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-</script> <div style='text-align:left;font-family:"Gill Sans";font-weight:normal; font-style:normal;text-decoration:none;text-shadow:none;text-effect:none; mso-fareast-hint:no;layout-flow:horizontal;color:#E9EB5D;font-size:293%; mso-text-raise:0%;mso-line-spacing:"-358 0 -1";mso-margin-left-alt:233; mso-text-indent-alt:0;mso-line-spacing:"-358 0 -1";mso-margin-left-alt:233; mso-text-indent-alt:0'><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> </layer></div> </layer><script>
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- <layer> <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">&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><font face=Verdana size=3>The red line and the red axis to the far right provides 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">&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 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 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 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> </div> </layer> <script language=JavaScript><!--
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generated by cgit v1.2.3 (git 2.44.0) at 2024-11-27 00:23:58 +0000