Latitudinal variation of the polymorphic sn-glycerol-3-phosphate (α-Gpdh) locus in Drosophila melanogaster has been characterized on several continents; however, apparent clinal patterns are potentially confounded by linkage with an inversion, close associations with other genetic markers that vary clinally, and a tandem α-Gpdh pseudogene. Here we compare clinal patterns in α-Gpdh with those of other linked markers by testing field flies from eastern Australian locations collected in two separate years. The α-Gpdh variation exhibited a consistent non-linear cline reflecting an increase in the α-GpdhF allele at extreme latitudes. This pattern was not influenced by the In(2L)t inversion wherein this locus is located, nor was it influenced by the presence of the α-Gpdh pseudogene, whose presence was ubiquitous and highly variable among populations. The α-Gpdh pattern was also independent of a cline in allozyme frequencies at the alcohol dehydrogenase (Adh) locus, and two length polymorphisms in the Adh gene. These results suggest clinal selection at the α-Gpdh locus that is partially or wholly unrelated to linear climatic gradients along the eastern coast of Australia.

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.

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.

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.

To comprehensively investigate the genetic architecture of growth and obesity, we performed Bayesian analyses of multiple epistatic quantitative trait locus (QTL) models for body weights at five ages (12 days, 3, 6, 9 and 12 weeks) and body composition traits (weights of two fat pads and five organs) in mice produced from a cross of the F1 between M16i (selected for rapid growth rate) and CAST/Ei (wild-derived strain of small and lean mice) back to M16i. Bayesian model selection revealed a temporally regulated network of multiple QTL for body weight, involving both strong main effects and epistatic effects. No QTL had strong support for both early and late growth, although overlapping combinations of main and epistatic effects were observed at adjacent ages. Most main effects and epistatic interactions had an opposite effect on early and late growth. The contribution of epistasis was more pronounced for body weights at older ages. Body composition traits were also influenced by an interacting network of multiple QTLs. Several main and epistatic effects were shared by the body composition and body weight traits, suggesting that pleiotropy plays an important role in growth and obesity.

Two growth-selected lines in chickens have been developed from a single founder population by divergent selection for body weight at 56 days of age. After more than 40 generations of selection they show a nine-fold difference in body weight at selection age and large differences in growth rate, appetite, fat deposition and metabolic characteristics. We have generated a large intercross between these lines comprising more than 800 F2 birds. QTL mapping revealed 13 loci affecting growth. The most striking observation was that the allele in the high weight line in all cases was associated with enhanced growth, but each locus explained only a small proportion of the phenotypic variance using a standard QTL model (1·3–3·1%). This result is in sharp contrast to our previous study where we reported that the two-fold difference in adult body size between the red junglefowl and White Leghorn domestic chickens is explained by a small number of QTLs with large additive effects. Furthermore, no QTLs for anorexia or antibody response were detected despite large differences for these traits between the founder lines. The result is an excellent example where a large phenotypic difference between populations occurs in the apparent absence of any single locus with large phenotypic effects. The study underscores the need for powerful experimental designs in genetic studies of multifactorial traits. No QTL at all would have reached genome-wide significance using a less powerful design (e.g. approx. 200 F2 individuals) regardless of the nine-fold phenotypic difference between the founder lines for the selected trait.

W. G. Hill and X.-S. Zhang (2004). Effects on phenotypic variability of directional selection arising through genetic differences in residual variability. Genetical Research 83, 121–132.

We thank Herman Mulder for noting errors in the published equations. The correct formulae were used in calculations.

The extent to which epistasis contributes to adaptation and speciation has been a controversial topic in evolutionary genetics. One experimental approach to study epistasis is based on quantitative trait locus (QTL) mapping using molecular markers. Comparisons can be made among all possible pair-wise combinations of the markers, irrespective of whether an additive QTL is associated with a marker; several software packages have been developed that facilitate this. We review several examples of using this approach to identify epistatic QTLs for traits of evolutionary or ecological interest. While there is variability in the results, the number of epistatic QTL interactions is often greater than or equal to the number of additive QTLs. The magnitude of epistatic effects can be larger than the additive effects. Thus, epistatic interactions seem to be an important part of natural genetic variation. Future studies of epistatic QTLs could lead to descriptions of the genetic networks underlying variation for fitness-related traits.

Mammalian mitochondrial genomes are organized in a conserved and extremely compact manner, encoding molecules that play a vital role in oxidative phosphorylation (OXPHOS) and carry out a number of other important biological functions. A large-scale screening of the normalized mitochondrial gene expression profiles generated from publicly available mammalian serial analysis of gene expression (SAGE) datasets (over 17·7 millions of tags) was performed in this study. Acquired SAGE libraries represent an extensive range of human, mouse, rat, bovine and swine cell and tissue samples (normal and pathological) in a variety of conditions. Using a straightforward in silico algorithm, variations in total mitochondrial gene expression, as well as in the expression of individual genes encoded by mitochondrial genomes are addressed, and common patterns in the species- and tissue-specific mitochondrial gene expression profiles are discussed.

Markers with segregation ratio distortion are commonly observed in data sets used for quantitative trait locus (QTL) mapping. In this study, a multipoint method of maximum likelihood (ML) was newly developed to estimate the positions and effects of the segregation distortion loci (SDLs) in two F2 populations of rice (Oryza sativa L.), i.e. Taichung65/Bhadua (TB; japonica–indica cross) and CPSLO17/W207-2 (CW; japonica–japonica). Of the four parents, W207-2 and Bhadua were found to be spikelet semi-sterile and stably inherited through selfing, and spikelet fertility segregated in the two populations. Therefore, recombination frequencies were recalculated after mapping the SDLs by using the multipoint method, and the molecular linkage maps of the two F2 populations were constructed to detect QTLs underlying spikelet fertility. As a result, five SDLs in the TB population were mapped on chromosomes 1, 3, 8 and 9, respectively. Two major QTLs underlying spikelet fertility, namely qSS-6a and qSS-8a, were detected on chromosomes 6 and 8, respectively. In the CW population, a total of 12 SDLs were detected on all 12 chromosomes except 1, 5, 7 and 11. Three QTLs underlying spikelet sterility, namely qSS-2, qSS-6b and qSS-8b on chromosomes 2, 6 and 8, were determined on the whole genome scale. Interestingly, both qSS-6a and qSS-6b, detected in the two F2 populations respectively, were located on a similar position as the S5 gene on chromosome 6; while qSS-8a and qSS-8b were also simultaneously detected on similar positions of the short arm of chromosome 8 in the two populations, which should be a new sterility gene showing the same type of zygotic selection.

Sperm competition is an important fitness component in many animal groups. Drosophila melanogaster males exhibit substantial genetic variation for sperm competitive ability and females show considerable genetic variation for first versus second male sperm use. Currently, the forces responsible for maintaining genetic variation in sperm competition related phenotypes are receiving much attention. While several candidate genes contributing to the variation seen in male competitive ability are known, genes involved in female sperm use remain largely undiscovered. Without knowledge of the underlying genes, it will be difficult to distinguish between different models of sexual selection such as cryptic female choice and sexual conflict. We used quantitative trait locus (QTL) mapping to identify regions of the genome contributing to female propensity to use first or second male sperm, female refractoriness to re-mating, and early-life fertility. The most well supported markers influencing the phenotypes include 33F/34A (P2), 57B (refractoriness) and 23F/24A (fertility). Between 10% and 15% of the phenotypic variance observed in these recombinant inbred lines was explained by these individual QTLs. More detailed investigation of the regions detected in this experiment may lead to the identification of genes responsible for the QTLs identified here.

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.