Progress 10/01/21 to 09/30/22
Outputs
PROGRESS REPORT Objectives (from AD-416): Objective 1. Expand genomic data used in prediction by selecting new variants that more precisely track the true gene mutations that cause phenotypic differences. Objective 2. Evaluate new traits that can all be predicted at birth from the same inexpensive DNA sample. Objective 3. Improve efficiency of genomic prediction and computation by developing faster algorithms, testing new adjustments and models, and accounting for genomic pre-selection in evaluation. Approach (from AD-416): Obj. 1: Variant selection strategies will be tested with 1000 Bull Genomes data. Two-stage imputation will be used; imputation accuracy will be compared by simulation. Local sequence data will be generated for families with new fertility defects or other health conditions and bulls homozygous for less frequent haplotypes. Animals will be selected for sequencing with an algorithm maximizing coverage of rare haplotypes and minimizing resequencing of common haplotypes. Previous data will be realigned to a new reference map. Candidate variants will be reselected using improved annotation, better bioinformatics, and information from discoveries across species. Lists of candidate variants with the largest effects will be supplied for array design. Best strategies to include gene-edited animals in breeding programs, their potential value, and confirmation of phenotypic effects of gene edits will be determined. Simulation will reveal optimum strategies for combining favorable haplotypes. Obj. 2: Genetic evaluations will be developed for traits already measured but with low heritability or moderate economic value. Economic values and reliability for new traits will be estimated; options for choosing the most profitable animals to phenotype and genotype will be explored. Data editing and analysis methods will be developed for new data. Computer simulation will be used to determine the best combination of direct and indirect phenotypes for genetic improvement. Relative economic values will be calculated for selection indexes; index sensitivity will be determined based on forecast economic value. Selection index methodology will be used to study effect on annual rates of genetic gain from adding recessives to the index. Incidence, correlations, and effects of more traits will be documented. Constant monitoring of input data will ensure continued high-quality evaluations. Obj. 3: Algorithms will be developed to improve aligning sequence segments to a reference genome while simultaneously calling variants. Genomic models will be designed to include more informative priors. Tests will compare predictive ability for future data within or across breed. Multibreed marker effects will be estimated as correlated traits. Potential biases from genomic pre-selection will be monitored using differences across time in percentages of genotyped mates or daughters. Use of single-step models to correct bias will be explored using recent algorithms to approximate the inverse of genomic relationships and model marker effects directly. Genomic evaluations of crossbred animals will be developed by weighting marker effects from each breed by genomic breed composition. Prediction of nonadditive effects and recombination loss will be continued. Genomic future inbreeding will be improved by computing average genomic relationship to a more recent group of potential mates instead of to breed reference population. Test-day models will be considered when appropriate. Adjustments will be tested using truncated data to predict more recent data. Multitrait processing will be used to obtain greater benefits from new traits without losing information from previous correlated traits. Progress was made on all three objectives of project 8042-31000-002-000D (Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals). Under Objective 1 (expand genomic data used in prediction by selecting new variants), the genetic basis of a new defect Jersey neuropathy with splayed forelimbs (JNS) was published and methods developed by the Animal Genomics and Improvement Lab (AGIL) were used by CDCB to provide JNS carrier status for 0.6 million genotyped Jerseys, trends in carrier frequencies were documented over the 10 year period 2011 to 2021 as evidence of genetic selection against 15 recessive defects, and annual quality control methods were updated for 79,000 markers from 49 genotyping arrays used in the national evaluation. Under Objective 2 (evaluate new traits that can be predicted at birth), genomic evaluations for 3 fertility traits were revised to account for unreported embryo transfer, feed intake records for 2,227 lactations from 5 additional countries were incorporated into a multi-trait national genomic evaluation of feed efficiency provided for 5 million genotyped Holsteins, reliabilities of genomic predictions for 10 traits were compared by including or excluding foreign data from Interbull, phenotypic and genotypic relationships among dry matter intake, milk components, and indicators of bodyweight were estimated for use in deriving selection indexes, and national selection indexes and bull rankings from 15 countries were compared to indicate expected vs. actual benefits from using foreign sires. Under Objective 3 (improve efficiency of genomic prediction and computation), single-step, multi-breed evaluations including 580,000 crossbreds were developed and tested in cooperation with the University of Georgia, benefits from single-step vs. multi-step genomic evaluations were documented for national yield trait records, methods to account for unknown parents in single-step evaluations were reviewed, accuracy of ancestor discovery was tested and improved and now automatically provides more complete and accurate pedigrees for hundreds of thousands of genotyped dairy cattle from 70 countries. Under Objectives 1 and 2, faster methods were developed to estimate allele effects for millions of genetic variants for millions of animals. Under Objectives 2 and 3, genetic evaluation software and edits were revised to include nearly a million more lactation records for many traits from herds that record milk weights but without taking component samples. Under Objectives 1, 2, and 3, the definitions, computation, and genetic biology underlying genomic evaluations for mastitis resistance were described. ACCOMPLISHMENTS 01 National fertility evaluations revised to account for embryo transfer. In just five years, the number of calves produced via embryo transfer (ET) increased five-fold in the United States. By 2021 these represented more than 1 million total births. Regrettably, the national pedigree database lacked information necessary to track the success of this technique. Therefore, ARS scientists modified software programs to exclude to avoid potential biases. The edits removed about 1% of fertility records in the most recent 4 years. This change primarily affected the rankings of younger bulls, popular for ET use, with large effects on genomic selection. These edits were implemented in April of 2022 and are already being used by dairy producers seeking to improve the fertility of their herds by selecting bulls with higher conception rate evaluations. 02 Improved discovery of cattle pedigrees. Cattle breeders often lack accurate pedigrees which would hasten improvement by avoiding inbreeding. Therefore, ARS Rresearchers in Beltsville, Maryland, sought to improve methods to discover missing ancestors. They used genotypes for 5.2 million dairy animals to improve the methods. They then determined how well the methods correctly identified grandsires and great grandsires in a sample of 78,000 animals with verified pedigrees. The improved methods correctly identified the true grandsires 92% of the time and correctly suggested another 5%. The methods erred <2% of the time. The researchers then further improved ancestor discovery, adding >100,000 more grandsires and reducing the error rate by adjusting the birth year and haplotype sharing limits in the software. Using these tools, the team has already added > 400,000 discovered grandsires to dams and maternal granddams with previously unknown sires. The team will add >1 million more to the national pedigree in 2022 by linking the discovered grandsires to their genotyped descendants using constructed IDs for the unknown dams and granddams. This research, completed in close cooperation with the Council on Dairy Cattle Breeding, has provided improved pedigrees to thousands of dairy producers in the U.S. and in >50 other countries, enabling them to make management decisions that minimize inbreeding and maximize selection for beneficial traits. 03 Use of foreign data and sires to increase national genetic progress. Genetic evaluations and genomic predictions are often computed separately within nations, but progress can be improved by including foreign data and sires. Methods were developed and implemented to include feed intake data from 5 additional countries into U.S. evaluations. Reliabilities of genomic predictions for 10 other traits were compared by including or excluding foreign data from Interbull. The largest benefits from foreign data were for less heritable traits such as productive life and somatic cell score. For milk production traits with higher heritability, benefits were also large (5 to 11%) for foreign Jerseys, small (1 to 2%) for foreign Holsteins, and near 0 for U.S. bulls. Correlations of genetic evaluations on 15 national scales revealed that selection index definitions generally caused more reranking than genotype by environment interactions across national scales. Foreign bulls were >80% of the top bulls in nearly all countries but often sired <50% of domestic cows. In most countries, foreign sires and particularly US sires are the better choice to maximize genetic progress. Countries that use foreign bulls only as sires of their elite bulls but not for the general cow population always remain at least 1 generation behind. Actual use of foreign sires from each country in each of >30 other countries is now updated every 4 months in a web report developed in this research.
Impacts
(N/A)
Publications
- Wu, X., Parker Gaddis, K.L., Burchard, J., Norman, H.D., Nicolazzi, E., Cole, J.B., Connor, E.E., Durr, J. 2021. An alternative interpretation of residual feed intake by phenotypic recursive relationships in dairy cattle. Journal of Dairy Science Communications. 2(6):371-375. https://doi.org/10. 3168/jdsc.2021-0080.
