Genetic influences on plasma CFH and CFHR1 concentrations and their role in susceptibility to age-related macular degeneration

Abstract

It is a longstanding puzzle why non-coding variants in the complement factor H (CFH) gene are more strongly associated with age-related macular degeneration (AMD) than functional coding variants that directly influence the alternative complement pathway. The situation is complicated by tight genetic associations across the region, including the adjacent CFH-related genes CFHR3 and CFHR1, which may themselves influence the alternative complement pathway and are contained within a common deletion (CNP147) which is associated with protection against AMD. It is unclear whether this association is mediated through a protective effect of low plasma CFHR1 concentrations, high plasma CFH or both. We examined the triangular relationships of CFH/CFHR3/CFHR1 genotype, plasma CFH or CFHR1 concentrations and AMD susceptibility in combined case-control (1256 cases, 1020 controls) and cross-sectional population (n = 1004) studies and carried out genome-wide association studies of plasma CFH and CFHR1 concentrations. A non-coding CFH SNP (rs6677604) and the CNP147 deletion were strongly correlated both with each other and with plasma CFH and CFHR1 concentrations. The plasma CFH-raising rs6677604 allele and raised plasma CFH concentration were each associated with AMD protection. In contrast, the protective association of the CNP147 deletion with AMD was not mediated by low plasma CFHR1, since AMD-free controls showed increased plasma CFHR1 compared with cases, but it may be mediated by the association of CNP147 with raised plasma CFH concentration. The results are most consistent with a regulatory locus within a 32 kb region of the CFH gene, with a major effect on plasma CFH concentration and AMD susceptibility.

Figure 1.

Results of GWAS of plasma CFH and CFHR1 in a normal population cohort. Variants in the CFH gene and the CNP147 deletion show the strongest genetic associations with both plasma CFH and plasma CFHR1 concentrations. (A) Manhattan plot summarizing GWAS results of plasma CFH in a Croatian cross-sectional population cohort (n = 1004) showing transformed (−log₁₀) P-values for all SNPs used in the study. A similar plot for plasma CFHR1 is shown in Supplementary Material, Figure S3. (B) GWAS results using a full mixed model showing chromosomal region 1q32 in more detail: the locations and genetic association P-values for 27 SNPs in the CFH to CFHR5 genomic region are shown. (C) The corresponding results for the plasma CFHR1 GWAS (n = 1004). (D) Genomic annotations showing the boundaries of the CFHR3/CFHR1 (CNP147) deletion (dotted vertical lines), direction of transcription (arrows) and genomic location of exons (to scale, vertical lines within the genes).

Figure 2.

Effects of CFH SNP rs6677604 and the CNP147 deletion on plasma CFH and CFHR1 concentrations, respectively. (A) Means and 95% confidence intervals of plasma CFH in the Croatian population cohort showing the association with rs6677604 SNP genotypes (GG, GA, AA). The minor allele (A) is associated with an increase in plasma CFH under an additive model [r = 0.52, P = 2.9 × 10⁻³³ (two-tailed, n = 451)]. Error bars are 95% confidence intervals of the mean (black squares). n is the number of unrelated individuals in each group. (B) The CNP147 (CFHR3/CFHR1) deletion (D) genotype and plasma CFHR1 concentrations are correlated in the Croatian cross-sectional population cohort. As the CNP147 copy number changes from 0 to 2, plasma CFHR1 concentrations are reduced accordingly [r = −0.62, P = 4.3 × 10⁻⁴² (two-tailed, n = 505)]. Error bars are 95% confidence intervals of the mean (black squares). A similar result was obtained in the SAMD series (see text).

Figure 3.

GWAS of plasma CFH and CFHR1 concentrations in the Croatian cohort (n = 1004), including both genotyped and imputed SNPs. Manhattan plots showing GWAS results for plasma CFH (A) and CFHR1 (B) using 1000 Genomes Project imputed data, in which the circles show the statistical strength of the association (−log₁₀ P-value), coloured according to the pair-wise correlations (r²) between surrounding markers and the most strongly associated variant (imputed SNP rs16840522). Variants that were poorly imputed (imputation Rsq ≤ 0.4) were filtered out and the results are shown for a 500 kb window encompassing the CFH and CFHR1-5 genes. The right-hand y-axis and purple line show recombination rates across the region, whereas the x-axis shows the position on chromosome 1 and genomic annotations showing the boundaries of the CFHR3/CFHR1 (CNP147) deletion (dotted vertical lines), direction of transcription (arrows) and genomic location of exons (to scale, vertical lines within the genes).

Figure 4.

Comparison of plasma CFH and CFHR1 concentrations in the SAMD series. Unadjusted plasma CFH (A) and CFHR1 (B) concentrations in 382 AMD cases and 201 disease-free controls. The mean and 95% confidence intervals are shown. The differences in mean value were statistically significant for plasma CFH and CFHR1 after adjusting for age, sex and smoking status. ***P < 0.001, using a t-test (two-tailed).

Figure 5.

Univariate analysis predicts that SNPs located within a 32 kb region of the CFH gene have strong (but opposite) effects on plasma CFH and CFHR1 concentration. (A) Results of univariate logistic regression analysis showing the direction and magnitude of minor allele effect sizes of 12 CFH/CFHR1 genetic variants influencing plasma CFH, plasma CFHR1 and AMD risk. Univariate linear regression coefficients are shown for the effects of minor allele genotype on plasma CFH and CFHR1, adjusted for age and sex, based on a combined analysis of SAMD control and Croatian samples. SNPs lying within a 32 kb region of CFH proposed by Sivakumaran et al. (25) to influence AMD risk are highlighted in orange. (B) Univariate logistic regression coefficients for the effects of genotype on AMD risk, adjusted for age, sex and cohort (left panel) or age, sex, cohort and three non-synonymous SNPs in CFH and CFHR1 (right panel), based on the combined analysis of SAMD and EAMD case–control series.