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Génétique Quantitative et Évolution - Le Moulon

GQMS - Quantitative Genetics and Plant Breeding Methodology

Our research is characterized by quantitative genetics approaches closely related to breeding methodology and the management of genetic diversity. They 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 specific loci
  • investigating the genetic determinism of complex traits, in view of direct applications in breeding through marker assisted selection and to gain a better understanding of the type of genetic effects which are involved. Specific attention is paid to flowering time, productivity under abiotic environmental constraints and heterosis,
  • optimizing the breeding process, from genetic resources to variety development.

This research involves theoretical and experimental approaches, development of statistical methods and decision support tools. Experimental approaches involve the development of original genetic materials in maize, their genotyping and their phenotyping. They are conducted by team members and also benefit to a large extent of the support of INRA experimental structures.

Research Topics

Our research is characterized by quantitative genetics approaches closely related to breeding methodology and the management of genetic diversity. They 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 specific loci. investigating the genetic determinism of complex traits, in view of direct applications in breeding through marker assisted selection and to gain a better understanding of the type of genetic effects which are involved. A specific attention is paid to flowering time, productivity under abiotic environmental constraints and heterosis, optimizing the breeding process, from genetic resources to variety development.

This research involves theoretical and experimental approaches, the development of statistical methods and decision support tools. Experimental approaches involve the development of original genetic materials in maize, their genotyping and their phenotyping. They are conducted by team members and also benefit to a large extent from the support of INRA experimental structures.

  1. Effect of migrations and breeding on the organization of maize genetic diversity

1.1. Molecular polymorphism in maize germplasm

New technologies of genotyping and sequencing offer new opportunities for genetic studies. We contribute to the development of new methods and tools such as:

  • the first 50k SNP chip (collaborative project, Ganal et al., 2011, Plos one). We used this chip to genotype of around 1200 maize lines, 250 landraces and 230 Highly Recombinant Inbred Lines.
  • the discovery of new polymorphisms such as SNPs or indels or more complex ones as CNVs in collaboration with ABI and GEAR teams. To discover these new polymorphisms, we achieved deep reseqencing of 5 inbred lines including the French reference line Fv2 (projects Amaizing and CNV-maize) and of 120 lines at a lower depth (projects Amaizing, http://www.amaizing.fr/, and Cornfed).

1.2 Consequences of evolution and breeding on the organization of genetic diversity

We aim at deciphering how evolution and modern breeding shaped the genetic diversity.

Following our previous studies on traditional maize varieties in Europe (Tenaillon & Charcosset, 2011, C R Biol), we extended our research to Asia and Africa within an international cooperation (Generation Challenge Program). More than 800 landraces were genotyped to evaluate the contribution of American maize genetic groups to the world diversity, hybridization areas were pointed out (Mir et al., 2013, Theor Appl Genet). We showed the emergence of new genetic groups resulting from recent breeding (Truntzler et al., 2011, Theor Appl Genet).

1.3 Modelling of linkage desequilibrium

We have been studying the range and variability of linkage disequilibrium (LD) along the genome in different panels and evaluated the impact of genetic structure on LD.

In a panel of 375 lines, the average LD has been found to be quite broad (197 kb for a R2 of 0.1) and to be amplified by the genetic structure of the population (Bouchet et al., 2013, Plos One). Population structure and kinship must therefore be taken into account in the different models of association mapping (Veyrieras et al., 2007b, Crop Sci, Mezmouk et al., 2011, Theor Appl Genet) We evaluated several tools for haplotype reconstruction and imputation. Our results show that lines can share large IBD regions (coll. B. Servin et B. Mangin, INRA Toulouse). We use this information on local identity to develop models for GWAS and QTL detection in multiparental context (see below).

