|YAN, ZE - China Agricultural University|
|HUANG, HETIAN - China Agricultural University|
|FREEBERN, ELLEN - University Of Maryland|
|SANTOS, DANIEL - University Of Maryland|
|DAI, DONGMEI - China Agricultural University|
|SI, JINGFANG - China Agricultural University|
|MA, CHONG - China Agricultural University|
|CAO, JIE - China Agricultural University|
|GUO, GANG - China Agricultural University|
|Liu, Ge - George|
|MA, LI - University Of Maryland|
|FANG, LINGZHAO - University Of Edinburgh|
|ZHANG, YI - China Agricultural University|
Submitted to: BMC Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/13/2020
Publication Date: 7/17/2020
Citation: Yan, Z., Huang, H., Freebern, E., Santos, D.J., Dai, D.D., Si, J., Ma, C., Cao, J., Guo, G., Liu, G., Ma, L., Fang, L., Zhang, Y. 2020. Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle. BMC Genomics. 21(1):489. https://doi.org/10.1186/s12864-020-06909-z.
Interpretive Summary: RNA sequencing generates a major source of genomic data. By performing integrative analyses of RNA sequencing data with large-scale genome-wide association studies in cattle, we provided novel insights into the molecular mechanism underlying ketosis in cattle. 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: Ketosis is a common metabolic disease during the transition period in dairy cattle, resulting in long-term economic loss to the dairy industry worldwide. While genetic selection of resistance to ketosis has been adopted by many countries, the genetic and biological basis underlying ketosis is poorly understood. Results: We collected a total of 24 blood samples from 4 healthy and 8 ketosis-diagnosed Holstein cows before (2 weeks) and after (5 days) calving, respectively. We then generated RNA-Sequencing (RNA-Seq) data and seven blood biochemical indicators (bio-indicators) for each of these samples. By employing a weighted gene co-expression network analysis (WGCNA), we determined that 4 out of 16 gene-modules were transcriptionally (FDR < 0.05) correlated with postpartum ketosis and several bio-indicators (e.g., high-density lipoprotein and low-density lipoprotein), which were significantly engaged in lipid metabolism and immune responses. By conducting a GWAS signal enrichment analysis for all gene-modules across six common disease traits (ketosis, mastitis, displaced abomasum, metritis, hypocalcemia and livability), we found four modules that were genetically (FDR < 0.05) associated with ketosis, among which three were shared with the WGCNA analysis above. We further identified five candidate genes for ketosis, including GRINA, MAF1, MAFA, C14H8orf82 and RECQL4. Our phenome-wide association analysis (Phe-WAS) demonstrated that human orthologues of these candidate genes were also significantly associated with many metabolic, endocrine, and immune traits in humans. For instance, MAFA, which is involved in insulin secretion, glucose response, and transcriptional regulation, showed a significantly higher association with metabolic and endocrine traits compared to other types of traits in humans. Conclusion: In summary, our study provides novel insights into the molecular mechanism underlying ketosis in cattle, and highlights that an integrative analysis of omics data and cross-species mapping are promising for illustrating the genetic architecture underpinning complex traits.