# Available Commands

This page contains all command available in PRSice.

Tips

When constructing new parameters, we follow the following rule: if the command has effect on any file that is not the target, it will have a prefix of the file name. For example, --base-info applies INFO score filtering on the base file, --ld-info perform INFO score filtering on the LD reference file and --info applies the INFO score filtering on the target file.

## Base File

• --a1

Column header containing the effective allele. There isn't any standardized label for the effective allele, therefore extra care must be taken to ensure the correct label is provided, otherwise, the effect will be flipped.

• --a2

Column header containing non-effective allele.

• --base | -b

Base (i.e. GWAS) association file. This is a whitespace delimited file containing association results for SNPs on the base phenotype. This file can be gzipped (must have the .gz suffix). For PRSice to run, the base file must contain the effective allele (--A1), effect size estimates (--stat), p-value for association (--pvalue), and the SNP ID (--snp).

• --beta

This flag is used to indicate if the test statistic is in the form of BETA. If set, PRSice assume the statistic is in the form BETA. Mutually exclusive from --or

• --bp

Column header containing the coordinate of SNPs. When provided, the coordinate of the SNPs will be scrutinized between the base and target file. SNPs with mismatched coordinate will be excluded.

• --chr

Column header containing the chromosome information. When provided, the chromosome information of the SNPs will be scrutinized between the base and target file. SNPs with mismatched chromosome information will be automatically excluded.

• --index

If set, assume the base columns are INDEX instead of the name of the corresponding columns. Index should be 0-based (start counting from 0)

• --base-info

Base INFO score filtering. Format should be <Column name>:<Threshold>. SNPs with info score less than <Threshold> will be ignored. It is useful to perform INFO score filtering to remove SNPs with low imputation confidence score. By default, PRSice will search for the INFO column in your base file and perform info score filtering with threshold of 0.9. You can disable this behaviour by using --no-default

• --base-maf

Base minor allele frequency (MAF) filtering. Format should be <Column name>,:<Threshold>. SNPs with MAF less than <Threshold> will be ignored. Additional column can be provided (e.g. different filtering threshold for case and control), using the following format: <Column name>:<Threshold>,<Column name>:<Threshold>

• --no-default Remove all default options. If set, PRSice will not set any defaults.

• --or

This flag is used to indicate if the test statistic is in the form of odd ratios. If set, PRSice assume the statistic is in the form OR. Mutually exclusive from --beta

• --pvalue | -p

Column header containing the p-value. The p-value information must be provided

• --snp

Column header containing the SNP ID. This is required to allow SNP matching between the base and target file.

Note

While it is possible to implement a feature to allow SNP matching purely based on the chromosome number and coordinate of a variant, the possibiliy of flipping and multi-allelic input complicates the matter. Therefore this feature will not be implemented until an elegant solution can be provided.

• --stat

Column header containing the summary statistic. If --beta is set, default to BETA; likewise, if --or is set, default to OR. Otherwise, will try and search for OR or BETA from the header of the base file. If both OR and BETA is presented in the header, PRSice will terminate.

## Target File

• --binary-target

Indicate whether the target phenotype is binary or not. Either T or F should be provided where T represent a binary phenotype. For multiple phenotypes, the input should be separated by comma without space.

Default: F if --beta is set and T if --or is set

• --geno

Filter SNPs based on gentype missingness. Must be a value between 0.0 and 1.0.

• --info Filter SNPs based on info score. Only used for imputed target data. The INFO score is calculated as the MaCH imputation r-squared value, represented by the following pseudo code:

    m=Mean of expected genotype
v=variance of expected genotype
p=m/2
p_a = 2p(1-p)
INFO = v/p_a


• --keep

File containing the sample(s) to be extracted from the target file. First column should be FID and the second column should be IID. If --ignore-fid is set, first column should be IID Mutually exclusive from --remove

• --maf

Filter SNPs based on minor allele frequency (MAF). MAF is calculated using only the founder samples

Note

When perform MAF filtering on dosage data, the MAF is calculated using the hard-coded genotype

• --nonfounders

By default, PRSice will exclude all non-founders from the analysis. When this flag is set, non-founders will be included in the regression model but will still be excluded from LD estimation.

