| A resolution of the mutation load paradox in humans. | |
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MedLine Citation:
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PMID: 22661324 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Current information on the rate of mutation and the fraction of sites in the genome that are subject to selection suggests that each human has received, on average, at least two new harmful mutations from its parents. These mutations were subsequently removed by natural selection through reduced survival or fertility. It has been argued that the mutation load, the proportional reduction in population mean fitness relative to the fitness of an idealized mutation-free individual, allows a theoretical prediction of the proportion of individuals in the population that fail to reproduce as a consequence of these harmful mutations. Application of this theory to humans implies that at least 88% of individuals should fail to reproduce and that each female would need to have more than 16 offspring to maintain population size. This prediction is clearly at odds with the low reproductive excess of human populations. Here, we derive expressions for the fraction of individuals that fail to reproduce as a consequence of recurrent deleterious mutation () for a model in which selection occurs via differences in relative fitness, such as would occur through competition between individuals. We show that is much smaller than the value predicted by comparing fitness to that of a mutation-free genotype. Under the relative fitness model, we show that depends jointly on U and the selective effects of new deleterious mutations and that a species could tolerate 10's or even 100's of new deleterious mutations per genome each generation. |
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Authors:
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Yann Lesecque; Peter D Keightley; Adam Eyre-Walker |
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Publication Detail:
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Type: Journal Article Date: 2012-06-01 |
Journal Detail:
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Title: Genetics Volume: 191 ISSN: 1943-2631 ISO Abbreviation: Genetics Publication Date: 2012 Aug |
Date Detail:
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Created Date: 2012-08-10 Completed Date: 2012-12-17 Revised Date: 2013-04-16 |
Medline Journal Info:
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Nlm Unique ID: 0374636 Medline TA: Genetics Country: United States |
Other Details:
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Languages: eng Pagination: 1321-30 Citation Subset: IM |
Affiliation:
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School of Life Sciences, University of Sussex, Brighton BN1 9QG, United Kingdom. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Computer Simulation Evolution, Molecular Female Genes, Recessive Genetic Fitness Genetics, Population Humans Male Models, Genetic* Mutation* Mutation Rate Reproduction / genetics Selection, Genetic* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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