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Human Molecular Genetics Advance Access originally published online on June 18, 2007
Human Molecular Genetics 2007 16(16):1986-1992; doi:10.1093/hmg/ddm146
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Protective effect of complement factor B and complement component 2 variants in age-related macular degeneration

Kylee L. Spencer1, Michael A. Hauser3, Lana M. Olson1, Silke Schmidt3, William K. Scott5, Paul Gallins5, Anita Agarwal2, Eric A. Postel4, Margaret A. Pericak-Vance5 and Jonathan L. Haines1,*

1 Center for Human Genetics Research, 2 Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, USA, 3 Center for Human Genetics, 4 Department of Ophthalmology, Duke University Eye Center, Duke University Medical Center, Durham, NC, USA and 5 Institute for Human Genomics, University of Miami, Miami, FL, USA

* To whom correspondence should be addressed. Tel: +1 6153435851; Fax: +1 6153438619; Email: Jonathan{at}chgr.mc.vanderbilt.edu

Received April 16, 2007; Revised June 5, 2007; Accepted June 5, 2007


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Age-related macular degeneration (AMD) is a devastating disorder of the central retina, causing significant visual impairment for 7.5 million elderly Americans. Abnormal regulation of the complement system likely caused by the Y402H polymorphism in the complement factor H gene is a recognized risk factor for AMD, as is the A69S variant in the poorly characterized LOC387715 gene. Recently, polymorphisms in the factor B (CFB) and complement component 2 (CC2) genes were associated with decreased susceptibility to AMD. To validate this association in independent family-based and case–control Caucasian data sets, we genotyped two single-nucleotide polymorphisms (SNPs) in CC2 and four SNPs in CFB. The R32Q variant of CFB was significantly associated with protection from AMD in the family-based data set (P = 0.025). Three SNPs in CC2 and CFB were strongly associated with decreased risk of AMD in the case–control data set (CC2 E318D: P = 0.02; CC2 rs547154: P = 9 x 10–6; and CFB R32Q P = 2 x 10–5). The minor alleles at CC2 rs547154 and CFB R32Q are present in 4% of cases versus 10% of controls, and as these SNPs are in strong linkage disequilibrium (r2=0.92), these results likely represent the same protective signal. After controlling for age, Y402H, A69S and smoking, the effect of CFB R32Q remained quite strong (OR 0.21, 95% confidence interval 0.11–0.39; P < 10–4). Likelihood ratio testing and conditional analyses in the case–control data set suggest that a weaker, independent protective effect exists for CC2 E318D.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Significant advances have been made in the field of age-related macular degeneration (AMD) genetics with the identification of risk and protective haplotypes in the complement factor H (CFH) gene on chromosome 1 (1–3) and the refined localization of the AMD risk gene on chromosome 10q26. Polymorphisms in both the poorly characterized LOC387715 gene and the serine protease HTRA1 have been strongly associated with AMD and replicated in multiple populations (4–8). As these genes are adjacent on chromosome 10 and extensive linkage disequilibrium (LD) exists between them, identification of one or more ‘functional’ variants will be difficult, but in the meantime the tagging SNP A69S in LOC387715 can be used as a proxy for the risk contributed by this genomic region. These associations continue to be refined as our knowledge of the interplay between genetic and lifestyle risk factors, such as cigarette smoking and increased body mass index, continues to grow (9–11).

CFH inhibits activation of the alternative complement cascade, and thereby avoids injury to self-tissues by preventing an excessive immune response. Inflammatory processes play a central role in AMD by contributing to the formation of drusen (12), a hallmark feature of AMD in which deposits of extracellular debris form between Bruch's membrane and the retinal pigment epithelium. Both risk and protective haplotypes in the CFH gene modify susceptibility to AMD (1–3,13). Therefore, it is only logical to ask whether polymorphisms in other genes within the alternative complement pathway also impart either risk or protection for AMD.

