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<title>PowerPoint Presentation - Complex trait analysis, develop-ment, and
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<div id=SlideObj class=sld style='position:absolute;top:0px;left:0px;
width:755px;height:566px;font-size:16px;background-color:#484848;clip:rect(0%, 101%, 101%, 0%);
visibility:hidden'><img src="master03_background.png" v:shapes="_x0000_s1026"
style='position:absolute;top:0%;left:0%;width:100.0%;height:100.0%'>
<div style='position:absolute;top:-.53%;left:1.72%;width:101.19%;height:8.65%;
filter:DropShadow(Color=#000000, OffX=2, OffY=2)'>
<div class=T style='mso-line-spacing:"-358 0 -1";mso-margin-left-alt:233;
mso-text-indent-alt:0;position:absolute;top:18.36%;left:.91%;width:98.95%;
height:75.51%'><span style='font-family:Verdana;font-size:64%'><i>WebQTL
searches for upstream controllers</i></span><span style='font-family:Verdana;
font-size:73%;mso-special-format:lastCR;display:none'><i><br>
</i></span></div>
</div>
<div style='position:absolute;top:71.02%;left:34.96%;width:45.96%;height:19.78%;
filter:DropShadow(Color=#000000, OffX=2, OffY=2)'>
<div class=O style='position:absolute;top:3.57%;left:2.3%;width:97.69%;
height:91.96%'><span style='position:absolute;top:0%;left:0%;width:98.82%'><span
style='font-size:167%;color:#E9EB5D'><i>App maps on Chr 16 (blue </i></span></span><span
style='position:absolute;top:25.24%;left:0%;width:100.0%'><span
style='font-size:167%;color:#E9EB5D'><i>arrow points to the orange </i></span></span><span
style='position:absolute;top:49.51%;left:0%;width:86.72%'><span
style='font-size:167%;color:#E9EB5D'><i>triangle) but the best </i></span></span><span
style='position:absolute;top:74.75%;left:0%;width:86.72%'><span
style='font-size:167%;color:#E9EB5D'><i>locus is on Chr 7.</i></span><span
style='font-size:233%;color:#E9EB5D;mso-special-format:lastCR;display:none'><i><br>
</i></span></span></div>
</div>
<img border=0 src="slide0005_image066.png" style='position:absolute;top:8.83%;
left:1.19%;width:98.27%;height:53.88%'><img border=0
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width:15.89%;height:53.35%'><img border=0 src="slide0005_image068.png"
style='position:absolute;top:46.64%;left:80.0%;width:10.99%;height:53.35%'><img
border=0 src="slide0005_image069.png" style='position:absolute;top:41.34%;
left:76.02%;width:11.65%;height:22.79%'><img border=0
src="slide0005_image070.png" style='position:absolute;top:41.51%;left:29.13%;
width:12.71%;height:24.38%'></div>
<div id=NotesObj style='display:none'>
<table style='color:white' border=0 width="100%">
<tr>
<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 a major 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 chromosomes only have a single long arm and the centromere
represents the origin of each chromosome for numerical purpose: 0
centimorgans at almost 0 megabases). The blue labels along the bottom of the
figure list a subset of the 3795 markers that were used in mapping.</font><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 chromosomes summarizes the strength of association between
variation in the phenotype (App expression differences) and the two genotypes
of all 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><font face=Verdana size=3>The red line and the red
axis to the far right provide an estimate of the effect that a QTL has on
expression of App (this estimate of the so-called additive effect tends to be
too high). 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 trait values. Multiply the additive effect
size by 2 to estimate the difference between the set of strains that have the
B/B genotype and those that have the D/D genotype at a specific marker. For
example, on distal Chr 7 the red line peaks at a value of about 0.2. That
means that this region of chromosome 2 is responsible for a 0.4 unit
expression difference between B/B strains and the D/D strains.</font><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 a method to
evaluate 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 using the 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 3 and Chr 7 under the LRS peaks. That
is somewhat reassuring. But notice that a substantial number of bootstrap are
scattered around on other chromosomes. About 30% of the bootstrap resamples
have a peak on Chr 7. That is pretty good, but does makes us realize that the
sample we are working with is still quite small and fragile.</font><br>
</td>
</tr>
<tr>
<td colspan=1></td>
<td align=left colspan=1><font face=Verdana size=3>The horizontal dashed
lines at 10.5 and 17.3 are the likelihood ratio statistic (LRS) values
associated with the suggestive and significant genome-wide probabilities that
were established by permutations of phenotypes across genotypes. We shuffle
randomly 2000 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 17.3. We call that level the 0.05
significance threshold for a whole genome scan. The p = 0.67 point is 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 3 is probably more than just suggestive.</font><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. 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 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 Analysis window
with the genotypes loaded into the window just like any other trait. More on
this in Section 3.</font><br>
</td>
</tr>
<tr>
<td colspan=1></td>
<td align=left colspan=1><font face=Verdana size=3>4. You can drag these maps
off of the browser window and onto your desktop. They will be saved as PNG or
PDF files. You can import them into Photoshop or other programs.</font><br>
</td>
</tr>
<tr>
<td colspan=1></td>
<td align=left colspan=1><font face=Verdana size=3>5. There is also an option
at the bottom of the page to download a 2X higher resolution image of this
plot for papers and presentations.</font><br>
</td>
</tr>
<tr>
<td colspan=1></td>
<td align=left colspan=1><font face=Verdana size=3>6. You can also download
the results of the analysis in a text format</font><br>
</td>
</tr>
</table>
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