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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 #342503

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

Location: Animal Genomics and Improvement Laboratory

Title: Genome wide association analyses based on a multiple trait approach for modeling feed efficiency

Author
item Lu, Yongfang - Michigan State University
item Vandehaar, Michael - Michigan State University
item Spurlock, Diane - Iowa State University
item Weigel, Kent - University Of Wisconsin
item Armentano, Lou - University Of Wisconsin
item Connor, Erin
item Coffey, Mike - Scottish Agricultural College
item Veerkamp, Roel - Wageningen University And Research Center
item De Haas, Yvette - Wageningen University And Research Center
item Staples, Charlie - University Of Florida
item Wang, Zhiquan - University Of Alberta
item Hanigan, Mark - Virginia Polytechnic Institution & State University
item Tempelman, Rob - Michigan State University

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/27/2017
Publication Date: 3/16/2018
Citation: Lu, Y., Vandehaar, M.J., Spurlock, D.M., Weigel, K.A., Armentano, L.E., Connor, E.E., Coffey, M., Veerkamp, R.F., De Haas, Y., Staples, C.R., Wang, Z., Hanigan, M.D., Tempelman, R.J. 2018. Genome wide association analyses based on a multiple trait approach for modeling feed efficiency. Journal of Dairy Science. 101(4):3140-3154. https://doi.org/10.3168/jds.2017-13364.
DOI: https://doi.org/10.3168/jds.2017-13364

Interpretive Summary: Selection for higher feed efficiency in dairy cattle is important for economic and environmental sustainability. Genome-wide association analyses based on the use of high-density genetic markers are useful to locate regions within the genome containing important causal genetic mutations influencing feed efficiency, which can then be targeted for genetic selection. In the present study, genome-wide association analysis on feed efficiency and its component traits of feed intake, body weight, and milk energy was conducted using 2 different statistical models. Two regions were identified on bovine chromosomes 12 and 26 that were associated with feed efficiency. One region contains a very promising candidate gene, ADAM12, that regulates muscle development and fatty acid use. The work provides insights into genetic control of feed intake and feed efficiency traits and provides targets for selection in dairy cattle to improve production efficiency.

Technical Abstract: Genome wide association (GWA) of feed efficiency (FE) could help target important genomic regions influencing FE. Data provided by an international dairy FE research consortium consisted of phenotypic records on dry matter intakes (DMI), milk energy (MILKE), and metabolic body weight (MBW) on 6,937 cows from 16 stations in 4 counties. Of these cows, 4,916 had genotypes on 57,347 single nucleotide polymorphism (SNP) markers. We compared a GWA analysis based on the more classical residual feed intake (RFI) model with a GWA analysis based on a previously proposed multiple trait (MT) approach for modeling FE using an alternative measure (DMI|MILKE,MBW). Both models were based on a single-step genomic BLUP procedure that allowed the use of phenotypes from both genotyped and non-genotyped cows. Estimated effects for single SNP markers were small and not statistically important, but virtually identical for either FE measure (RFI versus DMI|MILKE,MBW). However, upon further refining this analysis to develop joint tests within non-overlapping 1-Mb windows, significant associations were detected between either measure of FE with a window on each of BTA12 and BTA26. There was, as expected, no overlap between detected genomic regions for DMI|MILKE,MBW and genomic regions influencing the energy sink traits (i.e., MILKE and MBW) because of clearly defined orthogonal relationships between the various traits. Conversely, GWA inferences on DMI can be demonstrated to be partly driven by genetic associations between DMI with these same energy sink traits, thereby having clear implications when comparing GWA studies on DMI to GWA studies on FE-like measures such as RFI.