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.

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.

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.

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.

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.

Estimation of genetic parameters for BW and body measurements in Brahman cattle.

Body weight and body measurements are commonly used to represent growth and measured at several growth stages in beef cattle. Those economically important traits should be genetically improved. To achieve breeding programs, genetic parameters are prerequisite, as they are needed for designing and predicting outcomes of breeding programs, as well as estimating of breeding values. (Co)variance components were estimated for BW and body measurements on Brahman cattle born between 1990 and 2016 from 17 research herds across Thailand. The traits measured were BW, heart girth (GR), hip height (HH) and body length (BL) and were measured at birth, 200 days, 400 days and 600 days of age. The number of records varied between traits from 18 890 for birth BW to 876 for GR at 600 days. Estimation of variance components was performed using restricted maximum likelihood using univariate and multivariate animal models. Pre-weaning traits were influenced by genetic and/or permanent environmental effects of the dam, except for BL. Heritability estimates from birth to 600 days of age ranged from 0.28±0.01 to 0.50±0.06 for BW, 0.27±0.01 to 0.43±0.09 for GR, 0.28±0.01 to 0.58±0.08 for HH and 0.34±0.01 to 0.51±0.08 for BL using univariate analysis. Heritability estimates for the traits studied increased with age. A similar trend was observed for the phenotypic and genetic correlations between subsequent BW and body measurements. A positive correlation was observed between different traits measured at a similar age, ranging from 0.22±0.01 to 0.72±0.01 for the phenotypic correlation and 0.25±0.04 to 0.97±0.11 for the genetic correlation. Also, a positive correlation was observed for similar traits across different age classes ranging from 0.07±0.03 to 0.76±0.02 for the phenotypic correlation and 0.24±0.11 to 0.92±0.05 for the genetic correlation. Therefore, all correlations between body measurements at the same age and across age classes were positive. The results show the potential improvement of growth traits in Brahman cattle, and those traits can be improved simultaneously under the same breeding program.

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.

Genetic correlations between wool traits and meat quality traits in Merino sheep.

Genetic correlations between 29 wool production and quality traits and 25 meat quality and nutritional value traits were estimated for Merino sheep from an Information Nucleus (IN). Genetic correlations among the meat quality and nutritional value traits are also reported. The IN comprised 8 flocks linked genetically and managed across a range of sheep production environments in Australia. The wool traits included over 5,000 yearling and 3,700 adult records for fleece weight, fiber diameter, staple length, staple strength, fiber diameter variation, scoured wool color, and visual scores for breech and body wrinkle. The meat quality traits were measured on samples from the and included over 1,200 records from progeny of over 170 sires for intramuscular fat (IMF), shear force of meat aged for 5 d (SF5), 24 h postmortem pH (pHLL; also measured in the , pHST), fresh and retail meat color and meat nutritional value traits such as iron and zinc levels, and long-chain omega-3 and omega-6 polyunsaturated fatty acid levels. Estimated heritabilities for IMF, SF5, pHLL, pHST, retail meat color lightness (), myoglobin, iron, zinc and across the range of long-chain fatty acids were 0.58 ± 0.11, 0.10 ± 0.09, 0.15 ± 0.07, 0.20 ± 0.10, 0.59 ± 0.15, 0.31 ± 0.09, 0.20 ± 0.09, 0.11 ± 0.09, and range of 0.00 (eicosapentaenoic, docosapentaenoic, and arachidonic acids) to 0.14 ± 0.07 (linoleic acid), respectively. The genetic correlations between the wool production and meat quality traits were low to negligible and indicate that wool breeding programs will have little or no effect on meat quality. There were moderately favorable genetic correlations between important yearling wool production traits and the omega-3 fatty acids that were reduced for corresponding adult wool production traits, but these correlations are unlikely to be important in wool/meat breeding programs because they have high SE, and the omega-3 traits have little or no genetic variance. Significant genetic correlations among the meat quality traits included IMF with SF5 (-0.76 ± 0.24), fresh meat color * (0.50 ± 0.18), and zinc (0.41 ± 0.19). Selection to increase IMF will improve meat tenderness and color which may address some of the issues with Merino meat quality. These estimated parameters allow Merino breeders to combine wool and meat objectives without compromising meat quality.

