Recently, the genetic variant Y402H in the CFH (complement factor H) gene was associated with an increased risk for MI (myocardial infarction) in a prospective Caucasian cohort. In another nested case-control study, however, the CFH-Y402H variant did not carry susceptibility to MI. The aim of the present study was to test for an association between the CFH-Y402H variant and MI in a large case-control sample with a familial background for CAD (coronary artery disease). A total of 2161 individuals from the German MI family study were studied by questionnaire, physical examination and biochemical analyses. MI patients (n=1188; 51.4±8.6 years at first MI) were recruited from families with at least two members affected by MI and/or severe CAD. Spouses, sisters-in-law and brothers-in-law respectively, without MI/CAD were included as unaffected controls (n=973; 56.9±9.8 years). Genotyping was performed using a TaqMan assay. The common Y402H variant in the CFH gene was not associated with classical cardiovascular risk factors (diabetes, hypercholesterolaemia, hypertension, obesity, smoking and C-reactive protein serum levels). No association was found between the CFH-Y402H variant and susceptibility to MI. Separate analyses in both men and women revealed no gender-specific influence of the gene variant on cardiovascular risk factors or MI. This investigation was unable to replicate the association between the common CFH-Y402H variant and susceptibility to MI in our large Caucasian population which is enriched for genetic factors. We conclude that the CFH-Y402H variant has no relevant risk-modifying effect in our population.

INTRODUCTION

CAD (coronary artery disease) and MI (myocardial infarction) are the leading causes of morbidity and mortality in the Western world [1]. In epidemiological studies, factors such as smoking, obesity, high blood pressure, elevated cholesterol levels and diabetes have been identified to increase cardiovascular risk. Additionally, a strong genetic component has also been documented in the aetiology of CAD [2,3]. To unravel the underlying genes, several genome-wide analyses have been performed and have revealed chromosomal loci with linkage to CAD or MI [412]. However, currently only a few genes and variants responsible for prevalence to MI in the general population are known [9,13].

In the pathophysiology of atherosclerosis, inflammation is hypothesized to play an important role for plaque formation and its destabilization, therefore causing CAD and MI [14,15]. CFH (complement factor H), as a part of innate immunity, contributes to the process of inflammation. CFH provides binding sites for C3b, heparin, as well as sialic acids, and also interacts with CRP (C-reactive protein) [16], which has been linked in several studies to CAD, MI and stroke [17]. Recently, an association between a common CFH gene variant and increased risk for MI was reported in a prospective cohort study: an SNP (single nucleotide polymorphism) rs1061170, representing a tyrosine →histidine change at amino acid position 402 (X402H) in the CFH protein, showed hazard ratios up to 1.77 in 226 MI cases during a mean of 8.4 years of follow-up of 5237 individuals from the Rotterdam Study [18]. However, another prospective study with 335 MI cases did not show an influence of the Y402H variant on susceptibility to MI [19].

The German MI family study [4,2022] provides well-characterized MI patients as well as unrelated healthy controls for association studies. In the present study, we investigated the association between the CFH-Y402H genetic variant and susceptibility to MI and known cardiovascular risk factors, as well as CRP serum levels.

MATERIALS AND METHODS

Study sample

The study sample consisted of individuals from the German MI family study. Selection criteria have been described previously [4,20]. All participants were studied using a standardized questionnaire, physical examination and biochemical analyses at inclusion (n=2161) and 5-year follow-up (n=1780). Baseline characteristics of the 2161 investigated participants in the present study at the time point of inclusion are summarized in Table 1. The present analyses included independent MI cases (n=1188) with a positive family history (at least one additional family member who had suffered from MI or severe CAD, defined as treatment with percutaneous coronary intervention or coronary artery bypass graft). Control individuals (n=973) consisted of married-in spouses, sisters-in-law and brothers-in-law. Of these controls, 617 were confirmed to be free of any cardiovascular symptoms and events during the follow-up period. Of the remaining 356 control individuals, follow-up examination was not completed by the time of the present study. Consanguineous individuals were excluded. The research was performed in accordance with the Declaration of Helsinki (2000) of the World Medical Association. Written informed consent was obtained from all subjects, and the Ethics Committee of the University of Regensburg approved the study.

