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 3 10 À8). 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.

Genetic drug target validation using Mendelian randomization

“…This standard approach has often been applied regardless of the position of the exposure of interest in the biological pathway connecting genetic variation to disease risk. For example, an MR analysis investigating the causal relevance of C-reactive protein in a range of disease end-points 17 , and another between educational attainment on cardiovascular disease risk were conducted using broadly similar methodology and assumptions 18 .…”

Bayesian Genome-wide TWAS method to leverage both cis- and trans- eQTL information through summary statistics

“…We noted that the association between the GReX values of ZC3H12B and AD phenotypes was completely driven by trans-eQTL, while the association between the GReX values of KCTD12 and ߚ -amyloid load was driven by both cis-and trans-eQTL. Four of the top driven trans-eQTL (rs12721051, rs4420638, rs56131196, rs157592; Table 2) for ZC3H12B are located in gene APOC1 that is a known risk gene for AD 26 and blood lipids [27][28][29] , which is <12KB away from the well-known risk gene APOE of AD 30 . Particularly, rs12721051 located in the 3' UTR region of APOC1 was identified as a GWAS signal of total cholesterol levels 29 ; rs4420638 located in the downstream of APOC1 is in linkage disequilibrium (LD) with the APOE E4 allele (rs429358) and was identified to be a GWAS signal of various blood lipids measurements (i.e., low density lipoprotein cholesterol measurement, C-reactive protein measurement, triglyceride measurement, and total cholesterol measurement) 27 and AD 31 ;…”

“…This association was missed by the PrediXcan method 11 because it was completely driven by trans-eQTL. Importantly, a potential biological mechanism was revealed by showing that the top driven trans-eQTL of ZC3H12B are known GWAS signals of AD 26 and blood lipids [27][28][29] and <12KB away from the famous well-known risk gene of AD (APOE) 30 . Since recent studies generally identified a handful of eQTL with relatively large effect sizes for majority genes and trans-eQTL are often presented 8; 48 , we expect our Bayesian approach that selects sparse signals and leverages trans-eQTL information will have potential for making great impact in practice.…”

“…In particular, our simulation studies revealed that this Bayesian approach achieved higher TWAS power by considering both cis-and trans-SNPs for predicting GReX. Our application studies identified two novel risk genes associated with AD phenotypes ---ZC3H12B associated with both clinical diagnosis of AD (p-value=2.15 rs4420638, rs56131196, rs157592 located in gene APOC1) are located <12KB away from the well-known risk gene APOE of AD 30 , and are also known GWAS signals of AD 26 and blood lipids [27][28][29] . Free software for implementing our proposed Bayesian TWAS approach is available on Github.…”

Why are education, socioeconomic position and intelligence genetically correlated?

“…CRP is a biomarker of inflammation that is associated with a range of complex diseases and is unlikely to be influenced by the biases that relate to the social phenotypes that we investigate. Systematic population differences (population stratification) in CRP have been found to be insubstantial; 73,74 parental phenotypic expression of CRP is unlikely to influence offspring CRP (dynastic effects); and parents are very unlikely to selectively mate based upon CRP (assortative mating). Offspring CRP was measured from non-fasting blood assays taken during direct assessment when the children were aged 9.…”