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Video Tutorial of Downstream Analysis with SNP Markers Using MSA, GGT, and STRUCTURE software

Last Updated: May 16, 2012

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Authors:

Sung-Chur Sim, The Ohio State University; Heather L. Merk, The Ohio State University

This webinar tutorial and the accompanying pdf and zip files were presented at the 2010 Tomato Disease Workshop. These materials outline the utility of MSA, GGT, and STRUCTURE software for downstream analysis with single nucleotide polymorphism (SNP) markers. In addition, a case study demonstrates the use of STRUCTURE for association mapping of bacterial spot resistance in tomato.

The pdf at the bottom of the page (Downstream_SNP_Analysis) is a copy of Dr. Sim's presentation. The accompanying zip files (Downstream_Supplemental_Files) at the bottom of the page include a sample data set, an Excel file with best K analysis, and SAS code for association analysis that incorporates the Q matrix.

In the first video, Dr. Sung-Chur Sim, The Ohio State University, outlines the utility of MSA, GGT, and STRUCTURE software for downstream analysis with single nucelotide polymorphism (SNP) markers. Dr. Sim also explains where and how to download the software and how to format input data.

In the second video, Dr. Sim demonstrates the use of STRUCTURE for association mapping of bacterial spot resistance in tomato. Dr. Sim introduces association analysis models and emphasizes the use of the Q matrix to correct for population structure in association mapping. In addition, Dr. Sim explains the detailed steps in STRUCTURE to infer the best "K" (number of populations), which is required to obtain the Q matrix.

Find all the presentations from the 2010 Tomato Disease Workshop

References Cited

  • Evanno, G., S. Regnaut, and J. Goudet. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Molecular Ecology 14: 2611–2620. (Available online at: http://dx.doi.org/10.1111/j.1365-294X.2005.02553.x) (verified 12 May 2012).

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Funding Statement

Development of this lesson was supported in part by the National Institute of Food and Agriculture (NIFA) Solanaceae Coordinated Agricultural Project, agreement 2009-85606-05673, administered by Michigan State University. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the United States Department of Agriculture.

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