Genotype-by-environment interaction of fertility traits in Danish Holstein cattle using a single-step genomic reaction norm model

Zhang, Zhe; Kargo, Morten; A, Liu; Thomasen, Jørn Rind; Pan, Yuchun; Su, Guosheng

doi:10.1038/s41437-019-0192-4

Cited by 39 publications

(58 citation statements)

References 47 publications

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“…The variance components estimated using reaction norm models with A and H matrices were very similar. This result is consistent with previous reports on pigs (Silva et al., 2014), cattle (Oliveira et al., 2018) and Danish Holstein cattle (Zhang et al., 2019). However, regardless of the existence of G × E interactions, higher prediction accuracies were observed in models based on H matrix compared to A matrix, for both reaction norm models and reduced models.…”

Section: Discussionsupporting

confidence: 94%

“…For the amount of variance of the slope (

σ_{a_{1}}^{2}

) in relation to variance of the intercept (

σ_{a_{0}}^{2}

σ_{a_{1}}^{2} / σ_{a_{0}}^{2}

were 0.002 and 0.348 based on H matrix for AGE and BFT, respectively. Low correlation between the intercept and slope as well as large

σ_{a_{1}}^{2}

could potentially mean the re‐ranking of animals across different environments, which means that the best genotype (phenotype) in one environment may not be the best in another environment (Oliveira et al., 2018; Santana, Eler, Cardoso, Albuquerque, & Ferraz, 2013; Su et al., 2006; Zhang et al., 2019), high correlation between intercept and slope as well as small variance of slope indicate small or no G × E interactions for AGE. In addition, the effects estimated for the 10 top SNPs based on the p ‐values of the slopes were plotted against the EV (Figure 7), which also showed that there were no changes in the effects of the SNPs across EVs for AGE.…”

Section: Discussionmentioning

confidence: 99%

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Genomic prediction of growth traits for pigs in the presence of genotype by environment interactions using single‐step genomic reaction norm model

Song

Zhang

Misztal

et al. 2020

J Animal Breeding Genetics

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show abstract

Section: Discussionsupporting

confidence: 94%

“…For the amount of variance of the slope (

σ_{a_{1}}^{2}

) in relation to variance of the intercept (

σ_{a_{0}}^{2}

σ_{a_{1}}^{2} / σ_{a_{0}}^{2}

were 0.002 and 0.348 based on H matrix for AGE and BFT, respectively. Low correlation between the intercept and slope as well as large

σ_{a_{1}}^{2}

Section: Discussionmentioning

confidence: 99%

Genomic prediction of growth traits for pigs in the presence of genotype by environment interactions using single‐step genomic reaction norm model

Song

Zhang

Misztal

et al. 2020

J Animal Breeding Genetics

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“…G × E interactions play an important role in pig populations, and they should be considered in breeding programs to select the best animals in different environments [ 11 , 24 ]. The detection of G × E interactions relies on a genetic correlation of 0.8 for one trait in different environments, the threshold suggested by Robertson [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

The superiority of multi-trait models with genotype-by-environment interactions in a limited number of environments for genomic prediction in pigs

Song¹,

Zhang²,

Ding³

2020

J Animal Sci Biotechnol

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Background Different production systems and climates could lead to genotype-by-environment (G × E) interactions between populations, and the inclusion of G × E interactions is becoming essential in breeding decisions. The objective of this study was to investigate the performance of multi-trait models in genomic prediction in a limited number of environments with G × E interactions. Results In total, 2,688 and 1,384 individuals with growth and reproduction phenotypes, respectively, from two Yorkshire pig populations with similar genetic backgrounds were genotyped with the PorcineSNP80 panel. Single- and multi-trait models with genomic best linear unbiased prediction (GBLUP) and BayesC π were implemented to investigate their genomic prediction abilities with 20 replicates of five-fold cross-validation. Our results regarding between-environment genetic correlations of growth and reproductive traits (ranging from 0.618 to 0.723) indicated the existence of G × E interactions between these two Yorkshire pig populations. For single-trait models, genomic prediction with GBLUP was only 1.1% more accurate on average in the combined population than in single populations, and no significant improvements were obtained by BayesC π for most traits. In addition, single-trait models with either GBLUP or BayesC π produced greater bias for the combined population than for single populations. However, multi-trait models with GBLUP and BayesC π better accommodated G × E interactions, yielding 2.2% – 3.8% and 1.0% – 2.5% higher prediction accuracies for growth and reproductive traits, respectively, compared to those for single-trait models of single populations and the combined population. The multi-trait models also yielded lower bias and larger gains in the case of a small reference population. The smaller improvement in prediction accuracy and larger bias obtained by the single-trait models in the combined population was mainly due to the low consistency of linkage disequilibrium between the two populations, which also caused the BayesC π method to always produce the largest standard error in marker effect estimation for the combined population. Conclusions In conclusion, our findings confirmed that directly combining populations to enlarge the reference population is not efficient in improving the accuracy of genomic prediction in the presence of G × E interactions, while multi-trait models perform better in a limited number of environments with G × E interactions.

