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This function acts as a front-end for TASSEL's extensive association analysis methods. Using this function, users can run the following TASSEL association methods:

  • best linear unbiased estimates (BLUEs)

  • generalized linear model (GLM)

  • mixed linear model

  • Fast association (Shabalin 2012)


  fitMarkers = FALSE,
  kinship = NULL,
  fastAssociation = FALSE,
  maxP = 0.001,
  maxThreads = NULL,
  minClassSize = 0,
  outputFile = NULL,
  biallelicOnly = FALSE,
  appendAddDom = FALSE



An object of class TasselGenotypePenotype.


An R-based linear model formula. The general layout of this formula uses the following TASSEL data scheme: <data> ~ <factor> and/or <covariate>. If all traits in a Phenotype object should be ran, a simplified formula (. ~ .) can be used. This scheme can also be used for running all <data> or <factor> and/or <covariate> data as well. Single variables are separated witha + operator. See vignette for further clarification.


Should marker data be fitted? If TRUE, GLM analysis will be executed. If FALSE, BLUEs will be calculated. Defaults to FALSE.


Should kinship data be accounted for in the model? If so, a TASSEL kinship matrix object of class TasselDistanceMatrix must be submitted. Defaults to NULL.


Should TASSEL's Fast Association plugin be used? Consider setting to TRUE if you have many phenotypes in your data set.


Maximum p-value (0 - 1) to be reported. Currently works with fast association only. Defaults to a p-value of 0.001 will be used as a threshold. Note: p-value parameter will not be used for BLUE analysis.


Maximum threads to be used when running fast association. If NULL, all threads on machine will be used.


The minimum acceptable genotype class size. Genotypes in a class with a smaller size will be set to missing. Defaults to 0.


Output file prefix to be specified in case you want to write data directly to disk. Highly recommended for large datasets. If NULL, no data will be saved to disk. If a character


Only test sites that are bi-allelic. The alternative is to test sites with two or more alleles. Defaults to FALSE


If true, additive and dominance effect estimates will be added to the stats report for bi-allelic sites only. The effect will only be estimated when the data source is genotype (not a probability). The additive effect will always be non-negative. Defaults to FALSE.


Returns an R list containing DataFrame-based data frames