Hoof diseases are a problem in many dairy herds. To study one aspect of the problem, genetic correlations between 4 hoof diseases, protein yield, clinical mastitis, number of inseminations, and days from calving to first insemination were estimated in first-parity Swedish Red cows using trivariate linear animal models. Occurrence of dermatitis, heel horn erosion, sole hemorrhage, and sole ulcer were reported by hoof trimmers. The data set contained about 314,000 animals with records on at least one of the traits; among these, about 64,000 animals had records on hoof diseases. Heritabilities were low for all hoof diseases (0.03 to 0.05). The hoof diseases fell into 2 groups: (1) dermatitis and heel horn erosion (i.e., diseases related to hygiene) and (2) sole hemorrhage and sole ulcer (i.e., diseases related to feeding). The genetic correlations between traits within the 2 groups were high (0.87 and 0.73, respectively), whereas the genetic correlations between traits in different groups were low (≤0.23). These results indicate that the 2 groups of hoof diseases are partly influenced by the same genes. All genetic correlations between hoof diseases and protein yield were low to moderate and unfavorable. Moderate and favorable genetic correlations were found between the feed-related hoof diseases and clinical mastitis (0.35 and 0.32), whereas the genetic correlations between the hygiene-related hoof diseases and clinical mastitis were low and not significantly different from zero. The genetic correlations between the hygiene-related hoof diseases and number of inseminations were low to moderate and favorable (0.32 and 0.22), and the genetic correlations between the feed-related hoof diseases and number of inseminations were low and not significantly different from zero. A moderate genetic correlation was found between sole ulcer and days from calving to first insemination (0.33), whereas the genetic correlations between days from calving to first insemination and sole hemorrhage and the hygiene-related hoof diseases were low and not significantly different from zero. In general, the 2 groups of hoof diseases showed different patterns of genetic correlations to the other functional traits, but both were unfavorably correlated to protein yield. A simulation study showed that inclusion of hoof diseases in the selection index will not only reduce the genetic decline in resistance to hoof diseases but also be favorable for other functional traits and improve overall genetic merit.

We tested the following hypotheses: (i) breeding schemes with genomic selection are superior to breeding schemes without genomic selection regarding annual genetic gain of the aggregate genotype (ΔG(AG) ), annual genetic gain of the functional traits and rate of inbreeding per generation (ΔF), (ii) a positive interaction exists between the use of genotypic information and a short generation interval on ΔG(AG) and (iii) the inclusion of an indicator trait in the selection index will only result in a negligible increase in ΔG(AG) if genotypic information about the breeding goal trait is known. We examined four breeding schemes with or without genomic selection and with or without intensive use of young bulls using pseudo-genomic stochastic simulations. The breeding goal consisted of a milk production trait and a functional trait. The two breeding schemes with genomic selection resulted in higher ΔG(AG) , greater contributions of the functional trait to ΔG(AG) and lower ΔF than the two breeding schemes without genomic selection. Thus, the use of genotypic information may lead to more sustainable breeding schemes. In addition, a short generation interval increases the effect of using genotypic information on ΔG(AG) . Hence, a breeding scheme with genomic selection and with intensive use of young bulls (a turbo scheme) seems to offer the greatest potential. The third hypothesis was disproved as inclusion of genomically enhanced breeding values (GEBV) for an indicator trait in the selection index increased ΔG(AG) in the turbo scheme. Moreover, it increased the contribution of the functional trait to ΔG(AG) , and it decreased ΔF. Thus, indicator traits may still be profitable to use even when GEBV for the breeding goal traits are available.

Today, almost all reference populations consist of progeny tested bulls. However, older progeny tested bulls do not have reliable estimated breeding values (EBV) for new traits. Thus, to be able to select for these new traits, it is necessary to build a reference population. We used a deterministic prediction model to test the hypothesis that the value of cows in reference populations depends on the availability of phenotypic records. To test the hypothesis, we investigated different strategies of building a reference population for a new functional trait over a 10-year period. The trait was either recorded on a large scale (30 000 cows per year) or on a small scale (2000 cows per year). For large-scale recording, we compared four scenarios where the reference population consisted of 30 sires; 30 sires and 170 test bulls; 30 sires and 2000 cows; or 30 sires, 2000 cows and 170 test bulls in the first year with measurements of the new functional trait. In addition to varying the make-up of the reference population, we also varied the heritability of the trait (h 2 5 0.05 v. 0.15). The results showed that a reference population of test bulls, cows and sires results in the highest accuracy of the direct genomic values (DGV) for a new functional trait, regardless of its heritability. For small-scale recording, we compared two scenarios where the reference population consisted of the 2000 cows with phenotypic records or the 30 sires of these cows in the first year with measurements of the new functional trait. The results showed that a reference population of cows results in the highest accuracy of the DGV whether the heritability is 0.05 or 0.15, because variation is lost when phenotypic data on cows are summarized in EBV of their sires. The main conclusions from this study are: (i) the fewer phenotypic records, the larger effect of including cows in the reference population; (ii) for small-scale recording, the accuracy of the DGV will continue to increase for several years, whereas the increases in the accuracy of the DGV quickly decrease with large-scale recording; (iii) it is possible to achieve accuracies of the DGV that enable selection for new functional traits recorded on a large scale within 3 years from commencement of recording; and (iv) a higher heritability benefits a reference population of cows more than a reference population of bulls.