Complement factor B (CFB) aids initiation of the alternative complement cascade, and complement component 2 (CC2) activates the classical component pathway. CC2 is paralogous to CFB and resides adjacent to CFB on chromosome 6. Gold et al. (14) demonstrated that variation in both CFB and CC2 is associated with decreased risk for AMD in two independent cohorts with a total of approximately 900 cases and 400 matched controls. Because of the strong LD within the CFB/CC2 region, they were unable to completely determine which SNP(s) in these genes are the functional variant(s). Specifically, L9H in CFB, which is in strong LD with CC2 E318D, and CFB R32Q, which is in strong LD with an intronic SNP of CC2, were all highly protective for AMD in their study. Using stepwise logistic regression, Maller et al. (15) have excluded the intronic SNP of CC2 in favor of CFB R32Q. This agrees well with functional data showing that the CFB 32Q variant has reduced hemolytic activity compared with the CFB 32R variant and the hypothesis that reduced CFB activity would prevent the chronic complement response that is thought to cause formation of drusen and progression to AMD. However, ambiguity remains as to whether CFB L9H, CC2 E318D or another variant in LD with these SNPs represents a second protective locus in this region. Further dissection of the CFB/CC2 region on chromosome 6 will be essential for synthesizing a complete picture of the contribution of these loci to AMD genetic susceptibility.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
To characterize the contribution of CC2 and/or CFB to AMD susceptibility, we genotyped six SNPs in these genes in a family-based data set with 559 individuals from 223 families, and a completely independent data set of 698 cases and 282 unrelated controls (Table 1). All SNPs were in Hardy–Weinberg equilibrium (HWE) in the family data set and in controls from the case–control data set.


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  		Table 1. Characteristics of the study populations

 
We observed weak evidence of association of the CFB R32Q SNP with decreased susceptibility to AMD in the family-based data set, both in the overall analysis and when examining only neovascular AMD cases [Association in the Presence of Linkage (APL): P = 0.025 (overall); APL: P = 0.014 (neovascular only); Table 2]. In contrast, we saw strong association of SNPs in both CC2 and CFB in the case–control data set in both the overall and neovascular AMD only analyses (Tables 3 and 4). As CC2 IVS10 and CFB R32Q are in strong LD (r2=0.92; Fig. 1), we believe that these two SNPs are capturing the same protective effect. After controlling for age, CFH Y402H and LOC387715 A69S, the minor alleles at both CC2 E318D and CFB R32Q were protective for AMD (model 1, Table 5), although the association was much stronger for CFB R32Q than CC2 E318D (E318D: P = 0.048, odds ratio = 0.48, 95% confidence interval 0.24–0.99; R32Q: P < 0.0001, odds ratio = 0.29, 95% confidence interval 0.17–0.48).


Figure 1
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  		Figure 1. LD for SNPs in CC2 and CFB on chromosome 6 in the case–control data set. Darker shading represents stronger LD, and the r2 values are shown inside each square. Results in the family-based data set are quite similar (data not shown).

 


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  		Table 2. APL association results in the family-based data set

 


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  		Table 3. Allelic and genotypic association results in the case–control data set comparing all AMD cases (grades 3–5) to all controls (grades 1 and 2)

 


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  		Table 4. Allelic and genotypic association results in the case–control data set comparing neovascular AMD cases (grade 5) to controls (grade 1)

 


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  		Table 5. Logistic regression analyses1

 
CFB R32Q and CC2 E318D do not exhibit strong LD (r2=0.002; Fig. 1), and the results of model 1 in the logistic regression analysis suggest that these two SNPs may be exerting independent effects. Conditional analyses were used to further tease this apart. Conditioning on the C allele of CC2 E318D, the A allele of CFB R32Q is more frequent in controls than would be expected by chance and vice versa (Table 6). For example, in carriers of the C allele of CC2 E318D, the frequency of the A allele of CFB R32Q is much higher in controls than cases (8% versus 0%, P = 0.032). In individuals that do not carry the C allele of CC2 E318D, the A allele of CFB R32Q is more frequent in controls than cases (11% versus 5%, P = 2.3 x 10–5). Therefore, CFB R32Q is associated with AMD regardless of genotype at CC2 E318D. In carriers of the A allele of CFB R32Q, the frequency of the C allele of CC2 E318D is higher in controls than cases (4% versus 0%, P = 0.039) and in individuals that do not carry the A allele of CFB R32Q, the C allele of CC2 E318D is again more frequent in controls than cases (5% versus 3%, P = 0.034). Therefore, CC2 E318D is associated with AMD regardless of genotype at CFB R32Q and vice versa, suggesting independent effects of both loci.


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  		Table 6. Conditional analyses for CC2 E318D and CFB R32Q

 


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  		Table 7. Comparison of logistic regression models

 
To further test the independent protective effects of CC2 E318D and CFB R32Q, we compared the fit of logistic regression models with and without these variables with a likelihood ratio test. Specifically, after adjusting for age, CFH Y402H and LOC387715 A69S, a model including CFB R32Q fit the data significantly better than the model without CFB R32Q (P < 0.001, model 2 versus model 3; Table 7). Furthermore, after adjusting for age, CFH Y402H, LOC387715 A69S and CFB R32Q, a model including CC2 E318D fit the data significantly better than the model without CC2 E318D (P = 0.049, model 1 versus model 2, Table 7).

