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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>
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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"> </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>
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