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This is a major output
type: a so-called full-genome interval map.
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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.
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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.
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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.
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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.
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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.
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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.
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CLICKABLE REGIONS:
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1. If you click on the
Chromosome number then you will generate a new map just for that chromosome.
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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.
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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.
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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.
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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.
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6. You can also download
the results of the analysis in a text format
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