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The aim of this study was to assess the accuracy of genomic predictions for 19 traits including feed efficiency, growth, and carcass and meat quality traits in beef cattle. The 10,181 cattle in our study had real or imputed genotypes for 729,068 SNP although not all cattle were measured for all traits. Animals included Bos taurus, Brahman, composite, and crossbred animals. Genomic EBV (GEBV) were calculated using 2 methods of genomic prediction (BayesR and genomic BLUP (GBLUP)) either using a common training dataset for all breeds or using a training dataset comprising only animals of the same breed. Accuracies of GEBV were assessed using 5-fold cross-validation. The accuracy of genomic prediction varied by trait and by method. Traits with a large number of recorded and genotyped animals and with high heritability gave the greatest accuracy of GEBV. Using GBLUP, the average accuracy was 0.27 across traits and breeds, but the accuracies between breeds and between traits varied widely. When the training population was restricted to animals from the same breed as the validation population, GBLUP accuracies declined by an average of 0.04. The greatest decline in accuracy was found for the 4 composite breeds. The BayesR accuracies were greater by an average of 0.03 than GBLUP accuracies, particularly for traits with known genes of moderate to large effect mutations segregating. The accuracies of 0.43 to 0.48 for IGF-I traits were among the greatest in the study. Although accuracies are low compared with those observed in dairy cattle, genomic selection would still be beneficial for traits that are hard to improve by conventional selection, such as tenderness and residual feed intake. BayesR identified many of the same quantitative trait loci as a genomewide association study but appeared to map them more precisely. All traits appear to be highly polygenic with thousands of SNP independently associated with each trait.

A computing strategy to update the inverse of the genomic relationship matrix when new genotypes become available is described. It is shown that re-using results of previous computations can result in substantial reductions in computing time required. For instance, when the number of individuals increased by about 1% for matrices larger than 15,000, the time required for updating was less than 7% of that used for direct inversion from scratch.

The objectives of this study were to use genotypes for 12 commercially available GeneSTAR gene markers on 12,330 animals to estimate gene frequencies of the markers across a range of beef cattle breeds and to determine the effects of these markers on target traits using a subset of animals with both genotypic and phenotypic data (n = 9,414) for at least one trait. Tenderness markers (T1, T2, T3, T4) were assessed against shear force of 2 muscles, marbling markers (M1, M2, M3, M4) were assessed against intramuscular fat percent and marbling score, and the feed-efficiency markers (N1, N2, N3, N4) were assessed on daily feed intake and residual feed intake. Animals used were from 5 beef cattle research populations: Beef Cooperative Research Centre 1 (CRC1) temperate breeds (Angus, Hereford, Murray Grey, Shorthorn; n = 3,721), Beef CRC1 tropical breeds (Brahman, Santa Gertrudis, Belmont Red; n = 3,899), Beef CRC2 tropically adapted genotypes (Brahman and Tropical Composite; n = 4,446), and progeny test programs in Angus (n = 742) and Shorthorn (n = 347). Gene frequencies varied significantly across breeds and markers, with 86% of the markers estimated to be in Hardy-Weinberg equilibrium. Tenderness markers T1 and T2 had significant effects (P 0.05) in CRC1 temperate breeds but was significant (P < 0.0001) in tropically adapted breeds, with a large effect in Brahman. Marker T4 was significant for shear force in 2 CRC1 tropical breeds but with a different favorable allele. The 4 marbling markers were generally not significantly associated with intramuscular fat percent or marbling score across the 5 populations studied. Feed-efficiency markers N3 and N4 were significantly associated with residual feed intake and daily feed intake in the CRC1 temperate data set, in which a subset of the CRC1 data was used in their discovery, but were not significant in the other 4 populations. Markers N1 and N2 were generally not significant, but when they were significant, their direction of effects differed. The 12 GeneSTAR markers were studied in populations consisting of different breeds and genetic variability and showed gene frequencies and estimated effects that varied greatly across traits, suggesting large differences between the markers for their utility as selection tools in these populations.

