Using random regression models to estimate genetic variation in growth pattern and its association with sexual maturity of Thai native chickens.

1. Genetic (co)variances and parameters between body weights (BW) across the growth trajectory were estimated using a univariate random regression (RR) animal model. The effect of growth rates (GH) on age at first egg (AFE) and egg weight at first egg (EWFE) were explored using a series of univariate and bivariate analyses. 2. Body weights were taken from Thai native chickens at hatch day to 168 days of age. The model included interactions between age with hatch nested within year and sex as fixed effects, and random effects of direct additive genetic, direct permanent environmental, maternal genetic and maternal permanent environmental effects. All random effects were fitted as regressions to animals' age via quadratic Legendre polynomials and fitting six classes of residual variances was identified as an optimal variance structure to estimate parameters. 3. Genetic and phenotypic variances for BW increased with increasing age. Estimated heritabilities for direct additive (h2 a) and maternal genetic (h2 m) effects on BW traits ranged from 0.34 to 0.54, and 0.04 to 0.06, respectively. Estimated variance ratios for direct (c2 ape) and maternal permanent environmental (c2 mpe) effects ranged from 0.19 to 0.48 and 0.10 to 0.12, respectively. Estimated correlations between weights at different ages were high for all random effects. 4. Estimated h2 a for six GH traits ranged from 0.06 to 0.28, while for AFE and EWFE these were 0.24 and 0.16, respectively. Estimated h2 m and c2 mpe were low for GH. Estimated genetic correlations between GH and AFE ranged from -0.22 to 0.02 and, between GH and EWFE, ranged from -0.05 to 0.40. These estimates suggested that selecting high GH chickens at 28 days of age can be expected to reduce AFE and to increase EWFE.

Assessment of the genetic and economic impact of performance recording and genotyping in Australian commercial sheep operations.

With the new opportunities from DNA technology, multitier breeding schemes have the potential to become more effective and more integrated. Integrated breeding schemes can also be better adapted to account for potential genotype by environment interactions (G × E) between tiers. In this case, phenotypic and genotype information from lower tiers becomes more valuable as it involves measurement of traits that directly represent the breeding objective. The objective of this study was to compare scenarios that represented different selection strategies and their economic effectiveness in fine-wool commercial sheep operations that exploit multitier breeding structures. Genomic selection (GS) applied in the multiplier and the commercial tier presented the largest additional revenue among all scenarios, as it resulted in the largest amount of genetic progress. The largest benefits from GS were outweighed by the genotyping costs, which made DNA parentage the most feasible strategy for the multiplier tier, resulting in the highest cumulative net present value (CNPV). The benefits of phenotypes and genotype information from the commercial environment were larger in the presence of G × E between the nucleus and the commercial tier. The CNPV was larger with a 50% reduction in genotyping costs, which increased the returns of GS scenarios by 2.7-fold on average. Higher selection intensity when selecting multiplier rams also resulted in larger benefits. In this case, returns for the breeding scheme were 3.5-fold higher when 33% of multiplier males were selected based on commercial information, compared to scenarios selecting 50% of the available multiplier rams. The benefits of collecting commercial phenotypes and genotypes were long term, which means that return on investment often took more than 10 years to be achieved, and were largely dependent on two-stage selection to reduce cost while maintaining selection efficiency and on the cost of a genotype test.

MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information.

UNLABELLED: We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations. AVAILABILITY AND IMPLEMENTATION: MTG2 is available in https://sites.google.com/site/honglee0707/mtg2 CONTACT: hong.lee@une.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genomic prediction of reproduction traits for Merino sheep.

Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17-0.61) were higher than were those from sire family cross-validations (range 0.00-0.51). The GEBV accuracies of 0.41-0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy, it identified several candidate genes which are known to be associated with NLB and LSIZE. The approach provides a way to make use of all data available in genomic prediction for traits that have limited recording.

Genomic estimation of additive and dominance effects and impact of accounting for dominance on accuracy of genomic evaluation in sheep populations.

