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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'><span style='font-size:267%;color:#E9EB5D'><i>Right
<br>
</i></span></div>
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<div style='text-align:center'><span style='font-size:267%;color:#E9EB5D'><i>&<br>
</i></span></div>
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<div style='text-align:center'><span style='font-size:267%;color:#E9EB5D'><i>high
r</i></span></div>
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<div style='text-align:center'><span style='font-size:267%;color:#E9EB5D'><i>good<br>
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<div style='text-align:center'><span style='font-size:267%;color:#E9EB5D'><i>candidates</i></span></div>
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<td width="100%"></td>
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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"> </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>
</td>
</tr>
<tr>
<td colspan=1></td>
<td align=left colspan=1><br>
</td>
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<tr>
<td colspan=1></td>
<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>
</td>
</tr>
<tr>
<td colspan=1></td>
<td align=left colspan=1><br>
</td>
</tr>
<tr>
<td colspan=1></td>
<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"> </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 ÒcausativeÓ factor since it is likely to be polymorphic.</font><br>
</td>
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<tr>
<td colspan=1></td>
<td align=left colspan=1><br>
</td>
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