Progress 07/24/07 to 07/23/12
Outputs
Progress Report 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: Collect genotypes, specifically single-nucleotide polymorphisms (SNPs), and new phenotypes to improve accuracy and comprehensiveness of the national dairy database. Subobjective 1.A: Increase the accuracy of pedigree information by using SNP genotypes to verify and to assign parentage. Subobjective 1.B: Obtain additional data on health and management traits, and improve consistency of national database. Objective 2: Characterize phenotypic measures of dairy practices, and provide the dairy industry with information needed to determine the impact of various herd management decisions on profitability. Objective 3: Improve accuracy of prediction of economically important traits currently evaluated, determine merit and potential for developing genetic predictions for new traits, and investigate methods to incorporate high-density genomic data. Subobjective 3.A: Develop methodology for calculation of genome-enhanced breeding values using SNP genotypes. Subobjective 3.B: Develop methodology for accurate genetic predictions for new traits such as fertility and health. Objective 4. Investigate economic value of traits and correlations among them to most efficiently combine evaluations to select for healthy dairy animals capable of producing quality milk at a low cost in many environments. Approach (from AD-416): Objective 1: Extensive data on selected single nucleotide polymorphisms (SNPs) will be stored in a database, and intensive checks for accuracy will be conducted. Subobjective 1.A: A subset of SNPs will be selected for use in parentage verification and determination. Pedigree verification will be performed by comparing SNP genotypes of animals with those of recorded parents. Subobjective 1.B: Data for health and management traits will be obtained from dairy records processing centers. A system for submission and editing of data for new traits will be developed to allow routine data processing. Objective 2: Reports will be developed to describe industry progress and various statistics from the national genetic evaluation system for dairy animals, including statistics for Dairy Herd Information programs; breed reports for cow longevity and culling rate; summaries for reproductive traits; crossbreeding summaries and comparison tables for breed performance; heifer and cow inventories by breed composition; evaluation averages, distributions, and changes; genetic trends; progeny-test profiles; and selection intensity changes. Need for separate rankings for a grazing environment will be investigated. Objective 3: Test-day model methodology will be investigated. After patent issues with Cornell University are resolved, a test-day system will be implemented that provides parity-specific evaluations that account for maturity rate as well as evaluations for lactation persistency. Subobjective 3.A: Prediction of genetic merit using SNP information will be combined with results from the national dairy cattle genetic evaluation system to create an integrated prediction of genetic merit. Subobjective 3.B: Quality of available health data will be determined. Variance components will be estimated for individual and composite traits using threshold sire models. Methodology for genetic evaluation of health traits will be developed. Relationships among health and other traits of economic value will be examined. Environmental and genetic factors that affect gestation length of U.S. dairy cattle will be documented. Methods to improve accuracy of male-fertility evaluations from field data will be examined; effect of genetic and phenotypic factors on bull fertility will be studied. Methods and data for genetic evaluation of components of female fertility will be investigated. Objective 4: Selection goals that improve dairy farm profit most rapidly will be determined by economic analysis. Costs associated with additional health and management variables will be examined to determine if national genetic evaluations for those traits are needed. Optimal indexes for specific target populations will be determined. Interactions of genotype with environment will be investigated. Tools for making breed comparisons will be developed. Project 1265-31000-096-00D began on July 24, 2007, and was terminated on July 23, 2012. Under objectives 1 and 3, Holstein genomic predictions for yield (milk, fat, and protein), somatic cell score (mastitis resistance), productive life (longevity), daughter pregnancy rate (cow fertility), calving ease, final score (conformation), and net merit (a genetic- economic index) were developed based on genetic effects estimated for nearly 40,000 single-nucleotide polymorphisms (FY2008); those predictions were transitioned from a research project to a production system, and the United States became the first country to replace official traditional genetic evaluations with genomic evaluations in January 2009. Genomic evaluation methods were improved by including polygenic effects and information from foreign cows, enhancing approximation of evaluation accuracy, adjusting cow evaluations to be comparable with bull evaluations, and using haplotypes to imput missing genotypes and breed- specific markers to verify breed (FY2010). Technical procedures were developed to enable combining genomic information from low- and high- density marker sets; genomic evaluations for animals with low-density genotypes became official in December 2010. A new version of the computer program for haplotyping and imputation was developed and used to increase the proportion of correctly imputed genotypes (FY2011); additional adjustments for genetic evaluations of cows were applied to improve accuracy of genomic predictions. Genomic and pedigree relationships and predictions of breed composition were compared for 3 breeds (FY2011). Two genomic methods of maternal grandsire discovery and confirmation were developed (FY2012). Five new lethal recessive defects causing embryo loss were discovered (FY2011) from the absence of homozygous haplotypes in genomic data; accuracy of detection of deleterious recessives was determined using genetic markers (FY2012). New low-density genotyping arrays were developed to optimize imputation (FY2012); the contribution from high-density genotyping arrays was documented. Simultaneous calculation of traditional and genomic evaluations were developed to avoid genomic preselection bias (FY2012). Under objective 3, interim evaluations to evaluate daughter performance of progeny-test bulls between official evaluations were developed and implemented, and a new procedure to rank bull fertility phenotypically (sire conception rate) was developed to improve accuracy of estimated relative conception rate (FY2008); genetic evaluations for conception rate were developed and implemented for cows and heifers (FY2010). Under objective 4, genetic-economic indexes for lifetime merit of dairy cattle were revised to include updated economic values for all traits (FY2010). Under objective 2, consequences for U.S. Dairy Herd Improvement herds of changing national standards for somatic cell count were determined (FY2010). Under objective 3, genetic evaluations for mobility were developed for Brown Swiss dairy cattle (FY2012). This is the final report for this project. Accomplishments 01 New bovine low-density genotyping arrays optimized for imputation. A major challenge in implementing genomic selection in most species is the cost of genotyping, and an appealing approach is to use an economical, reduced-density chip with markers optimized for predicting unknown genotypes for animals from observed genotypes (imputation). The Illumin Infinium BovineLD Genotyping BeadChip with 6,909 single-nucleotide polymorphisms (SNPs) was developed to replace the Bovine3K chip and provide higher density genotypes in dairy and beef breeds to improve imputation for SNPs, provide specific gene tests, and allow imputation f microsatellite markers; accuracy of imputation to BovineSNP50 genotypes using the BovineLD chip was over 99% when both parents were genotyped in the North American BovineSNP50 reference population. The BovineLD chip was released to industry in the fall of 2011 at the same cost as the Bovine3K chip, and genotypic information from the BovineLD chip has been included in U.S. genomic evaluations since November 2011; using the BovineLD chip�s add-on capability, the GeneSeek Genomic Profiler (GGP) BeadChip with 8,655 SNPs that include specific gene tests was developed and released to the dairy industry in March 2012. As of June 2012, the USDA national genotype database for dairy cattle included LD and GGP genotypes for 63,061 animals (3,698 males), and their accuracy was about percentage points higher than for Bovine3K genotypes. The BovineLD BeadChip facilitates low-cost genomic selection in beef and dairy cattle and its design criteria are useful for other species for which an �imputation chip� could dramatically lower the cost of implementing genomic selection. 