The objectives of this study were to estimate the additive and dominance variance component of several weight and ultrasound scanned body composition traits in purebred and combined cross-bred sheep populations based on single nucleotide polymorphism (SNP) marker genotypes and then to investigate the effect of fitting additive and dominance effects on accuracy of genomic evaluation. Additive and dominance variance components were estimated in a mixed model equation based on "average information restricted maximum likelihood" using additive and dominance (co)variances between animals calculated from 48,599 SNP marker genotypes. Genomic prediction was based on genomic best linear unbiased prediction (GBLUP), and the accuracy of prediction was assessed based on a random 10-fold cross-validation. Across different weight and scanned body composition traits, dominance variance ranged from 0.0% to 7.3% of the phenotypic variance in the purebred population and from 7.1% to 19.2% in the combined cross-bred population. In the combined cross-bred population, the range of dominance variance decreased to 3.1% and 9.9% after accounting for heterosis effects. Accounting for dominance effects significantly improved the likelihood of the fitting model in the combined cross-bred population. This study showed a substantial dominance genetic variance for weight and ultrasound scanned body composition traits particularly in cross-bred population; however, improvement in the accuracy of genomic breeding values was small and statistically not significant. Dominance variance estimates in combined cross-bred population could be overestimated if heterosis is not fitted in the model.

Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy.

Genotyping sheep for genome-wide SNPs at lower density and imputing to a higher density would enable cost-effective implementation of genomic selection, provided imputation was accurate enough. Here, we describe the design of a low-density (12k) SNP chip and evaluate the accuracy of imputation from the 12k SNP genotypes to 50k SNP genotypes in the major Australian sheep breeds. In addition, the impact of imperfect imputation on genomic predictions was evaluated by comparing the accuracy of genomic predictions for 15 novel meat traits including carcass and meat quality and omega fatty acid traits in sheep, from 12k SNP genotypes, imputed 50k SNP genotypes and real 50k SNP genotypes. The 12k chip design included 12 223 SNPs with a high minor allele frequency that were selected with intermarker spacing of 50-475 kb. SNPs for parentage and horned or polled tests also were represented. Chromosome ends were enriched with SNPs to reduce edge effects on imputation. The imputation performance of the 12k SNP chip was evaluated using 50k SNP genotypes of 4642 animals from six breeds in three different scenarios: (1) within breed, (2) single breed from multibreed reference and (3) multibreed from a single-breed reference. The highest imputation accuracies were found with scenario 2, whereas scenario 3 was the worst, as expected. Using scenario 2, the average imputation accuracy in Border Leicester, Polled Dorset, Merino, White Suffolk and crosses was 0.95, 0.95, 0.92, 0.91 and 0.93 respectively. Imputation scenario 2 was used to impute 50k genotypes for 10 396 animals with novel meat trait phenotypes to compare genomic prediction accuracy using genomic best linear unbiased prediction (GBLUP) with real and imputed 50k genotypes. The weighted mean imputation accuracy achieved was 0.92. The average accuracy of genomic estimated breeding values (GEBVs) based on only 12k data was 0.08 across traits and breeds, but accuracies varied widely. The mean GBLUP accuracies with imputed 50k data more than doubled to 0.21. Accuracies of genomic prediction were very similar for imputed and real 50k genotypes. There was no apparent impact on accuracy of GEBVs as a result of using imputed rather than real 50k genotypes, provided imputation accuracy was >90%.

Genetic parameters for egg production traits in purebred and hybrid chicken in a tropical environment.

