Invited review: A quarter of a century-International genetic evaluation of dairy sires using MACE methodology.

For the past few decades, the international exchange of genetic materials has accelerated. This acceleration has been more substantial for dairy cattle compared with other species. The industry faced the need to put international genetic evaluation (IGE) systems in place. The Interbull Centre has been conducting IGE for various dairy cattle breeds and traits. This study reviews the past and the current status of IGE for dairy cattle, emphasizing the most prominent and well-established method of IGE, namely multiple across-country evaluation (MACE), and the challenges that should be addressed in the future of IGE. The first IGE methods were simple conversion equations. Only a limited number of common bulls between pairs of countries were considered. These bulls were a biased sample of highly selected animals, with their daughters under preferential treatment in the importing countries. Genetic relationships among animals were not considered either. The MACE method was the first IGE method based on mixed-model theory that could handle genotype by environment interaction (G × E) between countries. The G × E between countries is handled by treating the same trait in different countries as different traits, with genetic correlations less than unity between the traits. The G × E between countries is not solely due to different genetic expressions in different environments (countries), but is also attributable to different units or ways of measuring the trait, data editing, and statistical approaches and models used in different countries. The MACE method also considers different genetic means, genetic groups for unknown parents, heterogeneous genetic and residual variances among countries, and heterogeneous residual variances (precision weights for observations) within countries. Other IGE methods that came after MACE are rooted in MACE. The genomic revolution of the industry created new needs and opportunities. However, an unwanted aspect of it was genomic preselection bias. Genomic preselection causes directional information loss from pre-culled animals (bias) in statistical models for genetic and genomic evaluations, and preselected progeny of a mating are no longer a random sample of possible progeny from that mating. National genetic evaluations without genotypes are input to MACE, and biases in national evaluations are propagated internationally through MACE. Genomic preselection for the Holstein breed is a source of concern for introducing bias to MACE, especially when genomic preselection is practiced intensively in the population. However, MACE continues to be useful for other breeds, among other species, or for non-IGE purposes. Future methods will need to make optimum use of genomic information and be free of genomic preselection bias.

Multiple-trait multiple-country genetic evaluation of Holstein bulls for female fertility and milk production traits.

The aim of this study was to investigate the effect of including milk yield data in the international genetic evaluation of female fertility traits to reduce or eliminate a possible bias because of across-country selection for milk yield. Data included two female fertility traits from Great Britain, Italy and the Netherlands, together with milk yield data from the same countries and from the United States, because the genetic trends in other countries may be influenced by selection decisions on bulls in the United States. Potentially, female fertility data had been corrected nationally for within-country selection and management biases for milk yield. Using a multiple-trait multiple across-country evaluation (MT-MACE) for the analysis of female fertility traits with milk yield, across-country selection patterns both for female fertility and milk yield can be considered simultaneously. Four analyses were performed; one single-trait multiple across-country evaluation analysis including only milk yield data, one MT-MACE analysis including only female fertility traits, and one MT-MACE analysis including both female fertility and milk yield traits. An additional MT-MACE analysis was performed including both female fertility and milk yield traits, but excluding the United States. By including milk yield traits to the analysis, female fertility reliabilities increased, but not for all bulls in all the countries by trait combinations. The presence of milk yield traits in the analysis did not considerably change the genetic correlations, genetic trends or bull rankings of female fertility traits. Even though the predicted genetic merits of female fertility traits hardly changed by including milk yield traits to the analysis, the change was not equally distributed to the whole data. The number of bulls in common between the two sets of Top 100 bulls for each trait in the two analyses of female fertility traits, with and without the four milk yield traits and their rank correlations were low, not necessarily because of the absence of the US milk yield data. The joint international genetic evaluation of female fertility traits with milk yield is recommended to make use of information on several female fertility traits from different countries simultaneously, to consider selection decisions for milk yield in the genetic evaluation of female fertility traits for obtaining more accurate estimating breeding values (EBV) and to acquire female fertility EBV for bulls evaluated for milk yield, but not for female fertility.

Short communication: Quantifying bias in a single-trait international model ignoring covariances from multiple-trait national models.

The current method in use for international genetic evaluations, called single-trait multiple across-country evaluation (ST-MACE), does not consider residual covariances among traits, making possible only the inclusion of one trait per country in an analysis. The aim of this study was to quantify the effect of bias resulting from treating traits from the same country as nationally independent in an international genetic evaluation. Data from the September 2007 Interbull test evaluation for Holstein female fertility traits were used. Data included were 1 trait from Belgium, Switzerland, Spain, and the United States of America, and 2 traits from Canada, Germany-Austria, and Denmark-Finland-Sweden. The biased results were obtained from a 10-variate ST-MACE analysis including all country traits. The unbiased results were obtained from 8 different 7-variate ST-MACE analyses, each including only 1 trait per country. Average absolute bias in the genetic correlations among 2-trait countries (0.11) was higher than for between 1-trait countries and 2-trait countries (0.07) and for among 1-trait countries (0.03). The results of the biased and the unbiased analyses were different, not only due to bias, but also because of different number of traits involved in the analyses. Differences were considerable (on average, 0.08 to 6.91) for reliabilities, which were higher for traits with lower heritability. Average differences were minor (-0.04 to 0.03 standard deviations) for predicted genetic merits. However, for the top 100 bulls in each country trait, these differences were important (on average, -0.26 to 0.11 standard deviation of predicted genetic merit), which caused considerable changes in bull rankings. The results of this study showed that the effect of bias, caused by ignoring covariances from multiple-trait national models in an ST-MACE analysis, is of such a magnitude that necessitates the use of another method such as multiple-trait multiple across-country evaluation.

