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Genetics  |   April 2014
Different Hereditary Contribution of the CFH Gene Between Polypoidal Choroidal Vasculopathy and Age-Related Macular Degeneration in Chinese Han People
Author Affiliations & Notes
  • Lvzhen Huang
    Department of Ophthalmology, Peking University People's Hospital, Beijing, China
  • Yingjie Li
    Department of Abdominal Surgical Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
  • Shicheng Guo
    State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
    Human Genetics Center, University of Texas School of Public Health, Houston, Texas, United States
  • Yaoyao Sun
    Department of Ophthalmology, Peking University People's Hospital, Beijing, China
  • Chunfang Zhang
    Department of Clinical Epidemiology, Peking University People's Hospital, Beijing, China
  • Yujing Bai
    Department of Ophthalmology, Peking University People's Hospital, Beijing, China
  • Shanshan Li
    Department of Ophthalmology, Peking University People's Hospital, Beijing, China
  • Fei Yang
    Department of Ophthalmology, Peking University People's Hospital, Beijing, China
  • Min Zhao
    Department of Ophthalmology, Peking University People's Hospital, Beijing, China
  • Bin Wang
    Department of Ophthalmology, Peking University People's Hospital, Beijing, China
  • Wenzhen Yu
    Department of Ophthalmology, Peking University People's Hospital, Beijing, China
  • Mingwei Zhao
    Department of Ophthalmology, Peking University People's Hospital, Beijing, China
  • Chiea Chuen Khor
    Singapore Eye Research Institute, National University of Singapore, Singapore
  • Xiaoxin Li
    Department of Ophthalmology, Peking University People's Hospital, Beijing, China
  • Correspondence: Mingwei Zhao, Peking University People's Hospital, Xizhimen South Street 11, 100044 Beijing, China; zhaomingwei@medmail.com.cn
  • Chiea Chuen Khor, Division of Human Genetics, Genome Institute of Singapore, 60 Biopolis Street, Singapore 138672; khorcc@gis.a-star.edu.sg
  • Xiaoxin Li, Peking University People's Hospital, Xizhimen South Street 11, 100044 Beijing, China; drlixiaoxin@163.com
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 2534-2538. doi:10.1167/iovs.13-13437
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      Lvzhen Huang, Yingjie Li, Shicheng Guo, Yaoyao Sun, Chunfang Zhang, Yujing Bai, Shanshan Li, Fei Yang, Min Zhao, Bin Wang, Wenzhen Yu, Mingwei Zhao, Chiea Chuen Khor, Xiaoxin Li; Different Hereditary Contribution of the CFH Gene Between Polypoidal Choroidal Vasculopathy and Age-Related Macular Degeneration in Chinese Han People. Invest. Ophthalmol. Vis. Sci. 2014;55(4):2534-2538. doi: 10.1167/iovs.13-13437.

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Abstract

Purpose.: To investigate whether 11 variants in complement factor H gene contributed differently in patients with neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) of Chinese descent.

Methods.: We performed a case-control study in a group of Chinese patients with nAMD (n = 344) or PCV (n = 368) and contrasted the results against an independent control group comprising 511 mild cataract patients without any evidence of age-related maculopathy. Association analysis of allele and genotype frequencies was performed for 11 haplotype-tagging single-nucleotide polymorphisms (SNPs) at the CFH locus (rs1061170, rs1329428, rs1410996, rs2284664, rs375396, rs529825, rs551397, rs7540032, rs800292, rs2274700, and rs1065489). Multinomial logistic regression analyses were performed to estimate and compare the effect of these 11 CFH polymorphisms on AMD and PCV, using the wild-type genotype as reference. Differences in the observed genotypic distributions between cases and controls were tested by using χ2 tests, with age and sex adjusted for using logistic regression.

Results.: CFH rs1065489 was not significantly associated with the nAMD phenotype in Chinese collections either on univariate or multivariate analysis (P > 0.05 for all comparisons). The other 10 SNPs of CFH were significantly associated with the nAMD phenotype. As for PCV, all 11 SNP markers were significantly associated with risk of PCV before or after correction for age and sex differences. Eight of the 11 SNP markers showed significant evidence of heterogeneity between AMD and PCV (P < 0.05 for all comparisons).

