The key idea behind meta-analysis with RAREMETAL is that various gene-level test statistics can be reconstructed from single variant score statistics and that, when the linkage disequilibrium relationships between variants are known, the distribution of these gene-level statistics can be derived and used to evaluate signifi-cance. Single variant statistics are calculated using the Cochran-Mantel-Haenszel method. The main formulae are tabulated in the following:
We denote the following to describe our methods:
is the score statistic for the variant from the study
is the covariance of the score statistics between the and the variant from the study
and are described in detail in RAREMETALWORKER method.
is the vector of score statistics of rare variants in a gene from the study.
is the variance-covariance matrix of score statistics of rare variants in a gene from the study, or
is the number of studies
is the vector of weights for rare variants in a gene.
SINGLE VARIANT META ANALYSIS
Single variant meta-analysis score statistic can be reconstructed from score statistics and their variances generate by each study, assuming that samples are unrelated across studies. Define meta-analysis score statistics as
and its variance
Then the score test statistics for the variant asymptotically follows standard normal distribution
BURDEN META ANALYSIS
Once single variant meta analysis statistics are constructed, burden test score statistic can be easily reconstructed as
VT META ANALYSIS
SKAT META ANALYSIS
SKAT is most powerful when detecting genes with rare variants having opposite directions in effect sizes. Meta-analysis statistic can also be re-constructed using single variant meta-analysis scores and their covariances
As shown in Wu et. al, the null distribution of the statistic follows a mixture chi-sqaured distribution described as
where are eigen values of