Application of a multiple-trait, multiple-country genetic evaluation model for female fertility traits.

The need to implement a method that can handle multiple traits per country in international genetic evaluations is evident. Today, many countries have implemented multiple-trait national genetic evaluations and they may expect to have their traits simultaneously analyzed in international genetic evaluations. Traits from the same country are residually correlated and the method currently in use, single-trait multiple across-country evaluation (ST-MACE), cannot handle nonzero residual correlations. Therefore, multiple-trait, multiple across-country evaluation (MT-MACE) was proposed to handle several traits from the same country simultaneously. To test the robustness of MT-MACE on real data, female fertility was chosen as a complex trait with low heritability. Data from 7 Holstein populations, 3 with 2 traits and 4 with 1 trait, were used. The differences in the estimated genetic correlations by MT-MACE and the single ST-MACE analysis (average absolute deviation of 0.064) were due to the bias of considering several traits from the same country in the ST-MACE analysis. However, the differences between the estimated genetic correlations by MT-MACE and multiple ST-MACE analyses avoiding more than one trait per country in each analysis (average absolute deviation of 0.066) were due to the lack of analysis of the correlated traits from the same country together and using the reported within-country genetic correlations. Applying MT-MACE resulted in reliability gain in international genetic evaluations, which was different from trait to trait and from bull to bull. The average reliability gain by MT-MACE over ST-MACE was 3.0 points for domestic bulls and 6.3 points for foreign bulls. Even countries with 1 trait benefited from the joint analysis of traits from the 2-trait countries. Another superiority of MT-MACE over ST-MACE is that the bulls that do not have national genetic evaluation for some traits from multiple trait countries will receive international genetic evaluations for those traits. Rank correlations were high between ST-MACE and MT-MACE when considering all bulls. However, the situation was different for the top 100 bulls. Simultaneous analysis of traits from the same country affected bull ranks, especially for top 100 bulls. Multi-trait MACE is a recommendable and robust method for international genetic evaluations and is appropriate for handling multiple traits per country, which can increase the reliability of international genetic evaluations.

A study on some physical attributes of Naeini sheep wool for textile industry.

In order to evaluate some physical and qualitative characteristics of Naeini sheep wool for using in textile industry, herds from 6 different regions of Isfahan province were chosen. Staple length from 3 different body parts (shoulder, side and britch) was measured prior to shearing. Wool samples were taken from a 100 cm2 surface on the mid-side of the Animals. Then the samples were transferred to the Laboratory of Animal Sciences for evaluations of some quality characteristics and to the Fiber Physics Laboratory for measuring tenacity, breaking strength and elongation at break of fibers. The means and standard deviations of staple length were estimated (10.8 +/- 2.36), (9.71 +/- 3.14) and (10.99 +/- 2.49) cm for shoulder, side and britch parts, respectively. The total average staple length of Naeini sheep (10.5 cm) is suitable for using in textile industry. Wool fibers of Naeini sheep have desirable tenacity and breaking strength (1.22 cN/dtex and 13.76 centi-Newton) to resist against mechanical tensions of the spinning step. However, Naeini sheep wool fibers have an adequate, but not a desirable % elongation at break (28.6%). Based on the fiber diameter mean of Naeini sheep and its variation (28.51 +/- 4.33 microm) the fleece grade of Naeini sheep was predicted 54's, which is an intermediate grade. However, by conducting breeding programs toward enhancing fleece grade, Naeini sheep wool will be simply applicable in textile industry.

Effect of age at first calving on some productive and longevity traits in Iranian Holsteins of the Isfahan province.

Production and pedigree data of Iranian Holsteins were collected from 1991 to the end of 2001 on 45 herds in Isfahan province. Data on culled cows (birth and culling dates) were used to estimate the effect of age at first calving on total lifetime and productive life; and the effect of age at first calving on first-lactation yields was estimated from corrected (2x 305 d) first-lactation records of 12,082 dairy heifers that calved between 1995 and 2001. The estimate of heritability of age at first calving obtained in this study was 0.086. This low heritability indicates the importance of using available information on relatives for selection on this trait. Age at first calving significantly affected all the traits investigated, including: milk yield, fat yield, fat percentage, lifetime, and productive life. Results indicated a positive effect of reducing age at first calving on milk yield and productive life, although reducing age at first calving to 21 mo of age had a negative effect on yields of milk and milk fat. Lifetime did not show a similar trend with age at first calving. However, a slight positive phenotypic correlation (0.052) was detected between age at first calving and lifetime. We conclude that due to negative effects of age at first calving on productive life and because of optimum age at first calving for milk yield was 24 mo in this study, the reduction of age at first calving to 24 mo of age could be an effective management practice.