MyVisionTest News Archive
Feb 24, 2012
Genetic model estimates risk of developing wet AMD
AMD has been one of the success stories of the genome revolution and 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.
Read more...
Hum Genomics. 2011 Jul;5(5):420-40
Tags: risk factors, genetics, prognosis, wet AMD
AMD has been one of the success stories of the genome revolution and 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.
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.
Read more...
Hum Genomics. 2011 Jul;5(5):420-40
