Author

Mahmood Gholami

KWS Saat (Germany)

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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Bioinformatics· 1 Genetics· 1

Signature Assays, Organisms & Methods

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Research Focus:imputation· 2 Organism:plant (other)· 2 Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Research Focus:benchmarking· 1 Approach:experimental assay· 1 Research Focus:variant calling· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Andrew WhaleniD· 1 shared Helena Sofia da Silva· 1 shared John M. HickeyiD· 1 shared S. ThorniD· 1 shared Sonja KollersiD· 1 shared Timothy M. BeissingeriD· 1 shared Tobias A. M. NiehoffiD· 1 shared Torsten PookiD· 1 shared

Activity by Year

2021-2022 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 18 mutual co-author links

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1 shared paperGenetics

Andrew WhalenORCID

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2 papers

Recent·Most cited

Imputation of Low-density Marker Chip Data in Plant Breeding: Evaluation of Methods Based on Sugar Beet

Tobias A. M. Niehoff, Torsten Pook, Mahmood Gholami, Timothy M. Beissinger

This paper evaluates imputation methods for low-density marker data in sugar beet breeding, finding that AlphaPlantImpute2 performs well with default settings while Beagle benefits from parameter tuning, and combining both tools yields the highest accuracy.

The Plant Genome · Oct 18, 2022Peer-reviewedBioinformatics

Performance of genetic imputation across commercial crop species

S. Thorn, Andrew Whalen, Sonja Kollers, Mahmood Gholami, Helena Sofia da Silva, Valentin Wimmer, John M. Hickey

The paper demonstrates accurate genetic imputation in maize, sugar beet, and wheat without pedigrees, outperforming Beagle 5.1 in most scenarios.

bioRxiv · Dec 1, 2021 · PreprintGenetics