Conclusions.: Our data suggest that the genetic architecture at the CFH locus is complex with some markers showing significant skewing of the genotypes toward nAMD or PCV in Asians. This further supports the clinical observation that nAMD and PCV could have distinct pathogenesis mechanisms, which will require larger studies to accurately dissect.

Introduction
Age-related macular degeneration (AMD) is a major cause of severe visual loss in elderly populations in the developed countries. 1 Late-stage AMD can be divided into two forms: geographic atrophy (dry AMD) and neovascular AMD (wet AMD, or nAMD). Neovascular AMD is an advanced form of disease, characterized by the development of choroidal neovascular (CNV) membranes, which is the main cause of visual impairment in macular degeneration. 2  
Polypoidal choroidal vasculopathy (PCV) is characterized by aneurysmal dilations with interconnecting vessels, which are often best demonstrated by indocyanine green (ICG) angiography. 3 Sharing some similar clinical manifestations, it still remains controversial whether PCV represents a subtype of nAMD or is a specific entity on its own. However, the prevalence of PCV and nAMD varies depending on ethnicity. 4,5 Besides, emerging evidence suggests some other differences in basic pathomechanisms may exist between nAMD and PCV, including clinical morphologic features, histopathology features, clinical behavior, disease progression, and responses to photodynamic therapy or anti-VEGF therapy. 4,68  
Recently, genetic variants in the complement factor H gene (CFH) have been found to play an important role in the pathogenesis of both CNV and PCV. Although results vary with different ethnicities and different single-nucleotide polymorphisms (SNPs), most studies support the association between CFH variants and both nAMD and PCV. It remains uncertain whether the CFH SNPs play the same role in nAMD and PCV and there is a lack of sufficient evidence to interpret the difference between the two diseases. 
To see whether the differences in clinical feature between these two phenotypes could be attributed to differences in genetic components, we attempted to investigate the relationships between these CFH SNPs and nAMD and PCV, and their effects between nAMD and PCV were compared as well. 
Methods
Subjects
For eight of the 11 SNPs, 1223 unrelated Chinese subjects were studied in this case-control cohort. A total of 344 patients had nAMD, and 368 patients had PCV; 511 individuals without age-related maculopathy (ARM) were studied as controls. For rs800292, rs2274700, and rs1065489, 900 unrelated Chinese subjects were studied in this case-control cohort: 300 nAMD, 300 PCV, and 300 controls. The sex and age of the controls and cases are given in Table 1. The study participants were recruited at the Department of Ophthalmology in the Peking University People's Hospital, and the study was approved by the Ethical Committee of Peking University People's Hospital. An informed consent process was established, following the guidelines of the Declaration of Helsinki, and consent forms were signed by all subjects. All subjects received a comprehensive ophthalmic examination, including visual acuity measurements, slit-lamp biomicroscopy, and dilated fundus examination performed by a retinal specialist. All cases with nAMD and PCV underwent fluorescein angiography, optic coherence tomography, and ICG angiograms with HRA2 (Heidelberg Engineering, Heidelberg, Germany). The diagnosis of nAMD or ARM was defined by International Classification System for ARM. 9 The diagnosis of PCV was based on ICG angiography results that showed a branching vascular network terminating in aneurysmal enlargements. Exclusion criteria included any eye with any other macular abnormalities, such as pathologic myopia, idiopathic choroidal neovascularization (CNV), presumed ocular histoplasmosis, angioid streaks, and any other secondary CNV. Normal controls were defined as having no clinical evidence of nAMD or PCV in either eye or any other eye diseases, excluding mild age-related cataracts. Subjects with severe cataracts were excluded from the study. 
Table 1
 
