Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding

Rodrigo Silva Alves,Fabyano Fonseca E Silva,Leonardo Lopes Bhering,João Romero Do Amaral Santos De Carvalho Rocha Rocha,Gleison Augusto Dos Santos,Paulo Eduardo Teodoro,Marcos Deon Vilela De Resende,Marco Antônio Peixoto

doi:10.1590/1678-4499.20200125

Rodrigo Silva Alves, Fabyano Fonseca E Silva + Show 6 more

Open Access

https://doi.org/10.1590/1678-4499.20200125

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Abstract

ABSTRACT An accurate, efficient and informative statistical method for analyses of genotype × environment (G × E) interactions is a key requirement for progress in any breeding program. Thus, the objective of this study was to quantify individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus (Eucalyptus spp.) breeding. To this end, a data set with 215 eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height (DBH) and Pilodyn penetration (PP) was used. Variance components were estimated by restricted maximum likelihood, and genetic values were predicted by best linear unbiased prediction. The best-fitted model for DBH and PP was indicated by the Akaike information criterion, and the significance of the genotype effects was tested using the likelihood ratio test. Genetic variability between eucalyptus clones and very high accuracies (rg^g ≥ 0.90) were detected for both traits. Reaction norms and eigenfunctions generated genetic insights into G × E interactions. This is the first study that quantified individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus breeding and demonstrated the great potential of this technique.

Highlights

Eucalyptus (Eucalyptus spp.) is widely cultivated in tropical and subtropical regions
The objective of this study was to quantify individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus (Eucalyptus spp.) breeding
A data set with 215 eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height (DBH) and Pilodyn penetration (PP) was used

Summary

Introduction

Eucalyptus (Eucalyptus spp.) is widely cultivated in tropical and subtropical regions. The international pulp trade and the intense search for alternative energy sources have increasingly motivated the establishment of eucalyptus plantations in several countries worldwide (Fonseca et al 2010). In this sense, eucalyptus breeding programs have sought to identify more efficient selection techniques to increase yield and quality of traits of industrial interest. The genotype × environment (G × E) interactions are characterized by the differential behavior of genotypes in relation to environmental variations (Resende 2015) These interactions can be expressed in various ways and with different intensities and can generate significant obstacles for genetic selection (Li et al 2017; van Eeuwijk et al 2016), including eucalyptus (Nunes et al 2017; Resende et al 2017; 2018). The use of accurate, efficient and informative statistical methods that capture the information present in this source of variation and advantageously exploit its effects is fundamental in any breeding program

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