Leveraging probability concepts for cultivar recommendation in multi-environment trials

Author(s)

Dias, Kaio O. G. ; dos Santos, Jhonathan P. R. ; Krause, Matheus D. ; Piepho, Hans-Peter ; Guimaraes, Lauro J. M. ; Pastina, Maria M. ; Garcia, Antonio A. F.

• Document type: Journal article
• Source: THEORETICAL AND APPLIED GENETICS; v. 135, n. 4, p. 15-pg., 2022-02-22.

Abstract

Key message We propose using probability concepts from Bayesian models to leverage a more informed decision-making process toward cultivar recommendation in multi-environment trials. Statistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate offspring, and obtain highly productive genotypes for target environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the No-U-Turn sampler algorithm to get Hamiltonian Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our findings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specific adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defined intensity of selection that results in a more informed decision-making process toward cultivar recommendation in multi-environment trials. (AU)

• FAPESP's process: 16/12977-7 - Genomic selection implementation in maize using a statistical-genetics model that accounts for genotype-by-environment interaction, additive and non-additive genetic effects
• Grantee: Kaio Olimpio das Graças Dias
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 17/03625-2 - Development of temporal genomic selection models via Bayesian Networks applied to sorghum bicolor
• Grantee: Jhonathan Pedroso Rigal dos Santos
• Support Opportunities: Scholarships in Brazil - Doctorate