Lastly, we examined the effect of these two SNPs in the context of smoking, in addition to controlling for age, CFH Y402H and LOC387715 A69S. Owing to the incomplete participant response to the smoking questions on our lifestyle questionnaire, the sample size of this analysis was reduced from 612 cases and 242 controls with complete age and genotype data to 400 cases and 204 controls with complete data for age, genotypes and smoking history. The effect of CFB R32Q remained strong in this analysis, but the evidence for association of CC2 E318D was much diminished (CFB R32Q: P < 0.0001, odds ratio = 0.21, 95% confidence interval 0.11–0.39; CC2 E318D: P = 0.26, odds ratio= 0.60, 95% confidence interval 0.25–1.47, Table 5; model 4). Estimating the odds ratio for CFB R32Q controlling for age, CFH Y402H, LOC387715 A69S and smoking without including CC2 E318D did not substantially alter the magnitude of this effect (CFB R32Q: P < 0.0001, odds ratio = 0.21, 95% confidence interval 0.11–0.39, model 4, Table 5). When we compared models with and without CC2 E318D and CFB R32Q in our reduced sample with complete smoking data after adjusting for age, CFH Y402H and LOC387715 A69S, the addition of CFB R32Q significantly improved model fit (P < 0.001, model 5 versus model 6; Table 7), but the effect of CC2 E318D was no longer statistically significant in the smaller sample (P = 0.26, model 4 versus model 5; Table 7).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
We have replicated the association of CFB R32Q and CC2 E318D in our case–control data set. The conditional analyses and results of likelihood ratio testing suggest that these two polymorphisms exert independent, protective effects. However, after controlling for age, CFH Y402H, LOC387715 A69S and smoking in logistic regression model 2, the effect of CC2 E318D was no longer statistically significant. This may be caused by reduced power to detect an effect in the smaller data set with complete smoking covariate data. Reassuringly, CFB R32Q, which showed much stronger evidence for association in the allelic and genotypic association tests and in the conditional analysis, was still strongly associated with reduced AMD susceptibility after controlling for age, CFH Y402H, LOC387715 A69S and smoking. The magnitude of this effect was not altered by including or excluding CC2 E318D variant from the model, which further suggests an independent effect of these two loci. However, statistical analyses can only shed so much light on genetic associations, and functional studies will be needed to unravel the mechanism behind these results.

To our knowledge, we are the first to study the association of CFB and CC2 polymorphisms with AMD in a family-based data set. We observed weak evidence for association of CFB R32Q in the families, but did not see an association of CC2 E318D. Lack of replication of CC2 E318D in the families could be a true negative result, but it is most likely due to reduced power, since this variant has a frequency of only 2.6% in our families. One concern with any positive association result is the possibility that it derives from population substructure. One of the motivations for family-based studies is their insensitivity to population substructure, and our positive result for CFB R32Q in the family data set provides further support that population stratification is not the cause of the observed association.

Finally, we have referred to the association of these variants with AMD as ‘protective’ because the minor alleles of these polymorphisms are more frequent in controls than cases and to be consistent with previously published reports. However, since the actual biological mechanism by which these variants are acting to influence AMD susceptibility has not yet been described, and it is possible that the major allele may be acting as a risk allele, it may be more correct to term these results ‘inverse associations’ rather than ‘protective effects’ until more is known about the underlying pathophysiology.

In summary, polymorphisms in CC2 and CFB are associated with protection from AMD, but future functional studies will be needed to confirm that these variants are the source of the decreased AMD susceptibility.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Study populations
Multiplex and singleton families and an independent data set of unrelated cases and controls, all of Caucasian, non-Hispanic descent, were ascertained at Vanderbilt University Medical Center (VUMC) and Duke University Medical Center (DUMC). All patients and controls received an eye exam and had stereoscopic fundus photographs graded according to a modified version of the Age-Related Eye Disease Study (AREDS) grading system as described elsewhere (16,17). Briefly, grades 1 and 2 represent controls. Grade 1 controls have no evidence of drusen or small non-extensive drusen without pigmentary abnormalities, whereas grade 2 controls may show signs of either extensive small drusen or non-extensive intermediate drusen and/or pigmentary abnormalities. Grade 3 AMD cases have extensive intermediate drusen or large, soft drusen with or without drusenoid retinal pigment epithelial detachment. Grade 4 AMD cases exhibit geographic atrophy and grade 5 individuals have exudative AMD, which includes non-drusenoid retinal pigment epithelial detachment, choroidal neovascularization and subretinal hemorrhage or disciform scarring. Individuals were classified according to status in the more severely affected eye. Table 1 describes additional features of the data sets, including age of exam, gender and a brief description of family structure for the family-based data set. Approval for the study was obtained from the appropriate institutional review boards at VUMC and DUMC, all study participants gave informed consent, and this research adhered to the tenets of the Declaration of Helsinki.

