REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits

Abstract

ABSTRACT. 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/BLUPbased 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 yieldrelated 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 genotypevs-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 (R 2 > 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 highyield 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.

Multi‐trait stability index: A tool for simultaneous selection of soya bean genotypes in drought and saline stress

“…To manage and understand these variations when evaluating a relatively large set of genotypes, as well as several stresses at the same time, it is necessary to employ appropriate techniques for this purpose. Techniques such as PCA (principal component analysis) (Füzy et al, 2019;Giordani et al, 2019;Ramzan et al, 2019), ranking (Mazengo et al, 2019), BLUP (best linear unbiased prediction) (Nardino et al, 2016;Olivoto et al, 2017), AMMI (additive main effects and multiplicative interaction) (Bocianowski, Niemann, & Nowosad, 2019;Veenstra, Santantonio, Jannink, & Sorrells, 2019), and the combination of BLUP and AMMI (Olivoto, Lúcio, Silva, Marchioro, et al, 2019) and MTSI (multi-trait stability index) (Olivoto, Lúcio, Silva, Sari, et al, 2019) have been employed.…”

Stability analysis of tuber yield using unbalanced data from potato variety trials

“…In this context, the BLUP method could be adequately used to predict yield of genotypes in the environments where they are not examined (PIEPHO et al, 2003, BUNTARAN et al, 2019. Recently, REML/BLUP methods have been used to investigate the G×E interaction in different crops such as: wheat (YAN et al, 2002;STUDNICKI et al, 2015), maize (SO and EDWARDS, 2011;OLIVOTO et al, 2017) soybean RAJCAN, 2003), cowpea (SANTOS et al, 2016), sorghum (ALMEIDA FILHO et al, 2014; and sweat potatoes (TICONA-BENAVENTE and SILVA FILHO, 2015).…”

metan: An R package for multi‐environment trial analysis

“…Multi‐environment trials often generate data on several traits, and these data should be exploited. In breeding trials (as well as in many other areas), indirect selection helps geneticists and breeders to select superior genotypes (Ferrari et al, 2018; Fonseca, Lima, Dardengo, Silva, & Xavier, 2019; Gediya et al, 2019; Lopes Costa, Melo, & Oliveira Mano, 2019; Meira et al, 2017; Olivoto, de Souza, et al, 2017; Olivoto, Nardino, et al, 2017; Santos et al, 2018); thus, any tool that facilitates this work is welcome. metan provides useful functions for implementing biometrical models easily.…”