A resolution of the mutation load paradox in humans.
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.
School of Life Sciences, University of Sussex, Brighton BN1 9QG, United Kingdom.
Yann
Y
Lesecque
Peter D
PD
Keightley
Adam
A
Eyre-Walker
D000465:Algorithms; D003198:Computer Simulation; D019143:Evolution, Molecular; D005260:Female; D005808:Genes, Recessive; D056084:Genetic Fitness; D005828:Genetics, Population; D006801:Humans; D008297:Male; D008957:Models, Genetic; D009154:Mutation; D059645:Mutation Rate; D012098:Reproduction / Q000235:genetics; D012641:Selection, Genetic