Abstract
ABSTRACT Mixed models and multivariate analysis are powerful tools for selecting superior genotypes in plant breeding programs. The BLUP (best linear unbiased prediction) method has been used to predict genetic values without environmental effects. Furthermore, the FAI-BLUP (ideotype-design index) procedure is especially valuable for plant breeding because of multiple-trait selection. This study aimed to determine the genetic potential of advanced wheat generations using REML/BLUP in combination with multivariate techniques for the selection of superior genotypes. The experiment consisted of eleven wheat (Triticum aestivum L.) genotypes. The experimental design was randomized blocks, with three replications. Plant height, spike insertion height, number of tillers, number of spikelets, kernel width, hectoliter weight and kernel weight per plant were determined. The genetic parameters were estimated using the REML/BLUP methodology, and the FAI-BLUP index was calculated using predicted genetic values. The genotypes UFSMFW 1-02, UFSMFW 1-05 and UFSMFW 1-04 show potential to increase the grain yield. The selection gains for number of tillers (14.63 %) and kernel weight per plant (22.35 %) indicate the potential to select superior genotypes.
Highlights
Wheat (Triticum aestivum L.) is a staple food worldwide and, as such, an important part of the daily diet and a carbohydrate source of millions of people (Litoriya et al 2018)
One of the most appropriate selection procedures involves the estimation of variance components and the prediction of genotypic values (Resende 2007)
This study aimed to determine the genetic potential of advanced wheat genotypes using Restricted Maximum Likelihood (REML)/ BLUP combined with multivariate techniques to select superior genotypes
Summary
Wheat (Triticum aestivum L.) is a staple food worldwide and, as such, an important part of the daily diet and a carbohydrate source of millions of people (Litoriya et al 2018). The use of mixed models facilitates the prediction of genotypic values and improves selection efficiency (Pimentel et al 2014). In this context, one of the most appropriate selection procedures involves the estimation of variance components (i.e., restricted maximum likelihood - REML) and the prediction of genotypic values (i.e., best linear unbiased prediction - BLUP) (Resende 2007). Plant breeding studies use these methods, such as for white oat (Avena sativa L.; Olivoto et al 2019), soybean (Glycine max L.; Follmann et al 2019), bean (Phaseolus vulgaris L.; Santos et al 2019) and corn (Zea mays L.; Olivoto et al 2017), as well as in early-segregating generations of wheat (Pimentel et al 2014, Woyann et al 2019b)
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