First two principal components of the genetic relatedness 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 using the rank based inverse normal transformation (INT). The primary contribution is an omnibus test, which synthesizes two complementary INT-based approaches. In simulations against phenotypes with skewed and heavy tailed residuals, the omnibus test provided valid inference in the absence of genetic effects. When genetic effects were present, the omnibus test provided power comparable to the more efficient of the component methods. Under these settings, standard linear regression variously failed to control the type I error in the absence of genetic effects, and was underpowered in the presence of genetic effects.

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