Heterogeneous_Effect

<p>An example dataset with simulated genotypes and traits for 2 traits and 2 common causal variants with heterogeneous effects</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

Heterogeneous_Effect function


<div class="section" id="heterogeneous-effect">
<h3><code>Heterogeneous_Effect</code></h3><p></p><p>
</p><p>A list with 3 elements</p><dl>
<dt>X</dt>
<dd><p>List of genotype matrices</p></dd><p></p><p><dt>Y</dt>
<dd><p>List of traits</p></dd></p><p><dt>variant</dt>
<dd><p>indices of two causal variants</p></dd></p><p>
</p></dl><p></p><p></p></div>


Format

Individual level data for 2 traits and 2 causal variants with heterogeneous effects — Heterogeneous_Effect


<div class='section' id='heterogeneous-effect'>
<h3><code>Heterogeneous_Effect</code></h3>


<p>A list with 3 elements</p><dl>
<dt>X</dt>
<dd><p>List of genotype matrices</p></dd>

<dt>Y</dt>
<dd><p>List of traits</p></dd>

<dt>variant</dt>
<dd><p>indices of two causal variants</p></dd>


</dl>

</div>


Heterogeneous_Effect: Individual level data for 2 traits and 2 causal variants with heterogeneous effects

Description

Usage

Arguments

Format

`Heterogeneous_Effect`

See Also