Two independent phenotypes simulated under the null hypothesis of no 
genotypic effect. A subject specific mean was calculated based on
age, sex, pc1, and pc2. The normal phenotype was generated by adding N(0,1)
deviates to the subject means. The T3 phenotype was generated by adding \(t_{3}/sqrt{3}\) deviates
to the subject means.

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

Y function

A numeric matrix with 1000 rows and 2 columns.<dl class="dl-horizontal">
 <dt>YN</dt><dd>Normal phenotype.</dd>
 <dt>YT3</dt><dd>T3 phenotype.</dd>
</dl>

Format

Simulated Phenotypes — Y

A numeric matrix with 1000 rows and 2 columns.<dl class='dl-horizontal'>
 <dt>YN</dt><dd>Normal phenotype.</dd>
 <dt>YT3</dt><dd>T3 phenotype.</dd>
</dl>

Y: Simulated Phenotypes

Description

Usage

Arguments

Format