Author

Reyna Persa

University of Florida

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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Genomics· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Approach:statistical model· 3 Research Focus:variant calling· 1

Frequent Co-Authors

Diego JarquíniD· 3 shared Chen Taishen· 1 shared Damaris A. OdenyiD· 1 shared David Kalule OkelloiD· 1 shared Davis M. Gimode· 1 shared Fernando Silva AguilariD· 1 shared Hiromi Kajiya‐KanegaeiD· 1 shared Hiroshi NakagawaiD· 1 shared

Activity by Year

2020-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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3 shared papersGenomics

Diego JarquínORCID

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Author profile →

3 papers

Recent·Most cited

Genomic prediction by modelling genotype by environment interaction for yield in groundnut

Nelson Lubanga, Velma Okaron, Davis M. Gimode, Reyna Persa, James Kyalo Mwololo, David Kalule Okello, Mildred Ochwo‐Ssemakula, Thomas Lapaka Odong, Damaris A. Odeny, Diego Jarquín

This paper evaluates genomic prediction models for groundnut yield traits across environments, finding that incorporating genotype-by-environment interaction improves predictive ability in some cross-validation schemes.

bioRxiv · Oct 30, 2024 · PreprintGenomics

Sparse Testing Designs for Optimizing Predictive Ability in Sugarcane Populations

Julián García‐Abadillo, Paul Adunola, Fernando Silva Aguilar, Jhon Henry Trujillo‐Montenegro, John Jaime Riascos, Reyna Persa, Julio Isidro y Sánchez, Diego Jarquín

This study uses sparse testing designs with genomic prediction models to optimize predictive accuracy for sugarcane traits across environments.

Frontiers in Plant Science · Jul 23, 2024Peer-reviewedGenomics

Coupling Day Length Data and Genomic Prediction tools for Predicting Time-Related Traits under Complex Scenarios

Diego Jarquín, Hiromi Kajiya‐Kanegae, Chen Taishen, Shiori Yabe, Reyna Persa, Jianming Yu, Hiroshi Nakagawa, Masanori Yamasaki, Hiroyoshi Iwata

The paper develops methods that integrate day length data with genomic prediction tools to improve prediction of time-related traits like days to heading in unobserved environments.

Scientific Reports · Aug 7, 2020Peer-reviewedGenomics