02 Detection of deleterious recessives using genetic markers and causative mutations. Five haplotypes that are never found in a homozygous state (HH1, HH2, and HH3 in Holsteins, JH1 in Jerseys, and BH1 in Brown Swiss) had been confirmed to have deleterious effects on dairy cow conception rate, but exact locations of causative mutations were not known. Region suspected to contain the mutations were narrowed to 3.2 megabase pairs (Mbp) for HH1 and 0.8 Mbp for JH1, and sequence data revealed the causative mutation in the APAF1 gene on Bos taurus autosome (BTA) 5 for HH1 and in the CWC15 gene on BTA 15 for JH1; those 2 genes are conserved across many species, but a single nucleotide mutation in each causes los of homozygous embryos and fetuses. Accuracy of detecting the deleteriou fertility haplotypes using the Illumina BovineLD Genotyping BeadChip wit 6,909 markers was tested by reducing genotypes from the BovineSNP50 chip to BovineLD genotypes for 1,000 animals (500 heterozygous and 500 normal and then imputing them back to BovineSNP50 genotypes; concordance was 10 between BovineSNP50 and imputed BovineLD status but can be less if imputation is not done first or pedigrees are incomplete. To improve accuracy for such animals and allow laboratories to determine status without imputation, nearby markers were selected that had lowest frequen within breed for alleles present in each fertility haplotype; the best 4 markers per haplotype provided high concordance without requiring pedigrees or imputation and were included in the Genomic Profiler BeadCh which was developed collaboratively with GeneSeek and released in March 2012. For animals genotyped with other chips, inheritance of several other recessive defects can also be determined at 95 to 100% accuracy an used in selection and mating programs; if sequence data reveal causative mutations for remaining fertility haplotypes, those can be added to futu chips so that nearby markers are no longer needed. 03 Documentation of the contribution from high-density genotyping arrays to genetic evaluations. Although many different genotyping arrays (chips) with varying marker densities are available for dairy cattle, the effect on U.S. genetic evaluations from using information from genotypes based high-density (HD) chips was not known. Four evaluation studies were conducted using actual Holstein genotypes from 778,000 (HD), 56,000 (50K and low density (3,000, 6,000, and 8,000) single-nucleotide polymorphism (SNP) genotyping chips; although HD genotypes provided more markers, missing alleles had to be imputed for animals genotyped at lower than th highest density. The largest marker effects were located at very simila positions, but new markers from the HD chip often had larger effects tha the best markers from the 50K chip. The use of HD genotypes resulted in gain of only 0.4 percentage points in the average accuracy of genomic predictions, and increasing the number of HD genotypes beyond 1,074 did not improve accuracy; however, the use of HD genotypes for the calculati of genomic predictions is still warranted to support improved accuracy, across-breed genomic evaluation, and genomic evaluation of crossbreds. addition, HD data can be used to determine which SNPs are most informati for lower-cost genotyping chips that capture most of the information provided by HD chips and enable increased accuracy by better tracking of causative mutations; an 80,000-SNP chip that includes the most informati SNPs as well as additional SNPs for proprietary single-gene tests, detection of haplotypes that affect fertility, imputation of microsatellite alleles to facilitate parentage validation, and improved imputation of lower density chips was recently developed collaboratively with GeneSeek and is scheduled for dairy industry release in September 2012. 04 Discovery and confirmation of maternal grandsire lineage in dairy cattle through genomic information. Accurate pedigree information is essential for selecting dairy animals to improve economically important traits, bu identification information for older ancestors is not always available o accurate. Two methods of maternal grandsire (MGS) discovery that ranked the most likely MGS were developed using single- nucleotide polymorphism (SNP) and haplotypes; the SNP method can be performed as soon as a genotype is received because no imputation is required, but the haplotyp method had greater accuracy (95 to 97% compared with 91 to 95% for the S method). When the haplotype method was extended to great-grandsires, accuracy of maternal great-grandsire confirmation and discovery was 92% for Holsteins, 95% for Jerseys, and 85% for Brown Swiss. Because most dairy bulls have been genotyped, parentage and MGS analysis can accurate confirm, correct, or discover parents and more distant ancestors for mos animals, thereby increasing the accuracy of genetic evaluations. 05 Genetic evaluation of mobility for Brown Swiss dairy cattle. Mobility i a major concern of dairy producers because lameness is the third most likely cause for culling when production is not considered. Genetic parameters were estimated for mobility score and 16 current linear type traits for Brown Swiss dairy cattle, where mobility was defined as a composite trait that measures a cow's ability to move as well as the structure of her feet, pasterns, and legs. Mobility was determined to b 21% heritable; the traits with the highest genetic correlation with mobility were final score, rear legs (rear view), rear udder width, and foot angle. Genetic evaluations for mobility were calculated using the current Brown Swiss multitrait type evaluation system; mobility evaluations were most highly correlated with evaluations for final score rear legs (rear view), rear udder height, and rear udder width, and when matched with official evaluations from August 2011, mobility evaluations had moderately high correlations with evaluations for milk, fat, and protein yields and for productive life. National genetic evaluations f Brown Swiss mobility are expected to be officially released to the dairy industry in August 2012; selection on those evaluations is expected to improve genetic progress for mobility as well as increase the accuracy a timeliness of predictions of productive life (longevity) by increasing evaluation accuracy for foot-and-leg composite.
Impacts
(N/A)
Publications
- Boichard, D., Chung, H., Dassonneville, R., David, X., Eggen, A., Fritz, S. , Gietzen, K.J., Hayes, B.J., Lawley, C.T., Sonstegard, T.S., Van Tassell, C.P., Van Raden, P.M., Viaud, K., Wiggans, G.R. 2012. Design of a bovine low-density SNP array optimized for imputation. PLoS One. 7(3):e34130.
- Wiggans, G.R., Van Raden, P.M., Cooper, T.A. 2011. The genomic evaluation system in the United States: Past, present, future. Journal of Dairy Science. 94(6):3202-3211.
- Dawtwyler, H.D., Wiggans, G.R., Hayes, B.J., Woolliams, J.A., Goddard, M.E. 2011. Imputation of missing genotypes from sparse to high density using long-range phasing. Genetics. 189:317-327.
