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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
- Van Tassell, Curtis - Curt
- Rosen, Benjamin - Ben
- Toghiani, Sajjad
- 2023
- Publications
- Food Animal Production (101)

Research Project: Increasing Accuracy of Genomic Prediction, Developing Algorithms, Selecting Markers, and Evaluating New Traits to Improve Dairy Cattle **Location:
                            Animal Genomics and Improvement Laboratory** 2023 Annual Report **Objectives**
                                

Objective 1:  Expand the data used in genomic prediction by selecting new variants from five dairy breeds that more precisely track the true gene mutations that cause phenotypic differences.
The goal in Objective 1 is to use high-quality sequence data from multiple dairy breeds to identify new variants for inclusion in genomic predictions, making them more representative of all dairy animals, and improving their reliability compared to currently used single-nucleotide polymorphism markers in genotyping arrays. 
Sub-objective 1.A: Validate genomic predictions for U.S. cows.
Sub-objective 1.B: Track haplotypes to identify new, large-effect QTL for inclusion in genomic evaluations. 
Sub-objective 1.C: Interrogate whole genome sequences (WGS) from multiple dairy breeds to characterize their genetic differences and select high-impact variants. 

Objective 2:  Evaluate new traits that can all be predicted at birth from the same inexpensive DNA sample. 
Sub-objective 2.A: Identify new traits or data types via analysis of industry trends and determine their suitability for inclusion in genomic evaluations. 
Sub-objective 2.B: Develop new traits to promote animal health and welfare by creating new data pipelines and eventual GPTA. 
Sub-objective 2.C: Update NM$ with new traits and changing prices. 

Objective 3:  Improve efficiency of genomic prediction and computation by developing faster algorithms with better statistical properties.
Sub-objective 3.A: Expand the use of international phenotypes to improve the accuracy of prediction because U.S. bulls are the most frequently used worldwide.
Sub-objective 3.B: Develop and test methods to select for heat tolerance and efficiency in variable climates by examining the genotype by environment interaction.
Sub-objective 3.C: Update lactation record adjustment factors and yield projection factors used in predictive models.  
Sub-objective 3.D: Account for genomic pre-selection biases by comparing multi-step and single-step evaluation methods towards the goal of applying single-step to routine national evaluations. **Approach**
                                

Obj. 1: Genomic predictions will be validated using > 1 million U.S. cows genotyped early in life that were phenotyped later in life. Methods to detect and impute lethal recessive alleles will continue to be improved by investigating haplotypes with no or few homozygotes, estimating if conception or stillbirth rates are affected, inspecting sequence variants to determine the most likely causal variant, and incorporating lethal recessives discovered by other researchers into the U.S. evaluation. To improve genetic progress, genetic effects will be estimated for haplotypes already defined from array genotypes. By matching haplotypes with largest effects to sequence data, better markers for net merit and individual traits will be discovered. Documenting how well genomic predictions correspond to cow performance will increase breeder confidence and participation in national evaluation systems.

Obj. 2: New data on Johne’s disease, milking speed, milk mid-infrared spectra, hoof health, beef x dairy inseminations, and beef x dairy calving ease will be explored to develop further management or selection tools.  High-throughput phenotyping and big data analytics will be explored, industry trends documented, and economic values estimated for genetic evaluation for current and new traits to update the national selection index. Edits will be revised to accept data from herds that record milk yield but without approved fat or protein component testing. Such herds will add to reliability for traits with less data or lower heritability even if their additional records add little to the already high reliability of genomic prediction for yield traits. Embryo transfer (ET) has grown exponentially in the last few years but has not yet been reflected in reporting breeding records. To prevent bias resulting from ET calves with missing implantation breeding events or erroneous AI events, program alterations for unreported ET will be explored for reproductive performance evaluations (e.g., conception rates, gestation length, early first calving, and daughter pregnancy rate) and improved data pipelines for ET reporting will be developed.

