The degree to which genetic variation in a given trait varies among different populations of the same species and across different environments has seldom been quantified in wild vertebrate species. We investigated the expression of genetic variability and maternal effects in three larval life-history traits of the amphibian Rana temporaria. In a factorial laboratory experiment, five widely separated populations (max. 1600 km) were subjected to two different environmental treatments. Animal model analyses revealed that all traits were heritable (h2≈0·20) in all populations and under most treatment combinations. Although the cross-food treatment genetic correlations were close to unity, heritabilities under a restricted food regime tended to be lower than those under an ad libitum food regime. Likewise, maternal effects (m2≈0·05) were detected in most traits, and they tended to be most pronounced under restricted food conditions. We detected several cross-temperature genetic and maternal effects correlations that were lower than unity, suggesting that genotype–environment interactions and maternal effect–environment interactions are a significant source of phenotypic variation. The results reinforce the perspective that although the expression of genetic and maternal effects may be relatively homogeneous across different populations of the same species, local variation in environmental conditions can lead to significant variation in phenotypic expression of quantitative traits through genotype–environment and maternal effect–environment interactions.

The ancestral karyotype of the house mouse (Mus musculus) consists of 40 acrocentric chromosomes, but numerous races exist within the domesticus subspecies characterized by different metacentric chromosomes formed by the joining at the centromere of two acrocentrics. An exemplary case is present on the island of Madeira where six highly divergent chromosomal races have accumulated different combinations of 20 metacentrics in 500–1000 years. Chromosomal cladistic phylogenies were performed to test the relative performance of Robertsonian (Rb) fusions, Rb fissions and whole-arm reciprocal translocations (WARTs) in resolving relationships between the chromosomal races. The different trees yielded roughly similar topologies, but varied in the number of steps and branch support. The analyses using Rb fusions/fissions as characters resulted in poorly supported trees requiring six to eight homoplasious events. Allowance for WARTs considerably increased nodal support and yielded the most parsimonious trees since homoplasy was reduced to a single event. The WART-based trees required five to nine WARTs and 12 to 16 Rb fusions. These analyses provide support for the role of WARTs in generating the extensive chromosomal diversification observed in house mice. The repeated occurrence of Rb fusions and WARTs highlights the contribution of centromere-related rearrangements to accelerated rates of chromosomal change in the house mouse.

The effect was investigated of the hypomorphic DNA double-strand break repair, notably synthesis-dependent strand annealing, deficient mutation mus309 on the third chromosome of Drosophila melanogaster on intergenic and intragenic meiotic recombination in the X chromosome. The results showed that the mutation significantly increases the frequency of intergenic crossing over in two of three gene intervals of the X chromosome studied. Interestingly the increase was most prevalent in the tip of the X chromosome where crossovers normally are least frequent per physical map unit length. In particular crossing over interference was also affected, indicating that the effect of the mus309 mutation involves preconditions of crossing over but not the event of crossing over itself. On the other hand, the results also show that most probably the mutation does not have any effect on intragenic recombination, i.e. gene conversion. These results are fully consistent with the present molecular models of meiotic crossing over initiated by double-strand breaks of DNA followed by formation of a single-end-invasion intermediate, or D-loop, which is subsequently processed to generate either crossover or non-crossover products involving formation of a double Holliday junction. In particular the results suggest that the mus309 gene is involved in resolution of the D-loop, thereby affecting the choice between double-strand-break repair (DSBR) and synthesis-dependent strand annealing (SDSA) pathways of meiotic recombination.

