A mating advice system in dairy cattle incorporating genomic information

This study investigated the effects of alternative mating programs that incorporate genomic information on expected progeny herd performance and inbreeding, as well as methods to include un-genotyped animals in such mating programs. A total of 54,535 Holstein-Friesian cattle with imputed high-density genotypes (547,650 SNP after edits) were available. First, to quantify the accuracy of imputing un-genotyped animals (often an issue in populations), a sub-population of 729 genotyped animals had their genotypes masked, and their allele dosages were imputed, using linear regression exploiting information on genotyped relatives. The reference population for imputation included all genotyped animals, excluding the 729 selected animals and their sires, dams, and grandsires, and had either (1) their sires' genotypes, (2) their dams' genotypes (3) both their sires' and their dams' genotypes, or (4) both their sires' and maternal grandsires' genotypes introduced into the reference population. The correlations between true genotypes and the imputed allele dosages ranged from 0.58 (sire only) to 0.68 (both sire and dam). A herd of 100 cows was then simulated (1,000 replicates) from the sub-population of 729 imputed animals. The top 10 bulls from the genotyped population, based on their total genetic merit index (TMI) were selected to be used as sires. Three mating allotment methods were investigated: (1) random mating, (2) sequential mating based on maximizing only the expected TMI of the progeny, and (3) linear programming to maximize a generated index constructed to maximize genetic merit and minimize expected progeny inbreeding as well as intra- and inter-progeny variability in genetic merit. Relationships among candidate parents were calculated using either the pedigree relationship matrix or the genomic relationship matrix; the latter was constructed using either the true genotypes of both parents or the true genotypes of the sire plus the imputed allele dosages of the dam. Using the genomic co-ancestry estimates resulted in lower average herd expected genomic inbreeding levels compared with using the pedigree-based co-ancestry estimates. Additionally, if the dams were not genotyped, using their imputed allele dosages also resulted in lower average herd expected inbreeding levels compared with using the pedigree co-ancestry estimates. The inter-progeny coefficient of variation for selected traits, milk and fertility, estimated breeding values were reduced by 12 to 65% using the linear programing method compared with sequential mating.     

Symposium review: The genomic architecture of inbreeding: How homozygosity affects health and performance

Inbreeding depression is a growing concern in livestock because it can detrimentally affect animal fitness, health, and production levels. Genomic information can be used to more effectively capture variance in Mendelian sampling, thereby enabling more accurate estimation of inbreeding, but further progress is still required. The calculation of inbreeding for herd management purposes is largely still done using pedigree information only, although inbreeding coefficients calculated in this manner have been shown to be less accurate than genomic inbreeding measures. Continuous stretches of homozygous genotypes, so called runs of homozygosity, have been shown to provide a better estimate of autozygosity at the genomic level than conventional measures based on inbreeding coefficients calculated through conventional pedigree information or even genomic relationship matrices. For improved and targeted management of genomic inbreeding at the population level, the development of methods that incorporate genomic information in mate selection programs may provide a more precise tool for reducing the detrimental effects of inbreeding in dairy herds. Additionally, a better understanding of the genomic architecture of inbreeding and incorporating that knowledge into breeding programs could significantly refine current practices. Opportunities to maintain high levels of genetic progress in traits of interest while managing homozygosity and sustaining acceptable levels of heterozygosity in highly selected dairy populations exist and should be examined more closely for continued sustainability of both the dairy cattle population as well as the dairy industry. The inclusion of precise genomic measures of inbreeding, such as runs of homozygosity, inbreeding, and mating programs, may provide a path forward. In this symposium review article, we describe traditional measures of inbreeding and the recent developments made toward more precise measures of homozygosity using genomic information. The effects of homozygosity resulting from inbreeding on phenotypes, the identification and mapping of detrimental homozygosity haplotypes, management of inbreeding with genomic data, and areas in need of further research are discussed.     

