Improving dairy animals by increasing accuracy of genomic prediction, evaluating new traits, and redefining selection goals
Genetic progress of dairy cattle for traits of economic importance can be advanced more rapidly through improved genomic prediction methods, thereby leading to immediate benefits to producers and consumers worldwide. This project will use whole-genome or targeted DNA sequence data to discover naturally occurring variants that cause trait differences between animals or genetic markers closely associated with those differences to improve genotyping arrays and the potential for gene editing. The rapid growth in number and size of international databases and larger variant sets require deriving more accurate imputation methods, advanced statistical models, and efficient computer programs for processing the big data associated with dairy cattle records. New models will allow monitoring and removing potential biases caused by genomic pre-selection. Additional traits will be evaluated if the estimated economic value and heritability are sufficiently high to justify selection and use. Updated genetic-economic indexes for combining all traits will guide breeders on selection goals, and producer profits from alternative breeding programs and potential investments in data will be compared. Development of more cost-effective genotyping tools will be optimized through collaboration with other scientists in ARS, universities, and industry. Genomic predictions for crossbreds will be developed from phenotypic data using an all-breed scale instead of separate within-breed scales. Phenotypic effects of management practices and interactions of genotype with environment will also be documented and predicted. Collecting and combining information from phenotypes, genotypes, and pedigrees into more accurate predictions will allow breeders to greatly improve the production efficiency of future dairy cattle. Other species will also be improved by using the genomic selection methods and programs developed in this research.