1. bookVolume 17 (2017): Issue 3 (July 2017)
Journal Details
License
Format
Journal
eISSN
2300-8733
First Published
25 Nov 2011
Publication timeframe
4 times per year
Languages
English
access type Open Access

Genomic prediction by considering genotype × environment interaction using different genomic architectures

Published Online: 01 Aug 2017
Volume & Issue: Volume 17 (2017) - Issue 3 (July 2017)
Page range: 683 - 701
Received: 22 Sep 2016
Accepted: 20 Jan 2017
Journal Details
License
Format
Journal
eISSN
2300-8733
First Published
25 Nov 2011
Publication timeframe
4 times per year
Languages
English
Abstract

In this study, accuracies of genomic prediction across various scenarios were compared using single- trait and multiple-trait animal models to detect genotype × environment (G × E) interaction based on REML method. The simulated high and low linkage disequilibrium (HLD and LLD) genome consisted of 15,000 and 50,000 SNP chip applications with 300 and 600 QTLs controlling the trait of interest. The simulation was done to create the genetic correlations between the traits in 4 environments and heritabilities of the traits were 0.20, 0.25, 0.30 and 0.35 in environments 1, 2, 3 and 4, respectively. Two strategies were used to predict the accuracy of genomic selection for cows without phenotypes. In the first strategy, phenotypes for cows in three environments were kept as a training set and breeding values for all animals were estimated using three-trait model. In the second one, only 25, 50 or 75% of records in the fourth environment and all the records in the other three environments were used to predict GBV for non-phenotyped cows in the environment 4. For the first strategy, the highest accuracy of 0.695 was realized in scenario HLD with 600 QTL and 50K SNP chip for the fourth environment and the lowest accuracy of 0.495 was obtained in scenario LLD with 600QTL and 15K SNP chips for the first environment. Generally, the accuracy of prediction increased significantly (P<0.05) with increasing the number of markers, heritability and the genetic correlation between the traits, but no significant difference was observed between scenarios with 300 and 600 QTL. In comparison with models without G × E interaction, accuracies of the GBV for all environments increased when using multi-trait models. The results showed that the level of LD, number of animals in training set and genetic correlation across environments play important roles if G × E interaction exists. In conclusion, G × E interaction contributes to understanding variations of quantitative trait and increasing accuracy of genomic prediction. Therefore, the interaction should be taken into account in conducting selection in various environments or across different genotypes.

Keywords

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