From d0911a04958a04042da02a334ccc528dae79cc17 Mon Sep 17 00:00:00 2001 From: zsloan Date: Fri, 27 Mar 2015 20:28:51 +0000 Subject: Removed everything from 'web' directory except genofiles and renamed the directory to 'genotype_files' --- web/tutorial/ppt/html/webqtl_demo2.ppt.htm | 4 - .../ppt/html/webqtl_demo2.ppt_files/Slide0001.gif | Bin 75611 -> 0 bytes .../ppt/html/webqtl_demo2.ppt_files/Slide0002.gif | Bin 65191 -> 0 bytes .../ppt/html/webqtl_demo2.ppt_files/Slide0003.gif | Bin 64339 -> 0 bytes .../ppt/html/webqtl_demo2.ppt_files/Slide0004.gif | Bin 59695 -> 0 bytes .../ppt/html/webqtl_demo2.ppt_files/Slide0005.gif | Bin 88092 -> 0 bytes .../ppt/html/webqtl_demo2.ppt_files/Slide0006.gif | Bin 100241 -> 0 bytes .../ppt/html/webqtl_demo2.ppt_files/Slide0007.gif | Bin 72138 -> 0 bytes .../ppt/html/webqtl_demo2.ppt_files/Slide0008.gif | Bin 94020 -> 0 bytes .../ppt/html/webqtl_demo2.ppt_files/Slide0009.gif | Bin 75651 -> 0 bytes .../ppt/html/webqtl_demo2.ppt_files/Slide0010.gif | Bin 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Open the default .htm file to view this Web presentation.

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This presentation contains content that your browser is unable to display. This presentation was optimized for the recent version of Microsoft Internet Explorer and Netscape Navigator 4.

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Memphis Microarray 2003
June 11, 2003, Rob Williams
Ü#Ý
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The GeneNetwork and WebQTL : PART 2  
link to www.genenetwork.org
Slide 2
Slide 3
Discovering upstream modulatory loci
WebQTL searches for upstream controllers
Genetic versus Physical maps for App expression
Physical map for distal chromosome 7
Evaluating candidate genes
Physical maps are zoomable
Evaluating Ctbp2 as a candidate QTL for App
Evaluating Ctbp2 using other resources
"Summary of Part 2"
Test Questions
Contact for comments and improvements:
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The GeneNetwork and WebQTL : PART 2  
link to www.genenetwork.org
Part 1. How to study expression variation and genetic correlation (slides 2–17)
Part 2. Discovering upstream modulators (slides 18–29)

Slide 2
Slide 3
Discovering upstream modulatory loci
WebQTL searches for upstream controllers
Genetic versus Physical maps for App expression
Physical map for distal chromosome 7
Evaluating candidate genes
Physical maps are zoomable
Evaluating Ctbp2 as a candidate QTL for App
Evaluating Ctbp2 using other resources
"Summary of Part 2"
Summary of Part 2

Test Questions
Contact for comments and improvements:
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-function LoadSld( slideId ) -{ - if( !g_supportsPPTHTML ) return - if( slideId ) - parent.base.SldUpdated(slideId) - g_origSz=parseInt(SlideObj.style.fontSize) - g_origH=SlideObj.style.posHeight - g_origW=SlideObj.style.posWidth - g_scaleHyperlinks=(document.all.tags("AREA").length>0) - if ( IsWin("PPTSld") && !parent.IsFullScrMode() ) - parent.base.highlite(); - if( g_scaleHyperlinks ) - InitHLinkArray() - if( g_scaleInFrame||(IsWin("PPTSld") && parent.IsFullScrMode() ) ) - document.body.scroll="no" - _RSW() - if( IsWin("PPTSld") && (parent.IsFullScrMode() || CtxAlwaysOn ) ) { - document.oncontextmenu=parent._CM; - self.focus(); - - } -} -function MakeSldVis( fTrans ) -{ - fTrans=fTrans && g_showAnimation - if( fTrans ) - { - if( g_bgSound ) { - idx=g_bgSound.indexOf(","); - pptSound.src=g_bgSound.substr( 0, idx ); - pptSound.loop= -(parseInt(g_bgSound.substr(idx+1))); - } - SlideObj.filters.revealtrans.Apply() - } - SlideObj.style.visibility="visible" - if( fTrans ) - SlideObj.filters.revealtrans.Play() -} -function MakeNotesVis() -{ - if( !IsNts() ) return false - SlideObj.style.display="none" - nObj = document.all.item("NotesObj") - parent.SetHasNts(0) - if( nObj ) { - nObj.style.display="" - parent.SetHasNts(1) - } - return 1 -} -function Redirect( frmId,sId ) -{ - var str=document.location.hash,idx=str.indexOf('#') - if(idx>=0) str=str.substr(1); - if( window.name != frmId && ( sId != str) ) { - obj = document.all.item("Main-File") - window.location.href=obj.href+"#"+sId - return 1 - } - return 0 -} -function HideMenu() { if( frames["PPTSld"] && PPTSld.document.all.item("ctxtmenu") && PPTSld.ctxtmenu.style.display!="none" ) { PPTSld.ctxtmenu.style.display='none'; return true } return false } -function IsWin( name ) { return window.name == name } -function IsNts() { return IsWin("PPTNts") } -function IsSldOrNts() { return( IsWin("PPTSld")||IsWin("PPTNts") ) } -function SupportsPPTAnimation() { return( navigator.platform == "Win32" && navigator.appVersion.indexOf("Windows")>0 ) } -function SupportsPPTHTML() -{ - var appVer=navigator.appVersion, msie=appVer.indexOf( "MSIE " ), inex = appVer.indexOf( "Internet Explorer " ), ver=0 - if( msie >= 0 ) - ver=parseFloat( appVer.substring( msie+5, appVer.indexOf(";",msie) ) ) - else if( inex >= 0 ) - ver=parseFloat( appVer.substring( inex+18, appVer.indexOf(";",inex) ) ) - else - ver=parseInt(appVer) - - return( ver >= 4 ) -} -var MHTMLPrefix = CalculateMHTMLPrefix(); -function CalculateMHTMLPrefix() -{ - if ( document.location.protocol == 'mhtml:') { - href=new String(document.location.href) - Start=href.indexOf('!')+1 - End=href.lastIndexOf('/')+1 - if (End < Start) - return href.substring(0, Start) - else - return href.substring(0, End) - } - return ''; -} - -function LoadNavSld(slideId) { -playList(); -parent.createCM(); - if( !g_supportsPPTHTML ) return - if( IsWin("PPTSld") && slideId ) - parent.base.SldUpdated(slideId) - self.focus(); - -} -var hasNarration = false; -function _RSW() -{ - if( !g_supportsPPTHTML || IsNts() || - ( !g_scaleInFrame && (( window.name != "PPTSld" ) ) ) ) - return - - cltWidth=document.body.clientWidth - cltHeight=document.body.clientHeight - factor=(1.0*cltWidth)/g_origW - if( cltHeight < g_origH*factor ) - factor=(1.0*cltHeight)/g_origH - - newSize = g_origSz * factor - if( newSize < 1 ) newSize=1 - - s=SlideObj.style - s.fontSize=newSize+"px" - s.posWidth=g_origW*factor - s.posHeight=g_origH*factor - s.posLeft=(cltWidth-s.posWidth)/2 - s.posTop=(cltHeight-s.posHeight)/2 - - if ( hasNarration ) { - obj = document.all.NSPlay.style; - mySld = document.all.SlideObj.style; - obj.position = 'absolute'; - obj.posTop = mySld.posTop + mySld.posHeight - 20; - obj.posLeft = mySld.posLeft + mySld.posWidth - 20; - } - if( g_scaleHyperlinks ) - ScaleHyperlinks( factor ); -} -function IsMac() { - return (window.navigator.platform.indexOf("Mac") >= 0 ); -} - -function HitOK( evt ) { - //Nav Only function - return (evt.which == 1 || (IsMac() && evt.which == 3) ); -} -function _KPH(event) -{ - - if ( parent.base.msie < 0 ) { - - if ( ( (event.target.name && event.target.name == "hasMap" ) || (event.target.href && event.target.href != "") ) && parent.g_docTable[0].type != "jpeg" && HitOK( event ) ) { - return; /* to make hyperlinks in fullscreen mode traversable */ - } - if( IsContextMenu() ) - return parent.KPH(event); - if ( parent.IsFullScrMode() && event.which == 27 ) - parent.base.CloseFullScreen(); - else if ( parent.base.IsFullScrMode() && ( (!IsMac() && event.which == 3) || ( IsMac() && (event.modifiers & Event.CONTROL_MASK) && event.which == 1 ) ) ) - return parent.KPH(event); - else if( (event.which == 32) || (event.which == 13) || HitOK( event ) ) { - if( window.name == "PPTSld" ) - parent.PPTSld.DocumentOnClick(); - else - parent.M_GoNextSld(); - } - else if ( parent.IsFullScrMode() && ((event.which == 78) || (event.which == 110) || (event.which == 29) || (event.which == 31) || (event.which == 12)) ) - parent.M_GoNextSld(); - else if ( parent.IsFullScrMode() && ( (event.which == 80) || (event.which == 112) || (event.which == 30) || (event.which == 28) || (event.which == 11) || (event.which == 8)) ) - parent.M_GoPrevSld(); - - return; - } - - if( IsNts() ) return; - - if(parent.IsFullScrMode() && event.keyCode == 27 && !parent.HideMenu() ) - parent.base.CloseFullScreen(); - else if( (event.keyCode == 32) || (event.keyCode == 13) ) - { - if( window.name == "PPTSld" ) - parent.PPTSld.DocumentOnClick(); - else - parent.M_GoNextSld(); - } - else if ( parent.IsFullScrMode() && ((event.keyCode == 78) || (event.keyCode == 110)) ) - parent.M_GoNextSld(); - else if ( parent.IsFullScrMode() && ((event.keyCode == 80) || (event.keyCode == 112)) ) - parent.M_GoPrevSld(); -} - -function DocumentOnClick(event) -{ - if ( g_doAdvOnClick && !parent.IsFullScrMode() ) { - parent.base.TP_GoToNextSld(); - return; - } - - if ( parent.base.msie < 0 ) - { - if( ( g_allowAdvOnClick && parent.IsFullScrMode() ) || g_doAdvOnClick || - (event && ( (event.which == 32) || (event.which == 13) ) ) ) - parent.M_GoNextSld(); - return; - } - if( IsNts() || (parent.IsFullScrMode() && parent.HideMenu() ) ) return; - if( ( g_allowAdvOnClick && parent.IsFullScrMode() ) || g_doAdvOnClick || - (event && ( (event.keyCode==32) || (event.keyCode == 13) ) ) ) - parent.M_GoNextSld(); -} - - -var g_supportsPPTHTML = SupportsPPTHTML(), g_scaleInFrame = true, gId="", g_bgSound="", - g_scaleHyperlinks = false, g_allowAdvOnClick = true, g_showInBrowser = false, g_doAdvOnClick = false; - - var g_showAnimation = 0; -var g_hasTrans = false, g_autoTrans = false, g_transSecs = 0; -var g_animManager = null; - -var ENDSHOW_MESG="End of slide show, click to exit.", SCREEN_MODE="Frames", gIsEndShow=0, NUM_VIS_SLDS=14, SCRIPT_HREF="script.js", FULLSCR_HREF="fullscreen.htm"; -var gCurSld = gPrevSld = 1, g_offset = 0, gNtsOpen = gHasNts = gOtlTxtExp = gNarrationPaused = false, gOtlOpen = true -window.gPPTHTML=SupportsPPTHTML() -var g_hideNav = 0; -function UpdNtsPane(){ PPTNts.location.replace( MHTMLPrefix+GetHrefObj( gCurSld ).mNtsHref ) } -function UpdNavPane( sldIndex ){ if(gNavLoaded) PPTNav.UpdNav() } -function UpdOtNavPane(){ if(gOtlNavLoaded) PPTOtlNav.UpdOtlNav() } -function UpdOtlPane(){ if(gOtlLoaded) PPTOtl.UpdOtl() } -function SetHasNts( fVal ) -{ - if( gHasNts != fVal ) { - gHasNts=fVal - UpdNavPane() - } -} - -function ToggleVNarration() -{ - if ( base.msie < 0 ) { - PPTSld.ToggleSound( false, PPTSld.document.NSPlay ); - return; - } - - rObj=PPTSld.document.all("NSPlay") - if( rObj ) { - if( gNarrationPaused ) - rObj.Play() - else - rObj.Pause() - - gNarrationPaused=!gNarrationPaused - } -} - -function PrevSldViewed(){ GoToSld( GetHrefObj(gPrevSld).mSldHref ) } -function HasPrevSld() { return ( gIsEndShow || ( g_currentSlide != 1 && GetHrefObj( g_currentSlide-1 ).mVis == 1 )||( GetCurrentSlideNum() > 1 ) ) } -function HasNextSld() { return (GetCurrentSlideNum() != GetNumSlides()) } -function StartEndShow() -{ -// g_hideNav = 1; -// PPTNav.location.reload(); - if( PPTSld.event ) PPTSld.event.cancelBubble=true - - doc=PPTSld.document - doc.open() - doc.writeln('


' + ENDSHOW_MESG + '

