February 2009
Volume 50, Issue 2
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Biochemistry and Molecular Biology  |   February 2009
Further Assessment of the Complement Component 2 and Factor B Region Associated with Age-Related Macular Degeneration
Author Affiliations
  • Gareth J. McKay
    From the Centre for Vision Sciences and the
  • Giuliana Silvestri
    From the Centre for Vision Sciences and the
  • Christopher C. Patterson
    Centre for Public Health, Queen’s University of Belfast, Belfast, Northern Ireland, United Kingdom.
  • Ruth E. Hogg
    From the Centre for Vision Sciences and the
  • Usha Chakravarthy
    From the Centre for Vision Sciences and the
  • Anne E. Hughes
    Centre for Public Health, Queen’s University of Belfast, Belfast, Northern Ireland, United Kingdom.
Investigative Ophthalmology & Visual Science February 2009, Vol.50, 533-539. doi:10.1167/iovs.08-2275
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      Gareth J. McKay, Giuliana Silvestri, Christopher C. Patterson, Ruth E. Hogg, Usha Chakravarthy, Anne E. Hughes; Further Assessment of the Complement Component 2 and Factor B Region Associated with Age-Related Macular Degeneration. Invest. Ophthalmol. Vis. Sci. 2009;50(2):533-539. doi: 10.1167/iovs.08-2275.

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

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Abstract

purpose. Polymorphic variation in genes involved in regulation of the complement system has been implicated as a major cause of genetic risk, in addition to the LOC387715/HTRA1 locus and other environmental influences. Previous studies have identified polymorphisms in the complement component 2 (CC2) and factor B (CFB) genes, as potential functional variants associated with AMD, in particular CFB R32Q and CC2 rs547154, both of which share strong linkage disequilibrium (LD).

methods. Data derived from the HapMap Project were used to select 18 haplotype-tagging SNPs across the extended CC2/CFB region for genotyping, to measure the strength of LD in 318 patients with neovascular AMD and 243 age-matched control subjects to identify additional potential functional variants in addition to those originally reported.

results. Strong LD was measured across this region as far as the superkiller viralicidic activity 2–like gene (SKIV2L). Nine SNPs were identified to be significantly associated with the genetic effect observed at this locus. Of these, a nonsynonymous coding variant SKIV2L R151Q (rs438999; OR, 0.48; 95% confidence interval [CI], 0.31–0.74; P < 0.001), was in strong LD with CFB R32Q, rs641153 (r 2 = 0.95) and may exert a functional effect. When assessed within a logistic regression model measuring the effects of genetic variation at the CFH and LOC387715/HTRA1 loci and smoking, the effect remained significant (OR, 0.38; 95% CI, 0.22–0.65; P < 0.001). Additional variation identified within this region may also confer a weaker but independent effect and implicate additional genes within the pathogenesis of AMD.

conclusions. Because of the high level of LD within the extended CC2/CFB region, variation within SKIV2L may exert a functional effect in AMD.

