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RE2C

Random Effects model

RE2C is a free, open-source meta-analysis software tool,

designed to integrate the effects while accounting the heterogeneity between studies. The RE2C is built upon RE2, whose statistic is implemented under METASOFT analysis toolset. RE2C increases the power of RE2 by introducing two ideas. First, we generalize the likelihood model to account for correlations of statistics and achieve optimal power. We use an optimization technique based on spectral decomposition to maintain the efficiency of parameter estimations. Second, we modify the statistic to focus on the heterogeneous effects that FE cannot detect, thereby increasing the power to identify new associations.

OVERVIEW

The statistical model of RE2C


Two dimensional representation of SFE and SHet.
RE2 statistic can be decomposed into SFE and SHet whose null distributions are known, given an observed RE2 statistic, its p-value can be interpreted as an integral over a region in the two-dimentional space. Specifically, in the figure above, the RE2 p-value is the volume of the region excluding the bottome left triangle (i.e. region A + B). However, in RE2C, we only consider the region where PRE2PFE.

Power of Lin-Sullivan(FE,LS), Decoupling-RE2(DR2), and our new RE2C method for meta-analyzing correlated statistics.


Assuming statistics are correlated with correlation coefficient ρ, we simulated various effect size distribution with differing amount of heterogeneity. We considered the scenario that DR2 or RE2C is additionally applied to LS while accounting for multiple testing. DR2 and RE2C power is shown as two-color stacked bars, where we colored the proportion that LS was significant in light grey and the proportion that DR2/RE2C additionally identified as significant in dark grey.

DOWNLOAD

RE2C_v1_03.zip containing the followings,


RE2C.sh
RE2C code (shell script file)

R, data, example
containing all .R files, .RData, and example files, respectively.

README

LICENSE


RE2C_manual.pdf

VERSION/BUG INFO

V1.0.1 First major release. Bug fix.
V1.0.2 RE2C provides RE2Cp*. Bug fix.
V1.0.3 Fixed problems in Newton Raphson method.

USER GUIDE

- Usage -

Command line arguments: bash RE2C.sh [options]
--I [FILE]
or  --input [FILE]
Input file (Required)
--o [FILE]
or  --output [FILE]
Output file (default = output/out.txt)
--c [FILE]
or  --cor [FILE]
Correlation matrix
--h
or  --help
Print help



- Input File Format -


Input File format:

Each rows represent a SNPs. The first column of each rows is for rsID, and the following columns are pairs of effect size and its standard error of Nth study. (If we meta-analyze 5 summary statistics then each line must have 11 columns)
Correlation matrix is N x N symmetric matrix. An evaluated correlation matrix can be specified by using --cor option in RE2C. When --cor is unused, the code itself assumes an identity matrix of N rows and columns.



Example (Input File)

rsAAAAAA study1beta study1stderr study2beta study2stderr study3beta study3stderr

rsBBBBBB study1beta study1stderr study2beta study2stderr study3beta study3stderr

rsCCCCCC study1beta study1stderr study2beta study2stderr study3beta study3stderr



Example (Correlation Matrix)

1 a b c
a 1 d e
b d 1 h
c e h 1



- Example Running Command -

bash RE2C.sh --input ./example/example_input.txt --output ./out --cor ./example/example_cor.txt




- Output File -

Col. Num. Col. name Description
1 rsID SNP rsID
2 Nstudy Number of studies used in meta-analysis for the SNP
3 LSs Estimated Beta under Lin-Sullivan(LS)
4 LSse Standard error of LSs
5 LSp LS P-value (FEp-value)
6 Isq I-square heterogeneity statistic ( NA when cor matrix is non-diagonal )
7 Q Cochran's Q statistics ( NA when cor matrix is non-diagonal )
8 Qp Cochran's Q statistics's P-value ( NA when cor matrix is non-diagonal )
9 RE2Cs1 RE2C statistic mean effect part
10 RE2Cs2 RE2C statistic heterogeneity part
11 RE2Cp* RE2C P-value (RE2_Correlation)
12 RE2Cp RE2C P-value

PUBLICATION

If you use our software of refer to the new random effect model method, please cite.

Cue. H. Lee., Buhm. Han., Eleazar. Eskin, "Increasing power of meta-analysis of genome-wide association studies for detecting hetero-geneous effects", Bioinformatics, vol. 33, no. 14, pp. i379-i388, Jul. 2017.

FUNDING INFORMATION

C.H.L B.H are supported by a grant of the Korean Health Technology R&D project, Ministry of Health & Welfare,
Republic of Korea (Hl14C1731).

CONTACT