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Journal of Ophthalmology
Volume 2015 (2015), Article ID 821918, 8 pages
Research Article

Joint Effect of CFH and ARMS2/HTRA1 Polymorphisms on Neovascular Age-Related Macular Degeneration in Chinese Population

1Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
2Department of Ophthalmology, Peking University People’s Hospital, Beijing 100044, China
3Key Laboratory of Vision Loss and Restoration, Ministry of Education, Beijing 100044, China
4Beijing Centers of Disease Control and Prevention, Beijing 100013, China
5Department of Hygiene Toxicology, Preventive Medical College, Third Military Medical University, Chongqing 400038, China

Received 31 December 2014; Accepted 10 March 2015

Academic Editor: Naoshi Kondo

Copyright © 2015 Kai Fang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

The supplementary material included the additional information about the genes and environmental risk factors selected to explore the interactions as well as the relative sensitivity analyses carried out. The full set of the genes and environmental risk factors used in GMDR for the selection of the most informative set were shown in Supplementary Table S1. Each row of the table represented a set with linkage disequilibrium or a single SNP. Within each LD set, the SNP with the largest OR was selected as the representative of the LD set it belonged to. We also included cigarette smoking status as the environmental risk factor with other genetic factors in the potential pool for the selection. Finally, 31 risk factors in the potential pool were loaded as the initial factors for GMDR, including genotypes of 30 SNPs and the smoking status.

The genotype frequency and associations of 43 SNPs with nAMD were shown in Supplementary Table S2. The minor allele frequencies ranged from 0.02 to 0.49. 11 SNPs in CFH, and 4 SNPs in ARMS2/HTRA1 were significantly associated with nAMD. Other SNPs in C3, SERPING1, VEGF, CETP, LPL, LIPC, and TIMP3 did not show statistically significant differences between nAMD patients and controls. Age and gender adjusted ORs in dominant, recessive, and additive models of the selected 4 SNPs with nAMD were shown in Supplementary Table S3.

Sensitivity analysis was carried out to allow for ten risk factors in the most informative set by GMDR. The corresponding result was shown in Supplementary Table S4. The best model remained unchanged, i.e. the two-factor set including rs3793917and rs1061170 which was shown the highest cross-validation consistency(10/10) and the best testing balanced accuracy of 64.50%.

  1. Supplementary Material