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mso-text-indent-alt:0;position:absolute;top:11.76%;left:.99%;width:99.0%;
height:82.35%'><span style='font-family:Verdana;font-size:73%'><i>WebQTL to
exploring upstream control.</i></span></div>

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<div class=O style='position:absolute;top:13.78%;left:19.33%;width:60.52%;
height:6.53%'><span style='font-size:233%;color:#E9EB5D'><i>App maps on Chr 16
here</i></span></div>

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left:68.34%;width:14.43%;height:37.98%'><img border=0
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<div style='position:absolute;top:0%;left:0%;width:83.33%;height:51.21%'><span
style='font-size:267%;color:#E9EB5D'><i>Is App modulated by Chr 2?<br>
</i></span></div>

<div style='position:absolute;top:50.0%;left:0%;width:100.0%;height:51.21%'><span
style='font-size:267%;color:#E9EB5D'><i>Probably, but don�t bet the farm. </i></span></div>

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  <td align=left colspan=1><font face=Verdana size=3>This is the main output
  type: a so-called full genome interval map.</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=Verdana size=3>The X-axis represents all
  19 autosomes and the X chromosome as if they were laid end to end with short
  gaps between the telomere of one chromosome and the centromere of the next
  chromosome (mouse chromsomes only have a single long arm and the centromere represents
  the origin of each chromosome for numerical purpose: 0 centimorgans and
  almost 0 megabases). The blue labels along the bottom of the figure list a
  subset of markers that were used in mapping. We used 753 markers to perform
  the mapping but here we just list five markers per chromosome.</font><br>
  </td>
 </tr>
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  <td colspan=1></td>
  <td align=left colspan=1><br>
  </td>
 </tr>
 <tr>
  <td colspan=1></td>
  <td align=left colspan=1><font face=Verdana size=3>The thick blue wavy line
  running across chromsomes summarizes the strength of association between
  variation in the phenotype (App expression differences) and the two genotypes
  of 753 markers and the intervals between markers (hence, interval mapping).<span
  style="mso-spacerun: yes">&nbsp; </span>The height of the wave (blue Y-axis
  to the left) provides the likelihood ratio statistic (LRS). Divide by 4.61 to
  convert these values to LOD scores.<span style="mso-spacerun: yes">&nbsp;
  </span>Or you can read them as a chi-square-like statistic.</font><br>
  </td>
 </tr>
 <tr>
  <td colspan=1></td>
  <td align=left colspan=1><br>
  </td>
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  <td colspan=1></td>
  <td align=left colspan=1><font face=Verdana size=3>The red line and the red
  axis to the far right provides an estimate of the effect<span
  style="mso-spacerun: yes">&nbsp; </span>that a QTL has on expression of App
  (this estimate of the addtive effect tends to be an overestimate). If the red
  line is below the X-axis then this means that the allele inherited from
  C57BL/6J (B6 or B) at a particular marker is associated with higher values.
  If the red line is above the X-axis then the DBA/2J allele (D2 or D) is
  associated with higher traits. Multiply the additive effect size by 2 to
  estimate the difference between the set of strains that have the B/B genotype
  and the D/D genotype at a specific marker. For example, on Chr 2 the red
  line<span style="mso-spacerun: yes">&nbsp; </span>peaks at a value<span
  style="mso-spacerun: yes">&nbsp; </span>of about 0.25. That means that this
  region of chromosome 2 is responsible for a 0.5 unit expression difference
  between B/B strains and the D/D strains. Since the units are log base 2, this
  is 2^0.5, or about a 41% difference in expression with the D/D group being
  high.</font><br>
  </td>
 </tr>
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  <td colspan=1></td>
  <td align=left colspan=1><br>
  </td>
 </tr>
 <tr>
  <td colspan=1></td>
  <td align=left colspan=1><font face=Verdana size=3>The yellow histogram bars:
  These summarize the results of a whole-genome bootstrap of the trait that is
  performed 1000 times. What is a bootstrap? A bootstrap provides you a metho
  of evaluating whether results are robust. If we drop out one strain, do we
  still get the same results? When mapping quantitative traits, each strain
  normally gets one equally weighted vote. But inthe bootstrap procedure, we
  give each strain a random weighting factor of between 0 and 1.<span
  style="mso-spacerun: yes">&nbsp; </span>We then remap the trait and find THE
  SINGLE BEST LRS VALUE per bootstrap. We do this 1000 times. In this example,
  most bootstrap results cluster on Chr 2 under the LRS peak. That is somewhat
  reassuring. But notice that a substantial number of bootstrap results prefer
  Chr 7 or Chr 18.</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=Verdana size=3>The horizontal dashed
  lines at 9.6 and 15.9. These lines are the LRS values associated with the
  suggestive and significant false positive rates for genome-wide scans
  established by permuations of phenotypes across genotypes. We shuffle
  randomly 1000 times and obtain a distribution of peak LRS scores to generate
  a null distribution. Five percent of the time, one of these permuted data
  sets will have a peak LRS higher than 15.9. We call that level the 0.05
  significance threshold for a whole genome scan. The p = 0.67 point is the the
  suggestive level, and corresponds to the green dashed line.<span
  style="mso-spacerun: yes">&nbsp; </span>These thresholds are conservative for
  transcripts that have expression variation that is highly heritable. The
  putative or suggestive QTL on Chr 2 is probably more than just suggestive.</font><br>
  </td>
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  <td colspan=1></td>
  <td align=left colspan=1><br>
  </td>
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  <td colspan=1></td>
  <td align=left colspan=1><font face=Verdana size=3>One other point: the
  mapping procedure we use is computationally very fast, but it is relatively
  simple. We are not looking for gene-gene interactions and we are not fitting
  multiple QTLs in combinations.<span style="mso-spacerun: yes">&nbsp;
  </span>Consider this QTL analysis a first pass that will highlight hot spots
  and warm spots that are worth following up on using more sophisticated
  models.</font><br>
  </td>
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  <td align=left colspan=1><br>
  </td>
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  <td align=left colspan=1><font face=Verdana size=3>CLICKABLE REGIONS:</font><br>
  </td>
 </tr>
 <tr>
  <td colspan=1></td>
  <td align=left colspan=1><font face=Verdana size=3>1. If you click on the
  Chromosome number then you will generate a new map just for that chromosome.</font><br>
  </td>
 </tr>
 <tr>
  <td colspan=1></td>
  <td align=left colspan=1><font face=Verdana size=3>2. If you click on the
  body of the map, say on the blue line, then you will generate a view<span
  style="mso-spacerun: yes">&nbsp; </span>on a 10 Mb window of that part of the
  genome from the UCSC Genome Browser web site.</font><br>
  </td>
 </tr>
 <tr>
  <td colspan=1></td>
  <td align=left colspan=1><font face=Verdana size=3>3. If you click on a
  marker symbol, then you will generate a new Trait data and editing window
  with the genotypes loaded into the window just like any other trait. More on
  this later.</font><br>
  </td>
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  </td>
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  <td colspan=1></td>
  <td align=left colspan=1><font face=Verdana size=3>NOTE: you can drag these
  maps off of the browser window and onto your desktop. The will be saved as
  PNG or PDF files. You can import them into Photoshop or other programs.</font><br>
  </td>
 </tr>
</table>

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