Relating pathogenic loss-of function mutations in humans to their evolutionary fitness costs
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-day 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 then 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.
Data availability
All source data are freely available to researchers, with sources listed in Table S4. Code for simulations, and output is available at https://github.com/zfuller5280/MutationSelection and https://github.com/agarwal-i/loss-of-function-fitness-effects. Estimates of fitness costs of LOF mutations are provided as Table S2.
Article and author information
Author details
Funding
National Institutes of Health (GM121372)
- Molly Przeworski
National Institutes of Health (HG011432)
- Molly Przeworski
National Institutes of Health (GM128318)
- Zachary L Fuller
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- George H Perry, Pennsylvania State University, United States
Version history
- Preprint posted: August 12, 2022 (view preprint)
- Received: September 1, 2022
- Accepted: January 16, 2023
- Accepted Manuscript published: January 17, 2023 (version 1)
- Version of Record published: February 17, 2023 (version 2)
Copyright
© 2023, Agarwal et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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- Evolutionary Biology
Changes in an organism’s environment, genome, or gene expression patterns can lead to changes in its metabolism. The metabolic phenotype can be under selection and contributes to adaptation. However, the networked and convoluted nature of an organism’s metabolism makes relating mutations, metabolic changes, and effects on fitness challenging. To overcome this challenge, we use the long-term evolution experiment (LTEE) with E. coli as a model to understand how mutations can eventually affect metabolism and perhaps fitness. We used mass spectrometry to broadly survey the metabolomes of the ancestral strains and all 12 evolved lines. We combined this metabolic data with mutation and expression data to suggest how mutations that alter specific reaction pathways, such as the biosynthesis of nicotinamide adenine dinucleotide, might increase fitness in the system. Our work provides a better understanding of how mutations might affect fitness through the metabolic changes in the LTEE and thus provides a major step in developing a complete genotype–phenotype map for this experimental system.
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