GQMS - Quantitative Genetics and Plant Breeding Methodology

 14/12/2022 -  GQE
Study of genetic bases of complex traits and mechanisms of response to selection, analysis of diversity in collections and experimental populations. Optimizing genetic resource management and selection process using the results of these studies and theoretical approaches.

Head :
Laurence Moreau (INRAE)

Quantitative Genetics and Plant Breeding Methodology

1. Understand the organization of diversity in Maize

We aim at understanding the effect of historical and modern breeding on the evolution and adaptation of open pollinated and hybrid varieties, in terms of phenotypic variation, global organization of genetic diversity and polymorphism at the genome scale.

2. Decipher quantitative trait architecture

We aim at investigating the genetic determinism of complex traits, in view of direct applications in breeding through marker assisted selection and/or genomic selection but also to gain a better understanding of the type of genetic effects which are involved. Our objective is also to valorize multi-omics data that are now available to get a better insight into agronomical trait variation and regulation.

Specific attention is paid to flowering time, productivity under abiotic environmental constraints (more particularly abiotic stresses), performances stability and hybrid performances in Maize in relationship to global climatic changes and the evolution of agricultural practices (reduction of inputs, agroecology…).

3. Optimize breeding schemes

Our objectives are to optimize the breeding process, from genetic resources to variety development. We investigate more particularly the potential of genomic and phenomic predictions for improving diversity management in breeding schemes, for identifying and introgressing new sources of diversity in elite germplasm and for optimizing hybrid breeding schemes.

This research involves experimental and theoretical approaches, development of statistical methods and decision support tools.

Experimental approaches involve the assembly of diversity panels, the development of original genetic materials in maize, their genotyping in targeted chromosome segments or at the whole genome scale with different marker densities integrating also sequencing approaches and their phenotyping. These experiments are conducted by team members and also benefit to a large extent of the support of INRAE experimental structures and private partners involved in our research projects. Theoretical approaches aim more particularly at developing methods and optimizing experiments for QTL detection (using linkage or association mapping and meta-analysis), marker-assisted selection (with a specific focus on genomic selection) and systems genetics (multi-omics integration). These approaches being generic beyond maize, we collaborate to different projects on other species (flax, cucumber, wheat, tomatoes, legumes, etc).

We more particularly collaborate with the ABI group for the development of:

Beyond our research projects, we provide to researchers and technicians of INRAE working on maize knowledge sharing and we organize the circulation of genetic resources (coordination of the maize group and of the activities of Maize Biological Resources Center).

Most recent projects/network coordinated by team members :

Teaching:

Our teaching activities are focused on our main expertise domains such as population genetics, quantitative genetics, plant breeding, modern breeding methods (from QTL detection to genomic or phenomic selection), statistical modelling, adaptation of plants to abiotic stresses and genomics. We mostly contributed to different specialization of the engineer school AgroparisTech by co-organizing the PISTv specialization and the BIP master degree (M1 and M2) form the Univesity of Paris-Saclay. Researchers of our team also contribute more ponctually to other formations such as AgroCampusOuest, SupAgro Montpellier, the CIHAM in Saragoza (Spain), ENS, Clermont-Auvergne University, Picardie Jules Verne University or the Reims-Champagne Ardennes University.

We also provide teaching to other researchers and breeders through the organization of (i) modules of the doctoral school ABIES (ii) workshops organized in the framework of research projects or INRAE meta-programs (iii) (iii) training establishment such as ASFIS and (iii) training session defined specifically for breeding companies.

GQMS is attached to the LabEx SPS   (Sciences des Plantes de Saclay) and to ABIES doctoral school   . Members

