What is a good (gene) network?

Chantier qualité GAInternational audienceWhat is a good (gene) network

An integrated method to analyze farm vulnerability to climatic and economic variability according to farm configurations and farmers' adaptations

The need to adapt to decrease farm vulnerability to adverse contextual events has been extensively discussed on a theoretical basis. We developed an integrated and operational method to assess farm vulnerability to multiple and interacting contextual changes and explain how this vulnerability can best be reduced according to farm configurations and farmers' technical adaptations over time. Our method considers farm vulnerability as a function of the raw measurements of vulnerability variables (e.g., economic efficiency of production), the slope of the linear regression of these measurements over time, and the residuals of this linear regression. The last two are extracted from linear mixed models considering a random regression coefficient (an intercept common to all farms), a global trend (a slope common to all farms), a random deviation from the general mean for each farm, and a random deviation from the general trend for each farm. Among all possible combinations, the lowest farm vulnerability is obtained through a combination of high values of measurements, a stable or increasing trend and low variability for all vulnerability variables considered. Our method enables relating the measurements, trends and residuals of vulnerability variables to explanatory variables that illustrate farm exposure to climatic and economic variability, initial farm configurations and farmers' technical adaptations over time. We applied our method to 19 cattle (beef, dairy, and mixed) farms over the period 20082013. Selected vulnerability variables, i.e., farm productivity and economic efficiency, varied greatly among cattle farms and across years, with means ranging from 43.0 to 270.0 kg protein/ha and 29.4-66.0% efficiency, respectively. No farm had a high level, stable or increasing trend and low residuals for both farm productivity and economic efficiency of production. Thus, the least vulnerable farms represented a compromise among measurement value, trend, and variability of both performances. No specific combination of farmers' practices emerged for reducing cattle farm vulnerability to climatic and economic variability. In the least vulnerable farms, the practices implemented (stocking rate, input use ...) were more consistent with the objective of developing the properties targeted (efficiency, robustness ...). Our method can be used to support farmers with sector-specific and local insights about most promising farm adaptations

Prédiction phénotypique et sélection de variables en grande dimension dans les modèles linéaires et linéaires mixtes

Les nouvelles technologies permettent l'acquisition de données génomiques et post-génomiques de grande dimension, c'est-à-dire des données pour lesquelles il y a toujours un plus grand nombre de variables mesurées que d'individus sur lesquels on les mesure. Ces données nécessitent généralement des hypothèses supplémentaires afin de pouvoir être analysées, comme une hypothèse de parcimonie pour laquelle peu de variables sont supposées influentes. C'est dans ce contexte de grande dimension que nous avons travaillé sur des données réelles issues de l espèce porcine et de la technologie haut-débit, plus particulièrement le métabolome obtenu à partir de la spectrométrie RMN et des phénotypes mesurés post-mortem pour la plupart. L'objectif est double : d'une part la prédiction de phénotypes d intérêt pour la production porcine et d'autre part l'explicitation de relations biologiques entre ces phénotypes et le métabolome. On montre, grâce à une analyse dans le modèle linéaire effectuée avec la méthode Lasso, que le métabolome a un pouvoir prédictif non négligeable pour certains phénotypes importants pour la production porcine comme le taux de muscle et la consommation moyenne journalière. Le deuxième objectif est traité grâce au domaine statistique de la sélection de variables. Les méthodes classiques telles que la méthode Lasso et la procédure FDR sont investiguées et de nouvelles méthodes plus performantes sont développées : nous proposons une méthode de sélection de variables en modèle linéaire basée sur des tests d'hypothèses multiples. Cette méthode possède des résultats non asymptotiques de puissance sous certaines conditions sur le signal. De part les données annexes disponibles sur les animaux telles que les lots dans lesquels ils ont évolués ou les relations de parentés qu'ils possèdent, les modèles mixtes sont considérés. Un nouvel algorithme de sélection d'effets fixes est développé et il s'avère beaucoup plus rapide que les algorithmes existants qui ont le même objectif. Grâce à sa décomposition en étapes distinctes, l algorithme peut être combiné à toutes les méthodes de sélection de variables développées pour le modèle linéaire classique. Toutefois, les résultats de convergence dépendent de la méthode utilisée. On montre que la combinaison de cet algorithme avec la méthode de tests multiples donne de très bons résultats empiriques. Toutes ces méthodes sont appliquées au jeu de données réelles et des relations biologiques sont mises en évidenceRecent technologies have provided scientists with genomics and post-genomics high-dimensional data; there are always more variables that are measured than the number of individuals. These high dimensional datasets usually need additional assumptions in order to be analyzed, such as a sparsity condition which means that only a small subset of the variables are supposed to be relevant. In this high-dimensional context we worked on a real dataset which comes from the pig species and high-throughput biotechnologies. Metabolomic data has been measured with NMR spectroscopy and phenotypic data has been mainly obtained post-mortem. There are two objectives. On one hand, we aim at obtaining good prediction for the production phenotypes and on the other hand we want to pinpoint metabolomic data that explain the phenotype under study. Thanks to the Lasso method applied in a linear model, we show that metabolomic data has a real prediction power for some important phenotypes for livestock production, such as a lean meat percentage and the daily food consumption. The second objective is a problem of variable selection. Classic statistical tools such as the Lasso method or the FDR procedure are investigated and new powerful methods are developed. We propose a variable selection method based on multiple hypotheses testing. This procedure is designed to perform in linear models and non asymptotic results are given under a condition on the signal. Since supplemental data are available on the real dataset such as the batch or the family relationships between the animals, linear mixed models are considered. A new algorithm for fixed effects selection is developed, and this algorithm turned out to be faster than the usual ones. Thanks to its structure, it can be combined with any variable selection methods built for linear models. However, the convergence property of this algorithm depends on the method that is used. The multiple hypotheses testing procedure shows good empirical results. All the mentioned methods are applied to the real data and biological relationships are emphasizedTOULOUSE-INSA-Bib. electronique (315559905) / SudocSudocFranceF

