BLUP Method Research Articles

Bayesian methods for genomic breeding value estimation

Estimation of genomic breeding values is the key step in genomic selection. The successful application of genomic selection depends on the accuracy of genomic estimated breeding values, which is mostly determined by the estimation method. Bayes-type and BLUP-type methods are the two main methods which have been widely studied and used. Here, we systematically introduce the currently proposed Bayesian methods, and summarize their effectiveness and improvements. Results from both simulated and real data showed that the accuracies of Bayesian methods are higher than those of BLUP methods, especially for the traits which are influenced by QTL with large effect. Because the theories and computation of Bayesian methods are relatively complicated, their use in practical breeding is less common than BLUP methods. However, with the development of fast algorithms and the improvement of computer hardware, the computational problem of Bayesian methods is expected to be solved. In addition, further studies on the genetic architecture of traits will provide Bayesian methods more accurate prior information, which will make their advantage in accuracy of genomic estimated breeding values more prominent. Therefore, the application of Bayesian methods will be more extensive.

Including dominance effects in the genomic BLUP method for genomic evaluation.

We evaluated the performance of GBLUP including dominance genetic effect (GBLUP-D) by estimating variances and predicting genetic merits in a computer simulation and 2 actual traits (T4 and T5) in pigs. In simulation data, GBLUP-D explained more than 50% of dominance genetic variance. Moreover, GBLUP-D yielded estimated total genetic effects over 1.2% more accurate than those yielded by GBLUP. In particular, when the dominance genetic variance was large, the accuracy could be substantially improved by increasing the number of markers. The dominance genetic variances in T4 and T5 accounted for 9.6% and 6.3% of the phenotypic variances, respectively. Estimates of such small dominance genetic variances contributed little to the improvement of the accuracies of estimated total genetic effects. In both simulation and pig data, there were nearly no differences in the estimates of additive genetic effects or their variance between GBLUP-D and GBLUP. Therefore, we conclude GBLUP-D is a feasible approach to improve genetic performance in crossbred populations with large dominance genetic variation and identify mating systems with good combining ability.

Genetic analysis of values, trends and relations between conformation and milk traits in Polish Holstein-Friesian cows

Abstract. In the work presented an analysis was performed of the genetic values of conformation traits in cows and their relations with the genetic value of dairy traits, basing on dairy performance (milk, fat and protein yield) and conformation data, obtained for Polish Holstein-Friesian cows of the Black-and-White variety, born between year 2001 and 2004, and maintained in 21 herds in the region of Wielkopolska (central Poland). The genetic values for the data analysed were estimated using the BLUP method and a mixed model. For the traits analysed a genetic trend was estimated as well as genetic correlations between the dairy and conformation traits. The population analysed was divided depending on year of birth and production level. The results obtained show a systematic increase in the traits analysed (positive genetic trends), both for the dairy and for conformation traits (with the exception of temperament). The highest genetic correlations between dairy and conformation traits were obtained for the udder, temperament and dairy type, while the lowest for height at sacrum and calibre. The analysis of the genetic value of conformation traits in cows of different dairy production genetic values indicated that the highest results were obtained for the group of animals with the highest genetic dairy production value. In turn, the analysis of the genetic value of traits of cows born in different years did not show statistically significant differences, with the exception of height in the youngest group.

Opportunities for genome-wide selection for pig breeding in developing countries1

Genetic improvement of exotic and indigenous pigs in tropical developing countries is desired. Implementations of traditional selection methods on tropical pig populations are limited by lack of data recording and analysis infrastructure. Genome-wide selection (GS) provides an approach for achieving faster genetic progress without developing a pedigree recording system. The implications of GS on long-term gain and inbreeding should be studied before actual implementation, especially where low linkage disequilibrium (LD) is anticipated in the target population. A simulation case study of this option was performed on the basis of the available 60,000 SNP panel for porcine genome. Computer simulation was used to explore the effects of various selection methods, trait heritability, and different breeding programs when applying GS. Genomic predictions were based on the ridge regression method. Genome-wide selection performed better than BLUP and phenotypic selection methods by increasing genetic gain and maintaining genetic variation while lowering inbreeding, especially for traits with low heritability. Indigenous pig populations with low LD can be improved by using GS if high-density marker panels are available. The combination of GS with repeated backcrossing of crossbreds to exotic pigs in developing countries promises to rapidly improve the genetic merit of the commercial population. Application of this novel method on a real population will need to be performed to validate these results.

