|ALLAN, MARK - Pfizer Global Research & Development|
|Thallman, Richard - Mark|
|FERRELL, CALVIN - Retired ARS Employee|
|JENKINS, THOMAS - Retired ARS Employee|
|NIELSEN, MERLYN - University Of Nebraska|
|ROLFE, KELSEY - University Of Nebraska|
Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/26/2011
Publication Date: 6/1/2011
Citation: Snelling, W.M., Allan, M.F., Keele, J.W., Kuehn, L.A., Thallman, R.M., Bennett, G.L., Ferrell, C.L., Jenkins, T.G., Freetly, H.C., Nielsen, M.K., Rolfe, K.M. 2011. Partial-genome evaluation of postweaning feed intake and efficiency of crossbred beef cattle. Journal of Animal Science. 89:1731-1741.
Interpretive Summary: Feed intake is an important cost factor for beef production but it is also expensive to measure and use for selection. This research was undertaken to determine if genetic markers could be used either in place of measuring feed intake or combined with intake to increase response to selection. Individual feed intake and weight gain of steers representing two-, three-, and four-breed crosses of seven beef breeds (Angus, Charolais, Gelbvieh, Hereford, Limousin, Red Angus, and Simmental) were measured during the finishing period prior to harvest. The steers were genotyped with the BovineSNP50 BeadChip, and associations between 44,163 genetic markers and feed intake, body weight, and gain were determined. Associations with feed efficiency expressed as residual feed intake (intake adjusted for weight and gain) were also evaluated. Ninety markers were strongly associated with at least one trait and generally coincided with previously detected chromosomal regions affecting growth or feed efficiency. Relationships between animals established with genotypes of these 90 markers explained about a quarter to half of the total heritability of each trait, and the remainder was explained by pedigree relationships. Relationships established with genotypes of 96 to 1,536 markers with the strongest associations with each trait explained most or all of the estimated heritability, while the 40,000 markers with weakest associations explained almost no genetic variation. Comparison between breeding values predicted with and without genotypes showed some re-ranking occurred when genotypes were included in the evaluations, but many of the same performance tested individuals would be selected with or without considering genotypes. Including genotypes of associated genetic markers increased expected accuracy of genetic evaluations for cattle growth, feed intake, and efficiency. The increased accuracy was sufficient to aid selection of individuals measured for feed intake who are not yet parents, but on-going performance testing appears necessary for highly accurate sire evaluations.
Technical Abstract: Effects of individual single nucleotide polymorphisms (SNP), and variation explained by sets of SNP associated with dry matter intake (DMI), metabolic mid-test weight (MBW), BW gain (GN) and feed efficiency expressed as phenotypic and genetic residual feed intake (RFIp; RFIg) were estimated from weights and individual feed intake of 1,159 drylotted steers offered a 3.0 Mcal/kg ration for at least 119 d prior to harvest were estimated. Parents of these F1 * F1 (F1**2) steers were AI-sired F1 progeny of Angus, Charolais, Gelbvieh, Hereford, Limousin, Red Angus, and Simmental bulls mated to U.S. Meat Animal Research Center (USMARC) Angus, Hereford, and MARC III composite females. Steers were genotyped with the BovineSNP50 BeadChip assay. Effects of 44,163 SNP having minor allele frequency > 0.05 in the F1**2 generation were estimated with a mixed model that included genotype, breed composition, heterosis, age of dam, and harvest date contemporary groups as fixed effects, and a random additive effect with recorded pedigree relationships among animals. Variance attributable to sets of SNP, containing 6 to 40,000 SNP selected according to association with phenotype and proximity to associated SNP, was estimated with models that partitioned the additive effect into a polygenic component due to pedigree relationships, and a genotypic component due to genotypic relationships. Ninety SNP were strongly associated (P < 0.0001) with at least one efficiency or component trait; these 90 accounted for 28% to 46% of total additive variance of each trait. Trait-specific sets containing 96 SNP having the strongest associations with each trait explained 56% to 87% of additive variance. Expected accuracy of steer breeding values predicted with pedigree and genotypic relationships exceeded accuracy of their sires predicted without genotypic information, although gains in accuracy were not sufficient to encourage that performance testing be replaced by genotyping and genomic evaluations.