L'analyse d'un réseau de co-expression génique met en valeur des groupes fonctionnels homogènes et des gènes importants relatifs a un phénotype d'intérêt

National audienceCet article présente l'analyse d'un réseau de co-expression entre gènes dont la particularité est d'être régulés génétiquement. Cette étude est menée selon deux axes : une classification des gènes impliqués dans le réseau permet de mettre en valeur des groupes fonctionnels homogènes. Par ailleurs, une analyse conjointe du réseau et d'un phénotype d'intérêt permet de mettre en évidence des gènes candidats importants

The structure of a gene network reveals 7 biological sub-graphs underlying eQTLs in pig

International audienceIntegrative and system biology is a very promising tool for deciphering the biological and genetic mechanisms underlying complex traits. Transcriptomic analyses, in combination with genomic polymorphism, for instance, can give interesting insight on the genetic control of gene expression (eQTL studies). When hundreds of genes are detected with a link between their expression and some genetic polymorphisms (eQTL), the following question raises: what are the biological underlying functions? One tool is the use of a gene network, displaying interactions between genes with a genetic control (having at least an eQTL). There exist several possibilities for inferring a gene network: literature mining (using softwares such as Ingenuity) or inference from gene expression data. Although the first framework is a useful tool, it has some limitations: there is still a serious problem of lack of annotation in the pig genome, and a bias in information provided by Ingenuity (literature mainly devoted to Human, Mouse and Rat). We will hence explore in this work the inference of gene network from expression data. One simple method of inference was focused on, that has proven useful: Gaussian networks (Schäfer and Strimmer 2005). The following problem to be faced is the interpretation of such a "large" network (more than 100 genes). The aim of this study is to propose an adequate method for deciphering the structure of large gene networks. With the use of a good clustering of graph, the structure of one graph can be highlighted, and can reveal several sub graphs, each corresponding to particular biological functions

Biodiversity of pig breeds from China and Europe estimated from pooled DNA samples: differences in microsatellite variation between two areas of domestication

Microsatellite diversity in European and Chinese pigs was assessed using a pooled sampling method on 52 European and 46 Chinese pig populations. A Neighbor Joining analysis on genetic distances revealed that European breeds were grouped together and showed little evidence for geographic structure, although a southern European and English group could tentatively be assigned. Populations from international breeds formed breed specific clusters. The Chinese breeds formed a second major group, with the Sino-European synthetic Tia Meslan in-between the two large clusters. Within Chinese breeds, in contrast to the European pigs, a large degree of geographic structure was noted, in line with previous classification schemes for Chinese pigs that were based on morphology and geography. The Northern Chinese breeds were most similar to the European breeds. Although some overlap exists, Chinese breeds showed a higher average degree of heterozygosity and genetic distance compared to European ones. Between breed diversity was even more pronounced and was the highest in the Central Chinese pigs, reflecting the geographically central position in China. Comparing correlations between genetic distance and heterozygosity revealed that China and Europe represent different domestication or breed formation processes. A likely cause is a more diverse wild boar population in Asia, but various other possible contributing factors are discussed

