Purebred-crossbred genetic parameters for reproductive traits in swine

For swine breeding programs, testing and selection programs are usually within purebred (PB) populations located in nucleus units that are generally managed differently and tend to have a higher health level than the commercial herds in which the crossbred (CB) descendants of these nucleus animals are expected to perform. This approach assumes that PB animals selected in the nucleus herd will have CB progeny that have superior performance at the commercial level. There is clear evidence that this may not be the case for all traits of economic importance and, thus, including data collected at the commercial herd level may increase the accuracy of selection for commercial CB performance at the nucleus level. The goal for this study was to estimate genetic parameters for five maternal reproductive traits between two PB maternal nucleus populations (Landrace and Yorkshire) and their CB offspring: Total Number Born (TNB), Number Born Alive (NBA), Number Born Alive > 1 kg (NBA > 1 kg), Total Number Weaned (TNW), and Litter Weight at Weaning (LWW). Estimates were based on single-step GBLUP by analyzing any two combinations of a PB and the CB population, and by analyzing all three populations jointly. The genomic relationship matrix between the three populations was generated by using within-population allele frequencies for relationships within a population, and across-population allele frequencies for relationships of the CB with the PB animals. Utilization of metafounders for the two PB populations had no effect on parameter estimates, so the two PB populations were assumed to be genetically unrelated. Joint analysis of two (one PB plus CB) vs. three (both PB and CB) populations did not impact estimates of heritability, additive genetic variance, and genetic correlations. Heritabilities were generally similar between the PB and CB populations, except for LWW and TNW, for which PB populations had about four times larger estimates than CB. Purebred-crossbred genetic correlations (rpc) were larger for Landrace than for Yorkshire, except for NBA > 1 kg. These estimates of rpc indicate that there is potential to improve selection of PB animals for CB performance by including CB information for all traits in the Yorkshire population, but that noticeable additional gains may only occur for NBA > 1 kg and TNW in the Landrace population.     

应用BLUP方法对猪繁殖性状和生长性状遗传评估的研究

【目的】估计杜洛克猪(Duroc,DD)、长白猪(Landrace,LL)、大白猪(Yorkshire,YY)繁殖性状和生长性状的遗传参数,分析不同年份育种值变化的遗传趋势,为制定合理的育种方案提供理论依据。【方法】以杜洛克猪、长白猪、大白猪繁殖性状和生长性状的性能测定数据为研究材料,其中繁殖性状数据10 963条,包括总产仔数(total number born,TNB)、产活仔数(number born alive,NBA)、出生窝重(litter born weight,LBW)和21日龄窝重(litter weight at 21 days,LW21);生长性状数据25 257条,包括达100 kg体重日龄(age at 100 kg live weight,AGE)和达100 kg体重背膘厚(backfat adjusted to 100 kg,BF)。采用基于动物模型的最佳线性无偏预测(best linear unbiased prediction,BLUP)方法,使用ASReml统计分析软件进行遗传力、遗传相关和育种值估计。【结果】TNB、NBA和LBW的遗传力在0.08~0.20之间,LW21的遗传力在0.02~0.05之间;AGE和BF的遗传力在0.22~0.37之间。繁殖性状TNB、NBA、LBW、LW21的遗传相关系数总体分布在0.20~0.97之间,呈中等偏上正相关;生长性状AGE和BF的遗传相关系数分布在-0.07~-0.03之间,呈微弱的负相关。杜洛克猪繁殖性状的遗传趋势上升幅度较大,长白猪、大白猪繁殖性状的遗传趋势上升幅度较小;生长性状中AGE的遗传趋势均呈下降趋势,且下降幅度较大,BF的遗传趋势变化幅度较小。【结论】本研究对杜洛克猪、长白猪、大白猪繁殖性状和生长性状的遗传参数和遗产趋势进行了准确的评估,结果可为该育种场的育种工作提供参考。     

Genetic parameters and purebred–crossbred genetic correlations for growth,meat quality,and carcass traits in pigs

