GWAS and fine-mapping of livability and six disease traits in Holstein cattle

Authors: FREEBERN, ELLEN - University Of Maryland; SANTOS, DANIEL - University Of Maryland; FANG, LINGZHAO - University Of Maryland; JIANG, JICAI - University Of Maryland; PARKER-GADDIS, KRISTEN - Council On Dairy Cattle Breeding; Liu, Ge - George; Vanraden, Paul; MALTECCA, CHRISTIAN - North Carolina State University; Cole, John; MA, LI - University Of Maryland

Submitted to: BMC Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/7/2020
Publication Date: 1/13/2020
Citation: Freebern, E., Santos, D.J.A., Fang, L., Jiang, J., Parker-Gaddis, K.L., Liu, G.E., Van Raden, P.M., Maltecca, C., Cole, J.B., Ma, L. 2020. GWAS and fine-mapping of livability and six disease traits in Holstein cattle. BMC Genomics. 21:41. https://doi.org/10.1186/s12864-020-6461-z.
DOI: https://doi.org/10.1186/s12864-020-6461-z

Interpretive Summary: Health traits are of significant economic importance to the dairy industry and genome-wide association studies (GWAS) provide a means to identify associated genomic variants and genes for complex traits and diseases. We report six significant associations and 20 candidate genes of cattle health. Farmers, scientist, and policy planners who need improve animal health and production based on genome-enable animal selection will benefit from this study.

Technical Abstract: Background: Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to explore the potential candidate genes of cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data. Results: We studied cow livability and six disease traits, mastitis, ketosis, hypocalcemia, displaced abomasum, metritis, and retained placenta, using de-regressed breeding values and over three million imputed sequence variants. After data edits and filtering on reliability, we included 11,880 to 14,699 Holstein bulls across the seven traits. GWAS was performed using a mixed-model association test, and a Bayesian fine-mapping procedure was conducted to calculate a posterior probability of causality to each variant and gene in the candidate regions. The GWAS results detected a total of eight genome-wide significant associations for three traits, cow livability, ketosis, and hypocalcemia, including the bovine MHC region associated with livability. Fine-mapping of associated regions identified 20 candidate genes with the highest posterior probabilities of causality for cattle health. Combined with transcriptome data across multiple tissues in cattle, we further exploited these candidate genes to identify specific expression patterns in disease-related tissues and relevant biological explanations such as the expression of GC in liver and association with mastitis as well as the CCDC88C expression in CD8 cells and association with cow livability. Conclusions: Collectively, our analyses report six significant associations and 20 candidate genes of cattle health. Integrating with multi-tissue transcriptome data, our results provide useful information for future functional studies and better understanding of the biological relationship between genetics and disease susceptibility in cattle.