- Masuda, Y., Van Raden, P.M., Tsuruta, S., Lourenco, D.A.L., Misztal, I. 2022. Invited review: Unknown-parent groups and metafounders in single- step genomic BLUP. Journal of Dairy Science. 105(2):923⿿939. https://doi. org/10.3168/jds.2021-20293.
- Al-Khudhair, A.S., Null, D.J., Cole, J.B., Wolfe, C.W., Steffen, D.J., Van Raden, P.M. 2022. Inheritance of a mutation causing neuropathy with splayed forelimbs in Jersey cattle. Journal of Dairy Science. 105(2) :1338⿿1345. https://doi.org/10.3168/jds.2021-20600.
- Mahnani, A., Sadeghi-Sefidmazgi, A., Ansari-Mahyari, S., Ghiasi, H., Toghiani, S. 2022. Genetic analysis of retained placenta and its association with reproductive disorder, production, and fertility traits of Iranian Holstein dairy cows. Theriogenology. 189:59-63. https://doi.org/ 10.1016/j.theriogenology.2022.04.008.
- Weinroth, M.D., Belk, A.D., Dean, C.J., Noyes, N.R., Dittoe, D.K., Rothrock Jr, M.J., Ricke, S.C., Myer, P.R., Henniger, M.T., Ramirez, G.A., Oakley, B.B., Summers, K.L., Miles, A.M., Ault-Seay, T.B., Yu, Z., Metcalf, J., Wells, J. 2022. Considerations and best practices in animal science 16S ribosomal RNA gene sequencing microbiome studies. Journal of Animal Science. 100:1018. https://doi.org/10.1093/jas/skab346.
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Progress 10/01/20 to 09/30/21
Outputs
PROGRESS REPORT Objectives (from AD-416): Objective 1. Expand genomic data used in prediction by selecting new variants that more precisely track the true gene mutations that cause phenotypic differences. Objective 2. Evaluate new traits that can all be predicted at birth from the same inexpensive DNA sample. Objective 3. Improve efficiency of genomic prediction and computation by developing faster algorithms, testing new adjustments and models, and accounting for genomic pre-selection in evaluation. Approach (from AD-416): Obj. 1: Variant selection strategies will be tested with 1000 Bull Genomes data. Two-stage imputation will be used; imputation accuracy will be compared by simulation. Local sequence data will be generated for families with new fertility defects or other health conditions and bulls homozygous for less frequent haplotypes. Animals will be selected for sequencing with an algorithm maximizing coverage of rare haplotypes and minimizing resequencing of common haplotypes. Previous data will be realigned to a new reference map. Candidate variants will be reselected using improved annotation, better bioinformatics, and information from discoveries across species. Lists of candidate variants with the largest effects will be supplied for array design. Best strategies to include gene-edited animals in breeding programs, their potential value, and confirmation of phenotypic effects of gene edits will be determined. Simulation will reveal optimum strategies for combining favorable haplotypes. Obj. 2: Genetic evaluations will be developed for traits already measured but with low heritability or moderate economic value. Economic values and reliability for new traits will be estimated; options for choosing the most profitable animals to phenotype and genotype will be explored. Data editing and analysis methods will be developed for new data. Computer simulation will be used to determine the best combination of direct and indirect phenotypes for genetic improvement. Relative economic values will be calculated for selection indexes; index sensitivity will be determined based on forecast economic value. Selection index methodology will be used to study effect on annual rates of genetic gain from adding recessives to the index. Incidence, correlations, and effects of more traits will be documented. Constant monitoring of input data will ensure continued high-quality evaluations. Obj. 3: Algorithms will be developed to improve aligning sequence segments to a reference genome while simultaneously calling variants. Genomic models will be designed to include more informative priors. Tests will compare predictive ability for future data within or across breed. Multibreed marker effects will be estimated as correlated traits. Potential biases from genomic pre-selection will be monitored using differences across time in percentages of genotyped mates or daughters. Use of single-step models to correct bias will be explored using recent algorithms to approximate the inverse of genomic relationships and model marker effects directly. Genomic evaluations of crossbred animals will be developed by weighting marker effects from each breed by genomic breed composition. Prediction of nonadditive effects and recombination loss will be continued. Genomic future inbreeding will be improved by computing average genomic relationship to a more recent group of potential mates instead of to breed reference population. Test-day models will be considered when appropriate. Adjustments will be tested using truncated data to predict more recent data. Multitrait processing will be used to obtain greater benefits from new traits without losing information from previous correlated traits. Much progress was made on all three objectives of project 8042-31000-002- 00D (Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals). Under Objective 1 (expand genomic data used in prediction by selecting new variants), major quantitative trait loci influencing milk production and conformation traits were detected for Guernsey dairy cattle, quality control methods were updated for markers and genotyping arrays used in the national evaluation, and the potential for using gene editing to produce both dairy and beef polled cattle was investigated and compared with traditional breeding. Under Objective 2 (evaluate new traits that can be predicted at birth), genomic evaluations were developed for five traits [rear udder width, body depth, mobility, rear teat placement (side view), and milking speed] of several breeds that had only traditional pedigree evaluations, and the genetic basis of Jersey neuropathy with splayed forelimbs were identified. Under Objective 3 (improve efficiency of genomic prediction and computation), the reliability and bias of genomic predictions that include unknown-parent groups was assessed for yield traits of U.S. Holsteins, unknown-parent groups and metafounders in single-step genomic best linear unbiased prediction (GBLUP) were reviewed, multibreed genomic evaluations were investigated for U.S. dairy cattle using single-step GBLUP, genomic evaluation of U.S. crossbred dairy cattle was enhanced, improved validation of genomic evaluations through the use of extra regressions was investigated, adjustments were developed and implemented to account for late-term abortions in genetic and genomic evaluation of fertility traits and inheritance of abortions was investigated, improvement of the model for genetic evaluation of calving traits was investigated for U.S. Holsteins and Brown Swiss, computation of genomic and pedigree inbreeding and relationships was made more efficient and accurate using parallel processing and adjusting for sex differences due to the X chromosome, faster genotype imputation procedures were implemented, weights for combining genomic and pedigree information were updated, lifetime genetic-economic merit indexes were updated, correlations between national genetic-economic indexes were estimated, and expected use of foreign sires was compared with actual use. Under Objectives 1 and 2, the genetic basis of a mutation causing neuropathy with splayed forelimbs was identified. Under Objectives 1 and 3, imputed high-density genotypes were used for marker selection and genomic prediction of economically important traits of five breeds of dairy cattle, a method was developed to partition heritability of genetic markers, and a scalable mixed-model approach for genomewide association studies with millions of genotyped animals was developed that allows finding omnigenic core genes that matter in functional studies and targeted genome editing. Under Objectives 2 and 3, phenotypic and genotypic effects of milk components and body weight composite on dry matter intake were investigated, genomic evaluations for feed efficiency (feed saved) were developed for Holsteins, a Bayesian multitrait random- aggression approach was adapted to model feed efficiency in dairy cattle, genetic mechanisms of heterosis for daughter pregnancy rate were identified using a genome-wide association study, genomic heritability and prediction accuracy of