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mso-text-indent-alt:0'><span style='font-family:Verdana;font-size:73%'><i>WebQTL
to exploring upstream control.</i></span></div>
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no;layout-flow:horizontal;color:black;mso-color-index:1;font-size:80%;
mso-text-raise:0%'><span style='font-size:233%;color:#E9EB5D'><i>App maps on
Chr 16 here</i></span></div>
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<div><span style='font-size:267%;color:#E9EB5D'><i>Is App modulated by Chr
2?<br>
</i></span></div>
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<div><span style='font-size:267%;color:#E9EB5D'><i>Probably, but don�t bet the
farm. </i></span></div>
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<table style='color:white' border=0 width="100%">
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<td width=5 nowrap></td>
<td width="100%"></td>
</tr>
<tr>
<td colspan=1></td>
<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>
<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 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"> </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"> </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>
</tr>
<tr>
<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"> </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"> </span>peaks at a value<span
style="mso-spacerun: yes"> </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>
<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 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"> </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>
</tr>
<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"> </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>
</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>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">
</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>
</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>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"> </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>
</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>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>
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