PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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 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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-
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.
\ No newline at end of file
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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
aa
aaaa
D2 strain
B6 strain
amount of transcript
4 units
2 units
D
B
Dand Bmay be
SNP-like variants in the promoter itself
(cis QTL) or in upstream
genes (trans QTLs).
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).
\ No newline at end of file
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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
Discovering upstream modulatory loci
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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-
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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-
PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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.
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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-
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
\ No newline at end of file
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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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.
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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-
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
Physical map for
distal chromosome 7
Distal Chr 7 from
~120 and 132 Mb may modulate
App
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..ÓThese physical maps
are zoomable, a feature we will exploit to evaluate candidate genes in this
QTL interval.
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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..ÓThese physical maps
are zoomable, a feature we will exploit to evaluate candidate genes in this
QTL interval.
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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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.
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).]
\ No newline at end of file
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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).]
\ No newline at end of file
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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
Physical maps
are zoomable
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.
\ No newline at end of file
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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.
\ No newline at end of file
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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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.
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.
\ No newline at end of file
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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.
\ No newline at end of file
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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
Evaluating Ctbp2 using other resources
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 NCBIhttp://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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-
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 NCBIhttp://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).
\ No newline at end of file
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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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)
END
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END
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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
The GeneNetwork and WebQTL
: PART 2 link to www.genenetwork.org
lPart 1. How to study expression variation and genetic correlation (slides 2–17)
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
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 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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
aa
aaaa
D2 strain
B6 strain
amount of
transcript
4 units
2 units
D
B
Dand Bmay be SNP-like variants in the promoter itself (cis
QTL) or in upstream genes
(trans QTLs).
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
Discovering upstream modulatory loci
-
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
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.
-
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 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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
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.
-
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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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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-
PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
Physical
map for distal chromosome 7
Distal Chr 7 from
~120 and 132 Mb may modulate
App
-
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..ÓThese physical maps
are zoomable, a feature we will exploit to evaluate candidate genes in this
QTL interval.
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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
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.
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
Physical
maps are zoomable
-
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
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.
-
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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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..ÓThese physical maps
are zoomable, a feature we will exploit to evaluate candidate genes in this
QTL interval.
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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
Evaluating
Ctbp2 using other resources
-
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 NCBIhttp://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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
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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. TheC-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 interactswith several transcription factors
including EVI1, ZFPM1, andZFHX1A (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 NCBIhttp://www.ncbi.nih.gov/entrez/query.fcgi?db=geneand 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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lApp and Atcay transcript scatterplot
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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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lApp transcript and eight of
its neighbors
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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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App transcript coexpression neighborhood
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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
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lCorrelations of App with classical traits
-
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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lNetwork Graph of App with classical traits
-
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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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)
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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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 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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aa
aaaa
D2 strain
B6 strain
amount of
transcript
4 units
2 units
D
B
Dand Bmay be SNP-like variants in the promoter itself (cis
QTL) or in upstream genes
(trans QTLs).
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
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Discovering upstream modulatory loci
-
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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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.
-
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
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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.
-
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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Physical
map for distal chromosome 7
Distal Chr 7 from
~120 and 132 Mb may modulate
App
-
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..ÓThese physical maps
are zoomable, a feature we will exploit to evaluate candidate genes in this
QTL interval.
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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.
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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Physical
maps are zoomable
-
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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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.
-
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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Evaluating
Ctbp2 using other resources
-
Ctbp2 should also be
considered a high priority biological candidate gene responsible for
modulating App expression levels. TheC-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 interactswith several transcription factors
including EVI1, ZFPM1, andZFHX1A (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 NCBIhttp://www.ncbi.nih.gov/entrez/query.fcgi?db=geneand 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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
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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)
-
END
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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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Navigation Bar
-
-
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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
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
Choose
database
Enter
APP
Select
search
lPART 1: How to study variation and
covariation
Choosespecies, group, and type
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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-
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
lPlease also use the Glossary, FAQ, and
News
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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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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-
PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
Highlight this probe set in red and
click. You do NOT have to select the checkbox
Search results
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
lFirst page of data: The
Trait Data and Analysis Form
Click here
to learn
about
data
source
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
lData sources: Metadata
for each resource
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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)
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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.
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
lApp expression after
windsorizing
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
lDiscovering shared
expression patterns
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
lTranscript neighborhoods
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
lApp and Atcay transcript scatterplot
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
lApp transcript and eight of
its neighbors
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
App transcript coexpression neighborhood
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
lCorrelations of App with classical traits
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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-
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
lNetwork Graph of App with classical traits
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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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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)
END
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END
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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
GeneNetwork and WebQTL:
lPart 1: How to study expression variation and covariation (slides 2–16)
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
-
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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END
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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
Choose
database
Enter
APP
Select
search
lPART 1: How to study
variation and covariation
Choosespecies, group, and type
-
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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lPlease also use the
Glossary, FAQ, and News
-
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
Highlight this probe set in red and
click. You do NOT have to select the checkbox
Search results
-
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
lFirst page of data: The
Trait Data and Analysis Form
Click here
to learn
about
data
source
-
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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lData sources: Metadata
for each resource
-
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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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)
-
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
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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.
-
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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
genomics
-
lApp expression after
windsorizing
-
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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lDiscovering shared
expression patterns
-
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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lTranscript neighborhoods
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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
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lApp and Atcay transcript scatterplot
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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
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lApp transcript and eight of
its neighbors
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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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PowerPoint Presentation - Complex trait analysis, develop-ment, and
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App transcript coexpression neighborhood
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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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lCorrelations of App with classical traits
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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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lNetwork Graph of App with classical traits
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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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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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END
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