additive and nonadditive effects for daughter pregnancy rate were estimated for crossbred dairy cows, and the use of international clinical mastitis as an independent trait in the U.S. genetic evaluation system was investigated. Record of Any Impact of Maximized Teleworking Requirement: The maximized telework posture due to the covid-19 pandemic had minimal impact on this research project. The research performed for the project is computational, and all staff can function well by telework. Increased phone calls and email plus weekly video meetings for both the Laboratory and for this specific project kept the research on track. Negative impacts were that (1) some staff without ARS-issued laptops had less access to files stored on local machines or to software that must be accessed through the ARS network, which required a physical presence in the office, and (2) university collaborators had some difficulty connecting to Laboratory computer servers. Positive impacts were that postdoctoral research associates were able to reside with family instead of needing to maintain a local residence. Several permanent staff members could greatly reduce their commutes, a savings in staff time, and a decrease in environmental impact. ACCOMPLISHMENTS 01 Development of genomic evaluations for feed efficiency of dairy cattle. Dairy cattle feed efficiency is of great interest to dairy farmers because feed accounts for the largest part of operating costs in dairy production. However, the data for analyzing feed efficiency have been very limited because of the high cost and difficulty in collecting individual feed intake records. Because genomic selection is well suited for difficult-to-measure traits, ARS researchers at Beltsville, Maryland, used data from 6,221 cow feeding trials (most funded by a previous $10 million USDA grant) to develop genomic evaluations for feed efficiency as a tool for dairy cattle breeding programs. Using ARS methodology, the Council of Dairy Cattle Breeding in Bowie, Maryland, released genomic evaluations expressed as expected pounds of feed saved per lactation in December 2020 for Holstein dairy cattle; those evaluations will be included in a lifetime genetic-economic index in August 2021. As more data become available, feed-efficiency evaluations could be provided for additional breeds. Selecting for more feed- efficient cows can reduce farm costs, improve profitability, and lessen the environmental footprint of dairy production by lowering methane emissions and limiting the natural resources and energy needed to produce and process feed. 02 Development of genomic evaluations for dairy heifer livability. Mortality of young cows that have not calved (heifers) is a major issue related to profitability, management, and animal welfare on dairy farms, and raising replacement heifers ranks as the second-largest cost on dairy farms after feed and forage costs. Although the U.S. genetic evaluation system for dairy cattle included stillbirth and cow survival (livability) as traits, little information was available on heifer livability. Using 4.2 million records, ARS researchers in Beltsville, Maryland, developed genomic evaluations for heifer livability, released by the Council on Dairy Cattle Breeding in Bowie, Maryland, for the first time in December 2020 for Holstein and Jersey dairy cattle. Genomic evaluations for heifer livability can increase dairy farm profitability, increase genetic gain for the U.S. dairy population, and improve animal health and welfare. Heifer livability has a heritability of less than 1%, and heifer livability will be included in a lifetime genetic-economic index in August 2021, emphasizing just 1% of the total index. However, economic progress still is expected to be about $50,000 per year, and additional records will improve accuracy and give faster future progress. 03 Lifetime merit indexes for dairy cattle updated to include new traits. Genetic-economic indexes for dairy cattle are used to improve the efficiency of the national population by ranking animals based on their combined genetic merit for economically important traits. However, new traits for feed efficiency, young cow livability, and the ability for young cows to calve early in their lives had not been included in national lifetime merit indexes. Therefore, after the Council on Dairy Cattle Breeding in Bowie, Maryland, released evaluations for feed saved (December 2020), heifer livability (December 2020), and early first calving (April 2019) for U.S. dairy cattle, ARS researchers in Beltsville, Maryland, added those traits to lifetime merit indexes and also updated income and cost variables such as milk prices and feed requirements to reflect prices expected in the next few years. The updated indexes were adopted and officially released to the dairy industry by the Council on Dairy Cattle Breeding in August 2021. Selection using the new indexes will produce cows with genes that keep them healthy, productive, fertile, and efficient and, therefore, more profitable and environmentally sustainable. If all U.S. dairy producers base their breeding decisions on the updated net merit index, an increase in genetic progress worth $20 million annually is expected on a national basis. 04 Discovery of an undesirable genetic factor in Jersey cattle. Genomic testing of dairy cattle has allowed accurate and inexpensive tracking of deleterious and beneficial genes for economically important traits. A new condition in newborn calves was reported to the American Jersey Cattle Association in Columbus, Ohio, by Jersey breeders and named Jersey neuropathy with splayed forelimbs (JNS) because affected calves were unable to stand on splayed forelimbs and displayed neurological symptoms. The genetic basis for JNS was identified by ARS researchers in Beltsville, Maryland, using pedigree and genetic analyses, and a common ancestor born in 1995 was identified. Inheritance of the defect began to be tracked and reported in December 2020 by the Council on Dairy Cattle Breeding in Bowie, Maryland, using ARS software, and direct genotype tests are expected soon. The American Jersey Cattle Association updated its comprehensive mating program JerseyMate in January 2021 to use the reported carrier status to account for the risk of a JNS mating. Although 6% of genotyped Jerseys were determined to be carriers, genetic testing and avoiding carrier-to-carrier matings can prevent the birth of about 300 affected calves annually.
Impacts
(N/A)
Publications
- McWhorter, T.M., Hutchison, J.L., Norman, H.D., Cole, J.B., Fok, G.C., Lourenco, D.A.L., Van Raden, P.M. 2020. Investigating conception rate for beef service sires bred to dairy cows and heifers. Journal of Dairy Science. 103(11):10374⿿10382. https://doi.org/10.3168/jds.2020-18399.
- Li, B., Van Raden, P.M., Null, D.J., O'Connell, J.R., Cole, J.B. 2021. Major quantitative trait loci influencing milk production and conformation traits in Guernsey dairy cattle detected on Bos taurus autosome 19. Journal of Dairy Science. 104(1):550⿿560. https://doi.org/10.3168/jds.2020- 18766.
- Mueller, M.L., Cole, J.B., Connors, N.K., Johnston, D.J., Randhawa, I.A.S., Van Eenennaam, A.L. 2021. Comparison of gene editing versus conventional breeding to introgress the POLLED allele into the tropically adapted Australian beef cattle population. Frontiers in Genetics. 12:593154. https://doi.org/10.3389/fgene.2021.593154.
- Cole, J.B., Durr, J.W., Nicolazzi, E.L. 2021. Invited review: The future of selection decisions and breeding programs: What are we breeding for, and who decides? Journal of Dairy Science. 104(5):5111⿿5124. https://doi. org/10.3168/jds.2020-19777.
- Cesarani, A., Masuda, Y., Tsuruta, S., Nicolazzi, E.L., Van Raden, P.M., Lourenco, D., Misztal, I. 2021. Genomic predictions for yield traits in US Holsteins with unknown parent groups. Journal of Dairy Science. 104(5) :5843⿿5853. https://doi.org/10.3168/jds.2020-19789.
- Miles, A.M., Posbergh, C.J., Huson, H.J. 2021. Direct phenotyping and principal component analysis of type traits implicate novel QTL in bovine mastitis through genome-wide association. Animals. 11(4):1147. https://doi. org/10.3390/ani11041147.
- Freebern, E., Santos, D.J.A., Fang, L., Jiang, J., Parker-Gaddis, K.L., Liu, G.E., Van Raden, P.M., Maltecca, C., Cole, J.B., Ma, L. 2020. GWAS and fine-mapping of livability and six disease traits in Holstein cattle. BMC Genomics. 21:41. https://doi.org/10.1186/s12864-020-6461-z.