Genetic parameters were estimated for 5 economically important egg production traits using records collected over 9 years in chickens reared under tropical conditions in Thailand. The data were from two purebred lines and two hybrid lines of layer parent stocks. The two purebred lines were Rhode Island Red (RIR) and White Plymouth Rock (WPR) and the hybrid lines were formed by crossing a commercial brown egg laying strain to Rhode Island Red (RC) and White Plymouth Rock (WC), respectively. Five egg production traits were analysed, including age at first egg (AFE), body weight at first egg (BWT), egg weight at first egg (EWFE), number of eggs from the first 17 weeks of lay (EN) and average egg weight over the 17th week of lay (EW). Fixed effects of year and hatch within year were significant for all 5 traits and were included in the model. Maternal genetic and permanent environmental effects of the dam were not significant, except for EN and EW in RIR and BWT and EW in WPR. Estimated heritability of AFE, BWT, EWFE, EN and EW were 0.45, 0.50, 0.29, 0.19 and 0.43 in RIR; 0.44, 0.38, 0.33, 0.20 and 0.38 in WPR; 0.37, 0.41, 0.38, 0.18 and 0.36 in RC; and 0.46, 0.53, 0.36, 0.38 and 0.45 in WC lines, respectively. The EN was negatively correlated with other traits, except for BWT in RC and AFE and BWT in WC. It was concluded that selection for increased EN will reduce other egg production traits in purebred and hybrid chicken and therefore EN needs to be combined with other egg production traits in a multi-trait selection index to improve all traits optimally according to a defined breeding objective.

Macro- and micro-genetic environmental sensitivity of yearling weight in Angus beef cattle.

Australian beef cattle experience variable conditions, which may give rise to genotype-by-environment interactions depending on the genotypes' macro- and/or micro-genetic environmental sensitivity (GES). Macro-GES gives rise to genotype-by-environment interactions across definable and shared environments, while micro-GES causes heritable variation of phenotypes, e.g., the performance of progeny from one sire may be more variable than other sires. Yearling weight (YW) is a key trait in Australian Angus cattle that may be impacted by both macro- and micro-GES. Current models for genetic evaluation of YW attempt to account for macro-GES by fitting sire-by-herd interactions (S × H). Variation in micro-GES had not yet been estimated for YW in Australian Angus. The aim of this study was to estimate genetic variation due to macro- and micro-GES in YW of Australian Angus cattle. A reaction norm with contemporary group effects as the environmental covariate was fitted either as an alternative to or in combination with a random S × H effect to account for macro-GES. Double hierarchical generalised linear models (DHGLM), fitted as sire models, were used to estimate the genetic variance of the dispersion as a measure of micro-GES. Variation due to both macro- and micro-GES were found in YW. The variance of the slope of the reaction norm was 0.02-0.03 (SEs 0.00), while the S × H variance accounted for 7% of the phenotypic variance in all models. Results showed that both a random S × H effect and a reaction norm should be included to account for both macro-GES and the additional variation captured by an S × H effect. The heritability of the dispersion on the measurement scale ranged from 0.06 to 0.10 (SEs 0.00) depending on which model was used. It should therefore be possible to alter both macro- and micro-GES of YW in Australian Angus through selection. However, care should be taken to ensure an appropriate data structure when including sire-by-herd interactions in the mean part of a DHGLM; otherwise, it can cause biased estimates of micro-GES.

Short communication: Accuracy of whole-genome sequence imputation in Angus cattle using within-breed and multi breed reference populations.

Genotype imputation is a standard approach used in the field of genetics. It can be used to fill in missing genotypes or to increase genotype density. Accurate imputed genotypes are required for downstream analyses. In this study, the accuracy of whole-genome sequence imputation for Angus beef cattle was examined using two different ways to form the reference panel, a within-breed reference population and a multi breed reference population. A stepwise imputation was conducted by imputing medium-density (50k) genotypes to high-density, and then to the whole genome sequence (WGS). The reference population consisted of animals with WGS information from the 1 000 Bull Genomes project. The within-breed reference panel comprised 396 Angus cattle, while an additional 2 380 Taurine cattle were added to the reference population for the multi breed reference scenario. Imputation accuracies were variant-wise average accuracies from a 10-fold cross-validation and expressed as concordance rates (CR) and Pearson's correlations (PR). The two imputation scenarios achieved moderate to high imputation accuracies ranging from 0.896 to 0.966 for CR and from 0.779 to 0.834 for PR. The accuracies from two different scenarios were similar, except for PR from WGS imputation, where the within-breed scenario outperformed the multi breed scenario. The result indicated that including a large number of animals from other breeds in the reference panel to impute purebred Angus did not improve the accuracy and may negatively impact the results. In conclusion, the imputed WGS in Angus cattle can be obtained with high accuracy using a within-breed reference panel.

