When a genetic marker and a quantitative trait locus (QTL) are in linkage disequilibrium (LD) in one population, they may not be in LD in another population or their LD phase may be reversed. The objectives of this study were to compare the extent of LD and the persistence of LD phase across multiple cattle populations. LD measures r and r 2 were calculated for syntenic marker pairs using genomewide single-nucleotide polymorphisms (SNP) that were genotyped in Dutch and Australian Holstein-Friesian (HF) bulls, Australian Angus cattle, and New Zealand Friesian and Jersey cows. Average r 2 was $0.35, 0.25, 0.22, 0.14, and 0.06 at marker distances 10, 20, 40, 100, and 1000 kb, respectively, which indicates that genomic selection within cattle breeds with r 2 $ 0.20 between adjacent markers would require $50,000 SNPs. The correlation of r values between populations for the same marker pairs was close to 1 for pairs of very close markers (,10 kb) and decreased with increasing marker distance and the extent of divergence between the populations. To find markers that are in LD with QTL across diverged breeds, such as HF, Jersey, and Angus, would require $300,000 markers.

Genomic selection uses total breeding values for juvenile animals, predicted from a large number of estimated marker haplotype effects across the whole genome. In this study the accuracy of predicting breeding values is compared for four different models including a large number of markers, at different marker densities for traits with heritabilities of 50 and 10%. The models estimated the effect of (1) each single-marker allele ½single-nucleotide polymorphism (SNP)1, (2) haplotypes constructed from two adjacent marker alleles (SNP2), and (3) haplotypes constructed from 2 or 10 markers, including the covariance between haplotypes by combining linkage disequilibrium and linkage analysis (HAP_IBD2 and HAP_IBD10). Between 119 and 2343 polymorphic SNPs were simulated on a 3-M genome. For the trait with a heritability of 10%, the differences between models were small and none of them yielded the highest accuracies across all marker densities. For the trait with a heritability of 50%, the HAP_IBD10 model yielded the highest accuracies of estimated total breeding values for juvenile and phenotyped animals at all marker densities. It was concluded that genomic selection is considerably more accurate than traditional selection, especially for a low-heritability trait.T HE availability of many thousands of singlenucleotide polymorphisms (SNPs) spread across the genome for different livestock species opens up possibilities to include genomewide marker information in prediction of total breeding values, to perform genomic selection. Compared to traditional breeding practice, including genomic information yields a considerable increase in selection responses for juvenile animals that do not have phenotypic records and potentially can reduce the costs of a breeding program up to 90% (Schaeffer 2006).Genomic selection as described by predicts total breeding values on the basis of a large number of marker haplotypes across the entire genome. The underlying assumption of genomic selection is that haplotypes at some loci are in linkage disequilibrium (LD) with QTL alleles that affect the traits that are subject to selection. Different ways of deriving haplotypes of combinations of marker alleles, and the relationship between haplotypes at a locus, have been described. One method (SNP1) is to consider each different marker allele at a single locus to be a different haplotype, considering no relationships between different haplotypes, and thus breeding values are estimated directly for the marker alleles (Xu 2003). A second method is to construct haplotypes from two alleles at adjacent markers, assuming a zero relation between haplotypes at the same locus (SNP2) . A third method is to construct haplotypes (HAP_IBD) using two or more surrounding marker alleles and derive identical-by-descent (IBD) probabilities between the different haplotypes at the same locus (Meuwissen and Goddard 2001).The SNP1 model considers only two haplotypes at a locus and therefore may be suited for applications in, for instance, double-haploid ...

Genomic prediction of future phenotypes or genetic merit using dense SNP genotypes can be used for prediction of disease risk, forensics, and genomic selection of livestock and domesticated plant species. The reliability of genomic predictions is their squared correlation with the true genetic merit and indicates the proportion of the genetic variance that is explained. As reliability relies heavily on the number of phenotypes, combining data sets from multiple populations may be attractive as a way to increase reliabilities, particularly when phenotypes are scarce. However, this strategy may also decrease reliabilities if the marker effects are very different between the populations. The effect of combining multiple populations on the reliability of genomic predictions was assessed for two simulated cattle populations, A and B, that had diverged for T ¼ 6, 30, or 300 generations. The training set comprised phenotypes of 1000 individuals from population A and 0, 300, 600, or 1000 individuals from population B, while marker density and trait heritability were varied. Adding individuals from population B to the training set increased the reliability in population A by up to 0.12 when the marker density was high and T ¼ 6, whereas it decreased the reliability in population A by up to 0.07 when the marker density was low and T ¼ 300. Without individuals from population B in the training set, the reliability in population B was up to 0.77 lower than in population A, especially for large T. Adding individuals from population B to the training set increased the reliability in population B to close to the same level as in population A when the marker density was sufficiently high for the marker-QTL linkage disequilibrium to persist across populations. Our results suggest that the most accurate genomic predictions are achieved when phenotypes from all populations are combined in one training set, while for more diverged populations a higher marker density is required.

No abstract

Subclinical ketosis is a metabolic disorder in high-producing dairy cattle that can be detected by ketone bodies in milk: acetone (Ac), acetoacetate (AcAc), and beta-hydroxybutyrate (BHBA). Fourier transform infrared (FTIR) spectrometry is to a growing extent used for determination of milk constituents in milk recording, but as yet there is no calibration for ketone bodies available. The objective of this study was therefore to build a calibration for the MilkoScan FT6000 (FOSS Analytical A/S, Hillerød, Denmark) for Ac, AcAc, and BHBA and to evaluate the FTIR predictions for detection of subclinical ketosis. From 217 herds, 1,080 milk samples were taken from fresh multiparous dairy cows. The Ac, AcAc, and BHBA concentrations were determined by chemical methods using segmented flow analysis. Because of its low concentration, AcAc seemed to be hardly detectable and was therefore not considered further. The correlation between the chemical method results of Ac and BHBA was 0.82, indicating that both ketone bodies were elevated in milk during subclinical ketosis. In wk 1 postpartum, however, most samples with a high Ac concentration did not have a high BHBA concentration, whereas after wk 5 postpartum most samples with a high BHBA concentration did not have a high Ac concentration. For Ac and BHBA, the correlation coefficients between the FTIR predictions and the chemical results were around 0.80 with standard error of cross validation values of 0.184 and 0.064 mM for Ac and BHBA, respectively. Using thresholds of 0.15 mM for Ac and 0.10 mM for BHBA, high values for Ac or BHBA were detected with a sensitivity of 69 to 70%, a specificity of 95%, with 25 to 27% false positives and 6 to 7% false negatives. It is argued that FTIR predictions for Ac and BHBA are valuable for screening cows on subclinical ketosis, especially when used in combination with other indicators, and can serve in the evaluation of the herd health status with respect to subclinical ketosis.