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Genotype x environment interaction in cassava multi-environment trials via analytic factor

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  • Juraci Souza Sampaio Filho
  • Isadora Cristina Martins Oliveira
  • Maria Marta Pastina
  • Marcos de Souza Campos
  • Eder Jorge de Oliveira

Abstract

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 (H^²) 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.

Suggested Citation

  • Juraci Souza Sampaio Filho & Isadora Cristina Martins Oliveira & Maria Marta Pastina & Marcos de Souza Campos & Eder Jorge de Oliveira, 2024. "Genotype x environment interaction in cassava multi-environment trials via analytic factor," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-37, December.
    • Handle: RePEc:plo:pone00:0315370
    DOI: 10.1371/journal.pone.0315370
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    References listed on IDEAS

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    1. Robin Thompson & Brian Cullis & Alison Smith & Arthur Gilmour, 2003. "A Sparse Implementation of the Average Information Algorithm for Factor Analytic and Reduced Rank Variance Models," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 45(4), pages 445-459, December.
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    1. Alison B. Smith & Lauren M. Borg & Beverley J. Gogel & Brian R. Cullis, 2019. "Estimation of Factor Analytic Mixed Models for the Analysis of Multi-treatment Multi-environment Trial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 573-588, December.

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