Survival from birth to a reproductive adult is a challenge that only robust individuals resistant to a variety of mortality factors will overcome. To assess whether survival traits share genetic architecture throughout the life cycle, we estimated genetic correlations for survival within fingerling stage, and across egg, fingerling and grow-out stages in farmed rainbow trout. Genetic parameters of survival at three life cycle stages were estimated for 249 166 individuals originating from ten year classes of a pedigreed population. Despite being an important fitness component, survival traits harboured significant but modest amount of genetic variation (h2=0·07–0·27). Weak associations between survival during egg-fry and fingerling periods, between early and late fingerling periods (rG=0·30) and generally low genetic correlations between fingerling and grow-out survival (mean rG=0·06) suggested that life-stage specific survival traits are best regarded as separate traits. However, in the sub-set of data with detailed time of death records, positive genetic correlations between early and late fingerling survival (rG=0·89) showed that during certain years the best genotypes in the early period were also among the best in the late period. That survival across fingerling period can be genetically the same, trait was indicated also by only slightly higher heritability (h2=0·15) estimated with the survival analysis of time to death during fingerling period compared to the analysis treating fingerling survival as a binary character (h2=0·11). The results imply that (1) inherited resistance against unknown mortality factors exists, but (2) ranking of genotypes changes across life stages.

We proposed hierarchical Poisson and binomial models for mapping multiple interacting quantitative trait loci (QTLs) for count traits in experimental crosses. We applied our methods to two counted reproductive traits, live fetuses (LF) and dead fetuses (DF) at 17 days gestation, in an F2 female mouse population. We treated observed number of corpora lutea (ovulation rate) as the baseline and the total trials in our Poisson and binomial models, respectively. We detected more than 10 QTLs for LF and DF, most having epistatic and pleiotropic effects. The epistatic effects were larger, involved more QTLs, and explained a larger proportion of phenotypic variance than the main effects. Our analyses revealed a complex network of multiple interacting QTLs for the reproductive traits, and increase our understanding of the genetic architecture of reproductive characters. The proposed statistical models and methods provide valuable tools for detecting multiple interacting QTLs for complex count phenotypes.

The genus Drosophila is diverse and heterogeneous and contains a large number of easy-to-rear species, so it is an attractive subject for comparative studies. The ability to perform such studies is currently compromised by the lack of a comprehensive phylogeny for Drosophila and related genera. The genus Drosophila as currently defined is known to be paraphyletic with respect to several other genera, but considerable uncertainty remains about other aspects of the phylogeny. Here, we estimate a phylogeny for 176 drosophilid (12 genera) and four non-drosophilid species, using gene sequences for up to 13 different genes per species (average: 4333 bp, five genes per species). This is the most extensive set of molecular data on drosophilids yet analysed. Phylogenetic analyses were conducted with maximum-likelihood (ML) and Bayesian approaches. Our analysis confirms that the genus Drosophila is paraphyletic with 100% support in the Bayesian analysis and 90% bootstrap support in the ML analysis. The subgenus Sophophora, which includes Drosophila melanogaster, is the sister clade of all the other subgenera as well as of most species of six other genera. This sister clade contains two large, well-supported subclades. The first subclade contains the Hawaiian Drosophila, the genus Scaptomyza, and the virilis-repleta radiation. The second contains the immigrans-tripunctata radiation as well as the genera Hirtodrosophila (except Hirtodrosophila duncani), Mycodrosophila, Zaprionus and Liodrosophila. We argue that these results support a taxonomic revision of the genus Drosophila.

Increasingly researchers are conducting quantitative trait locus (QTL) mapping in metabolomics and proteomics studies. These data often are distributed as a point-mass mixture, consisting of a spike at zero in combination with continuous non-negative measurements. Composite interval mapping (CIM) is a common method used to map QTL that has been developed only for normally distributed or binary data. Here we propose a two-part CIM method for identifying QTLs when the phenotype is distributed as a point-mass mixture. We compare our new method with existing normal and binary CIM methods through an analysis of metabolomics data from Arabidopsis thaliana. We then conduct a simulation study to further understand the power and error rate of our two-part CIM method relative to normal and binary CIM methods. Our results show that the two-part CIM has greater power and a lower false positive rate than the other methods when a continuous phenotype is measured with many zero observations.

As an emerging powerful approach for mapping quantitative trait loci (QTLs) responsible for dynamic traits, functional mapping models the time-dependent mean vector with biologically meaningful equations and are likely to generate biologically relevant and interpretable results. Given the autocorrelation nature of a dynamic trait, functional mapping needs the implementation of the models for the structure of the covariance matrix. In this article, we have provided a comprehensive set of approaches for modelling the covariance structure and incorporated each of these approaches into the framework of functional mapping. The Bayesian information criterion (BIC) values are used as a model selection criterion to choose the optimal combination of the submodels for the mean vector and covariance structure. In an example for leaf age growth from a rice molecular genetic project, the best submodel combination was found between the Gaussian model for the correlation structure, power equation of order 1 for the variance and the power curve for the mean vector. Under this combination, several significant QTLs for leaf age growth trajectories were detected on different chromosomes. Our model can be well used to study the genetic architecture of dynamic traits of agricultural values.

Epistasis underlying fertility plays an important role in crop breeding. Although a new female sterile mutant in wheat, XND126, has been identified and a major quantitative trait locus (QTL), taf1, for the female sterility has been mapped, the genetic architecture of the female sterility needs to be further addressed. To identify the interaction involving the gene(s) controlling the female sterility, an investigation was carried out for the seed setting ratio in an F2 population derived from the cross between XND126 and Gaocheng 8901. Among 1250 simple sequence repeat (SSR) primer pairs in the whole genome, a total of 21 markers, obtained by recessive class approach, along with other ten tightly linked markers on reference maps in wheat, were used to survey 243 F2 individuals. As a result, 28 markers were mapped into five genetic linkage groups. The performance for female sterility for each F2 individual was evaluated simultaneously at the Urumqi and Huai'an experimental stations in 2006–2007. The two phenotypic datasets along with marker information were jointly analysed in the detection of QTL using penalized maximum likelihood approach. A total of six QTLs, including two main-effect QTLs, three epistatic QTLs and one environmental interaction and accounting for 0·67–24·55% of the total phenotypic variance, were identified. All estimated effects accounted for 53·26% of the total phenotypic variation. The taf1 detected in previous study was also located on the same marker interval on chromosome 2DS. These results enrich our understanding of the genetic basis of the female sterility.