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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Research Project #433412

Research Project: Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals

Location: Animal Genomics and Improvement Laboratory

2022 Annual Report

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.

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 8042-31000-002-000D (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.

1. 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.

2. 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.

3. 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.

Review 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.
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.
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.
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.
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.