Table 1
Characteristics of study sample from the German MI family study

Values are means±S.D. with range in parentheses or percentage (n) unless indicated otherwise. To convert values for total cholesterol, HDL (high-density lipoprotein) cholesterol and LDL cholesterol into millimoles per litre, divide by 38.66. See the Materials and methods section for definitions of risk factors. *Significant difference between the groups (P<0.05).

Variable MI cases (n=1188) Controls (n=973) 
5-year follow-up available (n1163 617 
Age at first MI (years) 51.4±8.6 (24–77) – 
Age at inclusion (years) 58.7±8.6 (29–87) 56.9±9.8* (29–80) 
Gender (% male) 68.4 (813) 34.6 (337)* 
Hypercholesterolaemia (%) 82.9 (985) 39.7 (386)* 
Total cholesterol (mg/dl) 227.2±47.1 238.2±42.8* 
HDL cholesterol (mg/dl) 50.1±13.2 60.7±15.4* 
LDL cholesterol (mg/dl) 151.3±43.3 146.9±34.9* 
Triacylglycerol (mg/dl) 199.6±139.0 152.5±109.3* 
Hypertension (%) 93.7 (1113) 57.0 (554)* 
SBP (mmHg) 137.6±19.5 133.8±17.7* 
DBP (mmHg) 82.2±10.1 82.0±9.8 
Obesity (%) 22.6 (269) 18.5 (180)* 
BMI (kg/m227.5±3.6 26.7±4.2* 
Diabetes mellitus (%) 16.8 (199) 5.4 (53)* 
Smoking (%) 69.3 (823) 49.7 (484)* 
Variable MI cases (n=1188) Controls (n=973) 
5-year follow-up available (n1163 617 
Age at first MI (years) 51.4±8.6 (24–77) – 
Age at inclusion (years) 58.7±8.6 (29–87) 56.9±9.8* (29–80) 
Gender (% male) 68.4 (813) 34.6 (337)* 
Hypercholesterolaemia (%) 82.9 (985) 39.7 (386)* 
Total cholesterol (mg/dl) 227.2±47.1 238.2±42.8* 
HDL cholesterol (mg/dl) 50.1±13.2 60.7±15.4* 
LDL cholesterol (mg/dl) 151.3±43.3 146.9±34.9* 
Triacylglycerol (mg/dl) 199.6±139.0 152.5±109.3* 
Hypertension (%) 93.7 (1113) 57.0 (554)* 
SBP (mmHg) 137.6±19.5 133.8±17.7* 
DBP (mmHg) 82.2±10.1 82.0±9.8 
Obesity (%) 22.6 (269) 18.5 (180)* 
BMI (kg/m227.5±3.6 26.7±4.2* 
Diabetes mellitus (%) 16.8 (199) 5.4 (53)* 
Smoking (%) 69.3 (823) 49.7 (484)* 

Definition of risk factors

Diabetes was defined as a history of diabetes mellitus or intake of antidiabetic medication. Individuals with a former or current smoking habit were classified as smokers. Obesity was defined as a BMI (body mass index) ≥30 kg/m2. Study subjects receiving antihypertensive therapy or with a SBP (systolic blood pressure) ≥140 mmHg or DBP (diastolic blood pressure) ≥90 mmHg were classified as hypertensive. Hypercholesterolaemia was defined as LDL (low-density lipoprotein) cholesterol 160 mg/dl or the use of a lipid-lowering therapy.

Genetic analysis

Genomic DNA was isolated from whole blood samples using the PureGene DNA Purification System Blood Kit (Gentra). DNA samples were genotyped using 5′-exonuclease TaqMan® technology (Applied Biosystems) with different fluorescence-labelled probes, including non-fluorescence quencher and MGB (minor groove binder) [23,24]. For SNP rs1061170, a Custom TaqMan® SNP Genotyping Assay (Applied Biosystems) was used with: forward primer, 5′-CTT TAT TTA TTT ATC ATT GTT ATG GTC CTT AGG AAA ATG TTA TTT -3′; reverse primer, 5′-GGC AGG CAA CGT CTA TAG ATT TAC C -3′; probe 1, VIC-5′-TTT CTT CCA TAA TTT TG -3′-MGB; probe 2, FAM-5′-TTT CTT CCA TGA TTT TG -3′-MGB (VIC and FAM are the fluorophores VIC® and 6FAM™ respectively; the SNP position is indicated as bold and underlined; probes were designed on the reverse chromosomal strand).

