Genetic model estimates risk of developing wet AMD
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is probably one of the best characterized of the complex trait
diseases in terms of genetic predisposition.
Besides age, genetic background is the most significant non-modifiable
risk factor for all stages of AMD, while smoking is the most
significant modifiable risk factor.
Initial groundbreaking studies established that loci on chromosomes
(Chr) 1 and 10 — in particular the complement factor H (CFH) and the
age-related maculopathy susceptibility protein 2 (ARMS2)/high
temperature requirement factor A1 (HTRA1) genes, respectively - are
significantly associated with AMD risk and protection in populations
of various ethnicities. Although the specific role(s) of the Chr 10
genes in AMD pathobiology has not yet been elucidated, the role of the
alternative complement pathway, where CFH functions as a major
fluid-phase regulator, is well established. Early pathobiological
investigations showed dysregulation of the complement cascade to be a
critical early predisposing step in the development of AMD. This
spurred the discovery of the association of CFH variants with AMD
risk. Subsequent genetic investigations revealed additional
associations between AMD and risk/protective variants in various
complement pathway-associated genes, including complement component 2
(C2), complement factor B (CFB), complement component 3 (C3),
complement factor H-related 1 and 3 (CFHR1 and CFHR3) and complement
factor I (CFI).
A prerequisite for a new era in genetic testing and diagnosis for AMD
is a robust test that accurately captures the impact of consistently
replicated AMD risk variants in predicting the risk of developing CNV.
Patients with CNV represent an important segment of the AMD population
that would benefit from early diagnosis, given the current
availability of an effective therapeutic intervention.
In this study, researchers assessed the accuracy of a panel of 13 SNPs
without consideration of environmental risk factors such as smoking or
BMI, to predict the risk of developing CNV in Caucasian individuals 60
years of age and older.
Methods & Results
We report a multicenter assessment of a larger panel of single
nucleotide polymorphisms (SNPs) than previously analyzed, to improve
further the classification performance of a predictive test to
estimate the risk of developing choroidal neovascular (CNV) disease.
We developed a predictive model based solely on genetic markers and
avoided inclusion of self-reported variables (eg smoking history) or
non-static factors (BMI, education status) that might otherwise
introduce inaccuracies in calculating individual risk estimates. We
describe the performance of a test panel comprising 13 SNPs genotyped
across a consolidated collection of four patient cohorts obtained from
academic centers deemed appropriate for pooling. We report on
predictive effect sizes and their classification performance. By
incorporating multiple cohorts of homogeneous ethnic origin, we
obtained >80 per cent power to detect differences in genetic variants
observed between cases and controls. We focused our study on CNV, a
subtype of advanced AMD associated with a severe and potentially
treatable form of the disease. Lastly, we followed a two-stage
strategy involving both test model development and test model
validation to present estimates of classification performance
anticipated in the larger clinical setting. The model contained nine
SNPs tagging variants in the regulators of complement activation (RCA)
locus spanning the complement factor H (CFH), complement factor
H-related 4 (CFHR4), complement factor H-related 5 (CFHR5) and
coagulation factor XIII B subunit (F13B) genes; the four remaining
SNPs targeted polymorphisms in the complement component 2 (C2),
complement factor B (CFB), complement component 3 (C3) and age-related
maculopathy susceptibility protein 2 (ARMS2) genes. The pooled sample
size (1,132 CNV cases, 822 controls) allowed for both model
development and model validation to confirm the accuracy of risk
prediction.
At the validation stage, our test model yielded 82 per cent
sensitivity and 63 per cent specificity, comparable with metrics
reported with earlier testing models that included environmental risk
factors. Our test had an area under the curve of 0.80, reflecting a
modest improvement compared with tests reported with fewer SNPs.
Discussion & Conclusions
Predictive tests for estimating the risk of developing late-stage
neovascular age-related macular degeneration (AMD) are subject to
unique challenges. AMD prevalence increases with age, clinical
phenotypes are heterogeneous and control collections are prone to high
false-negative rates, as many control subjects are likely to develop
disease with advancing age. Risk prediction tests have been presented
previously, using up to ten genetic markers and a range of
self-reported non-genetic variables such as body mass index (BMI) and
smoking history. In order to maximize the accuracy of prediction for
mainstream genetic testing, we sought to derive a test comparable in
performance to earlier testing models but based purely on genetic
markers, which are static through life and not subject to
misreporting.
Although the incorporation of non-static and self-reported variables
is important in elucidating the modifiable risk factors that
contribute to disease, their inclusion can degrade test performance in
mainstream genetic testing. Ideally, a robust test panel, subject to
rigorous validation, which captures the maximal genetic component
should improve classification performance and accuracy of reporting.
In order to compare performance across tests, a ROC curve was
generated for each prediction panel to evaluate the AUC. By evaluating
each test across the large collective cohort using the same validation
procedure, we compared the power of the genetic variants to evaluate
classification performance.
