Answers.Body: <p>Sequence data is basically always better and at this point in history it is de rigueur for any serious study, but originally phylogenies were all based on (ideally discrete) phenotypic characters, so there's no reason why it can't work in principle. As @Pallie mentions in a comment, the clustering methods in e.g. NJ or UPGMA will work on any set of distance measures, and they were in fact <a href="https://en.wikipedia.org/wiki/Phenetics" rel="nofollow noreferrer">originally developed on phenotypic characters rather than sequence data</a>.</p>
  <p>I'd recommend taking a look at chapter 24 of Joe Felsenstein's book on phylogenies, which is digitized <a href="http://zmmu.msu.ru/files/%D0%91%D0%B8%D0%B1%D0%BB%D0%B8%D0%BE%D1%82%D0%B5%D0%BA%D0%B0%20%D0%9F%D0%B0%D0%B2%D0%BB%D0%B8%D0%BD%D0%BE%D0%B2%D0%B0/felsenstein-2004_inferr_phylog.pdf" rel="nofollow noreferrer">here</a> (yes the link looks sketchy, sorry). There is a section on p. 425 &quot;Inferring phylogenies and correlations&quot;, which discusses how you can in principle use quantitative characters to infer phylogenies. The theory and math for why this is supposed to work is earlier in the chapter.</p>
  <p>In many cases people prefer to code the characters as discrete because it makes it easier to work with. But there are also methods that work with the raw quantitative characters.</p>
  <p>In R, you should be able to do something like this:</p>
  <pre><code># some simulated data, species are rows and characters are columns
  &gt; data
        char1 char2 char3
  spec1     0     3     6
  spec2     1     4     7
  spec3     2     5     8
  
  &gt; library(ape)
  &gt; tr = nj(dist(data))
  &gt; plot(tr)
  </code></pre>
  <p><a href="https://i.sstatic.net/UJSGy.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/UJSGy.png" alt="enter image description here" /></a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-08-25T17:26:27.277Z
Answers.LastActivityDate: 2020-08-25T17:26:27.277Z
Answers.UserDisplayName: Maximilian Press
Body: <p>I am new to bioinformatics, I am reading ‘Analysis of Phylogenetics Second Edition and Evolution with R’ from Emmanuel Paradis.</p>
  <p>I can create phylogeny from DNA sequences, by first calculating the distances and then using (ie nj() function).</p>
  <p>My question is: if instead of DNA I have trait data (ie height and weight of species), how can I estimate a phylogeny? (is the same process valid? somehow calculate distances and pass that to nj()?)</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-08-24T12:38:36.497Z
Comments.Text: You can pass any distance matrix to the NJ algorithm, but if the distances are calculated on height & size you might be building a tree that discerns the entries by age  or health or something other than phylogeny.
Comments.UserDisplayName: Pallie
Tags: <phylogenetics><phylogeny>
Title: how to estimate phylogeny without DNA sequences?
ThreadText: # how to estimate phylogeny without DNA sequences?
  
  (Asked by: username_0 on 2020-08-23)
  
  I am new to bioinformatics, I am reading ‘Analysis of Phylogenetics Second Edition and Evolution with R’ from Emmanuel Paradis.
  
  I can create phylogeny from DNA sequences, by first calculating the distances and then using (ie nj() function).
  
  My question is: if instead of DNA I have trait data (ie height and weight of species), how can I estimate a phylogeny? (is the same process valid? somehow calculate distances and pass that to nj()?)
  
  **Tags:** phylogenetics, phylogeny
  
  > **Comments:**
  > **username_1**: You can pass any distance matrix to the NJ algorithm, but if the distances are calculated on height & size you might be building a tree that discerns the entries by age  or health or something other than phylogeny.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2020-08-25. Score: 2
  
  Sequence data is basically always better and at this point in history it is de rigueur for any serious study, but originally phylogenies were all based on (ideally discrete) phenotypic characters, so there's no reason why it can't work in principle. As @username_1 mentions in a comment, the clustering methods in e.g. NJ or UPGMA will work on any set of distance measures, and they were in fact [originally developed on phenotypic characters rather than sequence data](https://en.wikipedia.org/wiki/Phenetics).
  
  I'd recommend taking a look at chapter 24 of Joe Felsenstein's book on phylogenies, which is digitized [here](http://zmmu.msu.ru/files/%D0%91%D0%B8%D0%B1%D0%BB%D0%B8%D0%BE%D1%82%D0%B5%D0%BA%D0%B0%20%D0%9F%D0%B0%D0%B2%D0%BB%D0%B8%D0%BD%D0%BE%D0%B2%D0%B0/felsenstein-2004_inferr_phylog.pdf) (yes the link looks sketchy, sorry). There is a section on p. 425 "Inferring phylogenies and correlations", which discusses how you can in principle use quantitative characters to infer phylogenies. The theory and math for why this is supposed to work is earlier in the chapter.
  
  In many cases people prefer to code the characters as discrete because it makes it easier to work with. But there are also methods that work with the raw quantitative characters.
  
  In R, you should be able to do something like this:
  
  ```
  # some simulated data, species are rows and characters are columns
  > data
        char1 char2 char3
  spec1     0     3     6
  spec2     1     4     7
  spec3     2     5     8
  
  > library(ape)
  > tr = nj(dist(data))
  > plot(tr)
  
  ```
  
  [![enter image description here](https://i.sstatic.net/UJSGy.png)](https://i.sstatic.net/UJSGy.png)
  
  
  ---
  
  