') - doc.close() -} -function SetSldVisited(){ gDocTable[gCurSld-1].mVisited=true } -function IsSldVisited(){ return gDocTable[gCurSld-1].mVisited } -function hrefList( sldHref, visible, sldIdx ) -{ - this.mSldHref= this.mNtsHref = sldHref - this.mSldIdx = sldIdx - this.mOrigVis= this.mVis = visible - this.mVisited= false -} -var gDocTable = new Array( - new hrefList("slide0001.htm", 1, 1), - new hrefList("slide0002.htm", 1, 2), - new hrefList("slide0003.htm", 1, 3), - new hrefList("slide0004.htm", 1, 4), - new hrefList("slide0005.htm", 1, 5), - new hrefList("slide0006.htm", 1, 6), - new hrefList("slide0007.htm", 1, 7), - new hrefList("slide0008.htm", 1, 8), - new hrefList("slide0009.htm", 1, 9), - new hrefList("slide0010.htm", 1, 10), - new hrefList("slide0011.htm", 1, 11), - new hrefList("slide0012.htm", 1, 12), - new hrefList("slide0013.htm", 1, 13), - new hrefList("slide0014.htm", 1, 14) -); - -function ImgBtn( oId,bId,w,action ) -{ - var t=this - t.Perform = _IBP - t.SetActive = _IBSetA - t.SetInactive= _IBSetI - t.SetPressed = _IBSetP - t.SetDisabled= _IBSetD - t.Enabled = _IBSetE - t.ChangeIcon = null - t.UserAction = action - t.ChgState = _IBUI - t.mObjId = oId - t.mBorderId= bId - t.mWidth = w - t.mIsOn = t.mCurState = 0 -} -function _IBSetA() -{ - if( this.mIsOn ) { - obj=this.ChgState( gHiliteClr,gShadowClr,2 ) - obj.style.posTop=0 - } -} -function _IBSetI() -{ - if( this.mIsOn ) { - obj=this.ChgState( gFaceClr,gFaceClr,1 ) - obj.style.posTop=0 - } -} -function _IBSetP() -{ - if( this.mIsOn ) { - obj=this.ChgState( gShadowClr,gHiliteClr,2 ) - obj.style.posLeft+=1; obj.style.posTop+=1 - } -} -function _IBSetD() -{ - obj=this.ChgState( gFaceClr,gFaceClr,0 ) - obj.style.posTop=0 -} -function _IBSetE( state ) -{ - var t=this - GetObj( t.mBorderId ).style.visibility="visible" - if( state != t.mIsOn ) { - t.mIsOn=state - if( state ) - t.SetInactive() - else - t.SetDisabled() - } -} -function _IBP() -{ - var t=this - if( t.mIsOn ) { - if( t.UserAction != null ) - t.UserAction() - if( t.ChangeIcon ) { - obj=GetObj(t.mObjId) - if( t.ChangeIcon() ) - obj.style.posLeft=obj.style.posLeft+(t.mCurState-4)*t.mWidth - else - obj.style.posLeft=obj.style.posLeft+(t.mCurState-0)*t.mWidth - } - t.SetActive() - } -} -function _IBUI( clr1,clr2,nextState ) -{ - var t=this - SetBorder( GetObj( t.mBorderId ),clr1,clr2 ) - obj=GetObj( t.mObjId ) - obj.style.posLeft=obj.style.posLeft+(t.mCurState-nextState)*t.mWidth-obj.style.posTop - t.mCurState=nextState - return obj -} -function TxtBtn( oId,oeId,action,chkState ) -{ - var t=this - t.Perform = _TBP - t.SetActive = _TBSetA - t.SetInactive= _TBSetI - t.SetPressed = _TBSetP - t.SetDisabled= _TBSetD - t.SetEnabled = _TBSetE - t.GetState = chkState - t.UserAction = action - t.ChgState = _TBUI - t.mObjId = oId - t.m_elementsId= oeId - t.mIsOn = 1 -} -function _TBSetA() -{ - var t=this - if( t.mIsOn && !t.GetState() ) - t.ChgState( gHiliteClr,gShadowClr,0,0 ) -} -function _TBSetI() -{ - var t=this - if( t.mIsOn && !t.GetState() ) - t.ChgState( gFaceClr,gFaceClr,0,0 ) -} -function _TBSetP() -{ - if( this.mIsOn ) - this.ChgState( gShadowClr,gHiliteClr,1,1 ) -} -function _TBSetD() -{ - this.ChgState( gFaceClr,gFaceClr,0,0 ) - this.mIsOn = 0 -} -function _TBSetE() -{ - var t=this - if( !t.GetState() ) - t.ChgState( gFaceClr,gFaceClr,0,0 ) - else - t.ChgState( gShadowClr,gHiliteClr,1,1 ) - t.mIsOn = 1 -} -function _TBP() -{ - var t=this - if( t.mIsOn ) { - if( t.UserAction != null ) - t.UserAction() - if( t.GetState() ) - t.SetPressed() - else - t.SetActive() - } -} -function _TBUI( clr1,clr2,lOffset,tOffset ) -{ - SetBorder( GetObj( this.mObjId ),clr1,clr2 ) - Offset( GetObj( this.m_elementsId ),lOffset,tOffset ) -} -function GetObj( objId ){ return document.all.item( objId ) } -function Offset( obj, top, left ){ obj.style.top=top; obj.style.left=left } -function SetBorder( obj, upperLeft, lowerRight ) -{ - s=obj.style; - s.borderStyle = "solid" - s.borderWidth = 1 - s.borderLeftColor = s.borderTopColor = upperLeft - s.borderBottomColor= s.borderRightColor = lowerRight -} -function GetBtnObj(){ return gBtnArr[window.event.srcElement.id] } -function BtnOnOver(){ b=GetBtnObj(); if( b != null ) b.SetActive() } -function BtnOnDown(){ b=GetBtnObj(); if( b != null ) b.SetPressed() } -function BtnOnOut(){ b=GetBtnObj(); if( b != null ) b.SetInactive() } -function BtnOnUp() -{ - b=GetBtnObj() - if( b != null ) - b.Perform() - else - Upd() -} -function GetNtsState(){ return parent.gNtsOpen } -function GetOtlState(){ return parent.gOtlOpen } -function GetOtlTxtState(){ return parent.gOtlTxtExp } -function NtsBtnSetFlag( fVal ) -{ - s=document.all.item( this.m_flagId ).style - s.display="none" - if( fVal ) - s.display="" - else - s.display="none" -} - -var gHiliteClr="THREEDHIGHLIGHT",gShadowClr="THREEDSHADOW",gFaceClr="THREEDFACE" -var gBtnArr = new Array() -gBtnArr["nb_otl"] = new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOtlPane,GetOtlState ) -gBtnArr["nb_nts"] = new TxtBtn( "nb_nts","nb_ntsElem",parent.ToggleNtsPane,GetNtsState ) -gBtnArr["nb_prev"]= new ImgBtn( "nb_prev","nb_prevBorder",30,parent.GoToPrevSld ) -gBtnArr["nb_next"]= new ImgBtn( "nb_next","nb_nextBorder",30,parent.GoToNextSld ) -gBtnArr["nb_sldshw"]= new ImgBtn( "nb_sldshw","nb_sldshwBorder",18,parent.FullScreen ) -gBtnArr["nb_voice"] = new ImgBtn( "nb_voice","nb_voiceBorder",18,parent.ToggleVNarration ) -gBtnArr["nb_otlTxt"]= new ImgBtn( "nb_otlTxt","nb_otlTxtBorder",23,parent.ToggleOtlText ) -gBtnArr["nb_nts"].m_flagId= "notes_flag" -gBtnArr["nb_nts"].SetFlag = NtsBtnSetFlag -gBtnArr["nb_otlTxt"].ChangeIcon= GetOtlTxtState -var sNext="Next",sPrev="Previous",sEnd="End Show",sFont="Arial", alwaysOn = false -function ShowMenu() -{ - BuildMenu(); - var doc=PPTSld.document.body,x=PPTSld.event.clientX+doc.scrollLeft,y=PPTSld.event.clientY+doc.scrollTop - - m = PPTSld.document.all.item("ctxtmenu") - m.style.pixelLeft=x - if( (x+m.scrollWidth > doc.clientWidth)&&(x-m.scrollWidth > 0) ) - m.style.pixelLeft=x-m.scrollWidth - - m.style.pixelTop=y - if( (y+m.scrollHeight > doc.clientHeight)&&(y-m.scrollHeight > 0) ) - m.style.pixelTop=y-m.scrollHeight - - m.style.display="" -} -function _CM() -{ - if( !parent.IsFullScrMode() && !alwaysOn) return; - - if(!PPTSld.event.ctrlKey) { - ShowMenu() - return false - } else - HideMenu() -} - -function processNavKPH(event) { - if ( PPTSld && (event.keyCode != 13 || !event.srcElement.href || event.srcElement.href == "" ) ) - return PPTSld._KPH(event); -} -function processNavClick() { - HideMenu(); - return true; -} -function BuildMenu() -{ - if( PPTSld.document.all.item("ctxtmenu") ) return - - var mObj=CreateItem( PPTSld.document.body ) -mObj.id="ctxtmenu" - var s=mObj.style - s.position="absolute" - s.cursor="default" - s.width="100px" - SetCMBorder(mObj,"menu","black") - - var iObj=CreateItem( mObj ) - SetCMBorder( iObj, "threedhighlight","threedshadow" ) - iObj.style.padding=2 - if ( self.IsFullScrMode() ) { - CreateMenuItem( iObj,sNext,M_GoNextSld,M_True ) - CreateMenuItem( iObj,sPrev,M_GoPrevSld,M_HasPrevSld ) - } - else { - CreateMenuItem( iObj,sNext, base.TP_GoToNextSld, base.HasNextSld ) - CreateMenuItem( iObj,sPrev,base.GoToPrevSld, base.HasPrevSld ) - } - var sObj=CreateItem( iObj ) - SetCMBorder(sObj,"menu","menu") - var s=sObj.style - s.borderTopColor="threedshadow" - s.borderBottomColor="threedhighlight" - s.height=1 - s.fontSize="0px" - if ( self.IsFullScrMode() ) - CreateMenuItem( iObj,sEnd,M_End,M_True ) - else - CreateMenuItem( iObj,sEnd,M_End,M_False ) -} -function Highlight() { ChangeClr("activecaption","threedhighlight") } -function Deselect() { ChangeClr("threedface","menutext") } -function Perform() -{ - e=PPTSld.event.srcElement - if( e.type=="menuitem" && e.IsActive() ) - e.Action() - else - PPTSld.event.cancelBubble=true -} -function ChangeClr( bg,clr ) -{ - e=PPTSld.event.srcElement - if( e.type=="menuitem" && e.IsActive() ) { - e.style.backgroundColor=bg - e.style.color=clr - } -} - -function M_HasPrevSld() { return( base.HasPrevSld() ) } -function M_GoNextSld() { - base.SetFSMode(1); - if( gIsEndShow ) - M_End(); - else { - if ( base.HasNextSld() ) - base.GoToNextSld(); - else if ( base.EndSlideShow ) { - StartEndShow(); - gIsEndShow = 1; - - PPTNav.location.reload(); - } - else - base.CloseFullScreen(); - } -} -function M_GoPrevSld() { - base.SetFSMode(1); - g_hideNav = 0; - if( gIsEndShow ) { - gIsEndShow = 0; - if ( base.msie > 0 && IsMac() ) - ChangeFrame( SLIDE_FRAME, GetHrefObj( g_currentSlide ).m_slideHref ); - else - PPTSld.history.back(); - - PPTNav.location.reload(); - if( PPTSld.event ) - PPTSld.event.cancelBubble=true; - } - else - base.GoToPrevSld(); -} -function M_True() { return true } -function M_False() { return false } - -function M_End() { - base.CloseFullScreen(); - /*PPTSld.event.cancelBubble=true; - window.close( self )*/ -} - -function CreateMenuItem( node,text,action,eval ) -{ - var e=CreateItem( node ) - e.type="menuitem" - e.Action=action - e.IsActive=eval - e.innerHTML=text - - if( !e.IsActive() ) - e.style.color="threedshadow" - e.onclick=Perform - e.onmouseover=Highlight - e.onmouseout=Deselect - s=e.style; - s.fontFamily=sFont - s.fontSize="8pt" - s.paddingLeft=2 -} -function CreateItem( node ) -{ - var elem=PPTSld.document.createElement("DIV") - node.insertBefore( elem ) - return elem -} -function SetCMBorder( o,ltClr,rbClr ) -{ - var s=o.style - s.backgroundColor="menu" - s.borderStyle="solid" - s.borderWidth=1 - s.borderColor=ltClr+" "+rbClr+" "+rbClr+" "+ltClr -} - -/* netscape context menu */ -g_ctxmenu = 0; -function setRect( obj, X, Y, W, H ) { - obj.top = Y; - obj.left = X; - obj.clip.top = 0; - obj.clip.left = 0; - obj.clip.bottom = H; - obj.clip.right = W; -} - -function KPH(event) { - if ( ! base.IsFullScrMode() && !alwaysOn ) - return true; - - if ( (!IsMac() &&event.which == 3) || ( IsMac() && (event.modifiers & Event.CONTROL_MASK) && event.which == 1 ) ) { - PPTSld.g_ctxmenu = 1; - PPTSld.stripUobj.visibility = "show"; - PPTSld.stripDobj.visibility = "show"; - PPTSld.shadeUobj.visibility = "show"; - PPTSld.shadeDobj.visibility = "show"; - PPTSld.panelobj.visibility = "show"; - PPTSld.Fobj.visibility = "show"; - PPTSld.Bobj.visibility = "show"; - PPTSld.Eobj.visibility = "show"; - - setRect(PPTSld.shadeUobj, event.pageX-2, event.pageY-2, 82, 67 ); - setRect(PPTSld.shadeDobj, event.pageX, event.pageY, 82, 67 ); - setRect(PPTSld.panelobj, event.pageX, event.pageY, 80, 65 ); - setRect(PPTSld.Fobj, event.pageX, event.pageY, 80, 20 ); - setRect(PPTSld.Bobj, event.pageX, event.pageY+20, 80, 20 ); - setRect(PPTSld.stripUobj, event.pageX, event.pageY+41, 80, 1 ); - setRect(PPTSld.stripDobj, event.pageX, event.pageY+43, 80, 1 ); - setRect(PPTSld.Eobj, event.pageX, event.pageY+45, 80, 20 ); - return false; - } - if ( HitOK( event ) ) { - PPTSld.g_ctxmenu = 0; - PPTSld.stripUobj.visibility = "hide"; - PPTSld.stripDobj.visibility = "hide"; - PPTSld.shadeUobj.visibility = "hide"; - PPTSld.shadeDobj.visibility = "hide"; - PPTSld.panelobj.visibility = "hide"; - PPTSld.Fobj.visibility = "hide"; - PPTSld.Bobj.visibility = "hide"; - PPTSld.Eobj.visibility = "hide"; - } - return true; -} - -function overMe() { - this.bgColor = "blue"; -} - -function outMe() { - this.bgColor = "#AAAAAA"; -} - -function makeElement( whichEl, whichContainer ) { - if ( arguments.length == 1 ) { - whichContainer = PPTSld; - } - tmp = new Layer(100,whichContainer); - eval( whichEl + " = tmp" ); - return eval(whichEl); -} - -function initMe( obj, clr, text ) { - obj.bgColor = clr; -// obj.document.write("" + text + ""); - obj.document.write( "   " + text +" "); - obj.document.close(); - obj.captureEvents(Event.CLICK); - obj.color = "black"; -} - -function createCM() { - if ( base.IsFullScrMode() ) { - var clr = "#AAAAAA"; - PPTSld.shadeUobj = makeElement("SHADEU"); - PPTSld.shadeDobj = makeElement("SHADED"); - PPTSld.panelobj = makeElement("PANEL"); - PPTSld.stripUobj = makeElement("STRIPU"); - PPTSld.stripDobj = makeElement("STRIPD"); - PPTSld.shadeUobj.bgColor = "#BBBBBB"; - PPTSld.shadeDobj.bgColor = "#888888"; - PPTSld.stripUobj.bgColor = "#777777"; - PPTSld.stripDobj.bgColor = "#CCCCCC"; - PPTSld.panelobj.bgColor = clr; - PPTSld.Fobj = makeElement("Next"); - PPTSld.Bobj = makeElement("Previous"); - PPTSld.Eobj = makeElement("EndShow"); - initMe( PPTSld.Fobj, clr, "Next" ); - PPTSld.Fobj.onclick = M_GoNextSld; - - initMe( PPTSld.Bobj, clr, "Previous" ); - PPTSld.Bobj.onclick = M_GoPrevSld; - - initMe( PPTSld.Eobj, clr, "End Show"); - PPTSld.Eobj.onclick = base.CloseFullScreen; - } -} - -function IsContextMenu() { - return (g_ctxmenu == 1) -} -var g_notesTable = new Array() -var g_hiddenSlide = new Array() -makeSlide( 0,1,1); -makeSlide( 1,1,1); -makeSlide( 2,1,1); -makeSlide( 3,1,1); -makeSlide( 4,1,1); -makeSlide( 5,1,1); -makeSlide( 6,1,1); -makeSlide( 7,1,1); -makeSlide( 8,1,1); -makeSlide( 9,1,1); -makeSlide( 10,1,1); -makeSlide( 11,0,1); -makeSlide( 12,0,1); -makeSlide( 13,1,1); - -var END_SHOW_HREF = "endshow.htm", - OUTLINE_EXPAND_HREF = "outline_expanded.htm", - OUTLINE_COLLAPSE_HREF = "outline_collapsed.htm", - OUTLINE_NAVBAR_HREF = "outline_navigation_bar.htm", - NAVBAR_HREF = "navigation_bar.htm", - BLANK_NOTES_HREF = "blank_notes.htm", - NUM_VISIBLE_SLIDES = 14, - SIMPLE_FRAMESET = 0, - SLIDE_FRAME = "PPTSld", - NOTES_FRAME = "PPTNts", - OUTLINE_FRAME = "PPTOtl", - OUTLINE_NAVBAR_FRAME = "PPTOtlNav", - NAVBAR_FRAME = "PPTNav", - MAIN_FRAME = "MainFrame", - FS_NAVBAR_HREF = "fs_navigation_bar.htm", - isIEFiles = 2, - isNAVFiles = 8, - isFLATFiles = 16, - includeNotes = 1, - PPTPRESENTATION = 1; 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- otlobj.style.backgroundColor = backclr; - otlobj.style.color = textclr; - otlobj.all.AREF.style.color = textclr; - } - else - index = GetCurrentSlideNum(); - eval( "otlobj = frames[MAIN_FRAME].frames[OUTLINE_FRAME].document.all.p" + index ); - otlobj.style.backgroundColor = textclr; - otlobj.style.color = backclr; - otlobj.all.AREF.style.color = backclr; - g_prevotlobjidx = index; -} - -function ChangeFrame( frame, href ) -{ -if ( IsFramesMode() ) { - if ( NAVBAR_FRAME == frame || OUTLINE_NAVBAR_FRAME == frame ) { - frames[frame].location.replace(href); - } - else if( ! 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Part 2: Discovering upstream modulators and quantitative trait loci (QTLs). A quantitative trait locus is a chromosomal region that harbors one or a few polymorphic gene loci that influence a trait. We are going to be looking for QTLs that modulate the steady state expression level of App in the adult mouse forebrain.
\ No newline at end of file diff --git a/web/tutorial/ppt/html/webqtl_demo2.ppt_files/slide0002.htm b/web/tutorial/ppt/html/webqtl_demo2.ppt_files/slide0002.htm deleted file mode 100755 index b40dd6e1..00000000 --- a/web/tutorial/ppt/html/webqtl_demo2.ppt_files/slide0002.htm +++ /dev/null @@ -1,25 +0,0 @@ - PowerPoint Presentation - Complex trait analysis, develop-ment, and genomics \ No newline at end of file diff --git a/web/tutorial/ppt/html/webqtl_demo2.ppt_files/slide0002_image002.png b/web/tutorial/ppt/html/webqtl_demo2.ppt_files/slide0002_image002.png deleted file mode 100755 index 2eb797be..00000000 Binary files a/web/tutorial/ppt/html/webqtl_demo2.ppt_files/slide0002_image002.png and /dev/null differ diff --git a/web/tutorial/ppt/html/webqtl_demo2.ppt_files/slide0002_image003.png b/web/tutorial/ppt/html/webqtl_demo2.ppt_files/slide0002_image003.png deleted file mode 100755 index 6cb8411f..00000000 Binary files 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The next few slides provide a short introduction to mapping the loci that are responsible for variation in a trait such as App expression level. These modulatory regions of the genome are sometimes called quantitative trait loci or QTLs. You may want to do some independent reading on this topic if this is your first exposure to QTL analysis.
The genetic reference population (GRP) of BXD recombinant inbred strains were originally generated about 25 years ago by Benjamin Taylor at The Jackson Laboratory. He crossed female C57BL/6J mice with male DBA/2J mice to generate the F1 and F2 progeny. At the bottom of this slide we have schematized one chromosome pair from three of the BXD RI strains.  The dashed vertical lines that lead to the final BXD RI lines involve 21 full sib matings (about 7 years of breeding). Some lines die out during inbreeding. For example, there is no longer any BXD3 strain.
Notes:
1. Over the last decade, our group (Lu Lu and Rob Williams) and Jeremy Peirce and Lee Silver at Princeton have enlarged Ben TaylorÕs set. There are now just over 80 BXD strains. They have all been genotyped using about 13,700 markers (SNPs and microsatellites). These markers are used to define the ÒblueÓ and ÒredÓ regions of the chromosomes as shown in the figure above.
2. Chromosomes of RI GRPs usually have about 4 times as many recombinations as those of F2 animals. However, unlike an F2, both chromosomes of an RI are identical. Therefore, 50 RI strains contain as many recombinations as 100 F2 animals.
3. BXD43 through BXD100 were generated using a special method that resulted in a further doubling of the average recombination density per chromosome. The entire set of 80 BXDs therefore contains as many recombinations as about 260 F2 animals.
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This slide is illustrates two major types of QTLs that modulate variability in transcript-relative steady state abundance.

