Evaluating candidate genes
Right
position
and high correlation
= better
candidates
| 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. |
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| 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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