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.
Much 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), major quantitative trait loci influencing milk production and conformation traits were detected for Guernsey dairy cattle, quality control methods were updated for markers and genotyping arrays used in the national evaluation, and the potential for using gene editing to produce both dairy and beef polled cattle was investigated and compared with traditional breeding. Under Objective 2 (evaluate new traits that can be predicted at birth), genomic evaluations were developed for five traits [rear udder width, body depth, mobility, rear teat placement (side view), and milking speed] of several breeds that had only traditional pedigree evaluations, and the genetic basis of Jersey neuropathy with splayed forelimbs were identified. Under Objective 3 (improve efficiency of genomic prediction and computation), the reliability and bias of genomic predictions that include unknown-parent groups was assessed for yield traits of U.S. Holsteins, unknown-parent groups and metafounders in single-step genomic best linear unbiased prediction (GBLUP) were reviewed, multibreed genomic evaluations were investigated for U.S. dairy cattle using single-step GBLUP, genomic evaluation of U.S. crossbred dairy cattle was enhanced, improved validation of genomic evaluations through the use of extra regressions was investigated, adjustments were developed and implemented to account for late-term abortions in genetic and genomic evaluation of fertility traits and inheritance of abortions was investigated, improvement of the model for genetic evaluation of calving traits was investigated for U.S. Holsteins and Brown Swiss, computation of genomic and pedigree inbreeding and relationships was made more efficient and accurate using parallel processing and adjusting for sex differences due to the X chromosome, faster genotype imputation procedures were implemented, weights for combining genomic and pedigree information were updated, lifetime genetic-economic merit indexes were updated, correlations between national genetic-economic indexes were estimated, and expected use of foreign sires was compared with actual use. Under Objectives 1 and 2, the genetic basis of a mutation causing neuropathy with splayed forelimbs was identified. Under Objectives 1 and 3, imputed high-density genotypes were used for marker selection and genomic prediction of economically important traits of five breeds of dairy cattle, a method was developed to partition heritability of genetic markers, and a scalable mixed-model approach for genomewide association studies with millions of genotyped animals was developed that allows finding omnigenic core genes that matter in functional studies and targeted genome editing. Under Objectives 2 and 3, phenotypic and genotypic effects of milk components and body weight composite on dry matter intake were investigated, genomic evaluations for feed efficiency (feed saved) were developed for Holsteins, a Bayesian multitrait random-aggression approach was adapted to model feed efficiency in dairy cattle, genetic mechanisms of heterosis for daughter pregnancy rate were identified using a genome-wide association study, genomic heritability and prediction accuracy of additive and nonadditive effects for daughter pregnancy rate were estimated for crossbred dairy cows, and the use of international clinical mastitis as an independent trait in the U.S. genetic evaluation system was investigated.
1. Development of genomic evaluations for feed efficiency of dairy cattle. Dairy cattle feed efficiency is of great interest to dairy farmers because feed accounts for the largest part of operating costs in dairy production. However, the data for analyzing feed efficiency have been very limited because of the high cost and difficulty in collecting individual feed intake records. Because genomic selection is well suited for difficult-to-measure traits, ARS researchers at Beltsville, Maryland, used data from 6,221 cow feeding trials (most funded by a previous $10 million USDA grant) to develop genomic evaluations for feed efficiency as a tool for dairy cattle breeding programs. Using ARS methodology, the Council of Dairy Cattle Breeding in Bowie, Maryland, released genomic evaluations expressed as expected pounds of feed saved per lactation in December 2020 for Holstein dairy cattle; those evaluations will be included in a lifetime genetic-economic index in August 2021. As more data become available, feed-efficiency evaluations could be provided for additional breeds. Selecting for more feed-efficient cows can reduce farm costs, improve profitability, and lessen the environmental footprint of dairy production by lowering methane emissions and limiting the natural resources and energy needed to produce and process feed.
2. Development of genomic evaluations for dairy heifer livability. Mortality of young cows that have not calved (heifers) is a major issue related to profitability, management, and animal welfare on dairy farms, and raising replacement heifers ranks as the second-largest cost on dairy farms after feed and forage costs. Although the U.S. genetic evaluation system for dairy cattle included stillbirth and cow survival (livability) as traits, little information was available on heifer livability. Using 4.2 million records, ARS researchers in Beltsville, Maryland, developed genomic evaluations for heifer livability, released by the Council on Dairy Cattle Breeding in Bowie, Maryland, for the first time in December 2020 for Holstein and Jersey dairy cattle. Genomic evaluations for heifer livability can increase dairy farm profitability, increase genetic gain for the U.S. dairy population, and improve animal health and welfare. Heifer livability has a heritability of less than 1%, and heifer livability will be included in a lifetime genetic-economic index in August 2021, emphasizing just 1% of the total index. However, economic progress still is expected to be about $50,000 per year, and additional records will improve accuracy and give faster future progress.
3. Lifetime merit indexes for dairy cattle updated to include new traits. Genetic-economic indexes for dairy cattle are used to improve the efficiency of the national population by ranking animals based on their combined genetic merit for economically important traits. However, new traits for feed efficiency, young cow livability, and the ability for young cows to calve early in their lives had not been included in national lifetime merit indexes. Therefore, after the Council on Dairy Cattle Breeding in Bowie, Maryland, released evaluations for feed saved (December 2020), heifer livability (December 2020), and early first calving (April 2019) for U.S. dairy cattle, ARS researchers in Beltsville, Maryland, added those traits to lifetime merit indexes and also updated income and cost variables such as milk prices and feed requirements to reflect prices expected in the next few years. The updated indexes were adopted and officially released to the dairy industry by the Council on Dairy Cattle Breeding in August 2021. Selection using the new indexes will produce cows with genes that keep them healthy, productive, fertile, and efficient and, therefore, more profitable and environmentally sustainable. If all U.S. dairy producers base their breeding decisions on the updated net merit index, an increase in genetic progress worth $20 million annually is expected on a national basis.
4. Discovery of an undesirable genetic factor in Jersey cattle. Genomic testing of dairy cattle has allowed accurate and inexpensive tracking of deleterious and beneficial genes for economically important traits. A new condition in newborn calves was reported to the American Jersey Cattle Association in Columbus, Ohio, by Jersey breeders and named Jersey neuropathy with splayed forelimbs (JNS) because affected calves were unable to stand on splayed forelimbs and displayed neurological symptoms. The genetic basis for JNS was identified by ARS researchers in Beltsville, Maryland, using pedigree and genetic analyses, and a common ancestor born in 1995 was identified. Inheritance of the defect began to be tracked and reported in December 2020 by the Council on Dairy Cattle Breeding in Bowie, Maryland, using ARS software, and direct genotype tests are expected soon. The American Jersey Cattle Association updated its comprehensive mating program JerseyMate in January 2021 to use the reported carrier status to account for the risk of a JNS mating. Although 6% of genotyped Jerseys were determined to be carriers, genetic testing and avoiding carrier-to-carrier matings can prevent the birth of about 300 affected calves annually.
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