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“…Differential animal responses across environmental conditions have been assessed through reaction norm models (RNM), describing animal sensitivity to environmental changes (Calus & Veerkamp 2003;Kolmodin et al 2003). Genomic information has been used to assess G9E interactions, representing a promising alternative to evaluating traits strongly influenced by environmental factors (Lillehammer et al 2008(Lillehammer et al , 2009Silva et al 2014;Mota et al 2016;Zhang et al 2019). According to Hayes et al (2016), the inclusion of genomic information in RNM has the potential to overcome the low quantity of phenotypic information from sires with offspring in a wide range of environments, given that the genotyped animals are well distributed across different environments.…”

Section: Introductionmentioning

confidence: 99%

Genomic reaction norm models exploiting genotype × environment interaction on sexual precocity indicator traits in Nellore cattle

et al. 2020

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Summary Brazilian beef cattle are raised predominantly on pasture in a wide range of environments. In this scenario, genotype by environment (G×E) interaction is an important source of phenotypic variation in the reproductive traits. Hence, the evaluation of G×E interactions for heifer’s early pregnancy (HP) and scrotal circumference (SC) traits in Nellore cattle, belonging to three breeding programs, was carried out to determine the animal’s sensitivity to the environmental conditions (EC). The dataset consisted of 85 874 records for HP and 151 553 records for SC, from which 1800 heifers and 3343 young bulls were genotyped with the BovineHD BeadChip. Genotypic information for 826 sires was also used in the analyses. EC levels were based on the contemporary group solutions for yearling body weight. Linear reaction norm models (RNM), using pedigree information (RNM_A) or pedigree and genomic information (RNM_H), were used to infer G×E interactions. Two validation schemes were used to assess the predictive ability, with the following training populations: (a) forward scheme—dataset was split based on year of birth from 2008 for HP and from 2011 for SC; and (b) environment‐specific scheme—low EC (−3.0 and −1.5) and high EC (1.5 and 3.0). The inclusion of the H matrix in RNM increased the genetic variance of the intercept and slope by 18.55 and 23.00% on average respectively, and provided genetic parameter estimates that were more accurate than those considering pedigree only. The same trend was observed for heritability estimates, which were 0.28–0.56 for SC and 0.26–0.49 for HP, using RNM_H, and 0.26–0.52 for SC and 0.22–0.45 for HP, using RNM_A. The lowest correlation observed between unfavorable (−3.0) and favorable (3.0) EC levels were 0.30 for HP and −0.12 for SC, indicating the presence of G×E interaction. The G×E interaction effect implied differences in animals’ genetic merit and re‐ranking of animals on different environmental conditions. SNP marker–environment interaction was detected for Nellore sexual precocity indicator traits with changes in effect and variance across EC levels. The RNM_H captured G×E interaction effects better than RNM_A and improved the predictive ability by around 14.04% for SC and 21.31% for HP. Using the forward scheme increased the overall predictive ability for SC (20.55%) and HP (11.06%) compared with the environment‐specific scheme. The results suggest that the inclusion of genomic information combined with the pedigree to assess the G×E interaction leads to more accurate variance components and genetic parameter estimates.

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Genotype-by-environment interaction of fertility traits in Danish Holstein cattle using a single-step genomic reaction norm model

Cited by 39 publications

References 47 publications

Genomic prediction of growth traits for pigs in the presence of genotype by environment interactions using single‐step genomic reaction norm model

Genomic prediction of growth traits for pigs in the presence of genotype by environment interactions using single‐step genomic reaction norm model

The superiority of multi-trait models with genotype-by-environment interactions in a limited number of environments for genomic prediction in pigs

Genomic reaction norm models exploiting genotype × environment interaction on sexual precocity indicator traits in Nellore cattle

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