- Cole, J.B., Wiggans, G.R., Ma, L., Sonstegard, T.S., Lawlor, T.J., Crooker, B.A., Van Tassell, C.P., Yang, J., Wang, S., Matukumalli, L.K., Da, Y. 2011. Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary U.S. Holstein cows. Biomed Central (BMC) Genomics. Online, 12:408.
- Dikmen, S., Cole, J.B., Null, D.J., Hansen, P.J. 2012. Heritability of rectal temperature and genetic correlations with production and reproduction traits in dairy cattle. Journal of Dairy Science. 95(6):3401- 3405.
- Wiggans, G.R., Van Raden, P.M., Cooper, T.A. 2012. Technical note: Adjustment of all cow evaluations for yield traits to be comparable with bull evaluations. Journal of Dairy Science. 95(6):3444-3447.
- Cole, J.B., Newman, S., Foertter, F., Aguilar, I., Coffey, M. 2012. Really big data: Processing and analysis of large datasets. Journal of Animal Science. 90(3):723-733.
- Norman, H.D., Miller, R.H., Wright, J.R., Hutchison, J.L., Olson, K.M. 2012. Factors associated with frequency of abortions recorded through Dairy Herd Improvement test plans. Journal of Dairy Science. 95(7):4074- 4084.
- Cole, J.B., Ehrlich, J.L., Null, D.J. 2012. Short communication: Projecting milk yield using best prediction and the MilkBot lactation model. Journal of Dairy Science. 95(7):4041-4044.
- Wiggans, G.R., Cooper, T.A., Van Raden, P.M., Cole, J.B. 2011. Technical note: Adjustment of traditional cow evaluations to improve accuracy of genomic predictions. Journal of Dairy Science. 94(12):6188-6193.
- Norman, H.D., Hutchison, J.L., Van Raden, P.M. 2011. Evaluations for service-sire conception rate for heifer and cow inseminations with conventional and sexed semen. Journal of Dairy Science. 94(12):6135-6142.
- Van Raden, P.M., Olson, K.M., Wiggans, G.R., Cole, J.B., Tooker, M.E. 2011. Genomic inbreeding and relationships among Holsteins, Jerseys, and Brown Swiss. Journal of Dairy Science. 94(11):5673-5682.
- Norman, H.D., Lombard, J.E., Wright, J.R., Kopral, C.K., Rodriquez, J.M., Miller, R.H. 2011. Consequence of alternative standards for bulk tank somatic cell count of dairy herds in the United States. Journal of Dairy Science. 94(12):6243-6256.
- Van Raden, P.M., Olson, K.M., Null, D.J., Hutchison, J.L. 2011. Harmful recessive effects on fertility detected by absence of homozygous haplotypes. Journal of Dairy Science. 94(12):6153-6161.
- Garcia-Peniche, T.B., Montaldo, H.H., Valencia-Posadas, M., Wiggans, G.R., Hubbard, S.M., Torres-Vazquez, J.A., Shepard, L. 2012. Breed differences over time and heritability estimates for production and reproduction traits of dairy goats in the United States. Journal of Dairy Science. 95(5) :2707-2717.
- Wiggans, G.R., Cooper, T.A., Van Raden, P.M., Olson, K.M., Tooker, M.E. 2012. Use of the Illumina Bovine3K BeadChip in dairy genomic evaluation. Journal of Dairy Science. 95(3):1552-1558.
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Progress 10/01/10 to 09/30/11
Outputs
Progress Report 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: Collect genotypes, specifically single-nucleotide polymorphisms (SNPs), and new phenotypes to improve accuracy and comprehensiveness of the national dairy database. Subobjective 1.A: Increase the accuracy of pedigree information by using SNP genotypes to verify and to assign parentage. Subobjective 1.B: Obtain additional data on health and management traits, and improve consistency of national database. Objective 2: Characterize phenotypic measures of dairy practices, and provide the dairy industry with information needed to determine the impact of various herd management decisions on profitability. Objective 3: Improve accuracy of prediction of economically important traits currently evaluated, determine merit and potential for developing genetic predictions for new traits, and investigate methods to incorporate high-density genomic data. Subobjective 3.A: Develop methodology for calculation of genome-enhanced breeding values using SNP genotypes. Subobjective 3.B: Develop methodology for accurate genetic predictions for new traits such as fertility and health. Objective 4. Investigate economic value of traits and correlations among them to most efficiently combine evaluations to select for healthy dairy animals capable of producing quality milk at a low cost in many environments. Approach (from AD-416) Objective 1: Extensive data on selected single nucleotide polymorphisms (SNPs) will be stored in a database, and intensive checks for accuracy will be conducted. Subobjective 1.A: A subset of SNPs will be selected for use in parentage verification and determination. Pedigree verification will be performed by comparing SNP genotypes of animals with those of recorded parents. Subobjective 1.B: Data for health and management traits will be obtained from dairy records processing centers. A system for submission and editing of data for new traits will be developed to allow routine data processing. Objective 2: Reports will be developed to describe industry progress and various statistics from the national genetic evaluation system for dairy animals, including statistics for Dairy Herd Information programs; breed reports for cow longevity and culling rate; summaries for reproductive traits; crossbreeding summaries and comparison tables for breed performance; heifer and cow inventories by breed composition; evaluation averages, distributions, and changes; genetic trends; progeny-test profiles; and selection intensity changes. Need for separate rankings for a grazing environment will be investigated. Objective 3: Test-day model methodology will be investigated. After patent issues with Cornell University are resolved, a test-day system will be implemented that provides parity-specific evaluations that account for maturity rate as well as evaluations for lactation persistency. Subobjective 3.A: Prediction of genetic merit using SNP information will be combined with results from the national dairy cattle genetic evaluation system to create an integrated prediction of genetic merit. Subobjective 3.B: Quality of available health data will be determined. Variance components will be estimated for individual and composite traits using threshold sire models. Methodology for genetic evaluation of health traits will be developed. Relationships among health and other traits of economic value will be examined. Environmental and genetic factors that affect gestation length of U.S. dairy cattle will be documented. Methods to improve accuracy of male-fertility evaluations from field data will be examined; effect of genetic and phenotypic factors on bull fertility will be studied. Methods and data for genetic evaluation of components of female fertility will be investigated. Objective 4: Selection goals that improve dairy farm profit most rapidly will be determined by economic analysis. Costs associated with additional health and management variables will be examined to determine if national genetic evaluations for those traits are needed. Optimal indexes for specific target populations will be determined. Interactions of genotype with environment will be investigated. Tools for making breed comparisons will be developed. Five new lethal recessive defects causing embryo loss were discovered from the absence of homozygous haplotypes in genomic data. Genotypes from a 2,900 (3K) marker panel were included as a data source for genomic evaluations, and evaluations for animals with 3K genotypes became official in December 2010. Multiple marker sets were included in the same evaluation by imputing all genotypes to the highest density. Accuracy of imputation