Estimation and decomposition of genetic trends in a two-way cross using Hungarian pig breeds

Authors analyzed the estimation and partition of the genetic trends of lean meat percentage and average daily gain of the two-way cross Duroc (Du), Pietrain (Pi) pigs and their cross. The analysis was based on the data collected by the Agricultural Agency of Administration in course of field test between 1998 and 2010 from 68 herds. Total number of animals in the pedigree file was 60926. Genetic parameters and breeding values of average daily gain (ADG) and the lean meat percentage (LMP) were estimated separately by REML and BLUP methods using the VCE6 and PEST software applying a two-trait animal model. For the observed period the authors received a small genetic trend for ADG while it was negligible for LMP. Decomposing these trends to genotypes the highest contributions to the trends were observed for Duroc (ADG) and Pietrain (LMP), respectively.

Effectiveness of genomic prediction on milk flow traits in dairy cattle

BackgroundMilkability, primarily evaluated by measurements of milking speed and time, has an economic impact in milk production of dairy cattle. Recently the Italian Brown Swiss Breeders Association has included milking speed in genetic evaluations. The main objective of this study was to investigate the possibility of implementing genomic selection for milk flow traits in the Italian Brown Swiss population and thereby evaluate the potential of genomic selection for novel traits in medium-sized populations. Predicted breeding values and reliabilities based on genomic information were compared with those obtained from traditional breeding values.MethodsMilk flow measures for total milking time, ascending time, time of plateau, descending time, average milk flow and maximum milk flow were collected on 37 213 Italian Brown Swiss cows. Breeding values for genotyped sires (n = 1351) were obtained from standard BLUP and genome-enhanced breeding value techniques utilizing two-stage and single-step methods. Reliabilities from a validation dataset were estimated and used to compare accuracies obtained from parental averages with genome-enhanced predictions.ResultsGenome-enhanced breeding values evaluated using two-stage methods had similar reliabilities with values ranging from 0.34 to 0.49 for the different traits. Across two-stage methods, the average increase in reliability from parental average was approximately 0.17 for all traits, with the exception of descending time, for which reliability increased to 0.11. Combining genomic and pedigree information in a single-step produced the largest increases in reliability over parent averages: 0.20, 0.24, 0.21, 0.14, 0.20 and 0.21 for total milking time, ascending time, time of plateau, descending time, average milk flow and maximum milk flow, respectively.ConclusionsUsing genomic models increased the accuracy of prediction compared to traditional BLUP methods. Our results show that, among the methods used to predict genome-enhanced breeding values, the single-step method was the most successful at increasing the reliability for most traits. The single-step method takes advantage of all the data available, including phenotypes from non-genotyped animals, and can easily be incorporated into current breeding evaluations.

Comparison of five methods for genomic breeding value estimation for the common dataset of the 15th QTL-MAS Workshop

BackgroundGenomic breeding value estimation is the key step in genomic selection. Among many approaches, BLUP methods and Bayesian methods are most commonly used for estimating genomic breeding values. Here, we applied two BLUP methods, TABLUP and GBLUP, and three Bayesian methods, BayesA, BayesB and BayesCπ, to the common dataset provided by the 15th QTL-MAS Workshop to evaluate and compare their predictive performances.ResultsFor the 1000 progenies without phenotypic values, the correlations between GEBVs by different methods ranged from 0.812 (GBLUP and BayesCπ) to 0.997 (TABLUP and BayesB). The accuracies of GEBVs (measured as correlations between true breeding values (TBVs) and GEBVs) were from 0.774 (GBLUP) to 0.938 (BayesCπ) and the biases of GEBVs (measure as regressions of TBVs on GEBVs) were from 1.033 (TABLUP) to 1.648 (GBLUP). The three Bayesian methods and TABLUP had similar accuracy and bias.ConclusionsBayesA, BayesB, BayesCπ and TABLUP performed similarly and satisfactorily and remarkably outperformed GBLUP for genomic breeding value estimation in this dataset. TABLUP is a promising method for genomic breeding value estimation because of its easy computation of reliabilities of GEBVs and its easy extension to real life conditions such as multiple traits and consideration of individuals without genotypes.