Comparative analysis of inbreeding parameters and runs of homozygosity islands in 2 Italian autochthonous cattle breeds mainly raised in the Parmigiano-Reggiano cheese production region

Reggiana and Modenese are autochthonous cattle breeds, reared in the North of Italy, that can be mainly distinguished for their standard coat color (Reggiana is red, whereas Modenese is white with some pale gray shades). Almost all milk produced by these breeds is transformed into 2 mono-breed branded Parmigiano-Reggiano cheeses, from which farmers receive the economic incomes needed for the sustainable conservation of these animal genetic resources. After the setting up of their herd books in 1960s, these breeds experienced a strong reduction in the population size that was subsequently reverted starting in the 1990s (Reggiana) or more recently (Modenese) reaching at present a total of about 2,800 and 500 registered cows, respectively. Due to the small population size of these breeds, inbreeding is a very important cause of concern for their conservation programs. Inbreeding is traditionally estimated using pedigree data, which are summarized in an inbreeding coefficient calculated at the individual level (F_PED). However, incompleteness of pedigree information and registration errors can affect the effectiveness of conservation strategies. High-throughput SNP genotyping platforms allow investigation of inbreeding using genome information that can overcome the limits of pedigree data. Several approaches have been proposed to estimate genomic inbreeding, with the use of runs of homozygosity (ROH) considered to be the more appropriate. In this study, several pedigree and genomic inbreeding parameters, calculated using the whole herd book populations or considering genotyping information (GeneSeek GGP Bovine 150K) from 1,684 Reggiana cattle and 323 Modenese cattle, were compared. Average inbreeding values per year were used to calculate effective population size. Reggiana breed had generally lower genomic inbreeding values than Modenese breed. The low correlation between pedigree-based and genomic-based parameters (ranging from 0.187 to 0.195 and 0.319 to 0.323 in the Reggiana and Modenese breeds, respectively) reflected the common problems of local populations in which pedigree records are not complete. The high proportion of short ROH over the total number of ROH indicates no major recent inbreeding events in both breeds. ROH islands spread over the genome of the 2 breeds (15 in Reggiana and 14 in Modenese) identified several signatures of selection. Some of these included genes affecting milk production traits, stature, body conformation traits (with a main ROH island in both breeds on BTA6 containing the ABCG2, NCAPG, and LCORL genes) and coat color (on BTA13 in Modenese containing the ASIP gene). In conclusion, this work provides an extensive comparative analysis of pedigree and genomic inbreeding parameters and relevant genomic information that will be useful in the conservation strategies of these 2 iconic local cattle breeds.     

Mating programs including genomic relationships and dominance effects

Computerized mating programs using genomic information are needed by breed associations, artificial-insemination organizations, and on-farm software providers, but such software is already challenged by the size of the relationship matrix. As of October 2012, over 230,000 Holsteins obtained genomic predictions in North America. Efficient methods of storing, computing, and transferring genomic relationships from a central database to customers via a web query were developed for approximately 165,000 genotyped cows and the subset of 1,518 bulls whose semen was available for purchase at that time. This study, utilizing 3 breeds, investigated differences in sire selection, methods of assigning mates, the use of genomic or pedigree relationships, and the effect of including dominance effects in a mating program. For both Jerseys and Holsteins, selection and mating programs were tested using the top 50 marketed bulls for genomic and traditional lifetime net merit as well as 50 randomly selected bulls. The 500 youngest genotyped cows in the largest herd in each breed were assigned mates of the same breed with limits of 10 cows per bull and 1 bull per cow (only 79 cows and 8 bulls for Brown Swiss). A dominance variance of 4.1 and 3.7% was estimated for Holsteins and Jerseys using 45,187 markers and management group deviation for milk yield. Sire selection was identified as the most important component of improving expected progeny value, followed by managing inbreeding and then inclusion of dominance. The respective percentage gains for milk yield in this study were 64, 27, and 9, for Holsteins and 73, 20, and 7 for Jerseys. The linear programming method of assigning a mate outperformed sequential selection by reducing genomic or pedigree inbreeding by 0.86 to 1.06 and 0.93 to 1.41, respectively. Use of genomic over pedigree relationship information provided a larger decrease in expected progeny inbreeding and thus greater expected progeny value. Based on lifetime net merit, the economic value of using genomic relationships was >$3 million per year for Holsteins when applied to all genotyped females, assuming that each will provide 1 replacement. Previous mating programs required transferring only a pedigree file to customers, but better service is possible by incorporating genomic relationships, more precise mate allocation, and dominance effects. Economic benefits will continue to grow as more females are genotyped.     