Age-related macular degeneration (AMD; MIM 603075) is a degenerative retinal disease that causes progressive impairment of central vision. It is a progressive disorder of the macular fundus that commences as degenerative changes at the level of the retinal pigment epithelium of the eye (early AMD) and that progresses to the visually disabling late phenotypes of geographic atrophy (GA) and or neovascular AMD. Although it is genetically complex, significant advances have been made in our understanding of the genetic basis of the disease etiology in recent years. Risk and protective haplotypes have been identified in the chromosomes of several complement-related genes, factor H (CFH) at 1q32, 1 2 3 4 5 component 2 (CC2)/factor B region (CFB) at 6p21, 6 7 and component C3 at 19p13 8 9 in addition to the LOC387715/serine protease HTRA1 locus at 10q26. 10 11 12 13 14 Although one or more candidate SNPs at each locus have been proposed to exert functional significance, there is still inconclusive proof to determine whether single or multiple SNPs at each region underpin this effect. It is widely accepted that the knowledge generated from these disease-association studies has improved our understanding of the genetic interactions with lifestyle factors, such as cigarette smoking and, importantly, has assisted in the development of risk prediction models. 15 16  
Inflammation and the alternative complement cascade in particular, have been implicated in the pathobiology of AMD. A dysfunctional complement pathway is thought to be involved in retinal cell damage, increased formation of drusen deposits, atrophy and cell degeneration, 17 and the progression of choroidal neovascular membranes. 4 CFH is a major inhibitor of the alternative complement pathway, limiting the ability of the immune system to damage self-tissue by reducing its response. In contrast, CC2 and CFB, paralogous genes separated by 500 base pairs on chromosome 6, are activators of the complement cascade through the classic and alternative pathways, respectively. In a previous study, CC2 and CFB were investigated by sequencing the coding exons of both genes for polymorphic variation and assessing their frequencies within a large disease-association study. 6 Several polymorphic variants were identified to be strongly associated with AMD, with three nonsynonymous coding variants (E318D-rs9332739 in CC2; and L9H-rs4151667 and R32Q-rs641153 both in CFB) identified to be potential disease causing variants. A replication study has since corroborated the strong association observed at rs641153 with marginal significance reported for rs9332739 which was subsequently lost when assessed within a logistic regression model measuring the genetic effect of the LOC387715 and CFH loci and the effect of smoking status. 7 The extensive linkage disequilibrium (LD) that exists makes identification of functional variants difficult. 
We sought to ascertain the extent of LD and genetic variation across this region and to assess the potential of other candidate genes, such as superkiller viralicidic activity 2-like gene (SKIV2L, MIM 600478), which may influence the AMD disease process. 
Materials and Methods
Study Participants
Patients (n = 318) with neovascular AMD in at least one eye, confirmed by both clinical examination and fluorescein angiography were recruited opportunistically from ophthalmology clinics in the Royal Victoria Hospital (Belfast, UK), which is the regional referral center for Northern Ireland, during two intervals, the first between June 2002 and August 2003 and the second between June and September 2006. Stereoscopic digital fundus photographs were captured at the time of examination, and images were sent to the photographic reading center for grading using the definitions of the Wisconsin Age Related Maculopathy Grading System. 18 All patients included within this study had exudative AMD in at least one eye (grade 4b). Patients with pure geographic atrophy (i.e., grade 4a) were excluded, but those with mixed GA and exudative AMD (grade 4ab) were included. As this study focused on end-stage disease, patients with early AMD changes (i.e., grades 1a–3) were excluded. Cases of macular disease due to other primary causes that mimic neovascular AMD, such as myopic maculopathy, adult vitelliform, central serous retinopathy, diabetic retinopathy, and idiopathic macular telangiectasia, were excluded, leaving 318 participants (average age, 76 years). Those with lens opacities of a degree that obscured retinal detail were also excluded. An age-matched control group (n = 243) was recruited through random sampling of older adults (aged 65 years and above). Fundi of control subjects were either free of drusen, or had fewer than five hard drusen of diameter 63 μm and absence of focal pigmentary irregularities such as hyperpigmentation or hypopigmentation (i.e., grades 0a and 0b). All participants were from Northern Ireland and described themselves as of European descent. Current smoking status and smoking history were obtained through the use of a questionnaire previously validated in the Whitehall study, with participants classified as never smokers, ex-smokers, or current smokers. 19  
Informed written consent was obtained from all subjects involved. The study was approved by the Research Ethics Committee of the Queen’s University of Belfast and conformed to the tenets of the Declaration of Helsinki. 
Genotyping