was improved by correcting the locations of several markers on the bovine map using information provided by collaborators at the Universities of Maryland, Missouri, and Guelph. A new version of the computer program for haplotyping and imputation was developed and used to increase the proportion of correctly imputed genotypes. Holstein and Jersey genomic reliabilities were discounted further below theoretical reliabilities to match observed reliabilities from the most recent genomic validation; Brown Swiss reliabilities were not discounted further because published and observed reliabilities from the validation study were similar. Genomic relationships, pedigree relationships, and predictions of breed composition were compared for 3 breeds. Approximate multitrait methods were used to add missing genetics evaluations for heifer and cow conception rates rather than use parent averages, which are less accurate. The adjustment of yield trait evaluations of cows to match properties of bull evaluations was applied for all cows rather than just genotyped animals. Other research included: 1) genomic imputation and evaluation using high-density Holstein genotypes; 2) investigation of the impact of including foreign data in genomic evaluations of dairy cattle; 3) documentation of changes in the use of young bulls; 4) determination of consequences for U.S. Dairy Herd Improvement herds of changing national standards for somatic cell count; 5) investigation of cow culling to help meet compliance for somatic cell standards; 6) determination of association of high and low parent average with daughter performance for yield, somatic cell score, and productive life in individual herds; 7) estimation of heritability of rectal temperature and genetic correlations with production and reproduction traits in dairy cattle; 8) investigation of effects of dam's dry period length on heifer development; 9) determination of prevalence, transmission and impact of bovine leukosis in Michigan dairies; 10) verification of factors to estimate daily milk yield from one milking of cows milked twice daily; 11) determination of opportunities for improving milk production efficiency in dairy cattle; and 12) visualization of data structure. Accomplishments 01 Five new lethal recessive defects that reduce dairy cow fertility. Leth recessive defects that cause embryo loss are difficult to detect without genomic data even with very large sets of phenotypic and pedigree data because of too few observations per estimated mating interaction. Based on genomic testing, a method was developed to discover lethal defects by detecting the absence of haplotypes (a set of single nucleotide polymorphisms associated on a single chromosome) that had high populatio frequency but were never homozygous. Haplotype testing revealed that effect on sire conception rate for those 5 new (3 in Holsteins, 1 in Jerseys, and 1 in Brown Swiss) as well as 2 previously known defects wer negative and consistent with a lethal recessive. Once animals have been genotyped, dairy farmers could avoid mating carrier animals without further testing expense using the new haplotype test, thereby saving tim increasing profitability, and reducing those defects in the population. 02 Official national genomic evaluations for dairy cattle with genotypes based on a low-density marker panel. Because of recent availability of low-density marker panel at a low cost, the number of animals with genom information has increased greatly, which provided an opportunity to improve accuracy of genetic evaluations. Methods developed last year to combine genomic information from low-density genotypes with previous higher density information were implemented for national genetic evaluations of yield and fitness traits of Holsteins, Jerseys, and Brown Swiss and made official in December 2010. The availability of genomic evaluations for animals with low-density genotypes has increased the accuracy of their estimated genetic merit compared with their traditiona evaluations. For young animals with low-density genotypes, gain in accuracy over parent average was about 80% of the gain realized with higher density genotypes. Low-density genotypes also provide a low cost alternative to traditional parentage verification. 03 Impact of changing national standards for somatic cell count in milk. Consideration of changes for U.S. standards for bulk-tank somatic cell count are underway because of a European Union announcement that its standards will be enforced for any herds supplying imports. For herds participating in Dairy Herd Improvement testing or shipping milk to four Federal Milk Orders, noncompliance was determined to be 0.9 and 1.0%, respectively, based on U.S. standards of 750,000 cells/mL and 7.8 and 16 1% for European Union standards at 400,000 cells/mL. With no change in herd management, proposed changes in U.S. standards would increase noncompliance in Dairy Herd Improvement and Milk Order herds up to 14.1 and 23.3%, respectively. Because the alternative standards being considered are substantially more stringent than the current U.S. standa U.S. producers will need to place more emphasis on preventing and combating mastitis and doing more directed culling to improve milk quali 04 Comparison of genomic inbreeding and relationships of dairy cattle with pedigree measures. Pedigree relationships were the foundation of animal breeding and genetic selection, but genomic relationships are replacing pedigree relationships in many national evaluation systems. Methods to combine genomic and pedigree relationships among Holsteins, Jerseys, and Brown Swiss were compared by estimating adjustments for averages and regressions of genomic on pedigree relationships. Adjustments for base population allele frequencies and adjustments to make pedigree relationships match genomic relationships more closely in multibreed populations were also determined. Results showed that genomic inbreedin accurately detected pedigree inbreeding and that breed identity can be determined more accurately using all markers than marker subsets. The results provide a basis for future multibreed genomic evaluations. 05 Adjustment of genetic evaluations of cows to improve accuracy of genomic predictions. Upward bias in traditional evaluations of cows with high genetic merit was adversely affecting accuracy of genomic predictions wh those cows were added to the reference population for estimating marker effects. Initially, only evaluations of genotyped cows were adjusted to have the same average and variance as bulls. However, evaluations of genotyped cows then were not comparable to those of nongenotyped cows. Later the method was revised and extended to all cows so that genotyped and nongenotyped cows could be compared more fairly. The efficiency of selection programs will improve because cows will be ranked more accurately, which will benefit breeding organizations and dairy producer
Impacts
(N/A)
Publications
- Wiggans, G.R., Gengler, N. 2011. Selection: Evaluation and methods. In: Fuquay, J.W, Fox, P.F., and McSweeney, P.L.H., editors. Encyclopedia of Dairy Sciences. 2nd edition. San Diego, CA: Academic Press. p. 649-655.
- Norman, H.D., Hubbard, S.M., Van Raden, P.M. 2010. Dairy Cattle: Breeding and genetics. Encyclopedia of Animal Science, 2nd edition. Pond, W.G., and Bell, A.W. (editors). Taylor and Francis, New York, NY. pp. 262-265.
- Norman, H.D., Wright, J.R., Miller, R.H. 2011. Potential consequences of selection on gestation length on Holstein performance. Journal of Dairy Science. 94(2):1005-1010.
