Recombination is thought to have various evolutionary effects on genome evolution. In this study, we investigated the relationship between the base composition and recombination rate in the Drosophila melanogaster genome. Because of a current debate about the accuracy of the estimates of recombination rate in Drosophila, we used eight different measures of recombination rate from recent work. We confirmed that the G+C content of large introns and flanking regions is positively correlated with recombination rate, suggesting that recombination has a neutral effect on base composition in Drosophila. We also confirmed that this neutral effect of recombination is the main determinant of the correlation between synonymous codon usage bias and recombination rate in Drosophila.

The usage of preferred codons in Drosophila melanogaster is reduced in regions of lower recombination. This is consistent with population genetics theory, whereby the effectiveness of selection on multiple targets is limited by stochastic effects caused by linkage. However, because the selectively preferred codons in D. melanogaster end in C or G, it has been argued that base-composition-biasing effects of recombination can account for the observed relationship between preferred codon usage and recombination rate (Marais et al., 2003). Here, we show that the correlation between base composition (of protein-coding and intron regions) and recombination rate holds only for lower values of the latter. This is consistent with a Hill–Robertson interference model and does not support a model whereby the entire effect of recombination on codon usage can be attributed to its potential role in generating compositional bias.

The soil nematode Caenorhabditis elegans is an example of a species in which self-fertilizing hermaphrodites predominate, but functional males continue to persist – allowing outcrossing to persevere at low levels. Hermaphrodites can produce male progeny as a consequence of sex chromosome non-disjunction or via outcrossing with males. Consequently, the genetics of sex determination coupled with the efficiency by which males find, inseminate and obtain fertilizations with hermaphrodites will influence the frequency at which males and outcrossing occurs in such populations. Behavioural and physiological traits with a heritable basis, as well as ecological characters, may influence male reproductive success and therefore sex ratio. Because sex ratio is tied to male reproductive success, sex ratio greatly affects outcrossing rates, patterns of genetic variation, and the ability of natural selection to act within populations. In this paper we explore the determinants of male frequency in C. elegans with a mathematical model and experimental data. We address the role of the genetic machinery of sex determination via sex chromosome non-disjunction on sex ratio and the influence of physiological components of C. elegans' life history that contribute to variation in sex ratio by way of male reproductive success. Finally, we discuss the short-term and long-term factors that are likely to affect sex ratio and breeding system evolution in species like C. elegans.

As part of a population genetics survey of the hybrid zone between mouse subspecies Mus musculus domesticus and M. m. musculus, we identified and characterized the t haplotypes in 1068 mice from 186 different populations in a 2500 km2 area in central Jutland. On the basis of two t-specific PCR markers, 130 mice possessed this haplotype. The allele frequencies at six microsatellites on the third and fourth chromosomal inversions of the t region were sufficiently different between t-bearing and non-t-bearing mice, and linkage disequilibria sufficiently marked on the t haplotype, to be able to reconstitute the genotype of most t haplotypes. A total of three frequent and 15 rarer haplotypes were identified. These haplotypes resemble each other more than they resemble a panel of known haplotypes from a wide range of geographical regions, except for tw73, which was also extracted from Jutland. The patterns of variation at the microsatellite loci suggest that the Jutland haplotypes were derived from a small number of haplotypes, followed by recombination between complementing haplotypes. Further evidence of recombination came from complementation tests that we performed, showing the lack of concordance between the degrees of complementation and of molecular resemblance between haplotypes. This study shows that it is possible to characterize the presence and variation of t haplotypes by a population genetics approach using simple molecular markers. However recombination between t haplotypes has occurred frequently enough to obscure the links between this variation and the biological properties of distortion and lethality of the haplotypes that originally colonized Jutland.

Current sitewise methods for detecting positive selection on gene sequences (the de facto standard being the CODEML method (Yang et al., 2000)) assume no recombination. This paper presents simulation results indicating that violation of this assumption can lead to false positive detection of sites undergoing positive selection. Through the use of population-scaled mutation and recombination rates, simulations can be performed that permit the generation of appropriate null distributions corresponding to neutral expectations in the presence of recombination, thereby allowing for a more accurate estimation of positive selection.

