We report mapping of a quantitative trait locus (QTL) with a major effect on bovine stature to a ∼780-kb interval using a Hidden Markov Model-based approach that simultaneously exploits linkage and linkage disequilibrium. We re-sequenced the interval in six sires with known QTL genotype and identified 13 clustered candidate quantitative trait nucleotides (QTNs) out of >9,572 discovered variants. We eliminated five candidate QTNs by studying the phenotypic effect of a recombinant haplotype identified in a breed diversity panel. We show that the QTL influences fetal expression of seven of the nine genes mapping to the ∼780-kb interval. We further show that two of the eight candidate QTNs, mapping to the PLAG1-CHCHD7 intergenic region, influence bidirectional promoter strength and affect binding of nuclear factors. By performing expression QTL analyses, we identified a splice site variant in CHCHD7 and exploited this naturally occurring null allele to exclude CHCHD7 as single causative gene.

A method is described for the prediction of breeding values incorporating genomic information. The first stage involves the prediction of genomic breeding values for genotyped individuals. A novel component of this is the estimation of the genomic relationship matrix in the context of a multi-breed population. Because not all ancestors of genotyped animals are genotyped, a selection index procedure is used to blend genomic predictions with traditional ancestral information that is lost between the process of deregression of the national breeding values and subsequent re-estimation using the genomic relationship matrix. Finally, the genomically enhanced predictions are filtered through to nongenotyped descendants using a regression procedure.

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A method was developed for calculating approximate reliability for national systems of evaluation. The method combined the reliability of three information sources: parent average, animal's own records, and progeny records. This method provided good approximation to the actual values with minimal upward bias and was considerably better than the current national method of New Zealand genetic evaluation or Meyer's method for all accuracy measures. Our method had an average absolute bias of 0.006 compared with 0.026 and 0.035 for the current national method and Meyer's method, respectively. Our method was less computationally demanding than the current New Zealand method. One of the major advantages of the method is that it can be extended to accommodate more complex models by altering the selection index equations within the method. An example is given for which the method was extended to account for a genetic correlation other than unity between an incomplete lactation and a complete lactation yield.

Body condition score (BCS) data were collected on 169,661 first-parity cows from herds participating in progeny testing schemes and linear type assessment. Genetic and residual variances for BCS estimated across time using a quadratic random regression model were found to be largest at the start of lactation. Heritability estimates ranged from 0.32 to 0.23 from d 1 to 200 of lactation, with a mean of 0.26. Genetic correlations between BCS and other traits were estimated using 2 approaches: 1) a multivariate analysis that included BCS and live weight, both adjusted for stage of lactation; 270-d cumulative yields of milk, fat, and protein; average somatic cell score; and 2 measures of fertility; and 2) a bivariate random regression analysis in which BCS was considered to be a longitudinal trait across time, with the same measurements as in approach 1 for all other traits. Genetic correlations of BCS with the 2 fertility traits were 0.43 and 0.50 using the multivariate analysis; the corresponding random regression estimates between BCS as a longitudinal trait across time and 2 measures of fertility were 0.35 to 0.44 and 0.40 to 0.49, and tended to increase with stage of lactation. Genetic correlations estimated using the random regression model fluctuated around the multivariate estimates for live weight and somatic cell score, which were 0.50 and -0.12, respectively. Genetic correlations estimated using the multivariate analysis of BCS with fat and protein yields were close to zero. With the random regression model, genetic correlations between BCS and fat and protein yields were positive at d 1 of lactation (0.16 and 0.08, respectively) and were negative by d 200 of lactation (-0.25 and -0.20, respectively). In pastoral production systems, such as those typical in New Zealand, there appears to be an advantage in the total lactation yields of fat and protein for cows of higher BCS in early lactation, which is likely to be because these cows have body reserves that are available to be mobilized in later lactation, when feed resources are sometimes limited.