Abstract
Genotype by environment interactions (GEI) have attracted increasing attention in tropical breeding programs because of the variety of production systems involved. In this work, we assessed GEI in 450-day adjusted weight (W450) Nelore cattle from 366 Brazilian herds by comparing traditional univariate single-environment model analysis (UM) and random regression first order reaction norm models for six environmental variables: standard deviations of herd-year (RRMw) and herd-year-season-management (RRMw-m) groups for mean W450, standard deviations of herd-year (RRMg) and herd-year-season-management (RRMg-m) groups adjusted for 365-450 days weight gain (G450) averages, and two iterative algorithms using herd-year-season-management group solution estimates from a first RRMw-m and RRMg-m analysis (RRMITw-m and RRMITg-m, respectively). The RRM results showed similar tendencies in the variance components and heritability estimates along environmental gradient. Some of the variation among RRM estimates may have been related to the precision of the predictor and to correlations between environmental variables and the likely components of the weight trait. GEI, which was assessed by estimating the genetic correlation surfaces, had values < 0.5 between extreme environments in all models. Regression analyses showed that the correlation between the expected progeny differences for UM and the corresponding differences estimated by RRM was higher in intermediate and favorable environments than in unfavorable environments (p < 0.0001).
Highlights
Genotype by environment interactions (GEI) occur when the genotype responds differently to changes in the environment (Kolmodin et al, 2002)
When environmental groups (EG) were defined based on farm-year groups the records were concentrated in the central region of environmental gradient and led to a larger number of sires being excluded from the analysis compared to the farm-year-season-management groups (192 and 177 sires with 85,259 and 79,250 total records in RRMw and RRMg, and 220 and 242 sires with 89,784 and 90,735 total records in RRMw-m and RRMg-m and their iterative models, respectively)
The initial aim of using different environmental descriptors was to maximize the identification of GEI based on the concept that similarities between independent (EGs of W450 averages) and de
Summary
Genotype by environment interactions (GEI) occur when the genotype responds differently to changes in the environment (Kolmodin et al, 2002). The development of molecular genetics has revealed astonishing aspects of epigenetic and major gene by gene and gene by environment interactions (Lewontin, 1998; Schlichting and Pigliucci, 1998) that have revolutionized various genetic concepts (El Hani, 2007) These developments suggest that traditional quantitative genetic models may be underestimating GEI and indicate the need of more precise models for these analyses. Other studies that have shown important GEI could be questioned because they were local studies and the small number of data used was often a limitation (Bolton et al, 1987; Nobre et al, 1988) In parallel with these investigations, progress in statistical methodology has produced different models and random regression has become increasingly important in longitudinal data analyses. The application of these models to growth and lactation curves using the variable “time” in the longitudinal axis resulted in more precise estimates in different phases of lactation (Veerkamp and Thompson, 1999)
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