Leveraging probability concepts for cultivar recommendation in multi-environment trials

Autor(es)

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.

• Tipo de documento: Artigo Científico
• Fonte: THEORETICAL AND APPLIED GENETICS; v. 135, n. 4, p. 15-pg., 2022-02-22.

Resumo

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)

• Processo FAPESP: 16/12977-7 - Implementação de seleção genômica em milho por meio de modelo genético-estatístico que integra efeitos da interação genótipos por ambientes e efeitos genéticos aditivos e não aditivos
• Beneficiário: Kaio Olimpio das Graças Dias
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 17/03625-2 - Desenvolvimento de modelos genético-estatísticos temporais de seleção genômica via redes bayesianas: uma aplicação em sorgo
• Beneficiário: Jhonathan Pedroso Rigal dos Santos
• Modalidade de apoio: Bolsas no Brasil - Doutorado