2013 Annual Report
1a.Objectives (from AD-416):
The primary objective is to improve the productive efficiency of dairy animals for traits of economic interest through genetic evaluation and management characterization so that the United States and other countries can meet the dietary needs of their populations. Specific objectives include:
Objective 1. Expand national and international collection of phenotypic and genotypic data through collaboration with the Council on Dairy Cattle Breeding and the Bovine Functional Genomics Laboratory (BFGL).
Objective 2. Develop a more accurate genomic evaluation system with advanced, efficient methods to combine pedigrees, genotypes, and phenotypes for all animals.
Objective 3. Use economic analysis to maximize genetic progress and financial benefits from collected data focused on herd management practices, optimal systems for genetic improvement, quantification of economic values for potential new traits such as feed efficiency, economic values of individual traits, and methods to select healthy, fertile animals with high lifetime production.
1b.Approach (from AD-416):
Information from phenotypes, genotypes, and pedigrees will be collected and combined into more accurate genetic evaluations, which will aid in improving the production efficiency of future dairy animals. Statistical methods will be derived, and advanced and efficient computer programs will be developed to process the rapidly growing database of international genomic information and remove bias caused by genomic preselection. Evaluations for additional traits will be developed if their estimated economic values and heritabilities are sufficiently high to justify selection. All traits will be combined into updated genetic-economic indexes to guide breeders with selection goals, and an index targeted to grazing herds will be developed. Methods to combine genotypes from all breeds and crossbreds in the same model will be further developed and tested. Profits from alternative breeding programs and potential investments in data will be compared using simulations and deterministic models. Cooperation with other scientists in ARS, universities, and industry will result in more cost-effective genotyping tools and will maximize benefits from the data collected. Protocols for efficient international sharing of genotype and pedigree data will be developed. Phenotypic effects of management practices and interactions of genotype with environment will also be documented using the national database. Nonadditive genomic effects such as dominance and epistasis will be investigated, and precision mating strategies will be derived to reduce economic loss from inbreeding. Higher density genotyping and full or targeted sequencing may lead to discovering causative mutations that affect important traits and to including quantitative trait loci (QTLs) in predictions instead of only markers. The QTLs and markers with largest effects will be included on smaller, special-purpose chips to provide greater accuracy with less genotyping cost. Methods to impute higher density genotypes from less dense genotypes will be improved. Other species may also be improved by using the genomic selection methods developed in this research as an example.
Under objective 1,.
1)software for pedigree analysis was enhanced;.
2)the MilkBot lactation model was compared to best-prediction and test-interval methods;.
3)processing of incoming genotype files was automated;.
4)additional Brown Swiss genotypes were obtained from Interbull;.
5)relationship between call rate and accuracy of marker genotypes was determined;.
6)exact locations of loss-of-function mutations were determined within 2 Holstein and 1 Jersey fertility haplotypes, which allows more precise determination of carrier status and faster improvement of reproductive efficiency;.
7)haplotypes for imputing microsatellite genotypes for parentage verification were identified;.
8)41 mutations were identified in close linkage with Weaver Syndrome in Brown Swiss;.
9)a web query to display chromosomal genetic merit for genotyped animals was improved; and 10) tools were developed to exploit sequence data to find new markers and disease loci. Under objectives 1 and 2,.
1)predicted genetic merit for heifer and cow conception rates were improved by changing cutoff date edits and using genomic relationships;.
2)use of more markers in genomic evaluation was assessed for a new high-density marker array;.
3)genomic evaluations were developed for Ayrshires, and a haplotype that affects fertility was discovered; and.
4)procedures were developed to deliver Interbull genomic multitrait across-country evaluations for young bulls. Under objective 2,.
1)health trait evaluations and reliabilities were corrected and adjusted;.
2)traditional service-sire calving ease and stillbirth evaluations of young bulls were included in estimating marker effects;.
3)adjustments of genomic inbreeding and relationships to match pedigree inbreeding and relationships were improved;.
4)weights used to combine direct genomic values with traditional genetic merit were revised for Holsteins;.
5)specific chromosomal regions with significant effects were identified using a granddaughter design;.
6)specific adjustments for Holstein cow evaluations by country of evaluation were developed and implemented to improve comparability; and.
7)gains in evaluation accuracy from including foreign genotypes were estimated for Brown Swiss. Under objective 3,.
1)optimal ages for first calving were determined;.
2)trends in herd noncompliance for milk quality standards were documented;.
3)a mating program that includes genomic relationships and dominant genetic effects was developed; and.
4)annual Dairy Herd Information reports for participation, herd averages, processing center activity, somatic cell count, and lactation averages were made available as well as new reports for reproductive status and culling. Under objectives 1, 2, and 3,.