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Progress 10/01/19 to 09/30/20
Outputs
Progress Report Objectives (from AD-416): Objective 1. Expand genomic data used in prediction by selecting new variants that more precisely track the true gene mutations that cause phenotypic differences. Objective 2. Evaluate new traits that can all be predicted at birth from the same inexpensive DNA sample. Objective 3. Improve efficiency of genomic prediction and computation by developing faster algorithms, testing new adjustments and models, and accounting for genomic pre-selection in evaluation. Approach (from AD-416): Obj. 1: Variant selection strategies will be tested with 1000 Bull Genomes data. Two-stage imputation will be used; imputation accuracy will be compared by simulation. Local sequence data will be generated for families with new fertility defects or other health conditions and bulls homozygous for less frequent haplotypes. Animals will be selected for sequencing with an algorithm maximizing coverage of rare haplotypes and minimizing resequencing of common haplotypes. Previous data will be realigned to a new reference map. Candidate variants will be reselected using improved annotation, better bioinformatics, and information from discoveries across species. Lists of candidate variants with the largest effects will be supplied for array design. Best strategies to include gene-edited animals in breeding programs, their potential value, and confirmation of phenotypic effects of gene edits will be determined. Simulation will reveal optimum strategies for combining favorable haplotypes. Obj. 2: Genetic evaluations will be developed for traits already measured but with low heritability or moderate economic value. Economic values and reliability for new traits will be estimated; options for choosing the most profitable animals to phenotype and genotype will be explored. Data editing and analysis methods will be developed for new data. Computer simulation will be used to determine the best combination of direct and indirect phenotypes for genetic improvement. Relative economic values will be calculated for selection indexes; index sensitivity will be determined based on forecast economic value. Selection index methodology will be used to study effect on annual rates of genetic gain from adding recessives to the index. Incidence, correlations, and effects of more traits will be documented. Constant monitoring of input data will ensure continued high-quality evaluations. Obj. 3: Algorithms will be developed to improve aligning sequence segments to a reference genome while simultaneously calling variants. Genomic models will be designed to include more informative priors. Tests will compare predictive ability for future data within or across breed. Multibreed marker effects will be estimated as correlated traits. Potential biases from genomic pre-selection will be monitored using differences across time in percentages of genotyped mates or daughters. Use of single-step models to correct bias will be explored using recent algorithms to approximate the inverse of genomic relationships and model marker effects directly. Genomic evaluations of crossbred animals will be developed by weighting marker effects from each breed by genomic breed composition. Prediction of nonadditive effects and recombination loss will be continued. Genomic future inbreeding will be improved by computing average genomic relationship to a more recent group of potential mates instead of to breed reference population. Test-day models will be considered when appropriate. Adjustments will be tested using truncated data to predict more recent data. Multitrait processing will be used to obtain greater benefits from new traits without losing information from previous correlated traits. Progress was made on all three objectives of project 8042-31000-002-00D (Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals). Under Objective 1 (expand genomic data used in prediction by selecting new variants), a software package for calculating individual gametic diversity was developed; sequence genotypes from the 1000 Bull Genomes Project, high- density array genotypes for many of the same bulls, and additional sequence data were examined to determine optimal editing strategies; sequence genotypes for 6.74 million variants of 39,000 Holsteins were imputed and investigated for possible use in genomic evaluation; a comprehensive framework for identification and validation of genetic defects, including haplotype-based detection of defects, selection of variants from sequence data, and in vitro validation using CRISPR-Cas9 knockout embryos was developed; and genome-wide and region-specific changes in the U.S. Holstein cattle population from 1950 through 2015 were evaluated to allow identification of candidate quantitative trait loci regions under selection and associated with economically important traits. Under Objective 2 (evaluate new traits that can be predicted at birth), current literature related to deep phenotyping of dairy cattle was reviewed, and opportunities and challenges associated with new technology for measuring animal performance were identified; the use of beef service sires bred to dairy cows and heifers was investigated and a tool for dairy producers to evaluate conception rate of beef service sires was developed; and genomic evaluations for heifer livability were developed; and genetic and environmental changes in dairy traits were revealed from a genetic base update. Under Objective 3 (improve efficiency of genomic prediction and computation), crossbred evaluations were updated to be more consistent with traditional evaluations by using genomic information from the previous monthly evaluation, and the closest purebred reference population is used to compute reliability for each crossbred; new standard deviations were implemented for type traits in non-Holstein breeds; unknown-parent groups were modified for Red-and- White Holsteins to conform with international identification standards; the genetic base to which most dairy traits are expressed was updated; an automated procedure to discover and fill missing maternal identification information was developed to allow discovered male maternal ancestors to be used in imputation as well as in calculating breeding values for animals in the U.S. dairy cattle database; and accuracy of pedigree information in the Mexican registered Holstein population was determined using genomic data available in Mexico and for the U.S. Holstein population. Under Objectives 1 and 3, current dairy selection structure related to response to selection and accumulation of homozygosity were reviewed and approaches were outlined for managing inbreeding, overall variability, and accumulation of harmful recessives while maintaining sustained selection pressure; and a new set of single-nucleotide polymorphisms was used for national genomic evaluations to track inheritance better in additional breeds and traits. Under Objectives 2 and 3, calving ease and stillbirth evaluations were adjusted to account for differences between the evaluation system⿿s genetic bases and population incidence levels; genomic breeding values for residual feed intake and their prediction reliability were estimated for U.S. Holsteins; development and implementation of genetic evaluations for direct health traits of U.S. dairy cattle as well as potential future developments were reviewed; national health trait evaluations were extended to Jerseys, and new edits and a changed model were implemented for disease resistance traits for Holsteins and Jerseys; and revision of the net merit selection index to include residual feed intake, early first calving, and heifer livability was investigated. Under Objectives 1, 2, and 3, genomic selection implemented by dairy cattle breeders was reviewed and compared with implementation alternatives; procedures were suggested for breeders of other populations to use the knowledge gained during the last decade; and programs that receive international evaluations were revised to integrate mastitis resistance into routine processing and advances in dairy cattle breeding to improve resistance to mastitis were documented. Accomplishments 01 Improved accuracy of genomic prediction using more DNA markers with higher impact. ARS researchers in Beltsville, Maryland, and Madison, Wisconsin, in collaboration with a scientist at the University of Maryland School of Medicine in Baltimore, Maryland, and the staff of the Council on Dairy Cattle Breeding in Bowie, Maryland, used a new map to improve genotype imputation, sequence alignment, and marker locations of dairy cattle. The number of markers used in national genomic evaluations was increased from 60,000 to almost 80,000 and now includes more exact gene tests recently added to genotyping chips. The current marker set for genomic evaluations better tracks inheritance in additional breeds and traits, including new traits such as feed efficiency. Genomic selection for dairy cattle is now more precise because of the increased number of DNA markers used for routine genetic evaluation of economically important traits. 02 Improved accuracy of genomic evaluations by discovering ancestors and connecting relatives. Genetic evaluation relies heavily on complete pedigree information because often only a small proportion of a population has been genotyped. For quality control, the pedigree and genomic relationships should be consistent and methods to confirm, discover, and correct parentage and to connect relatives allow creating more complete and accurate pedigrees, which in turn increase the number of usable phenotypic records and prediction reliability. ARS researchers in Beltsville, Maryland, in collaboration with the Council on Dairy Cattle Breeding in Bowie, Maryland, and the National Institute of Agricultural Technology in Rafaela, Argentina, developed an automated procedure to fill missing maternal identification in pedigrees and to link discovered male ancestors by constructing virtual dam identification numbers. This system resulted in the discovery and use of 300,000 additional maternal grandsires and 150,000 maternal great-grandsires of animals in the U.S. dairy cattle pedigree file that is used to calculate national genetic evaluations for economically important traits. Ancestor discovery was also extended to other categories of relatives (such as clones, full siblings, and parents) in addition to grandsires. The procedures developed for pedigree completion provide a useful tool for improving the accuracy of national and international genomic evaluations and aid producers in making better mating decisions. 03 Documenting genetic and phenotypic progress leads to revised trait scales. The genetic bases to which most dairy traits are expressed in the United States have been updated every 5 years since 1980 so that users of genetic evaluations can become aware that past standards for choosing service bulls or valuing females may no longer meet the genetic quality needed to remain competitive because of genetic progress. ARS researchers in Beltsville, Maryland, collaborated with the staff of the Council on Dairy Cattle Breeding in Bowie, Maryland, to automate computer programs for the 2020 base change for 102 breed- traits for yield and fitness of the major dairy cattle breeds in the United States. Results of the April 2020 base change showed that the genomic revolution initiated by USDA in 2008 for dairy cattle increased the rate of genetic improvement, primarily because of reduced generation interval. In addition, the bases used for calving ease and stillbirth were found to be no longer consistent with current population incidence rates. Consequently, the scales for those calving traits were revised in August 2020 to reflect lower breed averages, and future base changes will include updates for both genetic and phenotypic bases. The updated genetic bases for all traits remind dairy producers to update their selection strategies to account for past genetic progress of economically important traits and thereby improve future progress in selecting for healthy, efficient, and productive animals to meet growing demands for dairy products.
Impacts
(N/A)
Publications
- Schmitt, M.R., Van Raden, P.M., De Vries, A. 2019. Ranking sires using genetic selection indices based on financial investment methods versus lifetime net merit. Journal of Dairy Science. 102(10):9060-9075.
