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Use of marker × environment interaction whole genome regression model to incorporate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle
Feed efficiency in dairy cattle has gained much attention recently. Due to the cost prohibitive measurement of individual feed intakes, combining data from multiple countries is usually necessary to ensure a large enough reference population. It may then be essential to model genetic heterogeneity when making inferences about feed efficiency or selecting efficient cattle using genomic information. In this study, we constructed a marker × environment interaction model that decomposed marker effects into main effects and interaction components that were specific to each environment. We compared environment-specific variance component estimates and prediction accuracies of the interaction model analysis, an across-environment analysis ignoring population stratification, and a within-environment analysis on the feed efficiency data set. Phenotype traits included residual feed intake (RFI), dry matter intake (DMI), net energy in milk (MilkE), and metabolic body weight (MBW) from 3,656 cows measured in 3 broadly defined environments: North America (NAM), the Netherlands (NLD), and Scotland (SAC). Genotypic data included 57,574 single nucleotide polymorphisms per animal. The interaction model gave the highest prediction accuracy for MBW, which had the largest estimated heritabilities ranging from 0.37 to 0.55. The within-environment model performed the best when predicting the trait of RFI which had the lowest estimated heritabilities, ranging from 0.13 to 0.41. For traits (DMI and MilkE) which had intermediate estimated heritabilities (0.21 to 0.50 and 0.17 to 0.53), performance of the 3 models was comparable. Genomic correlations between environments were also computed using the variance component estimates from the interaction model. Averaged across all traits, genomic correlation was the highest between NAM and NLD, and was the lowest between NAM and SAC. In conclusion, the interaction model provided a novel way to evaluate traits measured in multiple environments in which genetic heterogeneity may exist. It offered the capability of estimating environment-specific parameters and performed either the best or nearly the best in the genomic prediction.
Keywords: genomic selection, interaction model, feed efficiency