Sumstat_5traits

<p>An example dataset with simulated statistics for 5 traits</p>

datasets

A multi-task learning approach to variable selection regression with highly correlated predictors and sparse effects,
based on frequentist statistical inference. It provides statistical evidence to identify which subsets of predictors have non-zero
effects on which subsets of response variables, motivated and designed for colocalization analysis across genome-wide association studies (GWAS)
and quantitative trait loci (QTL) studies.
The ColocBoost model is described in Cao et. al. (2025) <doi:10.1101/2025.04.17.25326042>.

Xuewei Cao

colocboost

Multi-Context Colocalization Analysis for QTL and GWAS Studies

Haochen Sun

Ru Feng

Daniel Nachun

Kushal Dey

Gao Wang

Sumstat_5traits function


<div class="section" id="sumstat-traits">
<h3><code>Sumstat_5traits</code></h3><p></p><p>
</p><p>A list with 2 elements</p><dl>
<dt>sumstat</dt>
<dd><p>Summary statistics for 5 traits</p></dd><p></p><p><dt>true_effect_variants</dt>
<dd><p>List of causal variants</p></dd></p><p>
</p></dl><p></p><p></p></div>


Format

Summary level data for 5 traits — Sumstat_5traits


<div class='section' id='sumstat-traits'>
<h3><code>Sumstat_5traits</code></h3>


<p>A list with 2 elements</p><dl>
<dt>sumstat</dt>
<dd><p>Summary statistics for 5 traits</p></dd>

<dt>true_effect_variants</dt>
<dd><p>List of causal variants</p></dd>


</dl>

</div>


Sumstat_5traits: Summary level data for 5 traits

Description

Usage

Arguments

Format

`Sumstat_5traits`

See Also