Quantifying systematic heterogeneity in meta-analysis using R.
The M statistic aggregates heterogeneity information across multiple
variants to, identify systematic heterogeneity patterns and their direction
of effect in meta-analysis. It's primary use is to identify outlier studies,
which either show "null" effects or consistently show stronger or weaker
genetic effects than average across, the panel of variants examined in a
GWAS meta-analysis. In contrast to conventional heterogeneity metrics
(Q-statistic, I-squared and tau-squared) which measure random heterogeneity
at individual variants, M measures systematic (non-random)
heterogeneity across multiple independently associated variants. Systematic
heterogeneity can arise in a meta-analysis due to differences in the study
characteristics of participating studies. Some of the differences may
include: ancestry, allele frequencies, phenotype definition, age-of-disease
onset, family-history, gender, linkage disequilibrium and quality control
|Author||Lerato E Magosi [aut], Jemma C Hopewell [aut], Martin Farrall [aut], Lerato E Magosi [cre]|
|Date of publication||2017-06-08 23:08:47 UTC|
|Maintainer||Lerato E Magosi <[email protected]>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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