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1. High throughput genotyping of structural variations in a complex plant genome using an original Affymetrix® axiom® array

Author

Sébastien Praud, Hélène Rimbert, Jorge Duarte, Jean-Philippe Pichon, Clémentine Vitte, Aude Darracq, Ali Pirani, Clément Mabire, Stéphane Nicolas, Nathalie Rivière, Valérie Combes, Johann Joets, Delphine Madur, Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Paris-Sud - Paris 11 (UP11)-Institut National de la Recherche Agronomique (INRA), BIOGEMMA, Biogemma Clermont-Ferrand, Génétique et évolution des populations végétales (GEPV), Université de Lille, Sciences et Technologies-Centre National de la Recherche Scientifique (CNRS), Affymetrix, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS), Biogemma, Laboratoire des interactions plantes micro-organismes (LIPM), Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche de Chappes, Thermofisher Scientific, CNVMaize, Amaizing, Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), France Agrimer program CNV4sel Plant Biology and Breeding department of the French National Institute for Agricultural Research (INRA), ANR-10-GENM-0003,CNV-MAIZE,Étude d'association sur génome entier entre variation structurale, variation des caractères d'intérêt agronomique et hétérosis chez le maïs(2010), ANR-10-BTBR-0001,AMAIZING,Développer de nouvelles variétés de maïs pour une agriculture durable: une approche intégrée de la génomique à la sélection(2010), and Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)

Subjects
0106 biological sciences, Genotyping, lcsh:QH426-470, Genotyping Techniques, Chromosomal rearrangements, lcsh:Biotechnology, [SDV]Life Sciences [q-bio], Single-nucleotide polymorphism, Computational biology, Biology, Affymetrix® Axiom®, 01 natural sciences, Genome, Zea mays, [SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics, 03 medical and health sciences, INDEL Mutation, Present absent variation, lcsh:TP248.13-248.65, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Genotype, SNP, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Association mapping, Indel, High throughput genotyping of structural variations, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Oligonucleotide Array Sequence Analysis, 2. Zero hunger, 0303 health sciences, Genome assembly, Copy number variation, Methodology Article, Breakpoint, Array, High-Throughput Nucleotide Sequencing, food and beverages, lcsh:Genetics, Nucleic Acid Probes, complex plant genome, [SDE]Environmental Sciences, Structural variation, Genome, Plant, 010606 plant biology & botany
Abstract

BackgroundInsertions/deletions (InDels) and more specifically presence/absence variations (PAVs) are pervasive in several species and have strong functional and phenotypic effect by removing or drastically modifying genes. Genotyping of such variants on large panels remains poorly addressed, while necessary for approaches such as association mapping or genomic selection.ResultsWe have developed, as a proof of concept, a new high-throughput and affordable approach to genotype InDels. We first identified 141,000 InDels by aligning reads from the B73 line against the genome of three temperate maize inbred lines (F2, PH207, and C103) and reciprocally. Next, we designed an Affymetrix® Axiom® array to target these InDels, with a combination of probes selected at breakpoint sites (13%) or within the InDel sequence, either at polymorphic (25%) or non-polymorphic sites (63%) sites. The final array design is composed of 662,772 probes and targets 105,927 InDels, including PAVs ranging from 35bp to 129kbp. After Affymetrix® quality control, we successfully genotyped 86,648 polymorphic InDels (82% of all InDels interrogated by the array) on 445 maize DNA samples with 422,369 probes. Genotyping InDels using this approach produced a highly reliable dataset, with low genotyping error (~3%), high call rate (~98%), and high reproducibility (>95%). This reliability can be further increased by combining genotyping of several probes calling the same InDels (99.9% of call rate for 5 probes). This “proof of concept” tool was used to estimate the kinship matrix between 362 maize lines with 57,824 polymorphic InDels. This InDels kinship matrix was highly correlated with kinship estimated using SNPs from Illumina 50K SNP arrays.ConclusionsWe efficiently genotyped thousands of small to large InDels on a sizeable number of individuals using a new Affymetrix®Axiom®array. This powerful approach opens the way to studying the contribution of InDels to trait variation and heterosis in maize. The approach is easily extendable to other species and should contribute to decipher the biological impact of InDels at a larger scale.

