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 based on 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 residual distributions, the omnibus test provided valid inference in the absence of a genotypic effect, and provided power comparable to the more powerful of the component methods in the presence of a genotypic effect. Under these settings, standard linear regression often failed to control the type I error.

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