Genome-wide association study for feed efficiency traits using SNP and haplotype models

Kashly R. Schweer, Stephen D. Kachman, Larry A. Kuehn, Harvey C. Freetly, John E. Pollak, Matthew L. Spangler

Research output: Contribution to journalArticle

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 toward 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 ADG and ADFI. Animals were genotyped with the BovineSNP50v2 BeadChip containing approximately 54,000 SNP. 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 SD (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. ADFI 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 that explained the largest fraction of genetic variation 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 in the training data 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.

Original languageEnglish (US)
Pages (from-to)2086-2098
Number of pages13
JournalJournal of animal science
Volume96
Issue number6
DOIs
StatePublished - Jun 1 2018

Fingerprint

Genome-Wide Association Study
Haplotypes
Single Nucleotide Polymorphism
haplotypes
feed conversion
Genotype
DNA Shuffling
Phenotype
beef industry
phenotype
genome-wide association study
genotype
breeding value
beef cattle
Industry
sires
heifers
quantitative trait loci
Economics
animals

Keywords

  • Beef
  • Feed efficiency
  • Genome-wide association study
  • Haplotype models

ASJC Scopus subject areas

  • Food Science
  • Animal Science and Zoology
  • Genetics

Cite this

Schweer, K. R., Kachman, S. D., Kuehn, L. A., Freetly, H. C., Pollak, J. E., & Spangler, M. L. (2018). Genome-wide association study for feed efficiency traits using SNP and haplotype models. Journal of animal science, 96(6), 2086-2098. https://doi.org/10.1093/jas/sky119

Genome-wide association study for feed efficiency traits using SNP and haplotype models. / Schweer, Kashly R.; Kachman, Stephen D.; Kuehn, Larry A.; Freetly, Harvey C.; Pollak, John E.; Spangler, Matthew L.

In: Journal of animal science, Vol. 96, No. 6, 01.06.2018, p. 2086-2098.

Research output: Contribution to journalArticle

Schweer, KR, Kachman, SD, Kuehn, LA, Freetly, HC, Pollak, JE & Spangler, ML 2018, 'Genome-wide association study for feed efficiency traits using SNP and haplotype models', Journal of animal science, vol. 96, no. 6, pp. 2086-2098. https://doi.org/10.1093/jas/sky119
Schweer, Kashly R. ; Kachman, Stephen D. ; Kuehn, Larry A. ; Freetly, Harvey C. ; Pollak, John E. ; Spangler, Matthew L. / Genome-wide association study for feed efficiency traits using SNP and haplotype models. In: Journal of animal science. 2018 ; Vol. 96, No. 6. pp. 2086-2098.
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