Jakobsdottir and coworkers recently concluded that the diagnostic
value of three variants in the CFH, ARMS2/HTRA1 and C2 genes was not
sufficient to discriminate between individuals with and without AMD
because of the relatively low sensitivity and specificity of the
combined test panel, in combination with the relatively low prevalence
of late-stage disease in the general population. They applied a three
single nucleotide polymorphism (SNP) test to their cohort of 640
late-stage AMD cases and 142 controls to demonstrate a clinical
sensitivity of 74 per cent and a specificity of 69 per cent, with a
reported area under the curve (AUC) - a measure of how well a test or
classifier can distinguish between cases and controls - of 0.79.
Perfect test discrimination would yield an AUC of 1.0. Jakobsdottir
and colleagues also reported that the positive predictive value (PPV)
of the same test is affected by different values of disease prevalence
reflective of age.
The performance of the three-SNP panel described by Jakobsdottir and
colleagues revealed an AUC value of 0.77 in the current study,
compared with a value of 0.79 observed in the original study of 642
late-stage AMD cases and 142 controls. The differences in AUC values
obtained between the original and the current study are likely to
reflect the impact of testing across a large collection of
independently collected cohorts compared with a single study that is
potentially more sensitive to subject selection bias.
Seddon and colleagues evaluated six AMD risk-associated variants in
CFH, ARMS2/HTRA1, C2, CFB and C3 with the goal of developing a
predictive risk test for late-stage AMD. After controlling for
smoking, body mass index (BMI) and vitamin intake, they demonstrated a
strong association between these six risk variants and the prevalence
of late-stage AMD, as well as progression to late-stage disease in
early AMD patients. The progression test described by Seddon et al.,
which included genetic, environmental and treatment variables,
achieved a performance of 83 per cent sensitivity and 68 per cent
specificity, with a reported AUC of 0.82.
The performance of the six-SNP test panel reported by Seddon and
colleagues as part of a joint gene–environment model exhibited a
drop in AUC from 0.81 to 0.79 from training to validation in our data
(significant at P, 0.05), similar to most of the tests evaluated. This
decrease in AUC reveals the value of the inclusion of an independent
validation set to challenge test performance and estimate metrics
achievable in the broader clinical setting more accurately. We have
emphasized the importance of both study design features to report
performance more accurately and to anticipate utility in the more
diverse clinical testing market more closely.
Finally, modest gains in our 13-SNP panel were demonstrated with the
highest AUC value obtained among all models evaluated (0.80). The
additional variants that contributed to the performance of the
predictive test located in CFHR5 and F13B highlight the complexity of
the genetic structure of the RCA region and influence AMD disease
biology.
In summary, the 13-SNP panel had a clinical sensitivity of 82 per cent
and a specificity of 63 per cent, achieving clinical performance
metrics comparable with models with fewer SNPs that include
self-reported and/or non-static risk factors. The PPV of the panel was
evaluated at different levels of prevalence, reflecting ranges
covering estimates of late-stage disease in individuals >40, >65 and
>80 years of age in the general population. More favorable estimates
of PPV were observed as the prevalence of disease increases with age.
The values obtained revealed 11 per cent PPV at 5.5 per cent
prevalence, 20 per cent PPV at 10 per cent prevalence and 28 per cent
PPV at 15 per cent prevalence in the general population. The
prevalence figures reflect conservative estimates of late-stage
disease in the general population and would be further enhanced and
more clinically applicable in a setting of diseased patients, as in
the study conducted by Seddon and colleagues. The longitudinal study
design of the Age-Related Eye Disease Study (AREDS) cohort used in
Seddon’s study was ideal for evaluating incident AMD by
distinguishing between “progressors” and “non-progressors”
but, more importantly, it established that the same set of variants
were effective at distinguishing non-disease controls from patients
with late-stage disease. Not surprisingly, the same core panel of SNPs
covering the major genes associated with disease used in Seddon and
co-workers’ test panel was also utilized in the study conducted by
Jakobsdottir and colleagues, as well as in our current study.
The present confirmatory findings reflect the utility of these
variants to predict the development of CNV in non-diseased subjects in
our study, as well as the progression to late-stage disease in
patients diagnosed with early forms of AMD. PPVs improve significantly
when applied to the population of patients diagnosed with early stages
of disease. The utility of AMD genetic testing will advance if the
result of a predictive test translates into actionable information for
the physician. This study highlights the need to continue to explore
the biology of CNV, to improve our understanding of the genetics
associated with disease and extend these findings in future studies to
evaluate clinical performance metrics in the more acute clinical
population diagnosed with early-stage disease. A genetic test
identifying individuals at high risk of developing CNV holds the
promise for earlier detection through risk-based surveillance
protocols and improved outcomes arising from more timely intervention.
Hum Genomics. 2011 Jul;5(5):420-40
http://www.ncbi.nlm.nih.gov/pubmed/21807600
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