Genomic inbreeding coefficients using imputed genotypes: Assessing different estimators in Holstein-Friesian dairy cows

The objective of this study was to estimate inbreeding coefficients in Holstein dairy cattle using imputed SNPs data. A data set of 95,540 Italian Holstein dairy cows from the routine genomic evaluations of the Italian National Association of Holstein, Brown, and Jersey Breeders were analyzed, with 84,445 imputed SNP. Ten widely used genomic inbreeding estimators were tested, including 4 PLINK v1.9 estimators (F, F_HAT1, F_HAT2, F_HAT3), 3 genomic relationship matrix (GRM)-based methods [VanRaden's first method with observed allele frequencies (F_GRM) or with fixed frequencies at 0.5 (F_GRM05), VanRaden's third method, allelic frequency free and pedigree regressed (F_GRM2)], runs of homozygosity (ROH)-based estimators in a complete (F_ROH) and simplified version (F_ROH2), and proportion of homozygous SNP (F_PH). Pairwise comparisons among them were made, including the comparison with traditional pedigree-based inbreeding coefficients (F_PED). Our results showed variability among the genomic inbreeding estimators. Coefficients of F_GRM and F_HAT3 were >1, meaning that more variability has been lost than the variability that existed in the base population. Regarding the remaining ones, F_GRM05, F_ROH, F_ROH2, and F_PH provided coefficients within the [0,1] space and are considered comparable to F_PED. Not comparable to F_PED, yet with an interpretable value, can be considered the coefficients of F, F_HAT2, and F_GRM2. Estimators based on ROH had the highest correlation with pedigree-based coefficients (0.59–0.66), among all estimators tested. In this study, Spearman correlations were shown to possibly provide a clearer estimation of the strength of the relationship between estimators. We hypothesize that imputation might cause extreme genomic inbreeding values that deserves further investigation.     

Optimizing the design of small-sized nucleus breeding programs for dairy cattle with minimal performance recording

Dairy cattle breeding programs in developing countries are constrained by minimal and erratic pedigree and performance recording on cows on commercial farms. Small-sized nucleus breeding programs offer a viable alternative. Deterministic simulations using selection index theory were performed to determine the optimum design for small-sized nucleus schemes for dairy cattle. The nucleus was made up of 197 bulls and 243 cows distributed in 8 non-overlapping age classes. Each year 10 sires and 100 dams were selected to produce the next generation of male and female selection candidates. Conception rates and sex ratio were fixed at 0.90 and 0.50, respectively, translating to 45 male and 45 female candidates joining the nucleus per year. Commercial recorded dams provided information for genetic evaluation of selection candidates (bulls) in the nucleus. Five strategies were defined: nucleus records only [within-nucleus dam performance (DP)], progeny records in addition to nucleus records [progeny testing (PT)], genomic information only [genomic selection (GS)], dam performance records in addition to genomic information (GS+DP), and progeny records in addition to genomic information (GS+PT). Alternative PT, GS, GS+DP, and GS+PT schemes differed in the number of progeny per sire and size of reference population. The maximum number of progeny records per sire was 30, and the maximum size of the reference population was 5,000. Results show that GS schemes had higher responses and lower accuracies compared with other strategies, with the higher response being due to shorter generation intervals. Compared with similar sized progeny-testing schemes, genomic-selection schemes would have lower accuracies but these are offset by higher responses per year, which might provide additional incentive for farmers to participate in recording.     

Strategies for implementing genomic selection for feed efficiency in dairy cattle breeding schemes

Alternative genomic selection and traditional BLUP breeding schemes were compared for the genetic improvement of feed efficiency in simulated Norwegian Red dairy cattle populations. The change in genetic gain over time and achievable selection accuracy were studied for milk yield and residual feed intake, as a measure of feed efficiency. When including feed efficiency in genomic BLUP schemes, it was possible to achieve high selection accuracies for genomic selection, and all genomic BLUP schemes gave better genetic gain for feed efficiency than BLUP using a pedigree relationship matrix. However, introducing a second trait in the breeding goal caused a reduction in the genetic gain for milk yield. When using contracted test herds with genotyped and feed efficiency recorded cows as a reference population, adding an additional 4,000 new heifers per year to the reference population gave accuracies that were comparable to a male reference population that used progeny testing with 250 daughters per sire. When the test herd consisted of 500 or 1,000 cows, lower genetic gain was found than using progeny test records to update the reference population. It was concluded that to improve difficult to record traits, the use of contracted test herds that had additional recording (e.g., measurements required to calculate feed efficiency) is a viable option, possibly through international collaborations.     

