TASSEL aslo known as
Trait Analysis by aSSociation, Evolution and Linkage is a powerful statistical software to conduct
association mapping such as
General Linear Model (GLM) and
Mixed Linear Model (MLM). The GUI (graphical user interface) version of TASSEL is very well built for anyone who does not have a background or experience in working in
command line. In this tutorial, I will show how to prepare
input files and run assoication analysis in TASSEL. For detailed information on TASSEL, user’s guide and further documentation please visit:
1.1 Download and install TASSEL software
Download and install the latest version of the TASSEL software at this link:
1.2: Preparing the Input files
Prepare the phenotype file as shown below in the figure, and please remember if your data has
covariates such as
treatment, then, please categories them with header name
TASSEL allows various genotype file formats such as
VCF (variant call format),
plink. In this tutorial, I am using the
hmp.txt version of the genotype file. The below githe screenshot of the hmp.txt genotype file.
Step 1.2: Importing phenotype and genotype files
Import the files by following the steps shown below.
Tip! Both files can be opened at same time holding
CTRL and clicking the file names.
1.3 Phenotype distribution plot
It is always a wise idea to look at the phenotype distribution by plotting to check for any outliers. Follow below steps to plot histogram of your phenotype data.
1.4 Genotype summary analysis
Next crucial step is to look at the genotype data by simply following the steps shown. Couple of keys things to look at are:
- Minor allele frequency distribution
- Missing genotypic data to see if it requires to be imputed
- Proportion of heterozygous in the samples to check for self-ed samples
2.0 Conduct GWAS analysis
2.1 multidimensional scaling (MDS)
MDS output can be used as the covariate in the GLM or MLM to correct for population structure. Please follow the steps shown below:
2.2 Intersecting the files
MDS files by following the steps below:
3.0 running General Linear Model (GLM)
Run the GLM analysis by selecting the
intersected files following the steps below:
The output of the GLM analyis is produced ubder the
Result node. The GLM association test can be evaluated by plotting
Q-Q plot and the
Manhattan plot as shown below.
From the above Q-Q plot, we can see that are several markers that appear to be falsely associated with the trait, therefore, to control this confounding effect, use
Kinship matrix as an another covariate in the linear model
4.0 Calculating Kinship matrix
Follow the below steps to calcuate the kinship matrix.
4.1 running Mixed Linear Model (MLM)
Once the Kinship matrix has been calculated, MLM can be now be conducted by below:
4.2 Exporting results
One may export the results in .txt format by the following the below steps:
4.3 Significance Threshold
Bonferroni threshold can be deterimined to identify significantly markers associated with the trait by using the below equation:
P ≤ 1/N (α =0.05)
where, N is the total number of markers tested in association analysis) was used to identify the most significantly markers associated with the trait. Similarly, another way is to perform
False Discovey Rate) correction method.
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Thank you for reading this tutorial. If you have any questions or comments, please let me know in the comment section below or send me an email.
Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. (2007) TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633-2635.