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mso-text-indent-alt:0;position:absolute;top:18.48%;left:.95%;width:99.04%;
height:61.34%'><span style='position:absolute;top:0%;left:0%;width:100.0%'><span
style='font-size:68%'><i>Are there experimental results to corroborate a link </i></span></span><span
style='position:absolute;top:49.31%;left:4.25%;width:90.12%'><span
style='font-size:68%'><i>between App with Hsp84-1?</i></span></span></div>

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<div class=O style='position:absolute;top:29.5%;left:4.37%;width:99.33%;
height:13.07%'><span style='position:absolute;top:0%;left:0%;width:100.0%'><span
style='font-family:"Gill Sans";font-size:250%;color:#E9EB5D'><i>Yes: Heat shock
84-1 induces the expression of App, </i></span></span><span style='position:
absolute;top:51.35%;left:0%;width:85.33%'><span style='font-family:"Gill Sans";
font-size:250%;color:#E9EB5D'><i>ubiquitin, and pyruvate kinase</i></span></span></div>

<div class=O style='position:absolute;top:50.88%;left:5.16%;width:95.23%;
height:19.78%'><span style='position:absolute;top:0%;left:0%;width:100.0%'><span
style='font-family:"Gill Sans";font-size:250%;color:#E9EB5D'><i>Having
�confirmed� these known relations, we can </i></span></span><span
style='position:absolute;top:33.92%;left:0%;width:99.72%'><span
style='font-family:"Gill Sans";font-size:250%;color:#E9EB5D'><i>now add new
members to this family: Atp6l, Gnas, </i></span></span><span style='position:
absolute;top:67.85%;left:0%;width:89.01%'><span style='font-family:"Gill Sans";
font-size:250%;color:#E9EB5D'><i>Ndr4. A thin veneer of functional genomics.</i></span></span></div>

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  <td align=left colspan=1><font face=Helvetica size=3>Having worked with
  WebQTL now for 30 minutes, do we know anything new? The hypothesis that we
  have generated (but not validated) is that three transcripts: Atp6l, Gnas,
  and Ndr4 are part of a family of genes that are coregulated in normal mouse
  forebrain with App and Hsp84-1. We need to add functional and mechanistic
  significance to this hypothesis to make it biologically vibrant. But from a
  statiistical standpoint it is a strong inference.</font><br>
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  <td align=left colspan=1><font face=Helvetica size=3>Please don�t say: But
  these are mere correlations. A high correlation in this context has a
  biological basis. The real question is are we smart enough to understand the
  web (not chain) of causality that produced the correlation. Once we
  understand the web of causality, does it have utility? Very often the answer
  will be NO. This will often be the case when a high correlation is generated
  by linkage disequilibrium of sets of polymorphisms that modulate a set of
  mechanistically separated traits. Chromosomal linkage can produce
  correlations that are not mechanistic in the conventional sense used by
  molecular biologists. For example, clusters<span style="mso-spacerun:
  yes">&nbsp; </span>of hox transcription factor genes tend to be close
  physically to keratin gene clusters, and one might expect shared patterns of
  variance produced by this linkage in a mapping panel, no matter how large.</font><br>
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  <td align=left colspan=1><font face=Helvetica size=3>If Affymetrix designed
  probe sets with reasonable care, if we did the experiments correctly, if we
  sampled animals appropriately, then a correlation of 0.70 or higher between
  transcripts in the brain tells you that these two transcripts are effectively
  coupled in this set of animals under this set of conditions. More than 50%
  the variance in the expression of one transcript can be predicted from the
  other. That is a major piece of information that could be of significant
  clinical, economic, and predictive value, whatever its causes. Yes,
  correlation coefficients are noisy and have large error terms, but we have
  larger n of strains coming to the rescue. Expect more than 50 BXD lines soon.</font><br>
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  <td align=left colspan=1><font face=Helvetica size=3>This is a thin veneer of
  functional genomics. It is enough to generate some marvelous hypotheses in a
  semi-automated way.</font><br>
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