Multiancestral analysis of inflammation-related genetic variants and C-reactive protein in the population architecture using genomics and epidemiology study.

BACKGROUND: C-reactive protein (CRP) is a biomarker of inflammation. Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) associated with CRP concentrations and inflammation-related traits such as cardiovascular disease, type 2 diabetes mellitus, and obesity. We aimed to replicate previous CRP-SNP associations, assess whether these associations generalize to additional race/ethnicity groups, and evaluate inflammation-related SNPs for a potentially pleiotropic association with CRP.

METHODS AND RESULTS: We selected and analyzed 16 CRP-associated and 250 inflammation-related GWAS SNPs among 40 473 African American, American Indian, Asian/Pacific Islander, European American, and Hispanic participants from 7 studies collaborating in the Population Architecture using Genomics and Epidemiology (PAGE) study. Fixed-effect meta-analyses combined study-specific race/ethnicity-stratified linear regression estimates to evaluate the association between each SNP and high-sensitivity CRP. Overall, 18 SNPs in 8 loci were significantly associated with CRP (Bonferroni-corrected P<3.1×10(-3) for replication, P<2.0×10(-4) for pleiotropy): Seven of these were specific to European Americans, while 9 additionally generalized to African Americans (1), Hispanics (5), or both (3); 1 SNP was seen only in African Americans and Hispanics. Two SNPs in the CELSR2/PSRC1/SORT1 locus showed a potentially novel association with CRP: rs599839 (P=2.0×10(-6)) and rs646776 (P=3.1×10(-5)).

CONCLUSIONS: We replicated 16 SNP-CRP associations, 10 of which generalized to African Americans and/or Hispanics. We also identified potentially novel pleiotropic associations with CRP for two SNPs previously associated with coronary artery disease and/or low-density lipoprotein-cholesterol. These findings demonstrate the benefit of evaluating genotype-phenotype associations in multiple race/ethnicity groups and looking for pleiotropic relationships among SNPs previously associated with related phenotypes.