REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits.
Methodologies using restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) in combination with sequential path analysis in maize are still limited in the literature. Therefore, the aims of this study were: i) to use REML/BLUP-based procedures in order to estimate variance components, genetic parameters, and genotypic values of simple maize hybrids, and ii) to fit stepwise regressions considering genotypic values to form a path diagram with multi-order predictors and minimum multicollinearity that explains the relationships of cause and effect among grain yield-related traits. Fifteen commercial simple maize hybrids were evaluated in multi-environment trials in a randomized complete block design with four replications. The environmental variance (78.80%) and genotype-vs-environment variance (20.83%) accounted for more than 99% of the phenotypic variance of grain yield, which difficult the direct selection of breeders for this trait. The sequential path analysis model allowed the selection of traits with high explanatory power and minimum multicollinearity, resulting in models with elevated fit (R2> 0.9 and ε < 0.3). The number of kernels per ear (NKE) and thousand-kernel weight (TKW) are the traits with the largest direct effects on grain yield (r = 0.66 and 0.73, respectively). The high accuracy of selection (0.86 and 0.89) associated with the high heritability of the average (0.732 and 0.794) for NKE and TKW, respectively, indicated good reliability and prospects of success in the indirect selection of hybrids with high-yield potential through these traits. The negative direct effect of NKE on TKW (r = -0.856), however, must be considered. The joint use of mixed models and sequential path analysis is effective in the evaluation of maize-breeding trials.
Departamento de Ciências Agronômicas e Ambientais, Universidade Federal de Santa Maria, Frederico Westphalen, RS, Brasil tiagoolivoto@gmail.com.
T
T
Olivoto
Departamento de Matemática e Estatística, Universidade Federal de Pelotas, Capão do Leão, RS, Brasil.
M
M
Nardino
Centro de Genômica e Fitomelhoramento, Universidade Federal de Pelotas, Capão do Leão, RS, Brasil.
I R
IR
Carvalho
Departamento de Agronomia, Universidade Federal de Santa Maria, Santa Maria, RS, Brasil.
D N
DN
Follmann
Centro de Genômica e Fitomelhoramento, Universidade Federal de Pelotas, Capão do Leão, RS, Brasil.
M
M
Ferrari
Departamento de Fitotecnia, Universidade Federal de Pelotas, Capão do Leão, RS, Brasil.
V J
VJ
Szareski
Centro de Genômica e Fitomelhoramento, Universidade Federal de Pelotas, Capão do Leão, RS, Brasil.
A J
AJ
de Pelegrin
Universidade Federal do Pampa, Dom Pedrito, RS, Brasil.
V Q
VQ
de Souza
D003433:Crosses, Genetic; D002523:Edible Grain / Q000235:genetics; D060005:Genotyping Techniques / Q000379:methods; D016013:Likelihood Functions; D008957:Models, Genetic; D000069600:Plant Breeding / Q000379:methods; D011336:Probability; D040641:Quantitative Trait Loci; D012044:Regression Analysis; D015203:Reproducibility of Results; D012641:Selection, Genetic; D003313:Zea mays / Q000235:genetics*