Medical subject heading (MeSH) annotations illuminate maize genetics and evolution

Authors: Beissinger, Timothy; MOROTA, GOTA - University Of Nebraska

Submitted to: Plant Methods
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/18/2017
Publication Date: 2/23/2017
Publication URL: https://handle.nal.usda.gov/10113/5723915
Citation: Beissinger, T.M., Morota, G. 2017. Medical subject heading (MeSH) annotations illuminate maize genetics and evolution. Plant Methods. 13:8. doi: 10.1186/s13007-017-0159-5.

Interpretive Summary: Modern tools have made it increasingly routine to identify genes that potentially contribute to agricultural traits, but determining the biological mechanisms underlying thir contributions is still difficult. In this manuscript, we addressed this problem through the use of Medical Subject Headings (MeSH), a new tool for making biological inferences from gene sets. We investigated five sets of genes that have been shown to be affected by, or contribute to, corn improvement. Among the findings made possible by MeSH, we observe that genes corresponding to "seeds" and "flowers" were under selection during corn domestication, and that "flower" genes continue to be under selection in the modern era of corn improvement. Furthermore, through this publication we demonstrate how MeSH terms can be assigned to genes and used to make biological inferences by other corn researchers and by the broader agricultural genetics community, ultimately enhancing producer yields.

Technical Abstract: In the modern era, high-density marker panels and/or whole-genome sequencing,coupled with advanced phenotyping pipelines and sophisticated statistical methods, have dramatically increased our ability to generate lists of candidate genes or regions that are putatively associated with phenotypes or processes of interest. However, the speed at with which we can validate genes, or even make reasonable biological interpretations about the principles underlying them, has not kept pace. A promising approach that runs parallel to explicitly validating individual genes is analyzing a set of genes together and assessing the biological similarities among them. This is often achieved via gene ontology (GO) analysis, a powerful tool that involves evaluating publicly available gene annotations. However, GO has limitations including its automated nature and sometimes-difficult interpretability. Here, we describe using Medical Subject Headings (MeSH terms) as an alternative tool for evaluating sets of genes to make biological interpretations and to generate hypotheses. MeSH terms are assigned to PubMed-indexed manuscripts by the National Library of Medicine, and can be mapped to directly genes to develop gene annotations. Once mapped, these terms can be evaluated for enrichment in sets of genes or similarity between gene sets to provide biological insights. Here, we implement MeSH analyses in five maize datasets to demonstrate how MeSH can be leveraged by the maize and broader crop-genomics community.