Demographic Distribution of the Study Subjects
Table 1
 
Demographic Distribution of the Study Subjects
nAMD, n = 344 PCV, n = 368 Controls, n = 511
Females, n 125 143 285
Males, n 219 225 226
Age range, y* 50–90 42–87 45–96
Age, mean ± SD, y 69.2 ± 8.7 66.6 ± 9.6 67.2 ± 9.6
Genetic Analysis
Blood samples were collected from all participants and stored at −80°C before DNA extraction. Genomic DNA was extracted from venous blood leukocytes by using a genomic extraction kit (Beijing eBios Biotechnology Co., Ltd., Beijing, China), and genotyping was performed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), as previously described. 10 Briefly, approximately 30 ng genomic DNA was used to genotype each sample. The primer sequences for 11 SNPs of CFH gene are shown in Supplementary Table S1. The DNA samples were amplified, and the PCR products were used for locus-specific single-base extension reactions. The resulting products were desalted and transferred to a 384 SpectroCHIP array (Sequenom, San Diego, CA, USA). Allele detection was performed by using MALDI-TOF-MS. The mass spectrograms were analyzed by using MassARRAY Typer software version 4.0 (Sequenom, San Diego, CA, USA). 
Statistical Analysis
Data were analyzed by using SAS9.1.3 software (SAS Institute Inc., Cary, NC, USA). Descriptive statistics were calculated for the demographic and clinical variables according to the presence or absence of AMD and PCV. Multinomial logistic regression analyses were performed to estimate and compare the effect of CFH polymorphisms on AMD and PCV, using the wide-type genotype as reference. The results are reported as odds ratios (ORs) with their accompanying 95% confidence intervals. P = 0.05 was used as the threshold for declaring statistical significance. For primary analysis, we measured the association between CFH SNP markers and disease status by using the trend test, modeling for a trend-per-copy of the minor allele. 
Results
Demographics of this study are shown in Table 1. Overall, genotype and allele frequencies of the reported SNPs were analyzed in the 344 nAMD patients, 368 PCV patients, and contrasted against those of the 511 controls. For all study groups, the distributions of the genotypes are shown in Table 3. All 11 SNPs but rs1061170 and rs529825 showed no significant deviation from Hardy-Weinberg equilibrium in the AMD group (P > 0.05) (Table 2). The details of the allele, genotype frequencies, and summary statistics for these 11 SNPs are shown in Table 3 (Supplementary Tables S2–S4). 
Table 2
 
Hardy-Weinberg Equilibrium Test of the Study Subjects
Table 2
 
Hardy-Weinberg Equilibrium Test of the Study Subjects
SNP Test A1 A2 GENO O(HET) E(HET) P
rs1061170 AMD C T 8/52/252 0.1667 0.1942 0.01822
rs1061170 PCV C T 2/63/271 0.1875 0.1795 0.5549
rs1061170 Control C T 1/57/403 0.1236 0.1198 1
rs1065489 AMD G T 75/137/88 0.4567 0.4991 0.1646
rs1065489 PCV G T 52/142/104 0.4765 0.4848 0.8111
rs1065489 Control G T 79/156/66 0.5183 0.4991 0.5639
rs1329428 AMD A G 46/149/148 0.4344 0.4558 0.407
rs1329428 PCV A G 28/167/172 0.455 0.423 0.1748
rs1329428 Control A G 109/259/141 0.5088 0.498 0.6569
rs1410996 AMD T C 41/147/153 0.4311 0.4461 0.5445
rs1410996 PCV T C 25/160/180 0.4384 0.4098 0.2035
rs1410996 Control T C 103/252/150 0.499 0.4957 0.9285
rs2274700 AMD A G 35/125/131 0.4296 0.4456 0.5984
rs2274700 PCV A G 24/120/144 0.4167 0.4132 1
rs2274700 Control A G 60/137/95 0.4692 0.4928 0.4078
rs2284664 AMD A G 34/145/164 0.4227 0.4282 0.8017
rs2284664 PCV A G 19/147/198 0.4038 0.3791 0.2681
rs2284664 Control A G 81/241/188 0.4725 0.478 0.7818
rs3753396 AMD A G 80/163/101 0.4738 0.4981 0.3867
rs3753396 PCV A G 63/173/128 0.4753 0.4841 0.7456
rs3753396 Control A G 141/258/110 0.5069 0.4981 0.7225
rs529825 AMD T C 36/137/170 0.3994 0.4237 0.3078
rs529825 PCV T C 22/130/215 0.3542 0.3617 0.6667
rs529825 Control T C 81/251/178 0.4922 0.4819 0.7131
rs551397 AMD A G 35/122/155 0.391 0.426 0.1453
rs551397 PCV A G 22/129/191 0.3772 0.3779 1
rs551397 Control A G 81/211/166 0.4607 0.4828 0.3337
rs7540032 AMD T C 41/106/137 0.3732 0.4429 0.01054
rs7540032 PCV T C 23/122/153 0.4094 0.4048 1
rs7540032 Control T C 79/165/106 0.4714 0.497 0.3345
rs800292 AMD A G 34/123/141 0.4128 0.4355 0.3546
rs800292 PCV A G 19/115/164 0.3859 0.3816 1
rs800292 Control A G 48/147/104 0.4916 0.4825 0.8108
Table 3
 
