Author

Johnathon M. Shook

Iowa State University

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· 2 Bioinformatics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Organism:plant (other)· 3 Approach:statistical model· 3 Research Focus:genome assembly· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Approach:machine learning (classical)· 1 Research Focus:variant calling· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Asheesh Kumar SinghiD· 4 shared Arti SinghiD· 1 shared Baskar GanapathysubramanianiD· 1 shared Brian W. DiersiD· 1 shared Daniela A. L. LourencoiD· 1 shared Jiaoping ZhangiD· 1 shared Linjiang WuiD· 1 shared Sarah E. JonesiD· 1 shared

Activity by Year

2018-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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4 shared papersGenetics

Asheesh Kumar SinghORCID

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4 papers

Recent·Most cited

Genetic and environmental factors regulating soybean reproductive stages and their transitions

Swapan Chakrabarty, Johnathon M. Shook, Asheesh Kumar Singh

This study identifies genetic and environmental factors influencing soybean reproductive stages and develops a model to predict their effects.

bioRxiv · Sep 10, 2025 · PreprintGenetics

PATRIOT: A pipeline for tracing identical-by-descent chromosome segments to improve genomic prediction in self-pollinating crop species

Johnathon M. Shook, Daniela A. L. Lourenco, Asheesh Kumar Singh

PATRIOT improves genomic prediction accuracy in self-pollinating crops by tracing identical-by-descent segments, increasing accuracy by 16.6% over traditional methods.

Frontiers in Plant Science · Sep 29, 2021Peer-reviewedGenomics

Meta-GWAS for quantitative trait loci identification in soybean

Johnathon M. Shook, Jiaoping Zhang, Sarah E. Jones, Arti Singh, Brian W. Diers, Asheesh Kumar Singh

This study performs a meta-GWAS on soybean data to identify quantitative trait loci (QTL) associated with various traits, finding 483 QTL at 393 unique loci and 66 candidate genes.

G3 Genes Genomes Genetics · Apr 15, 2021Peer-reviewedGenetics

Integrating genotype and weather variables for soybean yield prediction using deep learning

Johnathon M. Shook, Linjiang Wu, Tryambak Gangopadhyay, Baskar Ganapathysubramanian, Soumik Sarkar, Asheesh Kumar Singh

This paper uses deep learning to predict soybean yield by integrating genotype and weather data from historical trials.

bioRxiv · May 25, 2018 · PreprintBioinformatics