Structure Software

Plant Breeding and Genomics May 22, 2013 Print Friendly and PDF

Authors:

David M. Francis, The Ohio State University; Sung-Chur Sim, The Ohio State University

Structure software assigns individuals to populations using genotype data. This tutorial includes a link to a free download. The tutorial provides screenshots to show users how to format genotypic data, how to import data, how to configure a parameter set, and how to run Structure. It also shows users two ways to estimate the best k (the true number of populations) and provides a screenshot of a Q matrix.

Introduction

Structure is a free software program developed by Pritchard et al. (2000) that uses multilocus genotype data (SNPs, SSRs, AFLPs, and RFLPs) to assign individuals to a population. One of the outputs from STRUCTURE is the Q matrix, which gives a probability that an individual belongs to a subpopulation. The Q matrix is incorporated as a fixed effect in the “Unified-Mixed Model” for association analysis (Yu et al., 2005).

Estimating and visualizing population structure has implications for breeding programs beyond providing a correction for association analysis. Breeders may want to preserve structure within programs when it corresponds to specific market niches. Alternatively, breeders may want to choose parents such that crosses and progeny reduce structure and bring breeding programs into Hardy–Weinberg equilibrium.

In the event that the slideshare tutorial does not work, the tutorial is also attached as a pdf at the bottom of the page.

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 30 Aug 2012).
  • Pritchard, J. K., M. Stephens, and P. Donnelly. 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945–959. (Available online at: http://www.genetics.org/cgi/content/abstract/155/2/945) (verified 30 Aug 2012).
  • Rosenberg, N. A., T. Burke, K. Elo, M. W. Feldman, P. J. Freidlin, M.A.M. Groenen, J. Hillel, A. Maki-Tanila, M. Tixier-Boichard, A. Vignal, K. Wimmers, and S. Weigend. 2001. Empirical evaluation of genetic clustering methods using multilocus genotypes from 20 chicken breeds. Genetics 159: 699–713. (Available online at: http://www.genetics.org/cgi/content/abstract/159/2/699) (verified 30 Aug 2012).
  • Yu, J., G. Pressoir, W. H. Briggs, I. V. Bi, M. Yamasaki, J. F. Doebley, M. D. McMullen, B. S. Gaut, D. M. Nielson, J. B. Holland, S. Kresovich, and E. S. Buckler. 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics 38: 203–208. (Available online at: http://dx.doi.org/10.1038/ng1702) (verified 30 Aug 2012).

External Links

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.

Attachments:

STRUCTURE Software Tutorial.pdf (1.87 MB)

PBGworks 923

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This work is supported by the USDA National Institute of Food and Agriculture, New Technologies for Ag Extension project.