The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes

BackgroundThe theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values.MethodsSimulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated.ResultsThe gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy.ConclusionsAn animal's relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.

Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures

BackgroundGenomic selection has become an important tool in the genetic improvement of animals and plants. The objective of this study was to investigate the impacts of breeding value estimation method, reference population structure, and trait genetic architecture, on long-term response to genomic selection without updating marker effects.MethodsThree methods were used to estimate genomic breeding values: a BLUP method with relationships estimated from genome-wide markers (GBLUP), a Bayesian method, and a partial least squares regression method (PLSR). A shallow (individuals from one generation) or deep reference population (individuals from five generations) was used with each method. The effects of the different selection approaches were compared under four different genetic architectures for the trait under selection. Selection was based on one of the three genomic breeding values, on pedigree BLUP breeding values, or performed at random. Selection continued for ten generations.ResultsDifferences in long-term selection response were small. For a genetic architecture with a very small number of three to four quantitative trait loci (QTL), the Bayesian method achieved a response that was 0.05 to 0.1 genetic standard deviation higher than other methods in generation 10. For genetic architectures with approximately 30 to 300 QTL, PLSR (shallow reference) or GBLUP (deep reference) had an average advantage of 0.2 genetic standard deviation over the Bayesian method in generation 10. GBLUP resulted in 0.6% and 0.9% less inbreeding than PLSR and BM and on average a one third smaller reduction of genetic variance. Responses in early generations were greater with the shallow reference population while long-term response was not affected by reference population structure.ConclusionsThe ranking of estimation methods was different with than without selection. Under selection, applying GBLUP led to lower inbreeding and a smaller reduction of genetic variance while a similar response to selection was achieved. The reference population structure had a limited effect on long-term accuracy and response. Use of a shallow reference population, most closely related to the selection candidates, gave early benefits while in later generations, when marker effects were not updated, the estimation of marker effects based on a deeper reference population did not pay off.

Single-step methods for genomic evaluation in pigs

Genetic evaluation based on information from phenotypes, pedigree and markers can be implemented using a recently developed single-step method. In this paper we compare accuracies of predicted breeding values for daily gain and feed conversion ratio (FCR) in Danish Duroc pigs obtained from different versions of single-step methods, the traditional pedigree-based method and the genomic BLUP (GBLUP) method. In particular, we present a single-step method with an adjustment of the genomic relationship matrix so that it is compatible to the pedigree-based relationship matrix. Comparisons are made for both genotyped and non-genotyped animals and univariate and bivariate models. The results show that the three methods with marker information (two single-step methods and GBLUP) produce more accurate predictions of genotyped animals than the pedigree-based method. In addition, single-step methods provide more accurate predictions for non-genotyped animals. The results also show that the single-step method with adjusted genomic relationship matrix produce more accurate predictions than the original single-step method. Finally, the results for the bivariate analyses show a somewhat improved accuracy and reduced inflation of predictions for FCR for the two single-step methods compared with the univariate analyses. The conclusions are: first, the methods with marker information improve prediction compared with the pedigree-based method; second, a single-step method, contrary to GBLUP, provides improved predictions for all animals compared to the pedigree-based method; and third, a single-step method should be used with an adjustment of the genomic relationship matrix.

Using the Pareto principle in genome-wide breeding value estimation

Genome-wide breeding value (GWEBV) estimation methods can be classified based on the prior distribution assumptions of marker effects. Genome-wide BLUP methods assume a normal prior distribution for all markers with a constant variance, and are computationally fast. In Bayesian methods, more flexible prior distributions of SNP effects are applied that allow for very large SNP effects although most are small or even zero, but these prior distributions are often also computationally demanding as they rely on Monte Carlo Markov chain sampling. In this study, we adopted the Pareto principle to weight available marker loci, i.e., we consider that x% of the loci explain (100 - x)% of the total genetic variance. Assuming this principle, it is also possible to define the variances of the prior distribution of the 'big' and 'small' SNP. The relatively few large SNP explain a large proportion of the genetic variance and the majority of the SNP show small effects and explain a minor proportion of the genetic variance. We name this method MixP, where the prior distribution is a mixture of two normal distributions, i.e. one with a big variance and one with a small variance. Simulation results, using a real Norwegian Red cattle pedigree, show that MixP is at least as accurate as the other methods in all studied cases. This method also reduces the hyper-parameters of the prior distribution from 2 (proportion and variance of SNP with big effects) to 1 (proportion of SNP with big effects), assuming the overall genetic variance is known. The mixture of normal distribution prior made it possible to solve the equations iteratively, which greatly reduced computation loads by two orders of magnitude. In the era of marker density reaching million(s) and whole-genome sequence data, MixP provides a computationally feasible Bayesian method of analysis.