1. cis QTLs are defined as QTLs that are closely linked to the gene whose transcript is the measured trait. For example, a polymorphism in the promoter that affects binding of a transcription factor. However, cis QTLs can be far upstream or downstream polymorphisms in enhancers or may be in 3Õ UTR binding sites that affect message stability.

2. trans QTLs map far enough away from the location of the gene that gives rise to the transcript that is being measured so that we can be fairly certain that the QTL is not in the gene itself. The most blatant type of trans QTL would be a polymorphism in a transcription factor. But in the majority of cases, the trans QTLs can be far removed in a mechanistic sense from the actual events modulating transcript abundance. That is why there are three overlapping arrows in the figure.  The way in which an upstream polymorphism influences a downstream difference in mRNA abundance can be indirect. Effects can:
   a.  cross tissue types (a polymorphic liver enzyme may affect CNS gene expression)
   b.  cross time (the modulator is only expressed for one day during development but has permanent effects in adults)
   c.  may be contingent on environmental factors (heat shock may trigger the expression of a polymorphic factor that affects mRNA abundance).
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Please bring the Trait Data and Analysis window to the front and look for the Interval Mapping button. Confirm that you are back to the trait amyloid beta precursor protein.  If so, then just click the button.

Notice that the default for:
Select Chrs (chromosomes) is ALL
Select Mapping Scale is set to GENETIC
Options: Permutation test YES  (2000 is the default number)
Options: Bootstrap test YES (2000 is the default number)
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This is a major output type: a so-called full-genome interval map.

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.
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).  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.  Or you can read them as a chi-square-like statistic.
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.
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.  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.
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.  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.
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.

CLICKABLE REGIONS:
1. If you click on the Chromosome number then you will generate a new map just for that chromosome.
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.
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.
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.
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.
6. You can also download the results of the analysis in a text format
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The map on the top has an X-axis scale based on frequency of recombinations events between markers (B to D transitions, see slide 19 for a color-coded example). These so-called genetic maps are scaled in centimorgan (recombinations per 100 gametes). In contrast, the physical map shown below the genetic map has an X-axis scale based on DNA length measured in nucleotides or base-pairs. Notice the large difference between the two maps in the size of Chr 19 (large on the genetic scale but small on the physical scale).
Also notice the large difference in the width of the chromosome 7 QTL peak. In mice, recombinations occur with higher frequency toward the telomeric side (right side) of each chromosome. As a result, genetic maps are stretched out more toward the telomere relative to a physical map. The QTL on distal Chr 7 is therefore actually more precisely mapped than might appear looking at the genetic map.
The physical scale is becoming more useful than the genetic scale primarily because many other data types can be easily superimposed on a physical map. You will see more examples in the next several slides.
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Physical map of variation in App expression in brain on distal Chr 7 (a blow up of the whole-genome map on the previous slide).

Notes:
1. You can now see that the X-axis is on a physical scale of megabases (Mb). The QTL peak is roughly between 120 and 132 Mb.
2. The small irregular colored blocks and marks toward the top of the map mark the locations of genes superimposed on the physical map. Neighboring genes are offset slightly in the vertical axis for display purpose. Note one region of very high gene density from about 120 to 123 Mb.
3. The orange hash marks along the X-axis represent the number of single nucleotide polymorphisms that distinguish the two parental strains (C57BL/6J and DBA/2J) from each other. We call this the SNP seismograph track (see Glossary for more details). Regions with low numbers of SNPs have closely matched sequences and are less likely to contain QTLs.
4. As before, the thin red line shows the additive effect size. By convention the positive values signify the D alleles are associated with higher expression of App in this region of Chr 7 than the B alleles. The maximum effect size is about +0.20 log2 expression units per D allele. The differences been the BB and DD genotypes (BB and DD because each strain has two alleles; one per chromosome) is therefore about 2^0.4 = 1.32 or a 32% increment in DD relative to BB at this locus.
5. If you scroll just under the Physical Map you will see text that reads ÒDISPLAY from XXX Mb TO YYY MbÉ..Ó  These physical maps are zoomable, a feature we will exploit to evaluate candidate genes in this QTL interval.
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Evaluating candidate genes (CHECKED BOXES) responsible for variability in APP expression:
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.  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.

Notes:
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.
2. Caveat:   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 QTL 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.
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 transcripts listed in the Correlation Table above (from Gtf3c1 to Zranb1) has variation in expression that maps to Chr 7 at about 120 Mb?  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).]

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An even higher blow-up of part of the Chr 7 physical map of variation in App expression in brain.  The QTL region actually extends from about 119 to 129.

Notes:
1. As mentioned in the previous slide another important approach to ranking candidates is based on the number of sequence variants that distinguish the parental strains. If we were sure that the sequences of the gene, its promoter, and its enhancers were identical between the strains then we could discount--but not eliminate--that gene as a candidate. The Gtf3c1 candidate almost falls into this category: of 663 known SNPs in and around this gene, only four differ between C57BL/6J and DBA/2J. Gtf3c1 is essentially identical-by-descent in these strains and is a less likely candidate. In contrast, if the two alleles of the gene have dozens of functional variants in exons, promoters, enhancers, and splice sites, then it becomes a higher priority candidate.
Of course it only takes a single critical sequence variant to generate downstream effects. The argument above is really about the prior probabilities. Where would you place your bets given the information at hand?
2.  If you scroll down the INTERVAL ANALYST you will find that Ctbp2 is a particularly interesting candidate that contains lots of SNPs (n = 75 and a SNP density of 0.55 SNP/Kb). Ctbp2 is also closer to our QTL peak than was Gtf3c1. Not only does Ctbp2 contain lots of SNPs but it is also is associated with a powerful cis QTL with an LRS of 24.2 (divide by 4.61 to get the equivalent LOD score of 5.25).
3.  At this high magnification, individual genes are distinct. They are color coded by their density of SNPs. Bright orange represents those genes that have a high SNP density (C57BL/6J versus DBA/2J), black represents genes with low SNP density. Roll the cursor over a gene block and its name will pop up, along with information on exon number.
4.  Beneath the physical map you will find an INTERVAL ANALYST table that lists information on known genes in the region on which you have zoomed the Physical Map.
5.  As always: error-checking is important. Some genes may be missing from the Interval Analyst (recent additions or errors of omission). In this case the Zranb1 gene that is located just proximal to Ctbp2 is not listed in the INTERVAL ANALYST. Double-check the interval using the Genome Browser links (blue and beige horizontal bars) at the top of the PHYSICAL MAP.
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This slide illustrates one reason why Ctbp2 should be considered a high priority positional candidate gene that may modulate the expression level of App.  Ctbp2 is a strong cis QTL in some brain regions (here the data are taken from the striatum).  If Ctbp2 contains variants that modulate its own expression then these expression differences may produce many downstream effects. Of course, we now want to know much more about the known biology of Ctbp2. What kind of gene is it? To begin to answer that question we can use a number of resources listed in the LINKS page.

Notes:
1. The App QTL is bimodal. Perhaps there are actually two causal factors in this region--one close to 123 Mb and the other close to 127 Mb.
2. The precision of QTL mapping depends on several factors, including the effect size and interactions among QTLs modulating a trait, the number of genetic individuals that are studied, and the distribution of recombinations in the study population.  In the case above, the QTL(s) are likely to be confined to the interval from 120 to 132 Mb. The bootstrap test (yellow bars shown in some of the previous slides) can be usual for estimating the consistency of QTL peaks.
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Ctbp2 should also be considered a high priority biological candidate gene responsible for modulating App expression levels. The C-terminal binding protein 2 is a transcriptional co-repressor also known as Ribeye. The gene produces two transcripts encoding distinct proteins. The short form is a transcriptional repressor that binds a Pro-X-Asp-Leu-Ser peptide motif and interacts with several transcription factors including EVI1, ZFPM1, and ZFHX1A (aka TCF8, deltaEF1). The longer isoform is a major component of specialized synapses in photoreceptors. Both proteins contain a NAD+ binding domain similar to NAD+-dependent 2-hydroxyacid dehydrogenases.

