papa Files

PAPA is a flexible tool for pleiotropic pathway analysis utilizing GWAS summary results.


1.Installing and Running PAPA
PAPA is developed by C to interface with R for efficient data analysis. Please make sure that R (http://www.r-project.org/) has been installed on your system. PAPA is a command line based program. Unzip the downloaded "PAPA" package and run "PAPA" program at your terminal window to start analysis.  
   Example:
   ./PAPA


2.Parameter Setting
PAPA will ask users to input a set of parameters before starting analysis:
[1]SNP-gene annotation file name: Please input the storage path and name of SNP-gene annotation file. We provide a SNP-gene annotation file, named ¡°SNPmap.txt¡± in PAPA package. ¡°SNPmap.txt¡± contains 419,513 entries. In this file, SNPs are assigned to a gene, if they located within the gene or < 500 kb upstream or downstream of the gene. Users¡¯ SNP-gene annotation files are also acceptable in PAPA. Please make your SNP-gene annotation files using following format:
   Example:
   rs2101576	AJAP1
   rs10915493	AJAP1
   rs10799221	AJAP1
   ....
        
   Each line of this file records a SNP (the first column) and corresponding gene (the second column). 

[2]Gene-pathway annotation file name: Please input the storage path and name of gene-pathway annotation file. We provide two pathway-gene annotation files in PAPA package. The first one (¡°PathmapGSEA.txt¡±) contains the pathways and gene ontology categories, derived from GSEA Molecular Signatures Database. The second one (¡°PathmapKEGG.txt¡±) contains the pathways and gene ontology terms, collected from KEGG, BioCarta, Ambion GeneAssist Pathway Atlas and Gene Ontology (GO) database. 
Users¡¯ gene-pathway annotation files are also acceptable in PAPA. Users can obtain pathway annotation information from public database, such as KEGG (http://www.genome.ad.jp/kegg/pathway.html), GSEA Molecular Signatures Database (http://www.broadinstitute.org/gsea/msigdb/ index.jsp), Gene Ontology (http://www.geneontology.org), Reactome (http://www.reactome.org/). Please make your gene-pathway annotation files using following format:
    Example:
    KEGG_GALACTOSE_METABOLISM	LALBA
    KEGG_GALACTOSE_METABOLISM	HK2
    KEGG_GALACTOSE_METABOLISM	HK1
    KEGG_GALACTOSE_METABOLISM	GLB1
    ....
    Each line of this file records a pathway (the first column) and corresponding gene (the second column)

[3]GWAS summary file name of phenotype 1: Please input the storage path  and name of GWAS summary file of phenotype 1. This file records the GWAS statistics of each SNP of phenotype 1. Each line of this file records a SNP (the first column) and corresponding association testing statistic (the second column) , for instance, chi-square values for qualitative traits.
   Example:
   rs3748597	0.524
   rs2465136	0.119
   rs13129	1.095
   rs9442372	0.919
   ....

[4]GWAS summary file name of phenotype 2: Please input the storage path and name of GWAS summary file of phenotype 2. This file records the GWAS statistics of each SNP of phenotype 2. Each line of this file records a SNP (the first column) and corresponding association testing statistic (the second column).
   Example:
   rs3748597	0.223
   rs2465136	0.321
   rs13129	1.019
   rs9442372	2.108
   ....

[5]Permuation times: Permutations are used for P value calculation in PAPA. More than 1,000 permutations are recommended for obtaining accurate P values. Note, too large permutation times will make PAPA taking a long time to complete data analysis.

[6]Max and min sizes of analyzed pathways: Users need to define the maximum (fault value = 200 genes) and minimum (fault value = 5 genes) sizes of pathways analyzed by PAPA. 

[7]Weight parameter of phenotype 1 and phenotype 2: Please input the weighting parameters of phenotype 1 and phenotype 2. 


3.Output files
PAPA will output two result files:
[1]Pathway_result.txt: Pleiotropic pathway analysis result files. Each line of this file records a pathway and corresponding pleitropy analysis results, including normalized centered enrichment score (NCES) value and P value.
   Example:
   Pathway name	NCES	P value
   BIOCARTA_ATM_PATHWAY	0.290	0.636
   BIOCARTA_CELLCYCLE_PATHWAY	0.353	0.363
   ....	
   NCES can be used to measure the strength of association between pathways and disease phenotypes. In PAPA, circular permutation approach (Cabrera, et al., 2012, G3, 2, 1067-1075) is used to calculate the emperical P value of NCES  of each pathway. The pathways with P values < 0.05 are considered as significant pathway associated with disease phenotypes. 

[2]Rplots.pdf: Plot of pleitropic pathway analysis results. In the generated figure, each point denotes a pathway. X-axis presents the total number of pathways analyzed by PAPA software. Y-axis shows ¨Clog10 (P values) calculated by PAPA.