Genotype Frequency Distribution of the 11 CFH SNPs and the Results of Association Tests (P Trend)*
Table 3
 
Genotype Frequency Distribution of the 11 CFH SNPs and the Results of Association Tests (P Trend)*
CHR SNP A1 A2 Test Control PCV AMD P (PCV vs. Control) P (AMD vs. Control) Phet
1 rs529825 T C TREND 413/607 174/560 209/477 3.86E-13 0.0000292 0.005023
1 rs551397 A G TREND 373/543 173/511 192/432 3.61E-10 0.0001195 0.03104
1 rs800292 A G TREND 243/355 151/437 191/405 5.62E-08 0.0023 0.065
1 rs1061170 C T TREND 59/863 67/605 68/556 0.007927 0.002196 0.594
1 rs2274700 A G TREND 257/327 166/402 195/387 3.78E-07 0.00034 0.28
1 rs3753396 A G TREND 540/478 299/429 323/365 9.09E-07 0.01407 0.02873
1 rs1410996 T C TREND 458/552 210/520 229/453 2.20E-12 1.731E-06 0.04928
1 rs7540032 T C TREND 323/377 168/428 188/380 1.10E-10 7.956E-06 0.07994
1 rs2284664 A G TREND 403/617 185/543 213/473 8.65E-10 0.0004103 0.01726
1 rs1329428 A G TREND 477/541 223/511 241/445 2.68E-12 1.892E-06 0.0551
1 rs1065489 G T TREND 312/286 241/347 287/313 0.00012 0.14 0.099
In keeping with findings from Western collections, 11 the CFH rs1065489 polymorphism was not significantly associated with the nAMD phenotype (P > 0.05), despite showing strong association with PCV (P < 0.0005, with or without adjustment for age and sex). All of the 11 SNPs tested at the CFH locus were significantly associated with PCV (Table 3), and 8 of 11 SNP markers tested showed significant evidence of heterogeneity between AMD and PCV (P < 0.05 for all comparisons) after adjusting for age and sex (Table 4). We noted no evidence of heterogeneity between PCV and nAMD for rs1161170, which encodes for the Y402H mutation, which was first reported to be very strongly associated with late-stage AMD in Europeans. 12 Haplotype analysis revealed strong evidence of linkage disequilibrium across most of the 11 common polymorphisms in AMD (Fig. A) and in PCV (Fig. B). 
Table 4
 