• --pheno | -f

Tab or space delimited phenotype file containing the phenotype(s). First column must be FID of the samples and the second column must be IID of the samples. When --ignore-fid is set, first column must be the IID of the samples. Must contain a header if --pheno-col is specified

• --pheno-col

Headers of phenotypes to be included from the phenotype file. When multiple phenotypes are provided, the phenotype name will be used as part of the file output prefix

• --prevalence | -k

Prevalence of all binary trait. If provided, PRSice will adjust the ascertainment bias of the R2.

Note

When multiple binary trait is found, prevalence information must be provided for all of them (Either adjust all binary traits, or don't adjust at all). For example, if there are 3 traits A, B and C, where A and C are binary traits with population prevalence of 0.1 and 0.2 respectively. The correct input should be --binary-target T,F,T --prevalence 0.1,0.2

• --remove

File containing the sample(s) to be removed from the target file. First column should be FID and the second column should be IID. If --ignore-fid is set, first column should be IID Mutually exclusive from --keep

• --target | -t

Target genotype file. Currently support both BGEN and binary PLINK format. For multiple chromosome input, simply substitute the chromosome number with #. PRSice will automatically replace # with 1-22. A separate fam/sample file can be specified by --target <prefix>,<fam or sample file>

• --target-list

File containing prefix of target genotype files. Similar to --target but allow for more flexibility. A separate fam/sample file can be specified by --target-list <list-file>,<fam or sample file>

• --type

File type of the target file. Support bed (binary plink) and bgen format. Default: bed

## Dosage

• --allow-inter

Allow the generate of intermediate file. This will speed up PRSice when using dosage data as clumping reference and for hard coding PRS calculation

• --dose-thres

Translate any SNPs with highest genotype probability less than this threshold to missing call. For example, with --dose-thres 0.9, sample with genotype probability of $$P(0/0)=0.2$$, $$P(0/1)=0.52$$, $$P(1/1)=0.28$$ will be set to missing

• --hard-thres

A hardcall is saved when the distance to the nearest hardcall is less than the hardcall threshold. Otherwise a missing code is saved. Default is: 0.1

The distance ($$D$$) to the nearest hardcall is calculated as:

$P(Ref) = 2 \times P(HomRef) + P(Het) \\ P(Alt) = 2 \times P(HomAlt) + P(Het) \\ D = 0.5 \times \left(|P\left(Ref\right)- round\left(P\left(Ref\right)\right)| + |P\left(Alt\right)- round\left(P\left(Alt\right)\right)|\right)$

Note

If dosage data is used as a LD reference, it will always be hard coded to calculate the LD

Default: 0.9

• --hard

When set, will use hard thresholding instead of dosage for PRS construction. Default is to use dosage.

## Clumping

• --clump-kb The distance for clumping in kb. For example, if --clump-kb 250 is provided, PRSice will clump any SNPs that is within 250kb to both end of the index SNP (therefore a 500kb window with the index SNP at the center). Now also support distance with a unit. e.g. --clump-kb 1M is a valid input. Default: 250kb for PRSice, 1mb for PRSet

• --clump-r2

The r2 threshold for clumping. Default: 0.1

• --clump-p

The p-value threshold use for clumping. Default: 1.

• --ld | -L

LD reference file. Use for estimation of LD during clumping. If not provided, will use the post-filtered target genotype for LD calculation. Support multiple chromosome input. Please see --target for more information. When the target sample is small (e.g. < 500) and external panel of the same population is available (e.g. 1000 genome), an external reference panel might be used to improve the LD estimation for clumping.

• --ld-dose-thres

Translate any SNPs with highest genotype probability less than this threshold to missing call. For example, with --ld-dose-thres 0.9, sample with genotype probability of $$P(0/0)=0.2$$, $$P(0/1)=0.52$$, $$P(1/1)=0.28$$ will be set to missing

• --ld-geno

Filter SNPs based on genotype missingness. Must be a value between 0.0 and 1.0.

