**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** WHILE GENOMIC SELECTION HAS BEEN SUCCESSFUL IN ACCELERATING GENETIC IMPROVEMENT, WE STILL KNOW VERY LITTLE ABOUT THE GENETIC ARCHITECTURE OF QUANTITATIVE TRAITS. THIS CHALLENGES RESEARCHERS IN DEVELOPING A MODEL THAT CAN ACHIEVE THE FULL POTENTIAL OF GENOMIC EVALUATIONS. RECENT PROGRESS ON THE FUNCTIONAL ANNOTATION OF ANIMAL GENOMES (FAANG) PROJECTS PROVIDES AN OPPORTUNITY TO ADDRESS THE CHALLENGE IN HOPES THAT THE FUNCTIONAL INFORMATION CAN HELP TO DECIPHER THE GENOTYPE-TO-PHENOTYPE RELATIONSHIP; HOWEVER, THERE IS A LACK OF METHODS FOR EFFECTIVELY USING FUNCTIONAL ANNOTATIONS IN QUANTITATIVE GENETIC ANALYSIS. TO TACKLE THIS ISSUE, WE PROPOSE TO DEVELOP A NEW MIXED-MODEL METHOD FOR INTEGRATING FUNCTIONAL ANNOTATIONS IN THE FRAMEWORK OF GENOMIC PARTITIONING.IN PRELIMINARY RESEARCH, WE HAVE DEVELOPED A NEW SOFTWARE TOOL FOR FAST AND EFFICIENT GENOMIC PARTITIONING, WHICH WE REFER TO AS MPH (HTTPS://JIANG18.GITHUB.IO/MPH/). THE PROJECT WILL BE ADDRESSING TWO MAIN OBJECTIVES, AIMED AT 1) REFINING THE MIXED-MODEL APPROACH AND IMPROVING THE SOFTWARE PERFORMANCE AND 2) APPLYING THE METHOD TO DAIRY CATTLE DATA TO INVESTIGATE DAIRY TRAITS'GENETIC ARCHITECTURE AND TRAIN GENOMIC PREDICTION MODELS WITH FUNCTIONAL ANNOTATIONS. WE WILL USE A LARGE DATA OF GENOTYPES AND PHENOTYPES MAINTAINED AT THE U.S. COUNCIL ON DAIRY CATTLE BREEDING AND A VARIETY OF PUBLICLY AVAILABLE FUNCTIONAL ANNOTATIONS. THIS PROJECT WILL PRESENT ONE OF THE FIRST METHODS FOR EFFECTIVELY INTEGRATING FAANG DATA INTO QUANTITATIVE GENETIC ANALYSIS.