2019
DOI: 10.3168/jds.2018-15329
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Abstract: The aim of this study was to compare genetic (co)variance components and prediction accuracies of breeding values from genomic random regression models (gRRM) and pedigree-based random regression models (pRRM), both defined with or without an additional environmental gradient. The used gradient was a temperaturehumidity index (THI), considered in statistical models to investigate possible genotype by environment (G×E) interactions. Data included 106,505 test-day records for milk yield (MY) and 106,274 test-day… Show more

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Cited by 29 publications
(23 citation statements)
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“…In the presence of interactions, the estimated heritability in bivariate GREML was biased due to unmodeled G×E and R×E interactions. This result agrees with previous studies, e.g., Bohlouli et al (2019) reported that neglecting G×E interaction results in an underestimated heritability in a simulation study. Moreover, Ni et al (2019) and Zhou et al (2020) showed that biased heritability can be estimated when R×E interaction is ignored as well.…”
Section: Discussionsupporting
confidence: 93%
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“…In the presence of interactions, the estimated heritability in bivariate GREML was biased due to unmodeled G×E and R×E interactions. This result agrees with previous studies, e.g., Bohlouli et al (2019) reported that neglecting G×E interaction results in an underestimated heritability in a simulation study. Moreover, Ni et al (2019) and Zhou et al (2020) showed that biased heritability can be estimated when R×E interaction is ignored as well.…”
Section: Discussionsupporting
confidence: 93%
“…THI values can be used as discrete variable by dividing the samples arbitrarily into multiple groups, e.g., the quartile of THI levels (Brügemann et al, 2011;Bohlouli et al, 2019). Although Jaffrezic et al (2000) reported that estimates from models fitting continuous and discrete THI values were not statistically different, it is obvious that individual differences in THI values within each discrete group are ignored, which can result in decreasing the power to detect the G×E interaction.…”
Section: Discussionmentioning
confidence: 99%
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“…We found contradicting trends for heritability estimates across the THI trajectory from the 2 analyses, which may be attributable, at least in part, to the way the heritability estimates were calculated. An alternative to RM, which perhaps could have allowed more comparable results, would have been to model (co)variance components for the interactions between DIM and THI, as in Bohlouli et al (2019), and then estimate residual variances and heritabilities within DIM × THI combinations, but unfortunately this was not computationally feasible in our study.…”
Section: Variance Componentsmentioning
confidence: 99%
“…The resulting model is a multiple-trait, multiple-environment model with a variety of interactions, in which computational issues may arise due to the increase in the number of parameters being estimated. It has been shown that RRMs can account simultaneously for the additive genetic effect and some degree of GxE in longitudinal traits in animal breeding by allowing for the estimation of genetic (co)variance components and breeding values over the whole trajectory of a time-dependent trait and environmentdependent covariate (Brügemann et al, 2011;Santana et al, 2016;Bohlouli et al, 2019). In plant breeding, therefore, this model may provide considerable biological insights into the mechanisms determining performance in specific environments, making it a worthwhile method for study in future research.…”
Section: Non-additive Effects and Gxementioning
confidence: 99%