Notes:
1. To find out more about CTBP2 protein and the Ctbp2 gene, link to iHOP at http://www.pdg.cnb.uam.es/UniPub/iHOP/ and type in CTBP2
Try Arrowsmith at http://arrowsmith.psych.uic.edu/cgi-test/arrowsmith_uic/pubsmith.cgi
2. Both APP and CTBP2 are involved in oxidoreducatase activity or Notch signaling. To establish this common gene ontology visit NCBI  http://www.ncbi.nih.gov/entrez/query.fcgi?db=gene and enter each gene symbol.
3. You can get interesting hints regarding Ctbp2 expression partners by examining the genetic correlations between Ctbp2 probe set 1422887_a_at and all other transcripts on the M430 Affymetrix array. Use the Striatum data set because we already know from previous work (the previous slide) that this gene is a cis QTL.  You should be able to show that Ctbp2 and Notch3 have antagonistic expression patterns in striatum. The negative genetic correlation with E2f4 is even stronger. The transcript also has a high positive genetic correlation with Rdh14. Of particular interest with respect to APP protein processing, Ctbp2 covaries positively with Bace2 (the transcript of the beta site APP-cleaving enzyme 2).


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By clicking on the CORRELATION of the Atcay transcript to the App transcript, you can generate a Correlation plot between these two transcripts. In this App and Atcay scatterplot, each point is a strain mean value. For example, BXD33 and BXD8 have low App and Atcay expressions. The two parental strains and the F1 are also included in this plot.
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A group of traits from many different databases can be selected and brought together for joint analysis. In this case all of the content of the BXD SELECTIONS is from a single BRAIN database, the top 20 neighbors of the App transcript from the Correlation Results table. Eight of these neighbors plus App is shown in the slide.
Notes:
1.All of items in the BXD SELECTIONS were selected using the SELECT ALL button
2. The buttons at the top (and bottom) of this page can do some cool stuff. We will work with NETWORK GRAPH first.
3. Think of the SELECTIONS as your shopping cart. You go to different aisles in the supermarket to acquire different types of items of interest. These could include transcripts, classical phenotypes (longevity, brain weight, prepulse inhibition, iron levels in midbrain). ÒChecking outÓ in this case involves doing some analysis with the items in the cart.
4. Different tools handle different numbers of items. Most will handle up to 100 traits.

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END
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The GeneNetwork and WebQTL : PART 2  
link to www.genenetwork.org
lPart 1. How to study expression variation and genetic correlation (slides 2–17)
lPart 2. Discovering upstream modulators (slides 18–29)
RNA

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Going back to the Trait Data and Analysis Form window, we have computed the correlations between strain variation in App expression level and other classical phenotypes that have already been measured in many of the same BXD strains.
Notes:
1.The number of common strains varies widely--in this case from 14 to 23 strains.
2. We can add these traits (four are selected) to our BXD SELECTIONS window.
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How to make recombinant inbred strains (RI)

C57BL/6J (B)

DBA/2J (D)

F1

20 generations brother-sister matings

BXD1

BXD2

BXD80
+ É +

F2

BXD RI
Strain set

fully
inbred

isogenic

hetero-
geneous

Recombined chromosomes are needed for mapping

female

male

chromosome pair

Inbred
Isogenic
siblings

BXD
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We have computed the Network Graph, now using other types of traits.
Saline Hot Plate Latency is the green node labeled 10020.
Freezing (fear) is the green node labeled 10447.
Notes:
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aa

aaaa

D2 strain

B6 strain

amount of transcript

4 units

2 units

D

B

D and B may be SNP-like variants in the promoter itself (cis QTL) or in upstream genes (trans QTLs).
UPSTREAM
modulators

High

D

B

cis QTL

Low

>>>>PROMOTER--ATG-Exon1-Intron1-Exon2-Intron2 - etc-3'UTR >>>>>










trans QTL

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Discovering upstream modulatory loci
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Part 2: Discovering upstream modulators and quantitative trait loci (QTLs). A quantitative trait locus is a chromosomal region that harbors one or a few polymorphic gene loci that influence a trait. We are going to be looking for QTLs that modulate the steady state expression level of App in the adult mouse forebrain.
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WebQTL searches for upstream controllers
App maps on Chr 16 (blue arrow points to the orange triangle) but the best locus is on Chr 7.
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The next few slides provide a short introduction to mapping the loci that are responsible for variation in a trait such as App expression level. These modulatory regions of the genome are sometimes called quantitative trait loci or QTLs. You may want to do some independent reading on this topic if this is your first exposure to QTL analysis.
The genetic reference population (GRP) of BXD recombinant inbred strains were originally generated about 25 years ago by Benjamin Taylor at The Jackson Laboratory. He crossed female C57BL/6J mice with male DBA/2J mice to generate the F1 and F2 progeny. At the bottom of this slide we have schematized one chromosome pair from three of the BXD RI strains.  The dashed vertical lines that lead to the final BXD RI lines involve 21 full sib matings (about 7 years of breeding). Some lines die out during inbreeding. For example, there is no longer any BXD3 strain.
Notes:
1. Over the last decade, our group (Lu Lu and Rob Williams) and Jeremy Peirce and Lee Silver at Princeton have enlarged Ben TaylorÕs set. There are now just over 80 BXD strains. They have all been genotyped using about 13,700 markers (SNPs and microsatellites). These markers are used to define the ÒblueÓ and ÒredÓ regions of the chromosomes as shown in the figure above.
2. Chromosomes of RI GRPs usually have about 4 times as many recombinations as those of F2 animals. However, unlike an F2, both chromosomes of an RI are identical. Therefore, 50 RI strains contain as many recombinations as 100 F2 animals.
3. BXD43 through BXD100 were generated using a special method that resulted in a further doubling of the average recombination density per chromosome. The entire set of 80 BXDs therefore contains as many recombinations as about 260 F2 animals.
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Genetic versus Physical maps for App expression
The difference between genetic and physical scale is analogous to measuring the separation between New York and Boston in either travel hours or kilometers.
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This slide is illustrates two major types of QTLs that modulate variability in transcript-relative steady state abundance.

1. cis QTLs are defined as QTLs that are closely linked to the gene whose transcript is the measured trait. For example, a polymorphism in the promoter that affects binding of a transcription factor. However, cis QTLs can be far upstream or downstream polymorphisms in enhancers or may be in 3Õ UTR binding sites that affect message stability.

2. trans QTLs map far enough away from the location of the gene that gives rise to the transcript that is being measured so that we can be fairly certain that the QTL is not in the gene itself. The most blatant type of trans QTL would be a polymorphism in a transcription factor. But in the majority of cases, the trans QTLs can be far removed in a mechanistic sense from the actual events modulating transcript abundance. That is why there are three overlapping arrows in the figure.  The way in which an upstream polymorphism influences a downstream difference in mRNA abundance can be indirect. Effects can:
   a.  cross tissue types (a polymorphic liver enzyme may affect CNS gene expression)
   b.  cross time (the modulator is only expressed for one day during development but has permanent effects in adults)
   c.  may be contingent on environmental factors (heat shock may trigger the expression of a polymorphic factor that affects mRNA abundance).
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Physical map for distal chromosome 7
Distal Chr 7 from ~120 and 132 Mb may modulate App
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Please bring the Trait Data and Analysis window to the front and look for the Interval Mapping button. Confirm that you are back to the trait amyloid beta precursor protein.  If so, then just click the button.

Notice that the default for:
Select Chrs (chromosomes) is ALL
Select Mapping Scale is set to GENETIC
Options: Permutation test YES  (2000 is the default number)
Options: Bootstrap test YES (2000 is the default number)
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Evaluating candidate genes
Right position
and high correlation
 = better
candidates
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This is a major output type: a so-called full-genome interval map.

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.
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).  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.  Or you can read them as a chi-square-like statistic.
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.
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.  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.
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.  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.
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.

CLICKABLE REGIONS:
1. If you click on the Chromosome number then you will generate a new map just for that chromosome.
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.
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.
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.
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.
6. You can also download the results of the analysis in a text format
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Physical maps are zoomable
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The map on the top has an X-axis scale based on frequency of recombinations events between markers (B to D transitions, see slide 19 for a color-coded example). These so-called genetic maps are scaled in centimorgan (recombinations per 100 gametes). In contrast, the physical map shown below the genetic map has an X-axis scale based on DNA length measured in nucleotides or base-pairs. Notice the large difference between the two maps in the size of Chr 19 (large on the genetic scale but small on the physical scale).
Also notice the large difference in the width of the chromosome 7 QTL peak. In mice, recombinations occur with higher frequency toward the telomeric side (righ sidet) of each chromosome. As a result, genetic maps are stretched out more toward the telomere relative to a physical map. The QTL on distal Chr 7 is therefore actually more precisely mapped than might appear looking at the genetic map.
The physical scale is becoming more useful than the genetic scale primarily because many other data types can be easily superimposed on a physical map. You will see more examples in the next several slides.
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Evaluating Ctbp2 as a candidate QTL for App
This is the Ctbp2 cis QTL, but is detected only in the Rosen striatum data set.
This is the App QTL in the INIA data set.
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Physical map of variation in App expression in brain on distal Chr 7 (a blow up of the whole-genome map on the previous slide).

Notes:
1. You can now see that the X-axis is on a physical scale of megabases (Mb). The QTL peak is roughly between 120 and 132 Mb.
2. The small irregular colored blocks and marks toward the top of the map mark the locations of genes superimposed on the physical map. Neighboring genes are offset slightly in the vertical axis for display purpose. Note one region of very high gene density from about 120 to 123 Mb.
3. The orange hash marks along the X-axis represent the number of single nucleotide polymorphisms that distinguish the two parental strains (C57BL/6J and DBA/2J) from each other. We call this the SNP seismograph track (see Glossary for more details). Regions with low numbers of SNP have closely matched sequences and are less likely to contain QTLs.
4. As before, the thin red line shows the additive effect size. By convention the positive values signify the D alleles are associated with higher expression of App in this region of Chr 7 than the B alleles. The maximum effect size is about +0.20 log2 expression units per D allele. The differences been the BB and DD genotypes (BB and DD because each strain has two alleles; one per chromosome) is therefore about 2^0.4 = 1.32; or a 32% increment in DD relative to BB at this locus.
5. If you scroll just under the Physical Map you will see text that reads ÒDISPLAY from XXX Mb TO YYY MbÉ..Ó  These physical maps are zoomable, a feature we will exploit to evaluate candidate genes in this QTL interval.
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Evaluating Ctbp2 using other resources
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Evaluating candidate genes (CHECKED BOXES) responsible for variability in APP expression:
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.  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.

Notes:
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.
2. Caveat:   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.
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?  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).]

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lSummary of Part 2
1. Covered the basics of QTL analysis and mapping.
2. Reviewed difference between genetic and physical maps.
3. Discussed interpreting features of QTL maps including the LRS function, the additive effect function, the bootstrap bars, and the permutation thresholds.
4. Illustrated techniques to generate a list of positional candidates.
5. Discussed some factors used to evaluate candidate genes.
What does a QTL signify? A good QTL is a claim that a particular chromosomal region contains a causal source of variation in the phenotype. The importance of this hypothesis depends on the quality and relevance of the phenotype and the statistical strength of the QTL. As usual, test and be skeptical.
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Even higher blow-up of part of the Chr 7 physical map of variation in App expression in brain.  The QTL region actually extends from about 119 to 129.

Notes:
1. As mentioned in the previous slide another important approach to ranking candidates is based on the number of sequence variants that distinguish the parental strains. If we were sure that the sequences of the gene, its promoter, and its enhancers were identical between the strains then we could discount--but not eliminate--that gene as a candidate. The Gtf3c1 candidate almost falls into this category: of 663 known SNPs in and around this gene, only four differ between C57BL/6J and DBA/2J. Gtf3c1 is essentially identical-by-descent in these strains and is a less likely candidate. In contrast, if the two alleles of the gene have dozens of functional variants in exons, promoters, enhancers, and splice sites, then it becomes a higher priority candidate.
Of course it only takes a single critical sequence variant to generate downstream effects. The argument above is really about the prior probabilities. Where would you place your bets given the information at hand?
2.  If you scroll down the INTERVAL ANALYST you will find that Ctbp2 is a particularly interesting candidate that contains lots of SNPs (n = 75 and a SNP density of 0.55 SNP/Kb). Ctbp2 is also closer to our QTL peak than was Gtf3c1. Not only does Ctbp2 contain lots of SNPs but it is also is associated with a powerful cis QTL with an LRS of 24.2 (divide by 4.61 to get the equivalent LOD score of 5.25).
3.  At this high magnification, individual genes are distinct. They are color coded by their density of SNPs. Bright orange represents those genes that have a high SNP density (C57BL/6J versus DBA/2J), black represents genes with low SNP density. Roll the cursor over a gene block and its name will pop up, along with information on exon number.
4.  Beneath the physical map you will find an INTERVAL ANALYST table that lists information on known genes in the region on which you have zoomed the Physical Map.
5.  As always: error-checking is important. Some genes may be missing from the Interval Analyst (recent additions or errors of omission). In this case the Zranb1 gene that is located just proximal to Ctbp2 is not listed in the INTERVAL ANALYST. Double-check the interval using the Genome Browser links (blue and beige horizontal bars) at the top of the PHYSICAL MAP.
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Test Questions
1. Evaluate candidates for the Chr 3 App QTL.
2. Do App and Ctbp2 expression share any other QTLs beside that on Chr 7?
3. Can you exploit literature mining tools to find a strong relationship between App and Ctbp2?
4. Why might the cis QTL for Ctbp2 expression only be detected in the striatum data set?
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This slide illustrates one reason why Ctbp2 should be considered a high priority positional candidate gene that may modulate the expression level of App.  Ctbp2 is a strong cis QTL in some brain regions (here the data are taken from the striatum).  If Ctbp2 contains variants that modulate its own expression then these expression differencess may produce many downstream effects. Of course, we now want to know much more about the known biology of Ctbp2. What kind of gene is it? To begin to answer that question we can use a number of resources listed in the LINKS page.

Notes:
1. The App QTL is bimodal. Perhaps there are actually two causal factors in this region--one close to 123 Mb and the other close to 127 Mb.
2. The precision of QTL mapping depends on several factors, including the effect size and interactions among QTLs modulating a trait, the number of genetic individuals that are studied, and the distribution of recombinations in the study population.  In the case above, the QTL(s) are likely to be confined to the interval from 120 to 132 Mb. The bootstrap test (yellow bars shown in some of the previous slides) can be usual for estimating the consiistency of QTL peaks.
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Contact for comments and improvements:
rwilliam@nb.utmem.edu


kmanly@utmem.edu
The App findings reviewed in this presentation are part of an ongoing study by R. Williams. R. Homayouni, and R. Clark (July 15, 2005)
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Ctbp2 should also be considered a high priority biological candidate gene responsible for modulating App expression levels. The  C-terminal binding protein 2 is a transcriptional co-repressor also known as Ribeye. The gene produces two transcripts encoding distinct proteins. The short form is a transcriptional repressor that binds a Pro-X-Asp-Leu-Ser peptide motif common to adenoviral oncoprotein E1a and a related motif in BKLF. This short form also interacts  with several transcription factors including EVI1, ZFPM1, and   ZFHX1A (aka TCF8, deltaEF1). The longer isoform is a major component of specialized synapses in photoreceptors. Both proteins contain a NAD+ binding domain similar to NAD+-dependent 2-hydroxyacid dehydrogenases.