Genetic correlations for foal and studbook traits with racing traits and implications for selection strategies in the Finnhorse and Standardbred trotter.

Genetic correlations for body measurements and conformation and functional traits in foals and studbook horses with racing traits were estimated in the Finnhorse and Standardbred. Genetic response and accuracy were estimated using records of animal, half-sibs and parents in selection scenarios for racing traits, for foal and racing traits, for studbook and racing traits, and using records of animal, half-sibs and parents for foal traits and racing traits of parents. Racing time and earnings were the breeding objective. Low-to-moderate genetic correlations for body measurements and racing traits indicated that selection favours bigger horses at all ages. Being mainly favourable for the breeding objective, genetic correlations for conformation and functional traits with racing traits were highest for the foal traits of type, trot and overall grade and for the studbook traits of character and movements. Genetic correlations for foal and studbook conformation with racing traits were low in the Finnhorse and moderate to high in the Standardbred. In foals, the highest genetic correlations were for trot with racing time (-0.54) and with earnings (0.52) in the Finnhorse, and for overall grade with racing time (-0.54) and with earnings (0.54) in the Standardbred. In studbook horses, genetic correlations were high for character with racing time and earnings in the Finnhorse (-0.68, 0.61) and in the Standardbred (-0.63, 0.70), and for movements with racing time and earnings in the Finnhorse (-0.70, 0.69) and in the Standardbred (-0.90, 0.88). To increase accuracy of conformation and functional traits, foal traits would be more useful in the index with racing traits, as being less preselected than studbook traits. The foal traits (type, trot, overall grade) having moderate heritability and genetic correlations with racing traits would be useful in multi-trait index before a racing career, where the greatest gain is because of a shorter generation interval. It would be feasible to implement for AI stallions.

Genomic evaluations using similarity between haplotypes.

Long-range phasing and haplotype library imputation methodologies are accurate and efficient methods to provide haplotype information that could be used in prediction of breeding value or phenotype. Modelling long haplotypes as independent effects in genomic prediction would be inefficient due to the many effects that need to be estimated and phasing errors, even if relatively low in frequency, exacerbate this problem. One approach to overcome this is to use similarity between haplotypes to model covariance of genomic effects by region or of animal breeding values. We developed a simple method to do this and tested impact on genomic prediction by simulation. Results show that the diagonal and off-diagonal elements of a genomic relationship matrix constructed using the haplotype similarity method had higher correlations with the true relationship between pairs of individuals than genomic relationship matrices built using unphased genotypes or assumed unrelated haplotypes. However, the prediction accuracy of such haplotype-based prediction methods was not higher than those based on unphased genotype information.

Accuracy of genotype imputation in sheep breeds.