Former members

Yacine DJABALI, M2 (1/2 PAPPSO)(2020); Alexis Vergne, M1 (2020); Antoine ALLIER, Doctorant (2017-2020); Clément MABIRE (2016-2019), Doctorant ; Simon RIO, Doctorant (2016-2019) ; Adama SEYE, Doctorant (2016-2019) ; Morgane Roth, Post-doc (3 mois en 2019); Romane Guilbaud (2018-2019); Camille Clipet, Ingénieur (2017-2018); Elodie Petitjean, M2(2018); Fabien Laporte, Doctorant (2015-2018); Alban Besnard, M2 (2017); Heloïse Giraud, Doctorante (2012-2016); Marie Gauvin, TR CDD (2017); Cecile Monteil, TR (2013-2017); Philippe Jamin, TR (1993-2017); Sandra Negro, Post-doc (2013-2016); Mariangela Arca, Post-doc (2012-2014); Franck Gauthier, Ingenieur (2011 puis 2013-2014); Fabio Valente, Ingenieur (2009-2013); Magali Joannin, TR (2011-2013); Denis Coubriche, TR ( ); Nicolas Bardol, Doctorant (2010-2013); Xiao Wang, Post-doc (2013); Amandine Larièpe, Doctorante (2008-2012); Ashwin Khobragade, Doctorant (2008-2012); Sophie Bouchet, Post-doc (2009-2012); Marion Trunztler, Doctorante (2008-2011); Yves Roussel, Post-doc (2011); Valerie Loywyck, Post-doc (2008-2010); Tatiana Zerjal, Post-doc (2009); Celine Mir, Post-doc (2006-2009); Yung-Fen Huang, M2 (2008)

