Author

Jérôme Bartholomé

Centre de Coopération Internationale en Recherche Agronomique pour le DéveloppementORCID 0000-0002-0855-3828Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetics· 2

Signature Assays, Organisms & Methods

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Organism:plant (other)· 2 Approach:statistical model· 2 Research Focus:variant effect prediction· 2 Assay:whole-genome sequencing· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:genome assembly· 1 Research Focus:GWAS / association mapping· 1

Frequent Co-Authors

Nourollah AhmadiiD· 2 shared ADITI BHANDARIiD· 1 shared Arvind KumariD· 1 shared Giampiero ValèiD· 1 shared Julien FrouiniD· 1 shared Manel Ben HasseniD· 1 shared Nilima Kumari· 1 shared Tuong-Vi Cao-Hamadoun· 1 shared

Activity by Year

2018-2019 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 21 mutual co-author links

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2 shared papersGenetics

Nourollah AhmadiORCID

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2 papers

Recent·Most cited

Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice

ADITI BHANDARI, Jérôme Bartholomé, Tuong-Vi Cao-Hamadoun, Nilima Kumari, Julien Frouin, Arvind Kumar, Nourollah Ahmadi

This study improves genomic prediction for drought tolerance in rice by using trait-specific markers and multi-environment models, achieving higher predictive ability than traditional methods.

PLoS ONE · May 6, 2019Peer-reviewedGenetics

Genomic prediction accounting for genotype by environment interaction offers an effective framework for breeding simultaneously for adaptation to an abiotic stress and performance under normal cropping conditions in rice

Manel Ben Hassen, Jérôme Bartholomé, Giampiero Valè, Tuong‐Vi Cao, Nourollah Ahmadi

This study uses genomic prediction with genotype by environment interaction to improve rice breeding for adaptation to alternate wetting and drying, showing multi-environment models outperform single-environment models.

G3 Genes Genomes Genetics · May 9, 2018Peer-reviewedGenetics