Published
2019
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2. SNP discovery and genetic mapping using genotyping by sequencing of whole genome genomic DNA from a pea RIL population

Author

Gilles Boutet, Olivier Bouchez, Alain Baranger, Nathalie Rivière, Matthieu Falque, Susete Alves Carvalho, Clément Lavaud, Emeline Lhuillier, Pierre Peterlongo, Marie-Laure Pilet-Nayel, PISOM, Institut National de la Recherche Agronomique (INRA), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Paris-Sud - Paris 11 (UP11)-Institut National de la Recherche Agronomique (INRA), Génome et Transcriptome - Plateforme Génomique (GeT-PlaGe), Institut National de la Recherche Agronomique (INRA)-Plateforme Génome & Transcriptome (GET), Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, BIOGEMMA, Centre de Recherche de Chappes, French National FASO (Fonds d'Action Stratégique des Oléoprotéagineux), AGROCAMPUS OUEST-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Recherche Agronomique (INRA), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec, GeT PlaGe, Genotoul, Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT], Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Toulouse (UT)-Université de Toulouse (UT)-École nationale supérieure agronomique de Toulouse (ENSAT), Université de Toulouse (UT)-Université de Toulouse (UT), 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), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse)

Subjects
polymorphisme nucléotidique simple (SNP), Genetic Markers, 0106 biological sciences, 0301 basic medicine, Cancer genome sequencing, carte génétique, DNA, Plant, Genotype, Genotyping Techniques, QTL, [SDV]Life Sciences [q-bio], Genomics, Biology, Molecular Inversion Probe, Polymorphism, Single Nucleotide, 01 natural sciences, DNA sequencing, 03 medical and health sciences, Marker assisted selection, Aphanomyces euteiches, Next generation sequencing, Genetics, Exome sequencing, Pisum sativum, 2. Zero hunger, Whole genome sequencing, Vegetal Biology, Peas, Chromosome Mapping, food and beverages, Sequence Analysis, DNA, Aphanomyces euteiches, Pisum sativum, Next generation sequencing, Marker assisted selection, QTL, rils population, ressource génomique végétale, 030104 developmental biology, Biologie végétale, Genome, Plant, sélection assistée par marqueurs, Research Article, 010606 plant biology & botany, Biotechnology, Reference genome
Abstract

Background Progress in genetics and breeding in pea still suffers from the limited availability of molecular resources. SNP markers that can be identified through affordable sequencing processes, without the need for prior genome reduction or a reference genome to assemble sequencing data would allow the discovery and genetic mapping of thousands of molecular markers. Such an approach could significantly speed up genetic studies and marker assisted breeding for non-model species. Results A total of 419,024 SNPs were discovered using HiSeq whole genome sequencing of four pea lines, followed by direct identification of SNP markers without assembly using the discoSnp tool. Subsequent filtering led to the identification of 131,850 highly designable SNPs, polymorphic between at least two of the four pea lines. A subset of 64,754 SNPs was called and genotyped by short read sequencing on a subpopulation of 48 RILs from the cross ‘Baccara’ x ‘PI180693’. This data was used to construct a WGGBS-derived pea genetic map comprising 64,263 markers. This map is collinear with previous pea consensus maps and therefore with the Medicago truncatula genome. Sequencing of four additional pea lines showed that 33 % to 64 % of the mapped SNPs, depending on the pairs of lines considered, are polymorphic and can therefore be useful in other crosses. The subsequent genotyping of a subset of 1000 SNPs, chosen for their mapping positions using a KASP™ assay, showed that almost all generated SNPs are highly designable and that most (95 %) deliver highly qualitative genotyping results. Using rather low sequencing coverages in SNP discovery and in SNP inferring did not hinder the identification of hundreds of thousands of high quality SNPs. Conclusions The development and optimization of appropriate tools in SNP discovery and genetic mapping have allowed us to make available a massive new genomic resource in pea. It will be useful for both fine mapping within chosen QTL confidence intervals and marker assisted breeding for important traits in pea improvement. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2447-2) contains supplementary material, which is available to authorized users.