Logistic Regression Adjustment for Sex and Age
Table 4
 
Logistic Regression Adjustment for Sex and Age
SNP A1 PCV* PCV AMD AMD§ Phet
OR P OR P OR P OR P Phet Phet
rs529825 T 0.4544 1.298E-12 0.4471 1.10E-12 0.6462 3.357E-05 0.6506 6.445E-05 0.00523 0.003022
rs551397 A 0.5028 7.56E-10 0.4903 3.74E-10 0.6629 0.0001333 0.6583 0.0001447 0.03155 0.02065
rs800292 A 0.4991 8.292E-8 0.4933 9.15E-08 0.6927 0.002417 0.6839 0.002555 0.01699 0.01091
rs1061170 C 1.651 0.008483 1.789 0.002657 1.733 0.002561 1.743 0.002824 0.5942 0.6279
rs2274700 A 0.5314 4.517E-07 0.5333 8.36E-07 0.6528 0.0003731 0.6511 0.0006022 0.1152 0.08588
rs3753396 A 0.6161 1.156E-06 0.6273 0.000003999 0.7581 0.01431 0.7817 0.01496 0.02909 0.02114
rs1410996 T 0.476 6.187E-12 0.4758 1.26E-11 0.6118 2.178E-06 0.6089 3.317E-06 0.04976 0.0275
rs7540032 T 0.4672 2.879E-10 0.465 3.39E-10 0.6059 9.888E-06 0.5987 1.078E-05 0.08055 0.05915
rs2284664 A 0.5161 1.617E-09 0.5221 4.74E-09 0.6924 0.0004413 0.7029 0.0009987 0.01763 0.01066
rs1329428 A 0.4799 7.357E-12 0.4772 1.16E-11 0.6156 2.365E-06 0.612 3.357E-06 0.05558 0.03596
rs1065489 G 0.6414 0.0001818 0.6517 0.0003729 0.8443 0.1389 0.8571 0.192 0.02679 0.01701
Figure
 
(A) Analysis of pairwise LD across CFH SNPs in nAMD cohort. (B) Analysis of pairwise LD across CFH SNPs in PCV cohort. LD, linkage disequilibrium.
Figure
 
(A) Analysis of pairwise LD across CFH SNPs in nAMD cohort. (B) Analysis of pairwise LD across CFH SNPs in PCV cohort. LD, linkage disequilibrium.
Discussion
Our study comprehensively evaluated 11 SNPs of CFH in both nAMD and PCV, and contrasted the differences in genotype distribution between controls, nAMD, and PCV. In keeping with earlier Western reports on nAMD, most SNPs of CFH appeared to be shared genetic risk factors for both nAMD and PCV, whereas SNP rs1065489 appeared to be more confined to PCV. Of note, no association has been observed between CFH rs1065489 and severe nAMD in patients of European descent, 11 consistent with our observations of no association with nAMD in Chinese persons. 
With the increased knowledge about the genetic determinants for AMD, researchers have tried to find genetic evidence to determine the association between nAMD and PCV. Single-nucleotide polymorphisms of CFH, replicated in multiple cohorts, have been proved to be associated with both nAMD and PCV, which plays as major genetic evidence to support the similarity between nAMD and PCV. In our study, significant heterogeneity between nAMD and PCV were observed in 8 of 11 SNPs tested of CFH. The result may indicate that although they share some common genetic determinants and even further functional mechanisms, PCV seems not to be just a simple variant of AMD, but could be a distinct disease entity. 
We also noted significant heterogeneity between 8 of the 11 CFH SNP markers tested between nAMD and PCV (Table 4), a difference which is unlikely to occur by chance alone. The reason why these different effects could not be detected previously may lie in the clinical classification, which may lead to misclassification between PCV patient samples and nAMD patients. Ethnic differences may also affect the associative significance. Besides, different effects of these SNPs of CFH on PCV and nAMD may indicate different SNP functions. 
The G allele of CFH rs1065489 has been found to confer resistance against meningococcal sepsis in Europeans, 13 as well as exert an opposite effect: certain alleles are associated with disease in atypical hemolytic syndrome but are protective against immunoglobulin A nephropathy, central serous chorioretinopathy, and AMD. 14,15 It can be speculated that environmental factors and interactions with other multiple genetic loci may affect phenotypic expression of variant alleles. 
Our result may imply a different pathogenesis, although further studies are necessary to clarify the link. Since it is still unclear how genetic differences play a role in the pathogenesis of diseases, studies with larger sample sizes and different cohorts may be necessary to reveal the effect of these alleles. Besides, functional studies of each independent SNP may be carried out for further information. 
In conclusion, this study provided evidence that CFH acts somehow as a susceptibility gene for both nAMD and PCV, and that the complement pathway has an important role in the pathogenesis. The different effects of the nine SNPs on PCV and AMD suggest there may be different pathogenesis mechanisms and different genetic factors that may play a role. This finding provides further insight into the underlying genetic character and pathophysiology of the development of nAMD and PCV. 
Supplementary Materials
Acknowledgments
Supported by the National Basic Research Program of China (973 Program, No. 2011CB510200), the National Natural Science Foundation of China (NSFC, No. 81100666 and No. 81170854), and the Research Fund for Science and Technology Program of Beijing (No. Z121100005312006). 
Disclosure: L. Huang, None; Y. Li, None; S. Guo, None; Y. Sun, None; C. Zhang, None; Y. Bai, None; S. Li, None; F. Yang, None; M. Zhao, None; B. Wang, None; W. Yu, None; M. Zhao, None; C.C. Khor, None; X. Li, None 
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Footnotes
 LH, YL, SG, and YS contributed equally to the work presented here and should therefore be regarded as equivalent authors.
Figure
 