  1. Genetic determinism of complex traits

2.1. Effect of linkage on the variation of quantitative traits. QTL fine mapping

Using advanced intermated populations has been proposed as a way to increase the accuracy of mapping experiments. An F3 population of 300 lines and an advanced intermated F3 population of 322 lines, both derived from the same parental maize inbred lines were jointly evaluated for agronomical traits. We observed that:

The genetic variance for grain yield is significantly lower in the intermated F3 population, which suggests that single QTL in the classical F3 population should generally correspond to clusters of QTL in coupling phase (Huang et al., 2009, Genetics) This hypothesis was validated by the fine mapping of one QTL that showed that its apparent pleiotropic effect for grain yield and quality was due to two different QTLs with small effects.

2.2. Multiparental QTL mapping designs and allelic series

We contributed to the development of the BioMercator software to carry out meta-analysis of QTLs (in collaboration with the ABI team of the lab, Sosnowski et al., 2011, Bioinformatics, available at

http://moulon.inra.fr/index.php/fr/equipestransversales/atelier-de-bioinformatique/projects/projets/135). We used this software to reduce the confidence intervals of QTLs for silage quality (Truntzler et al., 2011, Theor Appl Genet) and water drought tolerance traits (Welcker et al., 2011, Plant Physiol). To get further insight into the allelic diversity of QTLs, we performed QTL detection in connected-multiparental designs, considering IBD probabilities of chromosome segments of parental lines (Bardol et al., 2013, Theor Appl Genet).

2.3. Heterosis

We extended the North Carolina design III (NCIII) by using three populations of recombinant inbred lines derived from three parental lines belonging to different heterotic pools, crossed with each parental line to obtain nine families of hybrids. Most of the QTL detected for grain yield are located in pericentromeric regions and display apparent overdominance effects and limited differences between heterozygous genotypes, whereas for grain moisture predominance of additive effects was observed (Larièpe et al., 2012, Genetics).

  1. Fine mapping and diversity organisation at major QTL

The fine mapping of QTLs was carried out by analysing of a large number of recombinants or by association studies.

vgt1 region - The distribution of the early allele fits with the environmental conditions and thus, reinforces the role of this locus in adaptation to the temperate area (Ducrocq et al.,2008, Genetics).

ZmCCT - A major QTL was localised near ZmCCT (170 kb) in an hypo-recombinating region (Ducrocq et al. 2009, Genetics) and its effect on earliness was later confirmed by other colleagues. This locus displays a limited number of haplotypes (fewer than for vgt1 locus), the effects of which were confirmed using near isogenic lines.

Tga1 and Su1 - A QTL affecting different agronomic traits was mapped in the region of Tga1 (involved in the domestication process) and Su1 (carbohydrate metabolism) on chromosome 4. Both genes show a strong decrease of nucleotidic diversity compared to teosinte.

Zcn8 and genome wide studies - Association genetics conducted with the 50k SNP chip highlighted a major effect of Zcn8 locus, which most likely corresponds to QTL vgt2 (Bouchet et al., 2013, Plos One). This contributes to deciphering the major contribution of the vgt1 - vgt2 region to flowering time variation in maize.

  1. Optimization of marker assisted selection

Following our results on the advantages of leading recurrent marker assisted selection for the multiparental schemes (Blanc et al., 2006, Theor Appl Genet; Blanc et al., 2008, Euphytica), we developed the OptiMAS software (Valente et al., 2013, Journal of Heredity, available at http://moulon.inra.fr/optimas/) to follow the transmission of favorable alleles in breeding schemes in order to choose the best parents to be crossed to increase the probability of obtaining the molecular ideotype at the targeted QTL.

We also strongly invest in the evaluation of genomic selection, especially for complex traits. This approach is based on the genotyping and phenotyping of a reference sample of individuals for the calibration of a model, which is then applied to predict the value of individuals based only on genotyping data. This avoids QTL detection step, which increases prediction efficiency for highly complex traits.