Genotyping
Two SNPs in CC2 and four SNPs in CFB were selected for genotyping to validate the previously described association (14,15). SNPs rs9332739 (CC2 E318D), rs547154 (CC2 IVS10), rs1048709 (CFB R150R) and rs2072633 (CFB IVS17) were genotyped using Taqman Assays on Demand from Applied Biosystems. rs12614 (CFB R32W) and rs641153 (CFB R32Q) posed a more difficult problem for genotyping, as these SNPs are adjacent to each other on chromosome 6 (basepairs 32,022,158 and 32,022,159, respectively, NCBI Build 36). Taqman probes were designed to bind the four possible haplotypes for these two SNPs (CG, TG, CA and TA). Each individual was then assayed with all possible combinations of the four probes (CA vic-labelled probe, TA fam-labelled probe; CG vic-labelled probe, CA fam-labelled probe; CG vic-labelled probe, TA fam-labelled probe; CG vic-labelled probe, TG fam-labelled probe; TG vic-labelled probe, CA fam-labelled probe; TG vic-labelled probe, TA fam-labelled probe), and the genotype determined from these results. Quality control samples were duplicated within and between plates, and we required that 95% of individuals assayed received a genotype for SNPs to be used in further analyses.

Statistical analyses
We verified that all SNPs were in HWE and examined the LD between SNPs in both the family-based and case–control data sets using Haploview software (18). HWE and LD in the case–control data set were examined both in the overall data set and separately in cases and controls. The results were similar in each analysis (data not shown). We used only founders to estimate allele frequencies in the family-based data set, except when a family did not have any founders genotyped. For those families, one individual was selected at random to contribute the allele frequency calculation. We tested SNPs for association in the family-based data set using the APL method (19,20). In the case–control data set, we assessed association of each SNP with AMD using a {chi}2 test for allelic association and a 2 x 3 contingency table likelihood ratio test for genotypic association. We also used logistic regression in the case–control data set to estimate the effects of CC2 E318D and CFB R32Q after controlling for age, smoking status, the Y402H variant in complement factor H and the A69S variant in LOC387715. Smokers (those who had smoked at least 100 cigarettes) were coded as ‘1’ and non-smokers (those who had smoked fewer than 100 cigarettes over their lifetime) were coded as ‘0’. CFH Y402H and LOC387715 A69S genotypes were coded as ‘1’ for heterozygotes or homozygotes of the risk allele (Y402H risk allele=C, A69S risk allele=T) and ‘0’ for the non-risk allele homozygotes. To conduct the conditional analyses, the case–control data set was simply divided by minor allele carrier status for CC2 E318D or CFB R32Q, and the difference in allele frequency between cases and controls was tested by a {chi}2 test, or Fisher's exact test when the observed count in any cell was less than 5. We compared logistic regression models by calculating a likelihood ratio statistic (twice the difference in the deviance of the full models compared with the reduced logistic regression models) and determined significance by comparing the LRT to a {chi}2 distribution with one degree of freedom. All case–control analyses were performed using either SAS v9.1 software (SAS Institute, Cary, NC) or Intercooled Stata 9.1 (StataCorp LP, College Station, TX).


   ACKNOWLEDGEMENTS
 
We thank the patients, their families and the controls who participated in the study, and also M. de la Paz, M. Klein, J. Caldwell, R. Domurath, K. Haynes, V. Mitchell, M. Shaw, J.D.M. Gass and J. Galloway for diligently working to enroll them. We also thank the following clinics and clinicians for referring individuals to the study: Southern Retina, LLC (C. Harris); Vitreo-Retinal Surgeons (M. Duan and C. Devine); Georgia Retina, P.C., and The Retina Group of Washington. This work was supported by grants EY12118 (to M.A.P.-V. and J.L.H.) and EY015216 (to S.S.) from the NIH/National Eye Institute, and grant M01 RR-00095 from the NIH/National Center for Research Resources (to Vanderbilt University).

Conflict of Interest statement:No conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 

  1. Haines J.L., Hauser M.A., Schmidt S., Scott W.K., Olson L.M., Gallins P., Spencer K.L., Kwan S.Y., Noureddine M., Gilbert J.R., et al. Complement factor H variant increases the risk of age-related macular degeneration. Science (2005) 308:419–421.[Abstract/Free Full Text]

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J. L. Haines, K. M. Spencer, and M. A. Pericak-Vance
Bringing the genetics of macular degeneration into focus
PNAS, October 23, 2007; 104(43): 16725 - 16726.
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