|SCHWEER, KASHLY - University Of Nebraska|
|KACHMAN, STEPHEN - University Of Nebraska|
|SPANGLER, MATTHEW - University Of Nebraska|
Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/27/2018
Publication Date: 6/8/2018
Citation: Schweer, K.R., Kachman, S.D., Kuehn, L.A., Freetly, H.C., Pollak, E.J., Spangler, M.L. 2018. Genome-wide association study for feed efficiency traits using SNP and haplotype models. Journal of Animal Science. 96:2086-2098. https://doi.org/10.1093/jas/sky119.
Interpretive Summary: Feed represents the greatest variable cost associated with beef production. Therefore, reducing feed costs, even by marginal amounts, will result in direct increases in profit. One method to reduce feed cost is selection for reduced intake in beef cattle. However, the expense of recording individual feed intake records precludes large scale genetic evaluations in the traditional sense. Therefore, a genomic-enabled approach was applied using a research herd with 748 crossbred steers representing 7 sire breeds. The steers had individual feed intake and average daily gain recorded and were genotyped using a high-density (50,000 markers) array. Using a multiple-trait (gain and intake) haplotyping approach, we were able to identify influential regions for both traits with high resolutions. These regions can be validated with other research data sets to produce markers that would be effective in selecting for feed efficiency.
Technical Abstract: Feed costs comprise the majority of variable expenses in beef cattle systems making feed efficiency an important economic consideration within the beef industry. Due to the expense of recording individual feed intake phenotypes, a genomic-enabled approach could be advantageous towards improving this economically relevant trait complex. A Genome-wide association study (GWAS) was performed using 748 crossbred steers and heifers representing seven sire breeds with phenotypes for average daily gain (ADG) and average daily feed intake (ADFI). Animals were genotyped with the BovineSNP50v2 BeadChip. Both traits were analyzed using univariate SNP-based (BayesC) and haplotype-based (BayesIM) models and jointly using BayesIM to perform a bivariate GWAS. For BayesIM, a hidden Markov model (HMM) of haplotype segments of variable length was built where haplotypes were mapped to clusters based on local similarity. The estimated HMM was then used to assign haplotype cluster genotypes, instead of SNP genotypes, as latent covariates in a Bayesian mixture model. The number of haplotype clusters at each location was assumed to be either 8 (BayesIM8) or 16 (BayesIM16). A total of three univariate analyses for each trait and two bivariate analyses were performed. Posterior mean genomic heritability estimates (PSD) for ADG were 0.28 (0.08), 0.37 (0.11), 0.37 (0.11), 0.35 (0.11) and 0.35 (0.12) for BayesC, BayesIM8, BayesIM16, BayesIM8 bivariate and BayesIM16 bivariate, respectively. Average daily feed intake posterior mean genomic heritability estimates (PSD) were 0.30 (0.07), 0.44 (0.13), 0.42 (0.12), 0.38 (0.10) and 0.38 (0.10) for the same models. The top 1% of 1-Mb windows in common between univariate SNP and haplotype models ranged from 24% to 40% and from 20% to 32% for ADG and ADFI, respectively. Spearmen rank correlations between molecular breeding values from SNP and haplotype-based models were similar for both traits (> 0.96) suggesting that either model would lead to similar rankings of animals, although resolution of potential QTL appeared to be greater for BayesIM.