Browsing by Author "Santos, Daniel J. A."

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Fine mapping of genomic regions associated with female fertility in Nellore beef cattle based on sequence variants from segregating sires
    (Springer Nature, 2019-12-16) Oliveira, Gerson A., Jr; Santos, Daniel J. A.; Cesar, Aline S. M.; Boison, Solomon A.; Ventura, Ricardo V.; Perez, Bruno C.; Garcia, José F.; Ferraz, José Bento S.; Garrick, Dorian J.
    Impaired fertility in cattle limits the efficiency of livestock production systems. Unraveling the genetic architecture of fertility traits would facilitate their improvement by selection. In this study, we characterized SNP chip haplotypes at QTL blocks then used whole-genome sequencing to fine map genomic regions associated with reproduction in a population of Nellore (Bos indicus) heifers.
  • Thumbnail Image
    Item
    GWAS and fine-mapping of livability and six disease traits in Holstein cattle
    (Springer Nature, 2020-01-13) Freebern, Ellen; Santos, Daniel J. A.; Fang, Lingzhao; Jiang, Jicai; Parker Gaddis, Kristen L.; Liu, George E.; VanRaden, Paul M.; Maltecca, Christian; Cole, John B.; Ma, Li
    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 identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data.
  • Thumbnail Image
    Item
    Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle
    (Springer Nature, 2020-07-17) Yan, Ze; Huang, Hetian; Freebern, Ellen; Santos, Daniel J. A.; Dai, Dongmei; Si, Jingfang; Ma, Chong; Cao, Jie; Guo, Gang; Liu, George E.; Ma, Li; Fang, Lingzhao; Zhang, Yi
    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. We collected a total of 24 blood samples from 12 Holstein cows, including 4 healthy and 8 ketosis-diagnosed ones, before (2 weeks) and after (5 days) calving, respectively. We then generated RNA-Sequencing (RNA-Seq) data and seven blood biochemical indicators (bio-indicators) from leukocytes and plasma in each of these samples, respectively. By employing a weighted gene co-expression network analysis (WGCNA), we detected that 4 out of 16 gene-modules, which were significantly engaged in lipid metabolism and immune responses, were transcriptionally (FDR < 0.05) correlated with postpartum ketosis and several bio-indicators (e.g., high-density lipoprotein and low-density lipoprotein). By conducting genome-wide association signal (GWAS) enrichment analysis among six common health traits (ketosis, mastitis, displaced abomasum, metritis, hypocalcemia and livability), we found that 4 out of 16 modules were genetically (FDR < 0.05) associated with ketosis, among which three were correlated with postpartum ketosis based on WGCNA. 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. 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.