Members

Publications

  • Lorenzi A., Bauland C., Pin S., Madur D., Combes V., Palaffre C., Guillaume C., Touzy G., Mary-Huard T. , Charcosset A. , Moreau L. . (2024) Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles. Theor Appl Genet, 3 (137) 75
  • Arca M., Gouesnard B., Mary‐Huard T., Le Paslier MC., Bauland C., Combes V., Madur D., Charcosset A. , Nicolas SD.. (2023) Genotyping of DNA pools identifies untapped landraces and genomic regions to develop next‐generation varieties. Plant Biotechnology Journal, 6 (21) 1123-1139
  • De Walsche A., Vergne A., Rincent R., Roux F., Nicolas S., Welcker C., Mezmouk S., Charcosset A. , Mary-Huard T. . (2023) metaGE: Investigating Genotype × Environment interactions through meta-analysis. DOI.org (Crossref),
  • Fraunhoffer NA., Moreno Vega AI., Abuelafia AM., Morvan M., Lebarbier E., Mary-Huard T. , Zimmermann MT., Lomberk G., Urrutia R., Dusetti N., Blum Y., Nicolle R., Iovanna J.. (2023) Priming therapy by targeting enhancer-initiated pathways in patient-derived pancreatic cancer cells. eBioMedicine, (92) 104602
  • Galić V., Anđelković V., Kravić N., Grčić N., Ledenčan T., Jambrović A., Zdunić Z., Nicolas S., Charcosset A. , Šatović Z., Šimić D.. (2023) Genetic diversity and selection signatures in a gene bank panel of maize inbred lines from Southeast Europe compared with two West European panels. BMC Plant Biol, 1 (23) 315
  • Mary-Huard T. , Balding D., Weir BS.. (2023) Fast and accurate joint inference of coancestry parameters for populations and/or individuals. PLoS Genet, 1 (19) e1010054
  • Monnot S., Desaint H., Mary-Huard T. , Moreau L. , Schurdi-Levraud V., Boissot N.. (2021) Deciphering the Genetic Architecture of Plant Virus Resistance by GWAS, State of the Art and Potential Advances. Cells, 11 (10) 3080
  • Raffo MA., Cuyabano BCD., Rincent R., Sarup P., Moreau L. , Mary-Huard T. , Jensen J.. (2023) Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat. Front. Plant Sci., (13) 1075077
  • Revilla P., Butrón A., Rodriguez VM., Rincent R., Charcosset A. , Giauffret C., Melchinger AE., Schön CC., Bauer E., Altmann T., Brunel D., Moreno-González J., Campo L., Ouzunova M., Álvarez Ángel., Ruíz de Galarreta JI., Laborde J., Malvar RA.. (2023) Genetic Variation for Cold Tolerance in Two Nested Association Mapping Populations. Agronomy, 1 (13) 195
  • Rio S., Charcosset A. , Moreau L. , Mary-Huard T. , Endelman J.. (2023) Detecting directional and non-directional epistasis in bi-parental populations using genomic data. GENETICS, 3 (224) iyad089
  • Rishmawi L., Bauget F., Protto V., Bauland C., Nacry P., Maurel C.. (2023) Natural variation of maize root hydraulic architecture underlies highly diverse water uptake capacities. Plant Physiology, kiad213
  • Sanane I., Nicolas SD., Bauland C., Marion-Poll F., Noûs C., Legrand J., Dillmann C.. (2023) Large genetic variability of maize leaf palatability to european corn borer : metabolic insights. bioRxiv,
  • Sanchez D., Sadoun SB., Mary-Huard T. , Allier A., Moreau L. , Charcosset A. . (2023) Improving the use of plant genetic resources to sustain breeding programs’ efficiency. Proc. Natl. Acad. Sci. U.S.A., 14 (120) e2205780119
  • Ahmadi N., Bartholomé J., Rio S., Charcosset A. , Mary-Huard T. , Moreau L. , Rincent R.. (2022) Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches. DOI.org (Crossref), (2467) 77-112
  • Ahmadi N., Bartholomé J., Crossa J., Montesinos-López OA., Pérez-Rodríguez P., Costa-Neto G., Fritsche-Neto R., Ortiz R., Martini JWR., Lillemo M., Montesinos-López A., Jarquin D., Breseghello F., Cuevas J., Rincent R.. (2022) Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction. DOI.org (Crossref), (2467) 245-283
  • Ahmadi N., Bartholomé J., Robert P., Brault C., Rincent R., Segura V.. (2022) Phenomic Selection: A New and Efficient Alternative to Genomic Selection. DOI.org (Crossref), (2467) 397-420
  • Colombo M., Roumet P., Salon C., Jeudy C., Lamboeuf M., Lafarge S., Dumas AV., Dubreuil P., Ngo W., Derepas B., Beauchêne K., Allard V., Le Gouis J., Rincent R.. (2022) Genetic Analysis of Platform-Phenotyped Root System Architecture of Bread and Durum Wheat in Relation to Agronomic Traits. Front. Plant Sci., (13) 853601