(A) Analysis of pairwise LD across CFH SNPs in nAMD cohort. (B) Analysis of pairwise LD across CFH SNPs in PCV cohort. LD, linkage disequilibrium.
Figure
 
(A) Analysis of pairwise LD across CFH SNPs in nAMD cohort. (B) Analysis of pairwise LD across CFH SNPs in PCV cohort. LD, linkage disequilibrium.
Table 1
 
Demographic Distribution of the Study Subjects
Table 1
 
Demographic Distribution of the Study Subjects
nAMD, n = 344 PCV, n = 368 Controls, n = 511
Females, n 125 143 285
Males, n 219 225 226
Age range, y* 50–90 42–87 45–96
Age, mean ± SD, y 69.2 ± 8.7 66.6 ± 9.6 67.2 ± 9.6
Table 2
 
Hardy-Weinberg Equilibrium Test of the Study Subjects
Table 2
 
Hardy-Weinberg Equilibrium Test of the Study Subjects
SNP Test A1 A2 GENO O(HET) E(HET) P
rs1061170 AMD C T 8/52/252 0.1667 0.1942 0.01822
rs1061170 PCV C T 2/63/271 0.1875 0.1795 0.5549
rs1061170 Control C T 1/57/403 0.1236 0.1198 1
rs1065489 AMD G T 75/137/88 0.4567 0.4991 0.1646
rs1065489 PCV G T 52/142/104 0.4765 0.4848 0.8111
rs1065489 Control G T 79/156/66 0.5183 0.4991 0.5639
rs1329428 AMD A G 46/149/148 0.4344 0.4558 0.407
rs1329428 PCV A G 28/167/172 0.455 0.423 0.1748
rs1329428 Control A G 109/259/141 0.5088 0.498 0.6569
rs1410996 AMD T C 41/147/153 0.4311 0.4461 0.5445
rs1410996 PCV T C 25/160/180 0.4384 0.4098 0.2035
rs1410996 Control T C 103/252/150 0.499 0.4957 0.9285
rs2274700 AMD A G 35/125/131 0.4296 0.4456 0.5984
rs2274700 PCV A G 24/120/144 0.4167 0.4132 1
rs2274700 Control A G 60/137/95 0.4692 0.4928 0.4078
rs2284664 AMD A G 34/145/164 0.4227 0.4282 0.8017
rs2284664 PCV A G 19/147/198 0.4038 0.3791 0.2681
rs2284664 Control A G 81/241/188 0.4725 0.478 0.7818
rs3753396 AMD A G 80/163/101 0.4738 0.4981 0.3867
rs3753396 PCV A G 63/173/128 0.4753 0.4841 0.7456
rs3753396 Control A G 141/258/110 0.5069 0.4981 0.7225
rs529825 AMD T C 36/137/170 0.3994 0.4237 0.3078
rs529825 PCV T C 22/130/215 0.3542 0.3617 0.6667
rs529825 Control T C 81/251/178 0.4922 0.4819 0.7131
rs551397 AMD A G 35/122/155 0.391 0.426 0.1453
rs551397 PCV A G 22/129/191 0.3772 0.3779 1
rs551397 Control A G 81/211/166 0.4607 0.4828 0.3337
rs7540032 AMD T C 41/106/137 0.3732 0.4429 0.01054
rs7540032 PCV T C 23/122/153 0.4094 0.4048 1
rs7540032 Control T C 79/165/106 0.4714 0.497 0.3345
rs800292 AMD A G 34/123/141 0.4128 0.4355 0.3546
rs800292 PCV A G 19/115/164 0.3859 0.3816 1
rs800292 Control A G 48/147/104 0.4916 0.4825 0.8108
Table 3
 