We showed that his approach was suitable in a multiparental context for a complex trait such as yield (Bardol et al., submitted) and that its efficiency can be significantly increased by optimizing the calibration of the reference population (Rincent et al., 2013, Genetics). A R-code was developed to help choosing the lines to be included in the reference population to increase the reliability of genomic predictions and is available on request.


GQMS is attached to the LabEx SPS, Sciences des Plantes de Saclay.

Members

Publications

Allier A, 20 janvier 2020, Contributions to genetic diversity management in maize breeding programs using genomic selection
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
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, Teyssèdre S, Lehermeier C, Charcosset A, Moreau L. (2019) Genomic prediction with a maize collaborative panel: identification of genetic resources to enrich elite breeding programs. Theor Appl Genet,
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
Blein-Nicolas M, Negro SS, Balliau T, Welcker C, Bosquet LC, Nicolas SD, Charcosset A, Zivy M. (2019) A proteomics-based systems genetics approach reveals environment-specific loci modulating protein co-expression and drought-related traits in maize. bioRxiv, 636514
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
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
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
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
Vienne D, Fiévet JB. (2019) Heterosis indices: What do we really measure?. bioRxiv, 800441
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 JB, 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)
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
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
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
Mary-Huard T, 2017-06-12 06/12/17, Some contributions to statistical modeling and model selection with applications to genomics and quantitative genetics
Moreau L, 2017-06-13 13/06/17, Utilisation des marqueurs en sélection : des QTL à la Sélection Génomique
Giraud H, 2016-01-22 22/01/16, Genetic analysis of hybrid value for silage maize in multiparental designs : QTL detection and genomic selection
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 ?. ,
Nicolas SD, Peros JP, Lacombe T, Launay A, Le Paslier MC, Berard A, Mangin B, Valiere S, Martins F, Le Cunff L, Laucou V, Bacilieri R, Dereeper A, Chatelet P, This P, Doligez A. (2016) Genetic diversity, linkage disequilibrium and power of a large grapevine (Vitis vinifera L) diversity panel newly designed for association studies. BMC plant biology, 1 (16) 74
Tenaillon MI, Manicacci D, Nicolas SD, Tardieu F, Welcker C. (2016) Testing the link between genome size and growth rate in maize. PeerJ, (4) e2408
Bel L, Daudin JJ, Etienne M, Lebarbier E, Mary-Huard T, Robin S, Vuillet C, Daudin JJ. (2015) Plans d’expérience. , 218-246
Bel L, Daudin JJ, Etienne M, Lebarbier E, Mary-Huard T, Robin S, Vuillet C, Daudin JJ. (2015) Modèle mixte, modélisation de la variance. , 162-189
Gallais A, Editions Quae . (2015) Pour comprendre l'amélioration des plantes. Enjeux, méthodes, objectifs et critères de sélection. ,
Berthet E, Charcosset A, Lemarié S, Moreau L, Segrestin B, Debaeke P, Quilot-Turion B. (2014) Nouvelles questions pour la conception.. ,
Rincent R, Moreau L, Monod H, Kuhn E, Melchinger AE, Malvar RA, Moreno-Gonzalez J, Nicolas S, Madur D, Combes V, Dumas F, Altmann T, Brunel D, Ouzunova M, Flament P, Dubreuil P, Charcosset A, Mary-Huard T. (2014) Recovering power in association mapping panels with variable levels of linkage disequilibrium. Genetics, 1 (197) 375-87
