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.
Project 8042-31000-002-00D (Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals) began on July 24, 2017, and continues research from project 8042-31000-101-00D (Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information). Under Objective 1 (expand genomic data used in prediction by selecting new variants), an integrated analysis of a large-scale genomewide association study was conducted to assess why the DGAT1 gene had the most significant effects on milk production; genes, genomic regions, and gene networks associated with three measures of fertility (daughter pregnancy rate, heifer conception rate, and cow conception rate) and two measures of reproductive health (metritis and retained placenta) were identified for U.S. Holsteins using producer-reported data; potential benefits from using a new reference map in genomic prediction were investigated, and improvements were found for genotype imputation, sequence alignment, and marker location; copy number differences in the PRAME gene were identified for Gir, Holstein, and Girolando breeds; genomic regions associated with resistance to clinical mastitis were identified for U.S. Holsteins; a gene-transcription factor network associated with residual feed intake based on single nucleotide variations, insertions, and deletion was identified in Gir, Girolando, and Holstein cattle; introgression of the polled allele into a dairy cattle population via conventional breeding versus gene editing was simulated for three different polled mating schemes; mutational plasticity within the prolactin receptor gene was discovered through a search for causal mutations that produce smooth (slick) coats in criollo breeds; a genomewide association study was conducted to identify genetic variants associated with reproductive traits in Nelore beef cattle; and different strategies for genotype imputation in a population of crossbred Girolando dairy cattle were investigated. Under Objective 2 (evaluate new traits that can be predicted at birth), genetic evaluations of gestation length introduced last year for males were extended to all animals of both sexes. Under objective 3 (improve efficiency of genomic prediction and computation), a 100-year review of methods and impact of genetic selection in dairy cattle was prepared; methods for discovering and validating relationships among genotyped animals were examined; a statistical model was developed to determine variance of gametic diversity as a possible tool for identifying matings with above-average likelihood of producing high-merit offspring; modeling uncertain paternity was investigated to address differential pedigree accuracy; methods to validate genomic reliabilities and to estimate gains from phenotypic updates were developed; genomic predictability of single-step genomic best linear unbiased prediction was validated for production traits of U.S. Holsteins; multitrait modeling of first versus later parities was evaluated for U.S. yield, somatic cell score, and fertility traits; effect of genomic selection on lifetime merit of U.S. Holsteins, Jerseys, and Brown Swiss was determined with a four-path model of genetic improvement and compared with gains predicted by theory; ranking and value differences between lifetime net merit and annualized net present value were compared; a new method for approximating genomic reliabilities was developed to make them comparable across countries and consistent with conventional reliabilities; and pre-selection bias in traditional evaluations was compared with that in single-step genomic evaluations for U.S. Holsteins and possible effect of that pre-selection was examined in a validation study. Under Objectives 1 and 2, genetic cues from fertilization to pregnancy establishment were examined. Under objectives 1 and 3, a state-of-the art fine-mapping procedure was developed, and 36 candidate genes for production traits, 48 for reproduction traits, and 29 for body conformation traits were identified for 27,000 Holstein bulls; use of causative variants and single-nucleotide polymorphism weighting in a single-step genomic prediction was investigated; and Holstein, Brown Swiss and Jersey breed-specific dystocia networks were characterized and used in genomic prediction of calving ease. Under Objectives 2 and 3, national genomic evaluations for health traits were developed for U.S. Holsteins and incorporated into lifetime merit indexes; and potential reliabilities of genomic predictions for feed intake of U.S. Holsteins were estimated by three different methods.
1. National genomic evaluations for health traits of dairy cattle. Health problems of cows can result in additional culling, decreased and lost milk sales, veterinary expenses, and additional labor. One option for increasing herd profitability, improving animal welfare, and reducing antibiotic use while decreasing management costs is to breed for healthy, disease-resistant cows. However, fitness and fertility traits are difficult to select for because of their low heritability (transmission from parent to offspring) and the influence of nongenetic factors. Therefore, in collaboration with the Council on Dairy Cattle Breeding (CDCB), ARS researchers in Beltsville, Maryland, developed genetic evaluations for disease resistance to the six most common, costly health events for U.S. dairy cattle: clinical mastitis, ketosis (metabolic carbohydrate disorder), retained placenta, metritis (uterine inflammation), displacement of the fourth stomach, and milk fever (acute illness caused by calcium deficiency). The new evaluations were released to the dairy industry by CDCB in April 2018. Dairy producers now will be able to incorporate these new health traits into their breeding programs and use the new evaluations as a tool to select healthier, more profitable animals.
2. Lifetime merit indexes for dairy cattle that include health 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, but health traits had been included only indirectly in national lifetime merit indexes before direct genetic evaluations became available. Therefore, after the Council on Dairy Cattle Breeding released evaluations for disease resistance to the six most common, costly health events for U.S. dairy cattle in April 2018, ARS researchers in Beltsville, Maryland, added a health composite made up of genetic-economic values for clinical mastitis, ketosis, retained placenta, uterine inflammation, displacement of the fourth stomach, and milk fever (acute illness caused by calcium deficiency) to lifetime merit indexes. Economic emphasis was added for direct expenses (such as clinical mastitis treatment) while at the same time reducing emphasis on previously correlated traits (such as somatic cell score). The updated indexes were adopted and officially released to the dairy industry by the Council on Dairy Cattle Breeding in August 2018. Selection using the new indexes will produce cows with genes that keep them healthy and, therefore, more profitable than cows with health conditions that require extra farm labor, veterinary treatment, and medicine; if all breeders select on NM$, an increase in genetic progress worth $1.4 million/year is expected on a national basis.
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Zhou, Y., Shen, B., Jiang, J., Padhi, A., Park, K., Oswalt, A., Sattler, C., Telugu, B.P., Chen, H., Cole, J.B., Liu, G., Ma, L. 2017. Construction of PRDM9 allele-specific recombination maps in cattle using large-scale pedigree analysis and genome-wide single sperm genomics. DNA Research. 25(2):183–194. https://doi.org/10.1093/dnares/dsx048.
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