text: user499: I have a gene expression dataset that I want to investigate. Particularly, I would like to understand whether there is any correlation between each gene's expression and some quantitative or qualtitative data (say, correlation between gene 'XPTO' , body mass index, and race). 
  One possible way to test this would be through logistic regression, but is this a good approach or are there caveats that I should know about using such a statistic?
  My question is the following: which methods would you advise to measure such correlations, and why?
  (this post was crossposted on Biostars)
  
  user77: Logistic regression would generally be a bad choice for non-binary outcomes. In such cases, linear regression (or a GLM more generally) still works fine. You can already do that in the standard R packages for RNAseq (DESeq2, edgeR, and limma), where the fold-change is in whatever units you're measuring your quantitative trait in.
  
original_text: 499: <p>I have a gene expression dataset that I want to investigate. Particularly, I would like to understand whether there is any correlation between each gene's expression and some quantitative or qualtitative data (say, correlation between gene 'XPTO' , body mass index, and race). </p>
  
  <p>One possible way to test this would be through logistic regression, but is this a good approach or are there caveats that I should know about using such a statistic?</p>
  
  <p>My question is the following: which methods would you advise to measure such correlations, and why?</p>
  
  <p>(this post was <a href="https://www.biostars.org/p/258889/" rel="noreferrer">crossposted on Biostars</a>)</p>
   77: <p>Logistic regression would generally be a bad choice for non-binary outcomes. In such cases, linear regression (or a GLM more generally) still works fine. You can already do that in the standard R packages for RNAseq (DESeq2, edgeR, and limma), where the fold-change is in whatever units you're measuring your quantitative trait in.</p>
  