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left:1.41%;width:97.03%;height:82.35%'><span style='font-family:Verdana;
font-size:73%'><i>Which gene is the QTL?</i></span></div>

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<div style='text-align:center;position:absolute;top:25.0%;left:0%;width:100.0%;
height:25.6%'><span style='font-size:267%;color:#E9EB5D'><i>position<br>
</i></span></div>

<div style='text-align:center;position:absolute;top:50.0%;left:8.17%;
width:83.64%;height:25.6%'><span style='font-size:267%;color:#E9EB5D'><i>&amp;<br>
</i></span></div>

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width:83.64%;height:25.6%'><span style='font-size:267%;color:#E9EB5D'><i>high r</i></span></div>

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<div style='text-align:center;position:absolute;top:0%;left:8.71%;width:83.48%;
height:51.21%'><span style='font-size:267%;color:#E9EB5D'><i>good<br>
</i></span></div>

<div style='text-align:center;position:absolute;top:50.0%;left:0%;width:100.0%;
height:51.21%'><span style='font-size:267%;color:#E9EB5D'><i>candidates</i></span></div>

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  <td align=left colspan=1><font face="Times New Roman" size=4><b>Candidate
  Genes: </b><span style="mso-spacerun: yes">&nbsp;</span>The best we can do at
  this point is to make an educated guess about the candidacy status of all
  genes in the QTL support interval. For sake of argument, lets say that we are
  confident that the polymorphism is located between 130 and 150 Mb (20 Mb,
  equivalent to roughly 10 cM). There will typically be 12 to 15 genes per Mb,
  so we now would like to evaluate 240 to 300 positional candidates. We would
  like to highlight the biologically relevant subset of candidates. We could
  look through gene ontologies and expression levels to help us winnow the
  list. An alternate way avaiable using WebQTL is to generate a list of those
  genes in this 20 Mb interval that have transcripts that co-vary in expression
  with App expression.</font><br>
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  <td align=left colspan=1><font face="Times New Roman" size=4>To do this, go
  back to the Trait Data and Editing window. Sort the correlations by position.
  Select Return = 500. Then scroll down the list to see positional candidates
  that share expression with App.</font><br>
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  <td align=left colspan=1><font face="Times New Roman" size=4>There are
  several candidates that have high correlation with App even in this short 20
  Mb interval. We can rank them by correlation, but they are all close.<span
  style="mso-spacerun: yes">&nbsp; </span>There is one other imporant approach
  to ranking these candidates. Are they likely to contain polymorphisms? We can
  assess the likelihood that they contain polymorphisms by mapping each
  transcript to see if any have strong cis QTLs. The logic of this search is
  that a transcript that has a strong cis-QTL is likely to contain functional
  polymorphisms that effect its own expression. This make is more like that the
  transcript is a �causative� factor since it is likely to be polymorphic.</font><br>
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