The distribution of mutation effects on viability in Drosophila melanogaster.

Abstract

Parameters of continuous distributions of effects and rates of spontaneous mutation for relative viability in Drosophila are estimated by maximum likelihood from data of two published experiments on accumulation of mutations on protected second chromosomes. A model of equal mutant effects gives a poor fit to the data of the two experiments; higher likelihoods are obtained with leptokurtic distributions or for models in which there is more than one class of mutation effect. Minimum estimates of mutation rates (events per generation) at polygenes affecting viability on chromosome 2 are 0.14 and 0.068, but estimates are strongly confounded with other parameters in the model. Separate information on rates of molecular divergence between Drosophila species and from rates of movement of transposable elements is used to infer the overall genomic mutation rate in Drosophila, and the viability data are analyzed with mutation rate as a known parameter. If, for example, a mutation rate for chromosome 2 of 0.4 is assumed, maximum likelihood estimates of mean mutant effect on relative viability are 0.4% and 1%, but the majority of mutations have very much smaller effects than these values as distributions are highly leptokurtic. The methodology is applied to estimate viability effects of single P element insertional mutations. The mean effect per insertion is found to be higher, and their distribution is found to be less leptokurtic than for spontaneous mutations. The equilibrium genetic variance of viability predicted by a mutation-selection balance model with parameters estimated from the mutation accumulation experiments is similar to laboratory estimates of genetic variance of viability from natural populations of Drosophila.

What can we learn about the distribution of fitness effects of new mutations from DNA sequence data?

“…Given that the average gene length is approximately 1300 bp in humans (International Human Genome Sequencing Consortium 2001) and approximately 1800 bp in Drosophila (Adams et al 2000), this shows that it will generally be difficult to obtain gene-specific estimates of the DFE in humans, but this may be possible for longer genes in Drosophila if sufficient alleles are sequenced. The DFE is often modelled using a simple distribution, such as the gamma distribution (Keightley 1994;Piganeau & Eyre-walker 2003;Eyre-Walker et al 2006;Boyko et al 2008) or the lognormal distribution ). However, in reality, the distribution is likely to be more complex than these simple distributions, and may even be multimodal .…”

“…He was the first to appreciate that the proportion of adaptive substitutions could be inferred by adapting the McDonald -Kreitman test (McDonald & Kreitman 1991;Charlesworth 1994). He was also one of the first to use the DFE in a population genetic analysis when he used parameter estimates from Mukai and Ohnishi's mutation accumulation experiments in Drosophila (Keightley 1994) to test whether background selection could explain how patterns of DNA diversity vary with the level of recombination in Drosophila (Charlesworth 1996). More recently, he has developed, with Laurence Loewe, a method to infer the DFE for deleterious mutations using DNA sequence data .…”

Rate and effects of spontaneous mutations that affect fitness in mutatorEscherichia coli

“…In the model we used to estimate U d and s d , all deleterious mutations are assumed to have the same effect. To test how the estimates were affected by variation in s d values, we ran simulations assuming a gamma distribution, b a s aÀ1 d e Àbs d =GðaÞ; with different shape parameters (a), which is a distribution commonly used (Keightley 1994) to fit data from MA experiments. One hundred simulations were done for each set of parameters.…”

“…The Bateman-Mukai method gives an upper bound for U d as DM 2 /DV and a lower bound for the mean deleterious effect of a mutation, s d , as DV/DM (Mukai et al 1972). More recently a maximum likelihood method was developed (Keightley 1994) to estimate the rate and the distribution of fitness effects in MA experiments. Assuming that mutation effects follow a continuous gamma distribution, it is possible to estimate its shape and scale parameters and, importantly, test if this fits the MA data better than a model that assumes a single value for s d .…”

Two steps forward, one step back: the pleiotropic effects of favoured alleles

“…Data based on mutation-accumulation experiments in Drosophila melanogaster suggest that the coefficient of variation for new random mutations varies from 2 to 5 for bristle traits and viability (Keightley 1994). These estimates are, however, based on the full spectrum of mutations.…”