comorbidData

A dataset with sets of PGSs, Phenotypes and Covariates to demo the
comorbidPGS package

datasets

Using polygenic scores (PGS, or PRS/GRS for binary outcomes),
this package allows to investigate shared predisposition between different
conditions, and do fast association analysis, export plots and views
of the PGS distribution using 'ggplot2' object.

Vincent Pascat

comorbidPGS

Assessing Predisposition Between Phenotypes using Polygenic
Scores

comorbidData function

<div class="section" id="who">
<h3><code>who</code></h3>
A data frame with 10,000 rows (individuals) and 16 columns:<dl>
<dt>ID</dt>
<dd>Individual's identifier, characters</dd><dt>sex</dt>
<dd>Sex of the individuals, binary numeric values</dd><dt>age</dt>
<dd>Age of the individuals, numeric value</dd><dt>gen_array</dt>
<dd>The genotypic array used for those individuals, factor values</dd><dt>ethnicity</dt>
<dd>The ethnicity of individuals, can be also used as Categorical Phenotype, factor values</dd><dt>brc_PGS, t2d_PGS, ldl_PGS</dt>
<dd>Three distributions of PGS for Breast Cancer, Type 2 Diabetes and Hypertension respectively; numeric values</dd><dt>brc, t2d, hypertension</dt>
<dd>Three Cases/Controls Phenotypes, representing Breast Cancer, Type 2 Diabetes and Hypertension respectively; binary values</dd><dt>ldl, bmi, sbp</dt>
<dd>Three Continuous Phenotypes, representing low-density lipoprotein, body-mass index, and systolic blood pressure respectively; numeric values</dd><dt>log_ldl</dt>
<dd>A continuous Phenotype, based on log(ldl) to have a normal distribution; numeric values</dd><dt>sbp_cat</dt>
<dd>An Ordered Categorical Phenotype, with 3 possible outcomes: low, normal or high systolic blood pressure; factor values</dd>
</dl></div>

Format

Mock dataset for comorbidPGS package — comorbidData

<div class='section' id='who'>
<h3><code>who</code></h3>


A data frame with 10,000 rows (individuals) and 16 columns:<dl>
<dt>ID</dt>
<dd>Individual's identifier, characters</dd>

<dt>sex</dt>
<dd>Sex of the individuals, binary numeric values</dd>

<dt>age</dt>
<dd>Age of the individuals, numeric value</dd>

<dt>gen_array</dt>
<dd>The genotypic array used for those individuals, factor values</dd>

<dt>ethnicity</dt>
<dd>The ethnicity of individuals, can be also used as Categorical Phenotype, factor values</dd>

<dt>brc_PGS, t2d_PGS, ldl_PGS</dt>
<dd>Three distributions of PGS for Breast Cancer, Type 2 Diabetes and Hypertension respectively; numeric values</dd>

<dt>brc, t2d, hypertension</dt>
<dd>Three Cases/Controls Phenotypes, representing Breast Cancer, Type 2 Diabetes and Hypertension respectively; binary values</dd>

<dt>ldl, bmi, sbp</dt>
<dd>Three Continuous Phenotypes, representing low-density lipoprotein, body-mass index, and systolic blood pressure respectively; numeric values</dd>

<dt>log_ldl</dt>
<dd>A continuous Phenotype, based on log(ldl) to have a normal distribution; numeric values</dd>

<dt>sbp_cat</dt>
<dd>An Ordered Categorical Phenotype, with 3 possible outcomes: low, normal or high systolic blood pressure; factor values</dd>


</dl>

</div>

comorbidData: Mock dataset for comorbidPGS package

Description

Usage

Arguments

Format

`who`