Growth, meat quality, and carcass traits are of economic importance in swine breeding. Understanding their genetic basis in purebred (PB) and commercial crossbred (CB) pigs is necessary for a successful breeding program because, although the breeding goal is to improve CB performance, phenotype collection and selection are usually carried out in PB populations housed in biosecure nucleus herds. Thus, the selection is indirect, and the accuracy of selection depends on the genetic correlation between PB and CB performance (rpc). The objectives of this study were to 1) estimate genetic parameters for growth, meat quality, and carcass traits in a PB sire line and related commercial CB pigs and 2) estimate the corresponding genetic correlations between purebred and crossbred performance (rpc). Both objectives were investigated by using pedigree information only (PBLUP) and by combining pedigree and genomic information in a single-step genomic BLUP (ssGBLUP) procedure. Growth rate showed moderate estimates of heritability for both PB and CB based on PBLUP, while estimates were higher in CB based on ssGBLUP. Heritability estimates for meat quality traits were diverse and slightly different based on PB and CB data with both methods. Carcass traits had higher heritability estimates based on PB compared with CB data based on PBLUP and slightly higher estimates for CB data based on ssGBLUP. A wide range of estimates of genetic correlations were obtained among traits within the PB and CB data. In the PB population, estimates of heritabilities and genetic correlations were similar based on PBLUP and ssGBLUP for all traits, while based on the CB data, ssGBLUP resulted in different estimates of genetic parameters with lower SEs. With some exceptions, estimates of rpc were moderate to high. The SE on the rpc estimates was generally large when based on PBLUP due to limited sample size, especially for CBs. In contrast, estimates of rpc based on ssGBLUP were not only more precise but also more consistent among pairs of traits, considering their genetic correlations within the PB and CB data. The wide range of estimates of rpc (less than 0.70 for 7 out of 13 traits) indicates that the use of CB phenotypes recorded on commercial farms, along with genomic information, for selection in the PB population has potential to increase the genetic progress of CB performance.     

Community-based alternative breeding plans for indigenous sheep breeds in four agro-ecological zones of Ethiopia

Based on the results of participatory approaches to define traits in the breeding objectives, four scenarios of ram selection and ram use were compared via deterministic modelling of breeding plans for community-based sheep breeding programmes in four diverse agro-ecological regions of Ethiopia. The regions (and production systems) were Afar (pastoral/agro-pastoral), Bonga and Horro (both mixed crop-livestock) and Menz (sheep-barley). The schemes or scenarios differed in terms of selection intensity and duration of ram use. The predicted genetic gains per year in yearling weight (kilograms) were comparable across the schemes but differed among the breeds and ranged from 0.399 to 0.440 in Afar, 0.813 to 0.894 in Bonga, 0.850 to 0.940 in Horro, and 0.616 to 0.699 in Menz. The genetic gains per year in number of lambs born per ewe bred ranged from 0.009 to 0.010 in both Bonga and Horro. The predicted genetic gain in the proportion of lambs weaned per ewe joined was nearly comparable in all breeds ranging from 0.008 to 0.011. The genetic gain per year in milk yield of Afar breed was in the order of 0.018 to 0.020 kg, while the genetic gain per generation for greasy fleece weight (kg) ranged from 0.016 to 0.024 in Menz. Generally, strong selection and shorter duration of ram use for breeding were the preferred options. The expected genetic gains are satisfactory but largely rely on accurate and continuous pedigree and performance recording.     

Evaluation of genetic trends and determination of the optimal number of cumulative records of parity required in reproductive traits in a Large White pig population

Genetic improvement of the reproductive performance of pigs is important for pig breeding despite their low heritabilities. The objectives of this study were to investigate the effectiveness of selection concerning reproductive traits and to determine the optimal number of parity records required for accurate estimation of breeding values (BVs) in the open population of a commercial pig breeding company. The study used records of 2220 purebred Large White pigs (9845 litters) farrowed between 1998 and 2009 in the two herds of the Pacific Ocean Breeding Co. Ltd. The traits studied included farrowing interval (FI), total number of piglets at birth (TNB), average weaning weight per litter (AWW), and raising rate (RR). A statistical model was applied to the 4‐trait repeatability animal model. The heritabilities of FI, TNB, AWW and RR were low. The genetic trends in TNB (h² = 0.09) showed approximately 1.0 increase in 6 years from 2003 to 2008. The predicted error variances indicated that up to fourth parity records are necessary for accurate genetic evaluation. The present study results indicated that even reproductive traits with low heritability can be improved.     

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.     