For each genotyping experiment 10 ng of DNA was used in a total volume of 5 μl containing 1× TaqMan® Universal PCR Master Mix (Applied Biosystems). The PCR reaction and post-PCR end point plate reading was carried out according to the manufacturer's protocol using the Applied Biosystems 7900HT Real-Time PCR System. Sequence Detection System software version 2.2 (Applied Biosystems) was used to assign genotypes applying the allelic discrimination test [24]. Case and control DNA were genotyped together on the same plates. Duplicates of samples were employed to assess intraplate and interplate genotype quality. No genotyping discrepancies were detected. Assignment of genotypes was performed by a person without knowledge of the affection status.

Statistical analysis

Genotype distribution within the groups of cases and controls respectively, was compared with values predicted by Hardy–Weinberg equilibrium using the χ2-test. Differences in allele frequencies between dichotomous traits were calculated using the same method. Genotype distribution between cases and controls assuming dominant or recessive genetic models were performed using logistic regression analysis. Linear regression analysis was employed for comparison of genotype distributions with continuous variables, whereas ln(CRP) serum levels were used. The potential interaction between each traditional cardiovascular risk factor and genotype was tested in separate logistic regression analyses including the cross-product term. Prevalence ORs (odds ratios) with their 95% CIs (confidence intervals) were reported. A two-sided P value ≤0.05 was considered statistically significant. All analyses were carried out using JMP IN 5.1 (SAS Institute). Power analysis was performed applying the G*Power program [25].

RESULTS

From 2187 DNA specimens, a total of 2161 individuals were genotyped successfully and therefore the overall call rate was 98.8%. Baseline characteristics of the present study sample are shown in Table 1. The proportion of women was lower in the patient group (n=375) than in the control group (n=636). As expected, the MI patients (n=1188) had a higher prevalence of classical cardiovascular risk factors (hypercholesterolaemia, hypertension, obesity, diabetes and smoking habit) than did the control subjects (n=973). In MI patients, the mean age at first MI was 51.4±8.6 years. Anthropometric and biochemical measurements were performed at the time-point of inclusion at a mean age of 57.9±9.2 years (58.7±8.6 years for MI patients and 56.9±9.8 years for controls).

Genotype distribution of the CFH gene variant Y402H was analysed in the whole population and in sub-groups separately. The Hardy–Weinberg equilibrium was always fulfilled. Hence tests for allele frequency difference and the co-dominant model gave approximately the same P values (results not shown). Therefore in addition to the allele frequency comparisons we have reported results from dominant and recessive genetic models (Table 2). In the present study with 2161 different DNA samples, the frequency of the H allele was 36.7%. Genotype frequencies in the whole study group were 40.4%, 45.8% and 13.8% for YY, YH and HH respectively.

Table 2
CFH-Y402H genotype distribution in MI patients and controls

Unadjusted ORs are given. MAF, minor allele frequency. See the Materials and methods section for definitions of risk factors.

