Twenty-two biomarker traits (plus 4 additional CRP traits) were analyzed in 1012 Offspring participants, on log-transformed multivariable-adjusted residuals as outlined in Table (minimum-maximum per phenotype n = 507–1008). The phenotypes were collected at various Framingham Offspring examinations from cycles 2 to 7. At examination cycles 2 and 7 the mean age of the participants with both phenotype and genotype data was 41 ± 10 and 59 ± 10 years, and 51.2% and 51.1% were women, respectively. For details of biomarker phenotype-genotype association refer to http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007
There were 58 SNPs associated with biomarker concentrations with a p < 10-6 by GEE. The 25 most statistically significant GEE associations sorted by p-value, listed with their corresponding FBAT p-value are shown in Table . MCP1 concentrations were associated with rs2494250 (p = 1*10-14) and rs4128725 (p = 3.68*10-12), both on chromosome 1, near the FCER1A and the OR10J1 genes, respectively. CRP concentrations averaged over 3 examinations (about 20 years) were associated with rs2794520 (p = 2.83*10-8) and rs2808629 (p = 3.19*10-8).
Top genetic associations with biomarkers based on the lowest p value for GEE test (2a), FBAT (2b), and Linkage (2c)
We estimated the amount of variability in biomarker concentrations explained by the 4 most statistically significant SNPs in the GEE model using a pseudo measure of R2
based on log-likelihood estimates [36
]. The two most statistically significant GEE SNPs explained about 7% and 4% of the variability in MCP1 concentrations (R2
= 0.070 for rs2494250 and R2
= 0.043 for rs4128725); for CRP concentrations averaged over examinations 2, 6, and 7 the two most statistically significant GEE SNPs explained 2.3% of the variability [R2
= 0.023 for rs2794520 and rs2808629) [36
]. We also examined the linkage disequilibrium between the most statistically significant GEE SNPs: rs2494250 and rs4128725 had a D' = 0.724 and an r2
= 0.196, whereas rs2794520 and rs2808629 served as perfect proxies for each other (D' = 1; r2
With FBAT, 11 SNPs were associated with biomarker concentrations with a p < 10-6. The two most statistically significant SNPs for FBAT were the same two SNPs observed with GEE: MCP1 concentrations were significantly associated with rs4128725, p = 3.28*10-8, and rs2494250, p = 3.55*10-8 (Table ). In addition, B-type natriuretic peptide (rs437021, p = 1.01*10-6) and Vitamin K% undercarboxylated osteocalcin (rs2052028, p = 1.07*10-6) also were nominally statistically significantly associated.
In Table we list the magnitude and location of LOD scores > 2.5 observed for the circulating biomarker traits. Because we were concerned that some of the LOD scores might be inflated by individuals with extreme marker concentrations, we reanalyzed the LOD scores on Winsorized residuals. The peak Winsorized LOD scores observed were for the biomarkers MCP1 (4.38, chromosome 1), and CRP (3.23, chromosome 10; 3.28, chromosome 1). Of note the 1.5 LOD support intervals for the linkage peaks on chromosome 1 included the SNPs significantly associated with MCP1 and CRP reported above (GEE model).
In an effort to potentially uncover genetic pleiotropy we display in Table two ways to synthesize findings across phenotypes. We examined 3 correlated inflammatory biomarker phenotypes, interleukin-6, CRP and fibrinogen, and report SNPs that were significantly associated with all 3 phenotypes by GEE or FBAT at p < 0.01 (Table ). We also examined phenotypes within a specific biomarker category including CRP over multiple examinations, liver function tests and vitamin concentrations (nutrients involved in bone health [37
]), and display in Table SNPs significant by either FBAT or GEE at a p < 0.01 for all of the phenotypes in a given phenotype cluster.
In Table we compared our data with previously reported phenotype-genotype associations in the published literature on systemic biomarker concentrations: bilirubin concentrations (TA repeat in UGT1A1
]; CRP (CRP
], intercellular adhesion molecule-1 (ICAM1
], interleukin-6 (IL6
], and MCP1 (CCL2
= MCP1 gene
]). Unfortunately, there were no SNPs within 60 KB of the ICAM1
gene on the Affymetrix 100K chip. There was no association between bilirubin concentrations and 1 SNP within 30 kb (rs741159) + 2 more SNPs within 50 kb (rs726017 and rs6752792) of a previously reported TA repeat in UGT1A1
. Additionally, there was no association between interleukin-6 concentrations and SNPs in the IL6
region despite one SNP in high LD (linkage disequilibrium; r2
= 0.819) with the previously reported rs1800795 (-174G/C) SNP. Similarly, we did not observe an association between MCP1 concentrations and SNPs in the CCL2
region, despite one SNP with a high r2
(0.956) with the SNP previously reported in the literature. For CRP concentrations, we had 2 SNPs in perfect LD with rs1205, and we observed strong evidence for replication. However, it should be noted that this association has been previously reported by Framingham investigators in unrelated participants [32
]. Similarly, rs431568, which is in high LD (r2
= 0.83) with 2 previously associated SNPs (rs3116653 and rs1417938), was highly associated with many of the CRP phenotypes.
Comparison with the prior literature