- Van Raden, P.M., O'Connell, J.R., Wiggans, G.R., Weigel, K.A. 2011. Genomic evaluations with many more genotypes. Genetic Selection Evolution. 43:10.
- Cole, J.B., Null, D.J., De Vries, A. 2011. Short communication: Best prediction of 305-day lactation yields with regional and seasonal effects. Journal of Dairy Science. 94(3):1601-1604.
- Olson, K.M., Van Raden, P.M., Tooker, M.E., Cooper, T.A. 2011. Differences among methods to validate genomic evaluations for dairy cattle. Journal of Dairy Science. 94(5):2613-2620.
- Aguilar, I., Misztal, I., Tsuruta, S., Wiggans, G.R., Lawlor, T.J. 2011. Multiple trait genomic evaluation of conception rate in Holsteins. Journal of Dairy Science. 94(5):2621-2624.
- Cole, J.B., Van Raden, P.M. 2011. Use of Haplotypes to Estimate Mendelian Sampling Effects and Selection Limits. Journal of Animal Breeding and Genetics. 128(6):446-455.
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Progress 10/01/09 to 09/30/10
Outputs
Progress Report 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: Collect genotypes, specifically single-nucleotide polymorphisms (SNPs), and new phenotypes to improve accuracy and comprehensiveness of the national dairy database. Subobjective 1.A: Increase the accuracy of pedigree information by using SNP genotypes to verify and to assign parentage. Subobjective 1.B: Obtain additional data on health and management traits, and improve consistency of national database. Objective 2: Characterize phenotypic measures of dairy practices, and provide the dairy industry with information needed to determine the impact of various herd management decisions on profitability. Objective 3: Improve accuracy of prediction of economically important traits currently evaluated, determine merit and potential for developing genetic predictions for new traits, and investigate methods to incorporate high-density genomic data. Subobjective 3.A: Develop methodology for calculation of genome-enhanced breeding values using SNP genotypes. Subobjective 3.B: Develop methodology for accurate genetic predictions for new traits such as fertility and health. Objective 4. Investigate economic value of traits and correlations among them to most efficiently combine evaluations to select for healthy dairy animals capable of producing quality milk at a low cost in many environments. Approach (from AD-416) Objective 1: Extensive data on selected single nucleotide polymorphisms (SNPs) will be stored in a database, and intensive checks for accuracy will be conducted. Subobjective 1.A: A subset of SNPs will be selected for use in parentage verification and determination. Pedigree verification will be performed by comparing SNP genotypes of animals with those of recorded parents. Subobjective 1.B: Data for health and management traits will be obtained from dairy records processing centers. A system for submission and editing of data for new traits will be developed to allow routine data processing. Objective 2: Reports will be developed to describe industry progress and various statistics from the national genetic evaluation system for dairy animals, including statistics for Dairy Herd Information programs; breed reports for cow longevity and culling rate; summaries for reproductive traits; crossbreeding summaries and comparison tables for breed performance; heifer and cow inventories by breed composition; evaluation averages, distributions, and changes; genetic trends; progeny-test profiles; and selection intensity changes. Need for separate rankings for a grazing environment will be investigated. Objective 3: Test-day model methodology will be investigated. After patent issues with Cornell University are resolved, a test-day system will be implemented that provides parity-specific evaluations that account for maturity rate as well as evaluations for lactation persistency. Subobjective 3.A: Prediction of genetic merit using SNP information will be combined with results from the national dairy cattle genetic evaluation system to create an integrated prediction of genetic merit. Subobjective 3.B: Quality of available health data will be determined. Variance components will be estimated for individual and composite traits using threshold sire models. Methodology for genetic evaluation of health traits will be developed. Relationships among health and other traits of economic value will be examined. Environmental and genetic factors that affect gestation length of U.S. dairy cattle will be documented. Methods to improve accuracy of male-fertility evaluations from field data will be examined; effect of genetic and phenotypic factors on bull fertility will be studied. Methods and data for genetic evaluation of components of female fertility will be investigated. Objective 4: Selection goals that improve dairy farm profit most rapidly will be determined by economic analysis. Costs associated with additional health and management variables will be examined to determine if national genetic evaluations for those traits are needed. Optimal indexes for specific target populations will be determined. Interactions of genotype with environment will be investigated. Tools for making breed comparisons will be developed. Genetic evaluations for conception rate were developed and implemented for cows and heifers. Genetic bases for all traits of dairy cattle that are evaluated by USDA for genetic merit were updated by 5 years. Economic values of all traits were updated for the net merit, cheese merit, and fluid merit genetic-economic indexes. Genomic evaluation methods were improved by including polygenic effects and information from foreign cows, enhancing the procedure to approximate evaluation accuracy, adjusting cow evaluations to be comparable with bull evaluations, and using haplotypes to impute missing genotypes and breed-specific markers to verity parentage. U.S. genomic evaluations were validated for genomic multitrait across-country evaluation. A web query to track genotypes was implemented. Methods to include genotypes from low- and high-density marker sets in genomic predictions were developed. Other research included: 1) derivation of factors to estimate daily trait values from one milking of cows milked three times daily, 2) documentation of recent trends in mastitis and fertility indicators, 3) comparison of service- sire fertility with conventional and sexed semen, 4) estimation of genetic merit for age at first calving, 5) determination of effectiveness of genetic predictions of gestation length and relationship to lactation yield for the subsequent lactation, 6) investigation of relationship of reason for lactation termination with genetic merit, 7) determination of effectiveness of genetic evaluations in predicting daughter performance in individual herds, 8) refined detection of quantitative trait loci for evaluation traits through genomewide association analysis, 9) use of haplotypes to predict selection limits and Mendelian sampling, and 10) development of multibreed genomic evaluations. Accomplishments 01 Technical procedures were developed to enable combining genomic information from low- and high-density marker sets. Recent advances in technology for accurate detection of genetic markers have made genotypin of dairy cows