DNA was extracted from peripheral blood leukocytes or frozen buffy coat samples using standard protocols. Data for the HapMap CEU cohort were downloaded to determine the haplotype block structure and LD patterns for the extended CC2/CFB gene region. 20 We identified 16 tagged SNPs across the region which also included the RD RNA binding protein gene (RDBP) and part of SKIV2L (Fig. 1 , Table 1 ). SNP genotyping was undertaken with multiplex PCR and primer extension methodology (Snapshot; ABI, Warrington, UK). All primer and probe sequences are available on request. Genotype data were entered in linkage format into Haploview 21 for analysis. In addition, SNPs rs641153 and rs4151667, which had been reported previously to be associated with AMD, were genotyped to facilitate correlation of findings in this study with those reported previously. 6 7  
Statistical Analysis
All SNPs were verified, validated, and assessed for HWE and LD and r 2 values were examined between SNPs with the Haploview software. 20 This was done both in the complete dataset and separately for case and control groups, with minimal variation found. Allele frequency differences in cases and controls were assessed by Pearson’s χ2 test of association. Odd ratios for alleles and haplotypes were obtained using the Unphased program. 22 Assessment of the associations of genetic markers with AMD risk and of interactions between genetic markers and smoking were obtained using likelihood ratio χ2 tests in a logistic regression model. The fitting of terms in the model for CFH, LOC387715 (A69S), and CC2/CFB haplotypes and smoking status was performed as reported previously. 15  
Results
All SNPs assessed were in Hardy-Weinberg equilibrium (HWE) and 9 of the 18 SNPs showed statistically significant association with AMD (P < 0.05, Table 1 ). Genotype frequencies for each SNP assessed in the study have been presented (Table 2) . The association for SNPs rs1048709 (P = 0.11) and rs2072633 (P = 0.05) did not reach significance, in agreement with previous reports. 6 7 The odds ratio and marginally significant association of rs9332739 supported the findings of Spencer et al., 7 in contrast to those initially reported. 6 SNPs rs641153 (OR, 0.40; confidence interval [CI], 0.24–0.65; P < 0.0001), rs1042663 (OR, 0.47; CI, 0.30–0.72; P < 0.001), and rs438999 (OR, 0.48; CI, 0.31–0.74; P < 0.001) showed the strongest association, reporting a similar effect as expected for markers with r 2 ≥ 0.95 (Fig. 1) . Both of the previous studies reported rs641153 (CFB R32Q) to be significantly associated with AMD. We have shown in this study that rs438999, a nonsynonymous coding SNP (R151Q) located within exon 5 of the SKIV2L gene, and rs1042663, a synonymous coding SNP in exon 8 of CC2 (A341A), are also in strong LD (r 2 = 0.95). The minor alleles of SNPs rs3020644, rs4151657, and rs2072632 which showed a similar magnitude of risk (OR > 1.43; P < 0.01) and were in strong LD (r 2 = 0.71–0.89). SNPs rs9332739 and rs4151672 showed an identical effect (OR, 0.52; P = 0.04) and were in complete LD (r 2 = 1) and also with rs4151667 (CFB L9H), as reported previously. 7 Associated haplotype frequencies in case and control cohorts are presented with the respective odds ratios with confidence intervals and the probabilities calculated (Table 3)
We assessed the association of both haplotypes and individual SNPs within a logistic regression model (Table 4)which included genotype data for CFH and LOC387715/HTRA1 and smoking status. 15 The output showed a protective effect exerted by haplotype 2E which is tagged by rs641153 and those genetic variants sharing strong LD with it (OR, 0.37; CI, 0.21–0.64, P < 0.001). Inversely, haplotype 2A exhibited increased risk in association with AMD (OR, 1.53; CI, 1.07–2.19; P = 0.021). SNP rs9332739 appeared to be significantly associated with AMD when assessed independently, but failed to retain significance when assessed within the framework of the logistic regression model incorporating other known risk factors in support of previous findings. 7 SNPs rs1042663 (OR, 0.37), rs641153 (OR, 0.34), and rs438999 (OR, 0.38) were significantly associated individually and when combined on haplotype block 2E, within the logistic regression model containing other contributing parameters. 
Discussion
Previous studies have suggested that the genetic effect exerted by this region is likely to originate from a functional variant within either the CC2 or CFB gene. 6 7 Our data confirm the association of the effect observed at this locus as demonstrated by SNPs rs438999, rs1042663, and rs641153, and also rs547154 reported previously, 6 7 all of which were in strong LD (r 2 > 0.92). Undoubtedly, the nonsynonymous change caused by rs641153 (R32Q) in the CFB gene is an excellent candidate for functional importance, particularly on the basis of reduced hemolytic activity associated with this variation. 23 However, the high level of LD across this region, extending as far as the SKIV2L gene, suggests that other candidate SNPs, such as rs438999 (R151Q) located within SKIV2L, should not be excluded from functional consideration. SKIV2L was first identified from an expression library derived from bovine retinal epithelium cells 24 and is a putative RNA helicase which have been implicated in several cellular processes involving alteration of RNA secondary structure such as translation, initiation, nuclear and mitochondrial splicing, and ribosome and spliceosome assembly. R151Q is highly conserved across many of the vertebrates. The gene shows extensive similarity to the yeast Ski2p gene, which plays an important role in defense against single and double-stranded RNA viruses. 25 The yeast SKI2 is involved in an important defense mechanism against virus propagation 26 and the high level of homology between the yeast and human gene suggests that the latter may also mediate antiviral effects, particularly against RNA viruses that use nonpolyadenylated RNA during their life cycle. 27  
