RAREMETALWORKER METHOD

Useful Links

Here are some useful links to key pages:

• The RAREMETALWORKER documentation
• The RAREMETALWORKER command reference
• The RAREMETALWORKER special topics
• The RAREMETALWORKER quick start tutorial
• The RAREMETAL method
• The FAQ

Brief Introduction

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

NOTATIONS

We use the following notations to describe our methods:

$\mathbf {y}$ is the vector of observed quantitative trait

$\mathbf {X}$ is the design matrix

$\mathbf {G_{i}}$ is the genotype vector of the $i^{th}$ variant

${\bar {\mathbf {G_{i}} }}$ is the vector of average genotype of the $i^{th}$ variant

${\boldsymbol {\beta _{c}}}$ is the vector of covariate effects

$\beta _{i}$ is the scalar of fixed genetic effect of the $i^{th}$ variant

$\mathbf {g}$ is the random genetic effects

${\boldsymbol {\varepsilon }}$ is the non-shared environmental effects

${\hat {\boldsymbol {\Omega }}}$ is the estimated covariance matrix of $\mathbf {y}$

$\mathbf {K}$ is the kinship matrix

$\mathbf {K_{X}}$ is the kinship matrix of Chromosome X

$\sigma _{g}^{2}$ is the genetic component

${{\sigma _{g}}_{X}}^{2}$ is the genetic component for markers on chromosome X

$\sigma _{e}^{2}$ is the non-shared-environment component.

SINGLE VARIANT SCORE TEST

We used the following model for the trait:

$\mathbf {y} =\mathbf {X} {\boldsymbol {\beta _{c}}}+\beta _{i}(\mathbf {G_{i}} -{\bar {\mathbf {G_{i}} }})+\mathbf {g} +{\boldsymbol {\varepsilon }}$.

Here, the quantitive trait for an individual is a sum of covariate effects, additive genetic effect from the $i^{th}$ variant and the polygenic background effects together with non-shared environmental effect.

In this model, $\beta _{i}$ is to measure the additive genetic effect of the $i^{th}$ variant. As usual, the score statistic for testing $H_{0}:\beta _{i}=0$ is:

$U_{i}=(\mathbf {G_{i}} -\mathbf {\bar {G_{i}}} )^{T}{\hat {\boldsymbol {\Omega }}}^{-1}(\mathbf {y} -\mathbf {X} {\boldsymbol {\beta }})$

We further derive the variance-covariance matrix of these statistics as

$\mathbf {V} =(\mathbf {G} -{\bar {\mathbf {G} }})^{T}({\hat {\boldsymbol {\Omega }}}^{-1}-{\hat {\boldsymbol {\Omega }}}^{-1}\mathbf {X} (\mathbf {X^{T}} {\hat {\boldsymbol {\Omega }}}^{-1}\mathbf {X} )^{-1}\mathbf {X^{T}} {\hat {\boldsymbol {\Omega }}}^{-1})(\mathbf {G} -{\bar {\mathbf {G} }})$.

The score test statistic, $T_{i}=(U_{i}^{2})/V_{ii}$, 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 ( $U_{i}$) together with quality information of that marker, including HWE p-value, call rate, and allele counts.

RAREMETALWORKER also stores the covariance matrices ($\mathbf {V}$) of the score statistics of markers within a window, size of which can be specified through command line.

MODELING RELATEDNESS

We use a variance component model to handle familial relationships. We estimate the variance components under the null model:

$\mathbf {y} =\mathbf {X} {\boldsymbol {\beta }}+\mathbf {g} +{\boldsymbol {\varepsilon }}$

We assume that genetic effects are normally distributed, with mean $\mathbf {0}$ and covariance $\mathbf {K} \sigma _{g}^{2}$ where the matrix $\mathbf {K}$ summarizes kinship coefficients between sampled individuals and $\sigma _{g}^{2}$ is a positive scalar describing the genetic contribution to the overall variance. We assume that non-shared environmental effects are normally distributed with mean $\mathbf {0}$ and covariance $\mathbf {I} \sigma _{e}^{2}$, where $\mathbf {I}$ is the identity matrix.

To estimate $\mathbf {K}$, we either use known pedigree structure to define $\mathbf {K}$ or else use the empirical estimator

$\mathbf {K} ={\frac {1}{l}}\sum _{i=1}^{l}{(G_{i}-2f_{i}\mathbf {1} )(G_{i}-2f_{i}\mathbf {1} ) \over 4f_{i}(1-f_{i})}$,

where $l$ is the count of variants, $G_{i}$ and $f_{i}$ are the genotype vector and estimated allele frequency for the $i^{th}$ variant, respectively. Each element in $G_{i}$ encodes the minor allele count for one individual. Model parameters ${\hat {\boldsymbol {\beta }}}$, ${\hat {\sigma _{g}^{2}}}$ and ${\hat {\sigma _{e}^{2}}}$, are estimated using maximum likelihood and the efficient algorithm described in Lippert et. al. For convenience, let the estimated covariance matrix of $\mathbf {y}$ be ${\hat {\boldsymbol {\Omega }}}={\hat {\sigma _{g}^{2}}}\mathbf {K} +{\hat {\sigma _{e}^{2}}}\mathbf {I}$.

ANALYZING MARKERS ON CHROMOSOME X

To analyze markers on chromosome X, we fit an extra variance components ${{\sigma _{g}}_{X}}^{2}$, to model the variance explained by chromosome X. A kinship for chromosome X, ${\boldsymbol {K_{X}}}$, can be estimated either from a pedigree, or from genotypes of marker from chromosome X. Then the estimated covariance matrix can be written as ${\hat {\boldsymbol {\Omega }}}={\hat {\sigma _{g}^{2}}}\mathbf {K} +{\hat {{\sigma _{g}}_{X}^{2}}}\mathbf {K_{X}} +{\hat {\sigma _{e}^{2}}}\mathbf {I}$.