affordable for commercial dairy producers, but those genotypes are based on various marker densities and contain different amounts of genomic information. ARS researchers at Beltsville, MD developed and implemented computing software to determine (impute) missi genomic information based on haplotypes (closely linked genetic markers that tend to be inherited together) and to handle genotypes from detecti chips of various marker densities; the first application (April 2010) wa to impute genotypes of dams from their genotyped progeny. The new computer programs for haplotyping and imputation allow multiple marker sets to be included in the same genetic evaluation. For young Holsteins genotyped with approximately 3,000 markers, the gain in accuracy of estimated net genetic-economic merit was almost 80% of the gain from genotyping 43,000 markers; simulated imputation of genotypes for 500,000 markers from 50,000 markers increased accuracy by 1.4%. Including a combination of marker densities for genotypes in genetic evaluations can improve evaluation accuracy at lower costs for dairy producers. 02 Genetic-economic indexes for lifetime merit of dairy cattle were revised Since 2006, feed costs have dramatically risen, which affected the emphasis that should be placed on a number of traits in national indexes (net merit, cheese merit, and fluid merit). ARS researchers at Beltsvil MD, updated key economic values as well as milk utilization statistics, and recent changes in premiums paid for somatic cell score were consider Compared with indexes developed in 2006, less weight now is placed on fat and protein yields and calving ability (an index that includes calvi ease and stillbirth), and more emphasis is placed on longevity, mastitis resistance, udder and leg traits, body size (favoring smaller cows), and cow fertility. The revised indexes should improve accuracy of selection of animals to be parents of the next generation of U.S. dairy cattle; th increase in genetic progress from use of the revised indexes is estimate to be worth $6 million annually on a national basis. 03 Evaluations for conception rate are available to the dairy industry for both bulls and cows. Declining fertility in the U.S. dairy herd has bee a concern of the dairy industry since the 1970s, and the increased use o estrous synchronization as part of reproductive management programs has intensified the importance of conception rate as a fertility trait. ARS researchers at Beltsville, MD, developed and implemented national geneti evaluations for heifer and cow conception rate for bulls (January 2009) and cows (August 2010), and the two new traits are included in genetic estimates for longevity. In addition, phenotypic evaluations for conception rate of service sires, which were temporarily suspended in April 2010 because of reduced data availability, resumed in August 2010 after a new agreement on data transfer was reached with industry partner Genetic evaluations for fertility traits enhance animal well-being and welfare as well as provide an improved understanding of relationships between yield and functional traits. The availability of conception rat evaluations allows international comparisons that can enhance exports of semen, embryos, and animals and positively impact the U.S. trade balance
Impacts
(N/A)
Publications
- Weigel, K.A., Van Tassell, C.P., O'Connell, J.R., Van Raden, P.M., Wiggans, G.R. 2010. Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms. Journal of Dairy Science. 93(5):2229-2238.
- Norman, H.D., Wright, J.R., Miller, R.H. 2010. Response to alternative genetic-economic indices for Holsteins across 2 generations. Journal of Dairy Science. 93(6):2695-2702.
- Cole, J.B., Van Raden, P.M. 2010. Visualization of Results from Genomic Evaluations. Journal of Dairy Science. 93(6):2727-2740.
- Norman, H.D., Hutchison, J.L., Miller, R.H. 2010. Use of sexed semen and its effect on conception rate, calf sex, dystocia, and stillbirth of Holsteins in the United States. Journal of Dairy Science. 93(8):3880-3890.
- Montaldo, H.H., Valencia-Posadas, M., Wiggans, G.R., Shepard, L., Torres- Vazquez, J.A. 2010. Short Communication: Genetic and environmental relationships between milk yield and kidding interval in dairy goats. Journal of Dairy Science. 93(1):370-372.
- Attalla, S.A., Seykora, A.J., Cole, J.B., Heins, B.J. 2010. Genetic Parameters of Milk ELISA scores for Johne's Disease. Journal of Dairy Science. 93(4):1729-1735.
- Wiggans, G.R., Van Raden, P.M., Bacheller, L.R., Tooker, M.E., Hutchison, J.L., Cooper, T.A., Sonstegard, T.S. 2010. Selection and management of DNA markers for use in genomic evaluation. Journal of Dairy Science. 93(5) :2287-2292.
- Van Raden, P.M., Sullivan, P. 2010. International genomic evaluation methods for dairy cattle. Genetics Selection Evolution. 42:7.
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Progress 10/01/08 to 09/30/09
Outputs
Progress Report 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: Collect genotypes, specifically single-nucleotide polymorphisms (SNPs), and new phenotypes to improve accuracy and comprehensiveness of the national dairy database. Subobjective 1.A: Increase the accuracy of pedigree information by using SNP genotypes to verify and to assign parentage. Subobjective 1.B: Obtain additional data on health and management traits, and improve consistency of national database. Objective 2: Characterize phenotypic measures of dairy practices, and provide the dairy industry with information needed to determine the impact of various herd management decisions on profitability. Objective 3: Improve accuracy of prediction of economically important traits currently evaluated, determine merit and potential for developing genetic predictions for new traits, and investigate methods to incorporate high-density genomic data. Subobjective 3.A: Develop methodology for calculation of genome-enhanced breeding values using SNP genotypes. Subobjective 3.B: Develop methodology for accurate genetic predictions for new traits such as fertility and health. Objective 4. Investigate economic value of traits and correlations among them to most efficiently combine evaluations to select for healthy dairy animals capable of producing quality milk at a low cost in many environments. Approach (from AD-416) Objective 1: Extensive data on selected single nucleotide polymorphisms (SNPs) will be stored in a database, and intensive checks for accuracy will be conducted. Subobjective 1.A: A subset of SNPs will be selected for use in parentage verification and determination. Pedigree verification will be performed by comparing SNP genotypes of animals with those of recorded parents. Subobjective 1.B: Data for health and management traits will be obtained from dairy records processing centers. A system for submission and editing of data for new traits will be developed to allow routine data processing. Objective 2: Reports will be developed to describe industry progress and various statistics from the national genetic evaluation system for dairy animals, including statistics for Dairy Herd Information programs; breed reports for cow longevity and culling rate; summaries for reproductive traits; crossbreeding summaries and comparison tables for breed