Because of the high level of LD shared by several SNPs with rs641153, it is important to assess the potential contribution that they may exert on the functional effect at this locus (Fig. 2) . The synonymous change observed at the CC2 SNP rs1042663 (A341A) provides no evidence to suggest a functional effect, while rs547154, rs550605, rs653414, rs497239, and rs609061 are located within CC2 introns and show no evidence of contribution toward alternative splicing. Although rs541862 is intronic within CFB, there are EST data to suggest some alternative splicing events within this region (e.g., T67767 and CV407035). There is no support for a functional effect caused by variation at rs550513 or rs403569 within introns of RDBP or rs522162 or rs760070 in the 3′ untranslated region of the same gene. 
Of interest, the initial studies of involvement of this locus reported highly significant associations for SNPs rs9332739 (CC2 E318D) and rs4151667 (CFB L9H) with suggestions that L9H, which resides in the signal peptide of the CFB gene, could exert a functional effect by modulating CFB secretion. 6 These SNPs are in high LD (r 2 = 0.95, Fig. 1 ) and as such are replicative of the effect observed. The strong association reported initially by Gold et al. 6 (OR, 0.36; P < 4 × 10−6) became barely significant in both a follow-up study 7 (OR, 0.48; P < 0.05) and the present study (OR, 0.52; P = 0.04), and when assessed within a logistic regression model with other contributing factors, was no longer statistically significant. Our findings and those of Spencer et al. 7 suggest that the effect estimates from the original study implied a much greater contribution from this particular SNP because of the large sample sizes used. 
Previous studies that have shown strong association of this locus with AMD included participants with early AMD (grades 2a–3) as well as late-stage AMD (grades 4a, 4b, and 4ab). By contrast the present study limited the case definition to the neovascular phenotype of late-stage AMD and did not include patients with early AMD or pure geographic atrophy. There is a growing awareness that neovascular AMD itself comprises a wide range of phenotypes, of which some subsets have a greater propensity for drusen formation. 28 29 A range of complement-derived products are found in drusen and activation of complement has been linked to their pathogenesis and potentially to the progression to late AMD. Therefore, it may be of interest to examine associations within new and previously conducted studies in clearly defined independent subphenotypes, such as end-stage neovascular AMD alone, or indeed within cohorts composed solely of individuals with early AMD changes (i.e., drusen and pigmentary changes) or cohorts exhibiting only geographic atrophy. Such an analysis could shed further light on the role of genetic variation in the pathogenesis of drusen and also on the pathways involved in progression to late AMD. 
The data presented herein and elsewhere 6 7 suggest that there is more than one independent but related genetic effect associated with AMD and the CC2/CFB/SKIV2L region. Our data suggest that CFB and SKIV2L are more strongly associated than CC2 and that some SNPs in CC2 show residual association emanating from their LD with CFB/SKIV2L (r 2 = 0.91). A meta-analysis of the combined cohorts assessed in the multiple studies to date may facilitate a better understanding as to which variants—in high but not perfect LD—are more likely to exert a functional effect. Of course, one cannot exclude the possibility, that although in high LD, these SNPs may exert independent or combined effects. This is similar to the effect observed within the regulators of complement activation on chromosome 1, which contains CFH and other genes involved in the complement pathway and immune response, whereby tightly clustered related genes share high LD. It is important to point out that, in line with previous studies, the minor allele has been presented as protective due to its increased frequency in control samples but until the exact biological mechanisms associated with these genotypes are elucidated, the bi-allelic portrayal of risk and protective haplotypes may be misleading. 
The authors acknowledge that the cohort size of 560 individuals used within this study is relatively small compared with some undertaken previously; nonetheless, the findings derived from this study largely replicate those reported earlier and in some ways expand on their original findings. 6 7 In summary, the genetic effect observed at this locus is significant but weaker than that exerted by either CFH or LOC387715/HTRA1 (Table 4) . Although previous studies have suggested that the functional variant associated with AMD is likely to be located within the CC2 of CFB gene, we suggest that, due to strong LD (r 2 > 0.95 for several SNPs across the region), that polymorphic variation of functional significance within SKIV2L cannot be excluded from contributing to the genetic risk associated with AMD. Until the exact functional variant(s) has been determined, it is unlikely that an appropriate target for therapeutic intervention can be developed. 
 