Late Fetal Blood Transcriptomic Approach To Get Insight Into Biology Related To Birth Survival

In recent decades, improvement of prolificacy and body composition has been accompanied by a substantial increase in the mortality of piglets before weaning. The most critical period is the perinatal period, mostly during the first 24-48 hours following birth. The maturity of piglets, defined as the state of full development for survival at birth, is an important determinant of early mortality. The objective of our project is to take advantage of current knowledge about two pig breeds, Large White (LW) pigs selected for prolificacy and body composition and Meishan (MS) pigs being more robust. Maturity of several tissues and metabolite profiles of various fluids are analyzed on the fetuses (LW, MS and reciprocal F1) at day 90 or 110 of gestation (birth at day 114). Here we presented the transcriptomic analysis done on total blood samples (N=63). We did two different statistical analyses, a supervised one to reveal differential pathways for the interaction between gestational stages and genotypes and an unsupervised analysis (hclust and differential analyses) to identify potential predictors of a lesser maturity at birth. All p-values were adjusted with a Bonferroni correction < 1%. The 265 genes differential for the interaction (Bonferroni 1%) in blood samples revealed many genes for mitochondrial ATP synthesis, transcriptional regulation, and response to hypoxia (overexpressed in LW at day 110 of gestation)

Phenotypic prediction based on metabolomic data : lasso vs Bolasso, primary data vs wavelet transformation

International audienceUnderstanding the relations between various 'omics data (such as metabolomics or genomics data) and phenotypes of interest is one of the current major challenges in biology. This question can be addressed by trying to learn a way to predict the phenotype value from the omic from joint observations of the omic and of the phenotype. In this paper, we focus on the prediction of a phenotype related to the quality of the meat from metabolomic data. As metabolomic data are high dimensional data and as, conjointly, the number of observations is often restricted, model selection methods are a way both to obtain a relevant solution to the prediction problem but also to select the most important metabolomes related to the phenotype under study. During the past years, model selection has know a growing interest in the statistical community: the first - and also probably the most known - selection method has been introducted by \citep{Tibshirani:1996} under the name of LASSO. Several variants of this original approach has then been proposed such as, recently, a bootstraped LASSO, named BOLASSO, introduced in (Bach, 2009). The proposal of this paper is to combine a wavelet representation of the metabolome spectra (see (Mallat, 1999) and (Antonini, 1992) for a complete introduction to wavelets) with the BOLASSO approach. We compare this methodology to more classical methods using either the original spectra as predictors (instead of the wavelet representation) or the original LASSO to select the model. The following section deals with the methodological description of the approach whereas the next one details the experiments and results

Farmers compose with ecosystem services and disservices for managing on-farm trees: insights from a French case study

Rural forests, i.e. farm forests and trees outside forests (TOF), are part of traditional agroforestry systems in many European regions. Yet, the industrialization of agriculture has induced the decline of rural forests and promoted a physical and functional separation between trees and agriculture. Despite the recent promotion of TOF in the Common Agriculture Policy (CAP), most farmers do not reinforce them in their farms. In order to understand farmers’ attitudes towards rural forests, we conducted 19 face-to-face interviews in southwestern France. Farmers identified 32 positive contributions, including 29 ecosystem services (ES), associated with rural forests. Similarly, they emphasized 25 negative contributions, including 21 ecosystem disservices (EDS). Contributions varied with the type of forested area. For instance, hedgerows had high levels of positive and negative contributions, while woods had high levels of positive and low levels of negative contributions. Finally, farmers identified 19 stakeholders and institutions, especially the CAP, that influenced rural forest management. In focusing on the balance between positive and negative contributions, our study enabled us to uncover the complex rationale of local rural forest management. Ecosystem disservices and CAP policies tended to discourage farmers to reinforce rural forests in their farms. Taking into account farmers’ rationale and perceptions may give invaluable information to better target public policies