performance; heifer and cow inventories by breed composition; evaluation averages, distributions, and changes; genetic trends; progeny-test profiles; and selection intensity changes. Need for separate rankings for a grazing environment will be investigated. Objective 3: Test-day model methodology will be investigated. After patent issues with Cornell University are resolved, a test-day system will be implemented that provides parity-specific evaluations that account for maturity rate as well as evaluations for lactation persistency. Subobjective 3.A: Prediction of genetic merit using SNP information will be combined with results from the national dairy cattle genetic evaluation system to create an integrated prediction of genetic merit. Subobjective 3.B: Quality of available health data will be determined. Variance components will be estimated for individual and composite traits using threshold sire models. Methodology for genetic evaluation of health traits will be developed. Relationships among health and other traits of economic value will be examined. Environmental and genetic factors that affect gestation length of U.S. dairy cattle will be documented. Methods to improve accuracy of male-fertility evaluations from field data will be examined; effect of genetic and phenotypic factors on bull fertility will be studied. Methods and data for genetic evaluation of components of female fertility will be investigated. Objective 4: Selection goals that improve dairy farm profit most rapidly will be determined by economic analysis. Costs associated with additional health and management variables will be examined to determine if national genetic evaluations for those traits are needed. Optimal indexes for specific target populations will be determined. Interactions of genotype with environment will be investigated. Tools for making breed comparisons will be developed. Significant Activities that Support Special Target Populations Previously developed genomic predictions were transitioned from a research project to a production system, and the United States became the first country to replace official traditional genetic evaluations with genomic evaluations based on direct examination of DNA in January 2009. Numerous changes were made to the USDA genetic evaluation program to enable efficient management of genomic information, incorporate it in official USDA evaluations, and distribute those evaluations to stakeholders. Artificial-insemination and breed organizations now can use an online query to designate animals to be genotyped, determine if the animal has already been nominated, and check for the reason if a genotype was rejected; four commercial laboratories provide genotypes that are stored in the USDA national dairy database, and the most recent international evaluations are combined with genomic and traditional data into a single evaluation that includes all available information. The evaluation system is continuing to be streamlined to provide genomic evaluations that meet industry needs with available resources. The programs and edited genotypes were also used to compute Canadian national evaluations in August 2009; USDA and Canadian researchers cooperated in developing international evaluation methods to combine genomic information from all countries. Other research included: 1) visualization of results from genomic predictions; 2) derivation of factors to estimate daily fat, protein, and somatic cell score from one milking of cows milked twice daily; 3) development of best prediction procedures for lactation yields that account for regional and seasonal differences; 4) characterization and usage of sexed semen from U.S. field data and determination of the effect of sexed semen on U.S. Holstein conception rates; 5) comparison of former and current service-sire fertility evaluations; 6) characterization of milk ELISA scores for Johne's disease in U.S. dairy cows, investigation of factors that affect those scores; and estimation of genetic parameters and transmitting abilities for ELISA scores; 7) documentation of trends in the international flow of Holstein genes; and 8) characterization of somatic cell counts in dairy goat milk. Technology Transfer Number of New CRADAS: 1 Number of Web Sites managed: 1
Impacts
(N/A)
Publications
- Wiggans, G.R., Sonstegard, T.S., Van Raden, P.M., Matukumalli, L.K., Schnabel, R.D., Taylor, J.F., Schenkel, F.S., Van Tassell, C.P. 2009. Selection of single-nucleotide polymorphisms and quality of genotypes used in genomic evaluation of dairy cattle in the United States and Canada. Journal of Dairy Science. 92(7):3431-3436.
- Norman, H.D., Wright, J.R., Hubbard, S.M., Miller, R.H., Hutchison, J.L. 2009. Reproductive status of Holstein and Jersey cows in the United States. Journal of Dairy Science. 92(7):3517-3528.
- Cole, J.B., Null, D.J. 2009. Genetic Evaluation of Lactation Persistency for Five Breeds of Dairy Cattle. Journal of Dairy Science. 92(5):2248-2258.
- Wiggans, G.R., Tsuruta, S., Misztal, I. 2008. Technical Note: Adaptation of an Animal-Model Method for Approximation of Reliabilities to a Sire- Maternal Grandsire Model. Journal of Dairy Science. 91(10):4058-4061.
- Norman, H.D., Wright, J.R., Weigel, K.A. 2009. Alternatives for Examining Daughter Performance of Progeny-Test Bulls between Official Evaluations. Journal of Dairy Science. 92(5):2348-2355.
- Norman, H.D., Wright, J.R., Kuhn, M.T., Hubbard, S.M., Cole, J.B., Van Raden, P.M. 2009. Genetic and Environmental Factors That Impact Gestation Length in Dairy Cattle. Journal of Dairy Science. 92(5):2259-2269.
- Van Raden, P.M. 2008. Efficient Methods to Compute Genomic Predictions. Journal of Dairy Science. 91(11):4414-4423.
- Cole, J.B., Null, D.J., Van Raden, P.M. 2009. Best Prediction of Yields for Long Lactations. Journal of Dairy Science. 92(4):1796-1810.
- Appuhamy, A.D., Cassell, B.G., Cole, J.B. 2009. Phenotypic and Genetic Relationships of Common Health Disorders with Milk and Fat Yield Persistencies from Producer-Recorded Health Data and Test Day Yields. Journal of Dairy Science. 92(4):1785-1795.
- Van Raden, P.M., Van Tassell, C.P., Wiggans, G.R., Sonstegard, T.S., Schnabel, R.D., Taylor, J.F., Schenkel, F.S. 2009. Invited Review: Reliability of Genomic Predictions for North American Holstein Bulls. Journal of Dairy Science. 92(1):16-24.
- De Vries, A., Cole, J.B. 2009. Profitable Dairy Cow Traits for Hot Climatic Conditions. In: Klopcic, M., Reents, R., Philipsson, J., and Kuipers, A., editors. Breeding for Robustness in Cattle. Wageningen, The Netherlands: Wageningen Academic Publishers. p. 227-248.
- Miller, R.H., Kuhn, M.T., Norman, H.D., Wright, J.R. 2008. Death Losses for Lactating Cows in Herds Enrolled in Dairy Herd Improvement Test Plans. Journal of Dairy Science. 91(9):3710-3715.
- Miller, R.H., Norman, H.D., Wright, J.R., Cole, J.B. 2008. Impact of Genetic Merit for Milk Somatic Cell Score of Sires and Maternal Grandsires on Herd Life of Their Daughters. Journal of Dairy Science. 92(5):2224-2228.
- Cole, J.B., Van Raden, P.M., O'Connell, J.R., Van Tassell, C.P., Sonstegard, T.S., Schnabel, R.D., Taylor, J.F., Wiggans, G.R. 2009. Distribution and Location of Genetic Effects for Dairy Traits. Journal of Dairy Science. 92(6):2931-2946.
