WebQTL Demonstration One
please link to
www.webqtl.org/search.html
please link to
www.webqtl.org/search.html
Part
1: How to discover shared expression patterns (slides 2Ð14)
Part
2. Discovering upstream modulators (15Ð25)
3.Discovering downstream targets
RNA
PowerPoint ÒNormal viewÓ has notes that may be useful companions to these slides.
| Welcome to a short
demonstation of WebQTL. Please adjust the wize of windows on your monitor so
that you can see part of this page as well as WebQTL windows. WebQTL will
produce a potentially large number of new windows (pop-ups), 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. The product of this mRNA, the APP protein,
is associated with Alzheimer disease. |
|
| (Initial version of June
2003 by Rob Williams, Last edits June 16, 2003 by RW.) |
or webqtl.org/search.html (mirror)
choose a
database
enter
amyloid beta
select
search
link to www.webqtl.org/search.html
| WebQTL can be used to
enter your own trait data or to work with data that we have entered for you. |
|
| Linking to
http://www.webqtl.org/search.html will get you a version of the window above.
It may not be identical in layout but it will have the key features. Please
follow the intructions on the slide. Of course, we encourage you to enter
your own terms of interest. |
|
| Two points: If you make
a search term too complex you may get no hits. if you make it too simple (for
example, APP) then you may get too much. Experiment. |
|
| If you just link to |
|
| http://www.webqtl.org
you will NOT see the window above but will see text that will help you to
enter your own data. To get to a
version of the window shown above you will need to click on the phrase RNA expression and Phenotype
Databases in the upper banner. |
|
| If you do not get a new
page within 30 seconds then there is
a problem: try the mirror site http://webqtl.org/search.html. |
highlight
amyloid beta
then click
Search results
| If all goes well, you
will see a version of this window. WebQTL will display up to about 100 hits.
If a search generates larger numbers of hits then you will need to refine
your search terms. |
First page of data: The ÒTrait Data FormÓ
Click here
to learn
about
data
source
| The Trait Data and
Editing Form is the single more important page from the point of view of
working with WebQTL 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. |
Data sources: Phenotpyes and genotypes
| There are already five
databases in WebQTL. Each will eventually have a page like this describing
the data source and appropriate citations to these databases. The great
majority of data in WebQTL were generated in our own labs and those of our
collaborators. We welcome you to
use these data, but caution you that there are inevitably lots of little
problems and issues that may compromise some results. Be cautious and
skeptical. Ask us questions before you leap to publication. And please, if
you find the data useful or can verify or refute data, LET US KNOW. We would
like to add you to our reference section and add links to improvements. |
Return to Trait Data page
bottom of this page
Trait data for each strain with SE when known. For array data the scale is ~ log base 2.
F1=13.752
= 2^13.752 = 13796
| This slide shows you the lower parts of the Trait Data Page.
We expect to make many small modification of this page, so do not be
surprised if some elements have been moved around. |
Discovering shared expression patterns
| Finally, we can now
start an analysis. |
|
| We ask a simple
question: |
|
| Do differences in App
transcript expression correlate with those of any other transcripts in the
forebrain? |
The App transcript neighborhood
Question: How many transcripts have correlations >0.7? What does this imply.
| The answer is a strong
Yes. A very large number of transcripts have correlations above 0.7 (absolute
value) with App mRNA. The precise number today is 208. But this will change
as we add more strains and arrays. In any case, this is a fairly large number
and all of these correlations are significant at alpha .05 even when
correcting for the enormous numbers
of tests (12422 tests). |
|
| What does this imply? |
|
| That there can be
massive codependence of expression variance among transcripts. App is NOT an
isolated instance. This is improtant biologically and statistically. From a
statistical perspective, we would like to know how many ÒindependentÓ test we
effectively are performing when we use array data in this way. Are we testing
12000 independent transcripts or just 1200 transcriptional ÒmodulesÓ each
with blurred boarders but each with about 10 effective members. There is no
answer yet, but we probaby have a large enough data set to begin to answer
this question. |
Handdrawn sketch of the neighborhood
| Many of the data types
in the previous slide are hot-linked and it is easy to generate a small web
of correlations between any transcript of interest and many other
transcripts. In this case, we have used green lines between transcripts that
have positive correlations, and red lines between transcripts that have
negative correlations. Correlations have been multiplied by 100. The
correlation of 0.96 between App and Hsp84-1 reads 96. These are Pearson product moment
correlations and they are sensitive to outliers. If you prefer, you can
recompute Spearman rank order correlations. |
|
| Where did Ndr4 (lower
left) come from? It is not in the list in the previous slide. Actually it is.
