Skip to main content
ARS Home » Midwest Area » Ames, Iowa » Corn Insects and Crop Genetics Research » Research » Publications at this Location » Publication #327163

Research Project: MaizeGDB: Enabling Access to Basic, Translational, and Applied Research Information

Location: Corn Insects and Crop Genetics Research

Title: New trait data at MaizeGDB

Author
item Schaeffer, Mary
item Portwood, John
item Gardiner, Jack - Iowa State University
item Andorf, Carson

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/16/2016
Publication Date: 3/17/2016
Citation: Schaeffer, M.L., Portwood II, J.L., Gardiner, J.M., Andorf, C.M. 2016. New trait data at MaizeGDB. In: 58th Annual Maize Genetics Conference, March 17-20, 2016, Jacksonville, Florida. p. 62.

Interpretive Summary:

Technical Abstract: MaizeGDB has several ways to archive trait data used for QTL and GWAS analyses. The simplest is simple posting of files provided by researchers along with links to the publication. More recently we have begun to integrate these data for diversity recombinant germplasm, and association panels. The goal is to integrate these data at one location, so that they may be used with minimal processing by researchers. Access to the data is provided under the ‘Diversity’ button on the home page, and, new this year, on individual Stock and Trait pages. For germplasm distributed by the North Central Regional Plant Introduction Station, individual Stock records link to the corresponding GRIN record, from which the germplasm may be requested. Last year our focus was on NAM and IBM mapping population, where we added values for some 60 traits. this year we have integrated published data for two diversity panels: the expanded Goodman panel, (Flint-Garcia et al 2005 Plant J 44:1054-64); and the Ames panel (Romay et al 2013 Genome Biol 14:R55) for many of the same traits. Additional trait categories this year include root architecture; disease response; and shoot apical meristem architecture. All traits are linked to controlled vocabularies, notably trait ontology, and the plant ontology (anatomy, growth), which permit filtering datasets, e.g. all ear traits; and promote interoperability with other crop and model plant species data.