Negative correlations between F These authors reported a stronger correlation of the Mendelian bristlr mobile sampling variance (similar to the square of SDGBV) with FG than with FP, which is caused by pedigree errors.
For animals with a low standard deviation of fat yield, the Q-Q plot (Figure 3) showed a high divergence between the theoretical normal distribution and the sampled distribution. Cole and Null indicated that mutations with large effects like DGAT1 should explain a higher proportion of the genetic variance than the expected variance based on the relative length of the chromosome. To check if the DGAT1 locus has an effect on the distribution of SDGBV, two scenarios were analyzed (Figure 6). In the first scenario, the SDGBV for fat yield was predicted including all 43 586 SNPs. Results showed a bivariate distribution with SDGBV ranging from 0.25 to 0.6 ?a. In the second scenario, haplotypes in a region of 2.2 Mbp surrounding the DGAT1 locus were excluded from the SDGBV prediction. Under this scenario, SDGBV showed a normal distribution with a lower mean and lower range than for scenario 1. This indicates that the SDGBV for a specific trait depends on its genetic architecture. The larger the effect on the trait and the more the allele frequency of this mutation is close to 0.5, the higher is the influence on the SDGBV, which results in a deviation from the normal distribution. Thaller et al. reported an allele frequency of 0.55 for Holstein animals for the lysine-encoding variant (K232A) of the DGAT1 gene. Furthermore, for the direct genetic effect for stillbirth, several investigations[20, 21] have indicated the presence of a quantitative trait loci (QTL) on chromosome 18 with a high influence on calving traits. Haplotype analyses demonstrated that a haplotype of 19 SNPs explains 16% of the estimated breeding value variance for the direct genetic effect for stillbirth (results not shown here). However, the influence of this QTL on SDGBV for direct genetic effect for stillbirth was less than the effect of DGAT1 on the SDGBV for fat yield. Differences in allele frequencies of the DGAT1 gene and of the QTL for the direct genetic effect for stillbirth might explain these findings.
Simulated SDGBV can only be validated for sires that have large groups of offspring. A validation independent from genomic information is only possible by comparing the SDGBV of a bull with the standard deviation of the phenotype-based estimated breeding values of its sons. However, only some very popular sires have a large number of offspring with phenotype-based estimated breeding values. Using genomic information, many animals can be tested at a relatively low cost compared to the costs of progeny-testing of bulls, which makes it possible to investigate the standard deviation of genomic breeding values within groups of offspring. Another approach to investigate and validate the standard deviation within groups of offspring is to use daughter yield deviations corrected for the contribution of the dam. One benefit of this approach is that many sires have very large groups of female offspring because of artificial insemination. Figure 7 shows the trend over time of the mean haplotype breeding values that progeny inherit from their sire and dam. Results show a near linear trend for fat and protein yields, but the paternal haplotype had a higher intercept and steeper slope than the maternal haplotype. An interesting point is the . Analysis of the 2002, 2003 and 2004 tested birth cohorts (650 bulls per year) also indicate a decrease in mean breeding values for fat yield (0.33 ?a, 0.25 ?a, 0.43 ?a) and protein yield (0.55 ?a, 0.46 ?a, 0.71 ?a) for the 2003 birth cohort. This decrease is mainly caused by the offspring of three sires which pre-dominated in this birth year. On average, these groups had breeding values for fat and protein yields that were more than one ?a lower than the pre-dominating groups of offspring in the birth cohorts in 2002 and 2004. In contrast to the gamete breeding values for fat and protein yields, no clear difference in gamete breeding values between maternal and paternal haplotypes was found for somatic cell score until the 2010 birth year. From birth year 2010 to 2013, the paternal haplotype was superior to the maternal haplotype. One explanation is that more and more genomically selected sires were used to produce animals born between 2010 and 2013. In contrast, due to genotyping costs, many dams were not genomically selected, which results in lower genetic gain on the female side. However, Figure 7 shows that for fat and protein yields there is a difference between sires and dams, which has to be taken into account in the validation. The gap between estimated sire and dam haplotype breeding values can be reduced by increasing genotyping and selection intensity in the dams-to-bulls and dams-to-cows selection paths.