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Editorial
Defining the Pathogenicity of DNA Sequence Variation
Carolyn Y. Ho, MD; Calum A. MacRae, MD, PhD
U
detected rates of de novo DNA sequence variants are high,
reliable data to predict their clinical significance are scarce,
long-term outcomes are typically not available, and single
individuals are occasionally found to have mutations in
multiple genes.5 In these latter cases, multiple mutations are
often proposed to result in more profound phenotypes because of a putative gene dosage effect. Unfortunately, it can
be extremely challenging to determine the functional consequences of all sequence variants because the background
phenotypic heterogeneity is substantial. Furthermore, family
segregation data, detailed clinical outcomes, or functional
assays are often not available for 1, let alone 2, variants.
Rigorous evidence to support the true impact of double
mutations is available for remarkably few reported cases,
making it difficult to accurately speculate on the clinical
significance of multiple mutations on either an individual or
a population basis.1,5
The absence of robust genetic support in humans or in
animal models is further compounded by reliance on other
circumstantial lines of evidence. Resequencing studies of
disease genes have typically not subjected control populations to the same level of scrutiny as patient cohorts;
therefore, similar rare but benign variants in normal individuals go undetected. Even if available, in vitro functional data
obtained in heterologous systems may not accurately reflect
in vivo physiology as a result of failure to recapitulate key
components of the cellular or organismal biology.6 Partner
proteins, intracellular compartmentalization of signals, unknown environmental contributions, and significant pathway
redundancy all may modify the final phenotype.3 Nucleotide
variants with apparent phenotypes in vitro may have no effect
in the context of the powerful homeostatic influences of other
physiological pathways. Human genetics offers many examples of profound in vitro effects that fail to translate into an
obvious in vivo phenotype.7 As an end result, pathogenicity
may be attributed to DNA sequence variants using flawed and
somewhat circular logic.
nderstanding the relationship between genotype and
phenotype remains one of the most challenging hurdles
in human genetics, especially as efforts are made to translate
genetic data into clinical prediction of disease or therapeutic
outcomes.1
Mendelian Disorders
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For many cardiac or vascular conditions in which the initial
presentation may be fatal, a strong genotype–phenotype
correlation is of fundamental importance if genetic diagnosis
or prognostication is ever to be useful. Even in classic
monogenic disorders, in which large effect sizes are observed, genetic prediction is often confounded by reduced
penetrance, wide variation in the final phenotypes (pleiotropy), or sporadic phenocopies.2 Although such trait “complexity” has been viewed largely in terms of environmental or
genetic modifiers, such factors have proven difficult to
identify. It is also possible that strong selection pressures
against many cardiovascular traits may result in purely
stochastic events playing a larger role in shaping phenotype.3
Rigorous genetic studies have identified many cardiovascular
disease genes, leading to broader investigation in smaller
families and individual probands. Although identifying variants in these same genes in unrelated individuals provides
important confirmation and strengthens the initial data, the
clinical significance of the numerous additional variants
uncovered in subsequent resequencing efforts may be ambiguous.4 The level of support for these variants as disease
causing is often much less robust than for the original
discovery. As a result, rare (or not so rare) benign polymorphisms populate many mutation databases, often indistinguishable from pathogenic mutations.
Article see p 182
The interpretation of sequence data in the absence of
relevant genetic or functional data to support pathogenicity is
fraught with problems. The pertinent issues are well illustrated in research and commercial proband-based “gene
screening” efforts in several cardiac syndromes in which
Common Disease: The Challenges of
Genome-Wide Association Studies
The opinions expressed in this editorial are not necessarily those of the
authors or of the American Heart Association.
From the Cardiovascular Division (C.Y.H.), Brigham and Women’s
Hospital, Boston, Mass; and the Cardiovascular Research Center
and Cardiology Division (C.A.M.), Massachusetts General Hospital,
Boston, Mass.
Correspondence to Calum A. MacRae, MD, PhD, Cardiovascular
Research Center and Cardiology Division, Massachusetts General Hospital,
55
Fruit
St,
Boston,
MA
02114.
E-mail
[email protected]
(Circ Cardiovasc Genet. 2009;2:95-97.)
© 2009 American Heart Association, Inc.
These challenges for genotype–phenotype correlation are
only magnified in common traits, in which underlying etiologic heterogeneity may be considerable, heritability is less
clearly defined, and effect sizes are orders of magnitude
smaller.8 These very factors will also militate against rapid
mechanistic understanding of the role of individual variants.
Detection of subtle coding sequence alleles, noncoding regulatory functions, or remote effects in cis and trans are all
likely to be compromised by intrinsically low signal-to-noise
ratios.8 –10 Given the limited predictive utility of genetic data
in many Mendelian diseases, it is difficult to imagine at
present how individualized prediction at the genomic level
Circ Cardiovasc Genet is available at
http://circgenetics.ahajournals.org
DOI: 10.1161/CIRCGENETICS.109.864793
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Circ Cardiovasc Genet
April 2009
will be feasible with current data sets.1 The cumulative risk
attributable to the major common alleles for any given trait is
unlikely to translate into meaningful insights if, as has been
the case in most studies to date, molecular mechanisms and
larger genetic or environmental factors remain uncharacterized. Any attempt to define robust genotype–phenotype
correlations must address these unknown intermediate effect
alleles, many of which might have been predicted from
preexisting heritability data.