 MI patients Controls   Dominant model Recessive model 
 Genotype (n Genotype (n Difference in allele frequency (genotype HH + YH versus YY) (genotype HH versus YH + YY) 
 YY YH HH MAF YY YH HH MAF P value OR (95% CI) P value OR (95% CI) P value OR (95% CI) 
MI 484 540 164 0.365 390 449 134 0.368 0.83 0.99 (0.87–1.12) 0.76 0.97 (0.82–1.16) 0.98 1.00 (0.78–1.28) 
Diabetes mellitus 104 115 33 0.359 770 874 265 0.368 0.71 0.96 (0.79–1.17) 0.78 0.96 (0.74–1.26) 0.73 0.93 (0.62–1.36) 
Hypercholesterolaemia 558 637 176 0.361 316 349 122 0.377 0.29 0.93 (0.82–1.06) 0.80 0.98 (0.82–1.17) 0.08 0.80 (0.63–1.03) 
Hypertension 679 758 230 0.365 193 229 68 0.372 0.68 0.97 (0.84–1.12) 0.59 0.77 (0.86–1.16) 0.96 0.99 (0.75–1.34) 
Smoking habit 507 617 183 0.376 366 372 115 0.353 0.12 1.11 (0.97–1.26) 0.06 1.19 (1.00–1.41) 0.73 1.04 (0.81–1.35) 
Obesity 186 210 53 0.352 686 775 242 0.370 0.33 0.96 (0.79–1.08) 0.66 0.95 (0.77–1.18) 0.19 0.81 (0.58–1.10) 
 MI patients Controls   Dominant model Recessive model 
 Genotype (n Genotype (n Difference in allele frequency (genotype HH + YH versus YY) (genotype HH versus YH + YY) 
 YY YH HH MAF YY YH HH MAF P value OR (95% CI) P value OR (95% CI) P value OR (95% CI) 
MI 484 540 164 0.365 390 449 134 0.368 0.83 0.99 (0.87–1.12) 0.76 0.97 (0.82–1.16) 0.98 1.00 (0.78–1.28) 
Diabetes mellitus 104 115 33 0.359 770 874 265 0.368 0.71 0.96 (0.79–1.17) 0.78 0.96 (0.74–1.26) 0.73 0.93 (0.62–1.36) 
Hypercholesterolaemia 558 637 176 0.361 316 349 122 0.377 0.29 0.93 (0.82–1.06) 0.80 0.98 (0.82–1.17) 0.08 0.80 (0.63–1.03) 
Hypertension 679 758 230 0.365 193 229 68 0.372 0.68 0.97 (0.84–1.12) 0.59 0.77 (0.86–1.16) 0.96 0.99 (0.75–1.34) 
Smoking habit 507 617 183 0.376 366 372 115 0.353 0.12 1.11 (0.97–1.26) 0.06 1.19 (1.00–1.41) 0.73 1.04 (0.81–1.35) 
Obesity 186 210 53 0.352 686 775 242 0.370 0.33 0.96 (0.79–1.08) 0.66 0.95 (0.77–1.18) 0.19 0.81 (0.58–1.10) 

CFH-Y402H variant and susceptibility to MI and risk factors

The observed allele frequency and genotype distributions of the CFH-Y402H variant were not significantly different between the 1188 MI cases and 973 unaffected controls. In addition, no association was found between the Y402H variant and classical cardiovascular risk factors, namely diabetes, hypercholesterolaemia, hypertension, obesity and smoking habit (Table 2). We also performed adjusted analyses to exclude confounding effects of the differently distributed risk factors between MI cases and controls and found no significant association between the CFH-Y402H variant and MI (results not shown).

Additionally, no association between the CFH-Y402H variant and CRP serum levels at the time of inclusion was observed (P=0.16). Due to the limited sample size with CRP data points at the inclusion date (497 of 2161 individuals; 471 of them classified as MI patients), we used 5-year follow-up values (mean age 62.9±8.7 years), where 1284 CRP values were measured. Again, no association between the CFH-Y402H variant and CRP serum levels could be found (P=0.97). From the follow-up examination, the CRP measurements were available from 855 MI patients (mean age 63.2±8.1 years, i.e. on average of 11.8 years after first MI) and 429 controls (mean age 62.5±9.1 years) respectively. No significant association between CRP serum levels at follow-up examination and MI was observed (P=0.09). To test for gender-specific influences of the CFH-Y402H variant, we performed the analyses in both men and women separately. Neither susceptibility to MI nor cardiovascular risk factors (diabetes, hypercholesterolaemia, hypertension, obesity and smoking habit) had a gender-specific association with the Y402H variant. Likewise, no gender-related association between CFH-Y402H genotypes and CRP serum levels from the follow-up examination were found (results not shown). Additional analysis in a age- and sex-matched sub-sample (n=1200) with 300 cases and 300 controls from both men and women also showed no association between risk factors, MI and CFH-Y402H genotypes (results not shown).