Nomenclature changes rapidly. If you click on R74996 in the previous slide
(the active webqtl version) you will see that it now has a new symbol and
name. |
|
| What are all of the conventions in this correlation
network sketch. |
|
| 1.The official gene symbol = App |
|
| 2. OUr estimate of the
location of these gene in the Mouse Genome Sequencing Consoritum version 3
build (MGSCv3). Chromosome followed by the megabase position relative to the
centromere. (Mice only have one chromosome arm so this is an unambiguous
coordinate. ) |
|
| 3. The pair of numbers:
top is the highest expression among the strain set. The lower number is the
lowest expression of that transcript among the strain set. |
|
| 4. Vertical number on
the right side of each box: this is the probe set ID given by Affymetrix. We
have truncated these probe set IDs so you will not see the usual ÒatÓ. A single gene may be
represented by more than 10 probe sets. Thus this ID number is essential to
identify the actual data source. |
|
| 5. Lower right corner: a
two digit number followed by plus and minus signs. These numbers are the
correlation value (absolute value) of the 100th best correlated transcript.
The plus and minus signs indicate the mean polarity of the correlations. |
|
| 6. The set of numbers
that read 2@140* etc. These are the approximate locations of additive effect
QTLs detected by WebQTL that we will describe in other slides. Read this as:
App has a suggestive QTL on Chr 2 at about 140 Mb and the D allele inherited
from DBA/2J confirms a higher expression level at this marker. If there is no star symbol, then it
is not even formally ÒsuggestiveÓ but does make an interesting looking blip
on the QTL radar screen. |
What
a network is likely to look like (but App will not be center of universe).
App
| What networks are likely
to really look like. This slide is taken from Lumeta Inc. (www.lumeta.com). It actually
illustrates the structure of connections across the Internet. The large green area is a major Internet
provider (WorldCom before the fall?).
Check out Lumeta to see
some more lovely graphs of this sort. Most biologists are familiar with
simple sketches, but this is what we will have to be prepared to contend with
soon. |
Are there experimental results to corroborate a link between App with Hsp84-1?
Yes: Heat shock
84-1 induces the expression of App, ubiquitin, and pyruvate kinase
Having
ÒconfirmedÓ these known relations, we can now add new
members to this family: Atp6l, Gnas, Ndr4. A thin veneer of functional genomics.
| 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. |
2.45 billion scatter plots: here is one of the best
App
| The correlation between
App and heat shock protein 84-1 transcript is most impressive. Since WebQTL now contains total of
about 70,000 traits in the BXD strains, we could produce as many as to 70k x
35k scatter plots of this type. Since all of the correlations come for a common reference population,
none of the correlations are
blantantly silly. However the great majority may be uninterpretable and a
very large number may be meaningless given the signal-to-noise ratios of some
measurements. With about 30 strains, correlations above 0.7 have a reasonably
low false positive rate. |
Cross-tissue type correlations
| We can compare App
expression inthe forebrain against transcript expression in hematopoietic
stem cells. Some of these correlations are significant, but it may be
difficult to discovery of shared genetic (linkage disequilibrium) or
molecular processes that give rise to these correlation. |
|
| The GNF Hematopoietic
stem cell data belong to Gerald de Haan (University of Groningen) and Michael
Cooke (Genomics Institute of the Novartis Research Foundation). |
Cross-modal
correlations: From mRNA to to anatomy and to behavior and pharmacology
| Another example, but in
this case we are generating correlations between variation in transcript
levels with a database of approximately 430 published (and unpublished)
phenotypes from BXD strains. Notice that the N of strains is variable (from
21 to 28 above). Rank order statistics is more appropriate when N is under
30. |
|
| The Published Phenotypes
database was prepared by Elissa Chesler and Robert Williams from data
extracted from the literature or sent to us for inclusion by our colleagues.
