The printable version is no longer supported and may have rendering errors. Please update your browser bookmarks and please use the default browser print function instead.
RAREMETALWORKER generates single variant association test statistics for a single study prior to meta-analysis. This page provides a brief description of the statistics that
RAREMETALWORKER calculates, together with key formulae.
Key Statistics for Analysis of Single Study
We use the following notations to describe our methods:
is the observed phenotype vector
is the design matrix
is the genotype vector of the variant
is the vector of average genotype of the variant
is the vector of covariate effects
is the scalar of fixed genetic effect of the variant
is the random genetic effects
is the non-shared environmental effects
SINGLE VARIANT SCORE TEST
We used the following model for the trait:
Here, [explain the formula].
In this model, is to measure the additive genetic effect of the variant. As usual, the score statistic for testing is:
We further derive the variance-covariance matrix of these statistics as
The score test statistic, , is asymptotically distributed as chi-squared with one degree of freedom. The score test p-value is reported in RAREMETALWORKER.
SUMMARY STATISTICS AND COVARIANCE MATRICES
RAREMETALWORKER automatically stores the score statistics for each marker ( ) together with quality information of that marker, including HWE p-value, call rate, and allele counts.
RAREMETALWORKER also stores the covariance matrices () of the score statistics of markers within a window, size of which can be specified through command line.
we use a variance component model to handle familial relationships. The null model is:
We assume that genetic effects are normally distributed, with mean and covariance where the matrix summarizes kinship coefficients between sampled individuals and is a positive scalar describing the genetic contribution to the overall variance. We assume that non-shared environmental effects are normally distributed with mean and covariance , where is the identity matrix.
To estimate , we either use known pedigree structure to define or else use the empirical estimator
where is the count of variants, and are the genotype vector and estimated allele frequency for the variant, respectively. Each element in encodes the minor allele count for one individual. Model parameters , and , are estimated using maximum likelihood and the efficient algorithm described in Lippert et. al. For convenience, let the estimated covariance matrix of be .
To analyze markers on chromosome X, we fit an extra variance components , to model the variance explained by chromosome X. A kinship for chromosome X, , can be estimated either from a pedigree, or from genotypes of marker from chromosome X. Then the estimated covariance matrix can be written as .