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authorzsloan2015-03-27 20:28:51 +0000
committerzsloan2015-03-27 20:28:51 +0000
commitd0911a04958a04042da02a334ccc528dae79cc17 (patch)
tree3c48e2e937c1dbeaf00a5697c87ed251afa5c8f1 /web/tutorial/ppt/html/webqtl_demo2.ppt_files/slide0056.htm
parenta840ad18e1fe3db98a359a159e9b9b72367a2839 (diff)
downloadgenenetwork2-d0911a04958a04042da02a334ccc528dae79cc17.tar.gz
Removed everything from 'web' directory except genofiles and renamed the directory to 'genotype_files'
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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="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 QT 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 Òmap backÓ 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 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> </table> </div> </layer> <script language=JavaScript><!--
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