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ARS Home Northeast Area Beltsville, Maryland (BARC) Beltsville Agricultural Research Center Animal Genomics and Improvement Laboratory Research





- Vanraden, Paul
- Miles, Asha
- Toghiani, Sajjad
- Rosen, Benjamin - Ben
- Van Tassell, Curtis - Curt
- 2022
- 2021
- 2020
- 2019
- 2018
- Publications
- Food Animal Production (101)

Research Project: Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals **Location:
                            Animal Genomics and Improvement Laboratory** 2020 Annual Report **Objectives**
                                

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**
                                

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 Report**
                                

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**
                                

1. 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. 2. 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. 3. 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. **Review 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. https://doi.org/10.3168/jds.2018-16081.*
                                *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. https://doi.org/10.3168/jds.2019-16262.*
                                *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. https://doi.org/10.3389/fgene.2019.01197.*
                                *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. https://doi.org/10.3168/jds.2019-16634.*
                                *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. https://doi.org/10.3168/jds.2019-16645.*
                                *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. https://doi.org/10.3168/jds.2019-17332.*
                                *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. https://doi.org/10.3168/jds.2019-17580.*
                                *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. https://doi.org/10.1186/s12859-020-3417-x.*
                                *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. https://doi.org/10.3389/fgene.2019.00412.*
                                *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. https://doi.org/10.1038/s42003-019-0454-y.*
                                *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. https://doi.org/10.3168/jds.2018-15407.*
                                *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. https://doi.org/10.3168/jds.2019-16530.*
                                *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. https://doi.org/10.3168/jds.2018-15076.*
                                *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. https://doi.org/10.1093/af/vfaa007.*
                                *Van Raden, P.M. 2020. Symposium review: How to implement genomic selection. Journal of Dairy Science. 103(6):5291-5301. https://doi.org/10.3168/jds.2019-17684.*
                                *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. https://doi.org/10.3168/jds.2019-17687.*
                                *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. https://doi.org/10.3168/jds.2019-17846.*
                                *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. https://doi.org/10.19103/AS.2019.0058.13.*
                                *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. https://doi.org/10.1101/gr.250704.119.*
                                *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. https://doi.org/10.1093/gigascience/giaa021.*
                                *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. https://doi.org/10.1186/s12864-019-6228-6.*


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