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Start Over Descriptor "marqueur génomique" Topic 030304 developmental biology
2 results on '"marqueur génomique"'

1. Insertion Sequences as Highly Resolutive Genomic Markers for Sequence Type 1 Legionella pneumophila Paris

Author

Dominique Schneider, Max Maurin, Sophie Jarraud, Jean Thioulouse, Elisabeth Kay, Mike Vergnes, Christophe Ginevra, Philippe Normand, Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble] (LAPM), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS), Centre National de Référence des Légionella, Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM), Immunité infection vaccination (I2V), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-IFR128-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire d'Ecologie Microbienne - UMR 5557 (LEM), Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Ecole Nationale Vétérinaire de Lyon (ENVL)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Ecologie quantitative et évolutive des communautés, Département écologie évolutive [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Bacteriologie, BRON, Hospices Civils de Lyon (HCL), CHU Grenoble, Université Joseph Fourier - Grenoble 1 (UJF)-CHU Grenoble, French Centre National de la Recherche Scientifique, Bonus Qualité Recherche grant, University Joseph Fourier, Agence Française de Sécurité Sanitaire de l’Environnement et du Travail (AFSSET, grant EST-2007-1), French Ministry of Education and Research, Delorme, Christine, Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR128-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Ecole Nationale Vétérinaire de Lyon (ENVL), CNRS UMR 5163, Université Joseph Fourier - Grenoble 1 (UJF), Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Vétérinaire de Lyon (ENVL)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble] ( LAPM ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Centre National de la Recherche Scientifique ( CNRS ), Université Joseph Fourier - Grenoble 1 ( UJF ), Université de Lyon-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Immunité infection vaccination ( I2V ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-IFR128-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Ecologie microbienne ( EM ), Centre National de la Recherche Scientifique ( CNRS ) -Ecole Nationale Vétérinaire de Lyon ( ENVL ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique ( INRA ) -VetAgro Sup ( VAS ), Laboratoire de Biométrie et Biologie Evolutive ( LBBE ), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), Hospices Civils de Lyon ( HCL ), Université Joseph Fourier - Grenoble 1 ( UJF ) -CHU Grenoble, and Schneider, Dominique

Subjects
Microbiology (medical), Genotype, Legionella, colonisation, infection humaine, marqueur génomique, Legionella pneumophila, Microbiology, 03 medical and health sciences, eau, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Environmental Microbiology, medicine, Humans, Typing, Serotyping, Insertion sequence, 030304 developmental biology, Genetics, Molecular Epidemiology, 0303 health sciences, biology, 030306 microbiology, Genetic Variation, Bacteriology, biology.organism_classification, medicine.disease, DNA Fingerprinting, Bacterial Typing Techniques, Electrophoresis, Gel, Pulsed-Field, DNA profiling, [ SDV.BBM.GTP ] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], DNA Transposable Elements, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Legionnaires' disease, Legionnaires' Disease, Restriction fragment length polymorphism, france, Polymorphism, Restriction Fragment Length
Abstract

The causative agent of legionellosis, Legionella pneumophila , colonizes all natural and human-made water networks, thus constituting the source of contaminated aerosols responsible for airborne human infections. Efficient control of infections, especially during epidemics, necessitates the fastest and most resolutive identification possible of the bacterial source for subsequent disinfection of reservoirs. We thus compared recognized typing approaches for Legionella with a method based on characterization of insertion sequence (IS) content. A total of 86 clinical or environmental isolates of L. pneumophila , including 84 Paris isolates, sampled from 25 clinical investigations in France between 2001 and 2007, were obtained from the Legionella National Reference Center. All strains were typed by monoclonal antibody subgrouping, sequence-based typing, pulsed-field gel electrophoresis, and restriction fragment length polymorphism based on the presence or absence of IS elements. We identified six different types of IS elements in L. pneumophila Paris and used them as genomic markers in hybridization experiments. One IS type, IS Lpn11 , revealed a high discriminatory power. Simpson's index of discrimination, calculated from the distribution of IS elements, was higher than that obtained with the other typing methods used for L. pneumophila Paris. Moreover, specific IS Lpn11 copies were found only in strains isolated from particular cities. In more than half of the cases, each clinical isolate had an IS Lpn11 profile that was recovered in at least one environmental isolate from the same geographical location, suggesting that our method could identify the infection source. Phylogenetic analysis suggests a clonal expansion for the L. pneumophila Paris strain.

Published
2011
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2. Genome-Wide Prediction Methods in Highly Diverse and Heterozygous Species: Proof-of-Concept through Simulation in Grapevine

Author

Agota Fodor, Loïc Le Cunff, Philippe Chatelet, Félix Abdel Aziz Homa, Marie Denis, Thierry Lacombe, Patrice This, Samuel Neuenschwander, Vincent Segura, Alexandre Fournier-Level, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Unité de recherche Amélioration, Génétique et Physiologie Forestières (UAGPF), Institut National de la Recherche Agronomique (INRA), Department of Ecology and Evolution, Université de Lausanne (UNIL), Swiss Institute of Bioinformatics, Vital-IT, Department of Genetics, University of Melbourne, Swiss National Science Foundation grant 31003A_138180 to Dr. J. Goudet. French Ministry of Research and Higher Education and the French Ministry of Food, Agriculture and Fisheries (project CAS DAR n u10AAPIT n u1009). PhD grant from the French Grapevine and Wine Institute (IFV)., Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Unité de recherche Amélioration, Génétique et Physiologie Forestières (AGPF)

Subjects
0106 biological sciences, cartographie génomique, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Agricultural Biotechnology, marqueur génomique, Selection Markers, lcsh:Medicine, Population genetics, Breeding, 01 natural sciences, Linkage Disequilibrium, F30 - Génétique et amélioration des plantes, genomic selection, GWAS, Vitis, Statistical Analysis of Genetic Association, lcsh:Science, 2. Zero hunger, Genetics, 0303 health sciences, education.field_of_study, Multidisciplinary, Agriculture, Biodiversity, Biological Evolution, Phenotype, genome wide, association study, Quantitative Trait Association Studies, Research Article, Heterozygote, Marker-Assisted Selection, Quantitative Trait Loci, Population, Genomics, Context (language use), Biotechnologies, Computational biology, Biology, Quantitative trait locus, Research and Analysis Methods, 03 medical and health sciences, vitis vinifera, Quantitative Trait, Heritable, Genome-Wide Association Studies, valeur phénotypique, Computer Simulation, donnée génomique, sélection génomique, Selection, Genetic, Family-Based Association Studies, Molecular Biology Techniques, education, Molecular Biology, Genetic Association Studies, Selection (genetic algorithm), 030304 developmental biology, Genetic diversity, Models, Statistical, Models, Genetic, lcsh:R, Reproducibility of Results, Biology and Life Sciences, Computational Biology, étude d'association pangénomique, Marker Genes, Genome Analysis, Genetic architecture, modèle prédictif, détection de qtl, lcsh:Q, Genome-Wide Association Study, 010606 plant biology & botany
Abstract

INRA UMR 1334 AGAP, Equipe DAVEM = Diversité et Adaptation de la Vigne et des Espèces Méditerranéennes; Nowadays, genome-wide association studies (GWAS) and genomic selection (GS) methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9) using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.

Published
2014
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