Author

Sandra Silvia Negro

Centre National de la Recherche ScientifiqueORCID 0000-0002-8462-2354Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genomics· 1 Plant Biology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:gene regulatory network inference· 2 Organism:zebrafish· 2 Assay:bulk RNA-seq· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Assay:proteomics· 1

Frequent Co-Authors

Stéphane NicolasiD· 3 shared Alain CharcossetiD· 2 shared Claude WelckeriD· 2 shared Delphine MaduriD· 2 shared Alexandra Launay‐AvoniD· 1 shared Anca Lucau‐DanilaiD· 1 shared Andrea Rau· 1 shared Benoît Decaux· 1 shared

Activity by Year

2019-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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3 shared papersPlant Biology

Stéphane NicolasORCID

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3 papers

Recent·Most cited

Plant BiologybioRxivPreprintMar 6, 2026

A major chromosome 4 region modulates early vigor under chilling through brassinosteroid signaling associated genes in maize

Maxence James, Camille Clipet, Kristelle Lourgant…·Catherine Giauffret

1 citation

zebrafishbulk RNA-seq

𝕏 PostBluesky

Systems BiologyGenome ResearchPeer-reviewedNov 1, 2020

A systems genetics approach reveals environment-dependent associations between SNPs, protein co-expression and drought-related traits in maize

Mélisande Blein‐Nicolas, Sandra Silvia Negro, Thierry Balliau…·Michel Zivy

This study identifies environment-dependent SNP-protein co-expression and trait associations in maize under drought conditions, revealing pleiotropic loci and candidate genes for drought tolerance.

25 citations

zebrafishproteomics

𝕏 PostBluesky

GenomicsBMC Plant BiologyPeer-reviewedJul 16, 2019

Genotyping-by-sequencing and microarrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies

Sandra Silvia Negro, Emilie J. Millet, Delphine Madur…·Stéphane Nicolas

The paper compares genotyping-by-sequencing and microarrays for detecting QTLs, showing they complement each other by tagging different haplotypes and improving detection power and localization.

90 citationstop 2%

GWAS summary statisticsstatistical model

𝕏 PostBluesky