MixMAPpackage: MixMAP: Mixed Modeling of Meta-Analysis P-values
In gjm112/MixMAP: Implements the MixMAP Algorithm

Description Details Author(s) References See Also Examples

Mixed Modeling approach based on Meta-analysis p-values for detecting gene level associations from genome wide assocation studies (GWAS) or candidate gene studies.

This package uses raw p-values from previous GWAS, and information about gene-level groupings to search for gene-level assocations between complex disease phenotypes and genetic loci.

Package:	MixMAP
Type:	Package
Version:	1.0
Date:	2012-08-13
License:	GPL-3

This goal of this package is to implement the MixMAP algorithm. The aim of the algorithm is to search for assocations between genes and complex diseases by using individual SNP-level p-values. The user must provides a file with SNP name and SNP p-value as well as gene name, chromosome, and basepair location. The output of the function MixMAP is an object of class MixMAP, which contains information on genes that have been detected as being associated with the phenotype of interest. An object of class MixMAP has a plot method associated with it to visually display the result of the MixMAP algorithm in a Manhattan style plot as well as a summary method.

Gregory J. Matthews and Andrea Foulkes

Maintainer: Gregory J. Matthews <gjm112@gmail.com>

Foulkes, A.S., Matthews, G.J., Das, U., Ferguson, J., Reilly, M. (2013) “Mixed Modeling of Meta-Analysis P-Values (MixMAP) Suggests Multiple Novel Gene Loci for Low Density Lipoprotein Cholesterol". PLoS ONE 8(2): e54812.

Matthews, G.J. and Foulkes, A.S. (2015) “MixMAP: An R Package for Mixed Modeling of Meta-Analysis $p$ Values in Genetic Association Studies." Journal of Statistical Software. 66(3): 1-11.

lme4-package

library(MixMAP)  
#Load data
#This data has been prepared to be used as input to the MixMAP function directly
data(MixMAP_example)
str(MixMAP_example)
#Run mixmapTest
MixOut<-mixmapTest(MixMAP_example,pval="GC.Pvalue",snp="MarkerName",
  chr="Chr",coord="Coordinate",gene="Gene")
#Display first ten detected genes
summary(MixOut)
#MixManhattan Plot
plot(MixOut)