Although genomic selection offers the prospect of improving the rate of genetic gain in meat, wool and dairy sheep breeding programs, the key constraint is likely to be the cost of genotyping. Potentially, this constraint can be overcome by genotyping selection candidates for a low density (low cost) panel of SNPs with sparse genotype coverage, imputing a much higher density of SNP genotypes using a densely genotyped reference population. These imputed genotypes would then be used with a prediction equation to produce genomic estimated breeding values. In the future, it may also be desirable to impute very dense marker genotypes or even whole genome re-sequence data from moderate density SNP panels. Such a strategy could lead to an accurate prediction of genomic estimated breeding values across breeds, for example. We used genotypes from 48 640 (50K) SNPs genotyped in four sheep breeds to investigate both the accuracy of imputation of the 50K SNPs from low density SNP panels, as well as prospects for imputing very dense or whole genome re-sequence data from the 50K SNPs (by leaving out a small number of the 50K SNPs at random). Accuracy of imputation was low if the sparse panel had less than 5000 (5K) markers. Across breeds, it was clear that the accuracy of imputing from sparse marker panels to 50K was higher if the genetic diversity within a breed was lower, such that relationships among animals in that breed were higher. The accuracy of imputation from sparse genotypes to 50K genotypes was higher when the imputation was performed within breed rather than when pooling all the data, despite the fact that the pooled reference set was much larger. For Border Leicesters, Poll Dorsets and White Suffolks, 5K sparse genotypes were sufficient to impute 50K with 80% accuracy. For Merinos, the accuracy of imputing 50K from 5K was lower at 71%, despite a large number of animals with full genotypes (2215) being used as a reference. For all breeds, the relationship of individuals to the reference explained up to 64% of the variation in accuracy of imputation, demonstrating that accuracy of imputation can be increased if sires and other ancestors of the individuals to be imputed are included in the reference population. The accuracy of imputation could also be increased if pedigree information was available and was used in tracking inheritance of large chromosome segments within families. In our study, we only considered methods of imputation based on population-wide linkage disequilibrium (largely because the pedigree for some of the populations was incomplete). Finally, in the scenarios designed to mimic imputation of high density or whole genome re-sequence data from the 50K panel, the accuracy of imputation was much higher (86-96%). This is promising, suggesting that in silico genome re-sequencing is possible in sheep if a suitable pool of key ancestors is sequenced for each breed.

Breeding objectives for dairy cattle under low, medium and high production systems in the tropics.

A deterministic bio-economic model was developed to estimate economic weights for genetic improvement of lactation milk yield, fat yield, age at first calving, calving interval, mature weight and survival under low, medium and high production systems in the Tropics. Input parameters were obtained from dairy production systems in Kenya which has a tropical environment. The highest proportion of revenue is from the sale of milk followed by sale of heifers, cull cows and sale of male calves under all production systems. On the other hand, feed cost is the most important production cost followed by labour, marketing, reproduction and health costs, respectively. Economic values for the six traits were derived from a profit equation using revenue and production costs per cow per year. The economic values were then discounted using diffusion coefficients which account for differences between traits in the time when the improvement is expressed. Economic weights were robust to changes in input and output prices, changes in feeding strategies, and changes in milk and surplus heifer marketing strategies. Genetic standard deviations were multiplied by economic values to standardise the economic value of traits and to compare their potential for economic response. When expressed as proportion of their sum, these relative economic weights under the low, medium and high production systems for lactation milk yield were 51.36, 59.79 and 63.98%; for fat yield 4.50, 10.69 and 9.05%; for age at first calving 3.16, 2.66 and 0.55%; for calving interval 33.59, 19.88 and 20.05%; for mature weight 1.55, 1.34 and 1.19% and for survival rate 5.84, 5.64 and 5.18%, respectively. The predicted responses followed the same pattern as the relative economic weights. This shows that milk yield and calving interval were most important in all production systems but the value of response for traits differed between production systems with more emphasis on milk yield and less on calving interval in the high production systems. Moderate correlations were estimated between the breeding objective for the low, medium and high production systems. To maximise response in the overall breeding objective, different selection criteria are required for the three production systems.

QTL and gene expression analyses identify genes affecting carcass weight and marbling on BTA14 in Hanwoo (Korean Cattle).

Causal mutations affecting quantitative trait variation can be good targets for marker-assisted selection for carcass traits in beef cattle. In this study, linkage and linkage disequilibrium analysis (LDLA) for four carcass traits was undertaken using 19 markers on bovine chromosome 14. The LDLA analysis detected quantitative trait loci (QTL) for carcass weight (CWT) and eye muscle area (EMA) at the same position at around 50 cM and surrounded by the markers FABP4SNP2774C>G and FABP4_µsat3237. The QTL for marbling (MAR) was identified at the midpoint of markers BMS4513 and RM137 in a 3.5-cM marker interval. The most likely position for a second QTL for CWT was found at the midpoint of tenth marker bracket (FABP4SNP2774C>G and FABP4_µsat3237). For this marker bracket, the total number of haplotypes was 34 with a most common frequency of 0.118. Effects of haplotypes on CWT varied from a -5-kg deviation for haplotype 6 to +8 kg for haplotype 23. To determine which genes contribute to the QTL effect, gene expression analysis was performed in muscle for a wide range of phenotypes. The results demonstrate that two genes, LOC781182 (p = 0.002) and TRPS1 (p = 0.006) were upregulated with increasing CWT and EMA, whereas only LOC614744 (p = 0.04) has a significant effect on intramuscular fat (IMF) content. Two genetic markers detected in FABP4 were the most likely QTL position in this QTL study, but FABP4 did not show a significant effect on both traits (CWT and EMA) in gene expression analysis. We conclude that three genes could be potential causal genes affecting carcass traits CWT, EMA, and IMF in Hanwoo.