A comparison of dairy cattle breeding designs that use genomic selection

Different dairy cattle breeding schemes were compared using stochastic simulations, in which the accuracy of the genomic breeding values was dependent on the structure of the breeding scheme, through the availability of new genotyped animals with phenotypic information. Most studies that predict the gain by implementing genomic selection apply a deterministic approach that requires assumptions about the accuracy of the genomic breeding values. The achieved genetic gain, when genomic selection was the only selection method to directly identify elite sires for widespread use and progeny testing was omitted, was compared with using genomic selection for preselection of young bulls for progeny testing and to a conventional progeny test scheme. The rate of inbreeding could be reduced by selecting more sires every year. Selecting 20 sires directly on their genomic breeding values gave a higher genetic gain than any progeny testing scheme, with the same rate of inbreeding as the schemes that used genomic selection for preselection of bulls before progeny testing. The genomic selection breeding schemes could reduce the rate of inbreeding and still increase genetic gain, compared with the conventional breeding scheme. Since progeny testing is expensive, the breeding scheme omitting the progeny test will be the cheapest one. Keeping the progeny test and use of genomic selection for preselection still has some advantages. It gives higher accuracy of breeding values and does not require a complete restructuring of the breeding program. Comparing at the same rate of inbreeding, using genomic selection for elite sire selection only gives a 13% increase in genetic gain, compared with using genomic selection for preselection. One way to reduce the costs of the scheme where genomic selection was used for preselection is to reduce the number of progeny tested bulls. This was here achieved without getting lower genetic gain or a higher rate of inbreeding.     

Genomic selection strategies in a small dairy cattle population evaluated for genetic gain and profit

The objective of this study was to evaluate a genomic breeding scheme in a small dairy cattle population that was intermediate in terms of using both young bulls (YB) and progeny-tested bulls (PB). This scheme was compared with a conventional progeny testing program without use of genomic information and, as the extreme case, a juvenile scheme with genomic information, where all bulls were used before progeny information was available. The population structure, cost, and breeding plan parameters were chosen to reflect the Danish Jersey cattle population, being representative for a small dairy cattle population. The population consisted of 68,000 registered cows. Annually, 1,500 bull dams were screened to produce the 500 genotyped bull calves from which 60 YB were selected to be progeny tested. Two unfavorably correlated traits were included in the breeding goal, a production trait (h² = 0.30) and a functional trait (h² = 0.04). An increase in reliability of 5 percentage points for each trait was used in the default genomic scenario. A deterministic approach was used to model the different breeding programs, where the primary evaluation criterion was annual monetary genetic gain (AMGG). Discounted profit was used as an indicator of the economic outcome. We investigated the effect of varying the following parameters: (1) increase in reliability due to genomic information, (2) number of genotyped bull calves, (3) proportion of bull dam sires that are young bulls, and (4) proportion of cow sires that are young bulls. The genomic breeding scheme was both genetically and economically superior to the conventional breeding scheme, even in a small dairy cattle population where genomic information causes a relatively low increase in reliability of breeding values. Assuming low reliabilities of genomic predictions, the optimal breeding scheme according to AMGG was characterized by mixed use of YB and PB as bull sires. Exclusive use of YB for production cows increased AMGG up to 3 percentage points. The results from this study supported our hypothesis that strong interaction effects exist. The strongest interaction effects were obtained between increased reliabilities of genomic estimated breeding values and more intensive use of YB. The juvenile scheme was genetically inferior when the increase in reliability was low (5 percentage points), but became genetically superior at higher reliabilities of genomic estimated breeding values. The juvenile scheme was always superior according to discounted profit because of the shorter generation interval and minimizing costs for housing and feeding waiting bulls.     

Efficient parentage assignment and pedigree reconstruction with dense single nucleotide polymorphism data

Large numbers of dairy cattle are now routinely genotyped for dense single nucleotide polymorphism (SNP) arrays for the purpose of predicting genomic estimated breeding values. Such SNP arrays contain very good information for parentage assignment and pedigree reconstruction. The main challenge in using this information for parentage assignment and pedigree reconstruction is development of computationally efficient strategies that enable a candidate animal to be assigned its sire and dam with the large volume of data. Here we describe an efficient algorithm for parentage assignment with SNP data and demonstrate very accurate assignment with 50,000-SNP and 3,000-SNP panels. The computer code implementing the algorithm is given in the Appendix.     