  • Laporte F., Charcosset A. , Mary-Huard T. , Singh M.. (2022) Efficient ReML inference in variance component mixed models using a Min-Max algorithm. PLoS Comput Biol, 1 (18) e1009659
  • Lorenzi A., Bauland C., Mary-Huard T. , Pin S., Palaffre C., Guillaume C., Lehermeier C., Charcosset A. , Moreau L. . (2022) Genomic prediction of hybrid performance: comparison of the efficiency of factorial and tester designs used as training sets in a multiparental connected reciprocal design for maize silage. Theor Appl Genet,
  • Monnot S., Cantet M., Mary-Huard T. , Moreau L. , Lowdon R., Van Haesendonck M., Ricard A., Boissot N.. (2022) Unravelling cucumber resistance to several viruses via genome-wide association studies highlighted resistance hotspots and new QTLs. Horticulture Research, uhac184
  • Robert P., Auzanneau J., Goudemand E., Oury FX., Rolland B., Heumez E., Bouchet S., Le Gouis J., Rincent R.. (2022) Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection. Theor Appl Genet, 3 (135) 895-914
  • Robert P., Goudemand E., Auzanneau J., Oury FX., Rolland B., Heumez E., Bouchet S., Caillebotte A., Mary-Huard T. , Le Gouis J., Rincent R.. (2022) Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials. Theor Appl Genet, 10 (135) 3337-3356
  • Roth M., Beugnot A., Mary-Huard T. , Moreau L. , Charcosset A. , Fievet JB.. (2022) Improving genomic predictions with inbreeding and non-additive effects in two admixed maize hybrid populations in single and multi-environment contexts. Genetics, iyac018
  • Speck A., Trouvé JP., Enjalbert J., Geffroy V., Joets J., Moreau L. . (2022) Genetic Architecture of Powdery Mildew Resistance Revealed by a Genome-Wide Association Study of a Worldwide Collection of Flax (Linum usitatissimum L.). Front. Plant Sci., (13) 871633
  • Welcker C., Spencer NA., Turc O., Granato I., Chapuis R., Madur D., Beauchene K., Gouesnard B., Draye X., Palaffre C., Lorgeou J., Melkior S., Guillaume C., Presterl T., Murigneux A., Wisser RJ., Millet EJ., van Eeuwijk F., Charcosset A. , Tardieu F.. (2022) Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions. Nat Commun, 1 (13) 3225
  • Arca M., Mary-Huard T. , Gouesnard B., Bérard A., Bauland C., Combes V., Madur D., Charcosset A. , Nicolas SD.. (2021) Deciphering the Genetic Diversity of Landraces With High-Throughput SNP Genotyping of DNA Bulks: Methodology and Application to the Maize 50k Array. Front. Plant Sci., (11) 568699
  • Atanda SA., Olsen M., Crossa J., Burgueño J., Rincent R., Dzidzienyo D., Beyene Y., Gowda M., Dreher K., Boddupalli PM., Tongoona P., Danquah EY., Olaoye G., Robbins KR.. (2021) Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage. Front. Plant Sci., (12) 658978
  • Diaw Y., Tollon-Cordet C., Charcosset A. , Nicolas SD., Madur D., Ronfort J., David J., Gouesnard B., Chiang TY.. (2021) Genetic diversity of maize landraces from the South-West of France. PLoS ONE, 2 (16) e0238334
  • Gonzàlez-Diéguez D., Legarra A., Charcosset A. , Moreau L. , Lehermeier C., Teyssèdre S., Vitezica ZG.. (2021) Genomic Prediction of Hybrid Crops Allows Disentangling Dominance and Epistasis. Genetics, iyab026
  • Haug B., Messmer MM., Enjalbert J., Goldringer I., Forst E., Flutre T., Mary-Huard T. , Hohmann P.. (2021) Advances in Breeding for Mixed Cropping – Incomplete Factorials and the Producer/Associate Concept. Front. Plant Sci., (11) 620400
  • Mary-Huard T. , Perduca V., Martin-Magniette ML., Blanchard G.. (2021) Error rate control for classification rules in multiclass mixture models. The International Journal of Biostatistics, 0 (0)
  • Mary-Huard T. , Das S., Mukhopadhyay I., Robin S., Birol I.. (2021) Querying multiple sets of P -values through composed hypothesis testing. Bioinformatics, 1 (38) 141-148
  • Allier A., 20 janvier 2020, Contributions to genetic diversity management in maize breeding programs using genomic selection, PhD Thesis, Université Paris-Saclay
  • Allier A., Teyssèdre S., Lehermeier C., Charcosset A. , Moreau L. . (2020) Genomic prediction with a maize collaborative panel: identification of genetic resources to enrich elite breeding programs. Theor Appl Genet, 1 (133) 201-215
