GWAS-based Mendelian Randomization Path Analysis

GMRP can perform analyses of Mendelian randomization (MR),correlation, path of causal variables onto disease of interest and SNP annotation analysis. MR includes SNP selection with given criteria and regression analysis of causal variables on the disease to generate beta values of causal variables on the disease. Using the beta vectors, GMRP performs correlation and path analyses to construct path diagrams of causal variables to the disease. GMRP consists of 8 R functions: chrp,fmerg,mktable,pathdiagram,pathdiagram2,path,snpposit,ucscannot and 5 datasets: beta.data,cad.data,lpd.data,SNP358.data,SNP368annot.data. chrp is used to separate string vector hg19 into two numeric vectors: chromosome number and SNP chromosome position. Function fmerg is used to merge two GWAS result datasets into one dataset. Function mktable performs SNP selection and creates a standard beta table for function path to do MR and path analyses. Function pathdiagram is used to create a path diagram of causal variables onto the disease or onto outcome. Function pathdiagram2 can merge two-level pathdiagrams into one nested pathdiagram where inner pathdiagram is a pathdiagram of causal variables contributing to outcome and the outside pathdiagram is a path diagram of causal variables including outcome onto the disease. The five datasets provide examples for running these functions. lpd.data and cad.data provide an example to create a standard beta dataset for path function to do path analysis and SNP data for SNP annotation analysis by performing mktable and fmerg. beta.data are a standard beta dataset for path analysis. SNP358.data provide an example for snpposit to do SNP position annotation analysis and SNP368annot.data are for ucscannot to do SNP function annotation analysis.