We especially thank John Crabbe (Oregon HSU) and Byron Jones (Pennsylvania
SU) for providing us with large pre-compiled data tables. |
WebQTL
link to
www.webqtl.org/search.html
link to
www.webqtl.org/search.html
1.Discovering shared expression patterns
2.Discovering upstream modulators (QTLs)
3.Discovering downstream targets
RNA
| Part 2: Mapping upstream
modulators or 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. |
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
| The next set of slides
provide a very short interlude on QTL mapping. You will need to do some
independent reading on this topic if this is your first exposure to QTL
mapping. The recombinant inbred strains that we are using in WebQTL and in
this particular demo were generated about 25 years ago by Dr. Ben Taylor at
The Jackson Laboratory. He crossed a female C57BL/6J mouse with a male DBA/2J
mice. At the bottom of this slide we have schematized one chromosome pair
from three out of 80 BXD RI strains.
The dashed vertical lines that lead to the final BXD RI lines involve
20 full sib matings (about 6 years of breeding). Some lines die out during inbreeding. For example,
there is no extant BXD3 or BXD4 strain. |
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
| This slide is
illustrates two categories of QTLs that modulate variability in transcript
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 the binding
of a transcription factor. However, cis QTLs can be far upstream or downstream
polymorphisms in enhancers. They may also be polymorphisms in neigghboring
genes. |
|
| 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 quite 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 overlappoing arrows above. The way in which an upstream
polymorphism influences a downstream difference in mRNA abundance can be very
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). |
WebQTL to exploring upstream control
Just
click
| Back to the demo. Please
bring the Traiit Data and Editing window to the front and look for the
Interval Mapping button. Please 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 Probes is Probe Set |
|
| Options: Permuation test
YES (1000 is the default number) |
|
| Options: Bootstrap test
YES (1000 is the default number) |
WebQTL to
exploring upstream control.
App maps on Chr 16
here
Is App modulated by Chr 2?
Probably, but donÕt bet the farm.
| This is the main 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 chromsomes only have a single long arm and the centromere represents
the origin of each chromosome for numerical purpose: 0 centimorgans and
almost 0 megabases). The blue labels along the bottom of the figure list a
subset of markers that were used in mapping. We used 753 markers to perform
the mapping but here we just list five markers per chromosome. |
|
| The thick blue wavy line
running across chromsomes summarizes the strength of association between
variation in the phenotype (App expression differences) and the two genotypes
of 753 markers and the intervals between markers (hence, interval mapping). 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 addtive effect tends to be an overestimate). If the red
line is below the X-axis then this means that the allele inherited from
C57BL/6J (B6 or B) at a particular marker is associated with higher values.
If the red line is above the X-axis then the DBA/2J allele (D2 or D) is
associated with higher traits. Multiply the additive effect size by 2 to
estimate the difference between the set of strains that have the B/B genotype
and the D/D genotype at a specific marker. For example, on Chr 2 the red
line peaks at a value of about 0.25. That means that this
region of chromosome 2 is responsible for a 0.5 unit expression difference
between B/B strains and the D/D strains. Since the units are log base 2, this
is 2^0.5, or about a 41% difference in expression with the D/D group being
high. |
|
| The yellow histogram bars:
These summarize the results of a whole-genome bootstrap of the trait that is
performed 1000 times. What is a bootstrap? A bootstrap provides you a metho
of evaluating whether results are robust. If we drop out one strain, do we
still get the same results? When mapping quantitative traits, each strain
normally gets one equally weighted vote. But inthe bootstrap procedure, we
give each strain a random weighting factor of between 0 and 1. We then remap the trait and find THE
SINGLE BEST LRS VALUE per bootstrap. We do this 1000 times. In this example,
most bootstrap results cluster on Chr 2 under the LRS peak. That is somewhat
reassuring. But notice that a substantial number of bootstrap results prefer
Chr 7 or Chr 18. |
|
| The horizontal dashed
lines at 9.6 and 15.9. These lines are the LRS values associated with the
suggestive and significant false positive rates for genome-wide scans
established by permuations of phenotypes across genotypes. We shuffle
randomly 1000 times and obtain a distribution of peak LRS scores to generate
a null distribution. Five percent of the time, one of these permuted data
sets will have a peak LRS higher than 15.9. We call that level the 0.05
significance threshold for a whole genome scan. The p = 0.67 point is the the
suggestive level, and corresponds to the green dashed line. These thresholds are conservative for
transcripts that have expression variation that is highly heritable. The
putative or suggestive QTL on Chr 2 is probably more than just suggestive. |
|
| 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 editing window
with the genotypes loaded into the window just like any other trait. More on
this later. |
|
| NOTE: you can drag these
maps off of the browser window and onto your desktop. The will be saved as
PNG or PDF files. You can import them into Photoshop or other programs. |
The whole neighborhood is modulated!
| App has a suggestive QTL
on Chr 2. What about the neighbors that we defined as having shared
expression patterns. This figure shows that members of the immediate App
neigborhood share a weak Chr 2 QTL.