  • Allier A., Teyssèdre S., Lehermeier C., Moreau L. , Charcosset A. . (2020) Optimized breeding strategies to harness genetic resources with different performance levels. BMC Genomics, 1 (21) 349
  • Arca M., Mary-Huard T. , Gouesnard B., Bérard A., Bauland C., Combes V., Madur D., Charcosset A. , Nicolas SD.. (2021) Deciphering the Genetic Diversity of Landraces With High-Throughput SNP Genotyping of DNA Bulks: Methodology and Application to the Maize 50k Array. Front. Plant Sci., (11) 568699
  • Arca M., Gouesnard B., Mary-Huard T. , Le Paslier MC., Bauland C., Combes V., Madur D., Charcosset A. , Nicolas SD.. (2020) Genome-wide SNP genotyping of DNA pools identifies untapped landraces and genomic regions that could enrich the maize breeding pool. DOI.org (Crossref),
  • Benoist R., Capdevielle‐Dulac C., Chantre C., Jeannette R., Calatayud PA., Drezen JM., Dupas S., Le Rouzic A., Le Ru B., Moreau L. , Van Dijk E., Kaiser L., Mougel F.. (2020) Quantitative Trait Loci involved in the reproductive success of a parasitoid wasp. Mol Ecol, mec.15567
  • Blein-Nicolas M., Negro SS., Balliau T., Welcker C., Cabrera-Bosquet L., Nicolas SD., Charcosset A. , Zivy M.. (2020) A systems genetics approach reveals environment-dependent associations between SNPs, protein coexpression, and drought-related traits in maize. Genome Res., 11 (30) 1593-1604
  • Castelletti S., Coupel-Ledru A., Granato I., Palaffre C., Cabrera-Bosquet L., Tonelli C., Nicolas SD., Tardieu F., Welcker C., Conti L., de Meaux J.. (2020) Maize adaptation across temperate climates was obtained via expression of two florigen genes. PLoS Genet, 7 (16) e1008882
  • Diouf I., Derivot L., Koussevitzky S., Carretero Y., Bitton F., Moreau L. , Causse M., Rebetzke G.. (2020) Genetic basis of phenotypic plasticity and genotype × environment interactions in a multi-parental tomato population. Journal of Experimental Botany, eraa265
  • Rio S., Mary-Huard T. , Moreau L. , Bauland C., Palaffre C., Madur D., Combes V., Charcosset A. , Springer NM.. (2020) Disentangling group specific QTL allele effects from genetic background epistasis using admixed individuals in GWAS: An application to maize flowering. PLoS Genet, 3 (16) e1008241
  • Seye AI., Bauland C., Charcosset A. , Moreau L. . (2020) Revisiting hybrid breeding designs using genomic predictions: simulations highlight the superiority of incomplete factorials between segregating families over topcross designs. Theor Appl Genet, 6 (133) 1995-2010
  • de Vienne D., Fiévet J. . (2020) The Pitfalls of Heterosis Coefficients. Plants, 7 (9) 875
  • Allier A., Teyssèdre S., Lehermeier C., Claustres B., Maltese S., Melkior S., Moreau L. , Charcosset A. . (2019) Assessment of breeding programs sustainability: application of phenotypic and genomic indicators to a North European grain maize program. Theor Appl Genet, 5 (132) 1321-1334
  • Allier A., Lehermeier C., Charcosset A. , Moreau L. , Teyssèdre S.. (2019) Improving Short- and Long-Term Genetic Gain by Accounting for Within-Family Variance in Optimal Cross-Selection. Front. Genet., (10) 1006
  • Allier A., Moreau L. , Charcosset A. , Teyssèdre S., Lehermeier C.. (2019) Usefulness Criterion and Post-selection Parental Contributions in Multi-parental Crosses: Application to Polygenic Trait Introgression. G3: Genes, Genomes, Genetics, 5 (9) 1469-1479
  • Boussardon C., Martin-Magniette ML., Godin B., Benamar A., Vittrant B., Citerne S., Mary-Huard T. , Macherel D., Rajjou L., Budar F.. (2019) Novel Cytonuclear Combinations Modify Arabidopsis thaliana Seed Physiology and Vigor. Front Plant Sci, (10) 32
  • Courret C., Gérard PR., Ogereau D., Falque M., Moreau L. , Montchamp-Moreau C.. (2019) X-chromosome meiotic drive in Drosophila simulans: a QTL approach reveals the complex polygenic determinism of Paris drive suppression. Heredity, 6 (122) 906-915
  • Forst E., Enjalbert J., Allard V., Ambroise C., Krissaane I., Mary-Huard T. , Robin S., Goldringer I.. (2019) A generalized statistical framework to assess mixing ability from incomplete mixing designs using binary or higher order variety mixtures and application to wheat. Field Crops Research, (242) 107571