Figure 1.
 
Linkage disequilibrium and proposed haplotype block structure across the CC2/SKIV2L region (r 2 values shown). White: r 2 = 0; gray: 0 < r 2 < 1; black: r 2 = 1. Haplotype frequencies are as indicated for cases and control subjects.
Figure 1.
 
Linkage disequilibrium and proposed haplotype block structure across the CC2/SKIV2L region (r 2 values shown). White: r 2 = 0; gray: 0 < r 2 < 1; black: r 2 = 1. Haplotype frequencies are as indicated for cases and control subjects.
Table 1.
 
Tag SNPs across the CC2/SKIV2L Region
Table 1.
 
Tag SNPs across the CC2/SKIV2L Region
Gene SNP Name Position (bp) Major>Minor Allele Minor Allele Counts OR 95% CI P
Cases, n Controls, n
CC2 rs2734335 32001923 C>T 293 (0.46) 198 (0.41) 1.23 0.96–1.58 0.08
CC2 rs3020644 32002605 A>G 238 (0.37) 143 (0.30) 1.43 1.10–1.85 0.006
CC2 rs7746553 32003952 C>G 84 (0.13) 56 (0.12) 1.16 0.80–1.70 0.41
CC2 rs9332739 32011783 G>C 16 (0.03) 23 (0.05) 0.52 0.25–1.04 0.04
CC2 rs1042663 32013109 G>A 40 (0.06) 61 (0.13) 0.47 0.30–0.72 0.0003
CFB rs4151667* 32022003 G>A 18 (0.03) 24 (0.05) 0.63 0.32–1.23 0.16
CFB rs641153* 32022159 C>T 29 (0.05) 58 (0.12) 0.40 0.24–0.65 0.00008
CFB rs1048709 32022914 G>A 125 (0.20) 114 (0.24) 0.79 0.59–1.07 0.11
CFB rs4151651 32023593 G>A 31 (0.05) 21 (0.04) 1.13 0.62–2.10 0.67
CFB rs537160 32024379 C>T 272 (0.43) 203 (0.42) 1.03 0.81–1.33 0.78
CFB rs4151657 32025519 T>C 206 (0.32) 119 (0.25) 1.47 1.12–1.93 0.004
CFB rs1270942 32026839 T>C 119 (0.19) 78 (0.16) 1.20 0.87–1.66 0.26
CFB rs2072633 32027557 C>T 319 (0.50) 214 (0.44) 1.27 0.99–1.62 0.05
CFB rs4151672 32027809 C>T 16 (0.03) 23 (0.05) 0.52 0.25–1.04 0.04
RDBP rs4151664 32028852 C>T 37 (0.06) 20 (0.04) 1.44 0.80–2.65 0.20
RDBP rs2072632 32029454 C>T 192 (0.30) 108 (0.22) 1.51 1.14–2.00 0.003
SKIV2 rs440454 32035321 C>T 240 (0.38) 188 (0.39) 0.95 0.74–1.23 0.71
SKIV2L rs438999 32036285 T>C 41 (0.06) 61 (0.13) 0.48 0.31–0.74 0.0004
Table 2.
 
Genotype Distribution of SNPs Assessed Stratified by Disease Status
Table 2.
 
Genotype Distribution of SNPs Assessed Stratified by Disease Status
Gene SNP Name Major>Minor Allele Cases (n = 318) Controls (n = 243)
Mj Homo, n (%) Hetero, n (%) Mn Homo, n (%) Mj Homo, n (%) Hetero, n (%) Mn Homo, n (%)
CC2 rs2734335 C>T 95 (29.9) 153 (48.1) 70 (22.0) 85 (35.0) 116 (47.7) 42 (17.3)
CC2 rs3020644 A>G 120 (37.7) 158 (49.7) 40 (12.6) 122 (50.2) 98 (40.3) 23 (9.5)
CC2 rs7746553 C>G 242 (76.1) 68 (21.4) 8 (2.5) 191 (78.6) 48 (19.8) 4 (1.6)
CC2 rs9332739 G>C 303 (95.3) 14 (4.4) 1 (0.03) 219 (90.1) 24 (9.9) 0
CC2 rs1042663 G>A 280 (88.1) 36 (11.3) 2 (0.6) 185 (76.2) 55 (22.6) 3 (1.2)
CFB rs4151667* G>A 256 (94.5) 12 (4.4) 3 (1.1) 211 (89.8) 24 (10.2) 0
CFB rs641153* C>T 244 (90.0) 25 (9.2) 2 (0.8) 181 (77.0) 50 (21.3) 4 (1.7)
CFB rs1048709 G>A 207 (65.1) 97 (30.5) 14 (4.4) 141 (58.0) 90 (37.0) 12 (5.0)
CFB rs4151651 G>A 288 (90.6) 29 (9.1) 1 (0.3) 223 (91.8) 19 (7.8) 1 (0.4)
CFB rs537160 C>T 100 (31.4) 164 (51.6) 54 (17.0) 84 (34.6) 115 (47.3) 44 (18.1)
CFB rs4151657 T>C 142 (44.7) 146 (45.9) 30 (9.4) 140 (57.6) 86 (35.4) 17 (7.0)
CFB rs1270942 T>C 208 (65.4) 101 (31.8) 9 (2.8) 178 (73.3) 52 (21.4) 13 (5.3)
CFB rs2072633 C>T 77 (24.2) 163 (51.3) 78 (24.5) 76 (31.3) 118 (48.6) 49 (20.1)
CFB rs4151672 C>T 303 (95.3) 14 (4.4) 1 (0.3) 219 (90.1) 24 (9.9) 0
RDBP rs4151664 C>T 283 (89.3) 31 (9.8) 3 (0.9) 224 (92.2) 18 (7.4) 1 (0.4)
RDBP rs2072632 C>T 152 (47.8) 140 (44.0) 26 (8.2) 149 (61.3) 79 (32.5) 15 (6.2)
SKIV2 rs440454 C>T 115 (36.2) 166 (52.2) 37 (11.6) 93 (38.3) 112 (46.1) 38 (15.6)
SKIV2L rs438999 T>C 279 (87.8) 37 (11.6) 2 (0.6) 185 (76.2) 55 (22.6) 3 (1.2)
Table 3.
 
