rare Mendelian diseases versus common multi-factorial diseases

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rare Mendelian diseases versus common
multi-factorial diseases
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1980 DNA markers are the key to identifying
Mendelian disease genes
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1989 successful cloning of CFTR gene responsible
for cystic fibrosis
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Online Mendelian Inheritance in Man currently
lists 2284 phenotypes whose molecular basis is
known
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1990-6 birth and death of sib pair analysis for
linkage based studies of common multi-factorial
diseases
Risch N, Merikangas K. 1996. The future of
genetic studies of complex human diseases.
Science 273 1516-1517 linkage analysis had been
successfully used to find genes for Mendelian
diseases in 1990, Risch popularized a method
(sib pairs) to find genes for complex
multi-factorial diseases that method failed and
they wanted to propose a different method that
would be more powerful association studies were
to be performed on functional polymorphisms for
as many candidate genes as technically feasible,
the entire genome if need be, regardless of how
impractical that was at least the number of
patients would no longer be a limiting factor
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past, present, and (near) future genetic studies
of human diseases
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population bottleneck, subsequent recombination,
linkage disequilibrium
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we need not test all the functional
polymorphisms, just enough markers to be within
linkage disequilibrium
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common-disease-common-variant versus
common-disease-rare-variant
CDCV hypothesis a few common allelic variants
account for most of the genetic variance in
disease susceptibility Reich DE, Lander ES. 2001.
On the allelic spectrum of human disease. Trends
Genet 17 502-510 CDRV hypothesis a large
number of rare allelic variants account for the
genetic variance in disease susceptibility Terwill
iger JD, Weiss KM. 1998. Linkage disequilibrium
mapping of complex disease fantasy or reality?
Curr Opin Biotechnol 9 578-594 for complex
reasons having to do with human population
history, linkage disequilibrium would only work
in diseases where the CDCV hypothesis is valid
the best justification for the HapMap was that
one common variant has more public health impact
than many rare variants, so it makes sense to
find these first
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multiple rare alleles contribute to low plasma
HDL cholesterol levels
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International HapMap Consortiumhttp//www.hapmap.
org/thehapmap.html.en
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7 tag SNPs capture all the common variation in a
locus on chromosome 2
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Wellcome Trust Case Control genome wide
association studies
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genome wide scan in seven diseases y-axis
represents statistical significance using -log10
of a p-valuethe chromosomes are shown in
alternating colors significant SNPs with p-value
lt1?10-5 are in green
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a doubling in relative risk for a disease is not
as bad as it sounds
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but gene therapy remains elusive 19 years after
the cystic fibrosis gene
Jesse Gelsinger (June 18, 1981 to September 17,
1999) was the first person identified as having
died in a clinical trial for gene therapy. He was
only 18 years old. Gelsinger suffered from
ornithine transcarbamylase OTC deficiency, a
disease of the liver whose victims are unable to
metabolize ammonia, a byproduct of protein
breakdown. Gelsinger was injected with
adenoviruses containing the corrected gene in the
hope that it would manufacture the much needed
enzyme. He died four days later, having suffered
a massive immune response, triggered by the viral
vector used to transport the gene into his cells.
This led to multiple organ failure and brain
death. Food and Drug Administration
investigators concluded that scientists involved
in the trial, including lead researcher Dr. James
M. Wilson (University of Pennsylvania), broke
several rules of conduct (a) Inclusion of
Gelsinger as a substitute for another volunteer
who had dropped out, despite his having high
ammonia levels that should have led to his
exclusion from the trial, (2) Failure by the
university to report that 2 other patients had
experienced serious side effects from the
therapy, (3) Failure to mention the deaths of
monkeys given a similar treatment, as should be
been done for the informed consent. The
university paid the parents an undisclosed
amount.
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http//www.genetic-future.com/2008/03/why-do-genom
e-wide-scans-fail.htmlAlleles with small
effect sizes To separate true signals from
noise, researchers have to set an exceptionally
high threshold that a marker needs to exceed
before it is acceptable as a likely
disease-causing candidate. By increasing the
numbers of samples in their disease and control
groups, researchers will steadily dial down the
statistical noise until even disease genes with
small effects stand out above the crowd. However,
the logistical challenge of collecting a large
number of carefully-ascertained patients will
always be a serious obstacle.Rare variants Our
catalogue of human genetic variation, i.e. the
HapMap, is largely restricted to common variants,
since rare variants are much harder to identify.
The instrumentation has restrictions on how many
different SNPs one can analyze with a single
chip. Everyone agrees that some non-trivial
fraction of the genetic risk of common diseases
will be the result of rare variants, especially
as the latest results in a variety of diseases
failed to provide unambiguous support for the
CDCV hypothesis. The problem is not so much the
costs of sequencing itself, as that is plummeting
due to massive investment in rapid sequencing
technologies, but rather the interpretation of
the resultant data. Population differences
Markers that are associated with disease in one
population can never be assumed to show the same
associations in other human groups. This will be
especially true for rare variants. The more
difficult challenge will be in collecting large
numbers of ancestry-homogeneous samples of
validated disease patients and healthy controls.
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http//www.genetic-future.com/2008/03/why-do-genom
e-wide-scans-fail.htmlEpistatic interactions
Most genetic approaches assume that genetic risk
is additive, in other words, that the presence of
two risk factors in an individual will increase
risk by the sum of the two factors. There is
however no reason to expect that this will always
be the case. Epistatic interactions, in which
combined risk is greater (or less) than the sum
of the risk from individual genes, are difficult
to identify by genome scans and even harder to
untangle. Copy number variation CNVs are now
known to account for a substantial fraction of
human genetic variation, and have been shown to
play an important role in gene expression
variation and in human evolution. It seems highly
likely that CNVs will be responsible for a
non-trivial proportion of disease risk, but only
time will tell.Epigenetic inheritance Although
epigenetic inheritance does occur, the degree to
which it is influencing human physical variation
and disease risk is essentially unknown. It needs
to be established that epigenetically inherited
variations actually contribute to a non-trivial
fraction of human disease risk before we can
include them in our systematic scans. Disease
heterogeneity Lumping patients with
fundamentally different conditions into a single
patient cohort is a recipe for failure, even if
there are strong genetic risk factors for each of
the separate conditions, since each will be
drowned out by noise from the other. The
geneticists cannot fix this problem. It will take
a combined effort of clinicians and biomedical
researchers to stratify the disease into useful
subcategories.