Tristan MARY-HUARD

Statistical methods for quantitative genetics

 INRAE, Junior Investigator

 tristan.mary-huard@agroparistech.fr —   +33 (0)1 69 33 23 58


 Quantitative Genetics and Plant Breeding Methodology

Implication de Tristan Mary-Huard dans axe1 20% axe1 Understanding and shedding light on evolutionary mecanisms Axe1 Implication de Tristan Mary-Huard dans axe2 70% axe2 Understanding and predicting the genetic bases of interactions Axe2 Implication de Tristan Mary-Huard dans axe3 10% axe3 Understanding and mobilizing cultivated diversity to provide solutions for agriculture and its actors Axe3
 Publications    

  • Génétique Quantitative et Évolution - Le Moulon
  • Université Paris-Saclay, INRAE, CNRS, AgroParisTech
  • IDEEV
  • 12 route 128
  • 91190 Gif-sur-Yvette
Tristan MARY-HUARD

Positions and Education

  • Current: Researcher at INRA, GQE-Le Moulon and MIA Paris
  • 2007-2012: Associate professor in statistics at AgroParisTech
  • 2017: HDR Some contributions to statistical modeling and model selection with applications to genomics and quantitative genetics, Univ. Paris-Sud, 2017
  • 2006: PhD Dimension reduction and model selection for supervised classification, MIA Paris, Univ. Paris-Sud
  • 1998-2001: National School of Statistics and Data Analysis (ENSAI)

Research Interests

In statistics:

  • Model selection in classification and regression
  • Variable aggregation, variable selection
  • Latent variable models and Mixed models
  • Breakpoint detection

In genomics:

  • Data integration for multi-omics
  • Gene expression/methylation analysis

In quantitative/population genetics:

  • QTL detection
  • Genomic selection
  • Fst estimation

Teaching

  • Classification en grande dimension (avec C. Giraud), M2 Mathématiques pour les Sciences du Vivant (Univ. Paris Sud 11).
  • Statistique pour la génétique, (avec T. Van Dooren and ML. Martin-Magniette), M1 Sciences de la vie (ENS Paris).
  • Modèle linéaire pour l’ingénieur, (avec P. Barbillon), eq. M1 (IFP School).

Book and book chapters :

  • Le modèle linéaire et ses extensions, 2015, Ed. Ellipses (with L. Bel, JJ Daudin, M. Etienne, E. Lebarbier, T. Mary-Huard, S. Robin, C. Vuillet)
  • Introduction to Statistical Methods for Complex Systems, in Handbook of Statistical Systems Biology, 2011, John Wiley & Sons (with S. Robin)
  • Introduction to Statistical Methods for Microarray Data Analysis, in Mathematical and Computational Methods in Biology, 2006, Ed. Hermann: Paris (with F. Picard, S. Robin)

Selected publications

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
Celisse A, Mary-Huard T. (2018) Theoretical Analysis of Cross-Validation for Estimating the Risk of the k-Nearest Neighbor Classifier. Journal of Machine Learning Research, 58 (19) 1-54
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
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
Delatola EI, Lebarbier E, Mary-Huard T, Radvanyi F, Robin S, Wong J. (2017) SegCorr a statistical procedure for the detection of genomic regions of correlated expression. BMC Bioinformatics, 1 (18) 333
Roux F, Mary-Huard T, Barillot E, Wenes E, Botran L, Durand S, Villoutreix R, Martin-Magniette ML, Camilleri C, Budar F. (2016) Cytonuclear interactions affect adaptive traits of the annual plant Arabidopsis thaliana in the field. Proceedings of the National Academy of Sciences of the United States of America, 13 (113) 3687-92

