Coopération entre Optimisation Combinatoire et Statistiques pour la Sélection animale

National audienceL'objectif de cette étude est d'élaborer des modèles prédictifs permettant, à partir de données génomiques, de déterminer les individus les plus performants selon certains critères quantitatifs. L'approche proposée allie les forces des méthodes statistiques et des méthodes d'optimisation combinatoire

Feature selection for high dimensional regression using local search and statistical criteria

International audienceGenomic selection is a genetic evaluation of animals from their DNA, based on a huge number of markers covering the whole genome. It requires advanced approaches and in particular feature selection methods. Feature selection is a combinatorial problem that may be addressed by combinatorial optimization methods. We propose to combine an iterated local search (ILS) with a statistical evaluation of a multivariate regression and we compared three criteria in order to analyse their impact on the performance of the local search

Feature selection in high dimensional regression problems for genomic

International audienceIn the context of genomic selection in animal breeding, an important objective consists in looking for explicative markers for a phe- notype under study. In order to deal with a high number of markers, we propose to use combinatorial optimization to perform variable selection. Results show that our approach outperforms some classical and widely used methods on simulated and "closed to real" datasets

Combining combinatorial optimization and statistics to mine high-throughput genotyping data

National audienceDepuis quelques années, la génomique a grandement évolué avec le développement de nouvelles technologies telles que le séquençage et le génotypage haut-débit. En ce qui concerne le domaine animal, nous sommes aujourd'hui capables de lire les informations génomiques sur près de 800 000 marqueurs sur des ensembles d'individus de plus en plus larges (de 3 000 à 10 000). Ces données peuvent donner lieu à des études d'association entre les marqueurs (GWAS : Genome-Wide Association Studies). Outre les contraintes biologiques (stockage des échantillons, manipulations longues et coûteuses...), la partie analyse de données (étude et extraction de connaissances) doit aussi être adaptée en terme de méthodologie et d'architecture matérielle et logicielle. L'objectif est d'élaborer des modéles prédictifs permettant, à partir des données génomiques, de déterminer les individus les plus performants selon certains critères quantitatifs de sélection animale. Pour cela, l'objectif théorique est à terme de définir de nouvelles méthodes permettant la coopération entre statistique et optimisation combinatoire spécifiquement dédiées aux données issues de génotypage haut débit en vue d'une implémentation

Cell-to-Cell Stochastic Variation in Gene Expression Is a Complex Genetic Trait

The genetic control of common traits is rarely deterministic, with many genes contributing only to the chance of developing a given phenotype. This incomplete penetrance is poorly understood and is usually attributed to interactions between genes or interactions between genes and environmental conditions. Because many traits such as cancer can emerge from rare events happening in one or very few cells, we speculate an alternative and complementary possibility where some genotypes could facilitate these events by increasing stochastic cell-to-cell variations (or ‘noise’). As a very first step towards investigating this possibility, we studied how natural genetic variation influences the level of noise in the expression of a single gene using the yeast S. cerevisiae as a model system. Reproducible differences in noise were observed between divergent genetic backgrounds. We found that noise was highly heritable and placed under a complex genetic control. Scanning the genome, we mapped three Quantitative Trait Loci (QTL) of noise, one locus being explained by an increase in noise when transcriptional elongation was impaired. Our results suggest that the level of stochasticity in particular molecular regulations may differ between multicellular individuals depending on their genotypic background. The complex genetic architecture of noise buffering couples genetic to non-genetic robustness and provides a molecular basis to the probabilistic nature of complex traits

Raw_global BIOM file

The raw_global BIOM file was obtained in the third analytical step of the home-made bioinformatics pipeline. All the annotated OTU tables were merged into a global OTU table based on each OTU's taxonomic annotation, in which each column of this table represents a sample, and each line represents a taxon (identified by its OTU identifier in the first column, and the taxonomic annotation in the last column). Finally, this annotated OTU table was converted into a global BIOM file to obtain the raw_global BIOM file

Normalized_global BIOM file

The normalized_global BIOM file was obtained at the end of the third analytical step of the home-made bioinformatics pipeline, after using DESeq2 normalization and conversion into a full annotated and normalized global BIOM file

Home-made scripts used in the bioinformatics pipeline

Compressed file that contains the four home-made scripts used in the home-made bioinformatics pipeline: fasta_dealigner.py: Python script used in the first step of the home-made bioinformatics pipeline, that generates an unaligned FASTA file. rarefaction.R: R script (v2.14.1) used in the second step of the home-made bioinformatics pipeline, that generates intra-sample rarefaction curves. OTU_tables_format.pl: Perl script that generates OTU count tables at the end of the second step of the home-made bioinformatics pipeline. OTU_tables_merge.py: Python script that merges OTU count tables from all samples produced at the end of the second step of the home-made bioinformatics pipeline in order to produce a global OTU Table tsv file

OTU_count_tables tsv file

This compressed file contains the OTU_count_tables tsv file that is the output of the second analytical step (clustering analysis and OTU classification) of the home-made bioinformatics pipeline. It contains, for each sample, four columns: the first column is the consensus read name associated to the OTU, the second column is the OTU raw counts, the third column is the consensus read name (same as first column) and the fourth column is the associated taxon

Modèles mixtes en génétique animale : sélection de variables par optimisation combinatoire

National audienceEn sélection génomique animale, un des enjeux consiste à identifier un sous-ensemble de marqueurs génomiques explicatifs pour un trait d'intérêt quantitatif. La spécificité des études animales nécessite l'utilisation de modèles mixtes, du fait des liens de parenté entre individus. Nous proposons d'effectuer, dans ce cadre, une sélection des marqueurs d'intérêt à l'aide de méthodes d'optimisation combinatoire