Potential Solutions
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Ultimately, several different strategies must converge to
facilitate the development of quantitative relationships between nucleotide variants and clinically meaningful traits. As
they emerge, massively parallel sequencing technologies will
allow large-scale resequencing studies in patients and controls to accelerate the identification of genetic associations
with disease.11 Nevertheless, these studies will be subject to
many of the confounders noted above, particularly if rooted in
heterogeneous cohorts with imprecise phenotypes.
There is no substitute for a detailed understanding of the
mechanisms of disease. Disease-relevant functional assays
have proven useful in developing predictive frameworks,
especially when a biologically relevant activity of the protein
can be measured directly in vitro or in vivo. Yet, even in
deficiencies of a single enzyme, correlation between a specific in vitro assay and clinical severity can be difficult to
capture.12 When less is known of the causal chain between
DNA sequence variation and final phenotype, or when this
relationship is difficult to discriminate above the biological
noise, any such correlation falls off rapidly. For example,
disease may result from subtle effects on development decades earlier, the protein may have unsuspected attributes, or
mutation may lead to the gain of novel functions. It is no
surprise that these aspects are not often captured by conventional clinical phenotypes that typically reflect only macroscopic late-stage features of disease. Pleiotropy and reduced
penetrance, both commonly seen in heritable conditions,
challenge efforts to garner useful predictive genotypic data.
Experience with monogenic disorders as well as emerging
results from genome-wide association studies suggest that the
apparent complexity of common disease often resides in the
phenotype.3,13
Examples of this complexity abound. In cardiac repolarization, despite the mechanistic insight from the successful
cloning of long-QT genes, predicting sudden death from
simple genotype has proven challenging, even within a single
family. For many of the sequence variants identified to date,
there are few genetic data to substantiate a causal role in
arrhythmia. The QT interval varies widely with autonomic
and environmental influences, fails to encompass morphological abnormalities of repolarization, and is at best an imperfect reflection of the underlying proarrhythmic state.14,15
There is often discordance between the available in vitro data
from heterologous expression studies of ion channel function
and observed effects on ventricular electrophysiology in
animal models or in man.14,16 Few, if any, studies correlating
clinical outcome with genotype account for the relatedness of
individuals, and apparent risk factors for sudden death are
thus more likely to reflect attributes of specific families
within the cohorts than provide robust measures of the utility
of genotype.
The next generation of patient-oriented studies must offer
rigorous estimates of heritability and more precise definition
of disease traits. Currently, such efforts are constrained by the
limited resolution of traditional clinical assays.13,17 Identifying more granular novel disease traits or endophenotypes will
not only help to resolve etiologic heterogeneity (even if
acquired) but will also enable empirical estimates of the
genetic architecture of these traits, improve the power of all
forms of genetic study, and allow the cost effective prioritization of investigation based on magnitude of effect. These
new clinical studies will exploit the latest functional genomic,
proteomic, and metabolomic technologies, as well as reappraise traditional phenotypes ranging from biochemistry and
physiology to imaging.18,19 Perhaps the most effective strategies will integrate multiple approaches at a systems level,
building disease specific networks that may then be interrogated.18,20,21 As we understand the complete genetic architecture of each disease, the relative importance of specific
genotypes will be more readily estimated, and robust population attributable risks may be calculated.3 Validated endophenotypes will offer immediate benefits to all forms of
genetic study, expanding Mendelian families and resolving
heterogeneity in cohorts for genome-wide association.8,13
Ultimately, in the face of a need for both relevant functional assays to test the effects of genotypic variation and
more multidimensional clinical phenotypes, it may be most
efficient to focus efforts on the identification of end points
that will serve both purposes. Clinical relevance will be key,
and the most useful phenotypes are likely to encompass
dynamic responses and allow bidirectional translation from
bench to bedside. For most diseases, this will require systematic approaches to redefine the most informative components
of the underlying pathological traits.18 This phenome project
will benefit from the investment in postgenomic technologies
but must leverage the legacy of classic clinical investigation.13 Dynamic responses to small molecule probes (drugs or
diagnostics) are one way in which directed efforts at phenotype discovery might be undertaken. These efforts might
occur in vivo, as seen in recent work using ion-channel
blockade to characterize repolarization reserve in individual
subjects.15
The power of combining biologically relevant assays with
rich understanding of genetic mechanisms is already evident
in malignant clonal disorders, in which genotypic prediction
of the response to therapy has rapidly been implemented in
the clinical arena.22 Overcoming the challenges to accurate
definition of the pathogenicity of sequence variants is a major
hurdle in Mendelian disease, although critical for realizing
the full potential of genomic medicine. Developing more
sophisticated methods to redefine disease phenotypes will be
an important step in what may be a longer but exciting cycle
of discovery and translation in the cardiovascular field.
Ultimately, this new phenome may complement ongoing
efforts to redefine the healthcare delivery interface.
Ho and MacRae
Disclosures
None.
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KEY WORDS: genetics
䡲
pathogenicity
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genetic testing
Defining the Pathogenicity of DNA Sequence Variation
Carolyn Y. Ho and Calum A. MacRae
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Circ Cardiovasc Genet. 2009;2:95-97
doi: 10.1161/CIRCGENETICS.109.864793
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