Published
2016
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3. SNP Discovery and Genetic Mapping Using Genotyping by Sequencing of Whole Genome Genomic DNA from a Pea RIL Population

Author

Gilles Boutet, Susete Carvalho, Matthieu Falque, Pierre Peterlongo, Emeline Lhuillier, Olivier Bouchez, Clément Lavaud, Marie Pilet, Nathalie Rivière, and Alain Baranger

Subjects
0106 biological sciences, 0303 health sciences, 03 medical and health sciences, 01 natural sciences, 030304 developmental biology, 010606 plant biology & botany
Published
2016
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4. Cross-genome map based dissection of a nitrogen use efficiency ortho-metaQTL in bread wheat unravels concerted cereal genome evolution

Author

Séverine Foucrier, Gilles Charmet, Catherine Feuillet, Gregory Desmaizieres, Michael Abrouk, Alain Murigneux, Umar Masood Quraishi, Jacques Le Gouis, Caroline Pont, Nathalie Rivière, Jérôme Salse, Carole Confolent, Stéphane Lafarge, Laurent Guerreiro, Etienne Paux, and Florent Murat

Subjects
2. Zero hunger, 0106 biological sciences, Molecular breeding, Genetics, 0303 health sciences, Genome evolution, food and beverages, Locus (genetics), Cell Biology, Plant Science, Vernalization, Quantitative trait locus, Biology, 01 natural sciences, Genome, 03 medical and health sciences, Cellular localization, 030304 developmental biology, 010606 plant biology & botany, Synteny
Abstract

Monitoring nitrogen use efficiency (NUE) in plants is becoming essential to maintain yield while reducing fertilizer usage. Optimized NUE application in major crops is essential for long-term sustainability of agriculture production. Here, we report the precise identification of 11 major chromosomal regions controlling NUE in wheat that co-localise with key developmental genes such as Ppd (photoperiod sensitivity), Vrn (vernalization requirement), Rht (reduced height) and can be considered as robust markers from a molecular breeding perspective. Physical mapping, sequencing, annotation and candidate gene validation of an NUE metaQTL on wheat chromosome 3B allowed us to propose that a glutamate synthase (GoGAT) gene that is conserved structurally and functionally at orthologous positions in rice, sorghum and maize genomes may contribute to NUE in wheat and other cereals. We propose an evolutionary model for the NUE locus in cereals from a common ancestral region, involving species specific shuffling events such as gene deletion, inversion, transposition and the invasion of repetitive elements.

Published
2011
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5. Transcriptome sequencing for high throughput SNP development and genetic mapping in Pea

Author

Clément Lavaud, Jean-Philippe Pichon, Jorge Duarte, Grégoire Aubert, Laurent Cornet, Isabelle Lejeune-Hénaut, Judith Burstin, Marie-Laure Pilet-Nayel, Nathalie Rivière, Jean-Pierre Martinant, Alain Baranger, Gilles Boutet, Boutet, Gilles, BIOGEMMA, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), AGROCAMPUS OUEST-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Recherche Agronomique (INRA), Agroécologie [Dijon], Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Stress Abiotiques et Différenciation des Végétaux Cultivés (SADV), Institut National de la Recherche Agronomique (INRA)-Université de Lille, Sciences et Technologies, Limagrain Europe, FASO (Fonds d’Action Stratégique des Oléoprotéagineux) / SOFIPROTEOL, Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, 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), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Institut de Génétique, Environnement et Protection des Plantes ( IGEPP ), Institut National de la Recherche Agronomique ( INRA ) -Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -AGROCAMPUS OUEST, Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Stress Abiotiques et Différenciation des Végétaux Cultivés ( SADV ), and Institut National de la Recherche Agronomique ( INRA ) -Université de Lille, Sciences et Technologies