杜洛克猪专门化品系选育研究

杜洛克猪专门化品系是以丹系杜洛克为育种素材,采用不完全闭锁的群体继代选育法,运用最佳线性无偏估计(BLUP)法、综合选择指数和分子标记辅助选择(MAS)等育种新方法,主选日增重和活体背膘厚等性状。经5个世代选育,产仔数达10.48头,产活仔数9.85头;肥育期日增重831g,料重比2.55:1,活体背膘厚11.83mm;瘦肉率69.5%,肌内脂肪为2.95%,主选性状全面达到或超过育种目标,成功培育了一个高性能的专门化父本新品系(ZFD系)。     

Canine fertility: The consequences of selection for special traits

Pedigree dogs and cats are bred aiming to conform breed standards with very poor consideration for breeding stock fertility. At the same time, the genetic asset underlining reproductive traits could be effectively analysed like in other species under selection. The definition of selection targets is very important in breeding protocols determination. The aim of the present work is to present an overview of the different correlations between reproduction and genetics, starting from selection procedure and inbreeding coefficient moving to genomic and the application of SNPs and GWAS on population study and identification of genes involved in phenotypical variation of reproductive traits in dogs. Particular relevance has been given to the concept of inbreeding which effects on canine reproduction have been presented. The use of genomic information in inbreeding coefficient calculation can be considered an improved effective procedure in the evaluation of the genetic variability loss in canine population and its negative effects on reproductive traits.     

Persistency of accuracy of genomic breeding values for different simulated pig breeding programs in developing countries

Genetic improvement of pigs in tropical developing countries has focused on imported exotic populations which have been subjected to intensive selection with attendant high population‐wide linkage disequilibrium (LD). Presently, indigenous pig population with limited selection and low LD are being considered for improvement. Given that the infrastructure for genetic improvement using the conventional BLUP selection methods are lacking, a genome‐wide selection (GS) program was proposed for developing countries. A simulation study was conducted to evaluate the option of using 60 K SNP panel and observed amount of LD in the exotic and indigenous pig populations. Several scenarios were evaluated including different size and structure of training and validation populations, different selection methods and long‐term accuracy of GS in different population/breeding structures and traits. The training set included previously selected exotic population, unselected indigenous population and their crossbreds. Traits studied included number born alive (NBA), average daily gain (ADG) and back fat thickness (BFT). The ridge regression method was used to train the prediction model. The results showed that accuracies of genomic breeding values (GBVs) in the range of 0.30 (NBA) to 0.86 (BFT) in the validation population are expected if high density marker panels are utilized. The GS method improved accuracy of breeding values better than pedigree‐based approach for traits with low heritability and in young animals with no performance data. Crossbred training population performed better than purebreds when validation was in populations with similar or a different structure as in the training set. Genome‐wide selection holds promise for genetic improvement of pigs in the tropics.     

The efficiency of genome-wide selection for genetic improvement of net merit

Four methods of selection for net merit comprising 2 correlated traits were compared in this study: 1) EBV-only index (I?), which consists of the EBV of both traits (i.e., traditional 2-trait BLUP selection); 2) GEBV-only index (I?), which comprises the genomic EBV (GEBV) of both traits; 3) GEBV-assisted index (I?), which combines both the EBV and the GEBV of both traits; and 4) GBV-assisted index (I?), which combines both the EBV and the true genomic breeding value (GBV) of both traits. Comparisons of these indices were based on 3 evaluation criteria [selection accuracy, genetic response (ΔH), and relative efficiency] under 64 scenarios that arise from combining 2 levels of genetic correlation (r(G)), 2 ratios of genetic variances between traits, 2 ratios of the genomic variance to total genetic variances for trait 1, 4 accuracies of EBV, and 2 proportions of r(G) explained by the GBV. Both selection accuracy and genetic responses of the indices I?, I?, and I? increased as the accuracy of EBV increased, but the efficiency of the indices I? and I? relative to I? decreased as the accuracy of EBV increased. The relative efficiency of both I? and I? was generally greater when the accuracy of EBV was 0.6 than when it was 0.9, suggesting that the genomic markers are most useful to assist selection when the accuracy of EBV is low. The GBV-assisted index I? was superior to the GEBV-assisted I? in all 64 cases examined, indicating the importance of improving the accuracy of prediction of genomic breeding values. Other parameters being identical, increasing the genetic variance of a high heritability trait would increase the genetic response of the genomic indices (I?, I?, and I?). The genetic responses to I?, I?, and I(4) was greater when the genetic correlation between traits was positive (r(G) = 0.5) than when it was negative (r(G) = -0.5). The results of this study indicate that the effectiveness of the GEBV-assisted index I? is affected by heritability of and genetic correlation between traits, the ratio of genetic variances between traits, the genomic-genetic variance ratio of each index trait, the proportion of genetic correlation accounted for by the genomic markers, and the accuracy of predictions of both EBV and GBV. However, most of these affecting factors are genetic characteristics of a population that is beyond the control of the breeders. The key factor subject to manipulation is to maximize both the proportion of the genetic variance explained by GEBV and the accuracy of both GEBV and EBV. The developed procedures provide means to investigate the efficiency of various genomic indices for any given combination of the genetic factors studied.     