Notes:
1. To find out more about CTBP2 protein and the Ctbp2 gene, link to iHOP at http://www.pdg.cnb.uam.es/UniPub/iHOP/ and type in CTBP2
Try Arrowsmith at http://arrowsmith.psych.uic.edu/cgi-test/arrowsmith_uic/pubsmith.cgi
2. Both APP and CTBP2 are involved in oxidoreducatase activity or Notch signalling. To estabilish this common gene ontology visit NCBI  http://www.ncbi.nih.gov/entrez/query.fcgi?db=gene  and enter each gene symbol.
3. You can get intersting hints regarding Ctbp2 expression partners by examining the genetic correlations between Ctbp2 probe set 1422887_a_at and all other transcripts on the M430 Affymetrix array. Use the Striatum data set because we already know from previous work (the previous slide) that this gene is a cis QTL.  You should be able to show that Ctbp2 and Notch3 have antagonistic expression patterns in striatum. The negative genetic correlation with E2f4 is even stronger. The transcript also has a high positive genetic correlation with Rdh14. Of particualr interest with respect to APP protein processing, Ctbp2 covaries positiviely with Bace2 (the transcript of the beta site APP-cleaving enzyme 2).


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END
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lApp and Atcay transcript scatterplot
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lApp transcript and eight of its neighbors
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App transcript coexpression neighborhood
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lCorrelations of App with classical traits
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lNetwork Graph of App with classical traits
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lSummary of Part 1:
1. You have learned the basics about searching for traits
2. You know some methods to check data quality
3. You know how to edit bad or suspicious data
4. You know how to review the basic statistics of a trait
5. You know how to generate a scattergram between two traits using the Traits Correlation tool
6. You know how to add items to your SELECTIONS window
7. You know how to generate a Network Graph of traits that co-vary.
What does genetic covariance mean? The genetic covariance can be functional and mechanistic, but it can also be due to linkage disequilibrium. Finally, it can be due to sampling error or poor experimental design. Evaluate the biological plausibility of correlations. Test and be skeptical.
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The GeneNetwork and WebQTL : PART 2  
link to www.genenetwork.org
lPart 1. How to study expression variation and genetic correlation (slides 2–17)
lPart 2. Discovering upstream modulators (slides 18–29)
RNA

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How to make recombinant inbred strains (RI)

C57BL/6J (B)

DBA/2J (D)

F1

20 generations brother-sister matings

BXD1

BXD2

BXD80
+ É +

F2

BXD RI
Strain set

fully
inbred

isogenic

hetero-
geneous

Recombined chromosomes are needed for mapping

female

male

chromosome pair

Inbred
Isogenic
siblings

BXD
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aa

aaaa

D2 strain

B6 strain

amount of transcript

4 units

2 units

D

B

D and B may be SNP-like variants in the promoter itself (cis QTL) or in upstream genes (trans QTLs).
UPSTREAM
modulators

High

D

B

cis QTL

Low

>>>>PROMOTER--ATG-Exon1-Intron1-Exon2-Intron2 - etc-3'UTR >>>>>










trans QTL

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Discovering upstream modulatory loci
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WebQTL searches for upstream controllers
App maps on Chr 16 (blue arrow points to the orange triangle) but the best locus is on Chr 7.
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Genetic versus Physical maps for App expression
The difference between genetic and physical scale is analogous to measuring the separation between New York and Boston in either travel hours or kilometers.
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Physical map for distal chromosome 7
Distal Chr 7 from ~120 and 132 Mb may modulate App
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Evaluating candidate genes
Right position
and high correlation
 = better
candidates
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Physical maps are zoomable
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Evaluating Ctbp2 as a candidate QTL for App
This is the Ctbp2 cis QTL, but is detected only in the Rosen striatum data set.
This is the App QTL in the INIA data set.
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Evaluating Ctbp2 using other resources
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lSummary of Part 2:
1. Covered the basics of QTL analysis and mapping.
2. Reviewed difference between genetic and physical maps.
3. Discussed interpreting features of QTL maps including the LRS function, the additive effect function, the bootstrap bars, and the permutation thresholds.
4. Illustrated technics to generate a list of positional candidates.
5. Discussed some factors used to evaluate candidate genes.
What does a QTL signify? A good QTL is a claim that a particular chromosomal region contains a causal source of variation in the phenotype. The importance of this hypothesis depends on the quality and relevance of the phenotype and the statistical strength of the QTL. As usual, test and be skeptical.
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Test Questions
1. Evaluate candidates for the Chr 3 App QTL.
2. Do App and Ctbp2 expression share any other QTLs beside that on Chr 7?
3. Can you exploit literature mining tools to find strong relation between App and Ctbp2?
4. Why might the cis QTL for Ctbp2 expression only be detected in the striatum data set?
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Contact for comments and improvements:
rwilliam@nb.utmem.edu


kmanly@utmem.edu
The App findings reviewed in this presentation are part of an ongoing study of R. Wiliams and R. Homayouni (July 15, 2005)
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Open the default .htm file to view this Web presentation.

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Memphis Microarray 2003
June 11, 2003, Rob Williams
Ü#Ý
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GeneNetwork and WebQTL:
Slide 2
Slide 3
Search results
"First page of data:"
"Data sources:"
"Expression estimates for App on..."
"Critiquing the App data the..."
"App expression after windsorizing"
"Discovering shared expression patterns"
"Transcript neighborhoods"
"App and Atcay transcript scatterplot"
"App transcript and eight of..."
App transcript coexpression neighborhood
"Correlations of App with classical..."
"Network Graph of App with..."
"Summary of Part 1:"
Contact for comments and improvements:
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GeneNetwork and WebQTL:
Part 1: How to study expression variation and covariation (slides 2–16)
Part 2. Discovering upstream modulators (slides 17–30)

Slide 2
Slide 3
Search results
"First page of data:"
First page of data: The Trait Data and Analysis Form

"Data sources:"
Data sources: Metadata for each resource

"Expression estimates for App on..."
Expression estimates for App on the Trait Data form

"Critiquing the App data the..."
Critiquing the App data the Trait Data

"App expression after windsorizing"
App expression after windsorizing

"Discovering shared expression patterns"
Discovering shared expression patterns

"Transcript neighborhoods"
Transcript neighborhoods

"App and Atcay transcript scatterplot"
App and Atcay transcript scatterplot

"App transcript and eight of..."
App transcript and eight of its neighbors

App transcript coexpression neighborhood
"Correlations of App with classical..."
Correlations of App with classical traits

"Network Graph of App with..."
Network Graph of App with classical traits

"Summary of Part 1:"
Summary of Part 1:

Contact for comments and improvements:
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-function LoadSld( slideId ) -{ - if( !g_supportsPPTHTML ) return - if( slideId ) - parent.base.SldUpdated(slideId) - g_origSz=parseInt(SlideObj.style.fontSize) - g_origH=SlideObj.style.posHeight - g_origW=SlideObj.style.posWidth - g_scaleHyperlinks=(document.all.tags("AREA").length>0) - if ( IsWin("PPTSld") && !parent.IsFullScrMode() ) - parent.base.highlite(); - if( g_scaleHyperlinks ) - InitHLinkArray() - if( g_scaleInFrame||(IsWin("PPTSld") && parent.IsFullScrMode() ) ) - document.body.scroll="no" - _RSW() - if( IsWin("PPTSld") && (parent.IsFullScrMode() || CtxAlwaysOn ) ) { - document.oncontextmenu=parent._CM; - self.focus(); - - } -} -function MakeSldVis( fTrans ) -{ - fTrans=fTrans && g_showAnimation - if( fTrans ) - { - if( g_bgSound ) { - idx=g_bgSound.indexOf(","); - pptSound.src=g_bgSound.substr( 0, idx ); - pptSound.loop= -(parseInt(g_bgSound.substr(idx+1))); - } - SlideObj.filters.revealtrans.Apply() - } - SlideObj.style.visibility="visible" - if( fTrans ) - SlideObj.filters.revealtrans.Play() -} -function MakeNotesVis() -{ - if( !IsNts() ) return false - SlideObj.style.display="none" - nObj = document.all.item("NotesObj") - parent.SetHasNts(0) - if( nObj ) { - nObj.style.display="" - parent.SetHasNts(1) - } - return 1 -} -function Redirect( frmId,sId ) -{ - var str=document.location.hash,idx=str.indexOf('#') - if(idx>=0) str=str.substr(1); - if( window.name != frmId && ( sId != str) ) { - obj = document.all.item("Main-File") - window.location.href=obj.href+"#"+sId - return 1 - } - return 0 -} -function HideMenu() { if( frames["PPTSld"] && PPTSld.document.all.item("ctxtmenu") && PPTSld.ctxtmenu.style.display!="none" ) { PPTSld.ctxtmenu.style.display='none'; return true } return false } -function IsWin( name ) { return window.name == name } -function IsNts() { return IsWin("PPTNts") } -function IsSldOrNts() { return( IsWin("PPTSld")||IsWin("PPTNts") ) } -function SupportsPPTAnimation() { return( navigator.platform == "Win32" && navigator.appVersion.indexOf("Windows")>0 ) } -function SupportsPPTHTML() -{ - var appVer=navigator.appVersion, msie=appVer.indexOf( "MSIE " ), inex = appVer.indexOf( "Internet Explorer " ), ver=0 - if( msie >= 0 ) - ver=parseFloat( appVer.substring( msie+5, appVer.indexOf(";",msie) ) ) - else if( inex >= 0 ) - ver=parseFloat( appVer.substring( inex+18, appVer.indexOf(";",inex) ) ) - else - ver=parseInt(appVer) - - return( ver >= 4 ) -} -var MHTMLPrefix = CalculateMHTMLPrefix(); -function CalculateMHTMLPrefix() -{ - if ( document.location.protocol == 'mhtml:') { - href=new String(document.location.href) - Start=href.indexOf('!')+1 - End=href.lastIndexOf('/')+1 - if (End < Start) - return href.substring(0, Start) - else - return href.substring(0, End) - } - return ''; -} - -function LoadNavSld(slideId) { -playList(); -parent.createCM(); - if( !g_supportsPPTHTML ) return - if( IsWin("PPTSld") && slideId ) - parent.base.SldUpdated(slideId) - self.focus(); - -} -var hasNarration = false; -function _RSW() -{ - if( !g_supportsPPTHTML || IsNts() || - ( !g_scaleInFrame && (( window.name != "PPTSld" ) ) ) ) - return - - cltWidth=document.body.clientWidth - cltHeight=document.body.clientHeight - factor=(1.0*cltWidth)/g_origW - if( cltHeight < g_origH*factor ) - factor=(1.0*cltHeight)/g_origH - - newSize = g_origSz * factor - if( newSize < 1 ) newSize=1 - - s=SlideObj.style - s.fontSize=newSize+"px" - s.posWidth=g_origW*factor - s.posHeight=g_origH*factor - s.posLeft=(cltWidth-s.posWidth)/2 - s.posTop=(cltHeight-s.posHeight)/2 - - if ( hasNarration ) { - obj = document.all.NSPlay.style; - mySld = document.all.SlideObj.style; - obj.position = 'absolute'; - obj.posTop = mySld.posTop + mySld.posHeight - 20; - obj.posLeft = mySld.posLeft + mySld.posWidth - 20; - } - if( g_scaleHyperlinks ) - ScaleHyperlinks( factor ); -} -function IsMac() { - return (window.navigator.platform.indexOf("Mac") >= 0 ); -} - -function HitOK( evt ) { - //Nav Only function - return (evt.which == 1 || (IsMac() && evt.which == 3) ); -} -function _KPH(event) -{ - - if ( parent.base.msie < 0 ) { - - if ( ( (event.target.name && event.target.name == "hasMap" ) || (event.target.href && event.target.href != "") ) && parent.g_docTable[0].type != "jpeg" && HitOK( event ) ) { - return; /* to make hyperlinks in fullscreen mode traversable */ - } - if( IsContextMenu() ) - return parent.KPH(event); - if ( parent.IsFullScrMode() && event.which == 27 ) - parent.base.CloseFullScreen(); - else if ( parent.base.IsFullScrMode() && ( (!IsMac() && event.which == 3) || ( IsMac() && (event.modifiers & Event.CONTROL_MASK) && event.which == 1 ) ) ) - return parent.KPH(event); - else if( (event.which == 32) || (event.which == 13) || HitOK( event ) ) { - if( window.name == "PPTSld" ) - parent.PPTSld.DocumentOnClick(); - else - parent.M_GoNextSld(); - } - else if ( parent.IsFullScrMode() && ((event.which == 78) || (event.which == 110) || (event.which == 29) || (event.which == 31) || (event.which == 12)) ) - parent.M_GoNextSld(); - else if ( parent.IsFullScrMode() && ( (event.which == 80) || (event.which == 112) || (event.which == 30) || (event.which == 28) || (event.which == 11) || (event.which == 8)) ) - parent.M_GoPrevSld(); - - return; - } - - if( IsNts() ) return; - - if(parent.IsFullScrMode() && event.keyCode == 27 && !parent.HideMenu() ) - parent.base.CloseFullScreen(); - else if( (event.keyCode == 32) || (event.keyCode == 13) ) - { - if( window.name == "PPTSld" ) - parent.PPTSld.DocumentOnClick(); - else - parent.M_GoNextSld(); - } - else if ( parent.IsFullScrMode() && ((event.keyCode == 78) || (event.keyCode == 110)) ) - parent.M_GoNextSld(); - else if ( parent.IsFullScrMode() && ((event.keyCode == 80) || (event.keyCode == 112)) ) - parent.M_GoPrevSld(); -} - -function DocumentOnClick(event) -{ - if ( g_doAdvOnClick && !parent.IsFullScrMode() ) { - parent.base.TP_GoToNextSld(); - return; - } - - if ( parent.base.msie < 0 ) - { - if( ( g_allowAdvOnClick && parent.IsFullScrMode() ) || g_doAdvOnClick || - (event && ( (event.which == 32) || (event.which == 13) ) ) ) - parent.M_GoNextSld(); - return; - } - if( IsNts() || (parent.IsFullScrMode() && parent.HideMenu() ) ) return; - if( ( g_allowAdvOnClick && parent.IsFullScrMode() ) || g_doAdvOnClick || - (event && ( (event.keyCode==32) || (event.keyCode == 13) ) ) ) - parent.M_GoNextSld(); -} - - -var g_supportsPPTHTML = SupportsPPTHTML(), g_scaleInFrame = true, gId="", g_bgSound="", - g_scaleHyperlinks = false, g_allowAdvOnClick = true, g_showInBrowser = false, g_doAdvOnClick = false; - - var g_showAnimation = 0; -var g_hasTrans = false, g_autoTrans = false, g_transSecs = 0; -var g_animManager = null; - -var ENDSHOW_MESG="End of slide show, click to exit.", SCREEN_MODE="Frames", gIsEndShow=0, NUM_VIS_SLDS=18, SCRIPT_HREF="script.js", FULLSCR_HREF="fullscreen.htm"; -var gCurSld = gPrevSld = 1, g_offset = 0, gNtsOpen = gHasNts = gOtlTxtExp = gNarrationPaused = false, gOtlOpen = true -window.gPPTHTML=SupportsPPTHTML() -var g_hideNav = 0; -function UpdNtsPane(){ PPTNts.location.replace( MHTMLPrefix+GetHrefObj( gCurSld ).mNtsHref ) } -function UpdNavPane( sldIndex ){ if(gNavLoaded) PPTNav.UpdNav() } -function UpdOtNavPane(){ if(gOtlNavLoaded) PPTOtlNav.UpdOtlNav() } -function UpdOtlPane(){ if(gOtlLoaded) PPTOtl.UpdOtl() } -function SetHasNts( fVal ) -{ - if( gHasNts != fVal ) { - gHasNts=fVal - UpdNavPane() - } -} - -function ToggleVNarration() -{ - if ( base.msie < 0 ) { - PPTSld.ToggleSound( false, PPTSld.document.NSPlay ); - return; - } - - rObj=PPTSld.document.all("NSPlay") - if( rObj ) { - if( gNarrationPaused ) - rObj.Play() - else - rObj.Pause() - - gNarrationPaused=!gNarrationPaused - } -} - -function PrevSldViewed(){ GoToSld( GetHrefObj(gPrevSld).mSldHref ) } -function HasPrevSld() { return ( gIsEndShow || ( g_currentSlide != 1 && GetHrefObj( g_currentSlide-1 ).mVis == 1 )||( GetCurrentSlideNum() > 1 ) ) } -function HasNextSld() { return (GetCurrentSlideNum() != GetNumSlides()) } -function StartEndShow() -{ -// g_hideNav = 1; -// PPTNav.location.reload(); - if( PPTSld.event ) PPTSld.event.cancelBubble=true - - doc=PPTSld.document - doc.open() - doc.writeln('