Subjects
0106 biological sciences, Germplasm, [ SDV.BV ] Life Sciences [q-bio]/Vegetal Biology, Genotype, UniGene, Computational biology, syntenie, Genes, Plant, Polymorphism, Single Nucleotide, 01 natural sciences, DNA sequencing, Contig Mapping, 03 medical and health sciences, Gene mapping, Genetics, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, pisum sativum, 030304 developmental biology, Synteny, 2. Zero hunger, next generation sequencing, 0303 health sciences, Genetic diversity, Vegetal Biology, Contig, biology, synteny, Peas, Chromosome Mapping, Genetic Variation, High-Throughput Nucleotide Sequencing, food and beverages, Sequence Analysis, DNA, genetic diversity, medicago truncatula, biology.organism_classification, Medicago truncatula, composite genetic map, marker assisted selection, diversité génétique, Biologie végétale, Research Article, 010606 plant biology & botany, Biotechnology
Abstract

International audience; Pea has a complex genome of 4.3 Gb for which only limited genomic resources are available to date. Although SNP markers are now highly valuable for research and modern breeding, only a few are described and used in pea for genetic diversity and linkage analysis.[br/] We developed a large resource by cDNA sequencing of 8 genotypes representative of modern breeding material using the Roche 454 technology, combining both long reads (400 bp) and high coverage (3.8 million reads, reaching a total of 1,369 megabases). Sequencing data were assembled and generated a 68 K unigene set, from which 41 K were annotated from their best blast hit against the model species Medicago truncatula. Annotated contigs showed an even distribution along M. truncatula pseudochromosomes, suggesting a good representation of the pea genome. 10 K pea contigs were found to be polymorphic among the genetic material surveyed, corresponding to 35 K SNPs.We validated a subset of 1538 SNPs through the GoldenGate assay, proving their ability to structure a diversity panel of breeding germplasm. Among them, 1340 were genetically mapped and used to build a new consensus map comprising a total of 2070 markers. Based on blast analysis, we could establish 1252 bridges between our pea consensus map and the pseudochromosomes of M. truncatula, which provides new insight on synteny between the two species.[br/] Our approach created significant new resources in pea, i.e. the most comprehensive genetic map to date tightly linked to the model species M. truncatula and a large SNP resource for both academic research and breeding.

Published
2014
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6. Intra-platform comparison of 25-mer and 60-mer oligonucleotide Nimblegen DNA microarrays

Author

Godfrey Neutelings, Rudy Huis, Brigitte Thomasset, Nathalie Rivière, Anca Lucau-Danila, Malika Chabi, Stéphane Fénart, Sophie Gallina, Simon Hawkins, UMR 1281 SADV, Institut National de la Recherche Agronomique (INRA), Laboratoire de Génétique et Evolution des Populations Végétales, Université de Lille, Sciences et Technologies-Centre National de la Recherche Scientifique (CNRS), BIOGEMMA, Biogemma Clermont-Ferrand, Génie Enzymatique et Cellulaire (GEC), Université de Technologie de Compiègne (UTC)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Authors gratefully acknowledge financial support of the French Nord-Pas de Calais Region (Plant Teq 4 project), RH gratefully acknowledges the support of the French government (PhD grant) and MC gratefully acknowledges the support of the ANR (Agence National de Recherche) (PhD grant KBBE FIBRAGEN project). This study was also supported by the ANR project GENOLIN., ANR-06-GPLA-0017,GENOLIN,GENOLIN : Développement de puces à oligonucléotides pour la valorisation industrielle du lin(2006), CNRS, Université de Lille, Stress Abiotiques et Différenciation des Végétaux Cultivés [SADV], Génétique et évolution des populations végétales [GEPV], Biogemma, Génie Enzymatique et Cellulaire [GEC], and Institut Charles Viollette (ICV) - EA 7394 [ICV]

