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<title>PowerPoint Presentation  -  Complex trait analysis, develop-ment, and
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mso-text-raise:0%;mso-line-spacing:"-358 0 -1";mso-line-spacing:"-358 0 -1"'><span
style='font-family:Verdana;font-size:64%'>Evaluating candidate genes</span><span
style='font-family:Verdana;font-size:73%;mso-special-format:lastCR;display:
none'><i><br>
</i></span></div>

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<div style='text-align:center'><span style='font-size:200%;color:#E9EB5D'><i>Right
position<br>
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style='text-align:center'><span style='font-size:200%;color:#E9EB5D'><i>and
high </i></span></span></layer><script>
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style='text-align:center'><span style='font-size:200%;color:#E9EB5D'><i>correlation</i></span></span></layer></div>

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<div style='text-align:center'><span style='font-size:267%;color:#E9EB5D'><i><span
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color:#E9EB5D'><i>= better<br>
</i></span></div>

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<div style='text-align:center'><span style='font-size:200%;color:#E9EB5D'><i>candidates</i></span></div>

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   <td width="100%"></td>
  </tr>
  <tr>
   <td colspan=1></td>
   <td align=left colspan=1><font face="Times New Roman" size=4><b>Evaluating
   candidate genes (CHECKED BOXES) responsible for variability in APP
   expression:</b></font><br>
   </td>
  </tr>
  <tr>
   <td colspan=1></td>
   <td align=left colspan=1><font face="Times New Roman" size=4>A large number
   of genes are usually in the QTL interval and are therefore POSITIONAL
   CANDIDATES, but they will differ greatly in their biological and
   bioinformatic plausibility. Assume that the QTL has been located between 119
   and 131 Mb (12 Mb). There will typically be 12 to 15 genes per Mb, so we
   might need to evaluate several hundred positional candidates. In this
   particular case there are about 100 known genes in this interval. Eight of
   these are highlighted in the table above with check marks in the boxes to
   the left.<span style="mso-spacerun: yes">&nbsp; </span>We need to highlight
   and objectively score the biologically relevant subset of all 100 positional
   candidate genes. We could look through gene ontologies and expression levels
   to help us shorten the list. An alternate way available using WebQTL is to
   generate a list of those genes in this interval that have transcripts that
   co-vary in expression with App expression. That is what the table shows.</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>Notes:</font><br>
   </td>
  </tr>
  <tr>
   <td colspan=1></td>
   <td align=left colspan=1><font face="Times New Roman" size=4>1. To replicate
   this table go back to the Trait Data and Analysis Form. Choose to sort
   correlations by POSITION and select RETURN = 500. Then scroll down the list
   to Chr 7 and review the subset of positional candidates that share
   expression with App. You should see a list similar to that shown above.
   Gtf3c1 is a good biological candidate and has a high covariation in
   expression with App.</font><br>
   </td>
  </tr>
  <tr>
   <td colspan=1></td>
   <td align=left colspan=1><font face="Times New Roman" size=4>2. Caveat:<span
   style="mso-spacerun: yes">&nbsp;&nbsp; </span>Of course, the gene or genes
   that control App expression may not be in this list. A protein coding
   difference might be the ultimate cause of variation in App transcript level
   and the expression covariation might be close to zero. Our list may also
   simply be missing the right transcript since the microarray is not truly
   comprehensive. Furthermore, even if the list contains the QTL gene, an
   expression difference may only have been expressed early in development or
   even in another tissue such as liver. While it is important to recognize
   these caveats, it is equally important to devise a rational way to rank
   candidates given existing data. Coexpression is one of several criteria used
   to evaluate positional candidates. We will see others in the next slide.</font><br>
   </td>
  </tr>
  <tr>
   <td colspan=1></td>
   <td align=left colspan=1><font face="Times New Roman" size=4>3. We can also
   assess the likelihood that candidates contain functional polymorphism in
   promoters and enhancers that affect their expression simply by mapping the
   transcripts of all candidate genes to see if they �map back� to the location
   of gene itself. A transcript that maps to its own location is referred to as
   a cis QTL. We essentially ask: Which of the transcripts listed in the
   Correlation Table above (from Gtf3c1 to Zranb1) has variation in expression
   that maps to Chr 7 at about 120 Mb?<span style="mso-spacerun: yes">&nbsp;
   </span>The logic of this search is that if a gene controls the level of its
   own expression it is also much more likely to generate other downstream
   effects. The Gtf3c1 transcript is a weak cis QTL with a local LRS maximum of
   about 7.0 (D alleles are high). That is just about sufficient to declare it
   to be a cis QTL. [No whole genome correction is required and a point-wise
   p-value of 0.05 is the appropriate test. A p-value of 0.05 is roughly
   equivalent to an LRS of 6.0 (LOD = 1.3).]</font><br>
   </td>
  </tr>
  <tr>
   <td colspan=1></td>
   <td align=left colspan=1><br>
   </td>
  </tr>
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