' + ENDSHOW_MESG + '

') - doc.close() -} -function SetSldVisited(){ gDocTable[gCurSld-1].mVisited=true } -function IsSldVisited(){ return gDocTable[gCurSld-1].mVisited } -function hrefList( sldHref, visible, sldIdx ) -{ - this.mSldHref= this.mNtsHref = sldHref - this.mSldIdx = sldIdx - this.mOrigVis= this.mVis = visible - this.mVisited= false -} -var gDocTable = new Array( - new hrefList("slide0001.htm", 1, 1), - new hrefList("slide0002.htm", 1, 2), - new hrefList("slide0003.htm", 1, 3), - new hrefList("slide0004.htm", 1, 4), - new hrefList("slide0005.htm", 1, 5), - new hrefList("slide0006.htm", 1, 6), - new hrefList("slide0007.htm", 1, 7), - new hrefList("slide0008.htm", 1, 8), - new hrefList("slide0009.htm", 1, 9), - new hrefList("slide0010.htm", 1, 10), - new hrefList("slide0011.htm", 1, 11), - new hrefList("slide0012.htm", 1, 12), - new hrefList("slide0013.htm", 1, 13), - new hrefList("slide0014.htm", 1, 14), - new hrefList("slide0015.htm", 1, 15), - new hrefList("slide0016.htm", 1, 16), - new hrefList("slide0017.htm", 1, 17), - new hrefList("slide0018.htm", 1, 18) -); - -function ImgBtn( oId,bId,w,action ) -{ - var t=this - t.Perform = _IBP - t.SetActive = _IBSetA - t.SetInactive= _IBSetI - t.SetPressed = _IBSetP - t.SetDisabled= _IBSetD - t.Enabled = _IBSetE - t.ChangeIcon = null - t.UserAction = action - t.ChgState = _IBUI - t.mObjId = oId - t.mBorderId= bId - t.mWidth = w - t.mIsOn = t.mCurState = 0 -} -function _IBSetA() -{ - if( this.mIsOn ) { - obj=this.ChgState( gHiliteClr,gShadowClr,2 ) - obj.style.posTop=0 - } -} -function _IBSetI() -{ - if( this.mIsOn ) { - obj=this.ChgState( gFaceClr,gFaceClr,1 ) - obj.style.posTop=0 - } -} -function _IBSetP() -{ - if( this.mIsOn ) { - obj=this.ChgState( gShadowClr,gHiliteClr,2 ) - obj.style.posLeft+=1; obj.style.posTop+=1 - } -} -function _IBSetD() -{ - obj=this.ChgState( gFaceClr,gFaceClr,0 ) - obj.style.posTop=0 -} -function _IBSetE( state ) -{ - var t=this - GetObj( t.mBorderId ).style.visibility="visible" - if( state != t.mIsOn ) { - t.mIsOn=state - if( state ) - t.SetInactive() - else - t.SetDisabled() - } -} -function _IBP() -{ - var t=this - if( t.mIsOn ) { - if( t.UserAction != null ) - t.UserAction() - if( t.ChangeIcon ) { - obj=GetObj(t.mObjId) - if( t.ChangeIcon() ) - obj.style.posLeft=obj.style.posLeft+(t.mCurState-4)*t.mWidth - else - obj.style.posLeft=obj.style.posLeft+(t.mCurState-0)*t.mWidth - } - t.SetActive() - } -} -function _IBUI( clr1,clr2,nextState ) -{ - var t=this - SetBorder( GetObj( t.mBorderId ),clr1,clr2 ) - obj=GetObj( t.mObjId ) - obj.style.posLeft=obj.style.posLeft+(t.mCurState-nextState)*t.mWidth-obj.style.posTop - t.mCurState=nextState - return obj -} -function TxtBtn( oId,oeId,action,chkState ) -{ - var t=this - t.Perform = _TBP - t.SetActive = _TBSetA - t.SetInactive= _TBSetI - t.SetPressed = _TBSetP - t.SetDisabled= _TBSetD - t.SetEnabled = _TBSetE - t.GetState = chkState - t.UserAction = action - t.ChgState = _TBUI - t.mObjId = oId - t.m_elementsId= oeId - t.mIsOn = 1 -} -function _TBSetA() -{ - var t=this - if( t.mIsOn && !t.GetState() ) - t.ChgState( gHiliteClr,gShadowClr,0,0 ) -} -function _TBSetI() -{ - var t=this - if( t.mIsOn && !t.GetState() ) - t.ChgState( gFaceClr,gFaceClr,0,0 ) -} -function _TBSetP() -{ - if( this.mIsOn ) - this.ChgState( gShadowClr,gHiliteClr,1,1 ) -} -function _TBSetD() -{ - this.ChgState( gFaceClr,gFaceClr,0,0 ) - this.mIsOn = 0 -} -function _TBSetE() -{ - var t=this - if( !t.GetState() ) - t.ChgState( gFaceClr,gFaceClr,0,0 ) - else - t.ChgState( gShadowClr,gHiliteClr,1,1 ) - t.mIsOn = 1 -} -function _TBP() -{ - var t=this - if( t.mIsOn ) { - if( t.UserAction != null ) - t.UserAction() - if( t.GetState() ) - t.SetPressed() - else - t.SetActive() - } -} -function _TBUI( clr1,clr2,lOffset,tOffset ) -{ - SetBorder( GetObj( this.mObjId ),clr1,clr2 ) - Offset( GetObj( this.m_elementsId ),lOffset,tOffset ) -} -function GetObj( objId ){ return document.all.item( objId ) } -function Offset( obj, top, left ){ obj.style.top=top; obj.style.left=left } -function SetBorder( obj, upperLeft, lowerRight ) -{ - s=obj.style; - s.borderStyle = "solid" - s.borderWidth = 1 - s.borderLeftColor = s.borderTopColor = upperLeft - s.borderBottomColor= s.borderRightColor = lowerRight -} -function GetBtnObj(){ return gBtnArr[window.event.srcElement.id] } -function BtnOnOver(){ b=GetBtnObj(); if( b != null ) b.SetActive() } -function BtnOnDown(){ b=GetBtnObj(); if( b != null ) b.SetPressed() } -function BtnOnOut(){ b=GetBtnObj(); if( b != null ) b.SetInactive() } -function BtnOnUp() -{ - b=GetBtnObj() - if( b != null ) - b.Perform() - else - Upd() -} -function GetNtsState(){ return parent.gNtsOpen } -function GetOtlState(){ return parent.gOtlOpen } -function GetOtlTxtState(){ return parent.gOtlTxtExp } -function NtsBtnSetFlag( fVal ) -{ - s=document.all.item( this.m_flagId ).style - s.display="none" - if( fVal ) - s.display="" - else - s.display="none" -} - -var gHiliteClr="THREEDHIGHLIGHT",gShadowClr="THREEDSHADOW",gFaceClr="THREEDFACE" -var gBtnArr = new Array() -gBtnArr["nb_otl"] = new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOtlPane,GetOtlState ) -gBtnArr["nb_nts"] = new TxtBtn( "nb_nts","nb_ntsElem",parent.ToggleNtsPane,GetNtsState ) -gBtnArr["nb_prev"]= new ImgBtn( "nb_prev","nb_prevBorder",30,parent.GoToPrevSld ) -gBtnArr["nb_next"]= new ImgBtn( "nb_next","nb_nextBorder",30,parent.GoToNextSld ) -gBtnArr["nb_sldshw"]= new ImgBtn( "nb_sldshw","nb_sldshwBorder",18,parent.FullScreen ) -gBtnArr["nb_voice"] = new ImgBtn( "nb_voice","nb_voiceBorder",18,parent.ToggleVNarration ) -gBtnArr["nb_otlTxt"]= new ImgBtn( "nb_otlTxt","nb_otlTxtBorder",23,parent.ToggleOtlText ) -gBtnArr["nb_nts"].m_flagId= "notes_flag" -gBtnArr["nb_nts"].SetFlag = NtsBtnSetFlag -gBtnArr["nb_otlTxt"].ChangeIcon= GetOtlTxtState -var sNext="Next",sPrev="Previous",sEnd="End Show",sFont="Arial", alwaysOn = false -function ShowMenu() -{ - BuildMenu(); - var doc=PPTSld.document.body,x=PPTSld.event.clientX+doc.scrollLeft,y=PPTSld.event.clientY+doc.scrollTop - - m = PPTSld.document.all.item("ctxtmenu") - m.style.pixelLeft=x - if( (x+m.scrollWidth > doc.clientWidth)&&(x-m.scrollWidth > 0) ) - m.style.pixelLeft=x-m.scrollWidth - - m.style.pixelTop=y - if( (y+m.scrollHeight > doc.clientHeight)&&(y-m.scrollHeight > 0) ) - m.style.pixelTop=y-m.scrollHeight - - m.style.display="" -} -function _CM() -{ - if( !parent.IsFullScrMode() && !alwaysOn) return; - - if(!PPTSld.event.ctrlKey) { - ShowMenu() - return false - } else - HideMenu() -} - -function processNavKPH(event) { - if ( PPTSld && (event.keyCode != 13 || !event.srcElement.href || event.srcElement.href == "" ) ) - return PPTSld._KPH(event); -} -function processNavClick() { - HideMenu(); - return true; -} -function BuildMenu() -{ - if( PPTSld.document.all.item("ctxtmenu") ) return - - var mObj=CreateItem( PPTSld.document.body ) -mObj.id="ctxtmenu" - var s=mObj.style - s.position="absolute" - s.cursor="default" - s.width="100px" - SetCMBorder(mObj,"menu","black") - - var iObj=CreateItem( mObj ) - SetCMBorder( iObj, "threedhighlight","threedshadow" ) - iObj.style.padding=2 - if ( self.IsFullScrMode() ) { - CreateMenuItem( iObj,sNext,M_GoNextSld,M_True ) - CreateMenuItem( iObj,sPrev,M_GoPrevSld,M_HasPrevSld ) - } - else { - CreateMenuItem( iObj,sNext, base.TP_GoToNextSld, base.HasNextSld ) - CreateMenuItem( iObj,sPrev,base.GoToPrevSld, base.HasPrevSld ) - } - var sObj=CreateItem( iObj ) - SetCMBorder(sObj,"menu","menu") - var s=sObj.style - s.borderTopColor="threedshadow" - s.borderBottomColor="threedhighlight" - s.height=1 - s.fontSize="0px" - if ( self.IsFullScrMode() ) - CreateMenuItem( iObj,sEnd,M_End,M_True ) - else - CreateMenuItem( iObj,sEnd,M_End,M_False ) -} -function Highlight() { ChangeClr("activecaption","threedhighlight") } -function Deselect() { ChangeClr("threedface","menutext") } -function Perform() -{ - e=PPTSld.event.srcElement - if( e.type=="menuitem" && e.IsActive() ) - e.Action() - else - PPTSld.event.cancelBubble=true -} -function ChangeClr( bg,clr ) -{ - e=PPTSld.event.srcElement - if( e.type=="menuitem" && e.IsActive() ) { - e.style.backgroundColor=bg - e.style.color=clr - } -} - -function M_HasPrevSld() { return( base.HasPrevSld() ) } -function M_GoNextSld() { - base.SetFSMode(1); - if( gIsEndShow ) - M_End(); - else { - if ( base.HasNextSld() ) - base.GoToNextSld(); - else if ( base.EndSlideShow ) { - StartEndShow(); - gIsEndShow = 1; - - PPTNav.location.reload(); - } - else - base.CloseFullScreen(); - } -} -function M_GoPrevSld() { - base.SetFSMode(1); - g_hideNav = 0; - if( gIsEndShow ) { - gIsEndShow = 0; - if ( base.msie > 0 && IsMac() ) - ChangeFrame( SLIDE_FRAME, GetHrefObj( g_currentSlide ).m_slideHref ); - else - PPTSld.history.back(); - - PPTNav.location.reload(); - if( PPTSld.event ) - PPTSld.event.cancelBubble=true; - } - else - base.GoToPrevSld(); -} -function M_True() { return true } -function M_False() { return false } - -function M_End() { - base.CloseFullScreen(); - /*PPTSld.event.cancelBubble=true; - window.close( self )*/ -} - -function CreateMenuItem( node,text,action,eval ) -{ - var e=CreateItem( node ) - e.type="menuitem" - e.Action=action - e.IsActive=eval - e.innerHTML=text - - if( !e.IsActive() ) - e.style.color="threedshadow" - e.onclick=Perform - e.onmouseover=Highlight - e.onmouseout=Deselect - s=e.style; - s.fontFamily=sFont - s.fontSize="8pt" - s.paddingLeft=2 -} -function CreateItem( node ) -{ - var elem=PPTSld.document.createElement("DIV") - node.insertBefore( elem ) - return elem -} -function SetCMBorder( o,ltClr,rbClr ) -{ - var s=o.style - s.backgroundColor="menu" - s.borderStyle="solid" - s.borderWidth=1 - s.borderColor=ltClr+" "+rbClr+" "+rbClr+" "+ltClr -} - -/* netscape context menu */ -g_ctxmenu = 0; -function setRect( obj, X, Y, W, H ) { - obj.top = Y; - obj.left = X; - obj.clip.top = 0; - obj.clip.left = 0; - obj.clip.bottom = H; - obj.clip.right = W; -} - -function KPH(event) { - if ( ! base.IsFullScrMode() && !alwaysOn ) - return true; - - if ( (!IsMac() &&event.which == 3) || ( IsMac() && (event.modifiers & Event.CONTROL_MASK) && event.which == 1 ) ) { - PPTSld.g_ctxmenu = 1; - PPTSld.stripUobj.visibility = "show"; - PPTSld.stripDobj.visibility = "show"; - PPTSld.shadeUobj.visibility = "show"; - PPTSld.shadeDobj.visibility = "show"; - PPTSld.panelobj.visibility = "show"; - PPTSld.Fobj.visibility = "show"; - PPTSld.Bobj.visibility = "show"; - PPTSld.Eobj.visibility = "show"; - - setRect(PPTSld.shadeUobj, event.pageX-2, event.pageY-2, 82, 67 ); - setRect(PPTSld.shadeDobj, event.pageX, event.pageY, 82, 67 ); - setRect(PPTSld.panelobj, event.pageX, event.pageY, 80, 65 ); - setRect(PPTSld.Fobj, event.pageX, event.pageY, 80, 20 ); - setRect(PPTSld.Bobj, event.pageX, event.pageY+20, 80, 20 ); - setRect(PPTSld.stripUobj, event.pageX, event.pageY+41, 80, 1 ); - setRect(PPTSld.stripDobj, event.pageX, event.pageY+43, 80, 1 ); - setRect(PPTSld.Eobj, event.pageX, event.pageY+45, 80, 20 ); - return false; - } - if ( HitOK( event ) ) { - PPTSld.g_ctxmenu = 0; - PPTSld.stripUobj.visibility = "hide"; - PPTSld.stripDobj.visibility = "hide"; - PPTSld.shadeUobj.visibility = "hide"; - PPTSld.shadeDobj.visibility = "hide"; - PPTSld.panelobj.visibility = "hide"; - PPTSld.Fobj.visibility = "hide"; - PPTSld.Bobj.visibility = "hide"; - PPTSld.Eobj.visibility = "hide"; - } - return true; -} - -function overMe() { - this.bgColor = "blue"; -} - -function outMe() { - this.bgColor = "#AAAAAA"; -} - -function makeElement( whichEl, whichContainer ) { - if ( arguments.length == 1 ) { - whichContainer = PPTSld; - } - tmp = new Layer(100,whichContainer); - eval( whichEl + " = tmp" ); - return eval(whichEl); -} - -function initMe( obj, clr, text ) { - obj.bgColor = clr; -// obj.document.write("" + text + ""); - obj.document.write( "   " + text +" "); - obj.document.close(); - obj.captureEvents(Event.CLICK); - obj.color = "black"; -} - -function createCM() { - if ( base.IsFullScrMode() ) { - var clr = "#AAAAAA"; - PPTSld.shadeUobj = makeElement("SHADEU"); - PPTSld.shadeDobj = makeElement("SHADED"); - PPTSld.panelobj = makeElement("PANEL"); - PPTSld.stripUobj = makeElement("STRIPU"); - PPTSld.stripDobj = makeElement("STRIPD"); - PPTSld.shadeUobj.bgColor = "#BBBBBB"; - PPTSld.shadeDobj.bgColor = "#888888"; - PPTSld.stripUobj.bgColor = "#777777"; - PPTSld.stripDobj.bgColor = "#CCCCCC"; - PPTSld.panelobj.bgColor = clr; - PPTSld.Fobj = makeElement("Next"); - PPTSld.Bobj = makeElement("Previous"); - PPTSld.Eobj = makeElement("EndShow"); - initMe( PPTSld.Fobj, clr, "Next" ); - PPTSld.Fobj.onclick = M_GoNextSld; - - initMe( PPTSld.Bobj, clr, "Previous" ); - PPTSld.Bobj.onclick = M_GoPrevSld; - - initMe( PPTSld.Eobj, clr, "End Show"); - PPTSld.Eobj.onclick = base.CloseFullScreen; - } -} - -function IsContextMenu() { - return (g_ctxmenu == 1) -} -var g_notesTable = new Array() -var g_hiddenSlide = new Array() -makeSlide( 0,1,1); -makeSlide( 1,1,1); -makeSlide( 2,1,1); -makeSlide( 3,1,1); -makeSlide( 4,1,1); -makeSlide( 5,1,1); -makeSlide( 6,1,1); -makeSlide( 7,1,1); -makeSlide( 8,1,1); -makeSlide( 9,1,1); -makeSlide( 10,1,1); -makeSlide( 11,1,1); -makeSlide( 12,1,1); -makeSlide( 13,1,1); -makeSlide( 14,1,1); -makeSlide( 15,1,1); -makeSlide( 16,0,1); -makeSlide( 17,1,1); - -var END_SHOW_HREF = "endshow.htm", - OUTLINE_EXPAND_HREF = "outline_expanded.htm", - OUTLINE_COLLAPSE_HREF = "outline_collapsed.htm", - OUTLINE_NAVBAR_HREF = "outline_navigation_bar.htm", - NAVBAR_HREF = "navigation_bar.htm", - BLANK_NOTES_HREF = "blank_notes.htm", - NUM_VISIBLE_SLIDES = 18, - SIMPLE_FRAMESET = 0, - SLIDE_FRAME = "PPTSld", - NOTES_FRAME = "PPTNts", - OUTLINE_FRAME = "PPTOtl", - OUTLINE_NAVBAR_FRAME = "PPTOtlNav", - NAVBAR_FRAME = "PPTNav", - MAIN_FRAME = "MainFrame", - FS_NAVBAR_HREF = "fs_navigation_bar.htm", - isIEFiles = 2, - isNAVFiles = 8, - isFLATFiles = 16, - includeNotes = 1, - PPTPRESENTATION = 1; -var INITSLIDENUM = 1; - -var EndSlideShow = 0; -var g_outline_href = OUTLINE_COLLAPSE_HREF; -var g_fullscrMode = 0; -var FSWin = null; -var gtmpstr = document.location.href; -var g_baseURL = gtmpstr.substr(0, gtmpstr.lastIndexOf("/") ) + "/" + "webqtl_demo2_part1.ppt_files"; -var g_showoutline = 1; -var g_shownotes = includeNotes; -var g_currentSlide = INITSLIDENUM, g_prevSlide = INITSLIDENUM; -var saveFSSlideNum = saveTPSlideNum = g_currentSlide; -var saveFSprevSlide = saveTPprevSlide = g_prevSlide; -var g_slideType="ie"; -var appVer = navigator.appVersion; -var msie = appVer.indexOf( "MSIE " ) + appVer.indexOf( "Internet Explorer " ); -var isnav = ( navigator.appName.indexOf( "Netscape" ) >= 0 ); -var msieWin31 = (appVer.indexOf( "Windows 3.1" ) > 0); -var ver = 0; -var g_done = 0; -var g_prevotlobjidx = 0; -var g_ShowFSDefault = 0; -var g_lastVisibleSld = 1; -var g_allHidden = false; -function IsIE() { - return (msie >= 0 ); -} - -function IsNav() { - return (isnav); -} -var msiePos = appVer.indexOf( "MSIE " ); -var inexPos = appVer.indexOf( "Internet Explorer " ); -if ( msiePos >= 0 ) - ver = parseFloat( appVer.substring( msiePos+5, appVer.indexOf ( ";", msiePos ) ) ); -else if( inexPos >= 0 ) - ver=parseFloat( appVer.substring( inexPos+18, appVer.indexOf(";",inexPos) ) ) -else - ver = parseInt( appVer ); - -//var g_supportsPPTHTML = 0; //!msieWin31 && ( ( msie >= 0 && ver >= 3.02 ) || ( msie < 0 && ver >= 3 ) ); - -function GetCurrentSlideNum() -{ - obj = GetHrefObj( g_currentSlide ); - if ( GetHrefObj( g_currentSlide ).m_origVisibility == 1 ) - return obj.m_slideIdx; - else - return g_currentSlide; -} - -function GetNumSlides() -{ - if ( GetHrefObj( g_currentSlide ).m_origVisibility == 1 ) - return NUM_VISIBLE_SLIDES; - else - return g_docTable.length; -} - -function GetHrefObj( slideIdx ) -{ return g_docTable[slideIdx - 1]; -} - -function GetSlideNum( slideHref ) -{ - for (ii=0; ii 0 ) { - obj = GetHrefObj( targetIdx ); - while ( ( obj.m_visibility == 0 ) && ( targetIdx>0 ) ) - obj = GetHrefObj( targetIdx-- ); - GoToSld( obj.m_slideHref ); - } -} - -function GoToLast() -{ - targetIdx = g_docTable.length; - if ( targetIdx != g_currentSlide ) - GoToSld( GetHrefObj( targetIdx ).m_slideHref ); -} - -function GoToFirst() -{ GoToSld( GetHrefObj(1).m_slideHref ); -} - -function highlite() { - if ( IsFullScrMode() ) - return; - index = GetCurrentSlideNum(); - if ( !frames[MAIN_FRAME].frames[OUTLINE_FRAME] ) - return; - if ( msie < 0 ) { - if ( g_prevotlobjidx != 0 ) { - eval( "otlobj = frames[MAIN_FRAME].frames[OUTLINE_FRAME].document.LAYERID" + g_prevotlobjidx ); - otlobj.hidden = true; - } - else - index = GetCurrentSlideNum(); - eval( "otlobj = frames[MAIN_FRAME].frames[OUTLINE_FRAME].document.LAYERID" + index ); - otlobj.hidden = false; - - g_prevotlobjidx = index; - - return; - } - if ( !g_showoutline ) - return; - - backclr = frames[MAIN_FRAME].frames[OUTLINE_FRAME].document.body.bgColor; - textclr = frames[MAIN_FRAME].frames[OUTLINE_FRAME].document.body.text; - if ( g_prevotlobjidx != 0 ) { - eval( "otlobj = frames[MAIN_FRAME].frames[OUTLINE_FRAME].document.all.p" + g_prevotlobjidx ); - otlobj.style.backgroundColor = backclr; - otlobj.style.color = textclr; - otlobj.all.AREF.style.color = textclr; - } - else - index = GetCurrentSlideNum(); - eval( "otlobj = frames[MAIN_FRAME].frames[OUTLINE_FRAME].document.all.p" + index ); - otlobj.style.backgroundColor = textclr; - otlobj.style.color = backclr; - otlobj.all.AREF.style.color = backclr; - g_prevotlobjidx = index; -} - -function ChangeFrame( frame, href ) -{ -if ( IsFramesMode() ) { - if ( NAVBAR_FRAME == frame || OUTLINE_NAVBAR_FRAME == frame ) { - frames[frame].location.replace(href); - } - else if( ! ( ( OUTLINE_FRAME == frame && !g_showoutline) || (NOTES_FRAME == frame && !g_shownotes ) ) ){ - frames[MAIN_FRAME].frames[frame].location.href = href; - } - } - else { - if ( frame == NAVBAR_FRAME || frame == SLIDE_FRAME ) { - if( frame == NAVBAR_FRAME ) { - href = FS_NAVBAR_HREF; - - } - if( frame == NAVBAR_FRAME ) - window.frames[frame].location.replace(href); - else - window.frames[frame].location.href = href; - } - } - -} - -function shutEventPropagation() { - if ( IsNav() ) - return; - - var slideFrame; - if ( IsFramesMode() ) - slideFrame = frames[MAIN_FRAME].frames[SLIDE_FRAME]; - else - slideFrame = window.frames[SLIDE_FRAME]; - if ( slideFrame.event ) - slideFrame.event.cancelBubble=true; -} - -function GoToSld( slideHref ) -{ - shutEventPropagation(); - if ( slideHref != GetHrefObj( g_currentSlide ).m_slideHref || g_slideType != GetHrefObj( g_currentSlide ).type) { - g_prevSlide = g_currentSlide; - g_currentSlide = GetSlideNum( slideHref ); - g_slideType = GetHrefObj( g_currentSlide ).type; - obj = GetHrefObj( g_currentSlide ); - obj.m_visibility = 1; - ChangeFrame( SLIDE_FRAME, slideHref ); - if( !SIMPLE_FRAMESET ) - ChangeFrame( NOTES_FRAME, obj.m_notesHref ); - ChangeFrame( NAVBAR_FRAME, NAVBAR_HREF ); - - } -} - -function PrevSldViewed() -{ GoToSld( GetHrefObj( g_prevSlide ).m_slideHref ); -} - -function NoHref() {} - -function ExpandOutline( ) -{ - g_outline_href = OUTLINE_EXPAND_HREF; - ChangeFrame( OUTLINE_FRAME, OUTLINE_EXPAND_HREF ); - frames[OUTLINE_NAVBAR_FRAME].location.replace( OUTLINE_NAVBAR_HREF); -} - -function CollapseOutline() -{ - g_outline_href = OUTLINE_COLLAPSE_HREF; - ChangeFrame( OUTLINE_FRAME, OUTLINE_COLLAPSE_HREF ); - frames[OUTLINE_NAVBAR_FRAME].location.replace( OUTLINE_NAVBAR_HREF); - } - -function SlideUpdated( id ) -{ - if ( id != GetHrefObj( g_currentSlide ).m_slideHref ) { - g_prevSlide = g_currentSlide; - g_currentSlide = GetSlideNum( id ); - obj = GetHrefObj( g_currentSlide ); - if( !SIMPLE_FRAMESET ) - ChangeFrame( NOTES_FRAME, obj.m_notesHref ); - ChangeFrame( NAVBAR_FRAME, NAVBAR_HREF ); - } -} - -function hrefList( slideHref, notesHref, visible, slideIdx, type ) -{ - this.m_slideHref = slideHref; - this.m_notesHref = notesHref; - this.m_navbarHref = NAVBAR_HREF; - this.m_origVisibility = visible; - this.m_visibility = visible; - this.m_slideIdx = slideIdx; - this.type = type; -} - -function IsFullScrMode() { - return g_fullscrMode; -} - - -function IsFramesMode() { - return (1 - g_fullscrMode); -} - -function SldUpdated( id ) -{ - if ( ( id != GetHrefObj( g_currentSlide ).m_slideHref ) || ( g_currentSlide == g_lastVisibleSld ) ){ - g_prevSlide = g_currentSlide; - g_currentSlide = GetSlideNum( id ); - obj = GetHrefObj( g_currentSlide ); - if( !SIMPLE_FRAMESET ) - ChangeFrame( NOTES_FRAME, obj.m_notesHref ); - ChangeFrame( NAVBAR_FRAME, NAVBAR_HREF ); - } -} - -function ToggleOutline() { - g_showoutline = 1 - g_showoutline; - writeMyFrame(); -} - -function ShowHideNotes() { - g_shownotes = 1 - g_shownotes; - writeMyFrame(); -} - -function writeMyFrame() { - SetFSMode(0); - obj = frames[MAIN_FRAME]; - - var curslide = g_baseURL + "/" + GetHrefObj( g_currentSlide ).m_slideHref; - var curnotes = g_baseURL + "/" + GetHrefObj( g_currentSlide ).m_notesHref; - var otlhref = g_baseURL + "/" + g_outline_href; - if ( msie < 0 ) { - if ( ! g_showoutline && g_shownotes ) { - obj.document.write( ' \ No newline at end of file diff --git a/web/tutorial/ppt/html/webqtl_demo2_part1.ppt_files/slide0001_image001.png b/web/tutorial/ppt/html/webqtl_demo2_part1.ppt_files/slide0001_image001.png deleted file mode 100755 index a1cb20e4..00000000 Binary files a/web/tutorial/ppt/html/webqtl_demo2_part1.ppt_files/slide0001_image001.png and /dev/null differ diff --git a/web/tutorial/ppt/html/webqtl_demo2_part1.ppt_files/slide0001_notes_pane.htm b/web/tutorial/ppt/html/webqtl_demo2_part1.ppt_files/slide0001_notes_pane.htm deleted file mode 100755 index 7c670210..00000000 --- a/web/tutorial/ppt/html/webqtl_demo2_part1.ppt_files/slide0001_notes_pane.htm +++ /dev/null @@ -1,5 +0,0 @@ -
Welcome to a short demonstration of the GeneNetwork and its WebQTL module. Please adjust the size of the windows on your monitor so that you can see part of this page, as well as GeneNetwork windows. WebQTL produces a large number of new windows, so you may need to modify your browser preferences to permit pop-ups. In this demonstration, we explore one important transcript expressed in the brain: the amyloid beta precursor protein messenger RNA. A protein product of this mRNA, the APP protein, is associated with AlzheimerÕs disease.

