMIC, and OMIM) and population genomic data (gnomAD), our model for the human genome achieves outstanding performance on deleteriousness prediction for both coding and non-coding variants.

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Section: Resultsmentioning

confidence: 99%

GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction

Benegas,

Albors,

et al. 2023

Preprint

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“…We further tested for remaining selection signal in our CADD 6% model residuals by using gene-specific estimates of the fitness cost of loss-of-function (LoF) mutations from Agarwal et al (2023). These estimates are based on an Approximate Bayesian Computation approach that estimates the posterior distribution over LoF fitness costs from the observed dearth of LoF mutations per gene, and thus is an independent approach to assess the strength of purifying selection.…”

Section: Resultsmentioning

confidence: 99%

“…The yellow points are binned means, and the yellow line is the lowess curve through predicted and observed values. (C) CADD 6% residuals (YRI shown) plotted against the average LoF selection coefficient across genes in megabase windows (estimated by Agarwal et al 2023).…”

Section: Resultsmentioning

confidence: 99%

A Quantitative Genetic Model of Background Selection in Humans

Buffalo,

Kern

2023

Preprint

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Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This "linked selection signal" reflects the impact of selection according to the physical placement of functional regions and recombination rates along chromosomes. Previous work has shown that purifying selection acting against the steady influx of new deleterious mutations at functional portions of the genome shapes patterns of genomic variation. To date, statistical efforts to estimate purifying selection parameters from linked selection models have relied on classic Background Selection theory, which is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of linked selection, that models how polygenic additive fitness variance distributed along the genome increases the rate of stochastic allele frequency change. By jointly predicting the equilibrium fitness variance and substitution rate due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three geographically distinct human samples. While our model can accommodate weaker selection, we find evidence of strong selection operating similarly across all human samples. While our quantitative genetic model of linked selection fits better than previous models, substitution rates of the most constrained sites are poorly predicted. We find that a model incorporating selective interference better predicts observed divergence in conserved regions, but overall our results ultimately suggest considerable uncertainty remains about the processes generating fitness variation in humans.

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“…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%

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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Relating pathogenic loss-of-function mutations in humans to their evolutionary fitness costs

Cited by 22 publications

References 98 publications

GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction

GPN-MSA: an alignment-based DNA language model for genome-wide variant effect prediction

A Quantitative Genetic Model of Background Selection in Humans

Constraining models of dominance for nonsynonymous mutations in the human genome

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