The use of foal and studbook traits in the breeding programmes of Finnhorse and Standardbred trotters.

Genetic correlations for body measurements and subjectively scored traits between foals and studbook horses were estimated using bivariate linear mixed models. Observations for nine foal and eleven studbook traits in Finnhorses on 6529 foals and 6596 studbook horses and in Standardbred trotters on 3069 foals and 2112 studbook horses were available from the Finnish horse breeding shows. The number of sires with progeny in both foal and studbook data was 203 in Finnhorse and 145 in Standardbred trotters. Estimates of heritability for body measurements in foals and studbook horses using univariate models were high in both breeds (0.41-0.84). Heritability estimates for subjectively scored traits using univariate models were generally higher for foals (0.08-0.46) than for studbook horses (0.06-0.21) in both breeds. Genetic correlations between foals and studbook horses for body measurements were highly positive ranging from 0.74 to 0.96 in Finnhorses and from 0.79 to 0.99 in Standardbred trotters. Low to highly positive genetic correlations between foals and studbook horses for subjectively scored traits were obtained in Finnhorse trotters, whereas in Standardbred trotters genetic correlations for subjectively scored traits varied from moderately negative to highly positive. Higher estimates of heritability for foal traits and generally high genetic correlations between the foal and studbook traits indicate that an early selection for conformation traits would be efficient in the breeding programmes.

Comparison of two live-animal ultrasound systems for genetic evaluation of carcass traits in Angus cattle.

The improvement of carcass traits is an important breeding objective in beef cattle breeding programs. The most common way of selecting for improvement in carcass traits is via indirect selection using ultrasound scanning of selection candidates which are submitted to genetic evaluation programs. Two systems used to analyze ultrasound images to predict carcass traits are the Pie Medical Esaote Aquila (PIE) and Central Ultrasound Processing (CUP). This study compared the ability of the two systems to predict carcass traits for genetic evaluation in Australian Angus cattle. Genetic and phenotypic parameters were estimated using data from 1,648 Angus steers which were ultrasound scanned twice with both systems, first at feedlot entry and then following 100 d in the feedlot. The traits interpreted from ultrasound scanning included eye muscle area (EMA), rib fat (RIB) rump fat (RUMP), and intramuscular fat (IMF). Abattoir carcass data were collected on all steers following the full feedlot feeding period of 285 d. For all ultrasound scan traits, CUP resulted in higher phenotypic and genetic variances compared to the PIE. For IMF, CUP had higher heritability at feedlot intake (0.51 for CUP compared to 0.37 for PIE) and after 100 d feeding (0.54 for CUP compared to 0.45 PIE). CUP predicted IMF also tended to have stronger correlations with the breeding objective traits of carcass IMF and marbling traits, both genetically (ranging from 0.59 to 0.75 for CUP compared to 0.45-0.63 for PIE) and phenotypically (ranging from 0.27 to 0.43 for CUP compared to 0.19-0.28 for PIE). Ultrasound scan EMA was the only group of traits in which the heritabilities were higher for PIE (0.52 for PIE compared to 0.40 for CUP at feedlot intake and 0.46 for PIE compared to 0.43 for CUP at 100 d of feeding), however with similar relationships to the breeding objective carcass EMA observed. For subcutaneous fat traits of ultrasound RIB and RUMP, the heritabilites and genetic correlations to the related carcass traits were similar, with the exception being the higher heritability observed for CUP predicted RUMP at feedlot intake at 0.52 compared to 0.38 for PIE. The results from this study indicates that the CUP system, compared to PIE, provides an advantage for genetic evaluation of carcass traits in Angus cattle, particularly for the IMF and associated marbling traits.