Publications

  • Ali B., Huguenin-Bizot B., Laurent M., Chaumont F., Maistriaux LC., Nicolas S., Duborjal H., Welcker C., Tardieu F., Mary-Huard T. , Moreau L., Charcosset A., Runcie D., Rincent R.. (2024) High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions. Theor Appl Genet, 7 (137) 175
  • Balconi C., Galaretto A., Malvar RA., Nicolas SD., Redaelli R., Andjelkovic V., Revilla P., Bauland C., Gouesnard B., Butron A., Torri A., Barata AM., Kravic N., Combes V., Mendes-Moreira P., Murariu D., Šarčević H., Schierscher-Viret B., Vincent M., Zanetto A., Kessel B., Madur D., Mary-Huard T. , Pereira A., Placinta DD., Strigens A., Charcosset A., Goritschnig S.. (2024) Genetic and Phenotypic Evaluation of European Maize Landraces as a Tool for Conservation and Valorization of Agrobiodiversity. Biology, 6 (13) 454
  • De Walsche A., Gauthier F., Charcosset A., Mary-Huard T. . (2024) Large-scale composite hypothesis testing for omics analyses. ,
  • 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
  • Beugnot A., 07/02/2023, Hybrid performance in maize: from study of complementary between heterotic groups to genomic prediction, PhD, Université Paris-Saclay
  • 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. Plant Biology,
  • 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
  • Haug B., Messmer MM., Enjalbert J., Goldringer I., Flutre T., Mary-Huard T. , Hohmann P.. (2023) New insights towards breeding for mixed cropping of spring pea and barley to increase yield and yield stability. Field Crops Research, (297) 108923
  • 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
  • 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
  • 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
  • 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
  • Laporte F., Charcosset A., Mary-Huard T. . (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
  • 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
  • Bellucci E., Mario Aguilar O., Alseekh S., Bett K., Brezeanu C., Cook D., De la Rosa L., Delledonne M., Dostatny DF., Ferreira JJ., Geffroy V., Ghitarrini S., Kroc M., Kumar Agrawal S., Logozzo G., Marino M., Mary‐Huard T., McClean P., Meglič V., Messer T., Muel F., Nanni L., Neumann K., Servalli F., Străjeru S., Varshney RK., Vasconcelos MW., Zaccardelli M., Zavarzin A., Bitocchi E., Frontoni E., Fernie AR., Gioia T., Graner A., Guasch L., Prochnow L., Oppermann M., Susek K., Tenaillon M., Papa R.. (2021) The INCREASE project: Intelligent Collections of food‐legume genetic resources for European agrofood systems. The Plant Journal, 3 (108) 646-660
  • 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
  • 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
  • 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),
  • Rio S., Moreau L., Charcosset A., Mary-Huard T. . (2020) Accounting for Group-Specific Allele Effects and Admixture in Genomic Predictions: Theory and Experimental Evaluation in Maize. Genetics, 1 (216) 27-41
  • 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
  • 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
  • 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
  • Rio S., 2019-04, Contributions to genomic selection and association mapping in structured and admixed populations : application to maize, Theses, Université Paris Saclay (COmUE)
  • 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
  • Celisse A., Mary-Huard T. . (2018) Theoretical Analysis of Cross-Validation for Estimating the Risk of the k-Nearest Neighbor Classifier. Journal of Machine Learning Research, 58 (19) 1-54
  • 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
  • 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
  • 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
  • Brault V., Delattre M., Lebarbier E., Mary‐Huard T., Lévy‐Leduc C.. (2017) Estimating the Number of Block Boundaries from Diagonal Blockwise Matrices Without Penalization. Scandinavian Journal of Statistics, 2 (44) 563-580
  • Delatola EI., Lebarbier E., Mary-Huard T. , Radvanyi F., Robin S., Wong J.. (2017) SegCorr a statistical procedure for the detection of genomic regions of correlated expression. BMC Bioinformatics, 1 (18) 333
  • 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 JB., 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
  • Desclée de Maredsous C., Oozeer R., Barbillon P., Mary-Huard T. , Delteil C., Blachier F., Tomé D., van der Beek EM., Davila AM.. (2016) High-Protein Exposure during Gestation or Lactation or after Weaning Has a Period-Specific Signature on Rat Pup Weight, Adiposity, Food Intake, and Glucose Homeostasis up to 6 Weeks of Age. J. Nutr., 1 (146) 21-29
  • Friggens NC., Duvaux-Ponter C., Etienne MP., Mary-Huard T. , Schmidely P.. (2016) Characterizing individual differences in animal responses to a nutritional challenge: Toward improved robustness measures. Journal of Dairy Science, 4 (99) 2704-2718
  • Roux F., Mary-Huard T. , Barillot E., Wenes E., Botran L., Durand S., Villoutreix R., Martin-Magniette ML., Camilleri C., Budar F.. (2016) Cytonuclear interactions affect adaptive traits of the annual plant Arabidopsis thaliana in the field. Proceedings of the National Academy of Sciences of the United States of America, 13 (113) 3687-92
  • 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
  • Bel L., Daudin JJ., Etienne M., Lebarbier E., Mary-Huard T. , Robin S., Vuillet C., Daudin JJ.. (2015) Plans d’expérience. , 218-246
  • Desclée de Maredsous C., Oosting A., Delteil C., Blachier F., Barbillon P., Mary-Huard T. , Tome D., Oozeer R., Davila AM.. (2015) High-Protein Diet during Gestation Promotes Adiposity and Food Intake in Female Rat Pups in the Longer Term. Faseb J., 1 (29)
  • Zaag R., Tamby JP., Guichard C., Tariq Z., Rigaill G., Delannoy E., Renou JP., Balzergue S., Mary-Huard T. , Aubourg S., Martin-Magniette ML., Brunaud V.. (2015) GEM2Net: from gene expression modeling to -omics networks, a new CATdb module to investigate Arabidopsis thaliana genes involved in stress response. Nucleic Acids Res, D1 (43) D1010-D1017
  • 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