predict_auc_mt_continous

Expected AUC for prediction of a binary trait using information on a correlated continuous trait

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Provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses.
Rohde et al. (2019) <doi:10.1101/503631>.

Peter Soerensen

Statistical Tools for Quantitative Genetic Analyses

Palle Duun Rohde

Izel Fourie Soerensen

predict_auc_mt_continous function

<dl><dt>h2x</dt>
<dd>is the heritability of the target trait</dd>
<dt>Nx</dt>
<dd>is the number of samples for the target trait</dd>
<dt>Kx</dt>
<dd>is the prevalence of the target trait</dd>
<dt>Px</dt>
<dd>is the case-control proportion of the target trait</dd>
<dt>h2y</dt>
<dd>is the heritability of the correlated trait</dd>
<dt>Ny</dt>
<dd>is the number of samples for the correlated trait</dd>
<dt>rg</dt>
<dd>is the genetic correlation between the target and correlated trait</dd>
<dt>Me</dt>
<dd>is the number of independent chromosome segments</dd>
<dt>M</dt>
<dd>is the number of markers</dd>
<dt>Ky</dt>
<dd>is the prevalence of the correlated trait</dd>
<dt>Py</dt>
<dd>is the case-control proportion of the correlated trait</dd></dl>

Arguments

Expected AUC for prediction of a binary trait using information on a correlated continuous trait — predict_auc_mt_continous

<dl>

<dt>h2x</dt>
<dd>is the heritability of the target trait</dd>


<dt>Nx</dt>
<dd>is the number of samples for the target trait</dd>


<dt>Kx</dt>
<dd>is the prevalence of the target trait</dd>


<dt>Px</dt>
<dd>is the case-control proportion of the target trait</dd>


<dt>h2y</dt>
<dd>is the heritability of the correlated trait</dd>


<dt>Ny</dt>
<dd>is the number of samples for the correlated trait</dd>


<dt>rg</dt>
<dd>is the genetic correlation between the target and correlated trait</dd>


<dt>Me</dt>
<dd>is the number of independent chromosome segments</dd>


<dt>M</dt>
<dd>is the number of markers</dd>


<dt>Ky</dt>
<dd>is the prevalence of the correlated trait</dd>


<dt>Py</dt>
<dd>is the case-control proportion of the correlated trait</dd>

</dl>

predict_auc_mt_continous: Expected AUC for prediction of a binary trait using information on a correlated continuous trait

Description

Usage

Arguments