Explaining the repeated evolution of similar sets of traits under similar environmental conditions is an important issue in evolutionary biology. The extreme alternative classes of explanations for correlated suites of traits are optimal adaptation and genetic constraint resulting from pleiotropy. Adaptive explanations presume that individual traits are free to evolve to their local optima and that convergent evolution represents particularly adaptive combinations of traits. Alternatively, if pleiotropy is strong and difficult to break, strong selection on one or a few particularly important characters would be expected to result in consistent correlated evolution of associated traits. If pleiotropy is common, we predict that the pattern of divergence among populations will consistently reflect the within-population genetic architecture. To test the idea that the multivariate life-history phenotype is largely a byproduct of strong selection on body size, we imposed divergent artificial selection on size at maturity upon two populations of the cladoceran Daphnia pulicaria, chosen on the basis of their extreme divergence in body size. Overall, the trajectory of divergence between the two natural populations did not differ from that predicted by the genetic architecture within each population. However, the pattern of correlated responses suggested the presence of strong pleiotropic constraints only for adult body size and not for other life-history traits. One trait, offspring size, appears to have evolved in a way different from that expected from the within-population genetic architecture and may be under stabilizing selection.

When multiple related families derived from inbred lines are jointly analysed to detect quantitative trait loci (QTLs), the analysis should estimate allelic effects as accurately as possible and estimate the probability that different parents carry alleles that are identical in state. Analyses exist that assume that all parents carry unique alleles or that all parents but one carry the same allele. In practice, many configurations are possible that group different parents according to their identity-in-state condition at a putative QTL allele. Here, we propose a variable model Bayesian analysis that selects among possible identity-in-state configurations and jointly estimates the allelic effects of identical-in-state parents. We contrast this analysis with a fixed model analysis that estimates unique allelic effects for all parents. We analyse two simulated mating designs: an experimental design in which three inbred parents were crossed to generate two families of 150 doubled haploid lines; and a breeding design in which 20 inbred parents were crossed to generate 60 families of 20 doubled haploid lines, with each parent contributing to six families. In all cases where some parents were simulated to carry alleles of identical effect (that is, they were identical in state), the variable analysis estimated allelic effects with lower mean-squared error than the fixed analysis. The variable analysis showed that, unless each family contains many individuals (more than 100), there is insufficient information in DNA-marker and phenotypic data to determine with high probability the QTL allelic number.

Estimation of quantitative genetic parameters conventionally requires known pedigree structure. However, several methods have recently been developed to circumvent this requirement by inferring relationship structure from molecular marker data. Here, two such marker-assisted methodologies were used and compared in an aquaculture population of rainbow trout (Oncorhynchus mykiss). Firstly a regression-based model employing estimates of pairwise relatedness was applied, and secondly a Markov Chain Monte Carlo (MCMC) procedure was employed to reconstruct full-sibships and hence an explicit pedigree. While both methods were effective in detecting significant components of genetic variance and covariance for size and spawning time traits, the regression model resulted in estimates that were quantitatively unreliable, having both significant bias and low precision. This result can be largely attributed to poor performance of the pairwise relatedness estimator. In contrast, genetic parameters estimated from the reconstructed pedigree showed close agreement with ideal values obtained from the true pedigree. Although not significantly biased, parameters based on the reconstructed pedigree were underestimated relative to ideal values. This was due to the complex structure of the true pedigree in which high numbers of half-sibling relationships resulted in inaccurate partitioning of full-sibships, and additional unrecognized relatedness between families.

In this article, a new algorithm for obtaining the maximum likelihood estimators (MLEs) of parameters in the joint segregation analysis (JSA) of multiple generations of P1, F1, P2, F2 and F2[ratio ]3 (MG5) for quantitative traits was set up. Firstly, owing to the fact that the component variance of the heterogeneous genotype in F2[ratio ]3 included both the first-order genetic parameters (denoted by the means of distributions) and the second-order parameters, a simple closed form for the MLEs of the means of component distributions did not exist while the expectation and maximization (EM) algorithm was used. To simplify the estimation of parameters, the first partial derivative of the above variance on the mean in the sample log-likelihood function was omitted. However, this would be remedied by the iterated method. Then, variances of component distributions for segregating populations were partitioned into major-gene, polygenic and environmental variances so that the generally iterated formulae for estimating the means as well as polygenic and environmental variances of component distributions in the maximization step (M-step) of the EM algorithm were obtained. Therefore, the EM algorithm for estimating parameters in the JSA model for the MG5 was simplified. This is called the expectation and iterated maximization (EIM) algorithm. Finally, an example of the inheritance of the resistance of soybean to beanfly showed that the results of mixed inheritance analysis in this paper coincided with those in both Wang & Gai (2001) and Wei et al. (1989), so the EIM algorithm was appropriate.