This space is home to learning materials and tutorials created for CyVerse products and services. To search the entire CyVerse wiki, use the box at the upper right.






Skip to end of metadata
Go to start of metadata



PLINK is a free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner. Though PLINK is not quite as effective as some of the other tools listed above, it is nonetheless versatile and useful for PLINK file format conversion. The developer website for version 1.9 may be found here.

NOTE: For most any type of command you run in PLINK, the program will initially try and connect to the Internet to download the latest version of the software. Because this process is often unnecessary and time-consuming, we recommend using the --noweb flag in each of your commands to tell PLINK not to update.

The file formats PLINK uses for genotype data fall into several different groups:

Standard files with .ped and .map extensions detailing the genotype information of an individual or a group of individuals. The PED file is delimited by spaces or tabs, and the first six columns of a PED file must correspond to the following: Family ID, Individual ID, Paternal ID, Maternal ID, Sex, and Phenotype. The MAP file is a description of each genetic marker, and this file must have exactly 4 columns: Chromosome, SNP ID, Genetic distance (in morgans), and Base-pair position.

To run PED/MAP format files, use this command:

Alternatively, if the PED and MAP files have different names, use these commands:

If certain columns of the PED files are missing, the flags --no-parents, --no-sex, and --no-pheno may be used to denote PED files without the paternal and maternal IDs, sex, or phenotype respectively.

Binary equivalents of the PED/MAP files. Because of the conversion to binary, these files take up far less storage space and can be more efficiently loaded than an equivalent PED/MAP set would. Furthermore, do not attempt to read the BED file; it is a compressed file and a text editor will only show strange characters when opening the BED file. The BIM and FAM files, however, are still readable in a standard text editor. The BIM file is basically an extended MAP file with two extra columns designating the allele names. The FAM file is merely the first six columns of the PED file, and as such displays the mandatory information detailed above.

To run BED/BIM/FAM files, use this commmand:

These files represent a transposed fileset. The first 4 columns of a TPED file are the same as a standard 4-column MAP file. Then, all genotypes are listed for all individuals for each particular SNP on each line. The TFAM file, like the standard FAM file, is just the first six columns of a standard PED file.

To run the TPED/TFAM files, use this command:

Note that some or all of these file extensions are also usable with other genome wide association studies tools in the Validate Workflow (e.g. FaST-LMM, GEMMA).

In some cases, PLINK also supports covariates in a given dataset. A covariate file should be formatted with three columns: Family ID, Individual ID, and covariate value. To load said covariate file, type in the command line like so:

Depending on your purpose for your data, occasionally conversion between PLINK formats may be necessary. Fortunately, PLINK is very efficient in this regard, and even extremely large datasets can be converted in a reasonable amount of time.

Assuming that you are starting with the standard PED/MAP format, data may be converted into binary format with the command

The default output name for the new BED/BIM/FAM files will be plink; however, the output name can be changed by instead running

This --out option for specifying the new file name may be used with any other type of PLINK file conversion or operation done from the command line.

To instead create transposed datasets, run the command:

Finally, to convert a binary or transposed file set back to its original PED/MAP format, add the --recode option to the command line like so:

PLINK is also capable of running many types of basic quantitative analyses including epistasis, dosage analysis, and meta-analysis. For the purposes of this tutorial, however, we will only focus on the three algorithms for quantitative trait association.

The standard quantitative trait association analysis may be called using the command

This command will create a new file titled based on whatever name you specified in the command (will be called plink by default). Regardless of your chosen file name, the file extension will be .qassoc and it will have the following columns:




Chromosome number


SNP Identifier


Physical position (base-pair)


Number of missing genotypes


Regression coefficient


Standard Error


Regression r-squared (coefficient of determination)


Wald test (based on a t-distribution)


Wald test asymptotic p-value


Both linear and logistic regression are supported, and these models are actually more flexible than the standard quantitative trait association option. More specifically, these regression models allow options for the aforementioned covariate files (again using the --covar option), for the interaction between said covariates and the SNPs (--interaction), for confidence intervals with each value (--ci <decimal number between 0 and 1>), etc. Keep in mind that while these regression models have extra options, they come at the price of increased runtime.

To run a linear regression model, type the command like so:

This will generate an .assoc.linear file with the name specified.

to run a logistic regression model, type

This command will generate an .assoc.logistic file with the output name you specified. For both of these options, the standard columns in the output files will be the following:



Chromosome number


SNP Identifier


Physical position (base-pair)


Tested allele (minor allele by default)


Code for test (ADD for additive effects of allele dosage by default)


Number of non-missing individuals included in analysis


Regression coefficient (for linear regression) or odds ratio (for logistic)


Coefficient t-statistic


Asymptotic p-value for coefficient t-statistic


Any of these analyses outputs may be run through Winnow and therefore are compatible with the rest of the Validate Workflow (so long as a known-truth file is available as well).

Developer website for version 1.9:

Sample data:

  • No labels