DISCUSSION

Inflammation and components of innate immunity, and therefore potentially complement activation, play a major role in the pathophysiology of atherosclerosis [26]. The CFH gene, encoding a plasma protein essential for regulation of the alternative complement pathway, is a good candidate for genetic susceptibility to MI. Two recent studies have shown inconsistent results regarding the association of the CFH-Y402H variant with MI in prospective cohorts [18,19]. Thus additional data are needed to assess the impact of this variant on the genetic aetiology of MI. In the present case-control association study, the CFH-Y402H variant was neither associated with an increased risk for MI in patients with a strong familial background for cardiovascular disease nor with classical cardiovascular risk factors, such as diabetes mellitus, hypercholesterolaemia, hypertension, obesity and smoking habit (Table 2). Serum CRP levels as a non-specific indicator of inflammation has gained great importance as a risk marker in cardiovascular disease, although the magnitude of its risk stratification potential has been debated [27]. Since the CRP-binding site of the CFH protein is localized within the region of the Y402H variant [28] and its genotype potentially determines CRP levels [29], we analysed a possible association of CFH-Y402H and serum CRP levels in the present study group. At follow-up, serum CRP levels were available from 1284 participants. No association between serum CRP and CFH-Y402H genotypes was detectable, indicating that CRP serum levels are not influenced by the CFH gene variant in a measurable fashion in the present cohort. Additionally, CRP serum levels at follow-up examination were not associated with susceptibility to MI in the present study. However, it has to be noted that, in the present study group, baseline as well as follow-up values for serum CRP were measured after a mean of 7.3 and 11.8 years respectively, after suffering from MI. It is thus questionable whether these values can be related to the pathophysiology of the MI event. Within the CFH gene the amino acid position 402 is encoded in exon 9. This region is part of a strong LD (linkage disequilibrium) block that does not cover the whole gene with respect to an r2 value of 0.8 by analysing data from the HapMap project [30]. However, the Y402H variant is the only one so far having a positive association with MI in a prospective cohort study [18]. We are currently carrying out a GWA (genome-wide association) study on MI using an Affymetrix© GeneChip® Human Mapping 500K Array Set, but this chip does not contain the SNP rs1061170. Altogether, information on 18 SNPs within the 95.5-kb CFH gene will be available from these GWA studies. Recent studies on the CFH gene investigating the association between the Y402H variant and age-related macular degeneration have shown that other polymorphisms within the CFH gene also contribute to disease susceptibility independent of the Y402H genotype [31,32]. This emphasizes the relevance of analysing the Y402H variant separately. Additionally, in the background of GWA studies, future focus should be given on the remaining part of the CFH gene as a potential candidate locus for MI susceptibility.

Our non-replication of the recently reported association between the CFH-Y402H variant and MI [18] is unlikely to be a result of insufficient power. The sample size with 1188 MI cases and 973 controls has greater than 96% power to detect a weak-to-moderate gene effect with an effect size equal to 0.2 at a significance level of 0.05.

A possible limitation of the present study is the recruitment of our MI patients: only patients who survived at least one MI and had an affected sibling with CAD or MI were included. Hence there was a selection bias for both MI-surviving patients and the familial form of the disease. On the other hand, however, due to the family-based character of our patient selection, the genetic background for the aetiology of MI is high in the present study group thus enabling us to investigate even small genetic effects. Using married-in spouses or their relatives as control subjects, the risk of population stratification between affected MI patients and unaffected individuals is minimized. Additionally, a main part of the control group was validated as being free of any cardiovascular symptoms and events during the 5-year follow-up period.

In a similar manner to other candidate genes, analyses of CFH should be included in forthcoming association studies of CAD and MI to reveal more information about the contribution of this gene and its variants to the genetic aetiology of the disease.

Abbreviations

     
  • BMI

    body mass index

  •  
  • CAD

    coronary artery disease

  •  
  • CFH

    complement factor H

  •  
  • CI

    confidence interval

  •  
  • CRP

    C-reactive protein

  •  
  • DBP

    diastolic blood pressure

  •  
  • GWA

    genome-wide association

  •  
  • LDL

    low-density lipoprotein

  •  
  • MGB

    minor groove binder

  •  
  • MI

    myocardial infarction

  •  
  • OR

    odds ratio

  •  
  • SBP

    systolic blood pressure

  •  
  • SNP

    single nucleotide polymorphism

We gratefully acknowledge the excellent technical assistance of Martina Köhler, Josef Simon and Michaela Vöstner. We appreciate the support from the German MI family study team, and 15 German cardiac rehabilitation centres. This study was supported by the Deutsche Forschungsgemeinschaft (He1921/9-1, Ho1073/8-1 and Schu672/12-1), the National Genome Network (NGFN2; 01GS0417 and 01GS0418 to C. H. and H. S. respectively), the Ernst- und Berta-Grimmke-Stiftung (C. H. and H. S.), the Wilhelm-Vaillant-Stiftung (C. H., S. H. and H. S.), and the Deutsche Stiftung für Herzforschung (C. H. and H. S.).