Cluster analyses of gene expression data are usually conducted based on their associations with the phenotype of a particular disease. Many disease traits have a clearly defined binary phenotype (presence or absence), so that genes can be clustered based on the differences of expression levels between the two contrasting phenotypic groups. For example, cluster analysis based on binary phenotype has been successfully used in tumour research. Some complex diseases have phenotypes that vary in a continuous manner and the method developed for a binary trait is not immediately applicable to a continuous trait. However, understanding the role of gene expression in these complex traits is of fundamental importance. Therefore, it is necessary to develop a new statistical method to cluster expressed genes based on their association with a quantitative trait phenotype. We developed a model-based clustering method to classify genes based on their association with a continuous phenotype. We used a linear model to describe the relationship between gene expression and the phenotypic value. The model effects of the linear model (linear regression coefficients) represent the strength of the association. We assumed that the model effects of each gene follow a mixture of several multivariate Gaussian distributions. Parameter estimation and cluster assignment were accomplished via an Expectation-Maximization (EM) algorithm. The method was verified by analysing two simulated datasets, and further demonstrated using real data generated in a microarray experiment for the study of gene expression associated with Alzheimer's disease.

A Bayesian model and variable dimensional parameter estimation based on Markov chain Monte Carlo was applied to map quantitative trait loci (QTLs) in a doubled haploid mapping population of rainbow trout. To increase power, the analysis was performed using the multiple-QTL model, which simultaneously accounted for all the environmental and genetic main effects that influence the expression of early development life history traits. By doing so we obtained the posterior estimated effects for the environmental factors as well as the number, positions, and the effects for the QTLs. The analyses revealed QTLs for time at hatching, embryonic length and weight at swim-up stage. The posterior expectation of the number of QTLs in different linkage groups shows that at least four QTLs are needed to explain the observed differences in early development between the clonal lines. The Bayesian method effectively combined all the information available to accurately position these QTLs in the rainbow trout genome.

Haplotype inference has become an important part of human genetic data analysis due to its functional and statistical advantages over the single-locus approach in linkage disequilibrium mapping. Different statistical methods have been proposed for detecting haplotype – disease associations using unphased multi-locus genotype data, ranging from the early approach by the simple gene-counting method to the recent work using the generalized linear model. However, these methods are either confined to case – control design or unable to yield unbiased point and interval estimates of haplotype effects. Based on the popular logistic regression model, we present a new approach for haplotype association analysis of human disease traits. Using haplotype-based parameterization, our model infers the effects of specific haplotypes (point estimation) and constructs confidence interval for the risks of haplotypes (interval estimation). Based on the estimated parameters, the model calculates haplotype frequency conditional on the trait value for both discrete and continuous traits. Moreover, our model provides an overall significance level for the association between the disease trait and a group or all of the haplotypes. Featured by the direct maximization in haplotype estimation, our method also facilitates a computer simulation approach for correcting the significance level of individual haplotype to adjust for multiple testing. We show, by applying the model to an empirical data set, that our method based on the well-known logistic regression model is a useful tool for haplotype association analysis of human disease traits.

The Transforming growth factor-β (TGF-β) family control diverse cellular processes and specify cell-fate/differentiation during embryogenesis in vertebrates and invertebrates. Mutations disrupting TGF-β signalling lead to developmental abnormalities and a range of diseases such as cancer. Nodal is a major TGF-β signal, responsible for gastrulation in embryogenesis. Arkadia (Akd) was discovered by mouse gene-trap mutagenesis and encodes a nuclear E3 ubiquitin ligase. Akd allows the Nodal signal to reach its maximum level and Akd-null mice lack mammalian organiser (MO) and mesendodermal tissues. Although Akd RNA is ubiquitously expressed, Akd-null mice lose a subset of Nodal-dependent functions. The specificity of Akd function is therefore most likely to be regulated post-transcriptionally or by co-factors. Akd possesses differentially spliced 5′ untranslated regions (UTRs) and large 3′ UTR. We have employed bioinformatics and developed a reporter system to address Akd post-transcriptional regulation. Akd RNA may initiate from different promoters and 5′ UTR differential splicing, upstream AUGs (uAUGs) and open-reading frames upstream (uORFs) may regulate protein translation. 5′ and 3′ UTRs can interact to either destabilise or decrease translational efficiency of RNA. The nature of this interaction is cell-type and signal level dependent. These data may represent mechanisms by which translational control of Arkadia is achieved and ultimately how TGF-β/Nodal signalling is regulated during embryogenesis.