Dataset Information

BDNF contributes to the genetic variance of milk fat yield in german holstein cattle.

ABSTRACT: The gene encoding the brain-derived neurotrophic factor (BDNF) has been repeatedly associated with human obesity. As such, it could also contribute to the regulation of energy partitioning and the amount of secreted milk fat during lactation, which plays an important role in milk production in dairy cattle. Therefore, we performed an association study using estimated breeding values (EBVs) of bulls and yield deviations of German Holstein dairy cattle to test the effect of BDNF on milk fat yield (FY). A highly significant effect (corrected p-value?=?3.362?×?10(-4)) was identified for an SNP 168?kb up-stream of the BDNF transcription start. The association tests provided evidence for an additive allele effect of 5.13?kg of fat per lactation on the EBV for milk FY in bulls and 6.80?kg of fat of the own production performance in cows explaining 1.72 and 0.60% of the phenotypic variance in the analyzed populations, respectively. The analyses of bulls and cows consistently showed three haplotype groups that differed significantly from each other, suggesting at least two different mutations in the BDNF region affecting the milk FY. The FY increasing alleles also had low but significant positive effects on protein and total milk yield which suggests a general role of the BDNF region in energy partitioning, rather than a specific regulation of fat synthesis. The results obtained in dairy cattle suggest similar effects of BDNF on milk composition in other species, including man.

SUBMITTER: Zielke LG

PROVIDER: S-EPMC3268571 | biostudies-other | 2011

REPOSITORIES: biostudies-other

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BDNF contributes to the genetic variance of milk fat yield in german holstein cattle.

Zielke Lea G LG Bortfeldt Ralf H RH Tetens Jens J Brockmann Gudrun A GA

Frontiers in genetics 20110405

The gene encoding the brain-derived neurotrophic factor (BDNF) has been repeatedly associated with human obesity. As such, it could also contribute to the regulation of energy partitioning and the amount of secreted milk fat during lactation, which plays an important role in milk production in dairy cattle. Therefore, we performed an association study using estimated breeding values (EBVs) of bulls and yield deviations of German Holstein dairy cattle to test the effect of BDNF on milk fat yield ...[more]

PMID: 22303313

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Project description:BackgroundThe impact of additive-genetic relationships captured by single nucleotide polymorphisms (SNPs) on the accuracy of genomic breeding values (GEBVs) has been demonstrated, but recent studies on data obtained from Holstein populations have ignored this fact. However, this impact and the accuracy of GEBVs due to linkage disequilibrium (LD), which is fairly persistent over generations, must be known to implement future breeding programs.Materials and methodsThe data set used to investigate these questions consisted of 3,863 German Holstein bulls genotyped for 54,001 SNPs, their pedigree and daughter yield deviations for milk yield, fat yield, protein yield and somatic cell score. A cross-validation methodology was applied, where the maximum additive-genetic relationship (amax) between bulls in training and validation was controlled. GEBVs were estimated by a Bayesian model averaging approach (BayesB) and an animal model using the genomic relationship matrix (G-BLUP). The accuracy of GEBVs due to LD was estimated by a regression approach using accuracy of GEBVs and accuracy of pedigree-based BLUP-EBVs.ResultsAccuracy of GEBVs obtained by both BayesB and G-BLUP decreased with decreasing amax for all traits analyzed. The decay of accuracy tended to be larger for G-BLUP and with smaller training size. Differences between BayesB and G-BLUP became evident for the accuracy due to LD, where BayesB clearly outperformed G-BLUP with increasing training size.ConclusionsGEBV accuracy of current selection candidates varies due to different additive-genetic relationships relative to the training data. Accuracy of future candidates can be lower than reported in previous studies because information from close relatives will not be available when selection on GEBVs is applied. A Bayesian model averaging approach exploits LD information considerably better than G-BLUP and thus is the most promising method. Cross-validations should account for family structure in the data to allow for long-lasting genomic based breeding plans in animal and plant breeding.

Project description:Holstein cattle are well known for their high average milk yield but are more susceptible to disease and have lower fecundity than other breeds of cattle. The purpose of this study was to explore the relationship between ruminal metabolites and both milk performance and ruminal microbiota composition as a means of assessing the benefits of crossbreeding Montbéliarde and Holstein cattle. This experiment crossbred Holstein with Montbéliarde cattle, aimed to act as a reference for producing high-quality dairy products and improving the overall efficiency of dairy cattle breeding. Based on similar age, parity and lactation time, 46 cows were selected and divided into two groups (n = 23 per group) for comparison experiment and fed the same formula: Montbéliarde×Holstein (MH, DIM = 33.23 ± 5.61 d), Holstein (H, DIM = 29.27 ± 4.23 d). Dairy herd improvement (DHI) data is an important basis for evaluating the genetic quality of bulls, understanding the quality level of milk, and improving feeding management. We collected the DHI data of these cows in the early lactation, middle lactation and late lactation period of 10 months. The results showed that the average milk production and protein content in Montbéliarde×Holstein were 1.76 kg (34.41 kg to 32.65 kg, p > 0.05) and 0.1% (3.54 to 3.44%, p < 0.05) higher than in Holstein cattle. Moreover, milk from Montbéliarde×Holstein cattle had lesser somatic cell score (1.66 to 2.02) than Holstein cattle (p < 0.01). A total of 10 experimental cattle in early lactation were randomly selected in the two groups (Lactation time = 92.70 ± 6.81), and ruminal fluid were collected by oral gastric tube. Using 16S rRNA microbial sequencing, we compared the ruminal microbiota composition and found that Montbéliarde×Holstein cattle had a lower abundance of Alphaproteobacteria (p < 0.05) and higher abundance of Selenomonas than Holstein cattle (p < 0.05). These bacteria play roles in protein degradation, nitrogen fixation and lactic acid degradation. The abundance of Succiniclasticum was also greater in Montbéliarde×Holstein cattle (p = 0.053). Through ruminal metabolome analysis, we found that the levels of trans-ferulic acid, pyrrole-2-carboxylic acid, and quinaldic acid were significantly increased in Montbéliarde×Holstein cattle, while that of lathosterol was significantly decreased. The changes in the levels of these metabolites could confer improved antioxidant, anti-inflammatory, and antibacterial activities.

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BDNF contributes to the genetic variance of milk fat yield in german holstein cattle.

Publications

BDNF contributes to the genetic variance of milk fat yield in german holstein cattle.

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