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.

Genetic parameters for wool traits, live weight, and ultrasound carcass traits in Merino sheep.

Genetic correlations between 29 wool production and quality traits and live weight and ultrasound fat depth (FAT) and eye muscle depth (EMD) traits were estimated from the Information Nucleus (IN). The IN comprised 8 genetically linked flocks managed across a range of Australian sheep production environments. The data were from a maximum of 9,135 progeny born over 5 yr from 184 Merino sires and 4,614 Merino dams. The wool traits included records for yearling and adult fleece weight, fiber diameter (FD), staple length (SL), fiber diameter CV (FDCV), scoured color, and visual scores for breech and body wrinkle. We found high heritability for the major yearling wool production traits and some wool quality traits, whereas other wool quality traits, wool color, and visual traits were moderately heritable. The estimates of heritability for live weight generally increased with age as maternal effects declined. Estimates of heritability for the ultrasound traits were also higher when measured at yearling age rather than at postweaning age. The genetic correlations for fleece weight with live weights were positive (favorable) and moderate (approximately 0.5 ± 0.1), whereas those with FD were approximately 0.3 (unfavorable). The other wool traits had lower genetic correlations with the live weights. The genetic correlations for FAT and EMD with FD and SL were positive and low, with FDCV low to moderate negative, but variable with wool weight and negligible for the other wool traits. The genetic correlations for FAT and EMD with postweaning weight were positive and high (0.61 ± 0.18 to 0.75 ± 0.14) but were generally moderate with weights at other ages. Selection for increased live weight will result in a moderate correlated increase in wool weight as well as favorable reductions in breech cover and wrinkle, along with some unfavorable increases in FD and wool yellowness but little impact on other wool traits. The ultrasound meat traits, FAT and EMD, were highly positively genetically correlated (0.8), and selection to increase them would result in a small unfavorable correlated increase in FD, moderately favorable reductions in breech cover and wrinkle, but equivocal or negligible changes in other wool traits. The estimated parameters provide the basis for calculation of more accurate Australian Sheep Breeding Values and selection indexes that combine wool and meat objectives in Merino breeding programs.

Genetic correlations between wool traits and carcass traits in Merino sheep.

Genetic correlations between 29 wool production and quality traits and 14 whole carcass measures and carcass component traits were estimated from the Information Nucleus of 8 flocks managed across a range of Australian sheep production environments and genetically linked. Wool data were from over 5,000 Merino progeny born over 5 yr, whereas carcass data were from over 1,200 wether progeny of over 176 sires, slaughtered at about 21 kg carcass weight, on average. Wool traits included yearling and adult records for wool weight, fiber diameter, fiber diameter variation, staple strength, scoured color, and visual scores for breech and body wrinkle. Whole carcass measures included HCW, dressing percentage (DP), and various measures of fat depth and eye muscle dimensions. Carcass components were obtained by dissection, and lean meat yield (LMY) was predicted. Heritability estimates for whole carcass measures ranged from 0.12 ± 0.08 to 0.35 ± 0.10 and ranged from 0.17 ± 0.10 to 0.46 ± 0.10 for carcass dissection traits, with no evidence of important genotype × environment interactions. Genetic correlations indicated that selection for increased clean wool weight will result in reduced carcass fat (-0.17 to -0.34) and DP (-0.48 ± 0.15), with little effect on carcass muscle. Selection for lower fiber diameter will reduce HCW (-0.48 ± 0.15) as well as carcass fat (0.14 to 0.27) and muscle (0.21 to 0.50). There were high genetic correlations between live animal measures of fat and muscle depth and the carcass traits (generally greater than 0.5 in size). Selection to increase HCW (and DP) will result in sheep with fewer wrinkles on the body (-0.57 ± 0.10) and barer breeches (-0.74 ± 0.12, favorable), with minor deterioration in scoured wool color (reduced brightness and increased yellowness). Selection for reduced fat will also result in sheep with fewer body wrinkles (-0.42 to -0.79). Increasing LMY in Merinos through selection would result in a large reduction in carcass fat and DP (-0.66 to -0.84), with a smaller increase in carcass muscle and some increase in wool weight and wrinkles. Although no major antagonisms are apparent between the wool and carcass traits, developing selection indexes for dual-purpose wool and meat breeding objectives will require accurate estimates of genetic parameters to ensure that unfavorable relationships are suitably considered. The findings will aid development of dual-purpose wool and meat breeding objectives.

