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PANPRS

Installation

For the sparse matrix implementation, please install the R package as follows:

devtools::install_github("katherine-h-l/PANPRSnext@sparse", force = TRUE)

For the dense matrix implementation, please install the R package as follows:

devtools::install_github("katherine-h-l/PANPRSnext@master", force = TRUE)

Input for PANPRS incorporating multiple traits and functional annotations of SNPs.

summaryZ, The Z statistics of p SNPs from q GWA studies. A matrix with dimension p x q for p SNPs and q traits. The first column corresponds to the primary trait and the rest columns correspond to the secondary traits.

Nvec, A vector of length q for the sample sizes of q GWA studies.

plinkLD, LD matrix information.

NumIter, The number of maximum iterations for the estimation procedure.

funcIndex, Inputs for the functional annotations of SNPs. A p x k matrix with (0,1) entry; p is the number of SNPs and k is the number of functional annotations. For the element at i-th row, j-th column, the entry 0 means SNP i without j-th functional annotation; entry 1 means otherwise.

numfunc, The number of functional annotations.

dfMax The upper bound of the number of non-zero estimates of coefficients for the primary trait.

Usage:

The current version only work on Unix, Linux and Mac System, R(>=3.4.3), R packages "gtools" and "permutations" and GCC(>=4.4.7) are required.

Modify the parameters in the gsfPEN.R, and execute it.

Example

Please find it in the R package.

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Version

Install

install.packages('PANPRSnext')

Monthly Downloads

133

Version

1.2.1

License

GPL-3

Maintainer

Katherine Luo

Last Published

July 22nd, 2025

Functions in PANPRSnext (1.2.1)

test_gsfPEN

Run gsfPEN on a small sample of the provided data set (Only 100 samples)
gsPEN_sparse_cpp

Main CPP function
gsPEN_cpp

Main CPP function
gsfPEN_cpp

Main CPP function
funcIndex

Inputs for the functional annotations of SNPs.
gsPEN_R

Run the gsPEN algorithm for multiple traits, without functional annotations.
gsfPEN_R

Run the gsfPEN algorithm for multiple traits, with functional annotations.
gsfPEN_sparse_cpp

Main CPP function
test_gsPEN

Run gsPEN on a small sample of the provided data set (Only 100 samples)
plinkLD

The LD info from output of the software (plink)
summaryZ

The Z statistics from the univariate analysis of the association between 3614 SNPs and three traits respectively.
Nvec

A vector of sample sizes for the q traits of the summaryZ.