Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders

Abstract

C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10^-8). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.

Keywords: C-reactive protein; DEPICT; Mendelian randomization; coronary artery disease; genome-wide association study; inflammation; inflammatory disorders; schizophrenia; system biology.

Figure 1

Genome-wide Genetic Correlation between Serum Amounts of CRP and Different Phenotypes and Clinical Diseases The genetic correlation and its standard error are estimated by LDSC analysis. Abbreviations are as follows: ADHD, attention deficit and hyperactivity disorder; FEV1, forced expiratory volume in 1 s; FVC, forced vital capacity; HOMA-B, homeostatic model assessment β cell function; HOMA-IR, homeostatic model assessment insulin resistance; and HbA1C, hemoglobin A1c.

Figure 2

Results of the DEPICT Functional Annotation Analysis Each node represents an exemplar gene set from affinity-propagation clustering, and links represent corresponding Pearson correlation coefficients between individual enriched gene sets (only the links with r > 0.3 are shown). DEPICT GSE P refers to the gene-set enrichment p value for that DEPICT gene-set generated by DEPICT. As an example, outlined are the individual gene sets inside two clusters (“inflammatory response” and “negative regulation of peptidase activity”).

Figure 3

Heatmap Representing the Results of DEPICT Functional Annotation Analysis Each row represents enriched (FDR < 0.05) gene sets, and each column represents prioritized (FDR < 0.05) genes. Colors on the heatmap represent each gene’s contribution to gene-set enrichment (GSE; depicted as a Z score; only the top ten highest Z scores per gene set are visualized). Sidebars represent p values for GWAS, GSE, and gene prioritization (nominal p value on log₁₀ scale). The top ten gene sets per annotation category are visualized. Abbreviations are as follows: GO, Gene Ontology; KE, Kyoto Encyclopedia of Gene and Genomes; RE, REACTOME pathways; MP, mouse phenotype; and PI, protein-protein interaction.