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 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.
1. 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.
2. 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.
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