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authorroot2012-05-08 18:39:56 -0500
committerroot2012-05-08 18:39:56 -0500
commitea46f42ee640928b92947bfb204c41a482d80937 (patch)
tree9b27a4eb852d12539b543c3efee9d2a47ef470f3 /web/tutorial/ppt/WebQTLDemo_files/slide0019.htm
parent056b5253fc3857b0444382aa39944f6344dc1ceb (diff)
downloadgenenetwork2-ea46f42ee640928b92947bfb204c41a482d80937.tar.gz
Add all the source codes into the github.
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+<html> <head> <meta http-equiv=Content-Type content="text/html; charset=macintosh"> <meta name=ProgId content=PowerPoint.Slide> <meta name=Generator content="Microsoft Macintosh PowerPoint 10"> <link id=Main-File rel=Main-File href="WebQTLDemo.htm"> <title>PowerPoint Presentation - Complex trait analysis, develop-ment, and genomics</title> <link title="Presentation File" type="application/powerpoint" rel=alternate href=WebQTLDemo.ppt> <script>
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+</script> <meta name=Description content="Jun-19-03: WebQTL to exploring upstream control."> <link rel=Stylesheet href="master03_stylesheet.css"> <style media=print> <!--.sld {left:0px !important; width:6.0in !important; height:4.5in !important; font-size:76% !important;} --> </style> <script language=JavaScript src=script.js></script><script language=JavaScript><!--
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+</script> </head> <body style='margin:0px;background-color:black' onclick="DocumentOnClick(event)" onresize="_RSW()" onkeypress="_KPH(event)"> <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.gif" v:shapes="_x0000_s1026" style='position:absolute;top:0%;left:0%;width:100.0%;height:100.0%'> <div style='position:absolute;top:2.29%;left:4.1%;width:106.88%;height:9.01%; filter:DropShadow(Color=#000000, OffX=2, OffY=2)'> <div class=T style='mso-line-spacing:" 0 &#1;";mso-margin-left-alt:233; 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> </div> <img border=0 src="slide0019_image085.gif" style='position:absolute;top:27.2%; left:1.72%;width:96.68%;height:51.76%'> <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> <img border=0 src="slide0019_image086.gif" style='position:absolute;top:15.9%; left:68.34%;width:14.43%;height:37.98%'><img border=0 src="slide0019_image087.gif" style='position:absolute;top:53.18%;left:13.5%; width:14.43%;height:37.98%'> <div class=O style='position:absolute;top:82.86%;left:26.88%;width:90.59%; height:14.48%'> <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 donŐt bet the farm. </i></span></div> </div> </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 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">&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> </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">&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> <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">&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> </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">&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> </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">&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> </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">&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> </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> </table> </div> <script language=JavaScript><!--
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