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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #327605

Research Project: Understanding Genetic and Physiological Factors Affecting Nutrient Use Efficiency of Dairy Cattle

Location: Animal Genomics and Improvement Laboratory

Title: 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

Author
item Yao, Chen - University Of Wisconsin
item De Los Campos, Gustavo - Michigan State University
item Vandehaar, Michael - Michigan State University
item Spurlock, Diane - Iowa State University
item Armentano, Lou - University Of Wisconsin
item Coffey, Mike - Scottish Agricultural College
item De Haas, Yvette - Wageningen University
item Veerkamp, Roel - Wageningen University
item Staples, Charlie - University Of Florida
item Connor, Erin
item Wang, Z - University Of Alberta
item Hanigan, Mark - Virginia Polytechnic Institution & State University
item Tempelman, Rob - Michigan State University
item Weigel, Kent - University Of Wisconsin

Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: 4/10/2016
Publication Date: 7/19/2016
Citation: Yao, C., De Los Campos, G., Vandehaar, M.J., Spurlock, D.M., Armentano, L.E., Coffey, M., De Haas, Y., Veerkamp, R.F., Staples, C.R., Connor, E.E., Wang, Z., Hanigan, M.D., Tempelman, R.J., Weigel, K. 2016. 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. Journal of Dairy Science. 99(E-Suppl. 1):142. Abstract 307.

Interpretive Summary:

Technical Abstract: Feed efficiency in dairy cattle has gained much attention recently. Due to the cost prohibitive measurement of individual phenotypes, combining data from multiple countries is usually necessary to enlarge the reference population. In this scenario, it is 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 the prediction accuracy of the interaction model with the across-environment analysis ignoring population stratification and the within-environment analysis using 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 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), the performance of the 3 models was comparable. Genomic correlations between environments also were 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.