Methods for calculating the breeding values of missing traits and their comparison

• Published in: Journal of Central European agriculture Vol. 15; no. 4; pp. 51 - 63
• Main Authors: Krpalkova, Lenka, Pribyl, Josef, Vostry, Lubos, VACEK, Mojmir, STADNIK, Ludek
• Format: Journal Article
• Language: English
• Published: University of Zagreb, Faculty of Agriculture 2014
• Subjects: breeding value; de-regression; genetic correlation; holstein cattle; missing traits
• ISSN: 1332-9049; 1332-9049
• DOI: 10.5513/JCEA01/15.4.1507

The objective of this study was to calculate the breeding values (BVs) of traits missing in a selection index. Different traits can be evaluated within the breeding programs of given countries. The BV of a trait can be calculated based on genetic correlations with other traits. Similarly, the BV of a missing trait can be calculated for imported bulls. Two methods of calculation were used. Method A was based on a regression of BVs. Method B was based on performing a de-regression of BVs and their retroactive calculation. Both of these methods were tested using a Czech and a Canadian database of BVs for Holstein bulls. The Czech database of Holstein bulls contained 766 bulls and the Canadian database 851. Two calculations were performed for bulls with low reliability of estimated BVs, the first calculation with their genetic correlation matrix and the second with a genetic correlation matrix created from a set of bulls with high reliability of BVs. These newly calculated BVs (CBVs) were then compared with the national BVs (NBVs) using correlation coefficients. The highest correlations were achieved with high reliability bulls when all traits were included into the calculation (34 evaluated traits). The correlations of these bulls averaged 0.82, with an average standard deviation of 0.19. The lowest correlations were found when low reliability bulls were included and the genetic correlation matrix from the high reliability bulls was applied. That average correlation was 0.74 and standard deviation 0.25. When only 15 traits were evaluated in the model, the average correlation for all sets was 0.68 with standard deviation of 0.28. These results show that calculating the BV of a missing trait is possible using both methods. Method B was slightly more accurate in its prediction.