Optimal Genetic Improvement for the High Producing Cow

Many 40,000-lb (18,144 kg) cows exist in today's dairy population, but herds capable of 40,000-lb averages for all cows remain to be developed. Traditional genetic improvement practices, based on consistent use of current high-ranking AI bulls selected to improve economically important traits, will remain important tools to develop high producing cows. Future breeding strategies will likely include attention to how high production is achieved and may include direct selection for increased appetite or some measure of energy balance to support high production, reproduction, and immune function. Direct selection for improved fertility, perhaps involving traits not presently used in herd management, may prove to be necessary as yields increase. Roles for evolving technologies such as marker-assisted selection, manipulation of the bovine genome, and cloning remain unclear, but will likely be incorporated into traditional progeny testing schemes. Equipment to routinely monitor physiological functions may encourage the establishment of large progeny test herds with expanded data recording capability. The expense could lead to proprietary genetic lines and private genetic evaluation systems such as exist in poultry and swine. Pedigree information will become more important in commercial herds to manage inbreeding. The dairy industry can expect to benefit from current research efforts in human genetics. However, current funding of dairy breeding research in the United States will limit the number of individuals trained in methods to implement those results in dairy cattle.     

Genetic parameters for conformation traits in herds that differ in mean final score and completeness of pedigree and performance data

The objectives of this study were to assess differences in the heritability of type (conformation) traits between herds that differ in mean final score and completeness of pedigree and performance data and to estimate genetic correlations among these environments. Measurement of subjective characteristics, such as conformation traits, may be more difficult in herds with poor management conditions, and genetic evaluation of sires using data from such herds could lead to inaccurate selection decisions. Furthermore, missing pedigree data is a significant problem in many herds, and a lack of phenotypic data from maternal relatives may reduce the effectiveness of animal model evaluation systems. These hypotheses were examined using type classification scores of 1,728,836 first-parity Holstein cows (from 54,223 sires) that calved from 1993 to 2002 in 24,207 US dairy herds. These data included 480,927 records from progeny test daughters, but only 254,891 (47%) were from dams that had valid sire identification, and only 132,953 (28%) were from dams that had also been classified. Herds were grouped into quartiles by mean classification score, percentage of known maternal grandsires, and percentage of classified dams. Estimated heritability of final score was 0.20 in herds with mean score <74.5, 0.17 in herds with <25% known maternal grandsires, and 0.19 in herds with <18% classified dams. Conversely, estimates were 0.39 in herds with mean score >78.7, 0.35 in herds with 100% known maternal grandsires, and 0.37 in herds with >71% classified dams. Estimated genetic correlations between quartiles ranged from 0.86 to 0.95. Based on this study, it appears that improvements in animal identification and data collection in progeny test herds would lead to greater accuracy and stability of genetic evaluations for conformation traits in US Holstein cattle.     

Structure of breeding programs to capitalize on reproductive technology for genetic improvement.

Potential rates of genetic progress are limited by biological constraints, which along with genetic parameters determine the structure of breeding programs to be employed for maximum genetic improvement. The objective here is to determine whether current progeny-testing programs in dairy cattle, which have been dictated and constrained by low female reproductive rates, need to be changed to capitalize on new reproductive technologies and how these changes should be implemented. Many differences between breeding programs diminish when selection on animal model genetic evaluations across all age and population groups is adopted as a strategy. Progeny-testing schemes then evolve toward dispersed open nucleus breeding schemes when multiple ovulation and embryo transfer is used on bull-dams. Nucleus breeding schemes have been advocated to capitalize on embryo transfer technology. In nucleus breeding schemes utilizing high reproductive rates, inbreeding, rather than reproductive rate, poses a limit to genetic progress, and strategies that maximize response to selection while limiting inbreeding need to be employed. One strategy is mating each dam to several sires rather than only one sire. In vitro embryo production techniques can be used to facilitate such mating strategies. Large-scale in vitro embryo production programs, in which large numbers of embryos per female are tested in the commercial population, offer the greatest potential for genetic gain with low rates of inbreeding. Cloning has an impact mainly on methods for dissemination of genetic improvement. Breeding herds, genetically inferior to marketed clones, are needed for continuous genetic gain. Reproductive technologies offer the potential for genetic improvement. Whether new breeding programs require changes in population structure, e.g., by creation of nucleus breeding herds, depends mainly on logistics and on quantity and quality of field information.     