- Fragomeni, B.O., Lourenco, D.A.L., Legarra, A., Van Raden, P.M., Misztal, I. 2019. Alternative SNP weighting for single-step genomic best linear unbiased predictor evaluation of stature in US Holsteins in the presence of selected sequence variants. Journal of Dairy Science. 102(11) :10012⿿10019.
- Koltes, J.E., Cole, J.B., Clemmens, R., Dilger, R.N., Kramer, L.M., Lunney, J.K., Mccue, M.E., Mckay, S., Mateescu, R., Murdoch, B.M., Reuter, R., Rexroad III, C.E., Rosa, G.J.M., Serao, N.V.L., White, S.N., Woodward Greene, M.J., Worku, M., Zhang, H., Reecy, J.M., editors. 2019. A vision for development and utilization of high-throughput phenotyping and big data analytics in livestock. Frontiers in Genetics. 10:1197.
- Van Raden, P.M., Tooker, M.E., Chud, T.C.S., Norman, H.D., Megonigal, Jr, J.H., Haagen, I.W., Wiggans, G.R. 2020. Genomic predictions for crossbred dairy cattle. Journal of Dairy Science. 103(2):1620⿿1631.
- Li, B., Fang, L., Null, D.J., Hutchison, J.L., Connor, E.E., Van Raden, P. M., Cole, J.B. 2019. High-density genome-wide association study for residual feed intake in Holstein dairy cattle. Journal of Dairy Science. 102(12):11067-11080.
- Li, B., Van Raden, P.M., Guduk, E., O'Connell, J.R., Null, D.J., Connor, E. E., VandeHaar, M.J., Tempelman, R.J., Weigel, K.A., Cole, J.B. 2020. Genomic prediction of residual feed intake in US Holstein dairy cattle. Journal of Dairy Science. 103(3):2477-2486.
- Nani, J.P., Bacheller, L.R., Cole, J.B., Van Raden, P.M. 2020. Discovering ancestors and connecting relatives in large genomic databases. Journal of Dairy Science. 103(2):1729-1734.
- Santos, D.J., Cole, J.B., Liu, G., Van Raden, P.M., Ma, L. 2020. Gamevar. f90: A software package for calculating individual gametic diversity. BMC Bioinformatics. 21:100.
- Jiang, J., Ma, L., Prakapenka, D., Van Raden, P.M., Cole, J.B., Da, Y. 2019. A large-scale genome-wide association study in U.S. Holstein cattle. Frontiers in Genetics. 10:412.
- Jiang, J., Cole, J.B., Freebern, E., Da, Y., Van Raden, P.M., Ma, L. 2019. Functional annotation and Bayesian fine-mapping reveals candidate genes for important agronomic traits in Holstein bulls. Communications Biology. 2:212.
- Connor, E.E., Hutchison, J.L., Van Tassell, C.P., Cole, J.B. 2019. Defining the optimal period length and stage of growth or lactation to estimate residual feed intake in dairy cows. Journal of Dairy Science. 102(7):6131⿿6143.
- Cole, J.B., Null, D.J. 2019. Short communication: Phenotypic and genetic effects of the polled haplotype on yield, longevity, and fertility in US Brown Swiss, Holstein, and Jersey cattle. Journal of Dairy Science. 102(9) :8247⿿8250.
- Garcia-Ruiz, A., Wiggans, G.R., Ruiz-Lopez, F.J. 2019. Pedigree verification and parentage assignment using genomic information in the Mexican Holstein population. Journal of Dairy Science. 102(2):1806⿿1810.
- Cole, J.B., Eaglen, S.A.E., Maltecca, C., Mulder, H.A., Pryce, J.E. 2020. The future of phenomics in dairy cattle breeding. Animal Frontiers. 10(2) :37-44.
- Van Raden, P.M. 2020. Symposium review: How to implement genomic selection. Journal of Dairy Science. 103(6):5291-5301.
- Parker Gaddis, K.L., Van Raden, P.M., Cole, J.B., Norman, H.D., Nicolazzi, E., Durr, J.W. 2020. Symposium review: Development, implementation, and future perspectives of health evaluations in the United States. Journal of Dairy Science. 103(6):5354-5365.
- Maltecca, C., Tiezzi, F., Cole, J.B., Baes, C. 2020. Symposium review: Exploiting homozygosity in the era of genomics-- Selection, inbreeding, and genomic mating programs. Journal of Dairy Science. 103(6):5302-5313.
- Cole, J.B. 2019. Advances in dairy cattle breeding to improve resistance to mastitis. In: van der Werf, J., Pryce, J. , editors. Advances in Breeding of Dairy Cattle. Sawston, Cambridge, UK: Burleigh Dodds Science Publishing Ltd. p. 229⿿253.
- Fang, L., Cai, W., Liu, S., Canela-Xandri, O., Gao, Y., Jiang, J., Rawlik, K., Li, B., Schroeder, S.G., Rosen, B.D., Li, C., Sonstegard, T.S., Alexander, L.J., Van Tassell, C.P., Van Raden, P.M., Cole, J.B., Yu, Y., Zhang, S., Tenesa, A., Ma, L., Liu, G. 2019. Comprehensive analyses of 723 transcriptomes enhance biological interpretation and genomic prediction for complex traits in cattle. Genome Research. 30(5):790-801.
- Rosen, B.D., Bickhart, D.M., Schnabel, R.D., Koren, S., Elsik, C.G., Tseng, E., Rowan, T.N., Low, W.Y., Zimin, A., Couldrey, C., Hall, R., Li, W., Rhie, A., Ghurye, J., McKay, S.D., Thibaud-Nissen, F., Hoffman, J., Murdoch, B.M., Snelling, W.M., McDaneld, T.G., Hammond, J.A., Schwartz, J. C., Nandolo, W., Hagen, D.E., Dreischer, C., Schultheiss, S.J., Schroeder, S.G., Phillippy, A.M.,Cole, J.B., Van Tassell, C.P., Liu, G., Smith, T.P.L. , Medrano, J.F. 2020. De novo assembly of the cattle reference genome with single-molecule sequencing. GigaScience. 9(3):1-9.
- Liu, S., Fang, L., Zhou, Y., Santos, D.J.A., Xiang, R., Daetwyler, H.D., Chamberlain, A.J., Cole, J.B., Li, C., Yu, Y., Ma, L., Zhang, S., Liu, G. 2019. Analyses of inter-individual variations in sperm DNA methylation reveal their regulatory role in gene expression and association with reproduction traits in cattle. BMC Genomics. 20:888.
