FEma: A fixed effects (FE) meta-analysis (MA) of multiple GWAS with...
In baolinwu/GSmeta: Various meta-analysis methods with main applications to genetic studies
Description
Usage
Arguments
Value
References
Description
Under FE MA, we assume the same effect sizes across all studies. They can be efficiently estimated following the Lin-Sullivan approach.
Usage
1
Arguments
betas
effect size estimates from K studies
stders
standard errors for the effct size estimates
sigma
the correlation matrix of effect size estimates. Default to NULL for independent studies.
Value
- betam
estimated mean effect size
- Vm
variance of betam
- p.value
p-value for testing the mean effect size
References
Lin,D.Y. and Sullivan,P.F. (2009) Meta-Analysis of Genome-wide Association Studies with Overlapping Subjects. Am J Hum Genet 85, 862<e2><80><93>872.
Han,B. and Eskin,E. (2011) Random-Effects Model Aimed at Discovering Associations in Meta-Analysis of Genome-wide Association Studies. The American Journal of Human Genetics 88, 586<e2><80><93>598.
Wu,B. and Zhao,H. (2018) Powerful random effects modeling for meta-analysis of genome-wide association studies.
baolinwu/GSmeta documentation built on May 24, 2019, 7:13 a.m.
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Description Usage Arguments Value References
Description
Under FE MA, we assume the same effect sizes across all studies. They can be efficiently estimated following the Lin-Sullivan approach.
Usage
1 |
Arguments
betas |
effect size estimates from K studies |
stders |
standard errors for the effct size estimates |
sigma |
the correlation matrix of effect size estimates. Default to NULL for independent studies. |
Value
- betam
estimated mean effect size
- Vm
variance of betam
- p.value
p-value for testing the mean effect size
References
Lin,D.Y. and Sullivan,P.F. (2009) Meta-Analysis of Genome-wide Association Studies with Overlapping Subjects. Am J Hum Genet 85, 862<e2><80><93>872.
Han,B. and Eskin,E. (2011) Random-Effects Model Aimed at Discovering Associations in Meta-Analysis of Genome-wide Association Studies. The American Journal of Human Genetics 88, 586<e2><80><93>598.
Wu,B. and Zhao,H. (2018) Powerful random effects modeling for meta-analysis of genome-wide association studies.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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