Aggressiveness of eight Venturia inaequalis isolates virulent or avirulent to the major resistance gene Rvi6 on a non-Rvi6 apple cultivar

For sustainable management of scab-resistant apple cultivars, it is necessary to understand the role of aggressiveness in the adaptation of Venturia inaequalis populations and particularly the costs to the organism of acquiring additional virulence. The aims of the present study were (i) to identify the quantitative variables that are most important in determining the differences in aggressiveness among groups of V. inaequalis isolates, and (ii) to ascertain whether virulent and avirulent isolates of V. inaequalis differ significantly in aggressiveness. The aggressiveness of eight isolates that differed in their virulence to the major resistance gene Rvi6 was compared on the non-Rvi6 apple cv. Gala. Three components of aggressiveness, namely lesion density, the number of spores per square centimetre of leaf area, and the number of spores per lesion, were evaluated 21 days after inoculation, and the kinetics of lesion density over time were analysed in terms of maximum lesion density, length of latent period and rate of lesion appearance. On the second youngest but fully developed leaf at the time of inoculation, maximum lesion density in the virulent group was 20% lower and the latent period 7% longer, than in the avirulent group. However, the alternative hypothesis, namely that isolates had adapted to quantitative resistance present in cv. Gala depending on their cultivar of origin, could not be rejected. The analysis of the kinetics of lesion density by a non-linear mixed-effect model proved useful in the assessment of aggressiveness

Implicit responses in the judgment of attractiveness in faces with differing levels of makeup

Makeup is a form of body art which has been used for more than 7,000 years and is present in the great majority of human cultures, often used to enhance facial attractiveness and to accentuate features that represent femininity. This study examines how cumulative levels of facial makeup influenced approach and avoidance tendencies and on facial muscle responses associated with emotional response obtained through facial electromyography (EMG) in a passive viewing task. Experiment 1 used the joystick variant of the approach-avoidance task, where 30 subjects categorized female faces by visual orientation (portrait/landscape) in seven cumulatively added makeup levels. In Experiment 2, facial EMG was recorded from 40 subjects in the passive viewing of the same images. The present study shows that makeup application modulates implicit responses and reveals two distinct implicit preferences, behavioral and affective, with a male behavioral preference for heavy eye cosmetics, a female behavioral preference for light makeup, and an overall affective preference in both men and women for makeup accentuating visual contrast in the eye and mouth regions. These results are consistent with the conception that perceptual cues underlying cosmetic enhancement are key determinants in aesthetic facial preferences

A factor model to analyze heterogeneity in gene expression

<p>Abstract</p> <p>Background</p> <p>Microarray technology allows the simultaneous analysis of thousands of genes within a single experiment. Significance analyses of transcriptomic data ignore the gene dependence structure. This leads to correlation among test statistics which affects a strong control of the false discovery proportion. A recent method called FAMT allows capturing the gene dependence into factors in order to improve high-dimensional multiple testing procedures. In the subsequent analyses aiming at a functional characterization of the differentially expressed genes, our study shows how these factors can be used both to identify the components of expression heterogeneity and to give more insight into the underlying biological processes.</p> <p>Results</p> <p>The use of factors to characterize simple patterns of heterogeneity is first demonstrated on illustrative gene expression data sets. An expression data set primarily generated to map QTL for fatness in chickens is then analyzed. Contrarily to the analysis based on the raw data, a relevant functional information about a QTL region is revealed by factor-adjustment of the gene expressions. Additionally, the interpretation of the independent factors regarding known information about both experimental design and genes shows that some factors may have different and complex origins.</p> <p>Conclusions</p> <p>As biological information and technological biases are identified in what was before simply considered as statistical noise, analyzing heterogeneity in gene expression yields a new point of view on transcriptomic data.</p

Complex trait subtypes identification using transcriptome profiling reveals an interaction between two QTL affecting adiposity in chicken

<p>Abstract</p> <p>Background</p> <p>Integrative genomics approaches that combine genotyping and transcriptome profiling in segregating populations have been developed to dissect complex traits. The most common approach is to identify genes whose eQTL colocalize with QTL of interest, providing new functional hypothesis about the causative mutation. Another approach includes defining subtypes for a complex trait using transcriptome profiles and then performing QTL mapping using some of these subtypes. This approach can refine some QTL and reveal new ones.</p> <p>In this paper we introduce Factor Analysis for Multiple Testing (FAMT) to define subtypes more accurately and reveal interaction between QTL affecting the same trait. The data used concern hepatic transcriptome profiles for 45 half sib male chicken of a sire known to be heterozygous for a QTL affecting abdominal fatness (AF) on chromosome 5 distal region around 168 cM.</p> <p>Results</p> <p>Using this methodology which accounts for hidden dependence structure among phenotypes, we identified 688 genes that are significantly correlated to the AF trait and we distinguished 5 subtypes for AF trait, which are not observed with gene lists obtained by classical approaches. After exclusion of one of the two lean bird subtypes, linkage analysis revealed a previously undetected QTL on chromosome 5 around 100 cM. Interestingly, the animals of this subtype presented the same q paternal haplotype at the 168 cM QTL. This result strongly suggests that the two QTL are in interaction. In other words, the "q configuration" at the 168 cM QTL could hide the QTL existence in the proximal region at 100 cM. We further show that the proximal QTL interacts with the previous one detected on the chromosome 5 distal region.</p> <p>Conclusion</p> <p>Our results demonstrate that stratifying genetic population by molecular phenotypes followed by QTL analysis on various subtypes can lead to identification of novel and interacting QTL.</p

Optimal sampling from concomitant variables for regression problems

International audienc

Exact Distribution of the Regression Estimator in Double Sampling

International audienc

Prediction sur matrices de distances

National audienc

Linear Regression Models under Conditional Independence Restrictions

Maximum likelihood estimation is investigated in the context of linear regression models under partial independence restrictions. These restrictions aim to assume a kind of completeness of a set of predictors "Z" in the sense that they are sufficient to explain the dependencies between an outcome "Y" and predictors "X": L ("Y"|"Z", "X") =  L("Y"|"Z"), where L (·|·) stands for the conditional distribution. From a practical point of view, the former model is particularly interesting in a double sampling scheme where "Y" and "Z" are measured together on a first sample and "Z" and "X" on a second separate sample. In that case, estimation procedures are close to those developed in the study of double-regression by Engel & Walstra (1991) and Causeur & Dhorne (1998). Properties of the estimators are derived in a small sample framework and in an asymptotic one, and the procedure is illustrated by an example from the food industry context. Copyright 2003 Board of the Foundation of the Scandinavian Journal of Statistics..