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

Saatchi, Mahdi, Beever, Jonathan E., Decker, Jared E., Faulkner, Dan B., Freetly, Harvey C., Hansen, Stephanie L., Yampara-Iquise, Helen, Johnson, Kristen A., Kachman, Stephen D., Kerley, Monty S., Kim, JaeWoo, Loy, Daniel D., Marques, Elisa, Neibergs, Holly L., Pollak, E. John, Schnabel, Robert D., Seabury, Christopher M., Shike, Daniel W., Snelling, Warren M., Spangler, Matthew L., Weaber, Robert L., Garrick, Dorian J., Taylor, Jeremy F.
BMC Genomics 2014 v.15 no.1 pp. 1004
Angus, average daily gain, beef cattle, body weight, carbohydrates, cytokines, dry matter intake, fatty acids, feed conversion, food law, genes, genetic markers, genetic variance, genotype, long-chain-fatty-acid-CoA ligase, marker-assisted selection, nitrogen metabolism, phenotype, quantitative trait loci, signal transduction, single nucleotide polymorphism
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 50K and 770K SNP genotypes scored in 5,133 animals from 4 independent beef cattle populations with phenotypes for average daily gain (ADG), dry matter intake (DMI), metabolic mid-test body weight (MBW) and residual feed intake (RFI). Results: A total of 5, 5, 17 and 10 large-effect QTL (defined as 1-Mb genome windows explaining more than 1 % of the additive genetic variance) were identified for ADG, DMI, MBW and RFI, respectively. These QTL had little overlap across the 4 populations. The pleiotropic QTL on BTA 7 at 23 Mb identified in the Angus population harbours a promising candidate gene ACSL6 (acyl-CoA synthetase long-chain family member 6), and was the largest effect QTL associated with DMI and MBW explaining 10.39 % and 14.25 % of the additive genetic variance, respectively. Pleiotropic QTL associated with ADG and MBW were detected on BTA 6 at 38 Mb and BTA 7 at 93 Mb confirming previous reports. No QTL for RFI explained more than 2.5 % of the additive genetic variance in any population. Gene ontology (GO) analyses revealed that genes involved in the metabolism of nitrogen components, fatty acids and carbohydrates were significantly overrepresented in the QTL regions identified for RFI. GO analyses implicate the role of cytokines in the regulation of feed intake in beef cattle. Genes within the PI3K-Akt signaling pathway were enriched for their association with variation in both ADG and DMI. 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 sufficient additive genetic variance to enable the implementation of genomic selection for feed efficiency. These results enhance our understanding of the biology of growth, feed intake and utilisation in beef cattle.