QTLs associated with dry matter intake, metabolic mid-test weight, growth and feed efficiency have little overlap across 4 beef cattle studies

Authors

Mahdi Saatchi, Iowa State UniversityFollow
Jonathan E. Beever, University of Illinois at Urbana-ChampaignFollow
Jared E. Decker, University of Missouri, ColumbiaFollow
Dan B. Faulkner, University of ArizonaFollow
Harvey C. Freetly, ARS-USDAFollow
Stephanie L. Hansen, University of Illinois at Urbana-ChampaignFollow
Helen Yampara-Iquise, University of Missouri, Columbia
Kristen A. Johnson, Washington State UniversityFollow
Stephen D. Kachman, University of Nebraska-LincolnFollow
Monty S. Kerley, University of Missouri, Columbia
JaeWoo Kim, University of Missouri, ColumbiaFollow
Daniel D. Loy, Iowa State UniversityFollow
Elisa Marques, GeneSeek a Neogen Company, Lincoln NE
Holly L. Neibergs, Washington State UniversityFollow
E. John Pollak, USDA-ARSFollow
Robert D. Schnabel, University of Missouri - ColumbiaFollow
Christopher M. Seabury, Texas A&M University
Daniel W. Shike, University of Illinois at Urbana-ChampaignFollow
W. M. Snelling, USDA-ARSFollow
Matthew L. Spangler, University of Nebraska-LincolnFollow
Robert L. Weaber, Kansas State UniversityFollow
D. J. Garrick, Iowa State UniversityFollow
Jeremy F. Taylor, University of Missouri - ColumbiaFollow

ORCID IDs

Stephen D. Kachman

Document Type

Article

Date of this Version

2014

Citation

BMC Genomics 2014, 15:1004 (14 pp)

Comments

US government work & Creative Commons license.

Abstract

Background: The identification of genetic markers associated with complex traits that are expensive to record such as feed intake or feed efficiency would allow these traits to be included in selection programs. To identify large-effect QTL, we performed a series of genome-wide association studies and functional analyses using 50 K and 770 K SNP genotypes scored in 5,133 animals from 4 independent beef cattle populations (Cycle VII, Angus, Hereford and Simmental × Angus) with phenotypes for average daily gain, dry matter intake, metabolic mid-test body weight and residual feed intake.

Results: A total of 5, 6, 11 and 10 significant QTL (defined as 1-Mb genome windows with Bonferroni-corrected P-value

Conclusions: This GWAS study, which is the largest performed for feed efficiency and its component traits in beef cattle to date, identified several large-effect QTL that cumulatively explained a significant percentage of additive genetic variance within each population. Differences in the QTL identified among the different populations may be due to differences in power to detect QTL, environmental variation, or differences in the genetic architecture of trait variation among breeds. These results enhance our understanding of the biology of growth, feed intake and utilisation in beef cattle.