References

References
1
Murray
C. J.
Lopez
A. D.
Global mortality, disability, and the contribution of risk factors: Global Burden of Disease Study
Lancet
1997
, vol. 
349
 (pg. 
1436
-
1442
)
2
Marenberg
M. E.
Risch
N.
Berkman
L. F.
Floderus
B.
de Faire
U.
Genetic susceptibility to death from coronary heart disease in a study of twins
N. Engl. J. Med.
1994
, vol. 
330
 (pg. 
1041
-
1046
)
3
Zdravkovic
S.
Wienke
A.
Pedersen
N. L.
Marenberg
M. E.
Yashin
A. I.
de Faire
U.
Heritability of death from coronary heart disease: a 36-year follow-up of 20966 Swedish twins
J. Intern. Med.
2002
, vol. 
252
 (pg. 
247
-
254
)
4
Broeckel
U.
Hengstenberg
C.
Mayer
B.
, et al. 
A comprehensive linkage analysis for myocardial infarction and its related risk factors
Nat. Genet.
2002
, vol. 
30
 (pg. 
210
-
214
)
5
Farrall
M.
Green
F. R.
Peden
J. F.
, et al. 
Genome-wide mapping of susceptibility to coronary artery disease identifies a novel replicated locus on chromosome 17
PLoS Genet.
2006
, vol. 
2
 pg. 
e72
 