Use of computerized mate selection programs to control inbreeding of Holstein and Jersey cattle in the next generation

The expected role of computerized mate selection programs with regard to inbreeding and lifetime profitability of Holstein and Jersey cattle was examined using data from 25 large registered herds of each breed. Sire selection and mate allocation were carried out using linear programming with the following objectives: 1) minimum inbreeding, 2) maximum net merit subject to a fixed inbreeding threshold, and 3) maximum expected lifetime profit after adjustment for inbreeding depression. Inbreeding of actual matings was similar to inbreeding from random matings, indicating that current inbreeding avoidance programs in these herds are ineffective. Inbreeding was reduced by 1.6 and 1.9% in Holsteins and Jerseys, respectively, when a mate allocation program was applied with service sires and usage levels fixed at the actual values. Benefits of mate selection programs increased when both sire selection and mate pair allocation were considered. Maximization of mean net merit with inbreeding restricted to a fixed level (5% in Holsteins and 8% in Jerseys) led to decreases in inbreeding of 0.9 and 1.4% and increases in lifetime profit of $16.66 and $26.86 in Holsteins and Jerseys, respectively, relative to programs that ignored inbreeding. Maximization of mean expected lifetime profit after adjustment for inbreeding depression decreased inbreeding by 1.8 and 2.8% and increased lifetime profit by $37.37 and $59.77 in Holsteins and Jerseys, respectively. Inbreeding coefficients estimated with pedigree traced to 1985 were inadequate predictors of inbreeding coefficients estimated with pedigrees traced to 1960. Mate selection programs cannot function optimally unless extensive historical pedigree data are available, particularly for service sires. Computerized mate selection programs can reduce inbreeding in the next generation, which will lead to an increase in farm profitability. However, if genetic diversity is to be maintained in the long term, procedures for selecting parents of AI sires must also be considered.     

National single-step genomic method that integrates multi-national genomic information

The aim of this paper was to develop a national single-step genomic BLUP that integrates multi-national genomic estimated breeding values (EBV) and associated reliabilities without double counting dependent data contributions from the different evaluations. Simultaneous use of all data, including phenotypes, pedigree, and genotypes, is a condition to obtain unbiased EBV. However, this condition is not always fully met, mainly due to unavailability of foreign raw data for imported animals. In dairy cattle genetic evaluations, this issue is traditionally tackled through the multiple across-country evaluation (MACE) of sires, performed by Interbull Centre (Uppsala, Sweden). Multiple across-country evaluation regresses all the available national information onto a joint pedigree to obtain country-specific rankings of all sires without sharing the raw data. In the context of genomic selection, the issue is handled by exchanging sire genotypes and by using MACE information (i.e., MACE EBV and reliabilities), as a valuable source of “phenotypic” data. Although all the available data are considered, these “multi-national” genomic evaluations use multi-step methods assuming independence of various sources of information, which is not met in all situations. We developed a method that handles this by single-step genomic evaluation that jointly (1) uses national phenotypic, genomic, and pedigree data; (2) uses multi-national genomic information; and (3) avoids double counting dependent data contributions from an animal’s own records and relatives’ records. The method was demonstrated by integrating multi-national genomic EBV and reliabilities of Brown Swiss sires, included in the InterGenomics consortium at Interbull Centre, into the national evaluation in Slovenia. The results showed that the method could (1) increase reliability of a national (genomic) evaluation; (2) provide consistent ranking of all animals: bulls, cows, and young animals; and (3) increase the size of a genomic training population. These features provide more efficient and transparent selection throughout a breeding program.     

Analysis of levels of inbreeding and inbreeding depression in Jersey cattle.

A pedigree file of 157,015 male and female Jersey cattle (born after 1955) from the Canadian herdbooks was investigated for the occurrence of inbreeding. A large proportion of Jersey bulls and cows were inbred (32.4 and 36.3% for bulls and cows, respectively). However, average inbreeding coefficients of these inbred cows and of all cows were low. First lactation milk, fat, and fat percentage records for 53,592 Jersey cows were analyzed. Inbreeding was included in the animal model as a linear covariate. The regression coefficients of milk, fat, and fat percentage on inbreeding were -9.84 kg, -.55 kg, and -.0011% per 1% increase of inbreeding. Inbreeding depression was not enough to cause large reductions of milk and fat yield of a cow with average inbreeding. However, when the inbreeding coefficient was greater than 12.5%, the inbreeding depression was significantly higher than expected and such that intentional inbreeding is not justified unless the mating is to an animal with exceptionally high breeding value.     