  • Fustier MA., Martínez-Ainsworth NE., Aguirre-Liguori JA., Venon A., Corti H., Rousselet A., Dumas F., Dittberner H., Camarena MG., Grimanelli D., Ovaskainen O., Falque M., Moreau L. , Meaux J., Montes-Hernández S., Eguiarte LE., Vigouroux Y., Manicacci D., Tenaillon MI.. (2019) Common gardens in teosintes reveal the establishment of a syndrome of adaptation to altitude. PLOS Genetics, 12 (15) e1008512
  • Mabire C., 2019-04-23 23/04/19, Contribution des variations structurales de type insertions/délétions à l'adaptation, la variation des caractères et les performances hybrides chez le maïs, PhD Thesis, Université Paris-Saclay
  • Mabire C., Duarte J., Darracq A., Pirani A., Rimbert H., Madur D., Combes V., Vitte C., Praud S., Rivière N., Joets J., Pichon JP., Nicolas SD.. (2019) High throughput genotyping of structural variations in a complex plant genome using an original Affymetrix® axiom® array. BMC Genomics, 1 (20) 848
  • Mangin B., Rincent R., Rabier CE., Moreau L. , Goudemand-Dugue E.. (2019) Training set optimization of genomic prediction by means of EthAcc. PLOS ONE, 2 (14) e0205629
  • Millet EJ., Kruijer W., Coupel-Ledru A., Prado SA., Cabrera-Bosquet L., Lacube S., Charcosset A. , Welcker C., Eeuwijk F., Tardieu F.. (2019) Genomic prediction of maize yield across European environmental conditions. Nat Genet, 6 (51) 952-956
  • Negro SS., Millet EJ., Madur D., Bauland C., Combes V., Welcker C., Tardieu F., Charcosset A. , Nicolas SD.. (2019) Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies. BMC Plant Biol., 1 (19) 318
  • Rio S., 2019-04-26 26/04/19, Contributions to genomic selection and association mapping in structured and admixed populations : application to maize, PhD Thesis, Université Paris-Saclay
  • Rio S., Mary-Huard T. , Moreau L. , Charcosset A. . (2019) Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel. Theor Appl Genet, 1 (132) 81-96
  • Seye A., 2019-03-21 21/03/19, Prédiction assistée par marqueurs de la performance hybride dans un schéma de sélection réciproque : simulations et évaluation expérimentale pour le maïs ensilage, PhD Thesis, Université Paris-Saclay
  • Seye AI., Bauland C., Giraud H., Mechin V., Reymond M., Charcosset A. , Moreau L. . (2019) Quantitative trait loci mapping in hybrids between Dent and Flint maize multiparental populations reveals group-specific QTL for silage quality traits with variable pleiotropic effects on yield. Theor Appl Genet, 5 (132) 1523-1542
  • Virlouvet L., El Hage F., Griveau Y., Jacquemot MP., Gineau E., Baldy A., Legay S., Horlow C., Combes V., Bauland C., Palafre C., Falque M., Moreau L. , Coursol S., Méchin V., Reymond M.. (2019) Water Deficit-Responsive QTLs for Cell Wall Degradability and Composition in Maize at Silage Stage. Front. Plant Sci., (10) 488
  • Darracq A., Vitte C., Nicolas S., Duarte J., Pichon JP., Mary-Huard T. , Chevalier C., Bérard A., Le Paslier MC., Rogowsky P., Charcosset A. , Joets J.. (2018) Sequence analysis of European maize inbred line F2 provides new insights into molecular and chromosomal characteristics of presence/absence variants. BMC Genomics, 1 (19) 119
  • Fiévet J. , Nidelet T., Dillmann C., de Vienne D.. (2018) Heterosis Is a Systemic Property Emerging From Non-linear Genotype-Phenotype Relationships: Evidence From in Vitro Genetics and Computer Simulations. Front. Genet., (9)
  • Joets J., Vitte C., Charcosset A. , Bennetzen J., Flint-Garcia S., Hirsch C., Tuberosa R.. (2018) Draft assembly of the F2 European maize genome sequence and its comparison to the B73 genome sequence: a characterization of genotype-specific regions.. , In Press
  • Laporte F., 2018-03-13 13/03/18, Développement de méthodes statistiques pour l’identification de gènes d’intérêt en présence d’apparentement et de dominance, application à la génétique du maïs, PhD thesis, Université Paris-Saclay
  • Bedoya CA., Dreisigacker S., Hearne S., Franco J., Mir C., Prasanna BM., Taba S., Charcosset A. , Warburton ML.. (2017) Genetic diversity and population structure of native maize populations in Latin America and the Caribbean. PLoS One, 4 (12)
