PLINK is a very useful, easy to use, command prompt based application designed to help you with whole genome association analysis. This tool can also perform a range of basic, large-scale analyses in a computationally efficient manner.
The focus of PLINK is purely on analysis of genotype / phenotype data, so there is no support for steps prior to this (for example, study design and planning, generating genotype or CNV calls from raw data).
PLINK [Updated] 2022
* Analyze genetic association data using various techniques
* Evaluates measures of association
* Shows how to conduct family-based and case-control association analysis
* Shows how to conduct haplotype association analysis
* Shows how to calculate odds ratios for case-control association analysis
* Shows how to select SNPs for fine mapping
* Shows how to use family based association analysis
* Shows how to conduct self-defined sub-populations
* Can perform large-scale genotyping in a computationally efficient manner
* Can perform single-SNP haplotype analysis
* Supports special characteristics of genetic data (e.g. non-Mendelian inheritance, missing data)
* Output to variety of formats (text, csv, dbgraph, html)
* Supports special characteristics of genotype data (e.g. Mendelian inheritance, missing data)
* Supports special characteristics of phenotype data (e.g. standard errors, covariates)
* Full version of the PLINK documentation, help files, and sample data
* Documentation for the package, sample data and help files for the package
* Many examples of usage of the package, with the same format as the help files.
* All functions available in PLINK are now available as methods for PLINK, which means the user can select methods by class rather than by function.
Documentation is provided in the form of the commands, help files, and a helpfile for PLINK.
The PLINK command: plink
The basic PLINK command is:
PLINK ( ,  )
This will read input files and output a PLINK output file.
Use the plink command to perform basic analyses on single SNPs and a few common methods. It also performs self-defined sub-populations.
There are other commands available. The aim of this release is to provide a high quality commandline tool for association analysis.
* plink > output file
* plink input.txt output.plink
* plink input.txt > output.plink
* plink –help
plink Programming Guide:
* Input Parameters
PLINK has several input parameters:
* – The file where your data is located. It must be in PLINK format (see below). PLINK cannot read
PLINK can work with any PLINK-compatible input data file. The key to its efficiency is in the use of a key macro, which must be defined in the input data file. It is important that the key macro is defined at the sample level (rather than as an entire dataset).
A sample-level key macro is defined as follows:
is the same as
is the name for a SNP in the Illumina data file
is a label for a sample in the Illumina data file (a number)
is the number of the chromosome in the Illumina data file (1, 2,…, 10)
is the physical position of the SNP in the Illumina data file
is the minimum absolute difference between the genotype or CNV calls at the SNP for each sample (if any). This is optional, but can improve efficiency if you have small minimums. For example, to create a “weighted” key you could define:
PLINK can efficiently process many samples by creating temporary data files. The data files are stored in a “directory” of samples, which is constructed in the command-line when the program is run. The naming convention for the temporary data files is as follows:
PLINK With Serial Key [Latest] 2022
PLINK is a command-line based application for performing association tests on genotype or phenotype data.
The latest stable version of PLINK is available at:
You might want to visit
CAGAL is a C++ library for read-counting and variant-calling based on an algorithm that is similar to GATK’s variant discovery. It provides a variety of functionalities for sample and variant callers. Some useful tools are listed in the list of CAGAL_Tools.
Raptors-PLINK is an open-source implementation of the Raptor-linkage Linkage analysis package.
Raptors-PLINK contains the capability to create a user-defined quantity similar to a genotype quality score (e.g. -1, 0, 1, 2, or 100), and to use this score as a marker to be tested for association with a phenotype using PLINK’s -analysis option.
Raptors-PLINK differs from the original Raptor-linkage package in that it does not use a likelihood-based framework to test for linkage, but instead uses a simple chi-squared test to determine whether the number of trait-marker pairs is significantly greater than expected by chance (which is important for SNP arrays).
Raptors-PLINK also differs from the original package in that it adds a method for correcting for inflation due to overlapping linkage signals.
Raptors-PLINK implements a custom wrapper for PLINK and can be easily installed on Linux, Mac, and Windows.
The OCF was created to provide a general purpose, versatile, efficient and free bioinformatics software system for sequence alignment and sequence variation analysis. It is
What’s New In?
PLINK was originally designed for use with the 1.5 MB human genome. The current version can work with both the human and mouse genome sequences.
PLINK looks at two different kinds of variation for association analysis:
At single base pair resolution (SNPs), it looks at nucleotide counts at specific base positions in an alignment. It uses a sliding window of size M (default is 20) to look for statistical deviation from the expected distribution.
At larger scale, it can analyze repeated regions that are not limited by a given marker, but instead involve segments of a fixed length. PLINK calls these regions using a Hidden Markov Model.
PLINK analysis involves just two steps: calling variants, and association analysis.
The first step in PLINK is to use a base-level calling function, such as the one shown below.
For each sample, a set of variants is specified. For each sample, PLINK will call variants with a base quality score greater than 20. If you specify a list of variant positions, PLINK will generate all variants at those positions. If you do not specify variant positions, PLINK will call variants based on the genome.
The next step in PLINK is to cluster the variants into related groups. These related groups are subsequently used to assign a “call quality” score for each sample. The call quality score is an estimate of the probability that a variant call is correct. This score is computed by taking the average base quality score of a variant over all samples, and scaling the scores to a 0-1 range.
PLINK performs an association analysis between each group of variants and the phenotype of interest.
To perform this analysis, PLINK needs a VCF file for each group of variants, as well as a phenotype file, in the same order as the variant groups. For each group, PLINK will compute a chi-square statistic for each variant. The result is written to a PLINK result file, which includes all the results for a single analysis.
PLINK can optionally make use of the LD information in the VCF files, and can optimize the LD calculation based on the underlying population.
Read more about PLINK in the PLINK documentation. The PLINK website contains a number of informative pages as well, which you can explore here: PLINK.
To install PLINK, run the install command.
To run PLINK, run the run command. The PLINK manual contains the details of how to use PLINK. In brief:
The first command in PLINK (run) generates the VCF files.
The next two commands (call and run) use the generated VCF files to call variants, and compute the chi-square statistics for each variant.
The next three commands (stat, tabulate and write)
System Requirements For PLINK:
Windows 7, Windows 8, Windows 8.1, Windows 10
MacOS 10.6, 10.7, 10.8, 10.9, 10.10
(Right Stick + A Button)
(L Button + A Button)