Cow genotyping strategies for genomic selection in a small dairy cattle population

This study compares how different cow genotyping strategies increase the accuracy of genomic estimated breeding values (EBV) in dairy cattle breeds with low numbers. In these breeds, few sires have progeny records, and genotyping cows can improve the accuracy of genomic EBV. The Guernsey breed is a small dairy cattle breed with approximately 14,000 recorded individuals worldwide. Predictions of phenotypes of milk yield, fat yield, protein yield, and calving interval were made for Guernsey cows from England and Guernsey Island using genomic EBV, with training sets including 197 de-regressed proofs of genotyped bulls, with cows selected from among 1,440 genotyped cows using different genotyping strategies. Accuracies of predictions were tested using 10-fold cross-validation among the cows. Genomic EBV were predicted using 4 different methods: (1) pedigree BLUP, (2) genomic BLUP using only bulls, (3) univariate genomic BLUP using bulls and cows, and (4) bivariate genomic BLUP. Genotyping cows with phenotypes and using their data for the prediction of single nucleotide polymorphism effects increased the correlation between genomic EBV and phenotypes compared with using only bulls by 0.163 ± 0.022 for milk yield, 0.111 ± 0.021 for fat yield, and 0.113 ± 0.018 for protein yield; a decrease of 0.014 ± 0.010 for calving interval from a low base was the only exception. Genetic correlation between phenotypes from bulls and cows were approximately 0.6 for all yield traits and significantly different from 1. Only a very small change occurred in correlation between genomic EBV and phenotypes when using the bivariate model. It was always better to genotype all the cows, but when only half of the cows were genotyped, a divergent selection strategy was better compared with the random or directional selection approach. Divergent selection of 30% of the cows remained superior for the yield traits in 8 of 10 folds.     

Accuracy of direct genomic values derived from imputed single nucleotide polymorphism genotypes in Jersey cattle

The objective of the present study was to evaluate the predictive ability of direct genomic values for economically important dairy traits when genotypes at some single nucleotide polymorphism (SNP) loci were imputed rather than measured directly. Genotypic data consisted of 42,552 SNP genotypes for each of 1,762 Jersey sires. Phenotypic data consisted of predicted transmitting abilities (PTA) for milk yield, protein percentage, and daughter pregnancy rate from May 2006 for 1,446 sires in the training set and from April 2009 for 316 sires in the testing set. The SNP effects were estimated using the Bayesian least absolute selection and shrinkage operator (LASSO) method with data of sires in the training set, and direct genomic values (DGV) for sires in the testing set were computed by multiplying these estimates by corresponding genotype dosages for sires in the testing set. The mean correlation across traits between DGV (before progeny testing) and PTA (after progeny testing) for sires in the testing set was 70.6% when all 42,552 SNP genotypes were used. When genotypes for 93.1, 96.6, 98.3, or 99.1% of loci were masked and subsequently imputed in the testing set, mean correlations across traits between DGV and PTA were 68.5, 64.8, 54.8, or 43.5%, respectively. When genotypes were also masked and imputed for a random 50% of sires in the training set, mean correlations across traits between DGV and PTA were 65.7, 63.2, 53.9, or 49.5%, respectively. Results of this study indicate that if a suitable reference population with high-density genotypes is available, a low-density chip comprising 3,000 equally spaced SNP may provide approximately 95% of the predictive ability observed with the BovineSNP50 Beadchip (Illumina Inc., San Diego, CA) in Jersey cattle. However, if fewer than 1,500 SNP are genotyped, the accuracy of DGV may be limited by errors in the imputed genotypes of selection candidates.     

Estimation of pedigree errors in the UK dairy population using microsatellite markers and the impact on selection

The proportion of cows in the UK dairy herd whose sires were misidentified was estimated using DNA markers. Genetic marker genotypes were determined on 568 cows (from 168 milk samples and 400 hair samples) and 96 putative sires (from semen samples). The estimated pedigree error rate from the hair samples was 8.8%, and from the milk samples, 13.1%, giving an overall estimate of the error rate of 10%. This level of pedigree errors will have a relatively large impact on the efficiency of progeny testing and the accuracy of cow predicted breeding values. We predict a loss of response to selection of approximately 2 to 3% given this error rate.