This curriculum page provides links to learning modules and tutorials relevant to association analysis. The learning modules introduce linkage disequilibrium (LD) and association genetics. The tutorials focus on preparation of phenotypic (using an augmented experiment design and obtaining BLUPs) and genotypic (using MSA, Structure, and GGT2) data. Association analysis using TASSEL is also demonstrated.
Figure 1. Data pipeline for association analysis.
The Unified Mixed Model
y = μ + Sα + Qv + Zu + e
Phenotype Data (y)
Marker Matrix (Sα)
- m by q matrix, where m is the total number of observations and q is the number of genotypes at a marker locus
- Analyzing SNP quality
Population Structure (Q matrix - Qv)
Kinship Matrix (Polygene effect - Zu)
The Unified Mixed Model
- Unified Mixed Model [Online]. Buckler Lab for Maize Genetics and Diversity. Available at: http://www.maizegenetics.net/unified-mixed-model (verified 29 March 2012).
- Yu, J., G. Pressoir, W. H. Briggs, I. V. Bi, M. Yamasaki, J. F. Doebley, M. D. McMullen, et al. 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 29 March 2012).
Accounting for Population Structure
- Price, A. L., N. J. Patterson, R. M. Plenge, M. E. Weinblatt, N. A. Shadick, and D. Reich. 2006. Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics 38: 904-909. (Available online at: http://dx.doi.org/10.1038/ng1847) (verified 29 March 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 29 March 2012).
SAS Code for Association Analysis
Creating Matrix Equations Online
Development of this page 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.