Obj. 3: Foreign phenotypes from more countries will be incorporated into U.S. genomic predictions such as for feed intake. Differences in temperature and humidity across states and seasons will be used to rank bulls for their daughter production in hotter or cooler regions. Careful choice of scale for reporting the interactions and extra education will be needed to help breeders understand the new rankings. Models for evaluating crossbred animals will be refined. Age adjustment factors and yield projection factors used in predictive models will be updated to account for genetic and management changes in recent decades. Multi-step and single-step evaluation methods will be compared in cooperation with the University of Georgia to better account for genomic pre-selection biases with the goal to apply single-step to routine national evaluations. **Progress Report**
                                

Progress was made on all three objectives of project 8042-31000-113-000D (Increasing Accuracy of Genomic Prediction, Developing Algorithms, Selecting Markers, and Evaluating New Traits to Improve Dairy Cattle).
Under Objective 1 (Expand the data used in genomic prediction by selecting new variants), the genetic basis of a new defect termed recumbency was published in cooperation with Pennsylvania State University, improved methods to track 2 recent mutations (recumbency and cholesterol deficiency) affecting calf livability were programmed and used by CDCB to provide defect status for 6 million genotyped Holsteins. New algorithm using Fst approach has been tested to prioritize most influential variants from high density SNP panel in collaboration with University of Georgia.

Under Objective 2 (Evaluate new traits), an AGIL researcher chaired the industry task force to collect data and evaluate milking speed as a new trait, AGIL researchers cooperated with several universities in a newly funded grant to evaluate methane as a new trait, collection of individual milking data began for a new project  to expand the use of mid-infrared spectra as a prediction tool, and an AGIL scientist cooperated to implement feed efficiency in the Iranian Holstein breeding program.

Under Objective 3 (Improve efficiency of genomic prediction and computation), adjustment factors for age, parity, and season of calving were updated from those estimated in 1994, combined adjustments for milking interval and frequency were derived and reported to the industry, collection of individual milking data for updating test day factors and the prediction of total lactation yields has begun, models were improved to account for 7,000 sets of clones, split embryos, and identical twins, and multi-step and single-step evaluation methods were compared for fertility which showed several ways to improve both algorithms. **Accomplishments**
                                