Rincent R, Nicolas S, Bouchet S, Altmann T, Brunel D, Revilla P, Malvar RA, Moreno-Gonzalez J, Campo L, Melchinger AE, Schipprack W, Bauer E, Schoen CC, Meyer N, Ouzunova M, Dubreuil P, Giauffret C, Madur D, Combes V, Dumas F, Bauland C, Jamin P, Laborde J, Flament P, Moreau L, Charcosset A. (2014) Dent and Flint maize diversity panels reveal important genetic potential for increasing biomass production. Theor Appl Genet, 11 (127) 2313-31
Rincent R, 2014-11-04 11/04/14, Optimization of association genetics and genomic selection strategies for populations of different diversity levels. Application in maize (Zea mays L.)
Valente F, Gauthier F, Bardol N, Blanc G, Joets J, Charcosset A, Moreau L, Fleury D, Whitford R. (2014) OptiMAS: A Decision Support Tool to Conduct Marker-Assisted Selection Programs. , (1145) 97-116
*Houel C, Martin-Magniette ML, Nicolas SD, Lacombe T, Le Cunff L, Franck D, Torregrosa L, Conéjéro G, Lalet S, This P, Adam-Blondon AF. (2013) Genetic diversity of berry size in grapevine (Vitis vinifera L.). Aus J Grape and Wine R, 2 (19) 208-220
Bardol N, 2013-08-03 08/03/13, Interest of dense genotyping for selection in multi-parental designs. Comparison of two marker-assisted selection approaches: Genomewide selection and QTL-LDLA based selection. Application in maize (Zea mays L.)
Bouchet S, Servin B, Bertin P, Madur D, Combes V, Dumas F, Brunel D, Laborde J, Charcosset A, Nicolas S. (2013) Adaptation of maize to temperate climates: mid-density genome-wide association genetics and diversity patterns reveal key genomic regions, with a major contribution of the Vgt2 (ZCN8) locus. PloS one, 8 (8) e71377
Khobragade A, 2012-03-23 23/03/12, Combining linkage analysis and association genetics to finely map loci involved in maize grain production and maturation -- Cartographie fine de locus impliqués dans la production et la maturation du grain chez le maïs par une approche conjointe de linkage et de génétique d’association
Mangin B, Siberchicot A, Nicolas S, Doligez A, This P, Cierco-Ayrolles C. (2012) Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness. Heredity, 3 (108) 285-91
Nicolas SD, Monod H, Eber F, Chevre AM, Jenczewski E. (2012) Non-random distribution of extensive chromosome rearrangements in Brassica napus depends on genome organization. The Plant journal : for cell and molecular biology, 4 (70) 691-703
Rincent R, Laloe D, Nicolas S, Altmann T, Brunel D, Revilla P, Rodriguez VM, Moreno-Gonzalez J, Melchinger A, Bauer E, Schoen CC, Meyer N, Giauffret C, Bauland C, Jamin P, Laborde J, Monod H, Flament P, Charcosset A, Moreau L. (2012) Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.). Genetics, 2 (192) 715-28
Truntzler M, Ranc N, Sawkins MC, Nicolas S, Manicacci D, Lespinasse D, Ribiere V, Galaup P, Servant F, Muller C, Madur D, Betran J, Charcosset A, Moreau L. (2012) Diversity and linkage disequilibrium features in a composite public/private dent maize panel: consequences for association genetics as evaluated from a case study using flowering time. TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 4 (125) 731-47
Charcosset A, Moreau L, Ricroch A, Dattée Y, Fellous M. (2011) Cartographie de QTL, génétique d’association et application en sélection. , 112-125
Dereeper A, Nicolas S, Le Cunff L, Bacilieri R, Doligez A, Peros JP, Ruiz M, This P. (2011) SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects. BMC bioinformatics, (12) 134
Fiévet JB, Gallais A, de Vienne D, Prioul JL, Thévenot C, Molnar T, Mike Burrel . (2011) Heterosis, inbreeding depression and hybrid varieties. , 67-87
Hirel B, Gallais A, Prioul JL, Thévenot C, Molnar T, Mike Burrel . (2011) Nitrogen use efficiency – Physiological, molecular and genetic investigations towards crop improvement. , 285-310
Moreau L, Charcosset A, Prioul JL, Thévenot C, Molnar T, Mike Burrel . (2011) Marker-assisted selection in maize. , 411-434