Genetic polymorphisms of the bovine fatty acid binding protein 4 gene are significantly associated with marbling and carcass weight in Hanwoo (Korean Cattle).

The objective of this study was to investigate an association between polymorphisms in the FABP4 gene and phenotypic variation for marbling and carcass weight (CWT) in a population of Hanwoo steers. We re-sequenced 4.3 kb of the FABP4 gene region in 24 Hanwoo bulls and identified 16 SNPs and 1 microsatellite polymorphism. Of these 16 SNPs, three SNPs [g.2774G>C (intron I), g.3473A>T (intron II) and g.3631G>A (exon III, creating a p.Met >Val amino acid substitution)] were genotyped in 583 steers to assess their association with carcass traits. The g.3473A allele showed a significant increasing effect on CWT (P = 0.01) and the g.3631G allele was associated with higher marbling score (P = 0.006). One haplotype of these three SNPs (CAG) was significantly associated with CWT (P = 0.02) and marbling score (P = 0.05) and could potentially be of value for marker assisted selection in Hanwoo cattle. The CAG haplotype effect for CWT was larger (11.14 +/- 5.03 kg) than the largest single locus effect of g.3473A>T (5.01 +/- 2.2 kg).

A quantitative trait locus for faecal worm egg and blood eosinophil counts on chromosome 23 in Australian goats.

Three microsatellite markers on goat chromosome 23 adjacent to the MHC were used to test for quantitative trait loci (QTL) affecting faecal worm egg count (WEC) and leukocyte traits in ten Australian Angora and twelve Australian Cashmere half-sib families (n = 16-57 per family). Data were collected from 280 Angora and 347 Cashmere kids over a 3- and 4-year period. A putative QTL affecting trichostrongyle WEC was found in two small families at the 5% chromosome-wise threshold level. The biggest QTL effect for WEC of 1.65 standard deviations (sigma(p)) was found within the region of OarCP73-BM1258. A significant QTL affecting blood eosinophil counts at the 1% chromosome-wise threshold level was detected at marker BM1258 (at 26 cM) in two Angora and Cashmere families. The magnitude of the putative QTL was 0.69 and 0.85 sigma(p) in Angora and Cashmere families, respectively. Due to the comparatively low power of the study these findings should be viewed as indicative rather than definitive.

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.

Genetic evaluation of Australian dairy cattle for somatic cell scores using multi-trait random regression test-day model.

A multi-trait (MT) random regression (RR) test day (TD) model has been developed for genetic evaluation of somatic cell scores for Australian dairy cattle, where first, second and third lactations were considered as three different but correlated traits. The model includes herd-test-day, year-season, age at calving, heterosis and lactation curves modelled with Legendre polynomials as fixed effects, and random genetic and permanent environmental effects modelled with Legendre polynomials. Residual variance varied across the lactation trajectory. The genetic parameters were estimated using asreml. The heritability estimates ranged from 0.05 to 0.16. The genetic correlations between lactations and between test days within lactations were consistent with most of the published results. Preconditioned conjugate gradient algorithm with iteration on data was implemented for solving the system of equations. For reliability approximation, the method of Tier and Meyer was used. The genetic evaluation system was validated with Interbull validation method III by comparing proofs from a complete evaluation with those from an evaluation based on a data set excluding the most recent 4 years. The genetic trend estimate was in the allowed range and correlations between the two sets of proofs were very high. Additionally, the RR model was compared to the previous test day model. The correlations of proofs between both models were high (0.97) for bulls with high reliabilities. The correlations of bulls decreased with increasing incompleteness of daughter performance information. The correlations between the breeding values from two consecutive runs were high ranging from 0.97 to 0.99. The MT RR TD model was able to make effective use of available information on young bulls and cows, and could offer an opportunity to breeders to utilize estimated breeding values for first and later lactations.