Genetic parameters for litter traits at different parities in purebred Landrace and Yorkshire pigs

Comparison of the multi‐trait animal model and the traditional repeatability model was carried out using data obtained from 6,424 Landrace and 20,835 Yorkshire sows farrowed from January 2000 to April 2018 in order to estimate genetic parameters for litter traits at different parities. Specifically, records of the total number born (TNB), number born alive (NBA), total number of mortality (MORT), number of stillborn (NSB) and number of mummified pigs (MUM) were used. Although results showed the heterogeneity of heritability for litter traits at different parities, the mean heritability estimates from the multi‐trait model were found to be higher than those of the repeatability model for all traits in both pig breeds. In terms of genetic correlation between parities, a slight difference in genetic control in the first parity was noted for TNB and NBA in Landrace and Yorkshire pigs. The correlation between the first parity and later parities ranged from 0.48 to 0.74 for TNB and NBA in both breeds. Moreover, genetic correlation between parities for MORT and NSB was observed to be high for parities higher than 2 in Yorkshire pigs. For MUM, genetic correlation between the first and other parities was generally low in both breeds, indicating that culling pigs on the basis of MUM at the first parity could probably be unreasonable. Overall, the results of this study suggest that the multi‐trait approach for litter size traits is useful for the accurate estimation of genetic parameters.     

Estimation of genetic parameters for reproductive traits in connectedness groups of Duroc,Landrace and Yorkshire pigs in China

The objective of this study was to estimate group- and breed-specific genetic parameters for reproductive traits in Chinese Duroc, Landrace, and Yorkshire populations. Records for reproductive traits between April 1998 and December 2017 from 92 nucleus pig breeding farms, which were involved in the China Swine Genetic Improvement Program, were analysed. Due to weak genetic connectedness across all farms, connectedness groups consisting of related farms were used. Three, two and four connectedness groups for Duroc, Landrace and Yorkshire were firstly established according to the genetic connectedness rating among farms. For each connectedness group a five-trait animal model was implemented, and via restricted maximum likelihood procedure the genetic parameters were estimated for five reproductive traits i.e., total number born (TNB), number born alive (NBA), litter weight at farrowing (LWF), farrowing interval (FI) and age at first farrowing (AFF). The average of heritabilities among connectedness groups ranged from .01 (for FI in Yorkshire) to .30 (for AFF in Duroc). Estimates of repeatability for litter traits ranged from .14 to .20 and were consistent for each breed, and for FI, the estimates varied from .01 to .11 across breeds and groups. The estimated genetic correlations among litter traits (i.e., TNB, NBA and LWF) were all significantly high (>.56) and similar across breeds. Averaged genetic correlations over three breeds were −.25, −.27, −.18, −.04, −.10, −.02, and .28 for FI-TNB, FI-NBA, FI-LWF, AFF-TNB, AFF-NBA, AFF-LWF and FI-AFF, respectively. The standard errors of the estimates were all very low (<0.01) in most situations. Results from this study suggest that selection based on TNB which is currently used in dam line selection index can improve NBA and LWF simultaneously. However, care should be taken on FI and AFF as they are both greatly influenced by non-genetic factors such as management and measurement.     

Genome-wide association study of swine farrowing traits. Part I: Genetic and genomic parameter estimates