That is what the blue oval in the background is meant to represent.
But some transcripts, such as Ndr4 and Psen2 do not share this Chr 2
interval. |
|
| QUESTION: What kind of
headway can we make in detemining what polymorphism or polymorphisms on Chr 2
near 130 Mb might contribute to the variance in the expression of all of
these important transcripts? |
Which gene is the QTL?
Right
position
&
high r
good
candidates
| Candidate
Genes: The best we can do at
this point is to make an educated guess about the candidacy status of all
genes in the QTL support interval. For sake of argument, lets say that we are
confident that the polymorphism is located between 130 and 150 Mb (20 Mb,
equivalent to roughly 10 cM). There will typically be 12 to 15 genes per Mb,
so we now would like to evaluate 240 to 300 positional candidates. We would
like to highlight the biologically relevant subset of candidates. We could
look through gene ontologies and expression levels to help us winnow the
list. An alternate way avaiable using WebQTL is to generate a list of those
genes in this 20 Mb interval that have transcripts that co-vary in expression
with App expression. |
|
| To do this, go
back to the Trait Data and Editing window. Sort the correlations by position.
Select Return = 500. Then scroll down the list to see positional candidates
that share expression with App. |
|
| There are
several candidates that have high correlation with App even in this short 20
Mb interval. We can rank them by correlation, but they are all close. There is one other imporant approach
to ranking these candidates. Are they likely to contain polymorphisms? We can
assess the likelihood that they contain polymorphisms by mapping each
transcript to see if any have strong cis QTLs. The logic of this search is
that a transcript that has a strong cis-QTL is likely to contain functional
polymorphisms that effect its own expression. This make is more like that the
transcript is a ÒcausativeÓ factor since it is likely to be polymorphic. |
|
Only one of these candidates is a functional polymorphism
Hars2 = 0610006H08Rik
is a cis-QTL with a very high likelihood ratio statistic (LRS) score| When you do this you
will find that only the transcript 0610006H08Rik has a strong cis QTL. See
the slide above. The LRS peaks above 35
(LOD of greater than 7.5). It turns out that this transcript is really
Hars2, also known as histydl tRNA synthase 2. |
Hars2 probe level analysis: 16 PMs
mapped
SNP
C
in B6, T in D2
no
SNPs
| LetÕs look at Hars2 in
more detail by mapping all of the perfect match probes (16 of them) that
target this transcript. |
|
| Go back to the Trait
Data and Editing window and select Chr 2 (rather than ALL as shown above) and
also select PM Probes. Then click on Interval Mapping button. |
|
| You will get the
illustration above, but without the sequence data that we have added. The 16 perfect match probes are
arranged in sequence (red is 5 prime, blue is the 3 prime end). For example,
the 5 prime-most primer 307387 has the sequence CACTG..... It also has a
polymorphism at the 17 nucleotide of this 25 nt probe sequence. |
|
| How do we know that the
5 prime probe is polymorphic? By looking up the sequence in the Celera
Genomics databases which often contqains sequence data for C57BL/6J (B6
above) and for DBA/2J. But two
blue probes (14 and 15) do NOT contain SNPs but still have very large LRS
scores. The other probes do not perform so wel. Highly variable probe
performance is probably a result of the very different stacking energies of
DNA-RNA duplexes. |
Forebrain
Stem
cells
Cerebellum
| The vertical text says it
all: Even when using identical probes, mapping performance (and signal)
depends on tissue type and mRNA complexity. This is another gene in the Hars2
interval. Forebrain and tem cell mRNAs were run on the same U74Av2 array,
whereas the cerebellum mRNA was run on the 430A and 430B array set. |
Is there known biology to link
Hars2 with App?
