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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Research Project #442491

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

Project Number: 8042-31000-113-000-D
Project Type: In-House Appropriated

Start Date: Jul 24, 2022
End Date: Jul 23, 2027

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.

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.