Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers

• Published in: Genetics selection evolution (Paris) 2010-10, Vol.42 (1), p.37-37, Article 37
• Main Authors: Moser, Gerhard, Khatkar, Mehar S, Hayes, Ben J, Raadsma, Herman W
• Format: Article
• Language: English
• Subjects: Accuracy; Analysis; Animal populations; Animals; Assaying; bulls; Cattle; Cattle - genetics; Conformation; cows; Dairy cattle; Daughters; Density; equations; Female; Gene frequency; Genetic aspects; Genetic Markers; Genetics; Genomics; Genomics - methods; Genotype; Genotype & phenotype; Genotypes; Genotyping; Heritability; Holstein; least squares; Least squares method; Least-Squares Analysis; Male; marker-assisted selection; Milk; Milk production; Nucleotides; Phenotype; Polymorphism, Single Nucleotide - genetics; Population genetics; prediction; Predictions; Proteins; Quantitative Trait, Heritable; Regression coefficients; Reproducibility of Results; Single-nucleotide polymorphism; sires; Studies; Survival; Technology application
• ISSN: 1297-9686; 0999-193X; 1297-9686
• DOI: 10.1186/1297-9686-42-37

BACKGROUND: At the current price, the use of high-density single nucleotide polymorphisms (SNP) genotyping assays in genomic selection of dairy cattle is limited to applications involving elite sires and dams. The objective of this study was to evaluate the use of low-density assays to predict direct genomic value (DGV) on five milk production traits, an overall conformation trait, a survival index, and two profit index traits (APR, ASI). METHODS: Dense SNP genotypes were available for 42,576 SNP for 2,114 Holstein bulls and 510 cows. A subset of 1,847 bulls born between 1955 and 2004 was used as a training set to fit models with various sets of pre-selected SNP. A group of 297 bulls born between 2001 and 2004 and all cows born between 1992 and 2004 were used to evaluate the accuracy of DGV prediction. Ridge regression (RR) and partial least squares regression (PLSR) were used to derive prediction equations and to rank SNP based on the absolute value of the regression coefficients. Four alternative strategies were applied to select subset of SNP, namely: subsets of the highest ranked SNP for each individual trait, or a single subset of evenly spaced SNP, where SNP were selected based on their rank for ASI, APR or minor allele frequency within intervals of approximately equal length. RESULTS: RR and PLSR performed very similarly to predict DGV, with PLSR performing better for low-density assays and RR for higher-density SNP sets. When using all SNP, DGV predictions for production traits, which have a higher heritability, were more accurate (0.52-0.64) than for survival (0.19-0.20), which has a low heritability. The gain in accuracy using subsets that included the highest ranked SNP for each trait was marginal (5-6%) over a common set of evenly spaced SNP when at least 3,000 SNP were used. Subsets containing 3,000 SNP provided more than 90% of the accuracy that could be achieved with a high-density assay for cows, and 80% of the high-density assay for young bulls. CONCLUSIONS: Accurate genomic evaluation of the broader bull and cow population can be achieved with a single genotyping assays containing ~ 3,000 to 5,000 evenly spaced SNP.