• --ld-info

Filter SNPs based on info score. Only used for imputed LD reference. The INFO score is calculated as the MaCH imputation r-squared value, represented by the following pseudo code

    m=Mean of expected genotype
v=variance of expected genotype
p=m/2
p_a = 2p(1-p)
INFO = v/p_a

• --ld-hard-thres

A hardcall is saved when the distance to the nearest hardcall is less than the hardcall threshold. Otherwise a missing code is saved. Default is: 0.1

The distance ($$D$$) to the nearest hardcall is calculated as:

$P(Ref) = 2 \times P(HomRef) + P(Het) \\ P(Alt) = 2 \times P(HomAlt) + P(Het) \\ D = 0.5 \times \left(|P\left(Ref\right)- round\left(P\left(Ref\right)\right)| + |P\left(Alt\right)- round\left(P\left(Alt\right)\right)|\right)$

Note

If dosage data is used as a LD reference, it will always be hard coded to calculate the LD

Default: 0.9

• --ld-keep

File containing the sample(s) to be extracted from the LD reference file. First column should be FID and the second column should be IID. If --ignore-fid is set, first column should be IID. Mutually exclusive from --ld-remove. No effect if --ld was not provided

• --ld-list

File containing prefix of multiple LD reference files. Similar to --ld but allow more flexibility. A separate fam/sample file can be specified by --ld-list <list-file>,<fam or sample file>

• --ld-maf

Filter SNPs based on minor allele frequency (MAF)

Note

When perform MAF filtering on dosage data, MAF is calculated using the hard-coded genotype

• --ld-remove

File containing the sample(s) to be removed from the LD reference file. First column should be FID and the second column should be IID. If --ignore-fid is set, first column should be IID. Mutually exclusive from --ld-keep

• --ld-type

File type of the LD file. Support bed (binary plink) and bgen format. Default: bed\n"

• --no-clump

When set, PRSice will not perform clumping. This is useful a pre-clumped list of SNPs is available.

• --proxy

Proxy threshold for index SNP to be considered as part of the region represented by the clumped SNP(s). e.g. --proxy 0.8 means the index SNP will represent region of any clumped SNP(s) that has r2=0.8 even if the index SNP does not physically locate within the region

## Covariate

• --cov | -C Covariate file. First column should be FID and the second column should be IID. If --ignore-fid is set, first column should be IID

• --cov-col | -c Header of covariates. If not provided, will use all variables in the covariate file. By adding @ in front of the string, any numbers within [ and ] will be parsed. E.g. @PC[1-3] will be read as PC1,PC2,PC3. Discontinuous input are also supported: @cov[1.3-5] will be parsed as cov1,cov3,cov4,cov5

• --cov-factor

Header of categorical covariate(s). Dummy variable will be automatically generated. Any items in --cov-factor must also be found in --cov-col Also accept continuous input (start with @).

## P-value Thresholding

• --bar-levels

Level of barchart to be plotted. When --fastscore is set, PRSice will only calculate the PRS for threshold within the bar level. Levels should be comma separated without space

• --fastscore

Only calculate threshold stated in --bar-levels

• --no-full

By default, PRSice will include the full model, i.e. p-value threshold = 1. Setting this flag will disable that behaviour

• --interval | -i

The step size of the threshold. Default: 5e-05

• --lower | -l

The starting p-value threshold. Default: 5e-08

• --model

Genetic model use for regression. The genetic encoding is based on the base data where the encoding represent number of the effective allele Available models include:

• add - Additive model, code as 0/1/2 (default)
• dom - Dominant model, code as 0/1/1
• rec - Recessive model, code as 0/0/1
• het - Heterozygous only model, code as 0/1/0
• --missing

Method to handle missing genotypes. Available methods include:

• MEAN_IMPUTE - Missing genotypes contribute an amount proportional to imputed allele frequency (default)
• SET_ZERO - To throw out missing observations instead
• CENTER - shift all scores to mean zero.
• --no-regress

Do not perform the regression analysis and simply output all PRS.

