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Background: Gastrointestinal nematodes (GIN), liver flukes (Fasciola hepatica) and bovine lungworms (Dictyocaulus viviparus) are the most important parasitic agents in pastured dairy cattle. Endoparasite infections are associated with reduced milk production and detrimental impacts on female fertility, contributing to economic losses in affected farms. In quantitative-genetic studies, the heritabilities for GIN and F. hepatica were moderate, encouraging studies on genomic scales. Genome-wide association studies (GWAS) based on dense single nucleotide polymorphism (SNP) marker panels allow exploration of the underlying genomic architecture of complex disease traits. The current GWAS combined the identification of potential candidate genes with pathway analyses to obtain deeper insights into bovine immune response and the mechanisms of resistance against endoparasite infections., Results: A 2-step approach was applied to infer genome-wide associations in an endangered dual-purpose cattle subpopulation [Deutsches Schwarzbuntes Niederungsrind (DSN)] with a limited number of phenotypic records. First, endoparasite traits from a population of 1166 Black and White dairy cows [including Holstein Friesian (HF) and DSN] naturally infected with GIN, F. hepatica and D. viviparus were precorrected for fixed effects using linear mixed models. Afterwards, the precorrected phenotypes were the dependent traits (rFEC-GIN, rFEC-FH, and rFLC-DV) in GWAS based on 423,654 SNPs from 148 DSN cows. We identified 44 SNPs above the genome-wide significance threshold (p Bonf  = 4.47 × 10 - 7 ), and 145 associations surpassed the chromosome-wide significance threshold (range: 7.47 × 10 - 6 on BTA 1 to 2.18 × 10 - 5 on BTA 28). The associated SNPs identified were annotated to 23 candidate genes. The DAVID analysis inferred four pathways as being related to immune response mechanisms or involved in host-parasite interactions. SNP effect correlations considering specific chromosome segments indicate that breeding for resistance to GIN or F. hepatica as measured by fecal egg counts is genetically associated with a higher risk for udder infections., Conclusions: We detected a large number of loci with small to moderate effects for endoparasite resistance. The potential candidate genes regulating resistance identified were pathogen-specific. Genetic antagonistic associations between disease resistance and productivity were specific for specific chromosome segments. The 2-step approach was a valid methodological approach to infer genetic mechanisms in an endangered breed with a limited number of phenotypic records.

Background: To better understand the genetic determination of udder health, we performed a genome-wide association study (GWAS) on a population of 2354 German Holstein bulls for which daughter yield deviations (DYD) for somatic cell score (SCS) were available. For this study, we used genetic information of 44 576 informative single nucleotide polymorphisms (SNPs) and 11 725 inferred haplotype blocks., Results: When accounting for the sub-structure of the analyzed population, 16 SNPs and 10 haplotypes in six genomic regions were significant at the Bonferroni threshold of P ≤ 1.14 × 10-6. The size of the identified regions ranged from 0.05 to 5.62 Mb. Genomic regions on chromosomes 5, 6, 18 and 19 coincided with known QTL affecting SCS, while additional genomic regions were found on chromosomes 13 and X. Of particular interest is the region on chromosome 6 between 85 and 88 Mb, where QTL for mastitis traits and significant SNPs for SCS in different Holstein populations coincide with our results. In all identified regions, except for the region on chromosome X, significant SNPs were present in significant haplotypes. The minor alleles of identified SNPs on chromosomes 18 and 19, and the major alleles of SNPs on chromosomes 6 and X were favorable for a lower SCS. Differences in somatic cell count (SCC) between alternative SNP alleles reached 14 000 cells/mL., Conclusions: The results support the polygenic nature of the genetic determination of SCS, confirm the importance of previously reported QTL, and provide evidence for the segregation of additional QTL for SCS in Holstein cattle. The small size of the regions identified here will facilitate the search for causal genetic variations that affect gene functions.

Recently, we identified 6 genomic loci affecting daughter yield deviations (DYD) for somatic cell score (SCS) in a genome-wide association study (GWAS) performed with German Holstein bulls. In the current study, we tested if these loci were associated with SCS in cows using their own performance data. The study was performed with 1,412 German Holstein cows, of which 483 were daughters of 71 bulls that had been used in the GWAS. We tested 10 single nucleotide polymorphisms (SNP) representing 6 genomic regions that were associated with DYD for SCS in bulls. All tested SNP were significant in cows. Seven of them, located on Bos taurus autosomes (BTA) 6, 13, and 19, had the same direction of effect as those previously reported in the bull population. The most significant associations were detected on BTA6 and BTA19, accounting for 1.8% of the total genetic variance. The major allele of the 2 SNP on BTA6 and the minor allele of the 2 SNP on BTA19 were favorable for lower SCS. The differences between the homozygous genotype classes were up to 15,000 cells/mL. The verification of SNP associated with SCS in this study provides further evidence for the functional role of the linked genomic regions for immune response and contributes to identification of causative mutations. In particular, SNP with minor frequency of the favorable allele possess high potential to reduce SCS in German Holstein cattle by selection., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Phenotypic variation in milk production traits has been described over the course of a lactation as well as between different parities. The objective of this study was to investigate whether variation in production is affected by different loci across lactations. A genome-wide association study (GWAS) using a 50-k SNP chip was conducted in 152 divergent German Holstein Friesian cows to test for association with milk production traits over different lactations. The first four lactations were analysed regarding milk yield, fat, protein, lactose, milk urea nitrogen yield and content as well as somatic cell score. Two approaches were used: (i) Wilmink curve parameters were used to assess the genetic effects over the course of a lactation and (ii) test-day yield deviations (YD) were used as a normative approach for a GWAS. The significant effects were largest for markers affecting curve parameters for which there was a statistical power <0.8 of detection even in this small design. While significant markers for YDs were detected in this study, the power to detect effects of a similar magnitude was only 0.11, suggesting that many loci may have been missed with this approach in the present design. Furthermore, all significant effects were specific for a single lactation, leading to the conclusion that the variance explained by a certain locus changes from lactation to lactation. We confirm the common evidence that most production traits vary in the degree of persistency after the peak as a result of genetic influence., (© 2011 The Authors, Animal Genetics © 2011 Stichting International Foundation for Animal Genetics.)

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.

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., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)