Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle.

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.

Technical note: updating the inverse of the genomic relationship matrix.

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.

A single nucleotide polymorphism in suppressor of cytokine signalling-2 is associated with growth and feed conversion efficiency in pigs.

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).

Estimated gene frequencies of GeneSTAR markers and their size of effects on meat tenderness, marbling, and feed efficiency in temperate and tropical beef cattle breeds across a range of production systems.

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 < 0.0001) on shear force, with the size and direction of effects consistent across the range of breeds for the 3 populations with phenotypes. However, sizes of marker effects differed across muscles and reduced upon tenderstretch hanging. Marker T3 was not significant (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.

Genetic mapping of quantitative trait loci for resistance to Haemonchus contortus in sheep.

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.

Sire breed comparisons for meat and eating quality traits in Australian pig populations.

Sire breed comparisons for carcase (n = 1169), meat and eating quality (n = 686) traits were obtained using data recorded on progeny of known pedigree sired by Duroc (DU), Large White (LW), Landrace (LR) and Duroc Synthetic (DS) boars from crossbred (LW/LR) sows. Animals were reared in eco-shelters in large single-sex contemporary groups and slaughtered on an age constant basis at 22 weeks of age. Compared to progeny from other sire groups, animals sired by purebred Duroc boars tended to have improved eating quality traits (higher intramuscular fat and lower shear force) at the expense of poorer carcase characteristics (higher subcutaneous and belly fat). Animals sired by DS boars tended to be heavier and leaner than those sired by LW, LR or DU boars; intramuscular fat but not belly fat was correspondingly lower, while tenderness was generally consistent with that of the DU progeny. Significant variability of sire progeny groups within sire breed suggests that sire breed selection, potentially used for improving traits such as meat quality in commercial progeny, will be less accurate in the absence of sire-specific information, which is typically poorly recorded in this class of traits.

Genetic analyses of live-animal ultrasound and abattoir carcass traits in Australian Angus and Hereford cattle.

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.

Assessing the efficiency of multiplicative mixed model equations to account for heterogeneous variance across herds in carcass scan traits from beef cattle.

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 (h2). These estimates were incorporated into a multiplicative mixed model that simultaneously estimates breeding values (EBV) and heterogeneity factors. Re-estimation of h2 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 h2 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 h2 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.

Multiple-trait genetic evaluation for one polychotomous trait and several continuous traits with missing data and unequal models.

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.

Implications of using an average relationship matrix in genetic evaluation for a population using multiple-sire matings.

SUMMARY: Ambiguous paternity can be incorporated into the mixed model equations (MME) by including the average numerator relatinship matrix (average A), which averages the true sire-offspring relationship over the putative sires. A previous study has shown that some overestimation of genetic trend results from this substitution. A population of 40 breeding females and 2 breeding males was simulated 1,000 times with either random mating or sequential selection continuing for 8 breeding cycles. In the selection case candidates were ranked on estimated breeding values (EBVs) calculated from the MME with an animal model and the average A. Variances of the EBVs and prediction errors were computed. The results showed the average A incorrectly perceives both the variance of family sizes among males and the variance loss due to selection to be smaller. This will lead to an overestimation of genetic trend. ZUSAMMENFASSUNG: Folgerungen aus der Anwendung einer durchschnittlichen Verwandtschaftsmatrix bei der Zuchtwertschätzung für eine Population mit mehreren Vätern in einer Paarungsgruppe In den Mischmodellgleichungen kann eine unklare väterliche Abstammung durch die Verwendung einer durchschnittlichen Verwandtschaftsmatrix, die die Abstammung zu gleichen Teilen über die möglichen Väter aufteilt, berücksichtigt werden. Eine frühere Arbeit hat gezeigt, daß diese Maßnahme zu einer gewissen Überschätzung des genetischen Fortschritts führt. Eine Population mit 40 weiblichen und 2 männlichen Tieren wurde 1000mal über 8 Paarungsperioden simuliert und zwar mit und ohne gerichteter Selektion. Im Falle der Selektion wurden die Tiere aufgrund der mit einem Tiermodell und der durchschnittlichen Verwandtschaftsmatrix geschätzten Zuchtwerte geordnet. Die Varianzen der Zuchtwerte und der Schätzfehler wurden berechnet. Die Ergebnisse zeigen, daß durch eine durch-schnittliche Verwandtschaftsmatrix die Varianz der Größe der Nachkommensgruppen der Väter und der Verlust an genetischer Varianz aufgrund der Selektion unterschätzt wird. Dies führt zu einer Überschätzung des genetischen Fortschritts.

Implications of using an average relationship matrix in genetic evaluation of a population using multiple-sire matings.

SUMMARY: Ambiguous paternity can be incorporated into the mixed model equations (MME) by including the average numerator relationship matrix (average A), which averages the true sire-offspring relationship over the putative sires. A previous study has shown that some overestimation of genetic trend results from this substitution. A population of 40 breeding females and 2 breeding males was simulated 1000 times with either random mating or sequential selection continuing for eight breeding cycles. In the selection case, candidates were ranked on estimated breeding values (EBVs) calculated from the MME with an animal model and the average A. Variances of the EBVs and prediction errors were computed. The results showed that the average A incorrectly perceives both the variance of family sizes among males and the variance loss due to selection to be smaller. This leads to an overestimation of genetic trend. ZUSAMMENFASSUNG: Auswirkung der durchschnittlichen genetischen Verwandtschaftsmatrix auf die Zuchtwertschätzung in einer Population mit Paarungen mehrerer Vatertiere Zweifelhafte väterliche Abstammung kann in die Mischmodellgleichung (MME) mittels durch- schnittlicher Numerator Verwandtschaftsmatrix (Durchschnitt A) eingebaut werden, die die durchschnittlichen wahren Verwandtschaften der in Frage kommenden Vatertiere beinhaltet. Eine frühere Untersuchung hat gezeigt, daß der genetische Trend dadurch eine gewisse Überschätzung erfährt. Eine Population aus 40 weiblichen und 2 männlichen Zuchttieren wurde 1000 mal simuliert, entweder unter Panmixie oder unter sequentieller Selektion durch acht Zuchtzyklen. Im Selektionsfall wurden die Kandidaten nach dem geschätzten Zuchtwert (EBV) rangiert der aus MME des Tiermodells mit durchschnittlicher A geschätzt worden ist. Varianzen der EBV und Schätzungsfehler wurden berechnet. Es zeigt sich, daß durchschnittliche A die Varianzen der Familiengrößen zwischen Vatertieren und den Varianzverlust durch Selektion als zu gering erfaßt, was zu einer Überschätzung des genetischen Trends führt.