• --score

Method to calculate the polygenic score. Available methods include:

• avg - Take the average effect size (default)
• std - Standardize the effect size
• con-std - Standardize the effect size using mean and sd derived from control samples
• sum - Direct summation of the effect size
• --upper | -u

The final p-value threshold. Default: 0.5

## PRSet

• --background

String to indicate a background file. This string should have the format of Name:Type where type can be

• bed - 0-based range with 3 column. Chr Start End
• range - 1-based range with 3 column. Chr Start End
• gene - A file contain a column of gene name

As the name suggest, the background file inform PRSet of the background signal to be used for competitive p-value calculation.

When a background file is not provided, PRSet will construct the background using the GTF file. However, if both the background and the GTF file isn't provided, PRSet cannot perform the set base permutation. In this case, you can use --full-back to indicate that you'd like to use the whole genome as the background set

• --bed | -B

Bed file containing the selected regions. Name of bed file will be used as the region identifier.

Warning

Bed file is 0-based.

• --feature

Feature(s) to be included from the gtf file.

Default: exon,CDS,gene,protein_coding

• --full-back

Use the whole genome as background set for competitive p-value calculation

• --gtf | -g

GTF file containing gene boundaries. Required when --msigdb is used

Tip

Human Genome build GRCh38 can be downloaded from here.

• --msigdb | -m

MSigDB file containing the pathway information. Require the gtf file. The GMT file format used by MSigDB is a simple tab/space delimited text file where each line correspond to a single gene set following by Gene IDs:

    [Set A] [Gene 1] [Gene 2] ...
[Set B] [Gene 1] [Gene 2] ...


Tip

Curated MSigDB files can be downloaded from here after registration in here

• --set-perm

The number of set base permutation to perform. This is only used for calculating the competitive p-value. 10,000 permutation nshould generally be enough.

• --snp-set

Provide gene sets using SNP ID. Two different format is allowed:

• SNP Set list format: A file containing a single column of SNP ID. Name of the set will be the file name or can be provided using --snp-set File:Name
• MSigDB format: Each row represent a single SNP set with the first column containing the name of the SNP set.
• --wind-3

Add N base(s) to the 3' region of each gene regions. Unit suffix are allowed e.g. --wind-3 1M

• --wind-5

Add N base(s) to the 5' region of each gene regions. Unit suffix are allowed e.g. --wind-5 1M

## R specific commands

• --prsice

Location of the PRSice executable.

• --dir

Location to install require R packages. Only require if the required packages are not installed. We require the following packages: optparse, method, tools, ggplot2, data.table, grDevices, RColorBrewer

## Plotting

• --bar-col-high

Colour of the most predicting threshold. Default: firebrick

• --bar-col-low

Colour of the poorest predicting threshold. Default: dodgerblue

• --bar-col-p

When set, will change the colour of bar to p-value threshold instead of the p-value from the association with phenotype

• --bar-palatte

Colour palatte to be used for bar plotting when --bar_col_p is set. Default: YlOrRd

• --device

Select different plotting devices. You can choose any plotting devices supported by base R. Default: png

• --multi-plot

Plot the top N target phenotypes / gene sets in a summary plot

• --plot

When set, will only perform plotting using existing PRSice result files. All other parameters are still required such that PRSice can correctly locate the required input files for plotting.

• --plot-set

The default behaviour of PRSet is to plot the bar-chart, high-resolution plot and quantile plot of the "Base" gene set, which consider all SNPs within the genome. By using the --plot-set option, you can plot the specific set of interest.

• --quantile | -q

Number of quantiles to plot. No quantile plot will be generated when this is not provided.

• --quant-break

Parameter to indicate an uneven distribution of quantile. Values represent the upperbound of each quantile group.

e.g. With --quantile 10 --quant-break 1,5,10, the quantiles will be grouped into

$$0\lt Q \le 1$$, $$1\lt Q \le 5$$, $$5\lt Q \le 10$$

Note

To use --quant-break, you must set the correct amount of quantiles. For example, if the largest value in --quant-break is 100, then you must use --quantile 100

• --quant-extract | -e

File containing sample ID to be plot on a separated quantile e.g. extra quantile containing only schizophrenia samples. Must contain IID. Should contain FID if --ignore-fid isn't set.