  • Bouchet S., Bertin P., Presterl T., Jamin P., Coubriche D., Gouesnard B., Laborde J., Charcosset A. . (2017) Association mapping for phenology and plant architecture in maize shows higher power for developmental traits compared with growth influenced traits. Heredity (Edinb), 3 (118) 249-259
  • Brandenburg JT., Mary-Huard T. , Rigaill G., Hearne SJ., Corti H., Joets J., Vitte C., Charcosset A. , Nicolas SD., Tenaillon MI.. (2017) Independent introductions and admixtures have contributed to adaptation of European maize and its American counterparts. PLOS Genetics, 3 (13) e1006666
  • Cañas RA., Yesbergenova-Cuny Z., Simons M., Chardon F., Armengaud P., Quilleré I., Cukier C., Gibon Y., Limami AM., Nicolas S., Brulé L., Lea PJ., Maranas CD., Hirel B.. (2017) Exploiting the Genetic Diversity of Maize Using a Combined Metabolomic, Enzyme Activity Profiling, and Metabolic Modeling Approach to Link Leaf Physiology to Kernel Yield. The Plant Cell, 5 (29) 919-943
  • Giraud H., Bauland C., Falque M., Madur D., Combes V., Jamin P., Monteil C., Laborde J., Palaffre C., Gaillard A., Blanchard P., Charcosset A. , Moreau L. . (2017) Linkage Analysis and Association Mapping QTL Detection Models for Hybrids Between Multiparental Populations from Two Heterotic Groups: Application to Biomass Production in Maize (Zea mays L.). G3: Genes, Genomes, Genetics, g3.300121.2017
  • Giraud H., Bauland C., Falque M., Madur D., Combes V., Jamin P., Monteil C., Laborde J., Palaffre C., Gaillard A., Blanchard P., Charcosset A. , Moreau L. . (2017) Reciprocal Genetics: Identifying QTL for General and Specific Combining Abilities in Hybrids Between Multiparental Populations from Two Maize (Zea mays L.) Heterotic Groups. Genetics, 3 (207) 1167-1180
  • Gouesnard B., Negro S., Laffray A., Glaubitz J., Melchinger A., Revilla P., Moreno-Gonzalez J., Madur D., Combes V., Tollon-Cordet C., Laborde J., Kermarrec D., Bauland C., Moreau L. , Charcosset A. , Nicolas S.. (2017) Genotyping-by-sequencing highlights original diversity patterns within a European collection of 1191 maize flint lines, as compared to the maize USDA genebank. Theor Appl Genet, 10 (130) 2165-2189
  • Laporte F., Charcosset A. , Mary‐Huard T.. (2017) Estimation of the relatedness coefficients from biallelic markers, application in plant mating designs. Biometrics, 3 (73) 885-894
  • Larièpe A., Moreau L. , Laborde J., Bauland C., Mezmouk S., Décousset L., Mary-Huard T. , Fiévet J. , Gallais A., Dubreuil P., Charcosset A. . (2017) General and specific combining abilities in a maize (Zea mays L.) test-cross hybrid panel: relative importance of population structure and genetic divergence between parents. Theor. Appl. Genet., 2 (130) 403-417
  • Mary-Huard T. , 2017-06-12 06/12/17, Some contributions to statistical modeling and model selection with applications to genomics and quantitative genetics, HDR, Université Paris Sud
  • Moreau L. , 2017-06-13 13/06/17, Utilisation des marqueurs en sélection : des QTL à la Sélection Génomique, HDR, Université Paris Sud
  • Rincent R., Charcosset A. , Moreau L. . (2017) Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations. Theor Appl Genet, 11 (130) 2231-2247
  • Fernandez O., Urrutia M., Bernillon S., Giauffret C., Tardieu F., Le Gouis J., Langlade N., Charcosset A. , Moing A., Gibon Y.. (2016) Fortune telling: metabolic markers of plant performance. Metabolomics, 10 (12) 158
  • Giraud H., 2016-01-22 22/01/16, Genetic analysis of hybrid value for silage maize in multiparental designs : QTL detection and genomic selection, PhD thesis, Université Paris-Sud
  • Millet E., Welcker C., Kruijer W., Negro S., Nicolas S., Coupel-Ledru A., Bauland C., Praud S., Ranc N., Presterl T., Tuberosa R., Bedo Z., Draye X., Usadel B., Charcosset A. , van Eeuwijk F., Tardieu F.. (2016) Genome-wide analysis of yield in Europe: allelic effects as functions of drought and heat scenarios. Plant physiology, 2 (172) 749-764
  • Moreau L. , Charmet G., Charcosset A. , Le Gouis J., Deretz S.. (2016) Quelle place pour la selection génomique chez les espèces de grande culture ?. ,
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