An application of MeSH enrichment analysis in livestock

Authors

Gota Morota, University of Nebraska- LincolnFollow
F. Moreno, University of Florida
James L. Petersen, University of Nebraska-LincolnFollow
Daniel C. Ciobanu, University of Nebraska-LincolnFollow
K. Tsuyuzaki, Tokyo University of Science
I. Nikaido, Advanced Center for Computing and Communication

Document Type

Article

Date of this Version

2015

Citation

Stichting International Foundation for Animal Genetics, 46, pp. 381–387, doi: 10.1111/age.12307.

Comments

Copyright © 2015 The Authors. This is an open access article under the terms of the Creative Commons Attribution License. Used by permission.

Abstract

An integral part of functional genomics studies is to assess the enrichment of specific biological terms in lists of genes 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 overrepresentation 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 applied predominantly in human and model organism research, its full potential in livestock genetics is yet to be explored. In this study, MeSH ORA was evaluated to discern biological properties of identified genes and contrast them with the results obtained from GO enrichment analysis. Three published datasets 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 overrepresented 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 and enrich 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.