First two principal components of the centered and scaled subject by locus 
genotype matrix. Note that principal components were calculated using
genotype at more loci than are provided in the example data set.

datasets

Implementation of genetic association tests that utilize the rank based inverse normal transformation (INT). The primary contribution is an omnibus test, which synthesizes two INT-based approaches, termed direct INT (DINT) and partially indirect INT (PIINT). In genome scale simulations, the omnibus test routinely provided valid inference against continuous phenotypes with skewed and heavy tailed residual distributions. Association testing against these same phenotypes via standard linear regression led to an excess of false positive associations. In addition to controlling the type I error, the omnibus approach provided power comparable to the more powerful of the component methods.

Zachary McCaw

RNOmni

Omnibus Test for Genetic Association Analysis using the Rank
Normal Transformation

S function

A numeric matrix with 1000 rows and 2 columns<dl class="dl-horizontal">
 <dt>pc1</dt><dd>The first principal component.</dd>
 <dt>pc2</dt><dd>The second principal component.</dd>
</dl>

Format

Population Structure Adjustments — S

A numeric matrix with 1000 rows and 2 columns<dl class='dl-horizontal'>
 <dt>pc1</dt><dd>The first principal component.</dd>
 <dt>pc2</dt><dd>The second principal component.</dd>
</dl>

S: Population Structure Adjustments

Description

Usage

Arguments

Format