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Progress 10/01/18 to 09/30/19
Outputs
Progress Report Objectives (from AD-416): Objective 1. Expand genomic data used in prediction by selecting new variants that more precisely track the true gene mutations that cause phenotypic differences. Objective 2. Evaluate new traits that can all be predicted at birth from the same inexpensive DNA sample. Objective 3. Improve efficiency of genomic prediction and computation by developing faster algorithms, testing new adjustments and models, and accounting for genomic pre-selection in evaluation. Approach (from AD-416): Obj. 1: Variant selection strategies will be tested with 1000 Bull Genomes data. Two-stage imputation will be used; imputation accuracy will be compared by simulation. Local sequence data will be generated for families with new fertility defects or other health conditions and bulls homozygous for less frequent haplotypes. Animals will be selected for sequencing with an algorithm maximizing coverage of rare haplotypes and minimizing resequencing of common haplotypes. Previous data will be realigned to a new reference map. Candidate variants will be reselected using improved annotation, better bioinformatics, and information from discoveries across species. Lists of candidate variants with the largest effects will be supplied for array design. Best strategies to include gene-edited animals in breeding programs, their potential value, and confirmation of phenotypic effects of gene edits will be determined. Simulation will reveal optimum strategies for combining favorable haplotypes. Obj. 2: Genetic evaluations will be developed for traits already measured but with low heritability or moderate economic value. Economic values and reliability for new traits will be estimated; options for choosing the most profitable animals to phenotype and genotype will be explored. Data editing and analysis methods will be developed for new data. Computer simulation will be used to determine the best combination of direct and indirect phenotypes for genetic improvement. Relative economic values will be calculated for selection indexes; index sensitivity will be determined based on forecast economic value. Selection index methodology will be used to study effect on annual rates of genetic gain from adding recessives to the index. Incidence, correlations, and effects of more traits will be documented. Constant monitoring of input data will ensure continued high-quality evaluations. Obj. 3: Algorithms will be developed to improve aligning sequence segments to a reference genome while simultaneously calling variants. Genomic models will be designed to include more informative priors. Tests will compare predictive ability for future data within or across breed. Multibreed marker effects will be estimated as correlated traits. Potential biases from genomic pre-selection will be monitored using differences across time in percentages of genotyped mates or daughters. Use of single-step models to correct bias will be explored using recent algorithms to approximate the inverse of genomic relationships and model marker effects directly. Genomic evaluations of crossbred animals will be developed by weighting marker effects from each breed by genomic breed composition. Prediction of nonadditive effects and recombination loss will be continued. Genomic future inbreeding will be improved by computing average genomic relationship to a more recent group of potential mates instead of to breed reference population. Test-day models will be considered when appropriate. Adjustments will be tested using truncated data to predict more recent data. Multitrait processing will be used to obtain greater benefits from new traits without losing information from previous correlated traits. Progress was made on all 3 objectives of project 8042-31000-002-00D (Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals). Under Objective 1 (expand genomic data used in prediction by selecting new variants), genome changes due to artificial selection were documented for U.S. Holsteins; haplotype tests for economically important traits of dairy cattle were updated and converted to the new ARS-UCD genome assembly; effects of 2 deleterious recessive haplotypes on reproduction performance of Ayrshires were investigated; Bayesian fine-mapping was used to determine how 3 million DNA variants for more than 27,000 Holstein bulls were associated with 35 production, reproduction, and body conformation traits; potential use of variance of gametic diversity in selection programs for dairy cattle improvement was examined; gene editing was compared with conventional breeding for introgression of the polled allele into U.S. dairy cattle, and a simulation was conducted for Jerseys; an artificial neural network was examined as an alternative genome-wide association method; a large-scale genome-wide association study of 9 health and fitness traits of U.S. Holsteins was conducted; a genome-wide association study based on copy number variations detected by array comparative genomic hybridization was conducted for Holsteins to explore their relationship with reproduction and other economic traits; signals from sperm methylome analysis and genome-wide association were integrated for a better understanding of male fertility in cattle; and comparative analyses of sperm DNA methylomes among humans, mice, and cattle were performed to provide insights into epigenomic evolution and complex traits. Under Objective 2 (evaluate new traits that can be predicted at birth), potential to improve feed efficiency of dairy cattle through genomic prediction was examined; genetic and nongenetic profiling of milk pregnancy-associated glycoproteins was conducted for Holsteins; and optimal period length and stage of growth or lactation were defined for estimating residual feed intake in dairy cows. Under Objective 3 (improve efficiency of genomic prediction and computation), genomic predictions were implemented for crossbred dairy cattle; several approaches to account for missing pedigrees and genetic changes over time were evaluated; alternative covariance structures that include unknown- parent groups and metafounders were examined for single-step genomic best linear unbiased prediction; alternative input parameters for Wood⿿s curve within best prediction used for genetic evaluation of U.S. production traits were examined; age-parity adjustment factors for fertility traits were revised to improve stability of genetic trend estimates; and evaluations for health traits were pre-adjusted for variance across lactations. Under Objectives 1 and 2, contribution of genetic and epigenetic architecture of paternal origin to gestation length was investigated; a genome-wide association study for Holstein residual feed intake using high-density genotype was conducted to identify candidate genes and biological pathways associated with feed efficiency; relationship of the polled haplotype to phenotypic and genetic merit was examined for traits of economic importance in U.S. Brown Swiss, Holsteins, and Jerseys; and genome-wide association studies and fine-mapping of livability and 6 health traits were conducted for Holsteins. Under Objectives 1 and 3, genomic prediction and marker selection were examined using high-density genotypes from 5 dairy breeds; potential benefits from using a new reference map in genomic prediction were examined; more markers and gene tests were used in genomic prediction; alternative marker weighting in single-step genomic evaluation of U.S. Holstein stature was examined in the present of selected sequence variants; comprehensive analyses of 723 transcriptomes were conducted to enhance biological interpretation and genomic prediction for complex traits in cattle; and an approximate generalized least-squares method was developed for large-scale genome-wide association studies. Under Objectives 2 and 3, a genetic evaluation for early first calving (age at first calving) was developed and implemented; financial investment methods were applied to genetic merit predictions of 1,500 Holstein sires to create two new economic selection indexes; and future enhancement of health evaluations for U.S. dairy cattle was examined. Under Objectives 1, 2, and 3, how to implement genomic selection and recent enhancements to the U.S. evaluation system were documented; application of genetic engineering and genome engineering tools to genetic improvement of livestock for both single-gene and complex traits was reviewed; and a vision for development and utilization of high-throughput phenotyping and big data analytics was compiled. Accomplishments 01 National genomic evaluations for crossbred dairy cattle. Genomic evaluations are useful for crossbred as well as purebred dairy cattle when selection is applied to commercial herds. Although producers had spent more than $1 million to genotype more than 50,000 crossbred animals, they had no tools to test and select their whole herds based on genomic evaluation. In collaboration with the Council on Dairy Cattle Breeding (CDCB) and Sao Paulo State University, ARS researchers in Beltsville, Maryland, developed genomic evaluations for crossbred dairy cattle based on animals⿿ breed composition for the five dairy cattle breeds routinely evaluated (Holstein, Jersey, Brown Swiss, Ayrshire, and Guernsey). The new evaluation methodology was adopted by CDCB, and national genomic evaluations for crossbreds were released to the dairy industry for the first time in April 2019. Those evaluations will aid commercial producers in managing their breeding programs and selecting tens of thousands of replacement heifers each year. 02 National genomic evaluations for early first calving. Heifer rearing is a major expense for the U.S. dairy industry and accounts for 15 to 20% of the total cost of producing milk. Much effort has been made to estimate optimal ages of first calving for cows to reduce these costs, which can be as high as $2.50 per day, and ensure that animals are productive earlier in life. Because selection for an earlier age at first calving may improve herd performance over time and profitability, ARS researchers in Beltsville, Maryland, in collaboration with the Council on Dairy Cattle Breeding (CDCB) developed genomic evaluations for early first calving. The new evaluation methodology was adopted by CDCB, and national genomic evaluations for early first calving were released to the dairy industry for the first time in April 2019 and are scheduled to be included in U.S. selection indexes in April 2020. Selection for cows that have a younger age at first calving will minimize management costs, produce animals that are profitable earlier in their life, and improve production efficiency for millions of dairy cattle.
Impacts
(N/A)
Publications
- Ma, L., Sonstegard, T.S., Cole, J.B., Van Tassell, C.P., Wiggans, G.R., Crooker, B.A., Tan, C., Prakapenka, D., Liu, G., Da, Y. 2019. Genome changes due to artificial selection in U.S. Holstein cattle. BMC Genomics. 20:128.
- Santos, D.A., Cole, J.B., Null, D.J., Byrem, T.M., Ma, L. 2018. Genetic and nongenetic profiling of milk pregnancy-associated glycoproteins in Holstein cattle. Journal of Dairy Science. 101(11):9987-10000.
- Ma, L., Cole, J.B., Da, Y., Van Raden, P.M. 2019. Symposium review: Genetics, genome-wide association study, and genetic improvement of dairy fertility traits. Journal of Dairy Science. 102(4):3735-3743.
- Bradford, H.L., Masuda, Y., Cole, J.B., Misztal, I., Van Raden, P.M. 2019. Modeling pedigree accuracy and uncertain parentage in single-step genomic evaluations of simulated and US Holstein datasets. Journal of Dairy Science. 102(3):2308⿿2318.