Estimated Haplotype Frequencies across the CC2/SKIV2L Region as Represented in Figure 1
Table 3.
 
Estimated Haplotype Frequencies across the CC2/SKIV2L Region as Represented in Figure 1
Block Haplotype Cases, n Controls, n OR 95% CI P
Block 1
 A CACG 259.0 (0.41) 230.0 (0.48) 0.76 0.60–0.96 0.02
 B TGCG 238.0 (0.37) 143.0 (0.30) 1.43 1.11–1.84 0.006
 C CAGG 84.0 (0.13) 56.0 (0.12) 1.16 0.81–1.67 0.41
 D TACG 39.0 (0.06) 32.0 (0.07) 0.92 0.57–1.50 0.74
 E TACC 16.0 (0.03) 23.0 (0.05) 0.52 0.27–0.99 0.04
Block 2
 A GGCGGTTCCCCCTT 188.9 (0.30) 107.0 (0.22) 1.50 1.13–1.99 0.004
 B GGCGGCCTTCCTCT 119.0 (0.19) 78.0 (0.16) 1.20 0.87–1.67 0.25
 C GGCAGTTTCCCCTT 88.0 (0.14) 86.0 (0.18) 0.75 0.53–1.04 0.08
 D GGCGGCTTTCCCCT 50.8 (0.08) 46.0 (0.10) 0.83 0.54–1.29 0.38
 E AGTGGCTTCCCCCC 37.7 (0.06) 58.9 (0.12) 0.46 0.29–0.72 0.0003
 F GGCAGCTTTCCCCT 37.0 (0.06) 20.0 (0.04) 1.44 0.80–2.61 0.20
 G GGCGGCTTCCCCCT 35.0 (0.06) 17.0 (0.04) 1.61 0.86–3.03 0.11
 H GGCGGTTCCCCCTT 30.0 (0.05) 21.0 (0.04) 1.10 0.60–2.01 0.75
 I GGCGGCCTTCCTCT 16.0 (0.02) 24.0 (0.05) 0.50 0.25–0.98 0.03
 J GGCAGTTTCCCCTT 14.0 (0.02) 11.0 (0.02) 0.97 0.41–2.31 0.94
Table 4.
 
Estimates of AMD Risk from a Logistic Regression Model
Table 4.
 
Estimates of AMD Risk from a Logistic Regression Model
OR 95% CI P
Ex-smoker vs. never smoker 1.51 0.94–2.43 0.09
Current smoker vs. never smoker 2.44 1.31–4.56 0.005
Number of CFH haplotype 1 5.68 3.05–10.6 <0.001
Number of CFH haplotype 2 6.36 3.63–11.1 <0.001
Number of CFH haplotype 3 3.27 1.80–5.94 <0.001
Number of CFH haplotype 4 2.79 1.47–5.28 0.002
LOC387715 A69S heterozygous 3.31 2.09–5.24 <0.001
LOC387715 A69S homozygous 29.7 13.1–67.3 <0.001
Number of CFB haplotype 2A 1.53 1.07–2.19 0.021
Number of CFB haplotype 2E 0.37 0.21–0.64 <0.001
Figure 2.
 
HapMap CEU download of the CC2/SKIV2L region with r 2 values shown (as indicated in Figure 1 ) and 11 SNPs sharing strong LD with rs641153 circled.
Figure 2.
 
HapMap CEU download of the CC2/SKIV2L region with r 2 values shown (as indicated in Figure 1 ) and 11 SNPs sharing strong LD with rs641153 circled.
The authors thank the patients, their families, and the control subjects who participated in the study, and Evelyn Moore, Vittorio Silvestri, and David McGibbon for technical assistance. 
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Figure 1.
 
Linkage disequilibrium and proposed haplotype block structure across the CC2/SKIV2L region (r 2 values shown). White: r 2 = 0; gray: 0 < r 2 < 1; black: r 2 = 1. Haplotype frequencies are as indicated for cases and control subjects.
Figure 1.
 
Linkage disequilibrium and proposed haplotype block structure across the CC2/SKIV2L region (r 2 values shown). White: r 2 = 0; gray: 0 < r 2 < 1; black: r 2 = 1. Haplotype frequencies are as indicated for cases and control subjects.
Figure 2.
 