Subjects
0106 biological sciences, Candidate gene, DNA arrays, Microarray, MESH: DNA/chemistry, Computational biology, Biology, Nimblegen, Gene expression, 01 natural sciences, General Biochemistry, Genetics and Molecular Biology, Gene expression Background, MESH: Nucleic Acid Hybridization, 03 medical and health sciences, chemistry.chemical_compound, Nucleic acid thermodynamics, 030304 developmental biology, Oligonucleotide Array Sequence Analysis, Medicine(all), Genetics, 0303 health sciences, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], Biochemistry, Genetics and Molecular Biology(all), Oligonucleotide, Hybridization probe, Nucleic Acid Hybridization, Reproducibility of Results, General Medicine, DNA, MESH: Reproducibility of Results, chemistry, MESH: DNA Probes, MESH: Oligonucleotide Array Sequence Analysis, DNA microarray, DNA Probes, 010606 plant biology & botany, Research Article
Abstract

Background We performed a Nimblegen intra-platform microarray comparison by assessing two categories of flax target probes (short 25-mers oligonucleotides and long 60-mers oligonucleotides) in identical conditions of target production, design, labelling, hybridization, image analyses, and data filtering. We compared technical parameters of array hybridizations, precision and accuracy as well as specific gene expression profiles. Results Comparison of the hybridization quality, precision and accuracy of expression measurements, as well as an interpretation of differential gene expression in flax tissues were performed. Both array types yielded reproducible, accurate and comparable data that are coherent for expression measurements and identification of differentially expressed genes. 60-mers arrays gave higher hybridization efficiencies and therefore were more sensitive allowing the detection of a higher number of unigenes involved in the same biological process and/or belonging to the same multigene family. Conclusion The two flax arrays provide a good resolution of expressed functions; however the 60-mers arrays are more sensitive and provide a more in-depth coverage of candidate genes potentially involved in different biological processes.

Published
2013
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7. Combining gene expression and genetic analyses to identify candidate genes involved in cold responses in pea

Author

Odile Jaminon, Nasser Bahrman, Aurélie Petit, Nathalie Rivière, Alain Baranger, Jeroen A. Wilmer, Christelle Blassiau, Sylvain Legrand, Isabelle Lejeune-Hénaut, Julie Mautord, Aurélie Bluteau, Véronique Fontaine, Bruno Delbreil, Julie Morin, Gilles Marque, Anne-Sophie Canoy, Stress Abiotiques et Différenciation des Végétaux Cultivés (SADV), Institut National de la Recherche Agronomique (INRA)-Université de Lille, Sciences et Technologies, BIOGEMMA, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Université de Lille, Sciences et Technologies-Institut National de la Recherche Agronomique (INRA), AGROCAMPUS OUEST-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, and 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)

Subjects
0106 biological sciences, Candidate gene, Genotype, Physiology, [SDV]Life Sciences [q-bio], Molecular Sequence Data, Quantitative Trait Loci, Population, Locus (genetics), Plant Science, Quantitative trait locus, Biology, Genes, Plant, Polymerase Chain Reaction, 01 natural sciences, 03 medical and health sciences, Gene Expression Regulation, Plant, Cold acclimation, chilling, Jasmonate, education, Gene, pisum sativum, Gene Library, 030304 developmental biology, Expressed Sequence Tags, 2. Zero hunger, Genetics, 0303 health sciences, education.field_of_study, Cold-Shock Response, cold acclimation, Peas, Sequence Analysis, DNA, suppression subtractive hybridisation, freezing tolerance, Suppression subtractive hybridization, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