- Fang, L., Jiang, J., Li, B., Zhou, Y., Freebern, E., Van Raden, P.M., Cole, J.B., Liu, G., Ma, L. 2019. Genetic and epigenetic architecture of paternal origin contribute to gestation length in cattle. Communications Biology. 2:100.
- Mueller, M.L., Cole, J.B., Sonstegard, T.S., Van Eenennaam, A.L. 2019. Comparison of gene editing versus conventional breeding to introgress the POLLED allele into the US dairy cattle population. Journal of Dairy Science. 102(5):4215-4226.
- Bradford, H.L., Masuda, Y., Van Raden, P.M., Legarra, A., Misztal, I. 2019. Modeling missing pedigree in single-step genomic BLUP. Journal of Dairy Science. 102(3):2336-2346.
- Tiezzi, F., Arceo, M.E., Cole, J.B., Maltecca, C. 2018. Including gene networks to predict calving ease in Holstein, Brown Swiss and Jersey cattle. BioMed Central (BMC) Genetics. 19:20.
- Van Raden, P.M., Bickhart, D.M., O'Connell, J.R. 2019. Calling known variants and identifying new variants while rapidly aligning sequence data. Journal of Dairy Science. 102(4):3216⿿3229.
- Weller, J.I., Bickhart, D.M., Wiggans, G.R., Tooker, M.E., O'Connell, J.R., Jiang, J., Ron, M., Van Raden, P.M. 2018. Determination of quantitative trait nucleotides by concordance analysis between quantitative trait loci and marker genotypes of US Holsteins. Journal of Dairy Science. 101(10) :9089⿿9107.
- Fang, L., Zhou, Y., Liu, S., Jiang, J., Bickhart, D.M., Null, D.J., Li, B., Schroeder, S.G., Rosen, B.D., Cole, J.B., Van Tassell, C.P., Ma, L., Liu, G. 2019. Comparative analyses of sperm DNA methylomes among human, mouse and cattle provide insights into epigenomic evolution and complex traits. Epigenetics. 14(3):260-276.
- Guarini, A., Sargolzaei, M., Brito, L., Kroezen, V., Lourenco, D., Baes, C. , Miglior, F., Cole, J.B., Schenkel, F. 2019. Estimating the impact of the deleterious recessive haplotypes AH1 and AH2 on reproduction performance of Ayrshire cattle. Journal of Dairy Science. 102(6):5315-5322.
- Fang, L., Zhou, Y., Liu, S., Jiang, J., Bickhart, D.M., Null, D.J., Li, B., Schroeder, S.G., Rosen, B.D., Cole, J.B., Van Tassell, C.P., Ma, L., Liu, G. 2019. Integrating signals from sperm methylome analysis and genome-wide association study for a better understanding of male fertility in cattle. Epigenomes. 3(2):10.
- Santos, D.J., Cole, J.B., Lawlor, T.J., Van Raden, P.M., Tonhati, H., Ma, L. 2019. Variance of gametic diversity and its use in selection programs. Journal of Dairy Science. 102(6):5279-5294.
- Liu, M., Fang, L., Liu, S., Pan, M.G., Seroussi, E., Cole, J.B., Ma, L., Chen, H., Liu, G. 2019. Array CGH-based detection of CNV regions and their potential association with reproduction and other economic traits in Holsteins. BMC Genomics. 20:181.
- De Souza Iung, L.H., Petrini, J., Ramirez-Diaz, J., Salvian, M., Rovadoscki, G.A., Pilonetto, F., Dauria, B.D., Machado, P.F., Coutinho, L. L., Wiggans, G.R., Mourao, G.V. 2019. Genome-wide association study for milk production traits in a Brazilian Holstein population. Journal of Dairy Science. 102(6):5305-5314.
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Progress 10/01/17 to 09/30/18
Outputs
Progress Report Objectives (from AD-416): Objective 1. Expand genomic data used in prediction by selecting new variants that more precisely track the true gene mutations that cause phenotypic differences. Objective 2. Evaluate new traits that can all be predicted at birth from the same inexpensive DNA sample. Objective 3. Improve efficiency of genomic prediction and computation by developing faster algorithms, testing new adjustments and models, and accounting for genomic pre-selection in evaluation. Approach (from AD-416): Obj. 1: Variant selection strategies will be tested with 1000 Bull Genomes data. Two-stage imputation will be used; imputation accuracy will be compared by simulation. Local sequence data will be generated for families with new fertility defects or other health conditions and bulls homozygous for less frequent haplotypes. Animals will be selected for sequencing with an algorithm maximizing coverage of rare haplotypes and minimizing resequencing of common haplotypes. Previous data will be realigned to a new reference map. Candidate variants will be reselected using improved annotation, better bioinformatics, and information from discoveries across species. Lists of candidate variants with the largest effects will be supplied for array design. Best strategies to include gene-edited animals in breeding programs, their potential value, and confirmation of phenotypic effects of gene edits will be determined. Simulation will reveal optimum strategies for combining favorable haplotypes. Obj. 2: Genetic evaluations will be developed for traits already measured but with low heritability or moderate economic value. Economic values and reliability for new traits will be estimated; options for choosing the most profitable animals to phenotype and genotype will be explored. Data editing and analysis methods will be developed for new data. Computer simulation will be used to determine the best combination of direct and indirect phenotypes for genetic improvement. Relative economic values will be calculated for selection indexes; index sensitivity will be determined based on forecast economic value. Selection index methodology will be used to study effect on annual rates of genetic gain from adding recessives to the index. Incidence, correlations, and effects of more traits will be documented. Constant monitoring of input data will ensure continued high-quality evaluations. Obj. 3: Algorithms will be developed to improve aligning sequence segments to a reference genome while simultaneously calling variants. Genomic models will be designed to include more informative priors. Tests will compare predictive ability for future data within or across breed. Multibreed marker effects will be estimated as correlated traits. Potential biases from genomic pre-selection will be monitored using differences across time in percentages of genotyped mates or daughters. Use of single-step models to correct bias will be explored using recent algorithms to approximate the inverse of genomic relationships and model marker effects directly. Genomic evaluations of crossbred animals will be developed by weighting marker effects from each breed by genomic breed composition. Prediction of nonadditive effects and recombination loss will be continued. Genomic future inbreeding will be improved by computing average genomic relationship to a more recent group of potential mates instead of to breed reference population. Test-day models will be considered when appropriate. Adjustments will be tested using truncated data to predict more recent data. Multitrait processing will be used to obtain greater benefits from new traits without losing information from previous correlated traits. Project 8042-31000-002-00D (Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals) began on July 24, 2017, and continues research from project 8042-31000-101-00D (Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information). Under Objective 1 (expand genomic data used in prediction by selecting new variants), an integrated analysis of a large-scale genomewide association study was conducted to assess why the DGAT1 gene had the most significant effects on milk production; genes, genomic regions, and gene networks associated with three measures of fertility (daughter pregnancy rate, heifer conception rate, and cow conception rate) and two measures of reproductive health (metritis and retained placenta) were identified for U.S. Holsteins using producer-reported data; potential benefits from using a new reference map in genomic prediction were investigated, and improvements were found for genotype imputation, sequence alignment, and marker location; copy number differences in the PRAME gene were identified for Gir, Holstein, and Girolando breeds; genomic regions associated with resistance to clinical mastitis were identified for U.S. Holsteins; a gene-transcription factor network associated with residual feed intake based on single nucleotide variations, insertions, and deletion was identified in Gir, Girolando, and Holstein cattle; introgression of the polled allele into a dairy cattle population via conventional breeding versus gene editing was simulated for three different polled mating schemes; mutational plasticity within the prolactin receptor gene was discovered through a search for causal mutations that produce smooth (slick) coats in criollo breeds; a genomewide association study was conducted to identify genetic variants associated with reproductive traits in Nelore beef cattle; and different strategies for genotype imputation in a population of