Genotype x environment interaction in cassava multi-environment trials via analytic factor.
The variability in genetic variance and covariance due to genotype × environment interaction (G×E) can hinder genotype selection accuracy, especially for complex traits. This study analyzed G×E interactions in cassava to identify stable, high-performing genotypes and predict agronomic performance in...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315370 |
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| author | Juraci Souza Sampaio Filho Isadora Cristina Martins Oliveira Maria Marta Pastina Marcos de Souza Campos Eder Jorge de Oliveira |
| author_facet | Juraci Souza Sampaio Filho Isadora Cristina Martins Oliveira Maria Marta Pastina Marcos de Souza Campos Eder Jorge de Oliveira |
| author_sort | Juraci Souza Sampaio Filho |
| collection | DOAJ |
| description | The variability in genetic variance and covariance due to genotype × environment interaction (G×E) can hinder genotype selection accuracy, especially for complex traits. This study analyzed G×E interactions in cassava to identify stable, high-performing genotypes and predict agronomic performance in untested environments using factor analytic multiplicative mixed models (FAMM) within multi-environment trials (METs). We evaluated 22 cassava genotypes for fresh root yield (FRY), dry root yield (DRY), shoot yield (ShY), and dry matter content (DMC) across 55 Brazilian environments. FAMM was applied to estimate genetic values and environmental loads, revealing significant genetic variance, especially for FRY (0.16-0.92) and broad-sense heritability ([Formula: see text]) above 0.70 in advanced yield trials. In joint analyses, analytic factor FA4 explained over 88% of genetic variation for all traits despite high G×E and data imbalance. Positive genetic correlations were found between environments for ShY and DRY (0.99 and 1.0, respectively), while FRY and DMC showed negative correlations (-0.82 and -0.95). Latent regression analysis identified hybrids adaptable to a range of environments, as well as genotypes suited to specific conditions. Moderate correlations between environmental covariables (rainfall, altitude, solar radiation) and FA model loadings suggest these factors contribute to high G×E interactions, notably for FRY. The FAMM model provided a robust approach to G×E analysis in cassava, yielding practical insights for breeding programs. |
| format | Article |
| id | doaj-art-2c8c30146de1447381f58745cae59a7a |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-2c8c30146de1447381f58745cae59a7a2025-08-20T02:31:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031537010.1371/journal.pone.0315370Genotype x environment interaction in cassava multi-environment trials via analytic factor.Juraci Souza Sampaio FilhoIsadora Cristina Martins OliveiraMaria Marta PastinaMarcos de Souza CamposEder Jorge de OliveiraThe variability in genetic variance and covariance due to genotype × environment interaction (G×E) can hinder genotype selection accuracy, especially for complex traits. This study analyzed G×E interactions in cassava to identify stable, high-performing genotypes and predict agronomic performance in untested environments using factor analytic multiplicative mixed models (FAMM) within multi-environment trials (METs). We evaluated 22 cassava genotypes for fresh root yield (FRY), dry root yield (DRY), shoot yield (ShY), and dry matter content (DMC) across 55 Brazilian environments. FAMM was applied to estimate genetic values and environmental loads, revealing significant genetic variance, especially for FRY (0.16-0.92) and broad-sense heritability ([Formula: see text]) above 0.70 in advanced yield trials. In joint analyses, analytic factor FA4 explained over 88% of genetic variation for all traits despite high G×E and data imbalance. Positive genetic correlations were found between environments for ShY and DRY (0.99 and 1.0, respectively), while FRY and DMC showed negative correlations (-0.82 and -0.95). Latent regression analysis identified hybrids adaptable to a range of environments, as well as genotypes suited to specific conditions. Moderate correlations between environmental covariables (rainfall, altitude, solar radiation) and FA model loadings suggest these factors contribute to high G×E interactions, notably for FRY. The FAMM model provided a robust approach to G×E analysis in cassava, yielding practical insights for breeding programs.https://doi.org/10.1371/journal.pone.0315370 |
| spellingShingle | Juraci Souza Sampaio Filho Isadora Cristina Martins Oliveira Maria Marta Pastina Marcos de Souza Campos Eder Jorge de Oliveira Genotype x environment interaction in cassava multi-environment trials via analytic factor. PLoS ONE |
| title | Genotype x environment interaction in cassava multi-environment trials via analytic factor. |
| title_full | Genotype x environment interaction in cassava multi-environment trials via analytic factor. |
| title_fullStr | Genotype x environment interaction in cassava multi-environment trials via analytic factor. |
| title_full_unstemmed | Genotype x environment interaction in cassava multi-environment trials via analytic factor. |
| title_short | Genotype x environment interaction in cassava multi-environment trials via analytic factor. |
| title_sort | genotype x environment interaction in cassava multi environment trials via analytic factor |
| url | https://doi.org/10.1371/journal.pone.0315370 |
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