Stability of estimated breeding values for average daily gain in Pannon White rabbits

Stability of estimated breeding values for average daily gain (ADG) between 5 and 10 weeks of age was analysed for 47 242 Pannon White rabbits, reared in 7470 litters and born between 2000 and 2008. The dataset was divided into 5 successive 5-year periods: (1) 2000–2004, (2) 2001–2005, (3) 2002–2006, (4) 2003–2007, and (5) 2004–2008. Then, after selecting the appropriate part of the pedigree for these sub-datasets, genetic parameters and breeding values were estimated for ADG using REML and BLUP methods. In the applied models sex, year-month, animal and random litter effects were considered. Estimated heritabilities for all 5 periods from 1 to 5 were moderate and stable (0.28 ± 0.01, 0.28 ± 0.02, 0.29 ± 0.02, 0.27 ± 0.02, and 0.28 ± 0.02). Magnitudes of random litter effects were low and stable (0.14 ± 0.01, 0.15 ± 0.01, 0.15 ± 0.01, 0.16 ± 0.01, and 0.16 ± 0.01). After breeding value estimation the dataset of period 5 was merged pair-wise with the other periods 4, 3, 2 and 1 using an inner join. Thus only the common records of the datasets representing the periods 5-4, 5-3, 5-2, and 5-1 were included in the merged datasets. In these merged datasets each rabbit had two breeding values for ADG based on two different periods. Spearman's rank correlation coefficients were calculated between the breeding values based on the dataset of period 5 and the other periods. With the successive years the rank correlation coefficients decreased (0.989, 0.979, 0.965 and 0.924). The correlation coefficients between ranks remained moderately high, even when the proportion of the common rabbits in the merged datasets was low. However, a reasonable re-ranking occurred among the top animals. Rank correlations for the top 100 and 1000 animals varied from 0.41 to 0.55 and from 0.37 to 0.54, respectively, which could influence selection efficiency if the rolling base were used for genetic evaluation.

Short communication: Influence of the muscle hypertrophy mutation of the myostatin gene on milk production traits and milk fatty acid composition in dual-purpose Belgian Blue dairy cattle

The objective of this study was to estimate genetic effects for the muscle hypertrophy mutation (mh) of the myostatin gene for conventional milk production traits and for milk fatty acid composition in dual-purpose Belgian Blue dairy cows. For the present study, only cows from a single herd, in which genotype frequencies were as balanced as possible (0.266 for +/+, 0.523 for mh/+, and 0.211 for mh/mh), were chosen to avoid confounding between herd and genotype effects. A total of 109 cows with 3,190 test-day records for fat, protein, and milk yields and 1,064 test-day records for saturated and monounsaturated fatty acids were used for the calculations. Variance component and gene effect estimations were performed via expectation-maximization REML and BLUP methods, respectively, using a multi-trait mixed test-day model with an additional fixed regression on the muscle hypertrophy genotype. Results showed that one copy of the wild-type “+” allele led to a significant additive effect of 26.35 g/d for fat yield. Significant dominance effects of 23.22 g/d for protein yield and 30.28 g/d for fat yield were also observed. In contrast, a nonsignificant trend was observed in favor of lower saturated fatty acid contents in milk for one copy of the mutant “mh” allele. Concerning milk, fat, and protein yields, our results confirmed literature results indicating a superior effect of the “+” allele compared with the mutant allele. Therefore, the selection of the “+” allele has the potential to increase conventional milk production traits in the dual-purpose Belgian Blue breed. However, when focus is given to milk fatty acid profile, a possible antagonistic effect between the benefit of the “+” allele for higher milk production and the “mh” allele for reduced saturated fatty acid content in milk should be confirmed in further studies.

Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle1

The benefit of using genomic breeding values (GEBV) in predicting ADG, DMI, and residual feed intake for an admixed population was investigated. Phenotypic data consisting of individual daily feed intake measurements for 721 beef cattle steers tested over 5 yr was available for analysis. The animals used were an admixed population of spring-born steers, progeny of a cross between 3 sire breeds and a composite dam line. Training and validation data sets were defined by randomly splitting the data into training and testing data sets based on sire family so that there was no overlap of sires in the 2 sets. The random split was replicated to obtain 5 separate data sets. Two methods (BayesB and random regression BLUP) were used to estimate marker effects and to define marker panels and ultimately the GEBV. The accuracy of prediction (the correlation between the phenotypes and GEBV) was compared between SNP panels. Accuracy for all traits was low, ranging from 0.223 to 0.479 for marker panels with 200 SNP, and 0.114 to 0.246 for marker panels with 37,959 SNP, depending on the genomic selection method used. This was less than accuracies observed for polygenic EBV accuracies, which ranged from 0.504 to 0.602. The results obtained from this study demonstrate that the utility of genetic markers for genomic prediction of residual feed intake in beef cattle may be suboptimal. Differences in accuracy were observed between sire breeds when the random regression BLUP method was used, which may imply that the correlations obtained by this method were confounded by the ability of the selected SNP to trace breed differences. This may also suggest that prediction equations derived from such an admixed population may be useful only in populations of similar composition. Given the sample size used in this study, there is a need for increased feed intake testing if substantially greater accuracies are to be achieved.

Pre-selection of markers for genomic selection

BackgroundAccurate prediction of genomic breeding values (GEBVs) requires numerous markers. However, predictive accuracy can be enhanced by excluding markers with no effects or with inconsistent effects among crosses that can adversely affect the prediction of GEBVs.MethodsWe present three different approaches for pre-selecting markers prior to predicting GEBVs using four different BLUP methods, including ridge regression and three spatial models. Performances of the models were evaluated using 5-fold cross-validation.Results and conclusionsRidge regression and the spatial models gave essentially similar fits. Pre-selecting markers was evidently beneficial since excluding markers with inconsistent effects among crosses increased the correlation between GEBVs and true breeding values of the non-phenotyped individuals from 0.607 (using all markers) to 0.625 (using pre-selected markers). Moreover, extension of the ridge regression model to allow for heterogeneous variances between the most significant subset and the complementary subset of pre-selected markers increased predictive accuracy (from 0.625 to 0.648) for the simulated dataset for the QTL-MAS 2010 workshop.

The evaluation of breeding value of simmental bulls for milk performance in Serbia

The basis for selection work is knowledge of the quality of bull sires used for conception, as well as how the major traits are passed on to the progeny. BLUP method (Best Linear Unbiased Prediction) is the basis of the most favourable solution for evaluation of additive gene value in cattle production, and it is implemented in various variants depending on the structure of data used. This research included 2.121 Simmental first cavers under control, with lactations completed within one year. All first calvers were located on holdings of individual agricultural producers on the territory of the Republic of Serbia. Evaluation of the bull breeding value for lactation duration, milk production, milk fat yield, yield of 4% FCM and percentage of milk fat, was carried out by using the mixed model (BLUP), the calculation included random effect of bull sire and fixed effect of the region, year and season of calving. In this study, bull sires which had in two or three regions over 20 daughters - first calvers of Simmental breed. Number of first calving heifers ranged from 22 to 215 animals per bull sire. By using BLUP method in evaluation of breeding value of bulls in terms of yield of milk, milk fat, content of milk fat and 4% FCMI and by ranking, results were obtained showing superiority and inferiority of breeding males.

