May 2008
Volume 49, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2008
Comparison of Multiple Predictive Models for AMD Susceptibility
Author Affiliations & Notes
  • S. K. Iyengar
    Epidemiology & Biostatistics, Case Western Reserve University, Cleveland, Ohio
  • Y. Wang
    Epidemiology & Biostatistics, Case Western Reserve University, Cleveland, Ohio
  • R. P. Igo, Jr.
    Epidemiology & Biostatistics, Case Western Reserve University, Cleveland, Ohio
  • J. P. SanGiovanni
    National Eye Institute, National Institutes of Health, Bethesda, Maryland
  • A. K. Henning
    The EMMES Corporation, Rockville, Maryland
  • T. Clemons
    The EMMES Corporation, Rockville, Maryland
  • E. Y. Chew
    National Eye Institute, National Institutes of Health, Bethesda, Maryland
  • Footnotes
    Commercial Relationships  S.K. Iyengar, None; Y. Wang, None; R.P. Igo, None; J.P. SanGiovanni, None; A.K. Henning, None; T. Clemons, None; E.Y. Chew, None.
  • Footnotes
    Support  NIH Grant 1 R01 EY015810
Investigative Ophthalmology & Visual Science May 2008, Vol.49, 2654. doi:https://doi.org/
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    • Get Citation

      S. K. Iyengar, Y. Wang, R. P. Igo, Jr., J. P. SanGiovanni, A. K. Henning, T. Clemons, E. Y. Chew; Comparison of Multiple Predictive Models for AMD Susceptibility. Invest. Ophthalmol. Vis. Sci. 2008;49(13):2654. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: : To develop predictive models for age-related macular degeneration (AMD) based on SNP combinations

Methods: : The ca.106,000 (100K) Illumina SNP set and the associated phenotypes for any AMD (N= 391), advanced AMD (AAMD) (N=377), choroidal neovascularization (CNV) (N=194) and geographic atrophy (GA) (N=134) were obtained from the dbGaP AREDS project. Data cleaning led to removal of SNPs and samples that consistently showed low call rates (< 90% calls with GenCall score < 0.5) or poor quality (median GenCall score < 0.6) leaving 98,470 SNPs for analysis. Initial testing under additive (trend) models was used to find disease-associated SNPs for covariate-adjusted outcome variables. SNPs with p-values < 0.001 were examined using forward stepwise logistic regression to identify the best set of predictive SNPs for AMD, AAMD, CNV and GA after adjusting for age. A variety of methods including neural networks (NN) and classification trees (CT) were used to cross-validate the results of the predictive models.

Results: : Association testing showed that 98 - 278 SNPs were associated with AMD, AAMD, GA or CNV. Of these, SNPs in CFH and PLEKHA1-HTRA1 showed genome-wide significance. Using forward selection it was possible to reduce the set of greatest importance to 43-69 SNPs. Our final predictive models suggested that at least 10 SNPs, at independent genomic locations, were necessary to explain 75-80% of the variance for AMD, GA and CNV, but the AAMD model saturated at 4 SNPs and did not include CFH or PLEKHA1-HTRA1. Cross-validation using NN and CT showed that the predictive model generated via regression recapitulated poorly using alternative approaches. We are in the process of improving the initial SNP generation mechanism to derive more robust models.

Conclusions: : This evaluation shows that AMD has a pleiotropic basis and many loci are necessary to cause disease. We are currently evaluating the validity of the models for risk prediction.

Keywords: age-related macular degeneration • genetics • gene mapping 
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