1)a Nonfunded Cooperative Agreement with the Council of Dairy Cattle Breeding was implemented to transfer control and responsibility for evaluation service to the dairy industry;.
2)milking speed and rear legs (rear view) were added as officially evaluated traits for Milking Shorthorns; and.
3)a genetic evaluation for days from calving to first insemination was developed and implemented.
Development of a genomic mating program for dairy cattle. Breed associations, artificial-insemination organizations, and on-farm software providers have needed new computerized mating programs for genomic selection so that genomic inbreeding could be minimized by comparing genotypes of potential mates. Effective methods for transferring genomic relationships from a central database to customers were developed. Methods also were developed and tested to consider dominant effects of individual markers when assigning mates to improve the merit of offspring further. Mating programs that included genomic relationships were more effective than those using pedigree relationships because they improved expected value of offspring as well as decreased expected offspring inbreeding. The expected decrease in inbreeding currently is worth over $3 million annually for U.S. Holsteins. That economic value will grow as more cows are genotyped.
Development of international genomic evaluations for young bulls. Genomic evaluations have rapidly replaced traditional evaluation systems used for dairy cattle selection, but accurate, unbiased comparison of genomic evaluations from different countries has not been possible because of differences in national methodologies and data included in evaluations. In collaboration with the Canadian Dairy Network (Guelph, Ontario) and the Interbull Centre (Uppsala, Sweden), a genomic multitrait across-country evaluation (GMACE) system was developed by modifying techniques used in traditional international evaluations. Implementation of GMACE for young Holstein bulls is scheduled for August 2013 by Interbull Centre. The availability of accurate international genomic evaluations for young bulls will allow breeders to select globally from the best animals, thereby improving the rate of genetic progress for production efficiency worldwide.
Identification of specific chromosomal regions with significant effects on economically important traits. A granddaughter design is an analysis of genetic linkage of quantitative loci to DNA markers in which the markers are identified in grandsires and sons but the quantitative analysis is carried out on granddaughter performance. The granddaughter design has been applied to nearly all major commercial dairy cattle populations, but relatively little practical use had been made of the results. A granddaughter design was applied to the entire Holstein genome to determine specific chromosomal regions (haplotypes) that had significant effects on 33 economically important traits, including production, disease resistance, longevity, fertility, calving, conformation, and overall net merit. Each trait was found to have at least one significant haplotype within family. This identification method will be beneficial in providing information on gene function and the architecture of quantitative trait loci as well as determining the causative genetic variant for desired traits.
Improved accuracy of genomic evaluations for dairy cattle through use of more DNA markers. Accuracy of genomic evaluation is expected to increase when more DNA markers are used because of better tracking of causative genetic variants. However, high-density genotypes based on almost 800,000 markers have not been used for U.S. genomic evaluations because the small accuracy gain achieved did not justify the genotyping cost. To investigate the use of more than 50,000 markers in genomic evaluation, a genotyping chip with approximately 77,000 markers was developed with the markers specifically chosen to be highly informative. The approximately 90,000 markers from the union of that marker set with the 45,000 currently used in genomic evaluation resulted in a small increase in evaluation accuracy for most traits of economic interest. That accuracy is expected to increase because of increased imputation accuracy as more animals are genotyped with the new chip as well as two new low-density chips that also include some of the new markers. Use of the larger set of markers in genomic evaluations of dairy cattle is planned for implementation in August 2013 and will allow dairy producers to make more accurate breeding selections for economically important traits.
First national genomic evaluations for Ayrshire dairy cattle. Although genetic evaluations of the Holstein, Jersey, and Brown Swiss dairy breeds in the United States have benefited from the inclusion of genotypic information since 2009, too few Ayrshires had been genotyped to allow genomic evaluation. In February 2013, the data from over 1,100 genotyped Ayrshires with performance and pedigree records in the North American database made possible the development of genomic evaluations for Ayrshires. Compared with traditional parent averages, those evaluations improved accuracy of prediction of genetic merit by 8.2 percentage points over all traits (17 percentage points for milk and protein yields and 16 percentage points for stature). The availability of genomic information also made breed determination possible using procedures that had been implemented for other breeds. In addition, a DNA segment (haplotype) that affects fertility was discovered on autosomal chromosome 17; sire conception rate was 3.0 percentage points lower for carriers of the haplotype, and the carrier frequency for genotyped Ayrshires is 23%. Ayrshire breeders will be able to make better selection decisions and increase the rate of genetic gain for economically important traits as a result of improved knowledge of the genomic makeup and merit of their animals.