HapMap CEU download of the CC2/SKIV2L region with r 2 values shown (as indicated in Figure 1 ) and 11 SNPs sharing strong LD with rs641153 circled.
Figure 2.
 
HapMap CEU download of the CC2/SKIV2L region with r 2 values shown (as indicated in Figure 1 ) and 11 SNPs sharing strong LD with rs641153 circled.
Table 1.
 
Tag SNPs across the CC2/SKIV2L Region
Table 1.
 
Tag SNPs across the CC2/SKIV2L Region
Gene SNP Name Position (bp) Major>Minor Allele Minor Allele Counts OR 95% CI P
Cases, n Controls, n
CC2 rs2734335 32001923 C>T 293 (0.46) 198 (0.41) 1.23 0.96–1.58 0.08
CC2 rs3020644 32002605 A>G 238 (0.37) 143 (0.30) 1.43 1.10–1.85 0.006
CC2 rs7746553 32003952 C>G 84 (0.13) 56 (0.12) 1.16 0.80–1.70 0.41
CC2 rs9332739 32011783 G>C 16 (0.03) 23 (0.05) 0.52 0.25–1.04 0.04
CC2 rs1042663 32013109 G>A 40 (0.06) 61 (0.13) 0.47 0.30–0.72 0.0003
CFB rs4151667* 32022003 G>A 18 (0.03) 24 (0.05) 0.63 0.32–1.23 0.16
CFB rs641153* 32022159 C>T 29 (0.05) 58 (0.12) 0.40 0.24–0.65 0.00008
CFB rs1048709 32022914 G>A 125 (0.20) 114 (0.24) 0.79 0.59–1.07 0.11
CFB rs4151651 32023593 G>A 31 (0.05) 21 (0.04) 1.13 0.62–2.10 0.67
CFB rs537160 32024379 C>T 272 (0.43) 203 (0.42) 1.03 0.81–1.33 0.78
CFB rs4151657 32025519 T>C 206 (0.32) 119 (0.25) 1.47 1.12–1.93 0.004
CFB rs1270942 32026839 T>C 119 (0.19) 78 (0.16) 1.20 0.87–1.66 0.26
CFB rs2072633 32027557 C>T 319 (0.50) 214 (0.44) 1.27 0.99–1.62 0.05
CFB rs4151672 32027809 C>T 16 (0.03) 23 (0.05) 0.52 0.25–1.04 0.04
RDBP rs4151664 32028852 C>T 37 (0.06) 20 (0.04) 1.44 0.80–2.65 0.20
RDBP rs2072632 32029454 C>T 192 (0.30) 108 (0.22) 1.51 1.14–2.00 0.003
SKIV2 rs440454 32035321 C>T 240 (0.38) 188 (0.39) 0.95 0.74–1.23 0.71
SKIV2L rs438999 32036285 T>C 41 (0.06) 61 (0.13) 0.48 0.31–0.74 0.0004
Table 2.
 
Genotype Distribution of SNPs Assessed Stratified by Disease Status
Table 2.
 
Genotype Distribution of SNPs Assessed Stratified by Disease Status
Gene SNP Name Major>Minor Allele Cases (n = 318) Controls (n = 243)
Mj Homo, n (%) Hetero, n (%) Mn Homo, n (%) Mj Homo, n (%) Hetero, n (%) Mn Homo, n (%)
CC2 rs2734335 C>T 95 (29.9) 153 (48.1) 70 (22.0) 85 (35.0) 116 (47.7) 42 (17.3)
CC2 rs3020644 A>G 120 (37.7) 158 (49.7) 40 (12.6) 122 (50.2) 98 (40.3) 23 (9.5)
CC2 rs7746553 C>G 242 (76.1) 68 (21.4) 8 (2.5) 191 (78.6) 48 (19.8) 4 (1.6)
CC2 rs9332739 G>C 303 (95.3) 14 (4.4) 1 (0.03) 219 (90.1) 24 (9.9) 0
CC2 rs1042663 G>A 280 (88.1) 36 (11.3) 2 (0.6) 185 (76.2) 55 (22.6) 3 (1.2)
CFB rs4151667* G>A 256 (94.5) 12 (4.4) 3 (1.1) 211 (89.8) 24 (10.2) 0
CFB rs641153* C>T 244 (90.0) 25 (9.2) 2 (0.8) 181 (77.0) 50 (21.3) 4 (1.7)
CFB rs1048709 G>A 207 (65.1) 97 (30.5) 14 (4.4) 141 (58.0) 90 (37.0) 12 (5.0)
CFB rs4151651 G>A 288 (90.6) 29 (9.1) 1 (0.3) 223 (91.8) 19 (7.8) 1 (0.4)
CFB rs537160 C>T 100 (31.4) 164 (51.6) 54 (17.0) 84 (34.6) 115 (47.3) 44 (18.1)
CFB rs4151657 T>C 142 (44.7) 146 (45.9) 30 (9.4) 140 (57.6) 86 (35.4) 17 (7.0)
CFB rs1270942 T>C 208 (65.4) 101 (31.8) 9 (2.8) 178 (73.3) 52 (21.4) 13 (5.3)
CFB rs2072633 C>T 77 (24.2) 163 (51.3) 78 (24.5) 76 (31.3) 118 (48.6) 49 (20.1)
CFB rs4151672 C>T 303 (95.3) 14 (4.4) 1 (0.3) 219 (90.1) 24 (9.9) 0
RDBP rs4151664 C>T 283 (89.3) 31 (9.8) 3 (0.9) 224 (92.2) 18 (7.4) 1 (0.4)
RDBP rs2072632 C>T 152 (47.8) 140 (44.0) 26 (8.2) 149 (61.3) 79 (32.5) 15 (6.2)
SKIV2 rs440454 C>T 115 (36.2) 166 (52.2) 37 (11.6) 93 (38.3) 112 (46.1) 38 (15.6)
SKIV2L rs438999 T>C 279 (87.8) 37 (11.6) 2 (0.6) 185 (76.2) 55 (22.6) 3 (1.2)
Table 3.
 
