Drosophila melanogaster, like other organisms, move and orient themselves in response to the earth's gravitational force. The ability to sense and respond to gravity is essential for an organism to navigate and thrive in its environment. The genes underlying this behaviour in Drosophila remain elusive. Using 88 recombinant inbred lines, we have identified four quantitative trait loci (QTLs) that contribute to adult gravitaxis (geotaxis) behaviour in Drosophila. Candidate genes of interest were selected from the QTLs of highest significance based on their function in chordotonal organ formation. Quantitative complementation tests with these candidate genes revealed a role for skittles in adult gravitaxis behaviour in D. melanogaster.

In order to document further the phenomena of variance in reproductive success in natural populations of the European flat oyster Ostrea edulis, two complementary studies based on natural and experimental populations were conducted. The first part of this work was focused on paternity analyses using a set of four microsatellite markers for larvae collected from 13 brooding females sampled in Quiberon Bay (Brittany, France). The number of individuals contributing as the male parent to each progeny assay was highly variable, ranging from 2 to more than 40. Moreover, paternal contributions showed a much skewed distribution, with some males contributing to 50–100% of the progeny assay. The second part of this work consisted of the analysis of six successive cohorts experimentally produced from an acclimated broodstock (62 wild oysters sampled in the Quiberon Bay). Allelic richness was significantly higher in the adult population than in the temporal cohorts collected. Genetic differentiation (Fst estimates) was computed for each pair of samples and all significant values ranged from 0·7 to 11·9%. A limited effective number of breeders (generally below 25) was estimated in the six temporal cohorts. The study gives first indications of the high variance in reproductive success as well as a reduced effective size, not only under experimental conditions but also in the wild. Surprisingly, the pool of the successive cohorts, based on the low number of loci used, appeared to depict a random and representative set of alleles of the progenitor population, indicating that the detection of patterns of temporal genetic differentiation at a local scale most likely depends on the sampling window.

We analysed the family linkage data obtained from short tandem repeat (STR) genotyping of 212 unrelated Indian families having a single Down syndrome (DS) baby each, in order to explore the incidence and aetiology of this human aneuploidy in our cohort. The estimated values of maternal meiotic I and meiotic II non-disjunction (NDJ) errors of chromosome 21 (Ch 21) were ~78 and ~22%, respectively. Within the paternal outcome group, about 47 and 53% were accounted for NDJ at meiosis I and meiosis II, respectively. We estimated only ~2% post-zygotic mitotic errors. The comparison of average age of conception between controls and DS-bearing mothers revealed a significant difference (P<0·001) with DS-bearing women were on an average older than controls and meiotic II non-disjoined mothers were oldest among meiotic outcome groups. Our linkage analysis suggested an overall reduction in recombination by more than 50% on meiotic I non-disjoined maternal Ch 21 with error prone to susceptible chiasma formation within the ~5·1 kbp segment near the telomeric end. We stratified meiotic I non-disjoined women in three age groups, viz. young (⩽28 years), middle (29–34 years) and old (⩾35 years) and found linear decrease in the frequency of achiasmate meiosis from the young to the old group. In contrary, a linear increase in the multiple chiasma frequency from the young to the old group was observed. Considering these results together, we propose that the risk factors for Ch 21 NDJ are of two types, one being ‘maternal age-independent’ and the other being ‘maternal age-dependent’. Moreover, a comparison of our present Indian dataset with that of other published data of ethnically different populations suggested that the genetics that underlies the NDJ of Ch 21 is probably universal irrespective of racial difference across human populations. The present study is the first population-based report on any DS cohort from the Indian subcontinent and our work will help future workers in understanding better the aetiology of this birth defect.

Isolated founder populations which exhibit great genetic and environmental homogeneity provide an attractive setting for the study of quantitative traits (QTs). Geneticists have repeatedly turned to population isolates and the past successes have prompted increased interest among medical researchers. We studied nine small isolated villages of a secluded area of Sardinia (Ogliastra), all of them characterized by a few founders, high endogamy rates, slow population expansion and a distinct genetic makeup. Anthropometric and blood parameters, 43 QTs in all, were analysed in about 9000 voluntary subjects for whom extended genealogical information was available. We explored the distribution and examined mean differences of each trait among villages by analysis of variance (ANOVA). A heritability analysis with the variance component (VC) method was performed. Results show significant differences in the distribution of most traits between groups of villages located in two distinct geographical areas already identified by a previous population structure analysis, thus supporting the existence of differentiation among sub-populations in the same region. Heritability estimates range between 30 and 89%, demonstrating that genetic effects substantially contribute to phenotypic variation of all investigated traits and that this population provides excellent research conditions for gene-mapping projects. Results suggest that history, geographic location and population structure may have influenced the genetic and phenotypic features of these isolates. Our findings may be useful for the ongoing linkage and association studies in these isolates and suggest that a thorough characterization of population is valuable to better identify genes or variants that may be rare in the population at large and peculiar to single villages.