Introduction of free genetic tests for inherited defects of dairy cattle. A method to identify exact locations of loss-of-function mutations and DNA sequences associated with lethal or undesirable conditions of dairy cattle was developed and automated over the past 2 years. However, results from that method could not be made available to the dairy industry for DNA sequences are associated with patented genes. Although genetic tests were available for several lethal mutations, most females were not tested because individual gene tests were expensive and not included on genotyping chips until very recently. The June 2013 U.S. Supreme Court unanimous decision that biotechnology companies cannot patent genes that occur naturally has made possible the release of information from genetic tests for bovine leukocyte adhesion deficiency (BLAD), deficiency of uridine monophosphate synthase (DUMPS), and mulefoot in Holsteins and Weaver Syndrome, spinal dysmyelination (SDM), and spinal muscular atrophy (SMA) in Brown Swiss; for Holsteins, the method also can be applied to identify DNA markers associated with complex vertebral malformation (CVM) and brachyspina as well as for desired traits such as red coat color and polledness (no horns). In addition, four new deleterious DNA sequences have been identified for dairy cattle fertility, and those sequences have been incorporated into new genotyping chips. The first release of genomic status information for the inherited defects is scheduled for August 2013 and is expected to allow dairy producers to reduce or eliminate costs for genetic testing, decrease the frequency of undesired traits, and increase the rate of genetic progress for desired traits.
Cole, J.B., Lewis, R.M., Maltecca, C., Newman, S., Olson, K.M., Tait, R. 2013. Systems Biology in Animal Breeding: Identifying relationships among markers, genes, and phenotypes. Journal of Animal Science. 91(2):521-522.
Olson, K.M., Van Raden, P.M., Tooker, M.E. 2012. Multibreed genomic evaluations using purebred Holsteins, Jerseys, and Brown Swiss. Journal of Dairy Science. 95(9):5378-5383.
Parker Gaddis, K.L., Cole, J.B., Clay, J.S., Maltecca, C. 2012. Incidence validation and relationship analysis of producer-recorded health event data from on-farm computer systems in the United States. Journal of Dairy Science. 95(9):5422-5435.
Misztal, I., Tsuruta, S., Aguilar, I., Legarra, A., Van Raden, P.M., Lawlor, T.J. 2013. Methods to approximate reliabilities in single-step genomic evaluation. Journal of Dairy Science. 96(1):647-654.
Van Raden, P.M., Null, D.J., Sargolzaei, M., Wiggans, G.R., Tooker, M.E., Cole, J.B., Sonstegard, T.S., Connor, E.E., Winters, M., Van Kaam, J., Van Doormaal, B.J., Faust, M.A., Doak, G.A. 2013. Genomic imputation and evaluation using high density Holstein genotypes. Journal of Dairy Science. 96(1):668-678.
Sonstegard, T.S., Cole, J.B., Van Raden, P.M., Van Tassell, C.P., Null, D.J., Schroeder, S.G., Bickhart, D.M., Mcclure, M.C. 2013. Identification of a nonsense mutation in CWC15 associated with decreased reproductive efficiency in Jersey cattle. PLoS One. 8(1):e54872.
Hutchison, J.L., Van Raden, P.M., Norman, H.D., Cole, J.B. 2013. Technical Note: Changes to herd cutoff date in conception rate evaluations. Journal of Dairy Science. 96(2):1264-1268.
Wiggans, G.R., Cooper, T.A., Van Tassell, C.P., Sonstegard, T.S., Simpson, E.B. 2013. Technical note: Characteristics and use of the Illumina BovineLD and GeneSeek Genomic Profiler low-density bead chips for genomic evaluation. Journal of Dairy Science. 96(2):1258-1263.
Van Raden, P.M., Cooper, T.A., Wiggans, G.R., O'Connell, J.R., Bacheller, L.R. 2013. Confirmation and discovery of maternal grandsires and great grandsires in dairy cattle. Journal of Dairy Science. 96(3):1874-1879.
Cole, J.B., Null, D.J. 2013. Visualization of the transmission of direct genomic values for paternal and maternal chromosomes for 15 traits in US Brown Swiss, Holstein, and Jersey cattle. Journal of Dairy Science. 96(4):2713-2726.
Wright, J.R., Wiggans, G.R., Muenzenberger, J.R., Neitzel, R.R. 2013. Short communication: Genetic evaluation of mobility for Brown Swiss dairy cattle. Journal of Dairy Science. 96(4):2657-2660.
Cooper, T.A., Wiggans, G.R., Van Raden, P.M. 2013. Short communication: Relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle. Journal of Dairy Science. 96(5):3336-3339.
Ma, L., Wiggans, G.R., Wang, S., Sonstegard, T.S., Yang, J., Crooker, B.A., Cole, J.B., Van Tassell, C.P., Da, Y. 2012. Effect of sample stratification on dairy GWAS results. Biomed Central (BMC) Genomics. 13:536.