crossbred Girolando dairy cattle were investigated. Under Objective 2 (evaluate new traits that can be predicted at birth), genetic evaluations of gestation length introduced last year for males were extended to all animals of both sexes. Under objective 3 (improve efficiency of genomic prediction and computation), a 100-year review of methods and impact of genetic selection in dairy cattle was prepared; methods for discovering and validating relationships among genotyped animals were examined; a statistical model was developed to determine variance of gametic diversity as a possible tool for identifying matings with above-average likelihood of producing high-merit offspring; modeling uncertain paternity was investigated to address differential pedigree accuracy; methods to validate genomic reliabilities and to estimate gains from phenotypic updates were developed; genomic predictability of single-step genomic best linear unbiased prediction was validated for production traits of U.S. Holsteins; multitrait modeling of first versus later parities was evaluated for U.S. yield, somatic cell score, and fertility traits; effect of genomic selection on lifetime merit of U.S. Holsteins, Jerseys, and Brown Swiss was determined with a four-path model of genetic improvement and compared with gains predicted by theory; ranking and value differences between lifetime net merit and annualized net present value were compared; a new method for approximating genomic reliabilities was developed to make them comparable across countries and consistent with conventional reliabilities; and pre-selection bias in traditional evaluations was compared with that in single-step genomic evaluations for U.S. Holsteins and possible effect of that pre-selection was examined in a validation study. Under Objectives 1 and 2, genetic cues from fertilization to pregnancy establishment were examined. Under objectives 1 and 3, a state-of-the art fine-mapping procedure was developed, and 36 candidate genes for production traits, 48 for reproduction traits, and 29 for body conformation traits were identified for 27,000 Holstein bulls; use of causative variants and single-nucleotide polymorphism weighting in a single-step genomic prediction was investigated; and Holstein, Brown Swiss and Jersey breed-specific dystocia networks were characterized and used in genomic prediction of calving ease. Under Objectives 2 and 3, national genomic evaluations for health traits were developed for U.S. Holsteins and incorporated into lifetime merit indexes; and potential reliabilities of genomic predictions for feed intake of U.S. Holsteins were estimated by three different methods. Accomplishments 01 National genomic evaluations for health traits of dairy cattle. Health problems of cows can result in additional culling, decreased and lost milk sales, veterinary expenses, and additional labor. One option for increasing herd profitability, improving animal welfare, and reducing antibiotic use while decreasing management costs is to breed for healthy, disease-resistant cows. However, fitness and fertility traits are difficult to select for because of their low heritability (transmission from parent to offspring) and the influence of nongenetic factors. Therefore, in collaboration with the Council on Dairy Cattle Breeding (CDCB), ARS researchers in Beltsville, Maryland, developed genetic evaluations for disease resistance to the six most common, costly health events for U.S. dairy cattle: clinical mastitis, ketosis (metabolic carbohydrate disorder), retained placenta, metritis (uterine inflammation), displacement of the fourth stomach, and milk fever (acute illness caused by calcium deficiency). The new evaluations were released to the dairy industry by CDCB in April 2018. Dairy producers now will be able to incorporate these new health traits into their breeding programs and use the new evaluations as a tool to select healthier, more profitable animals. 02 Lifetime merit indexes for dairy cattle that include health traits. Genetic economic indexes for dairy cattle are used to improve the efficiency of the national population by ranking animals based on their combined genetic merit for economically important traits, but health traits had been included only indirectly in national lifetime merit indexes before direct genetic evaluations became available. Therefore, after the Council on Dairy Cattle Breeding released evaluations for disease resistance to the six most common, costly health events for U.S. dairy cattle in April 2018, ARS researchers in Beltsville, Maryland, added a health composite made up of genetic-economic values for clinical mastitis, ketosis, retained placenta, uterine inflammation, displacement of the fourth stomach, and milk fever (acute illness caused by calcium deficiency) to lifetime merit indexes. Economic emphasis was added for direct expenses (such as clinical mastitis treatment) while at the same time reducing emphasis on previously correlated traits (such as somatic cell score). The updated indexes were adopted and officially released to the dairy industry by the Council on Dairy Cattle Breeding in August 2018. Selection using the new indexes will produce cows with genes that keep them healthy and, therefore, more profitable than cows with health conditions that require extra farm labor, veterinary treatment, and medicine; if all breeders select on NM$, an increase in genetic progress worth $1.4 million/year is expected on a national basis.
Impacts
(N/A)
Publications
- Hardie, L.C., Vandehaar, M.J., Tempelman, R.J., Weigel, K.A., Armentano, L. E., Wiggans, G.R., Veerkamp, R.F., Haas, Y., Coffey, M.P., Connor, E.E., Hanigan, M.D., Staples, C., Zhiquan, W., Dekkers, J.C., Spurlock, D.M. 2017. The genetic and biological basis of feed efficiency in mid-lactation Holstein dairy cows. Journal of Dairy Science. 100(11):9061-9075.
- Cole, J.B., Bormann, J.M., Gill, C.A., Khatib, H., Koltes, J., Maltecca, C. , Milgior, F. 2017. Breeding and Genetics Symposium: Resilience of livestock to changing environments. Journal of Animal Science. 95(4):1777- 1779.
- Oliveira Jr, G., Chud, T., Ventura, R., Garrick, D., Cole, J.B., Munari, D. , Ferraz, J., Mullart, E., Denise, S., Smith, S., Da Silva, M. 2017. Genotype imputation in a tropical crossbred dairy cattle population. Journal of Dairy Science. 100(12):9623-9634.
- Weigel, K.A., Van Raden, P.M., Norman, H.D., Grosu, H. 2017. A 100-year review: Methods and impact of genetic selection in dairy cattle�From daughter�dam comparisons to deep learning algorithms. Journal of Dairy Science. 100(12):10234-10250.
- Heringstad, B., Egger-Danner, C., Charfeddine, N., Pryce, J., Stock, K., Kofler, J., Sogstad, A.M., Holzhauer, M., Fiedler, A., Mueller, K., Nielsen, P., Thomas, G., Gengler, N., De Jong, G., Odegard, C., Malchiodi, F., Miglior, F., Alsaaod, M., Cole, J.B. 2018. Invited review: Genetics and claw health: Opportunities to enhance claw health by genetic selection. Journal of Dairy Science. 101(6):4801�4821.
- Masuda, Y., Van Raden, P.M., Misztal, I., Lawlor, T.J. 2018. Differing genetic trend estimates from traditional and genomic evaluations for genotyped animals as evidence of pre-selection bias in US Holsteins. Journal of Dairy Science. 101(6):5194�5206.
- Cole, J.B., Van Raden, P.M. 2018. Symposium review: Possibilities in an age of genomics: The future of the breeding index. Journal of Dairy Science. 101(4):3686-3701.
- Oliveira Junior, G.A., Perez, B.C., Cole, J.B., Santana, M.H., Silveira, J. , Gianluca, M., Ventura, R.V., Junior, M.L., Kadarmideen, H.N., Garrick, D. J., Ferraz, J. 2017. Genomic study and Medical Subject Headings enrichment analysis of early pregnancy rate and antral follicle numbers in Nelore heifers. Journal of Animal Science. 95(11):4796-4812.
- Hutchison, J.L., Van Raden, P.M., Null, D.J., Cole, J.B., Bickhart, D.M. 2017. Genomic evaluation of age at first calving. Journal of Dairy Science. 100(8):6853-6861.
- Zhou, Y., Shen, B., Jiang, J., Padhi, A., Park, K., Oswalt, A., Sattler, C. , Telugu, B.P., Chen, H., Cole, J.B., Liu, G., Ma, L. 2017. Construction of PRDM9 allele-specific recombination maps in cattle using large-scale pedigree analysis and genome-wide single sperm genomics. DNA Research. 25(2):183�194.
- Porto-Neto, L.R., Bickhart, D.M., Landaeta-Hernandez, A.J., Utsunomiya, Y. T., Morales, M.P., Caban-Jimenez, E., Hansen, P.J., Dikmen, S., Schroeder, S.G., Sun, J., Crespo, E., Amati, N., Cole, J.B., Null, D.J., Garcia, J.F., Reverter, A., Barendse, W., Sonstegard, T.S. 2018. Convergent evolution of slick coat in cattle through truncation mutations in the prolactin receptor. Frontiers in Genetics. 9:57.
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