1. Discovery of a major new mutation causing dairy calf death loss. Scientists from ARS Beltsville, Maryland, and Pennsylvania State University jointly investigated a recessive genetic condition termed “recumbency” that makes young calves unable to stand and results in higher death rates. The researchers traced the source of this defect back many generations through sires that previously ranked #1 in the world using genotypes of 5.6 million Holsteins. They identified the new mutation that causes this condition in a gene affecting muscle movement by using sequence data for affected calves, relatives, and previously sequenced Holstein bulls. They also improved methods for using pedigree records to track new mutations within existing haplotypes and applied the methods to both recumbency and a previously identified mutation that causes cholesterol deficiency and calf death. ARS scientists then estimated significant effects of both defects on heifer livability from national data. The research resulted in a direct genetic test for the DNA mutation that is now widely used by Holstein breeders in selection and mating programs. Identification of harmful new mutations and selection against those improves fertility and reduces calf losses. 2. Better accounting for cloning in genetic and genomic evaluations. Many elite dairy cattle are being cloned in recent years, but the identical animals cause more complex relationships to model in genetic evaluations. ARS scientists in Beltsville, Maryland, improved statistical models and applied new programs to data for 4,762 pairs of natural identical twins, 1,776 split embryos, and 530 nuclear transfer clones of cells from other embryos, calves, or adults in close cooperation with the Council on Dairy Cattle Breeding. Genetic effects for the 7,068 animals reported to be a clone or copy of another animal were linked to the source animal and their own effects removed from the relationship matrix to simplify modeling. Milk production of adult clones was not significantly affected but their fertility and health traits were slightly below expected. Benefits of the new model were more exact pedigree inbreeding coefficients, improved ancestor discovery for descendants of clones, combined progeny counts for cloned bulls instead of reporting only the clone with the most progeny, and more precise genetic evaluations for the clones. Implementation of these edits in the national genetic evaluation system is underway and expected to be live by August 2023. 3. Fairer comparisons of cow milk production adjusted for age and season effects. From 1935-1994, adjustment factors for milk, fat, and protein yields were updated by ARS researchers 5 times and now again in 2023. The scientists in Beltsville, Maryland, documented changes in cow maturity and seasonality patterns across the decades, breeds, and regions, and adjusted to 36-month rather than mature equivalent yield so that standardized yields better reflect average production across the lifetime. New, climate data-based regions were generated to better represent environmental interactions due to geographic effects (especially heat stress) on yield traits. Seasonal effects were much smaller in recent than in older data within each of the 5 climate regions, indicating that improved housing and management has decreased the effect of the environment on lactation yields. The new factors were applied to 104 million lactation records of 41 million U.S. cows which improved the genetic evaluations especially for less numerous breeds whose maturity patterns differ most from Holsteins. For easier use and interpretation, the new factors are applied as a series of separate adjustments for age-parity, season, previous days open, and milking frequency. The updated research should improve management decisions on thousands of dairy farms. This research has been presented to the industry at several national meetings and implementation of these new factors is underway. **Review Publications**
                            *Nadri, S., Sadeghi-Sefidmazgi, A., Zamani, P., Ghorbani, G., Toghiani, S. 2023. Implementation of feed efficiency in Iranian Holstein breeding program. Animals. 13(7):1216. https://doi.org/10.3390/ani13071216.*
                                *Miles, A.M., Hutchison, J.L., Van Raden, P.M. 2023. Improving national fertility evaluations by accounting for the rapid rise of embryo transfer in US dairy cattle. Journal of Dairy Science. https://doi.org/10.3168/jds.2022-22298.*
                                *Miles, A.M., McArt, J.A., Lima, S.F., Neves, R.C., Ganda, E. 2022. The association of hyperketonemia with fecal and rumen microbiota at time of diagnosis in a case-control cohort of early lactation cows. BMC Veterinary Research. 18:411. https://doi.org/10.1186/s12917-022-03500-4.*
                                *Crandall, S.G., Miles, A.M., Chung, T., Cloutier, M.L., Garcia-Rodriguez, R., Schweigkofler, W., Couradeau, E. 2022. Temporal and spatial dynamics of bacterial and fungal microbiomes in nursery soils post-steaming. PhytoFrontiers. https://doi.org/10.1094/PHYTOFR-07-22-0071-R.*
                                *Wu, X., Wiggans, G.R., Norman, H.D., Miles, A.M., Van Tassell, C.P., Baldwin, R.L., Burchard, J., Durr, J. 2022. Daily milk yield correction factors: what are they? Journal of Dairy Science Communications. https://doi.org/10.3168/jdsc.2022-0230.*
                                *Wu, X., Wiggans, G.R., Norman, H.D., Miles, A.M., Van Tassell, C.P., Baldwin, R.L., Burchard, J., Durr, J. 2022. Statistical methods revisited for estimating daily milk yields: how well do they work? Frontiers in Genetics. 13:943705. https://doi.org/10.3389/fgene.2022.943705.*
                                *Guinan, F.L., Wiggans, G.R., Norman, H.D., Durr, J.W., Cole, J.B., Van Tassell, C.P., Misztal, I., Lourenco, D. 2023. Changes in genetic trends in US dairy cattle since the implementation of genomic evaluations. Journal of Dairy Science. 106(2):1110–1129. https://doi.org/10.3168/jds.2022-22205.*
                                *Lozada-Soto, E.A., Maltecca, C., Jiang, J., Cole, J.B., Van Raden, P.M., Tiezzi, F. 2022. Genomic characterization of autozygosity and recent inbreeding trends in all major breeds of US dairy cattle. Journal of Dairy Science. 105(11):8956-8971. https://doi.org/10.3168/jds.2022-22116.*
                                *Zaabza, H.B., Van Tassell, C.P., Vandenplas, J., Van Raden, P.M., Liu, Z., Eding, H., Mckay, S., Haugaard, K., Lidauer, M.H., Mäntysaari, E.A., Strandén, I. 2023. Invited review: Reliability computation from the animal model era to the single-step genomic model era. Journal of Dairy Science. 106(3):1518–1532. https://doi.org/10.3168/jds.2022-22629.*


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