Abstract #649

# 649
An application of MeSH enrichment analysis in livestock.
Gota Morota*1, Francisco Pe├▒agaricano2,3, Jessica L. Petersen1, Daniel C. Ciobanu1, Koki Tsuyuzaki4,5, Itoshi Nikaido5, 1University of Nebraska-Lincoln, Lincoln, NE, 2University of Florida, Gainesville, FL, 3University of Florida Genetics Institute, Gainesville, FL, 4Tokyo University of Science, 2641 Yamazaki, Noda, Chiba, Japan, 5RIKEN, 2-1 Hirosawa, Wako, Saitama, Japan.

It is an integral part of functional genomics studies to assess the enrichment of specific biological terms in gene lists found to be playing an important role in biological phenomena. Contrasting the observed frequency of annotated terms with those of the background is at the core of over-representation analysis (ORA). Gene Ontology (GO) is a means to consistently classify and annotate gene products and has become a mainstay in ORA. Alternatively, Medical Subject Headings (MeSH) offers a comprehensive life science vocabulary including additional categories that are not covered by GO. Although MeSH is predominantly applied in human and model organism research, its full potential in livestock genetics is yet to be explored. MeSH ORA was evaluated to discern biological properties of the identified genes and contrasted with the results obtained from GO enrichment analysis. Three published data sets were employed for this purpose representing a gene expression study in dairy cattle, the use of SNPs for genome-wide prediction in swine, and the identification of genomic regions targeted by selection in horses. We found that several over-represented MeSH annotations linked to these gene sets share similar concepts with those of GO terms. Moreover, MeSH yielded unique annotations, which are not directly provided by GO terms, suggesting that MeSH has the potential to refine the representation of biological knowledge. We demonstrated that MeSH can be regarded as another choice of annotation to draw biological inferences from genes identified via experimental analyses. When used in combination with GO terms, our results indicate that MeSH can enhance our functional interpretations for specific biological conditions or the genetic basis of complex traits in livestock species.

Key Words: MeSH, enrichment analysis, annotation