69 SNPs, 1 cSNP:
6 exons in NCBI,
2 exons in Celera
Not obvious
| Hars2 is not a well
characterized gene and their is no biology yet to support the hypothesis that
Hars2 modulates gene expression, let alone App expression in specific. There
are also serious database/assembly discrepancies between Celera and MGSCv3
regarding the genomic organization of this gene. But there appear to be
approximately 69 SNPs in Hars2, one of which results in a substitution. |
WebQTL
link to
www.webqtl.org/search.html
link to
www.webqtl.org/search.html
1.Discovering shared expression patterns
2.Discovering upstream modulators (QTLs)
3.Discovering downstream targets
| Part 3. Many investigators would like to
discover the set of downstream targets of a gene of interest. |
|
| In a genetic and
functional sense, that question can only be addressed effectively if there is
genetic variation in the particular gene. We know that Fos is an important transcription factor, but
unless it is polymorphic between C57BL/6J and DBA/2J, then it cannot generate
a genetic variance signal with which we can work. We can still study
covariance of Fos and hundreds of other transcripts (an interesting
exercise), but there are no genetic causes-and-effects. |
Requirement: The
gene must be polymorphic to be
genetically ÒupstreamÓ
What are
targets of the Hars2 polymorphisms?
App and many
other
correlated
transcripts and other traits.| Genes must be polymorphic
to generate downstream genetic effects (as opposed to downstream molecular
effects). Hars2 meets this condition because we have already mapped a
functional polymorphism in the gene. We can therefore posit that Hars2 is a
QT gene. What transcripts are downstream? App is one obvious
candidate, but there are many more. |
|
| The are several ways to
look for downstream targets. The best and most obvious is to look up all
transcripts that have high correlations with Hars2 itself. You should know
how to do this. An alternative method is shown here for teaching purpose and
to show you what to do if your gene of interest is not in our database. You
need to know: |
|
| 1. Where your gene is located. You need this information to find a
surrogate marker; a marker that is located very close to your gene of
interest. |
|
| 2. That your gene is
polymorphic between C57BL/6J and DBA/2J. |
|
| LetÕs look at the
correlation of Hars2 with BXD genotypes as shown in the slide above to
illustrate how to use markers as surrogate traits. |
|
| Go to the Trait Date and
Editing window one more time. We want the data for Hars2 this time, not App.
You should be able to show that Hars2 has a high correlation with D2Mit423 as shown in the slide above. |
|
| By clicking on the symbol
D2Mit423 in the Correlation window, you will generate a new Trait Data window
shown on the next slide. |
Direct
correlations between genotypes and traits
App and
correlated
traits would
be obvious candidates to correlate with D2Mit423
B = -1
D = 1
| We can review the set of
correlations between the marker D2Mit423 and all transcripts in
forebrain. This is in essence a
backwards way of mapping QTLs. We are considering one marker and asking what
traits correlate to the marker and how well. |
WhatÕs
downstream of Chr 2 near Hars2?
Notice
many Chr 2 hits: Linkage disequilibrium limits specificity
Click here
| The marker D2Mit423
correlates moderately well with a number of Chr 2 transcripts. This is due to
linkage disequilibrium. These correlations are not due to a molecular
interactions other than being close together on a chromosome. But we have circled one transcript,
actinin alpha 2, that has a moderately good correlation (0.59) with D2Mit423.
If we map this gene we expect to find a suggestive QTL that peaks near
D2Mit423 |
WhatÕs
downstream of Chr 2 near Hars2?
modest
support that Actn2 is modulated by the Hars2 region
| There is some support for
the hypothesis that Actn2 is downstream of a polymorphism in the Hars2
region. But again, due to the 10 to 20 Mb precision of the mapping data, this
relation could be generated by a large number of other polymorphisms close to
Hars2. In the absence of a biological connection between Actn2 and Hars2 we
have a weak hypothesis. If there were a plausible functional connection
between the two genes, then this hypothesis could be quickly upgraded. |
Does
Hars2 correlate with Actn2 strongly?
Plenty of high correlations, including 2 actins, but not
to Actn2 specifically.
Sort
by gene
| We can test the Hars2 to
Actn2 connection directly. This process weakens the putative association. We
are ready to move on and examine other candidates in the Hars2 region near
D2Mit423. Or in your case, please
start from the beginning using other genes and transcripts and tissues that
interest you more than this App-Hsp84-Hars2 example. |
|
| This concludes the first
demonstation of how to use some of the WebQTL features. Please explore.
