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Crop phenotype prediction using biclustering to explain genotype-by-environment interactions

Phenotypic variation in plants is attributed to genotype (G), environment (E), and genotype-by-environment interaction (GEI). Although the main effects of G and E are typically larger and easier to model, the GEI interaction effects are important and a critical factor when considering such issues as...

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Autores principales: Pham, Hieu, Reisner, John, Swift, Ashley, Olafsson, Sigurdur, Vardeman, Stephen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530907/
https://www.ncbi.nlm.nih.gov/pubmed/36204056
http://dx.doi.org/10.3389/fpls.2022.975976
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author Pham, Hieu
Reisner, John
Swift, Ashley
Olafsson, Sigurdur
Vardeman, Stephen
author_facet Pham, Hieu
Reisner, John
Swift, Ashley
Olafsson, Sigurdur
Vardeman, Stephen
author_sort Pham, Hieu
collection PubMed
description Phenotypic variation in plants is attributed to genotype (G), environment (E), and genotype-by-environment interaction (GEI). Although the main effects of G and E are typically larger and easier to model, the GEI interaction effects are important and a critical factor when considering such issues as to why some genotypes perform consistently well across a range of environments. In plant breeding, a major challenge is limited information, including a single genotype is tested in only a small subset of all possible test environments. The two-way table of phenotype responses will therefore commonly contain missing data. In this paper, we propose a new model of GEI effects that only requires an input of a two-way table of phenotype observations, with genotypes as rows and environments as columns that do not assume the completeness of data. Our analysis can deal with this scenario as it utilizes a novel biclustering algorithm that can handle missing values, resulting in an output of homogeneous cells with no interactions between G and E. In other words, we identify subsets of genotypes and environments where phenotype can be modeled simply. Based on this, we fit no-interaction models to predict phenotypes of a given crop and draw insights into how a particular cultivar will perform in the unused test environments. Our new methodology is validated on data from different plant species and phenotypes and shows superior performance compared to well-studied statistical approaches.
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spelling pubmed-95309072022-10-05 Crop phenotype prediction using biclustering to explain genotype-by-environment interactions Pham, Hieu Reisner, John Swift, Ashley Olafsson, Sigurdur Vardeman, Stephen Front Plant Sci Plant Science Phenotypic variation in plants is attributed to genotype (G), environment (E), and genotype-by-environment interaction (GEI). Although the main effects of G and E are typically larger and easier to model, the GEI interaction effects are important and a critical factor when considering such issues as to why some genotypes perform consistently well across a range of environments. In plant breeding, a major challenge is limited information, including a single genotype is tested in only a small subset of all possible test environments. The two-way table of phenotype responses will therefore commonly contain missing data. In this paper, we propose a new model of GEI effects that only requires an input of a two-way table of phenotype observations, with genotypes as rows and environments as columns that do not assume the completeness of data. Our analysis can deal with this scenario as it utilizes a novel biclustering algorithm that can handle missing values, resulting in an output of homogeneous cells with no interactions between G and E. In other words, we identify subsets of genotypes and environments where phenotype can be modeled simply. Based on this, we fit no-interaction models to predict phenotypes of a given crop and draw insights into how a particular cultivar will perform in the unused test environments. Our new methodology is validated on data from different plant species and phenotypes and shows superior performance compared to well-studied statistical approaches. Frontiers Media S.A. 2022-09-20 /pmc/articles/PMC9530907/ /pubmed/36204056 http://dx.doi.org/10.3389/fpls.2022.975976 Text en Copyright © 2022 Pham, Reisner, Swift, Olafsson and Vardeman. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Pham, Hieu
Reisner, John
Swift, Ashley
Olafsson, Sigurdur
Vardeman, Stephen
Crop phenotype prediction using biclustering to explain genotype-by-environment interactions
title Crop phenotype prediction using biclustering to explain genotype-by-environment interactions
title_full Crop phenotype prediction using biclustering to explain genotype-by-environment interactions
title_fullStr Crop phenotype prediction using biclustering to explain genotype-by-environment interactions
title_full_unstemmed Crop phenotype prediction using biclustering to explain genotype-by-environment interactions
title_short Crop phenotype prediction using biclustering to explain genotype-by-environment interactions
title_sort crop phenotype prediction using biclustering to explain genotype-by-environment interactions
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530907/
https://www.ncbi.nlm.nih.gov/pubmed/36204056
http://dx.doi.org/10.3389/fpls.2022.975976
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