In this package, we provide three JAM-family methods that utilize GWAS
summary statistics to perform post-GWAS analysis, including Mendelian
Randomization, TWAS, and multi-population fine-mapping.
hJAM (hJAM) is a hierarchical model which unifies the framework of
Mendelian Randomization and Transcriptome-wide association studies. The
hJAM-Egger (hJAM_egger) is a natural extension on hJAM that uses an
intercept term to account for the pleiotropy effect of the SNPs on the
outcome.
Additionally, we provide implementations to construct the weight matrix
A in hJAM. JAM_A converts the marginal summary statistics into
conditional ones by using the correlation matrix of the SNPs from a
reference data. susieJAM_A selects SNPs for one intermediate using the
marginal summary statistics from GWAS or other study summary data.
SHA-JAM (SHAJAM) is a scalable version of hJAM that handles
high-dimensional intermediates. SHA-JAM performs model selection from
highly correlated intermediates through SuSiE: Sum of Single Effect
Model (SHAJAM) or elastic-net (EN.hJAM).
mJAM is for multi-population fine-mapping using GWAS summary statistics
and credible set construction. We provide two implementations of mJAM:
one through SuSiE (mJAM_SuSiE) and another one through forward
selection (mJAM_Forward). mJAM_Forward also provides the flexiblity
of constructing credible sets for using user-defined index SNPs. A
tutorial to get started with mJAM can be found
here.
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hJAM: Jiang, L., Xu, S., Mancuso, N., Newcombe, P. J., & Conti, D. V. (2021). A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis. American journal of epidemiology, 190(6), 1148–1158. https://doi.org/10.1093/aje/kwaa287
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SHA-JAM: Jiang, L., Conti, D.V. SHA-JAM: A Scalable Hierarchical Approach to Joint Analysis for Marginal Summary Statistics with Omics Data. (In preparation).
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mJAM: Shen, J., Jiang, L., Wang, K., Wang, A., Chen, F., Newcombe, P.J., Haiman, C.A., & Conti, D.V. Fine-Mapping and Credible Set Construction using a Multi-population Joint Analysis of Marginal Summary Statistics from Genome-wide Association Studies. (Preprint available)
You can install the published version of hJAM from CRAN with:
install.packages("hJAM")Currently we are working on improving hJAM package and adding
genome-wide implementation of mJAM. If you want to use the most updated
version of this package, we recommend installing the development version
from GitHub with:
if (!require("devtools")) { install.packages("devtools") } else {}
devtools::install_github("USCbiostats/hJAM")sessionInfo()
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