Academic journal article Genetics

The Distribution of Fitness Effects of New Deleterious Amino Acid Mutations in Humans

Academic journal article Genetics

The Distribution of Fitness Effects of New Deleterious Amino Acid Mutations in Humans

Article excerpt

ABSTRACT

The distribution of fitness effects of new mutations is a fundamental parameter in genetics. Here we present a new method by which the distribution can be estimated. The method is fairly robust to changes in population size and admixture, and it can be corrected for any residual effects if a model of the demography is available. We apply the method to extensively sampled single-nucleotide polymorphism data from humans and estimate the distribution of fitness effects for amino acid changing mutations. We show that a gamma distribution with a shape parameter of 0.23 provides a good fit to the data and we estimate that >50% of mutations are likely to have mild effects, such that they reduce fitness by between one one-thousandth and one-tenth. We also infer that <15% of new mutations are likely to have strongly deleterious effects. We estimate that on average a nonsynonymous mutation reduces fitness by a few percent and that the average strength of selection acting against a nonsynonymous polymorphism is ~9 × 10^sup -5^. We argue that the relaxation of natural selection due to modern medicine and reduced variance in family size is not likely to lead to a rapid decline in genetic quality, but that it will be very difficult to locate most of the genes involved in complex genetic diseases.

(ProQuest Information and Learning: ... denotes formulae omitted.)

IT has been estimated that each of us receives more than one harmful amino acid mutation each generation (EYRE-WALKER and KEIGHTLEY 1999). But how harmful are these mutations on average, and what proportion of mutations are weakly, mildly, and strongly deleterious? In short, what is the distribution of fitness effects of new mutations? This question is central to understanding several topics in human biology, including the genetic basis of disease and the likely consequences of relaxing natural selection through modern medicine and better living standards (MULLER 1950; CROW 1997; LYNCH et al. 1999). Furthermore, the distribution of fitness effects is central to our understanding of many other problems in genetics and evolution, including the maintenance of genetic variation (CHARLESWORTH et al. 1993), the long-term survival of small populations (LANDE 1994; LYNCH et al. 1995), and the basis of the molecular clock (OHTA 1977).

Although the distribution of fitness effects is an important parameter in genetics and evolutionary biology, relatively little is known with certainty about its form. Mutagenesis and mutation-accumulation experiments suggest that the distribution of fitness effects is highly leptokurtic, such that most mutations appear to have effects of <1% (KEIGHTLEY 1994, 1996; DAVIES et al. 1999; VASSILIEVA et al. 2000; ESTES et al. 2004). This has been broadly corroborated by studies of DNA sequence evolution, although the precise form of the distribution inferred by different studies varies considerably. PIGANEAU and EYRE-WALKER (2003) and LOEWE et al. (2006) found that a gamma distribution, with a shape parameter of less than one, was consistent with non-synonymous data from animal mitochondria and Drosophila, respectively, whereas NIELSEN and YANG (2003) and SAWYER et al. (2002) showed that a normal distribution was consistent with similar data.

These studies were based on different methods, each with its own advantages and disadvantages. However, they all share two limitations. First, they are based on relatively little information. Generally these methods use either divergence data or divergence data in association with a single statistic summarizing polymorphism data, which limits the power of these analyses. Second, the use of divergence data introduces the problem of adaptive substitutions, which may influence estimates of the distribution of fitness effects (although note that this should not be a problem for the method of LOEWE et al. 2006, which estimates the proportion of adaptive substitutions). Furthermore, estimates based on a combination of divergence and polymorphism data may be affected by differences in effective population sizes associated with the polymorphism and divergence data, respectively. …

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