METASOFT
METASOFT is a free, opensource metaanalysis software tool for genomewide association study analysis, designed to perform a range of basic and advanced metaanalytic methods in an efficient manner.
ForestPMPlot is a free, opensource a pythoninterfaced R package tool for analyzing the heterogeneous studies in metaanalysis by visualizing the effect size differences between studies. The resulting plot can facilitate the better understanding of heterogeneous genetic effects on the phenotype in different study conditions.
Overview
METASOFT provides the following methods:
 Fixed Effects model (FE)
 Fixed effects model based on inversevarianceweighted effect size.
 Random Effects model (RE)
 Conventional random effects model based on inversevarianceweighted effect size (very conservative).
 Han and Eskin's Random Effects model (RE2)
 New random effects model optimized to detect associations under heterogeneity. (Han and Eskin, AJHG 2011)
 Binary Effects model (BE)
 New random effects model optimized to detect associations when some studies have an effect and some studies do not. (Han and Eskin, PLoS Genetics 2012)
METASOFT provides the following estimates:
Summary effect size estimates
Beta and standard errors for both fixed effects model and random effects model.
Heterogeneity estimates
Cochran's Q statistic and its pvalue, and I^{2}
Mvalues
Posterior probability that an effect exists in each study. (Han and Eskin, PLoS Genetics 2012)
ForestPMPlot is a visualization tool. The resulting plot
consists of the following components:
 Forest Plot
 For each study, it displays pvalue, study name, log odds ratio and its standard error and summary statistic.
 mvalue

mvalue is the posterior probability that the effect exists in each study. mvalues have the following interpretations.
Small mvalue (e.g. < 0.1): The study is predicted to not have an effect.
Large mvalue (e.g. > 0.9): The study is predicted to have an effect.
Otherwise: It is ambiguous to make a prediction.
mvalue is estimated utilizing crossstudy information (Han and Eskin, 2012
Link).
 PM Plot
 PMPlot visualizes the mvalue of each study along with its pvalue.
The Xaxis of the PMPlot represents the mvalue between 0 and 1 and the
Yaxis represents the statistical significance, −log10(pvalue).
PMPlot allows us to predict which study have an effect and which study does not have an effect. The dot size represents the study sample size.
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Version/bug info
METASOFT
v2.0.1 (20120821) PLINK format converter is added.
v2.0.0 (20120215) Mvalues are computed using exact calculation. User interface has been changed.
v1.0.0 (20110825) First major release. Bug fix.
v0.3.1 (20110707) Bug fix.
v0.3.0 (20110706) Bug fix. Binary effects model and Mvalues implemented.
v0.2.2 (20110519) Bug fix.
v0.2.1 (20110516) Small changes in output format.
v0.2.0 (20110510) Bug fix. Genomic control method implemented.
v0.1.0 (20110418) Initial prototype version deployed
ForestPMPlot
Publication
Buhm Han and Eleazar Eskin, “RandomEffects Model Aimed at Discovering Associations in MetaAnalysis of Genomewide Association Studies”, The American Journal of Human Genetics (2011) 88, 586598. LINK
Buhm Han and Eleazar Eskin, “Interpreting MetaAnalysis of Genomewide Association Studies”, In Press. PLoS Genetics (2012). (Manuscript available here)
Eun Yong Kang and Buhm Han and Nicholas Furlotte and Jong Wha J. Joo and Diana Shih and Richard C. Davis and Aldons J. Lusis and Eleazar Eskin, “ MetaAnalysis Identifies GenebyEnvironment Interactions as Demonstrated in a Study of 4,965 Mice ”, PLOS Genetics (2014) 10(1), e1004022. LINK
Eun Yong Kang and Buhm Han and Eleazar Eskin, “ ForestPMplot: a flexible tool for visualizing heterogeneity between studies in meta analysis”, Submitted
Jae Hoon Sul and Buhm Han and Chun Ye and Ted Choi and Eleazar Eskin, “ Effectively identifying eQTLs from multiple tissues by combining mixed model and metaanalytic approaches.”, PLOS Genetics (2013) 9, e1003491. LINK Software
Contact
METASOFT
Buhm Han :
buhan (AT) cs.ucla.edu ,
http:www.cs.ucla.du/~buhan
ForestPMPlot
Eun Yong Kang : ekang (AT) cs.ucla.edu
Funding information
B.H. and E.E. are supported by National Science Foundation grants 0513612, 0731455, 0729049 and 0916676, and NIH grants K25HL080079 and U01DA024417. B.H. is supported by the Samsung Scholarship. This research was supported in part by the University of California, Los Angeles subcontract of contract N01ES45530 from the National Toxicology Program and National Institute of Environmental Health Sciences to Perlegen Sciences.