A challenge when predicting total genetic values for complex quantitative traits is that an unknown number of quantitative trait loci may affect phenotypes via cryptic interactions. If markers are available, assuming that their effects on phenotypes are additive may lead to poor predictive ability. Non-parametric radial basis function (RBF) regression, which does not assume a particular form of the genotype–phenotype relationship, was investigated here by simulation and analysis of body weight and food conversion rate data in broilers. The simulation included a toy example in which an arbitrary non-linear genotype–phenotype relationship was assumed, and five different scenarios representing different broad sense heritability levels (0·1, 0·25, 0·5, 0·75 and 0·9) were created. In addition, a whole genome simulation was carried out, in which three different gene action modes (pure additive, additive+dominance and pure epistasis) were considered. In all analyses, a training set was used to fit the model and a testing set was used to evaluate predictive performance. The latter was measured by correlation and predictive mean-squared error (PMSE) on the testing data. For comparison, a linear additive model known as Bayes A was used as benchmark. Two RBF models with single nucleotide polymorphism (SNP)-specific (RBF I) and common (RBF II) weights were examined. Results indicated that, in the presence of complex genotype–phenotype relationships (i.e. non-linearity and non-additivity), RBF outperformed Bayes A in predicting total genetic values using SNP markers. Extension of Bayes A to include all additive, dominance and epistatic effects could improve its prediction accuracy. RBF I was generally better than RBF II, and was able to identify relevant SNPs in the toy example.

The L2-Boosting algorithm is one of the most promising machine-learning techniques that has appeared in recent decades. It may be applied to high-dimensional problems such as whole-genome studies, and it is relatively simple from a computational point of view. In this study, we used this algorithm in a genomic selection context to make predictions of yet to be observed outcomes. Two data sets were used: (1) productive lifetime predicted transmitting abilities from 4702 Holstein sires genotyped for 32 611 single nucleotide polymorphisms (SNPs) derived from the Illumina® BovineSNP50 BeadChip, and (2) progeny averages of food conversion rate, pre-corrected by environmental and mate effects, in 394 broilers genotyped for 3481 SNPs. Each of these data sets was split into training and testing sets, the latter comprising dairy or broiler sires whose ancestors were in the training set. Two weak learners, ordinary least squares (OLS) and non-parametric (NP) regression were used for the L2-Boosting algorithm, to provide a stringent evaluation of the procedure. This algorithm was compared with BL [Bayesian LASSO (least absolute shrinkage and selection operator)] and BayesA regression. Learning tasks were carried out in the training set, whereas validation of the models was performed in the testing set. Pearson correlations between predicted and observed responses in the dairy cattle (broiler) data set were 0·65 (0·33), 0·53 (0·37), 0·66 (0·26) and 0·63 (0·27) for OLS-Boosting, NP-Boosting, BL and BayesA, respectively. The smallest bias and mean-squared errors (MSEs) were obtained with OLS-Boosting in both the dairy cattle (0·08 and 1·08, respectively) and broiler (−0·011 and 0·006) data sets, respectively. In the dairy cattle data set, the BL was more accurate (bias=0·10 and MSE=1·10) than BayesA (bias=1·26 and MSE=2·81), whereas no differences between these two methods were found in the broiler data set. L2-Boosting with a suitable learner was found to be a competitive alternative for genomic selection applications, providing high accuracy and low bias in genomic-assisted evaluations with a relatively short computational time.

Quantitative trait loci (QTLs) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for correlation among the multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. In this paper, we developed a Bayesian multiple QTL mapping method for causally related traits using a mixture structural equation model (SEM), which allows researchers to decompose QTL effects into direct, indirect and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis–Hasting algorithm. The number of QTLs affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of QTL detection, accuracy and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.