International audience; Cold stress affects plant growth and development. In order to better understand the responses to cold (chilling or freezing tolerance), we used two contrasted pea lines. Following a chilling period, the Champagne line becomes tolerant to frost whereas the Terese line remains sensitive. Four suppression subtractive hybridisation libraries were obtained using mRNAs isolated from pea genotypes Champagne and Terese. Using quantitative polymerase chain reaction (qPCR) performed on 159 genes, 43 and 54 genes were identified as differentially expressed at the initial time point and during the time course study, respectively. Molecular markers were developed from the differentially expressed genes and were genotyped on a population of 164 RILs derived from a cross between Champagne and Terese. We identified 5 candidate genes colocalizing with 3 different frost damage quantitative trait loci (QTL) intervals and a protein quantity locus (PQL) rich region previously reported. This investigation revealed the role of constitutive differences between both genotypes in the cold responses, in particular with genes related to glycine degradation pathway that could confer to Champagne a better frost tolerance. We showed that freezing tolerance involves a decrease of expression of genes related to photosynthesis and the expression of a gene involved in the production of cysteine and methionine that could act as cryoprotectant molecules. Although it remains to be confirmed, this study could also reveal the involvement of the jasmonate pathway in the cold responses, since we observed that two genes related to this pathway were mapped in a frost damage QTL interval and in a PQL rich region interval, respectively.

Published
2013
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8. High-density SNP-based genetic map development and linkage disequilibrium assessment in Brassica napus L

Author

Isabelle André, Philippe Blanchard, Nicolas Ribière, Régine Delourme, Gwenn Trotoux, Maryline Pauchon, Nicole Lucante, Berline Fopa Fomeju, Marie Boillot, Jean-Philippe Pichon, Jean-Pierre Martinant, Jorge Duarte, Gilles Lassalle, Cyril Falentin, Valérie Gauthier, Jérôme Pauquet, Amandine Marty, Nathalie Rivière, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Euralis Semences, Dynamique et durabilité des écosystèmes : de la source à l’océan (DECOD), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), BIOGEMMA, Unité de Recherches Zootechniques (URZ), Institut National de la Recherche Agronomique (INRA), Limagrain, De la Préhistoire à l'Actuel : Culture, Environnement et Anthropologie (PACEA), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Institut national de recherches archéologiques préventives (Inrap), AGROCAMPUS OUEST-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, and 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)

Subjects
0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Linkage disequilibrium, carte génétique, Genetic Linkage, Quantitative Trait Loci, polymorphisme snp, Biology, Colza, Polymorphism, Single Nucleotide, 01 natural sciences, Linkage Disequilibrium, brassica napus, marqueurs SNP, 03 medical and health sciences, description génétique, Genetics, SNP, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [INFO]Computer Science [cs], Association mapping, Genotyping, 030304 developmental biology, Genetic association, 2. Zero hunger, 0303 health sciences, Vegetal Biology, Chromosome Mapping, Tag SNP, SNP genotyping, Agricultural sciences, [SDE]Environmental Sciences, Doubled haploidy, Genome, Plant, Sciences agricoles, Biologie végétale, Research Article, 010606 plant biology & botany, Biotechnology
Abstract

Background High density genetic maps built with SNP markers that are polymorphic in various genetic backgrounds are very useful for studying the genetics of agronomical traits as well as genome organization and evolution. Simultaneous dense SNP genotyping of segregating populations and variety collections was applied to oilseed rape (Brassica napus L.) to obtain a high density genetic map for this species and to study the linkage disequilibrium pattern. Results We developed an integrated genetic map for oilseed rape by high throughput SNP genotyping of four segregating doubled haploid populations. A very high level of collinearity was observed between the four individual maps and a large number of markers (>59%) was common to more than two maps. The precise integrated map comprises 5764 SNP and 1603 PCR markers. With a total genetic length of 2250 cM, the integrated map contains a density of 3.27 markers (2.56 SNP) per cM. Genotyping of these mapped SNP markers in oilseed rape collections allowed polymorphism level and linkage disequilibrium (LD) to be studied across the different collections (winter vs spring, different seed quality types) and along the linkage groups. Overall, polymorphism level was higher and LD decayed faster in spring than in “00” winter oilseed rape types but this was shown to vary greatly along the linkage groups. Conclusions Our study provides a valuable resource for further genetic studies using linkage or association mapping, for marker assisted breeding and for Brassica napus sequence assembly and genome organization analyses.