6
Francke
S.
Manraj
M.
Lacquemant
C.
, et al. 
A genome-wide scan for coronary heart disease suggests in Indo-Mauritians a susceptibility locus on chromosome 16p13 and replicates linkage with the metabolic syndrome on 3q27
Hum. Mol. Genet.
2001
, vol. 
10
 (pg. 
2751
-
2765
)
7
Harrap
S. B.
Zammit
K. S.
Wong
Z. Y.
, et al. 
Genome-wide linkage analysis of the acute coronary syndrome suggests a locus on chromosome 2
Arterioscler. Thromb. Vasc. Biol.
2002
, vol. 
22
 (pg. 
874
-
878
)
8
Hauser
E. R.
Crossman
D. C.
Granger
C. B.
, et al. 
A genomewide scan for early-onset coronary artery disease in 438 families: the GENECARD Study
Am. J. Hum. Genet.
2004
, vol. 
75
 (pg. 
436
-
447
)
9
Helgadottir
A.
Manolescu
A.
Thorleifsson
G.
, et al. 
The gene encoding 5-lipoxygenase activating protein confers risk of myocardial infarction and stroke
Nat. Genet.
2004
, vol. 
36
 (pg. 
233
-
239
)
10
Pajukanta
P.
Cargill
M.
Viitanen
L.
, et al. 
Two loci on chromosomes 2 and X for premature coronary heart disease identified in early- and late-settlement populations of Finland
Am. J. Hum. Genet.
2000
, vol. 
67
 (pg. 
1481
-
1493
)
11
Samani
N. J.
Burton
P.
Mangino
M.
, et al. 
A genomewide linkage study of 1933 families affected by premature coronary artery disease: The British Heart Foundation (BHF) Family Heart Study
Am. J. Hum. Genet.
2005
, vol. 
77
 (pg. 
1011
-
1020
)
12
Wang
Q.
Rao
S.
Shen
G. Q.
, et al. 
Premature myocardial infarction novel susceptibility locus on chromosome 1P34-36 identified by genomewide linkage analysis
Am. J. Hum. Genet.
2004
, vol. 
74
 (pg. 
262
-
271
)
13
Watkins
H.
Farrall
M.
Genetic susceptibility to coronary artery disease: from promise to progress
Nat. Rev. Genet.
2006
, vol. 
7
 (pg. 
163
-
173
)
14
Hansson
G. K.
Inflammation, atherosclerosis, and coronary artery disease
N. Engl. J. Med.
2005
, vol. 
352
 (pg. 
1685
-
1695
)
15
Ross
R.
Atherosclerosis: an inflammatory disease
N. Engl. J. Med.
1999
, vol. 
340
 (pg. 
115
-
126
)
16
Rodriguez de Cordoba
S.
Esparza-Gordillo
J.
Goicoechea de Jorge
E.
Lopez-Trascasa
M.
Sanchez-Corral
P.
The human complement factor H: functional roles, genetic variations and disease associations
Mol. Immunol.
2004
, vol. 
41
 (pg. 
355
-
367
)
17
Wilson
A. M.
Ryan
M. C.
Boyle
A. J.
The novel role of C-reactive protein in cardiovascular disease: risk marker or pathogen
Int. J. Cardiol.
2006
, vol. 
106
 (pg. 
291
-
297
)
18
Kardys
I.
Klaver
C. C.
Despriet
D. D.
, et al. 
A common polymorphism in the complement factor H gene is associated with increased risk of myocardial infarction: the Rotterdam Study
J. Am. Coll. Cardiol.
2006
, vol. 
47
 (pg. 
1568
-
1575
)
19
Zee
R. Y.
Diehl
K. A.
Ridker
P. M.
Complement factor H Y402H gene polymorphism, C-reactive protein, and risk of incident myocardial infarction, ischaemic stroke, and venous thromboembolism: a nested case-control study
Atherosclerosis
2006
, vol. 
187
 (pg. 
332
-
335
)
20
Baessler
A.
Hengstenberg
C.
Holmer
S.
, et al. 
Long-term effects of in-hospital cardiac rehabilitation on the cardiac risk profile. A case-control study in pairs of siblings with myocardial infarction
Eur. Heart J.
2001
, vol. 
22
 (pg. 
1111
-
1118
)
21
Fischer
M.
Broeckel
U.
Holmer
S.
, et al. 
Distinct heritable patterns of angiographic coronary artery disease in families with myocardial infarction
Circulation
2005
, vol. 
111
 (pg. 
855
-
862
)
22
Reinhard
W.
Holmer
S. R.
Fischer
M.
, et al. 
Association of the metabolic syndrome with early coronary disease in families with frequent myocardial infarction
Am. J. Cardiol.
2006
, vol. 
97
 (pg. 
964
-
967
)
23
de Kok
J. B.
Wiegerinck
E. T.
Giesendorf
B. A.
Swinkels
D. W.
Rapid genotyping of single nucleotide polymorphisms using novel minor groove binding DNA oligonucleotides (MGB probes)
Hum. Mutat.
2002
, vol. 
19
 (pg. 
554
-
559
)
24
Livak
K. J.
Allelic discrimination using fluorogenic probes and the 5′ nuclease assay
Genet. Anal.
1999
, vol. 
14
 (pg. 
143
-
149
)
25
Erdfelder
E.
Faul
F.
Buchner
A.
GPOWER: a general power analysis program
Behav. Res. Methods Instrum. Comput.
1996
, vol. 
28
 (pg. 
1
-
11
)
26
Oksjoki
R.
Kovanen
P. T.
Pentikainen
M. O.
Role of complement activation in atherosclerosis
Curr. Opin. Lipidol.
2003
, vol. 
14
 (pg. 
477
-
482
)
27
Danesh
J.
Wheeler
J. G.
Hirschfield
G. M.
, et al. 
C-reactive protein and other circulating markers of inflammation in the prediction of coronary heart disease
N. Engl. J. Med.
2004
, vol. 
350
 (pg. 
1387
-
1397
)
28
Discipio
R. G.
Daffern
P. J.
Schraufstatter
I. U.
Sriramarao
P.
Human polymorphonuclear leukocytes adhere to complement factor H through an interaction that involves αMβ2 (CD11b/CD18)
J. Immunol.
1998
, vol. 
160
 (pg. 
4057
-
4066
)
29
Johnson
P. T.
Betts
K. E.
Radeke
M. J.
Hageman
G. S.
Anderson
D. H.
Johnson
L. V.
Individuals homozygous for the age-related macular degeneration risk-conferring variant of complement factor H have elevated levels of CRP in the choroid
Proc. Natl. Acad. Sci. U.S.A.
2006
, vol. 
103
 (pg. 
17456
-
17461
)
30
The International HapMap Consortium
A haplotype map of the human genome
Nature
2005
, vol. 
437
 (pg. 
1299
-
1320
)
31
Li
M.
Atmaca-Sonmez
P.
Othman
M.
, et al. 
CFH haplotypes without the Y402H coding variant show strong association with susceptibility to age-related macular degeneration
Nat. Genet.
2006
, vol. 
38
 (pg. 
1049
-
1054
)
32
Maller
J.
George
S.
Purcell
S.
, et al. 
Common variation in three genes, including a noncoding variant in CFH, strongly influences risk of age-related macular degeneration
Nat. Genet.
2006
, vol. 
38
 (pg. 
1055
-
1059
)