My thanks to Dr. Robert F. Clark and Wenli Cai for testing this PowerPoint demonstration and making many improvements.

(Initial version of June 2003 by Rob Williams. Edits July 13, 2005 by RW and RFC. Edit July 14, 2005 by WC. Final edits by RF Clark, July 22, 2005.
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Please link to the web site:  http://www.genenetwork.org
To begin a search you make choices about what species, group, and database to explore.

For this demonstration enter APP as above and click on the SEARCH button. Make sure that the DEFAULT SETTINGS are species = Mouse, Group = BXD, Type = Whole Brain, and Database = INIA BRAIN mRNA M430 (Apr05) PDNN.

Notes:
1. The GeneNetwork and WebQTL are often used to work with public data sets. However, it is possible to enter and analyze your own data for specific genetic reference populations such as the BXD genetic reference population of mice or the HXB strains of rat. Entering your own data is a more advanced topic, but if you click on the HOME pop-down menu (upper left), you will see ÒEnter Trait DataÓ that will explain the process.
2. For help on advanced searching methods read the left side of the page (INTRODUCTION).  If you make a search term too complex, you may get no hits (try entering Òamyloid betaÓ for example). If you make it too simple, you may also get too many.
3. Use the asterisk * as a wildcard. For example, to find all Hoxb transcripts, search for Hoxb*.
4. In some cases you can also research for transcripts and genes using special search strings such as ÒMb = (Chr1, 100 102)Ó to find all genes on Chromosome 1 between 98 and 104 megabases (donÕt actually use the quotes). Details are described at http://www.genenetwork.org/searchHelp.html.
5.   These INFO buttons provide links to data about the different data types. Try them.
6.  The SET TO DEFAULT button is used to change the database default setting to match your typical search categories.
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SHORT DETOUR to the HELP menu. If you are new to the GeneNetwork, you may find it helpful to review the The Glossary and FAQ pages shown above. We are in the process of making ÒliveÓ demos for some of the key modules in the GeneNetwork. Check the NEWS every month or two to find out about new features.

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RESULTS OF THE APP SEARCH.  A search of the INIA Brain database generates 18 matches, 10 of which are shown above. The GeneNetwork will display several hundred matches in pages of 40 each. If a search generates a larger numbers of hits, then you will need to refine search terms.

Notes:
1. APP is a great transcript to introduce you to the complexity and power of new array platforms that often provide ÒalternativeÓ expression estimates for single genes. There are seven probe sets that target different parts of the APP transcript. Which of the alternative measurements is most appropriate and informative? Have a look at the FAQ page for more on this topic, but general advice: 1. be skeptical and try to validate that the correct transcript and gene is being measured; 2. check what part of the transcript is complementary to the probes; 3. evaluate the performance of individual probes based on expression level, signal-to-noise and other error terms such as the standard deviation and error.
2. In this particular case we have highlighted and selected # 5 on this SEARCH RESULTS page. The annotation for this probe set mentions that it targets the last three exons and the 3Õ untranslated region (UTR) of the amyloid precursor protein (APP).  That is just what we want.
3. Most probe sets have not been annotated in as much detail as App. Refer tot the FAQ to learn how to annotate probe sets yourself.
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The Trait Data and Analysis Form is the single most important page from the point of view of working with GeneNetwork data. Please read the text carefully. Explore the links, but do not close this page. We will need it many more times in this demonstration.
Notes:
1. What is this database? It is called INIA Brain mRNA M430 (Apr05) PDNN, but what does that actually mean. How much of the brain was used? How were the animals processed? Most of these types of questions can be answered by clicking on the DATABASE link.
2. Transcript/gene LOCATION data is usually from the most recent assembly. You can VERIFY the location of the probes and probe set using the two VERIFY buttons. VERIFY UCSC performs a sequence alignment (BLAT analysis) of the probes to the most recent assembly.
3. The PROBE TOOL button provides you with highly detailed information on the probe sequences used to assemble the probe set. For example, in this case you can find out which probes correspond to which of the three exons. You can also review the performance of the individual probes. Please check the GLOSSARY for additional details on probes.
4. The identifiers (IDs) provide links to other key resources.

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Most of the database components and resources in The GeneNetwork are linked to metadata pages that provide a human-readable summary of how, why, where, when, and with whom the data were generated. Before you get too involved with a data set, it is naturally important to read this information. While the data in The GeneNetwork may be accessible and useful, that does not always mean that the data is public domain and available for you to use in publication or for profit purposes. If you want to know more about the data ownership and usage, please read through the POLICIES pop-down menu items.
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This slide shows you the lower parts of the Trait Data and Analysis Form with the data for the first set of BXD strains
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The Basic Statistics page allows you to evaluate some aspects of the data quality. In this case, BXD8 is a potential problem. An outlier of this type may be generated by a technical artifact (bad sample?). However, it is also possible that BXD8 just has genuine low endogenous expression of App and may therefore be a particularly valuable model for research. There are different ways to treat problematic data of these types. One way is simply to discard this datum. The other way is to prevent outliers from have too much influence quantitatively, while leaving them in their low (or high positions). This is called windsorizing the data (after King Henry the VIII who had a habit of chopping heads). In this case, we have windsorized the BXD8 to a value of 16.0 and the BXD33 to a value of 16.02. Rank is retained. We are making a bet that the two lowest strains are really low, but we are hedging our bet and just making them a little lower than BXD90. This removes their ÒundueÓ influence.
Notes:
1. It turns out that BXD8 is a strain with many odd phenotypes. The whole strain is essentially an outlier for many traits. Therefore, the low App expression data may be quite accurate. Still, it would be comforting to have at least two more replicates.
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Now we get a much better feel for the variation in the error among the cases. Those without error bars are of course the ÒnoisiestÓ of all. This data set is not complete yet (the aim is to acquire at least one male-female sample for each BXD strain).
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Finally, we can now start an analysis.
We ask a simple question:
Do differences in this particular App transcript steady-state abundance level correlate with those of any other transcripts in the same INIA Brain mRNA M430 data set?
Notes:
1.You can CHOOSE many other DATABASES at this point if you want, but for now letÕs stick with the default.
2. There are different ways to sort the correlations. The most obvious is by p-value (most significant values at the top of the list), but it is also interesting to sort the top 100 or top 500 by their gene symbol (gene ID) or by their chromosomal location (position).
3. If you donÕt want your analysis to be sensitive to outliers, then you may want to choose to use the Spearman Rank Order method of calculating correlations.
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The Traits Correlation output window (Correlation Results) compares App expression data with all other traits in this INIA Brain data set. The most significant 100 or 500 transcripts are sorted by their p-values. The top correlation is that of the probe set to itself (often a value of 1.0, but in this case we modified the App values manually by windsorizing the data). The next best correlation is to another App probe set. The fourth correlation is interesting and suggests that there may be a link between App and a particular type of ataxia (Atcay).
Notes:
1.Use the checkboxes to the far left to select traits that you want to study together. Once you have selected interesting traits, click on the ADD SELECTION button. This puts all of the selected traits into a SELECTIONS WINDOW for other types of analysis.
2. The p-value is not corrected for multiple tests. A conservative approach for array data would be to assume 10,000 nominally independent tests. Subtract 4 from the exponent and if the value is still smaller than 0.05 you may have a real correlation.
3. The LITERATURE CORRELATION is a data type generated by Drs. Ramin Homayouni and Michael Berry. Click on the header column by the asterisk for more information on this highly useful data type.
4. We are using Pearson product moment correlations rather that the Spearman rank order correlation. But you can select either in the previous step.
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By clicking on the CORRELATION of the Atcay transcript to the App transcript, you can generate a Correlation plot between these two transcripts. In this App and Atcay scatterplot, each point is a strain mean value. For example, BXD33 and BXD8 have low App and Atcay expressions. The two parental strains and the F1 are also included in this plot.
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A group of traits from many different databases can be selected and brought together for joint analysis. In this case all of the content of the BXD SELECTIONS is from a single BRAIN database, the top 20 neighbors of the App transcript from the Correlation Results table. Eight of these neighbors plus App is shown in the slide.
Notes:
1.All of items in the BXD SELECTIONS were selected using the SELECT ALL button
2. The buttons at the top (and bottom) of this page can do some cool stuff. We will work with NETWORK GRAPH first.
3. Think of the SELECTIONS as your shopping cart. You go to different aisles in the supermarket to acquire different types of items of interest. These could include transcripts, classical phenotypes (longevity, brain weight, prepulse inhibition, iron levels in midbrain). ÒChecking outÓ in this case involves doing some analysis with the items in the cart.
4. Different tools handle different numbers of items. Most will handle up to 100 traits.

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Output of the Network Graph. Warm colors (orange and red) are positive correlations above 0.5 whereas cool colors (green and blue) are negative correlations. Notes:
1. All of the nodes (gene/transcripts) on this graph are clickable.
2. For this graph the App expression values have ÒrevertedÓ to their pre-Windsorized values.
3. To generate this graph, we used the default setting:  Size of 16 by 16 inches; Gene Symbols; Don't Show Correlations; Use curved lines (aka ÒedgesÓ).
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Going back to the Trait Data and Analysis Form window, we have computed the correlations between strain variation in App expression level and other classical phenotypes that have already been measured in many of the same BXD strains.
Notes:
1.The number of common strains varies widely--in this case from 14 to 23 strains.
2. We can add these traits (four are selected) to our BXD SELECTIONS window.
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We have computed the Network Graph, now using other types of traits.
Saline Hot Plate Latency is the green node labeled 10020.
Freezing (fear) is the green node labeled 10447.
Notes:
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END
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GeneNetwork and WebQTL:
lPart 1: How to study expression variation and covariation (slides 2–16)
lPart 2. Discovering upstream modulators (slides 17–30)
RNA

PowerPoint ÒNormal viewÓ has notes that may be useful companions to these slides.
a PowerPoint Presentation
RWW 07.23.2005
You can also download this PowerPoint at
ftp://atlas.utmem.edu/public/webqtl_demo2.ppt
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END
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Choose
database
Enter
APP
Select
search
lPART 1: How to study variation and covariation
Choose  species, group, and type
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lPlease also use the Glossary, FAQ, and News
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Highlight this probe set in red and
click. You do NOT have to select the checkbox
Search results
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lFirst page of data: The Trait Data and Analysis Form
Click here
to learn
about
data
source
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lData sources: Metadata for each resource
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lExpression estimates for App on the Trait Data form
Trait data for each strain with SE when known. For array data the scale is ~ log base 2.   F1 data = 16.723 = 2^16.723 = 108,174
These values can all be changed by the user. (Yes, there is a RESET)
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lCritiquing the App data the Trait Data
Use the BASIC STATISTICS button to evaluate the App data. You will find that App data from the different strains are not equally trustworthy. BXD8 is an obvious outlier without replication (no error bar). BXD33 is also suspiciously low. BXD5 is noisy.
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lApp expression after windsorizing
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lDiscovering shared expression patterns
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lTranscript neighborhoods
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lApp and Atcay transcript scatterplot
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lApp transcript and eight of its neighbors
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App transcript coexpression neighborhood
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lCorrelations of App with classical traits
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lNetwork Graph of App with classical traits
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lSummary of Part 1:
1. You have learned the basics about searching for traits
2. You know some methods to check data quality
3. You know how to edit bad or suspicious data
4. You know how to review the basic statistics of a trait
5. You know how to generate a scattergram between two traits using the Traits Correlation tool
6. You know how to add items to your SELECTIONS window
7. You know how to generate a Network Graph of traits that co-vary.
What does genetic covariance mean? The genetic covariance can be functional and mechanistic, but it can also be due to linkage disequilibrium. Finally, it can be due to sampling error or poor experimental design. Evaluate the biological plausibility of correlations. Test and be skeptical.
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Contact for comments and improvements:
rwilliam@nb.utmem.edu


kmanly@utmem.edu
The App findings reviewed in this presentation are part of an ongoing study by R. Williams. R. Homayouni, and R. Clark (July 15, 2005)
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