Published
2013
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9. Nitric oxide participates in the complex interplay of defense-related signaling pathways controlling disease resistance to Sclerotinia sclerotiorum in Arabidopsis thaliana

Author

Clotilde Claudel-Renard, Bruno Grezes-Besset, Dominique Roby, Nathalie Rivière, Laure Perchepied, Claudine Balagué, Catherine Riou, Laboratoire des interactions plantes micro-organismes (LIPM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), Laboratoire de Chimie des Substances Naturelles (LCSN), Université de Limoges (UNILIM)-Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST FR CNRS 3503), Laboratoire de Biologie Cellulaire et Moléculaire, BIOGEMMA, Upstream Genomics Research Group, Transcriptomics Team, Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), and French ANR : ANR 06 GPLA 015

Subjects
0106 biological sciences, Physiology, Arabidopsis, Cyclopentanes, Plant disease resistance, Nitric Oxide, 01 natural sciences, SCLEROTINIA SCLEROTIORUM, 03 medical and health sciences, chemistry.chemical_compound, Ascomycota, Botany, Plant defense against herbivory, Arabidopsis thaliana, Oxylipins, Abscisic acid, RELATION HOTE-PARASITE, Plant Diseases, 030304 developmental biology, 0303 health sciences, INTERACTION PLANTE-PATHOGENE, biology, [CHIM.ORGA]Chemical Sciences/Organic chemistry, Jasmonic acid, Brassica rapa, Sclerotinia sclerotiorum, ARABIDOPSIS THALIANA, General Medicine, Ethylenes, biology.organism_classification, Cell biology, Plant Leaves, chemistry, Host-Pathogen Interactions, Reactive Oxygen Species, Salicylic Acid, Agronomy and Crop Science, Salicylic acid, Abscisic Acid, Signal Transduction, 010606 plant biology & botany
Abstract

International audience; Studies of the interaction between Arabidopsis thaliana and the necrotrophic fungal pathogen Sclerotinia sclerotiorum have been hampered by the extreme susceptibility of this model plant to the fungus. In addition, analyses of the plant defense response suggested the implication of a complex interplay of hormonal and signaling pathways. To get a deeper insight into this host-pathogen interaction, we first analyzed the natural variation in Arabidopsis for resistance to S. sclerotiorum. The results revealed a large variation of resistance and susceptibility in Arabidopsis, with some ecotypes, such as Ws-4, Col-0, and Rbz-1, being strongly resistant, and others, such as Shahdara, Ita-0, and Cvi-0, exhibiting an extreme susceptibility. The role of different signaling pathways in resistance was then determined by assessing the symptoms of mutants affected in the perception, production, or transduction of hormonal signals after inoculation with S. sclerotiorum. This analysis led to the conclusions that i) signaling of inducible defenses is predominantly mediated by jasmonic acid and abscisic acid, influenced by ethylene, and independent of salicylic acid; and ii) nitric oxide (NO) and reactive oxygen species are important signals required for plant resistance to S. sclerotiorum. Defense gene expression analysis supported the specific role of NO in defense activation.

Published
2010
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10. Development and validation of a flax (Linum usitatissimum L.) gene expression oligo microarray

