An analysis of mutation accumulation in finite, asexual populations shows that by modeling discrete individuals, a necessary condition for mutation–selection balance is often not met. It is found that over a wide parameter range (whenever N e−μ/s < 1, where N is the population size, μ is the genome-wide mutation rate, and s is the realized strength of selection), asexual populations will fail to achieve mutation–selection balance. This is specifically because the steady-state strength of selection on the best individuals is too weak to counter mutation pressure. The discrete nature of individuals means that if the equilibrium level of mutation and selection is such that less than one individual is expected in a class, then equilibration towards this level acts to remove the class. When applied to the classes with the fewest mutations, this drives mutation accumulation. This drive is in addition to the well-known identification of the stochastic loss of the best class as a mechanism for Muller's ratchet. Quantification of this process explains why the distribution of the number of mutations per individual can be markedly hypodispersed compared to the Poisson expectation. The actual distribution, when corrected for stochasticity between the best class and the mean, is akin to a shifted negative binomial. The parameterization of the distribution allows for an approximation for the rate of Muller's ratchet when N e−μ/s < 1. The analysis is extended to the case of variable selection coefficients where incoming mutations assume a distribution of deleterious effects. Under this condition, asexual populations accumulate mutations faster, yet may be able to survive longer, than previously estimated.

A method of using information on the location of markers to improve the efficiency of markerassisted selection (MAS) in a population produced by a cross between two inbred lines is developed. The method is closer to mapping QTL than the selection index approaches to MAS described by previous authors. We use computer simulations to compare our method with phenotypic selection and two selection index approaches, simulations being performed on three genetic maps. The simulations show that whilst MAS can be considerably more efficient than phenotypic selection differences between the three MAS methods are slight. Which of the MAS methods is best depends on a number of factors: in particular the genetic map, the time scale under consideration and the population size are of importance.

The Clc2 gene of the mouse codes for the ubiquitously expressed chloride channel ClC-2, a member of a family of at least seven voltage gated chloride channels, some of which are implicated in hereditary diseases. Using a mouse interspecies back-cross panel, we have mapped Clc2 to Chr 16, proximal to the somatostatin gene Smst, extending a region of documented conserved synteny to human Chr 3q.

A Monte Carlo simulation study to evaluate the benefits of marker assisted selection (MAS) in small populations with one marked bi-allelic quantitative trait locus (QTL) is described. In the base generation, linkage phase equilibrium between the markers, QTL and polygenes was assumed and frequencies of 0·5 for the two QTL alleles were used. Six discrete generations of selection for a single character measured on both sexes followed. An additive genetic model was used with the QTL positioned midway between two highly polymorphic markers. Schemes were simulated with a distance of 10 cM between the QTL and either of the two markers and with the QTL explaining 1/8 of the total genetic variance in the base generation. Values of 0·5, 0·25 or 0·1 were assumed for the heritability. Eight males and 16, 32 or 64 females were selected each generation with each dam producing four sons and four daughters as candidates for the next generation. Animals were evaluated with a conventional BLUP animal model or with a model using marker information. MAS resulted in substantially higher QTL responses (4–54%), especially with low heritabilities, than conventional BLUP but lower polygenic responses (up to 4%) so that the overall effect on the total genetic response, although in the majority of cases favourable, was relatively small. With QTLs of larger size (explaining 25% of the genetic variance) comparable results were found. When the distance between the QTL and the markers was reduced to 2 cM, genetic responses were increased very slightly with a heritability of 0·5 whereas with a heritability of 0·1 responses were increased by up to 10%, compared with conventional BLUP. Results emphasize that MAS should be most useful for lowly heritable traits and that once QTLs for such traits have been identified the search for closely linked polymorphic markers should be intensified.

Wright first introduced the idea that random genetic drift and classical mass-action selection might combine in such a way as to allow populations to find the highest peak in complicated adaptive surfaces. His theory assumes large but structured populations, in which mating is spatially local. If gene flow is sufficiently low, and the subpopulations (demes) are small enough, they will be subject to genetic drift. Distant demes drift independently, allowing many independent searches of the adaptive surface to take place. A deme that has shifted to a higher peak can, by emigration, cause the rest of the demes to shift to the higher peak. The probability of this shift depends on the migration rate. Previous studies have concluded that very little migration is necessary to effect the shift in adaptive peaks that characterizes the last phase of Wright's Shifting Balance Process (SBP).

Here we present the results of a computer study that investigates the roles of dispersal distance, the degree of epistasis in the fitness surface, and recombination on the shifting balance process. In particular, we measure their effect on the population's mean fitness. We show that over a range of dispersal distances the advantage of the SBP is a monotonically increasing function of the amount of epistasis. Our results show that the extent of dispersal that results in the greatest effect of the SBP in increasing mean fitness depends on the extent of epistasis. Finally, for low levels of epistasis, higher recombination performs better, while for intermediate levels, lower recombination results in a greater advantage of the SBP.

The amount and pattern of genetic variation in a population can be estimated from genes or DNA sequences sampled from the population. Although random sampling is assumed in almost all cases, we often do not know whether sampling is random or not. Using a simple non-random sampling model, the effects of non-random sampling on the estimation of parameters in population genetics were investigated. This non-random sampling model assumes that n genes are randomly sampled with replacement from m genes which were randomly sampled from a large random mating population, and various degrees of non-randomness can be generated by changing the value of m. The results obtained show that the effect of non-random sampling on the number of alleles and the number of segregating sites is substantially large whereas the effect of non-random sampling on heterozygosity and the average number of nucleotide differences is negligibly small unless non-randomness is extremely large. The effects of non-random sampling on the tests of neutrality were also investigated, and the results obtained indicate that the effect of non-random sampling is stronger on Fu and Li's tests than on Tajima's test.