Background: This study focused on the dynamics of genome-wide effects on five milk production and eight fertility traits as well as genetic correlations between the traits. For 2,405 Holstein Friesian bulls, estimated breeding values (EBVs) were used. The production traits were additionally assessed in 10-day intervals over the first 60 lactation days, as this stage is physiologically the most crucial time in milk production. Results: SNPs significantly affecting the EBVs of the production traits could be separated into three groups according to the development of the size of allele effects over time: 1) increasing effects for all traits; 2) decreasing effects for all traits; and 3) increasing effects for all traits except fat yield. Most of the significant markers were found within 22 haplotypes spanning on average 135,338 bp. The DGAT1 region showed high density of significant markers, and thus, haplotype blocks. Further functional candidate genes are proposed for haplotype blocks of significant SNPs (KLHL8, SICLEC12, AGPAT6 and NID1). Negative genetic correlations were found between yield and fertility traits, whilst content traits showed positive correlations with some fertility traits. Genetic correlations became stronger with progressing lactation. When correlations were estimated within genotype classes, correlations were on average 0.1 units weaker between production and fertility traits when the yield increasing allele was present in the genotype. Conclusions: This study provides insight into the expression of genetic effects during early lactation and suggests possible biological explanations for the presented time-dependent effects. Even though only three markers were found with effects on fertility, the direction of genetic correlations within genotype classes between production and fertility traits suggests that alleles increasing the milk production do not affect fertility in a more negative way compared to the decreasing allele.

The effects of genes are commonly estimated using random regression models based on test-day data and only give a general gene effect. Alternatively, lactation curve models can be used to estimate biological and environmental effects, or to predict missing test-day data and perform breeding value estimation. This study combines lactation curve models and estimation of gene effects to represent gene effects in different stages of lactation. The lactation curve models used were based on the Wood, Wilmink, and Ali and Schaeffer models. A random regression test-day model was used to compare estimated gene effects with the results of commonly used models. The well-characterized DGAT1 gene with known effects on milk yield, milk fat, and milk protein production was chosen to test this new approach in a Holstein-Friesian dairy cattle population. The K232A polymorphism and the promoter VNTR (variable number of tandem repeats) of the DGAT1 gene were used. All lactation curve models predicted the production curves sufficiently. Nevertheless, for predicting genotype effects, the Wilmink curve indicated the closest fit to the data. This study shows that the characteristic gene effects for DGAT1 genotypes occur after lactation d 40, which might be explained by a link to other genes affecting metabolic traits. Furthermore, allele substitution effects of allele K of the K232A locus showed that the typical effect of low milk and protein yield is due mainly to a lower overall production level, whereas the higher fat and protein content is reached by increased production toward its peak and fat yield is increased because of a higher production after this peak. Predicting gene effects with production curves gives better insight into the timeline of gene effects. This can be used to form genetic groups, in addition to feeding groups, for managing livestock populations in a more effective way.

The bovine growth hormone receptor (GHR) gene has been identified as a strong positional and functional candidate gene influencing milk production. A non-synonymous single nucleotide polymorphism (SNP) in exon 8 leads to a phenylalanine to tyrosine amino acid substitution (F279Y) in the receptor. The aim of the study was to estimate the effects of the F279Y mutation on milk yield, fat, protein, casein, and lactose yield and content, as well as somatic cell score (SCS), in a German Holstein dairy cattle population. The analysis of 1,370 dairy cows confirmed a strong association of the F279Y polymorphism with milk yield, as well as with fat, protein, and casein contents. Furthermore, increasing effects on lactose yield and content for the 279Y allele were found. Even though the tyrosine variant occurred as the minor allele (16.5%), its substitution effects were 320 kg (305 d), 0.02 kg per day, and 0.07 kg per day for milk, casein, and lactose yields, respectively. The same allele had negative effects on fat, protein, and casein contents. Finally, the high-milk-yield tyrosine allele was also associated with lower SCS (p< 0.05). The data support the high potential of the F279Y polymorphism as a marker for the improvement of milk traits in selection programs.

High throughput methods have been recently developed to genotype individuals for 50,000 or more single nucleotide polymorphisms (SNPs) in a cost effective way. However, besides increasing the power of association studies by using dense marker maps the importance of the accurate selection of the phenotypic data should not be disregarded. The level of milk production differs between parities and shows a decrease for higher parities. Moreover, differences in milk yield can be observed between different lactations as well as within a single lactation. A lactation curve shows the peak production between day 35 and 50 after which production slowly decreases until the end of the lactation (Stanton et al. 1992, Dematawewa et al. 2007). This study aims to locate genetic effects underlying variation in milk production over time. In order to estimate genetic effects between lactations the average production level for each cow was considered. To estimate genetic effects within a lactation, lactation curve parameters were estimated.

Breeding indices have enabled farmers to select for multiple traits simultaneously, including negatively correlated traits such as milk production and fertility. This negative correlation is believed to be either caused by an energy deficit during early lactation or serves a functional purpose in providing optimal birth spacing. A linear regression was carried out between parameters describing a lactation curve and a fertility index (RZR) and milk yield EBVs (mEBVs) to determine the effects of selection on the lactation curve. Breeding values of first lactation milk yield and a RZR were available for 2,405 sires. Additionally, these sires had test-day records of the first lactation of ~2M daughters. There was a negative correlation between mEBVs and RZR (r=-0.27, P<0.0001). Selection for fertility resulted in higher initial milk yield with an early peak yield. This suggests that an early peak occurs to provide offspring with sufficient milk despite a potential energy deficit. Further, an early peak provides an increased duration over which milk production declines and therefore sufficient time for the cow to recover from the energy deficit prior to a subsequent pregnancy. Finally, current production environments could be optimised to fulfil the genetic potential of high producing dairy cows.

This study focused on haplotype effects and linkage disequilibrium (LD) for the K232A locus and the promoter VNTR in the DGAT1 gene. Analyses were carried out in three German Holstein Frisian populations (including 492, 305, and 518 animals) for milk yield, milk fat and protein yield, and milk fat and protein content. We found that effects of the promoter VNTR were not significant and explain only a small amount of the variation of the QTL on BTA14. Haplotype effects were less significant than the K232A locus by itself, but the haplotype containing the A allele of the K232A locus and allele 3 with five repeats of the promoter VNTR showed negative effects on protein content when paternally inherited, whereas the haplotype with the A allele and VNTR allele 2 (with six repeats) increased the protein content. Significant differences between these two haplotypes occurred for protein yield as well, pointing to a linked effect that is picked up by the haplotypes rather than a direct effect of the VNTR. The linkage disequilibrium, estimated by D', showed values between 0.29 and 0.59 which is unexpectedly low for a distance of ~10 kb. Only a very low correlation between the two loci was observed due to the almost similar frequencies of haplotypes containing the A or K allele of the K232A locus.