Difference between revisions of "RAREMETALWORKER METHOD"

From Genome Analysis Wiki
Jump to navigationJump to search
Line 5: Line 5:
  
 
== Modeling Relatedness ==
 
== Modeling Relatedness ==
we use a variance component model to handle familial relationships.In a sample of n individuals, we model the observed phenotype vector ('''y''') as a sum of covariate effects (specified by a design matrix '''X''' and a vector of covariate effects '''β'''), additive genetic effects (modeled in vector '''g''') and non-shared environmental effects (modeled in vector '''ε'''). Thus:
+
we use a variance component model to handle familial relationships.In a sample of n individuals, we model the observed phenotype vector ('''y''') as a sum of covariate effects (specified by a design matrix '''X''' and a vector of covariate effects '''β'''), additive genetic effects (modeled in vector '''g''') and non-shared environmental effects (modeled in vector '''ε'''). Thus the null model is:  
  
  <math>\mathbf{y}=\mathbf{X}\beta +\mathbf{g}+ \boldsymbol{\varepsilon}</math>
+
<math>\mathbf{y}=\mathbf{X}\beta +\mathbf{g}+ \boldsymbol{\varepsilon}</math>
  
 
== Single Variant Score Tests ==
 
== Single Variant Score Tests ==

Revision as of 10:39, 11 March 2014

Brief Introduction

RAREMETALWORKER(RMW) generates single variant association results from score test, together with summary statistics and covariance matrices of the score statistics.

In the following sections, we will go through the methods behind RWM including statistic model, handling sample relatedness, and definitions of statistics in the output.

Modeling Relatedness

we use a variance component model to handle familial relationships.In a sample of n individuals, we model the observed phenotype vector (y) as a sum of covariate effects (specified by a design matrix X and a vector of covariate effects β), additive genetic effects (modeled in vector g) and non-shared environmental effects (modeled in vector ε). Thus the null model is:

Single Variant Score Tests

Summary Statistics

Covariance Matrices

Chromosome X