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 aim of the present study was to test the effect of Enterococcus faecium NCIMB 10415 (E. faecium) on CD4+ T helper immune cell subpopulations and CD25+ cells in ileal lymphatic tissue after challenge with Salmonella (S.) Typhimurium DT 104. German Landrace piglets treated with E. faecium (n = 16) as a feed additive and untreated controls (n = 16) were challenged with S. Typhimurium 10 days after weaning. The expression of lineage specific T helper cell subtype master transcription factors on mRNA level was measured in the whole tissue of the gut associated lymphoid tissues (ileocecal mesenteric lymph node, ileum with Peyer’s patches and papilla ilealis) and in magnetically sorted T helper cells from blood and ileocecal mesenteric lymph nodes at two and 28 days post infection. CD25 protein expression of T helper cells was studied by flow cytometry in ileal Peyer’s patches, lymph nodes and blood. Distribution and morphology of CD25+ cells was demonstrated in situ by immunohistochemistry in paraffin embedded specimens of the ileum and the ileocecal mesenteric lymph nodes. The data provide evidence for a higher T helper 2 cell driven immune response in the control group compared to the E. faecium treated group (P < 0.05) in CD4+ magnetically sorted lymphocytes from the ileocecal mesenteric lymph nodes at two and 28 days post infection. We did not observe differences for CD25+ cells in immunohistochemistry and flow cytometry between E. faecium fed pigs and the control group, but provided a detailed description of the occurrence and morphology of these cells in the gut associate lymphoid tissues of piglets. In conclusion we suggest that (i) prolonged feeding with E. faecium can result in changes of the T helper cell response leading to a stronger infection with S. Typhimurium and (ii) that it is important to examine purified immune cells to be able to detect effects on T helper cell subpopulations.

The aims of this study were to estimate the genotype and allele frequencies and genotype effects located in the Acyl-CoA: diacylglycerol acyltransferase 1 (DGAT1), Leptin (Lep/ob),Growth hormone receptor (GHR), Prolactin receptor (PRLR), and Kappa casein (CSN3) genes on milk yield, composition and somatic cell score (SCS) in German Holstein cows. The association analyses were based on data from 1380 German Holstein cows. The allele frequencies of the DGAT1 K232A were 44.2% and 55.8% for the Lysine and Alanine variant, respectively. The allele substitution effect for the Lysine variant was significantly increased the fat (0.30%, p < 0.0001), protein (0.08%, p < 0.0001) and casein contents (0.06%, p < 0.0001) and fat yield (9.13 kg, p < 0.0001). In contrast, the effect was negative on milk yield (-372.77 kg, p < 0.0001), protein yield (-6.32 kg, p < 0.0001), and lactose yield (-0.05 kg, p < 0.0001). With respect to the Mbo1-RFLP in the Lep gene, the allele A was the major allele with a frequency of 90.3%. The substitution effect of the minor allele B had a significant influence on fat yield (6.3 kg, p < 0.05). The frequency of the Phenylalanine allele of the GHR F279Y polymorphism was 83%. The allele substitution effect of the minor Tyrosine variant was 320 kg (p < 0.0001), 0.02 kg (p < 0.05), 0.07 kg (p < 0.0001), and 0.03% (p < 0.05) for milk, casein, and lactose yields, and lactose content, respectively. Negative effects were evident for fat (-0.12%, p < 0.0001), protein (-0.09%, p < 0.0001) and casein (-0.07%, p < 0.0001) contents. The Tyrosine variant of GHR F279Y was associated with lower SCS (p <0.05). For the PRLR S18N polymorphism, Serine was the major allele (76.7%). The Asparagine variant had a significant (p < 0.05) effect on the casein content (0.02%). For the CSN3 gene locus, the allele encoding the protein variant A was higher frequent (85.1%) and the minor Allele B was associated with protein (0.03%, p < 0.05) and casein contents (0.03%, p < 0.05). This study demonstrated that power of candidate gene analyses. The gene effects are considered in genome wide genomic selection programs.

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.

Background: This study is aimed at the analysis of genetic and physiological effects of myostatin on economically relevant meat quality traits in a genetic background of high muscularity. For this purpose, we generated G3 populations of reciprocal crosses between the two hypermuscular mouse lines BMMI866, which carries a myostatin mutation and is lean, and BMMI806, which has high intramuscular and body fat content. To assess the relationship between muscle mass, body composition and muscle quality traits, we also analysed intramuscular fat content (IMF), water holding capacity (WHC), and additional physiological parameters in M. quadriceps and M. longissimus in 308 G3-animals. Results: We found that individuals with larger muscles have significantly lower total body fat (r = −0.28) and IMF (r = −0.64), and in females, a lower WHC (r = −0.35). In males, higher muscle mass was also significantly correlated with higher glycogen contents (r = 0.2) and lower carcass pH-values 24 hours after dissection (r = −0.19). Linkage analyses confirmed the influence of the myostatin mutation on higher lean mass (1.35 g), reduced body fat content (−1.15%), and lower IMF in M. longissimus (−0.13%) and M. quadriceps (−0.07%). No effect was found for WHC. A large proportion of variation of intramuscular fat content of the M. longissimus at the myostatin locus could be explained by sex (23%) and direction-of-cross effects (26%). The effects were higher in males (+0.41%). An additional locus with negative over-dominance effects on total fat mass (−0.55 g) was identified on chromosome 16 at 94 Mb (86–94 Mb) which concurs with fat related QTL in syntenic regions on SSC13 in pigs and BTA1 in cattle. Conclusion: The data shows QTL effects on mouse muscle that are similar to those previously observed in livestock, supporting the mouse model. New information from the mouse model helps to describe variation in meat quantity and quality, and thus contribute to research in livestock.

Lactation is a dynamic process, which evolved to meet dietary demands of growing offspring. At the same time, the mother's metabolism changes to meet the high requirements of nutrient supply to the offspring. Through strong artificial selection, the strain of milk production on dairy cows is often associated with impaired health and fertility. This led to the incorporation of functional traits in to breeding aims to counteract this negative association. Potentially, distributing the total quantity of milk per lactation cycle more equally over time could reduce the peak of physiological strain and improve health and fertility. During lactation many factors affect the production of milk: food intake; digestion, absorption, and transportation of nutrients; blood glucose levels; activity of cells in the mammary gland, liver, and adipose tissue; synthesis of proteins and fat in the secretory cells; and the metabolic and regulatory pathways that provide fatty acids, amino acids, and carbohydrates. Whilst the endocrine regulation and physiology of the dynamic process of milk production seems to be understood, the genetics that underlie these dynamics are still to be uncovered. Modeling of longitudinal traits and estimating the change in additive genetic variation over time has shown that the genetic contribution to the expression of a trait depends on the considered time-point. Such time-dependent studies could contribute to the discovery of missing heritability. Only very few studies have estimated exact gene and marker effects at different time-points during lactation. The most prominent gene affecting milk yield and milk fat, DGAT1, exhibits its main effects after peak production, whilst the case in genes have larger effects in early lactation. Understanding the physiological dynamics and elucidating the time-dependent genetic effects behind dynamically expressed traits will contribute to selection decisions to further improve productive and healthy breeding populations.