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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 #336137

Research Project: Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information

Location: Animal Genomics and Improvement Laboratory

Title: Replication and validation of genome-wide associations with feed efficiency of dairy cattle

Author
item Xu, Tingyang - University Of Connecticut
item Sun, Jiangwen - University Of Connecticut
item Qureshi, Fatir - University Of Connecticut
item Connor, Erin
item Cole, John
item Bi, Jinbo - University Of Connecticut

Submitted to: International Bioinformatics and Biomedicine Conference
Publication Type: Proceedings
Publication Acceptance Date: 11/20/2016
Publication Date: 12/15/2016
Citation: Xu, T., Sun, J., Qureshi, F., Connor, E.E., Cole, J.B., Bi, J. 2016. Replication and validation of genome-wide associations with feed efficiency of dairy cattle. International Bioinformatics and Biomedicine Conference. December 15-16, 2016. Shenzhen, China. Abstract S13202.

Interpretive Summary: Feed is the most expensive cost associated with milk production, so it is desirable to breed dairy cattle that consume less feed without a decrease in productivity. Studies that identify genomic regions associated with important traits often cannot be replicated when new data are available. In this study, two independent sets of data were used to search for genomic regions associated with improved feed efficiency. Several genetic variants were identified in both groups that are associated with greater feed efficiency.

Technical Abstract: Improving feed efficiency in dairy production is an important endeavor as it can reduce feed costs and mitigate negative impacts of production on the environment. Feed efficiency is a multivariate phenotype characterized by a variety of phenotypic variables such as dry matter intake, body weight gain, milk yield, and the composition of fat and protein in milk. In previous work we developed a method for homogeneously quantifying the feed efficiency of lactating dairy cattle for the purpose of breeding selection and utilizing genetic data directly in the development of feed efficiency measures. However, the method only examined one set of dairy cows with 700K genetic markers. No replication studies have been done to validate the usefulness of the method for genome-wide association analysis with feed efficiency of dairy cattle. In this paper we aim to reproduce the procedures on a different set of dairy cows and genetic information in order to identify cattle clusters with homogeneous feed efficiency features that are ready to link to genetic variants, and thus can have greater utility in breading selection. Using a separate set of feed efficiency data collected by USDA, three cattle subgroups, replicating those in our early work, have been identified by our analysis, and genetic variants have also been replicated to be significantly associated with the lactating efficient subgroup.