Weaning weights from 83 389 Limousin calves born between 1993 and 2002 in France and the Trans-Tasman block (Australia/New Zealand) were analysed to compare different strategies for running an international genetic evaluation for the breed. These records were a subset of the complete data for both countries and comprised a sample of herds that had recorded progeny of sires used across both countries. Genetic and phenotypic parameters for weaning weight were estimated within the countries. The estimates of direct genetic heritabilities were higher in France than in the Trans-Tasman block (0.31 vs. 0.22), while direct-maternal genetic correlations were less negative in the Trans-Tasman block (-0.10) than in France (-0.21). Different strategies for an international evaluation were studied, and the correlations between the estimated breeding values (EBV) of national evaluations and these strategies were derived. The international evaluation strategies were a) an animal model on raw performance data with non unity genetic correlations and heterogeneous residual and genetic variances across countries; b) the same animal model applied to pre-corrected (for fixed effects) performance data; and c) a sire model on de-regressed proofs (MACE). Estimates of the genetic correlations between weaning weight in both countries were 0.86 (0.80) for direct (maternal) genetic effects for the first strategy. Estimation of variance components by MACE appeared to be very sensitive to the sample of bulls and their reliability approximations. Variance component estimates obtained using pre-corrected data were inconsistent with estimates on raw data. However, the EBV predicted using pre-corrected data and parameters estimated from the raw data were similar to those predicted from raw data. Correlations between national and international EBV were always high (> 0.90) for sires, whichever genetic effect (direct or maternal) or international evaluation model was considered. The ranking of the bulls in the top 100 is of primary interest in terms of international genetic evaluation. In this study, some re-ranking of sires was observed for the top 100 bulls between countries and between the three international evaluation models. Thus, the origin of top sires may vary according to the implemented international evaluation strategy.

Feed efficiency and growth are the most important traits in pig production, and very few genetic markers have been reported to be associated with feed efficiency. The suppressor of cytokine signalling-2 (encoded by SOCS2) is the main negative regulator of somatic growth, and the knockout of SOCS2 and naturally mutant mice have high-growth phenotypes. Porcine SOCS2 was selected as a primary positional candidate for feed efficiency, because it is located on chromosome 5q, in the vicinity of a Quantitative Trait Locus (QTL) region for food conversion ratio in pigs. Here, we report five single nucleotide polymorphisms identified by sequencing of the promoter region and exon 1. One PCR-RFLP assay was designed for genotyping the polymorphism c.1667A > G (GenBank Accession No AY312266). Association analyses were performed in an Australian mapping resource pedigree population (PRDC-US43) for food conversion ratio, backfat, IGF1 level and growth traits and showed significant effects on average daily gain on test (ADG2) (P < 0.01) and marginal association with food conversion ratio (FCR) (P < 0.08). © 2010 The Authors, Animal Genetics

This paper presents results from a mapping experiment to detect quantitative trait loci (QTL) for resistance to Haemonchus contortus infestation in merino sheep. The primary trait analysed was faecal worm egg count in response to artificial challenge at 6 months of age. In the first stage of the experiment, whole genome linkage analysis was used for broad-scale mapping. The animal resource used was a designed flock comprising 571 individuals from four half-sib families. The average marker spacing was about 20 cM. For the primary trait, 11 QTL (as chromosomal/family combinations) were significant at the 5% chromosome-wide level, with allelic substitution effects of between 0.19 and 0.38 phenotypic standard deviation units. In general, these QTL did not have a significant effect on faecal worm egg count recorded at 13 months of age. In the second stage of the experiment, three promising regions (located on chromosomes 1, 3 and 4) were fine-mapped. This involved typing more closely spaced markers on individuals from the designed flock as well as an additional 495 individuals selected from a related population with a deeper pedigree. Analysis was performed using a linkage disequilibrium-linkage approach, under additive, dominant and multiple QTL models. Of these, the multiple QTL model resulted in the most refined QTL positions, with resolutions of < 10 cM achieved for two regions. Because of the moderate size of effect of the QTL, and the apparent age and/or immune status specificity of the QTL, it is suggested that a panel of QTL will be required for significant genetic gains to be achieved within industry via marker-assisted selection. Refereed/Peer-reviewed