Author

Simon Hawkins, Reynald Tavernier, Brigitte Thomasset, Godfrey Neutelings, Jeroen A. Wilmer, Stéphane Fénart, Anca Lucau, Brigitte Chabbert, Yves-Placide Assoumou Ndong, Nathalie Rivière, Xavier Guillot, Olivier Van Wuytswinkel, Emmanuelle Cariou, Jorge Duarte, Laurent Gutierrez, Génétique et évolution des populations végétales (GEPV), Université de Lille, Sciences et Technologies-Centre National de la Recherche Scientifique (CNRS), Génie Enzymatique et Cellulaire (GEC), Université de Technologie de Compiègne (UTC)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Stress Abiotiques et Différenciation des Végétaux Cultivés (SADV), Université de Lille, Sciences et Technologies-Institut National de la Recherche Agronomique (INRA), UMR1281, Institut National de la Recherche Agronomique (INRA), Biologie des Plantes et Innovation - UR UPJV 3900 (BIOPI), Université de Picardie Jules Verne (UPJV), BIOGEMMA, Institut Technique du Lin, Partenaires INRAE, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), Laboulet, Linea, Université de Technologie de Compiègne (UTC), Institut National de la Recherche Agronomique (INRA)-Université de Lille, Sciences et Technologies, Université de Picardie Jules Verne (UPJV)-Transfrontalière BioEcoAgro - UMR 1158 (BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Université d'Artois (UA)-Université de Liège-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Centre de ressources régionales en biologie moléculaire (CRRBM) (CRRBM), and Laboulet Semences S.A.

Subjects
0106 biological sciences, Linum, expression génique, lcsh:QH426-470, Genotype, lcsh:Biotechnology, UniGene, Genes, Plant, lin, 01 natural sciences, 03 medical and health sciences, Contig Mapping, Gene Expression Regulation, Plant, lcsh:TP248.13-248.65, Flax, GENOMIQUE, Gene expression, Genetics, 030304 developmental biology, Oligonucleotide Array Sequence Analysis, 2. Zero hunger, 0303 health sciences, Expressed sequence tag, [SDV.GEN]Life Sciences [q-bio]/Genetics, Principal Component Analysis, biology, Plant Stems, Microarray analysis techniques, Reverse Transcriptase Polymerase Chain Reaction, Gene Expression Profiling, linum usitatissimum, Gene Expression Regulation, Developmental, Reproducibility of Results, Molecular Sequence Annotation, Sequence Analysis, DNA, biology.organism_classification, Fold change, Gene expression profiling, lcsh:Genetics, Organ Specificity, DNA microarray, 010606 plant biology & botany, Biotechnology, Research Article
Abstract

Background Flax (Linum usitatissimum L.) has been cultivated for around 9,000 years and is therefore one of the oldest cultivated species. Today, flax is still grown for its oil (oil-flax or linseed cultivars) and its cellulose-rich fibres (fibre-flax cultivars) used for high-value linen garments and composite materials. Despite the wide industrial use of flax-derived products, and our actual understanding of the regulation of both wood fibre production and oil biosynthesis more information must be acquired in both domains. Recent advances in genomics are now providing opportunities to improve our fundamental knowledge of these complex processes. In this paper we report the development and validation of a high-density oligo microarray platform dedicated to gene expression analyses in flax. Results Nine different RNA samples obtained from flax inner- and outer-stems, seeds, leaves and roots were used to generate a collection of 1,066,481 ESTs by massive parallel pyrosequencing. Sequences were assembled into 59,626 unigenes and 48,021 sequences were selected for oligo design and high-density microarray (Nimblegen 385K) fabrication with eight, non-overlapping 25-mers oligos per unigene. 18 independent experiments were used to evaluate the hybridization quality, precision, specificity and accuracy and all results confirmed the high technical quality of our microarray platform. Cross-validation of microarray data was carried out using quantitative qRT-PCR. Nine target genes were selected on the basis of microarray results and reflected the whole range of fold change (both up-regulated and down-regulated genes in different samples). A statistically significant positive correlation was obtained comparing expression levels for each target gene across all biological replicates both in qRT-PCR and microarray results. Further experiments illustrated the capacity of our arrays to detect differential gene expression in a variety of flax tissues as well as between two contrasted flax varieties. Conclusion All results suggest that our high-density flax oligo-microarray platform can be used as a very sensitive tool for analyzing gene expression in a large variety of tissues as well as in different cultivars. Moreover, this highly reliable platform can also be used for the quantification of mRNA transcriptional profiling in different flax tissues.

Published
2010
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