Please also send feedback for improvements or additions to
rwilliam@nb.utmem.edu |
Contact
for comments and improvements:
rwilliam@
nb.utmem.edu
kenneth.manly@roswellpark.org
lulu@ nb.utmem.edu
echesler@ nb.utmem.edu
kenneth.manly@roswellpark.org
lulu@ nb.utmem.edu
echesler@ nb.utmem.edu
| END |
WebQTL Demonstration
One
please link to
www.webqtl.org/search.html
please link to
www.webqtl.org/search.html
Part 1: How to discover
shared expression patterns (slides 2Ð14)
Part 2. Discovering
upstream modulators (15Ð25)
3.Discovering downstream
targets
RNA
PowerPoint ÒNormal viewÓ has notes that may be useful companions to these slides.
or
webqtl.org/search.html (mirror)
choose a
database
enter
amyloid beta
select
search
link to
www.webqtl.org/search.html
highlight
amyloid beta
then click
Search results
First page of data: The
ÒTrait Data FormÓ
Click here
to learn
about
data
source
Data sources: Phenotpyes
and genotypes
Return to Trait Data page
bottom of this page
Trait data for each strain with SE when known.
For array data the scale is ~ log base 2. F1=13.752 = 2^13.752 = 13796
Discovering shared
expression patterns
The App transcript neighborhood
Question: How many transcripts have correlations >0.7? What does this imply.
Handdrawn sketch of the
neighborhood
What a network is likely
to look like (but App will not be center of universe).
App
Are there experimental
results to corroborate a link between App with
Hsp84-1?
Yes: Heat shock 84-1 induces the expression of App, ubiquitin, and pyruvate kinase
Having ÒconfirmedÓ these known relations, we can now add new members to this family: Atp6l, Gnas, Ndr4. A thin veneer of functional genomics.
2.45 billion scatter
plots: here
is one of the best
App
Cross-tissue type
correlations
Cross-modal
correlations: From mRNA to to anatomy and to behavior and pharmacology
WebQTL
link to
www.webqtl.org/search.html
link to
www.webqtl.org/search.html
1.Discovering shared
expression patterns
2.Discovering upstream modulators (QTLs)
3.Discovering downstream
targets
RNA
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
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
WebQTL to exploring upstream
control
Just click
WebQTL
to exploring upstream control.
App maps on
Chr 16 here
Is App modulated by Chr
2?
Probably, but donÕt bet the
farm.
The whole neighborhood is
modulated!
Which gene is the QTL?
Right
position
&
high
r
good
candidates
Only one
of these candidates is a functional polymorphism
Hars2 = 0610006H08Rik
is a
cis-QTL with a very high
likelihood ratio statistic
(LRS) scoreHars2 probe level analysis: 16 PMs
mapped
SNP
C in B6, T in D2
no SNPs
Forebrain
Stem cells
Cerebellum
Is there known biology to
link Hars2 with App?
69 SNPs, 1
cSNP:
6 exons in
NCBI,
2 exons in
Celera
Not obvious
WebQTL
link to
www.webqtl.org/search.html
link to
www.webqtl.org/search.html
1.Discovering shared
expression patterns
2.Discovering upstream modulators (QTLs)
3.Discovering downstream
targets
Requirement: The gene
must be polymorphic to be genetically
ÒupstreamÓ
What are targets of the Hars2 polymorphisms?
App
and many other
correlated transcripts and other traits.Direct correlations between genotypes and traits
App
and
correlated traits would be obvious candidates to correlate with D2Mit423B =
-1
D =
1
WhatÕs downstream of Chr 2 near Hars2?
Notice many Chr 2 hits: Linkage disequilibrium limits
specificity
Click here
WhatÕs downstream of Chr 2 near Hars2?
modest support that Actn2 is modulated by the Hars2 region
Does Hars2 correlate with Actn2 strongly?
Plenty of high correlations, including 2 actins, but not to Actn2 specifically.
Sort by gene
Contact for comments and improvements:
rwilliam@ nb.utmem.edu
kenneth.manly@roswellpark.org
lulu@ nb.utmem.edu
echesler@ nb.utmem.edu
kenneth.manly@roswellpark.org
lulu@ nb.utmem.edu
echesler@ nb.utmem.edu