Genotype Frequency Distribution of the 11 CFH SNPs and the Results of Association Tests (P Trend)*
Table 3
 
Genotype Frequency Distribution of the 11 CFH SNPs and the Results of Association Tests (P Trend)*
CHR SNP A1 A2 Test Control PCV AMD P (PCV vs. Control) P (AMD vs. Control) Phet
1 rs529825 T C TREND 413/607 174/560 209/477 3.86E-13 0.0000292 0.005023
1 rs551397 A G TREND 373/543 173/511 192/432 3.61E-10 0.0001195 0.03104
1 rs800292 A G TREND 243/355 151/437 191/405 5.62E-08 0.0023 0.065
1 rs1061170 C T TREND 59/863 67/605 68/556 0.007927 0.002196 0.594
1 rs2274700 A G TREND 257/327 166/402 195/387 3.78E-07 0.00034 0.28
1 rs3753396 A G TREND 540/478 299/429 323/365 9.09E-07 0.01407 0.02873
1 rs1410996 T C TREND 458/552 210/520 229/453 2.20E-12 1.731E-06 0.04928
1 rs7540032 T C TREND 323/377 168/428 188/380 1.10E-10 7.956E-06 0.07994
1 rs2284664 A G TREND 403/617 185/543 213/473 8.65E-10 0.0004103 0.01726
1 rs1329428 A G TREND 477/541 223/511 241/445 2.68E-12 1.892E-06 0.0551
1 rs1065489 G T TREND 312/286 241/347 287/313 0.00012 0.14 0.099
Table 4
 
Logistic Regression Adjustment for Sex and Age
Table 4
 
Logistic Regression Adjustment for Sex and Age
SNP A1 PCV* PCV AMD AMD§ Phet
OR P OR P OR P OR P Phet Phet
rs529825 T 0.4544 1.298E-12 0.4471 1.10E-12 0.6462 3.357E-05 0.6506 6.445E-05 0.00523 0.003022
rs551397 A 0.5028 7.56E-10 0.4903 3.74E-10 0.6629 0.0001333 0.6583 0.0001447 0.03155 0.02065
rs800292 A 0.4991 8.292E-8 0.4933 9.15E-08 0.6927 0.002417 0.6839 0.002555 0.01699 0.01091
rs1061170 C 1.651 0.008483 1.789 0.002657 1.733 0.002561 1.743 0.002824 0.5942 0.6279
rs2274700 A 0.5314 4.517E-07 0.5333 8.36E-07 0.6528 0.0003731 0.6511 0.0006022 0.1152 0.08588
rs3753396 A 0.6161 1.156E-06 0.6273 0.000003999 0.7581 0.01431 0.7817 0.01496 0.02909 0.02114
rs1410996 T 0.476 6.187E-12 0.4758 1.26E-11 0.6118 2.178E-06 0.6089 3.317E-06 0.04976 0.0275
rs7540032 T 0.4672 2.879E-10 0.465 3.39E-10 0.6059 9.888E-06 0.5987 1.078E-05 0.08055 0.05915
rs2284664 A 0.5161 1.617E-09 0.5221 4.74E-09 0.6924 0.0004413 0.7029 0.0009987 0.01763 0.01066
rs1329428 A 0.4799 7.357E-12 0.4772 1.16E-11 0.6156 2.365E-06 0.612 3.357E-06 0.05558 0.03596
rs1065489 G 0.6414 0.0001818 0.6517 0.0003729 0.8443 0.1389 0.8571 0.192 0.02679 0.01701
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