Optimising the proportion of selection candidates measured for feed intake for a beef cattle breeding objective that includes methane emissions.

Reducing daily methane production (DMP) via selection for lower estimated daily (pasture) feed intake (DFI) has the potential to be more cost effective than direct selection for DMP. Daily feed intake has a high heritability and high genetic correlation to DMP and has a potential lower cost of measurement. This study's main aim was to determine for a breeding nucleus the optimal proportion of randomly selected young male and female cattle in which to estimate DFI. This optimum proportion was determined by modeling the measurement costs and response to selection of Angus cattle on a (standard industry) Angus breeding index (ABI) augmented with DFI and DMP in a combined breeding objective (BO), but without DMP being measured. For the assumed herd structure and considering a 20 yr planning horizon, the highest net present value (NPV) occurred when 64% of males and no females were measured for DFI. The highest breakeven DFI test cost (A$41.51/head) and highest returns on investment (ROI) occurred when 36% of males and no females had DFI estimates. Higher ROI were achieved when all males had DFI estimates before any females had DFI estimates. There was a diminishing increase in rate of genetic gain when moving from 36% to 64% of males with DFI estimates, thus ROI decreased from 29.7% to 23.1%. When 36% of males had DFI estimates (and no females), herd DMP genetic gain was slightly positive as the DMP reduction per generation from male selection (-0.086) was more than offset by the DMP increase per generation from female selection (+0.110). The selection response for DMP only became negative when at least 40% of males had DFI estimates. Having 64% of males with DFI estimates resulted in a predicted genetic decrease in DMP (-0.018 kgCOe/head per yr), compared to an increase of 0.052 kgCOe/head per yr when no animals had DFI estimates. The optimum proportion of males with DFI estimates (36 to 64%) depends on the breeders attitude toward ROI and the value of genetic change for DMP. Sensitivity analysis showed that the economic value (EV), heritability and genetic variance of DFI had a higher impact on the NPV and ROI outcomes than parameters related to ABI and DMP, so future work should focus on obtaining robust estimates for DFI parameters. Higher EV for feed intake and DMP would result in higher percentages of animals being profitably measured for DFI, leading to larger reductions in DMP.

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.

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.

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.

Breeding objectives for sheep should be customised depending on variation in pasture growth across years.

Breeding programmes for livestock require economic weights for traits that reflect the most profitable animal in a given production system, which affect the response in each trait after selection. The profitability of sheep production systems is affected by changes in pasture growth as well as grain, meat and wool prices between seasons and across years. Annual pasture growth varies between regions within Australia's Mediterranean climate zone from low growth with long periods of drought to high growth with shorter periods of drought. Therefore, the objective of this study was to assess whether breeding objectives need to be adapted for regions, depending on how reliable the pasture growth is across years. We modelled farms with Merino sheep bred for wool and meat in 10 regions in Western Australia. Across these 10 regions, mean annual pasture growth decreased, and the CV of annual pasture growth increased as pasture growth for regions became less reliable. We calculated economic values for nine traits, optimising management across 11 years, including variation for pasture growth and wool, meat and grain prices between and within years from 2002 to 2012. These economic values were used to calculate responses to selection for each trait for the 10 regions. We identified two potential breeding objectives, one for regions with low or high reliability and the other for regions with medium reliability of pasture growth. Breeding objectives for high or low pasture growth reliability had more emphasis on live weight traits and number of lambs weaned. Breeding objectives for medium reliability of pasture growth had more emphasis on decreasing fibre diameter. Relative economic weights for fleece weight did not change across the regions. Regions with low or high pasture reliability had similar breeding objectives and response to selection, because the relationship between the economic values and CV of pasture growth were not linear for live weight traits and the number of lambs weaned. This non-linearity was caused by differences in distribution of pasture growth between regions, particularly during summer and autumn, when ewes were pregnant, with increases in energy requirements affecting the value of lambs weaned. In addition, increasing live weight increased the intake capacity of sheep, which meant that more poor quality pasture could be consumed during summer and autumn, which had more value in regions with low and high pasture reliability. We concluded that breeding values for sheep production systems should be customised depending on the reliability of pasture growth between years.

