BIG-DATA GENOMIC INVESTIGATION TO IMPROVE DAIRY CATTLE HEALTH

The overall goal of this proposal is to uncover the genetic mechanism of host resistance to bovine mastitis and to apply thesegenomic discoveries to improve cattle health and profitability of the dairy industry. The specific aims are to:Aim 1: Identify genomic regions and candidate genes associated with mastitis by using big data genome-wide associationanalysis of mastitis and other immune-related diseases.The working hypothesis is that genetics contributes a non-negligibleamount to dairy disease resistance and the proposed big-data investigation is powerful to identify genes and genomic regionsassociated with cattle health traits using the available data resources from the National Dairy Genomic and PhenotypicDatabase.Aim 2: Integrate sequence-level GWAS, transcriptome and functional validation of immune cells to identify health SNPs andapply them to optimize genomic selection of disease traits. The transcriptome and functions of immune cells will be examined incows genetically resistant or susceptible to mastitis, making the computational and experimental components complementary toeach other: computational analyses provide candidate SNPs for experimental validation and functional experiments helpinterpret and confirm computational results. The working hypothesis is that sequencing-based investigation will lead to a fruitfuldiscovery of candidate health variants for mastitis and integration of these candidate variants with transcriptome and functionalvalidation from immune cells into the genomic selection models will greatly improve disease resistance in cattle.