Estimated Haplotype Frequencies across the CC2/SKIV2L Region as Represented in Figure 1
Table 3.
 
Estimated Haplotype Frequencies across the CC2/SKIV2L Region as Represented in Figure 1
Block Haplotype Cases, n Controls, n OR 95% CI P
Block 1
 A CACG 259.0 (0.41) 230.0 (0.48) 0.76 0.60–0.96 0.02
 B TGCG 238.0 (0.37) 143.0 (0.30) 1.43 1.11–1.84 0.006
 C CAGG 84.0 (0.13) 56.0 (0.12) 1.16 0.81–1.67 0.41
 D TACG 39.0 (0.06) 32.0 (0.07) 0.92 0.57–1.50 0.74
 E TACC 16.0 (0.03) 23.0 (0.05) 0.52 0.27–0.99 0.04
Block 2
 A GGCGGTTCCCCCTT 188.9 (0.30) 107.0 (0.22) 1.50 1.13–1.99 0.004
 B GGCGGCCTTCCTCT 119.0 (0.19) 78.0 (0.16) 1.20 0.87–1.67 0.25
 C GGCAGTTTCCCCTT 88.0 (0.14) 86.0 (0.18) 0.75 0.53–1.04 0.08
 D GGCGGCTTTCCCCT 50.8 (0.08) 46.0 (0.10) 0.83 0.54–1.29 0.38
 E AGTGGCTTCCCCCC 37.7 (0.06) 58.9 (0.12) 0.46 0.29–0.72 0.0003
 F GGCAGCTTTCCCCT 37.0 (0.06) 20.0 (0.04) 1.44 0.80–2.61 0.20
 G GGCGGCTTCCCCCT 35.0 (0.06) 17.0 (0.04) 1.61 0.86–3.03 0.11
 H GGCGGTTCCCCCTT 30.0 (0.05) 21.0 (0.04) 1.10 0.60–2.01 0.75
 I GGCGGCCTTCCTCT 16.0 (0.02) 24.0 (0.05) 0.50 0.25–0.98 0.03
 J GGCAGTTTCCCCTT 14.0 (0.02) 11.0 (0.02) 0.97 0.41–2.31 0.94
Table 4.
 
Estimates of AMD Risk from a Logistic Regression Model
Table 4.
 
Estimates of AMD Risk from a Logistic Regression Model
OR 95% CI P
Ex-smoker vs. never smoker 1.51 0.94–2.43 0.09
Current smoker vs. never smoker 2.44 1.31–4.56 0.005
Number of CFH haplotype 1 5.68 3.05–10.6 <0.001
Number of CFH haplotype 2 6.36 3.63–11.1 <0.001
Number of CFH haplotype 3 3.27 1.80–5.94 <0.001
Number of CFH haplotype 4 2.79 1.47–5.28 0.002
LOC387715 A69S heterozygous 3.31 2.09–5.24 <0.001
LOC387715 A69S homozygous 29.7 13.1–67.3 <0.001
Number of CFB haplotype 2A 1.53 1.07–2.19 0.021
Number of CFB haplotype 2E 0.37 0.21–0.64 <0.001
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