Integrative Mapping of Pleiotropic Association
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information. Update Oct 4, 2017 Update Aug 18, 2018
annotation.txt annotation Oct 4, 2017
iMAP (Lasso and enet).R iMAP (Lasso and enet).R Feb 7, 2018
iMAP.R iMAP Oct 4, 2017
multisummary.R multisummary Oct 3, 2017
zvalue.txt zvalue Oct 4, 2017

iMAP: integrative MApping of Pleiotropic association


iMAP is a method which performs integrative mapping of pleiotropic association and functional annotations using penalized Gaussian mixture models. Specifically, iMAP directly models summary statistics from GWASs, uses a multivariate Gaussian distribution to account for phenotypic correlation between traits, simultaneously infers genome-wide SNP association pattern using mixture modeling, and has the potential to reveal causal relationship between traits. Importantly, iMAP can integrate a large number of binary and continuous SNP functional annotations to substantially improve association mapping power, and, with a sparsity-inducing penalty term, is capable of selecting informative annotations from a large, potentially noninformative set. To enable scalable inference of iMAP to association studies with hundreds of thousands of individuals and millions of SNPs, we further develop an efficient expectation maximization algorithm based on a recently proposed approximation algorithm for penalized regression.

iMAP is implemented in R statistical environment.


Ping Zeng, Xingjie Hao and Xiang Zhou. Pleiotropic Mapping and Annotation Selection in Genome-wide Association Studies with Penalized Gaussian Mixture Models. bioRxiv 2018. Doi: 10.1101/256461.


We are very grateful to any questions, comments, or bugs reports; and please contact Ping Zeng via or


2018-02-01  iMAP was updated to include elastic enet for selecting correlated annotations.