<b>Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model

Abstract

ABSTRACT. Variance components must be obtained to estimate genetic parameters and predict breeding values. This information can be obtained through Bayesian inference. When multiple traits are evaluated, Bayesian inference can be used in multi-trait models. The objective of this study was to obtain estimates of genetic parameters, gains with selection, and genetic correlations among traits. Likewise, we aim to predict the genetic values and select the best kale genotypes using the Bayesian approach in a multi-trait linear model. The following traits were evaluated: stem diameter, plant height, number of shoots, number of marketable leaves and fresh weight of leaves using Bayesian inference in 22 kale genotypes. The experiment consisted of a randomized block design with three replications and four plants per plot. Genetic effects predominated over environmental effects. The highest correlation estimates were found between the fresh weight of leaves and stem diameter and between the plant height and number of marketable leaves. The following commercial cultivars and genotypes are recommended for cultivation and to integrate into breeding programs: UFLA 11, UFLA 5, UFLA 6, UFVJM 3 and UFVJM 19. The estimates of the gain with selection indicate the potential for improvement of the studied population.Keywords: Brassica oleracea L. var. acephala DC., genetic parameters, crop breeding, statistical modeling, correlations.Parâmetros populacionais e seleção de genótipos de couve por inferência bayesiana em modelo linear multicaracterístico RESUMO. Para selecionar genitores em programas de melhoramento deve-se obter os componentes de variância para estimar parâmetros genéticos e predizer valores genéticos, os quais podem ser obtidos vantajosamente pela inferência bayesiana. Quando várias características são avaliadas a inferência bayesiana pode ser utilizada em modelos multicaracterísticos. Objetivou-se obter estimativas de parâmetros genéticos, ganhos de seleção, conhecer as correlações genéticas entre as características, predizer valores genéticos e selecionar melhores genótipos de couve utilizando a abordagem bayesiana em modelo linear multicaracterístico. Foram avaliados o diâmetro do caule, altura da planta, número de brotações, número de folhas comercializáveis e massa fresca de folhas por inferência bayesiana em 22 genótipos de couve. Foi utilizado o delineamento em blocos casualizados com três repetições e quatro plantas por parcela. Verificou-se a predominância dos efeitos genéticos sobre os ambientais. As maiores estimativas de correlação foram encontradas entre a matéria fresca de folhas e as características diâmetro do caule, altura de plantas e número de folhas comercializáveis. Além das testemunhas comerciais, são indicados para o cultivo e para integrar programas de melhoramento os genótipos UFLA 11, UFLA 5, UFLA 6, UFVJM 3 e UFVJM 19. As estimativas do ganho de seleção indicaram o potencial de melhoramento para a população estudada.

Genetic parameters and selection gain in tropical wheat populations via Bayesian inference

“…In this process, the selection of progenies occurs from a performance evaluation according to the breeder's criteria (SILVA et al, 2019). The literature reports different successful cases involving Bayesian inference, such as the selection of guava (Psidium guajava L.) (SILVA et al, 2020), kale (Brassica oleracea L. var acephala DC) (AZEVEDO et al, 2017), and eucalyptus populations (Eucalyptus globulus) (MORA et al, 2019).…”

“…Bayesian approaches have many practical applications in breeding programs, including the study of adaptability and stability in genotypes of Gossypium L. (NASCIMENTO et al, 2020) and Zea mays (OLIVEIRA et al, 2018), repeatability analysis in Jatropha curcas (PEIXOTO et al, 2021), and parameter estimates and population selection in Brassica oleracea L. (AZEVEDO et al, 2017). Moreover, the Bayesian approach produces information regarding distributions and credibility intervals; however, wheat breeding programs do not often report its use.…”

Study of repeatability and phenotypical stabilization in kale using frequentist, Bayesian and bootstrap resampling approaches

“…Kale is a vegetable with nutritional characteristics of interest, such as high levels of carbohydrates, proteins, vitamins and fibre (Novo, Prela-Pantano, Trani, & Blat, 2010). However, there is a need for cultivars with higher productivity and resistance to the crop's major pests (Azevedo et al, 2016a;Azevedo et al, 2017).…”

“…Intensive computing methodologies such as bootstrap resampling can also be used for this purpose, dispensing assumptions about the probability distribution of parameters (Efron & Tibshirani, 1993). Another possible approach for estimating reliability is the Bayesian approach to obtaining credibility intervals from an a posteriori density distribution (Zhang, Rojas, & Cuervo, 2010;Santos et al, 2011;Azevedo et al, 2017).…”