# Preparation¶

Before performing PRSice, you should perform quality control on your target samples. See here for an example.

## Input¶

• PRSice.R file: A wrapper for the PRSice binary and for plotting
• PRSice executable file: Perform all analysis except plotting
• Base data set: GWAS summary results, which the PRS is based on
• Target data set: Raw genotype data of target phenotype. Can be in the form of PLINK binary or BGEN

# Running PRSice¶

In most case, you can simply run PRSice using the following command, assuming your PRSice executable is located in ($HOME)/PRSice/ and you are working in ($HOME)/PRSice

Note

## Binary Traits¶

For binary traits, you can use the following command (commands specific to binary traits are highlighted in yellow)

Rscript PRSice.R --dir . \
--prsice ./PRSice \
--base TOY_BASE_GWAS.assoc \
--target TOY_TARGET_DATA \
--stat OR \
--binary-target T


## Quantitative Traits¶

For quantitative traits, you can use (commands specific to quantitative traits are highlighted in yellow)

Rscript PRSice.R --dir . \
--prsice ./PRSice \
--base TOY_BASE_GWAS.assoc \
--target TOY_TARGET_DATA \
--stat BETA \
--binary-target F


Note

If you do not provide the PRSice command line with the type of Effect (--stat) or data type (--binary-target) then PRSice will try to work these out from the header of your base file:

1. When BETA (case insensitive) is found in the header and --stat was not provided, --beta will be added to your command, and if --binary-target was not provided, --binary-target F will be added to your command

2. When OR (case insensitive) is found in the header and --binary-target was not provided, --binary-target T will be added to your command

PRSice will detail all effective options in its log file where you can simply copy and paste it to get the same output

# Quality Control of Target Samples¶

You can perform quality control on the target samples using PLINK. A good starting point is (assume ($target) is the prefix of your target binary file) plink --bfile ($target) \
--maf 0.05 \
--mind 0.1 \
--geno 0.1 \
--hwe 1e-6 \
--make-just-bim \
--make-just-fam \
--out ($target).qc  Then you can add --keep ($target).qc.fam --extract (\$target).qc.bim to PRSice command to filter out the samples and SNPs