Relating pathogenic loss-of-function mutations in humans to their evolutionary fitness costs
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eLife assessment
This paper directly estimates the fitness cost of loss-of-function mutations in almost every gene in the human genome, providing an interpretable measure of the severity of mutations. The authors then compare datasets of presumably healthy individuals and individuals affected by severe complex disorders or genetic disorders, finding enrichment of de novo loss-of-function mutations in highly constrained genes among probands alongside other illuminating results. This important study will be useful to researchers interested in interpreting and prioritizing disease-causing mutations and in the process of human evolution. Overall, the approach is elegant and the results are of high quality and compelling.
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Abstract
Causal loss-of-function (LOF) variants for Mendelian and severe complex diseases are enriched in 'mutation intolerant' genes. We show how such observations can be interpreted in light of a model of mutation-selection balance and use the model to relate the pathogenic consequences of LOF mutations at present to their evolutionary fitness effects. To this end, we first infer posterior distributions for the fitness costs of LOF mutations in 17,318 autosomal and 679 X-linked genes from exome sequences in 56,855 individuals. Estimated fitness costs for the loss of a gene copy are typically above 1%; they tend to be largest for X-linked genes, whether or not they have a Y homolog, followed by autosomal genes and genes in the pseudoautosomal region. We compare inferred fitness effects for all possible de novo LOF mutations to those of de novo mutations identified in individuals diagnosed with one of six severe, complex diseases or developmental disorders. Probands carry an excess of mutations with estimated fitness effects above 10%; as we show by simulation, when sampled in the population, such highly deleterious mutations are typically only a couple of generations old. Moreover, the proportion of highly deleterious mutations carried by probands reflects the typical age of onset of the disease. The study design also has a discernible influence: a greater proportion of highly deleterious mutations is detected in pedigree than case-control studies, and for autism, in simplex than multiplex families and in female versus male probands. Thus, anchoring observations in human genetics to a population genetic model allows us to learn about the fitness effects of mutations identified by different mapping strategies and for different traits.
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eLife assessment
This paper directly estimates the fitness cost of loss-of-function mutations in almost every gene in the human genome, providing an interpretable measure of the severity of mutations. The authors then compare datasets of presumably healthy individuals and individuals affected by severe complex disorders or genetic disorders, finding enrichment of de novo loss-of-function mutations in highly constrained genes among probands alongside other illuminating results. This important study will be useful to researchers interested in interpreting and prioritizing disease-causing mutations and in the process of human evolution. Overall, the approach is elegant and the results are of high quality and compelling.
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Reviewer #1 (Public Review):
This paper estimates the selective effects of loss-of-function mutations in each gene, ultimately providing an estimate of the overall distribution of fitness effects, and point estimates for each gene. Unlike some measures of intolerance such as pLI, the parameter the authors estimate (effectively the compound parameter hs) is interpretable in terms of evolutionary fitness. The most comparable analysis is by Weghorn et al (2019) which estimates the same parameter, but on a smaller sample and using a different approach.
The point estimates will be broadly useful for future analyses, and the overall distribution is an interesting result. The enrichment in various disease cohorts is unexpected but nice to demonstrate. Overall, I found the approach to be elegant and it has the nice property that it can be …
Reviewer #1 (Public Review):
This paper estimates the selective effects of loss-of-function mutations in each gene, ultimately providing an estimate of the overall distribution of fitness effects, and point estimates for each gene. Unlike some measures of intolerance such as pLI, the parameter the authors estimate (effectively the compound parameter hs) is interpretable in terms of evolutionary fitness. The most comparable analysis is by Weghorn et al (2019) which estimates the same parameter, but on a smaller sample and using a different approach.
The point estimates will be broadly useful for future analyses, and the overall distribution is an interesting result. The enrichment in various disease cohorts is unexpected but nice to demonstrate. Overall, I found the approach to be elegant and it has the nice property that it can be easily generalized to more complicated models. The data cleaning and filtering is quite extensive but all seems well done and appropriate. Qualitatively, the results clearly make a lot of sense (Figure 3 is an excellent figure) My only major questions are around how quantitatively robust this analysis is to the choice of parameters and hyperparameters including priors, mutation rates, and demography. I don't think that extensive work is required, but it would be helpful to see some quantification of this uncertainty.
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Reviewer #2 (Public Review):
This study models the fitness costs of loss-of-function mutations in a large cohort of a human database of 55,855 individuals. The modeling indicates different values for autosomal genes, X-linked genes, and those present in the pseudo-autosomal regions of the X and Y chromosomes. The study details the frequency of de novo mutations in zygotes and examined the relationship to a few specific genetic diseases. The authors have composed a well-written manuscript, have explicitly detailed their assumptions, and have noted caveats to interpretations. The results are a valuable documentation of the effects of loss-of-function mutations in humans.
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Reviewer #3 (Public Review):
This manuscript presents a new method to estimate the selective effect of heterozygous loss of function mutations. The authors offer a sequential Monte Carlo algorithm coupled with ABC estimates based on forward population genetics simulations. The method is of obvious interest to the field. The result confirms that DFE distribution for PTVs is broad with the mean and median exceeding 1% and ~20% of genes associated with more than 10% loss in fitness. The new quantitative estimates are likely an improvement over the state-of-the-art. Importantly, the authors include estimates for PTVs on the X chromosome, which are expectedly higher. The authors demonstrate that de novo PTVs leading to a substantial fitness loss are highly enriched in individuals affected by severe complex disorders including …
Reviewer #3 (Public Review):
This manuscript presents a new method to estimate the selective effect of heterozygous loss of function mutations. The authors offer a sequential Monte Carlo algorithm coupled with ABC estimates based on forward population genetics simulations. The method is of obvious interest to the field. The result confirms that DFE distribution for PTVs is broad with the mean and median exceeding 1% and ~20% of genes associated with more than 10% loss in fitness. The new quantitative estimates are likely an improvement over the state-of-the-art. Importantly, the authors include estimates for PTVs on the X chromosome, which are expectedly higher. The authors demonstrate that de novo PTVs leading to a substantial fitness loss are highly enriched in individuals affected by severe complex disorders including neuropsychiatric disorders. They also provide estimates of allelic ages for variants with specific selection coefficients. This work is of interest to both population and medical geneticists.
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