Impact of Including Calf Gender in Models to Predict Breeding Values for Lactation Yields in Dairy Cattle

Foetal calves produce sex hormones that can enter the maternal bloodstream. Male calves typically have longer gestations than female calves resulting in shorter lactations in pastoral production systems. Both of these phenomena could influence milk yields of the dam. North American and French studies have reported conflicting results as to the size of calf gender effects on milk yield. This study used a dataset from New Zealand dairy cattle to fit calf gender effects and quantify the impact of including calf gender when estimating breeding values. The regressions of lactation yield on days in milk were different for second parity cows according to whether the cows had produced male or female calves. The gender of a cow’s second calf had an effect on second lactation milk yield in Holstein Friesians. There was minimal re-ranking of animals when calf gender was included in the model used for breeding value estimation and the expected genetic gain was similar with and without calf gender included in the analytical model

Improving Accuracy of Genomic Prediction in Holstein Friesians

Three statistical models were considered to assess the advantage of including information of known causative mutations when estimating genomic breeding values. Data included phenotypic records and 50k genotypes from 5,661 Holstein Friesian cows. This study showed that when aknown causative mutation for milk traits, DGAT1, was fit as a fixed effect in genomic prediction, an increase in accuracy was seen compared to fitting it as either a random effect or not explicitly fitting it and relying on linked markers fitted as random effects. The regression coefficients of genomic prediction on phenotype were near one for all estimates, indicating that no major bias was present in the estimates. These results suggest that, when calculatinggenomic predictions, it is beneficial to include information from known major genes in the analysis to increase the accuracy of prediction

Improving the Accuracy of Genomic Prediction of Milk Fat

Four statistical models were considered to quantify any advantage of including the genotype of known causative mutations when calculating direct genomic values. Data included 50k genotypes from 5,661 Holstein Friesian cows and 2,287 bulls. This study showed that when a known QTL for milk traits, DGAT1, was fit as a fixed class or fixed covariate in genomic prediction, an increase in accuracy was seen compared to fitting it as either a random covariate or relying on linked 50k markers fit as random covariates. The regression coefficients of genomic prediction on phenotype were near one for all estimates, indicating no major bias was in the estimates. These results suggest it is beneficial to the accuracy of prediction to include information from known major QTL in genomic analyses

Genomic Prediction of Milk Fat using Fixed Length Haplotypes

Performance of genomic prediction from haplotype models utilizing fixed length haplotype blocks ranging from 125 kb – 2 Mb and haplotype allele frequency cutoffs ranging from 1-10% were compared to a model using SNP genotypes. Milk fat yield deviations and genotypes at 37,740 SNPs were from 38,385 Holstein Friesian, Jersey and KiwiCross cows (Training=23,907; Validation=14,478) from New Zealand. This study showed slight improvement in accuracy and bias of genomic prediction when using 125 kb haplotype blocks with a 1% filter; however this was associated with a large increase in run-time. Haplotype blocks larger than 1 Mb are not appropriate for genomic prediction in this population