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Progress 10/01/07 to 09/30/08
Outputs
Progress Report 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: Collect genotypes, specifically single-nucleotide polymorphisms (SNPs), and new phenotypes to improve accuracy and comprehensiveness of the national dairy database. Subobjective 1.A: Increase the accuracy of pedigree information by using SNP genotypes to verify and to assign parentage. Subobjective 1.B: Obtain additional data on health and management traits, and improve consistency of national database. Objective 2: Characterize phenotypic measures of dairy practices, and provide the dairy industry with information needed to determine the impact of various herd management decisions on profitability. Objective 3: Improve accuracy of prediction of economically important traits currently evaluated, determine merit and potential for developing genetic predictions for new traits, and investigate methods to incorporate high-density genomic data. Subobjective 3.A: Develop methodology for calculation of genome-enhanced breeding values using SNP genotypes. Subobjective 3.B: Develop methodology for accurate genetic predictions for new traits such as fertility and health. Objective 4. Investigate economic value of traits and correlations among them to most efficiently combine evaluations to select for healthy dairy animals capable of producing quality milk at a low cost in many environments. Approach (from AD-416) Objective 1: Extensive data on selected single nucleotide polymorphisms (SNPs) will be stored in a database, and intensive checks for accuracy will be conducted. Subobjective 1.A: A subset of SNPs will be selected for use in parentage verification and determination. Pedigree verification will be performed by comparing SNP genotypes of animals with those of recorded parents. Subobjective 1.B: Data for health and management traits will be obtained from dairy records processing centers. A system for submission and editing of data for new traits will be developed to allow routine data processing. Objective 2: Reports will be developed to describe industry progress and various statistics from the national genetic evaluation system for dairy animals, including statistics for Dairy Herd Information programs; breed reports for cow longevity and culling rate; summaries for reproductive traits; crossbreeding summaries and comparison tables for breed performance; heifer and cow inventories by breed composition; evaluation averages, distributions, and changes; genetic trends; progeny-test profiles; and selection intensity changes. Need for separate rankings for a grazing environment will be investigated. Objective 3: Test-day model methodology will be investigated. After patent issues with Cornell University are resolved, a test-day system will be implemented that provides parity-specific evaluations that account for maturity rate as well as evaluations for lactation persistency. Subobjective 3.A: Prediction of genetic merit using SNP information will be combined with results from the national dairy cattle genetic evaluation system to create an integrated prediction of genetic merit. Subobjective 3.B: Quality of available health data will be determined. Variance components will be estimated for individual and composite traits using threshold sire models. Methodology for genetic evaluation of health traits will be developed. Relationships among health and other traits of economic value will be examined. Environmental and genetic factors that affect gestation length of U.S. dairy cattle will be documented. Methods to improve accuracy of male-fertility evaluations from field data will be examined; effect of genetic and phenotypic factors on bull fertility will be studied. Methods and data for genetic evaluation of components of female fertility will be investigated. Objective 4: Selection goals that improve dairy farm profit most rapidly will be determined by economic analysis. Costs associated with additional health and management variables will be examined to determine if national genetic evaluations for those traits are needed. Optimal indexes for specific target populations will be determined. Interactions of genotype with environment will be investigated. Tools for making breed comparisons will be developed. Significant Activities that Support Special Target Populations Genotypes derived from DNA of 5,285 Holstein bulls and 75 Holstein cows were used in estimating genetic effects for nearly 40,000 single- nucleotide polymorphisms. Based on those effects, genomic predictions for yield (milk, fat, and protein), somatic cell score (indicator for mastitis resistance), productive life (longevity), daughter pregnancy rate (cow fertility), calving ease, final score (conformation), and net merit (a genetic-economic index) were developed. Two different evaluations were developed to predict the genetic merit of an animal's daughters and sons separately. Genomic predictions for genotyped bulls and cows (mostly calves) were distributed in April and July 2008 to owners and to organizations that paid for genotyping to aid in selection decisions. A new procedure to rank bull fertility phenotypically (sire conception rate) was developed to improve accuracy of the current procedure (estimated relative conception rate) and to broaden the data upon which bull fertility was evaluated. Factors related to the cow that is being inseminated that distort bull fertility measures were identified and removed to improve prediction of bull fertility. Other research included 1) development and implementation of interim evaluations to evaluate daughter performance of progeny-test bulls between official evaluations; 2) derivation of factors to estimate daily yield from single milkings for Holsteins milked two or three times daily; 3) enhancement of a data-exchange format and national database for producer-recorded health event data from on-farm management software; 4) determination of factors that affect abortion frequency in U.S. dairy herds; 5) investigation of the impact of selection for decreased somatic cell score on productive life and culling for mastitis; 6) characterization of reproductive trends of U.S. dairy herds; 7) investigation of the impact of selection for increased daughter fertility on productive life and culling for reproduction; and 8) documentation of the breed composition of the U.S. dairy cattle herd. For National Program 101 (Food Animal Production), progress on genomic predictions and genetic evaluations (including data collection and adjustment) relates to Component I (Understanding, Improving, and Effectively Using Animal Genetic and Genomic Resources) by enabling identification of functional genes and their interactions and development and implementation of genome-enabled genetic improvement programs; progress on measuring and ranking bull fertility and on characterizing reproductive traits relates to Component II (Enhancing Animal Adaptation, Well-Being, and Efficiency in Diverse Production Systems) through development of tools to help reduce reproductive losses. Technology Transfer Number of New/Active MTAs(providing only): 4 Number of Web Sites managed: 1
Impacts
(N/A)
Publications
- Bohmanova, J., Misztal, I., Tsuruta, S., Norman, H.D., Lawlor, T.J. 2008. Short Communication: Genotype by Environment Interaction Due to Heat Stress. Journal of Dairy Science. 91(2):840-846.
- Dechow, C.D., Norman, H.D., Zwald, N.R., Cowan, C.M., Meland, O.M. 2008. Relationship between individual herd-heritability estimates and sire misidentification rate. Journal of Dairy Science. 91(4):1640-1647.
- Dechow, C.D., Norman, H.D., Pelensky, C.A. 2008. Short Communication: Variance estimates among herds stratified by individual herd heritability. Journal of Dairy Science. 91(4):1648-1651.
- Kuhn, M.T., Hutchison, J.L. 2008. Prediction of dairy bull fertility from field data: Use of multiple services and identification and utilization of factors affecting bull fertility. Journal of Dairy Science. 91(6):2481- 2492.
- Appuhamy, J., Cassell, B.G., Dechow, C.D., Cole, J.B. 2007. Phenotypic Relationships of Common Health Disorders in Dairy Cows to Lactation Persistency Estimated from Daily Milk Weights. Journal of Dairy Science. 90(9):4424-4434.
- Kuhn, M.T., Hutchison, J.L., Norman, H.D. 2008. Modeling Nuisance Variables for Prediction of Service Sire Fertility. Journal of Dairy Science. 91(7):2823-2835.
- Powell, R.L., Sanders, A.H., Norman, H.D. 2008. Investigation of country bias in international genetic evaluations using full-brother information. Journal of Dairy Science. 91(7):2885-2892.
- Wiggans, G.R., Cole, J.B., Thornton, L.L. 2008. Multiparity Evaluation of Calving Ease and Stillbirth with Separate Genetic Effects by Parity. Journal of Dairy Science. 91(8):3173-3178.
- Mark, T., Fikse, W.F., Sullivan, P.G., Van Raden, P.M. 2007. Prediction of Correlations and International Breeding Values for Missing Traits. Journal of Dairy Science. 90(10):4805-4813.
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