Genetic correlations between body weight change and reproduction traits in Merino ewes depend on age.

Merino sheep in Australia experience periods of variable feed supply. Merino sheep can be bred to be more resilient to this variation by losing less BW when grazing poor quality pasture and gaining more BW when grazing good quality pasture. Therefore, selection on BW change might be economically attractive but correlations with other traits in the breeding objective need to be known. The genetic correlations (rg) between BW, BW change, and reproduction were estimated using records from approximately 7,350 fully pedigreed Merino ewes managed at Katanning in Western Australia. Number of lambs and total weight of lambs born and weaned were measured on approximately 5,300 2-yr-old ewes, approximately 4,900 3-yr-old ewes, and approximately 3,600 4-yr-old ewes. On a proportion of these ewes BW change was measured: approximately 1,950 2-yr-old ewes, approximately 1,500 3-yr-old ewes, and approximately 1,100 4-yr-old ewes. The BW measurements were for 3 periods. The first period was during mating period over 42 d on poor pasture. The second period was during pregnancy over 90 d for ewes that got pregnant on poor and medium quality pasture. The third period was during lactation over 130 d for ewes that weaned a lamb on good quality pasture. Genetic correlations between weight change and reproduction were estimated within age classes. Genetic correlations were tested to be significantly greater magnitude than 0 using likelihood ratio tests. Nearly all BW had significant positive genetic correlations with all reproduction traits. In 2-yr-old ewes, BW change during the mating period had a positive genetic correlation with number of lambs weaned (rg = 0.58); BW change during pregnancy had a positive genetic correlation with total weight of lambs born (rg = 0.33) and a negative genetic correlation with number of lambs weaned (rg = -0.49). All other genetic correlations were not significantly greater magnitude than 0 but estimates of genetic correlations for 3-yr-old ewes were generally consistent with these findings. The direction of the genetic correlations mostly coincided with the energy requirements of the ewes and the stage of maturity of the ewes. In conclusion, optimized selection strategies on BW changes to increase resilience will depend on the genetic correlations with reproduction and are dependent on age.

Genetic parameters for meat quality traits of Australian lamb meat.

Genetic parameters were estimated for a range of meat quality traits recorded on Australian lamb meat. Data were collected from Merino and crossbred progeny of Merino, terminal and maternal meat breed sires of the Information Nucleus programme. Lambs born between 2007 and 2010 (n=8968) were slaughtered, these being the progeny of 372 sires and 5309 dams. Meat quality traits were found generally to be of moderate heritability (estimates between 0.15 and 0.30 for measures of meat tenderness, meat colour, polyunsaturated fat content, mineral content and muscle oxidative capacity), with notable exceptions of intramuscular fat (0.48), ultimate pH (0.08) and fresh meat colour a* (0.08) and b* (0.10) values. Genetic correlations between hot carcass weight and the meat quality traits were low. The genetic correlation between intramuscular fat and shear force was high (-0.62). Several measures of meat quality (fresh meat redness, retail meat redness, retail oxy/met value and iron content) appear to have potential for inclusion in meat sheep breeding objectives.