Predicting the Accuracy of Breeding Values Using High Density Genome Scans

In this paper, simulation was used to determine accuracies of genomic breeding values for polygenic traits associated with many thousands of markers obtained from high density genome scans. The statistical approach was based upon stochastically simulating a pedigree with a specified base population and a specified set of population parameters including the effective and noneffective marker distances and generation time. For this population, marker and quantitative trait locus (QTL) genotypes were generated using either a single linkage group or multiple linkage group model. Single nucleotide polymorphism (SNP) was simulated for an entire bovine genome (except for the sex chromosome, n = 29) including linkage and recombination. Individuals drawn from the simulated population with specified marker and QTL genotypes were randomly mated to establish appropriate levels of linkage disequilibrium for ten generations. Phenotype and genomic SNP data sets were obtained from individuals starting after two generations. Genetic prediction was accomplished by statistically modeling the genomic relationship matrix and standard BLUP methods. The effect of the number of linkage groups was also investigated to determine its influence on the accuracy of breeding values for genomic selection. When using high density scan data (0.08 cM marker distance), accuracies of breeding values on juveniles were obtained of 0.60 and 0.82, for a low heritable trait (0.10) and high heritable trait (0.50), respectively, in the single linkage group model. Estimates of 0.38 and 0.60 were obtained for the same cases in the multiple linkage group models. Unexpectedly, use of BLUP regression methods across many chromosomes was found to give rise to reduced accuracy in breeding value determination. The reasons for this remain a target for further research, but the role of Mendelian sampling may play a fundamental role in producing this effect.

Genetic Variability of Hatchability Estimated Based on Multiple-trait Model

Rozempolska-Rucinska, I., Zieba, G., Lukaszewicz, M. and Ciechonska, M. 2010. Genetic variability of hatchability estimated based on multiple-trait model. J. Appl. Anim. Res., 38: 175–180. To verify whether hatchability rates in successive hatches of egg-laying chickens may be regarded as separate traits, 8 generations of layers from a maternal Rhode Island White (≈4200 hens) and paternal Rhode Island Red (≈2600 hens) lines were involved. (Co)variance components were estimated by means of the REML method using the multiple-trait animal model. The birds' breeding merit was assessed with the BLUP method. There was no ground for regarding the successive hatches as separate traits. The number of chicks hatched in consecutive hatches is a measure of the same trait assessed in various periods. High genetic correlations between the particular hatching periods facilitate assessment of birds' breeding merit on the basis of the first hatch results. Thus, it would be possible to verify the breeding stock rank and to select family flocks for subsequent reproduction of the offspring.

The level of chinchilla reproductive traits in relation to the behaviour of females

The aim of the study was to estimate the relations between elements of chinchilla female behaviour and the level of reproductive traits. The observations involved 380 deliveries from 130 females of the breeding stock of chinchillas, var. standard. In order to assess the temperament of the animals, they were tested three times and subsequently classified into one of the following groups: confident and curious, reserved and curious, and withdrawn animals. The animal breeding value of reproductive traits was estimated in the study with the use of the BLUP method combined with the multi-trait repeatability animal model. The significance of the factors included in the model was previously verified with the multivariate analysis of variance. The additive value of the animals was used in determining the mean breeding value of the number of born and weaned kits in the 3 classes of the behaviour type observed in the particular tests. Irrespective of the type of the behavioural test, no significant differences in the breeding value of reproductive traits were observed among the differently tempered chinchilla females.

Genetic improvement in the Australasian Merino – management of a diverse gene pool for changing markets

SummaryThe Australasian Merino population has declined significantly over the last 15 years in response to a decline in the price of apparel wool, both in absolute terms and relative to the price of sheep meat. Over the same period, a national genetic evaluation system based on BLUP methods has been introduced, that is achieving steady growth in adoption by breeders. Genetic parameter estimates for the population provide evidence for considerable genetic diversity for all recorded traits, providing ample opportunity for genetic improvement. More recently, there is considerable evidence for increasingly rapid progress, both in fleece traits and a range of meat production and adaptation traits. The Merino population is evolving towards two broad types – one focused on high quality apparel wools finer than 19 µm and used in enterprises with a wool/meat income ratio of about 3:1, and the other a more dual-purpose animal producing 19–21 µm wool and an enterprise wool/meat income ratio between 1.5:1 and 1:1. Underlying these trends is a growing focus on adaptation traits including worm resistance; reduced need for veterinary interventions; and increased early growth, fertility and mothering ability. Together these trends point to increasingly ‘easy-care’ sheep and exploitation of the available genetic diversity to rapidly increase profitability.