The primary objective of this study was to determine genetic and genomic parameters among swine (Sus scrofa) farrowing traits. Genetic parameters were obtained using MTDFREML. Genomic parameters were obtained using GENSEL. Genetic and residual variances obtained from MTDFREML were used as priors for the Bayes C analysis of GENSEL. Farrowing traits included total number born (TNB), number born alive (NBA), number born dead (NBD), number stillborn (NSB), number of mummies (MUM), litter birth weight (LBW), and average piglet birth weight (ABW). Statistically significant heritabilities included TNB (0.09, P = 0.048), NBA (0.09, P = 0.041), LBW (0.20, P = 0.002), and ABW (0.26, P < 0.0001). Statistically significant genetic correlations included TNB-NBA (0.97, P < 0.0001), TNB-LBW (0.74, P < 0.0001), NBA-LBW (0.56, P < 0.0017), NSB-LBW (0.87, P < 0.0395), and LBW-ABW (0.63, P < 0.0002). Genetic parameters are similar to others found in the literature. The proportion of phenotypic variance explained by genomic markers (GP) generated by GENSEL was TNB (0.04), NBA (0.06), NBD (0.00), NSB (0.01), MUM (0.00), LBW (0.11), and ABW (0.31). Limited information is available in the literature about genomic parameters. Only the GP estimate for NSB is significantly lower than what has been published. The GP estimate for ABW is greater than the estimate for heritability found in this study. Other traits with significant heritability had GP estimates half the value of heritability. This research indicates that significant genetic markers will be found for TNB, NBA, LBW, and ABW that will have either immediate use in industry or provide a roadmap to further research with fine mapping or sequencing of areas of significance. Furthermore, these results indicate that genomic selection implemented at an early age would have similar annual progress as traditional selection, and could be incorporated along with traditional selection procedures to improve genetic progress of litter traits.     

EPD估计的原理与应用

EPD是在BLUP理论的基础上对种用动物遗传值估计的一种参数。EPD值是估计育种值的一半，它是一个容易理解和应用的种畜遗传传递力的值，能很直观地反映种畜价值在其后代中的体现。EPD主要是用在同一品种中2个不同个体间直接遗传效应的比较，从而预测在同一平均遗传值水平上种用动物未来子女的性能差异。EPD适用于动物的出生、发育、母性性状、屠宰性状以及其它特征性状的估计。EPD广泛应用于肉用种公牛价值的评定。对肉牛重要经济性状EPD估计后，以出版物和（或）计算机网络的方式发布，肉牛生产者根据自己的不同需要选用种牛或精液。作者对EPD的估计原理与应用作较为详细的论述。     

Comparing deregression methods for genomic prediction of test‐day traits in dairy cattle

We aimed to investigate the performance of three deregression methods (VanRaden, VR; Wiggans, WG; and Garrick, GR) of cows’ and bulls’ breeding values to be used as pseudophenotypes in the genomic evaluation of test‐day dairy production traits. Three scenarios were considered within each deregression method: (i) including only animals with reliability of estimated breeding value (REL_EBV ) higher than the average of parent reliability (REL_PA ) in the training and validation populations; (ii) including only animals with REL_EBV higher than 0.50 in the training and REL_EBV higher than REL_PA in the validation population; and (iii) including only animals with REL_EBV higher than 0.50 in both training and validation populations. Individual random regression coefficients of lactation curves were predicted using the genomic best linear unbiased prediction (GBLUP), considering either unweighted or weighted residual variances based on effective records contributions. In summary, VR and WG deregression methods seemed more appropriate for genomic prediction of test‐day traits without need for weighting in the genomic analysis, unless large differences in REL_EBV between training population animals exist.     

Effect of carcass price fluctuations on genetic and economic evaluation of carcass traits in Japanese Black cattle

The objectives of this study were 1) to investigate the effect of changes in carcass market prices due to bovine spongiform encephalopathy (BSE) occurrences on estimates of genetic parameters and economic weights for carcass traits; and 2) to compare direct and indirect approaches for prediction of genetic merit of Japanese Black cattle for profitability of their progeny. The direct approach utilized estimated breeding values of carcass prices, whereas in the indirect approach, selection indices were constructed as products of economic weights and breeding values of component traits. Data were composed of 80,191 carcass records divided into 5 periods based on changes in carcass prices as a result of occurrences of BSE in Japan and the United States. The periods ranged from a period before occurrence of BSE in Japan to a period of beef import restrictions and a rise in prices. Carcass traits analyzed included HCW, LM area, rib thickness, subcutaneous fat thickness, and marbling score (MS). Price traits included carcass unit price and carcass sale price. Estimates of heritability for price traits were moderate (0.32 to 0.46) and slightly sensitive to changes in carcass market prices. Genetic correlations of HCW and LM area with price traits increased and that between MS and carcass sale price decreased with period, whereas estimates of genetic correlation between MS and carcass unit price were high in all periods (0.96 to 0.98). Economic weights for carcass traits varied with periods because carcass prices were highly sensitive to economic importance of traits. Nevertheless, correlations between within-period breeding values for price traits estimated using direct and indirect approaches were high (0.92 to 0.99). This result indicates that selection realized by direct and indirect approaches will provide very similar results. A comparison among within-approach breeding values estimated in different periods showed that the largest differences in breeding values of sires for price traits were between the periods after occurrences of BSE in Japan and in the United States. Economic effects of BSE occurrences influenced the importance of carcass traits and economic merits of price traits through a change of carcass prices from period to period, irrespective of the approach taken in determining the genetic merit of breeding animals for profitability of their progeny.     

Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM

Genomic selection is based on breeding values that are estimated using genome-wide dense marker maps. The objective of this paper was to investigate the effect of including or ignoring the polygenic effect on the accuracy of total genomic breeding values, when there is coverage of the genome with approximately one SNP per cM. The importance of the polygenic effect might differ for high and low heritability traits, and might depend on the design of the reference dataset. Hence, different scenarios were evaluated using stochastic simulation. Accuracies of the total breeding value of juvenile selection candidates depended on the number of animals included in the reference data. When excluding polygenic effects, those accuracies ranged from 0.38 to 0.55 and from 0.73 to 0.79 for traits with heritabilities of 10 and 50%, respectively. Accuracies were improved by including a polygenic effect in the model for the low heritability trait, when the LD-measure r2 between adjacent markers became smaller than approximately 0.10, while for the high heritability trait there was already a small improvement at r2 between adjacent markers of 0.14. In all situations, the estimated total genetic variance was underestimated, particularly when the polygenic effect was excluded from the model. The haplotype variance was less underestimated when more animals were added in the reference dataset.     

Simulation of genetic control of reproduction in beef cows. III. Within-herd breeding value estimation with known breeding dates

Procedures for breeding value estimation for reproductive traits with known breeding dates were developed and tested using results of a computer simulation model of genetic control of bovine reproduction. The model generated realized reproductive outputs as a function of underlying genetic variation in two independent traits: conception rate (CR), which was indicative of the ability to conceive given estrus, and PPI, the postpartum interval from calving to first estrus. Two scenarios were considered. In the first, all cows were assumed to be cycling at the start of breeding and to be bred artificially. For this scenario, breeding values for CR could be estimated from information on observed breeding and calving dates by using a categorical trait, multi-stage selection model. Breeding value estimation for PPI, however, required actual measurement of PPI because if PPI and CR are genetically independent and if all cows are cycling at the beginning of breeding, subsequent breeding and calving dates are independent of PPI. The second scenario recognized that not all females would be cycling at the start of breeding. For this scenario, the categorical trait, multi-stage selection model could still be applied for breeding value estimation for CR, but accumulation of data across years was complicated by a need to consider the lifetime reproductive pattern of the individual rather than just the sum of each year's performances. Breeding value estimates for PPI could be obtained from observed breeding and calving dates for this scenario, but required consideration of the distributional properties of PPI.     

Comparison of Breeding and Marketing Systems for Red Angus Cattle Using an Integrated Computer-Based Spreadsheet

An integrated computer-based spreadsheet was developed with data from 581 Red Angus-sired calves to compare synchronized AI and natural service breeding systems. This comparison was based on input costs, genetic merit of sires used for mating, and calf marketing system, using differences in net return. The spreadsheet integrated four elements into a decision summary: bull costs, AI costs, genetics merit, and marketing options. An economic sensitivity analysis was used to identify trends and key variables in the net return of each decision. Three prominent variables identified from economic analysis were bull purchase price, semen price, and percent genetic change. Bull purchase price was a primary factor in changes in net return; semen costs and genetic merit change explained rearrangements in ranking of net return. These two variables altered the ranking based on whether the estrous synchronization protocol used estrus detection or timed AI. The spreadsheet identified AI to be more cost effective than natural service when calves are marketed as finished cattle. Net revenue from AI calves was greater in all retained ownership scenarios; the weaned marketing scenario caused net return to vary by synchronization system for the combinations of costs and changes in genetic merit. However, there was a wide variance in identifying which breeding system provided the greatest benefit when calves were marketed as feeder cattle. Retaining ownership through finish and marketing either on the cash market or on a grid proved to be advantageous to AI in all of the estrous synchronization protocols provided. The economic advantage ranged from $142.98 to $214.16 per head compared with marketing at weaning. The spreadsheet developed provides a useful tool for evaluating the economic impacts of breeding system decisions.     

Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers

Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single‐nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic‐based [genomic best linear unbiased prediction (GBLUP)‐REML and BayesC] and pedigree‐based (PBLUP‐REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP‐REML across traits, from 0 to 0.03 with GBLUP‐REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic‐based methods were small (0.01–0.05), with GBLUP‐REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP‐REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population.