Comparative accuracies of genetic values predicted for economically important milk traits, genome-wide association, and linkage disequilibrium patterns of Canadian Holstein cows
Peters, S.O., Kızılkaya, K., Ibeagha-Awemu, E.M., Sinecen, M., Zhao, X. (2021). Comparative accuracies of genetic values predicted for economically important milk traits, genome-wide association, and linkage disequilibrium patterns of Canadian Holstein cows. Journal of Dairy Science (JDS), [online] 104(2), 1900-1916. http://dx.doi.org/10.3168/jds.2020-18489
Plain language summary
The methods of genomic selection and genetic associations at the whole gene level, are important statistic tools used to determine the relationship between genetic markers and phenotypes or traits (e.g. milk yield). For this study, data on actual 305-d milk , fat, and protein yield, and 76,355 genetic markers were used to characterize the non-random association of alleles at different loci (or linkage disequilibrium) in relation to these traits in Canadian Holstein cows. Also, this data was used to compare the performance of different methods used in genomic selection (pedigree-based BLUP, genomic BLUP (GBLUP), and Bayesian (BayesB)) and as well as performance of statistical methods used in whole genome associations studies (Bayesian ridge regression and BayesB statistical methods) for the traits (milk yield, fat yield and protein yield).The results show that, as the distance between markers decreases, the non-random association of alleles increases. The GBLUP and BayesB models or statistical methods resulted in similar heritability estimates for MY and PY; but, the GBLUP method resulted in higher heritability estimates than BayesB method for fat yield. Heritability refers to the amount of variation (or change) in a trait that is due to genetic variation.
The ability of the GBLUP method to predict heritability was considerably lower than that of BayesB method for milk yield, fat yield, and protein yield. The results identified 28 high-effect or important markers, and markers on cow chromosome 14 and located within 6 genes (DOP1B, TONSL, CPSF1, ADCK5, PARP10, and GRINA) associated significantly with fat yield. These markers can be used in genomic selection for the improvement of fat yield.
Genomic selection methodologies and genome-wide association studies use powerful statistical procedures that correlate large amounts of high-density SNP genotypes and phenotypic data. Actual 305-d milk (MY), fat (FY), and protein (PY) yield data on 695 cows and 76,355 genotyping-by-sequencing-generated SNP marker genotypes from Canadian Holstein dairy cows were used to characterize linkage disequilibrium (LD) structure of Canadian Holstein cows. Also, the comparison of pedigree-based BLUP, genomic BLUP (GBLUP), and Bayesian (BayesB) statistical methods in the genomic selection methodologies and the comparison of Bayesian ridge regression and BayesB statistical methods in the genome-wide association studies were carried out for MY, FY, and PY. Results from LD analysis revealed that as marker distance decreases, LD increases through chromosomes. However, unexpected high peaks in LD were observed between marker pairs with larger marker distances on all chromosomes. The GBLUP and BayesB models resulted in similar heritability estimates through 10-fold cross-validation for MY and PY; however, the GBLUP model resulted in higher heritability estimates than BayesB model for FY. The predictive ability of GBLUP model was significantly lower than that of BayesB for MY, FY, and PY. Association analyses indicated that 28 high-effect markers and markers on Bos taurus autosome 14 located within 6 genes (DOP1B, TONSL, CPSF1, ADCK5, PARP10, and GRINA) associated significantly with FY.