<p>Linear mixed models are used to analyze <abbr title="genome-wide association study">GWAS</abbr> data and detect <abbr title="quantitative trait loci">QTLs</abbr>.
<a href="https://www.linkedin.com/in/ziyatdinov">Andrey Ziyatdinov</a> recently released an R package, <a href="https://github.com/variani/lme4qtl">lme4qtl</a>, that can be used to
formulate and fit these models.
In this episode, Andrey and I discuss linear mixed models, genome-wide association studies, and strengths and weaknesses of lme4qtl.</p>


      

      
      <p>Links:</p>

<ul>
  <li><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2057-x">Paper: lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals</a></li>
  <li><a href="https://github.com/variani/lme4qtl">lme4qtl on GitHub</a></li>
</ul>

#22 smCounter2: somatic variant calling and UMIs with Chang Xu

A podcast about computational biology, bioinformatics, and next generation sequencing.