Data (n = 2,658) from live animal ultrasonic measures from 17 Angus herds were used to evaluate a multiplicative mixed model that incorporates scaling factors to correct for across-herd heterogeneity of variance. Traits included were ribeye muscle area (EMA), surface fat at the P8 site (P8), surface fat between the 12th and 13th ribs (RIB12), and weight at scanning (WEIGHT). Cattle ranged in age from 501 to 698 d and represented 291 contemporary groups. Data were initially analyzed using single-trait, animal model, Method R procedures to estimate variance components and heritabilities (h 2 ). These estimates were incorporated into a multiplicative mixed model that simultaneously estimates breeding values (EBV) and heterogeneity factors. Reestimation of h 2 after scaling the data with the correction factors was explored to obtain a measure of the improvement in the genetic evaluation and to detect changes in ranking of individuals and herds. Initial h 2 estimates for EMA, P8, RIB12, and WEIGHT were.36,.39,.29, and.48, respectively. Scaling factors ranged from.25 for P8 in a herd with eight records to 1.96 for RIB12 in a herd with 86 individuals. Re-estimates of h 2 increased by an average of 4.2% for all the traits as a result of correcting for heterogeneity. Deviations of new scaling factors were within expectations. Correlations between EBV with and without heterogeneity correction were greater than.97 for all the traits. However, some substantial re-rankings of herds were observed for some traits in the smaller herds.

A method for multiple-trait genetic evaluation for categorical and continuous traits was generalized to a polychotomous rather than a binary trait and to several continuous traits rather than one. Any missing data pattern was allowed. Breeding values were estimated based on an animal model with fixed and random effects differing among traits. Equations in location parameters were solved iteratively within each Fisher scoring step. In each round of scoring, new solutions of the residual covariances among the categorical and the continuous traits were computed based on maximum likelihood estimation and used to reevaluate all partial regression coefficients of liability on the continuous traits for each missing data pattern. Simulation was used to assess the estimation of the residual covariances. The other dispersion parameters were treated as known because their estimation has been treated elsewhere and is analogous to restricted maximum likelihood.

In order to estimate genetic parameters, abattoir carcass data on 1,713 Angus and 1,007 Hereford steers and heifers were combined with yearling live-animal ultrasound measurements on 8,196 Angus and 3,405 Hereford individuals from seedstock herds. Abattoir measures included carcass weight (CWT), percentage of retail beefyield (RBY), near-infrared measured intramuscular fat percentage (CIMF), preslaughter scanned eye muscle area (CEMA), and subcutaneous fat depth at the 12th rib (CRIB) and at the P8 site (CP8). Ultrasound scans on yearling animals included 12th-rib fat depth (SRIB), rump fat depth at the P8 site (SP8), eye muscle area (SEMA), and percentage of intramuscular fat (SIMF). Records on CWT were adjusted to 650-d slaughter age, and the remaining abattoir traits were adjusted to 300-kg CWT. Scan data were analyzed treating records on males and females as different traits. Multivariate analyses were performed on a variety of trait combinations using animal model and REML algorithm. Heritability (h2) estimates for CWT, RBY, CIMF, CP8, CRIB, and CEMA were .31, .68, .43, .44, .28, and .26, respectively, for Angus and .54, .36, .36, .08, .27, .38, respectively, for Hereford. Pooled across sexes, h2 estimates for SIMF, SP8, SRIB, and SEMA were .33, .55, .51, and .42, respectively, for Angus and .20, .31, .18, and .38, respectively, for Hereford. Genetic correlations (r(g)) between the same pair of carcass traits measured at yearling through scanning and directly at the abattoir were moderate to strongly positive, suggesting that selection using yearling ultrasound measurements of seedstock cattle should result in predictable genetic improvement for abattoir carcass characteristics. Estimates of r(g) between the scanned fat measurements and RBY were negative, ranging from -.85 for Angus heifers to -.05 for Hereford heifers. Also, the estimates of r(g) between SEMA and the fat records measured at the abattoir were negative and ranged from -.94 in Hereford heifers to -.02 in Angus heifers.

An efficient algorithm is described for computing restricted maximum likelihood estimates of variance components in a class of models. The class of models is characterized by effects to be absorbed, which are nested within herds, other fixed effects, random sire effects, one other group of random effects nested within herds, and a random residual. The model can be generalized to account for relationships among sires. Matrix inversion is not used. A realistic example is presented.