Author

Morten Greve

DLF Trifolium (Denmark)

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Genetics· 3

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Research Focus:genome assembly· 1 Approach:machine learning (classical)· 1 Assay:metabolomics· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Torben AspiD· 3 shared Christian Sig Jensen· 2 shared Dario FèiD· 2 shared Elesandro BornhofeniD· 2 shared Ingo LenkiD· 2 shared Luc JanssiD· 2 shared Thomas Didion· 2 shared Andrea Bellucci· 1 shared

Activity by Year

2022-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersGenetics

Torben AspORCID

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Author profile →

3 papers

Recent·Most cited

Genomic prediction of agronomic traits in perennial ryegrass (Lolium perenne L.) and genotype x environment interactions at the limit of the species distribution

Natasha H. Johansen, Andrea Bellucci, Pernille B. Hansen, Petter Marum, Helga Amdahl, Kristin H. Gylstrøm, Odd Arne Rognli, Vilma Kemešytė, Gintaras Brazauskas, Morten Greve, Christer Persson, Mika Isolahti, Áslaug Helgadóttir, Rene Aavola, Torben Asp, Guillaume P. Ramstein

The paper evaluates genomic prediction models for agronomic traits in perennial ryegrass under genotype x environment interactions, showing improved accuracy when accounting for these interactions.

Theoretical and Applied Genetics · Oct 25, 2025Peer-reviewedGenetics

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

Elesandro Bornhofen, Dario Fè, István Nagy, Ingo Lenk, Morten Greve, Thomas Didion, Christian Sig Jensen, Torben Asp, Luc Janss

This study uses multi-omics data from grass hybrids to identify gene and metabolite networks associated with agronomic traits, finding conserved hubs but limited predictive power of network features.

BMC Genomics · Apr 24, 2023Peer-reviewedGenetics

Leveraging spatio-temporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models

Elesandro Bornhofen, Dario Fè, Ingo Lenk, Morten Greve, Thomas Didion, Christian Sig Jensen, Torben Asp, Luc Janss

This study uses random regression models to analyze spatio-temporal genomic data in ryegrass, finding improved prediction accuracy for nutritive quality traits but not dry matter yield.

The Plant Genome · Oct 3, 2022Peer-reviewedGenetics