Note

This will only work if the base and target has a different phenotype or if the target phenotype is quantitative

• --quant-ref

Reference quantile for quantile plot. Default is number of quantiles divided by 2

Or in the event where --quant-break is used, represent the upper bound of the reference quantile

• --scatter-r2

When set, will change the y-axis of the high resolution scatter plot to R2 instead

## Miscellaneous

• --all-score

Output PRS for ALL threshold.

Warning

This will generate a huge file

• --exclude

File contains SNPs to be excluded from the analysis. Mutually exclusive from --extract

• --chr-id

Try to construct an RS ID for SNP based on its chromosome, coordinate, effective allele and non-effective allele.

For example, c:L-aBd is translated to: <chr>:<coordinate>-<effective><noneffective>d

This ID will always be used to represent SNPs on the target file, whereas for the base file, we will still prefer to use the column provided in the --snp parameter. SNPs in base file will only be represented by the --chr-id if the RS ID is not provided.

• --extract

File contains SNPs to be included in the analysis. Mutually exclusive from --exclude

• --id-delim

Delimiter used to concatinate FID and IID when performing ID matching. Especially useful for BGEN file processing

• --ignore-fid

Ignore FID for all input. When this is set, first column of all file will be assume to be IID instead of FID

• --keep-ambig

Keep ambiguous SNPs. PRSice will only perform dosage flipping but not strand flipping on ambiguous SNPs. e.g. If your base data contain A/T, with effective allele being A, and your target data is T/A with dosage of T, then PRSice will change the dosage in target to A/T with dosage of A. Only use this option when you are certain your base and target are on the same strand.

• --logit-perm

When performing permutation on binary phenotypes, use logistic regression instead of linear regression. This will substantially slow down PRSice.

Note

One problem with using --logit-perm is that some of the permuted phenotype might be suffer from perfect separation. This leads to the GLM logistic model not being able to be converge (thus terminating PRSice).

If you encounter such problem, you might want to exclude the --logit-perm option. In most case, the p-value of the linear model should be similar to the logistic model

• --memory

Maximum memory usage allowed. PRSice will try its best to honor this setting. For example, --memory 10Gb will restrict PRSice to use no more than 10Gb of memory.
However, as we are not using memory pool like PLINK, it is possible for PRSice to use more than the allowed amount. PRSice will mainly check the memory usage when:

• Perform Clumping
• Perform permutation analysis
• Perform set-based permutation
• --non-cumulate

Calculate non-cumulative PRS. PRS will be reset to 0 for each new P-value threshold instead ofadding up

• --out | -o

Prefix for all file output.

Note

If multiple target phenotypes are included (e.g. using --pheno-col), the phenotype will be appended to the output prefix

If multiple gene set are included, the name of the set will be appended to the output prefix (after the phenotype (if any))

• --perm

Number of permutation to perform. This will generate the empirical p-value. Recommend to use value larger than or equal to 10,000

Note

When permutation is required, PRSice will perform the following operation

1. Perform normal PRSice across all thresholds and obtain p-value of the most significant threshold
2. Repeat PRSice analysis N times with permuted phenotype. Count the number of time where the p-value of the most significant threshold for the permuted
• --print-snp

Print all SNPs that remains in the analysis after clumping is performed. For PRSet, 1 indicate the SNPs falls within the gene set of interest and 0 otherwise. If only PRSice is performed, a single "gene set" called "Base" will be indicated with all entries marked as 1

• --seed | -s

Seed used for permutation. If not provided, system time will be used as seed. This will allow the same results to be generated when the same seed and input is used

• --thread | -n

Number of thread use

Tip

Maximum number of thread can be specified by using --thread max

Note

PRSice will limit the maximum number of thread used to the number of core available on the system as detected by PRSice.

• --ultra

Ultra aggressive memory managememnt. Will store all genotype into the memory after clumping is performed. This will significant speed up PRSice and PRSet at the expense of increased memory usage.

• --x-range
Range of SNPs to be excluded from the whole analysis. It can either be a single bed file or a comma seperated list of range. Range must be in the format of chr:start-end or chr:coordinate

• --help | -h

Display the help messages