| 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. |
|
| Please donÕt 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 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. |
|
| 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. |
|
| This is a thin veneer of
functional genomics. It is enough to generate some marvelous hypotheses in a
semi-automated way. |