The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies.

The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).

Association of Methylation Signals With Incident Coronary Heart Disease in an Epigenome-Wide Assessment of Circulating Tumor Necrosis Factor α.

Importance: Tumor necrosis factor α (TNF-α) is a proinflammatory cytokine with manifold consequences for mammalian pathophysiology, including cardiovascular disease. A deeper understanding of TNF-α biology may enhance treatment precision. Objective: To conduct an epigenome-wide analysis of blood-derived DNA methylation and TNF-α levels and to assess the clinical relevance of findings. Design, Setting, and Participants: This meta-analysis assessed epigenome-wide associations in circulating TNF-α concentrations from 5 cohort studies and 1 interventional trial, with replication in 3 additional cohort studies. Follow-up analyses investigated associations of identified methylation loci with gene expression and incident coronary heart disease; this meta-analysis included 11 461 participants who experienced 1895 coronary events. Exposures: Circulating TNF-α concentration. Main Outcomes and Measures: DNA methylation at approximately 450 000 loci, neighboring DNA sequence variation, gene expression, and incident coronary heart disease. Results: The discovery cohort included 4794 participants, and the replication study included 816 participants (overall mean [SD] age, 60.7 [8.5] years). In the discovery stage, circulating TNF-α levels were associated with methylation of 7 cytosine-phosphate-guanine (CpG) sites, 3 of which were located in or near DTX3L-PARP9 at cg00959259 (ß [SE] = -0.01 [0.003]; P = 7.36 × 10-8), cg08122652 (ß [SE] = -0.008 [0.002]; P = 2.24 × 10-7), and cg22930808(ß [SE] = -0.01 [0.002]; P = 6.92 × 10-8); NLRC5 at cg16411857 (ß [SE] = -0.01 [0.002]; P = 2.14 × 10-13) and cg07839457 (ß [SE] = -0.02 [0.003]; P = 6.31 × 10-10); or ABO, at cg13683939 (ß [SE] = 0.04 [0.008]; P = 1.42 × 10-7) and cg24267699 (ß [SE] = -0.009 [0.002]; P = 1.67 × 10-7), after accounting for multiple testing. Of these, negative associations between TNF-α concentration and methylation of 2 loci in NLRC5 and 1 in DTX3L-14 PARP9 were replicated. Replicated TNF-α-linked CpG sites were associated with 9% to 19% decreased risk of incident coronary heart disease per 10% higher methylation per CpG site (cg16411857: hazard ratio [HR], 0.86; 95% CI, 0.78-1.95; P = .003; cg07839457: HR, 0.89; 95% CI, 0.80-0.94; P = 3.1 × 10-5; cg00959259: HR, 0.91; 95% CI, 0.84-0.97; P = .002; cg08122652: HR, 0.81; 95% CI, 0.74-0.89; P = 2.0 × 10-5). Conclusions and Relevance: We identified and replicated novel epigenetic correlates of circulating TNF-α concentration in blood samples and linked these loci to coronary heart disease risk, opening opportunities for validation and therapeutic applications.

Psoriasis and cardiometabolic traits: modest association but distinct genetic architectures.

Psoriasis has been linked to cardiometabolic diseases, but epidemiological findings are inconsistent. We investigated the association between psoriasis and cardiometabolic outcomes in a German cross-sectional study (n=4,185) and a prospective cohort of German Health Insurance beneficiaries (n=1,811,098). A potential genetic overlap was explored using genome-wide data from >22,000 coronary artery disease and >4,000 psoriasis cases, and with a dense genotyping study of cardiometabolic risk loci on 927 psoriasis cases and 3,717 controls. After controlling for major confounders, in the cross-sectional analysis psoriasis was significantly associated with type 2 diabetes (T2D, adjusted odds ratio (OR)=2.36; 95% confidence interval CI=1.26-4.41) and myocardial infarction (MI, OR=2.26; 95% CI=1.03-4.96). In the longitudinal study, psoriasis slightly increased the risk for incident T2D (adjusted relative risk (RR)=1.11; 95% CI=1.08-1.14) and MI (RR=1.14; 95% CI=1.06-1.22), with highest risk increments in systemically treated psoriasis, which accounted for 11 and 17 excess cases of T2D and MI per 10,000 person-years. Except for weak signals from within the major histocompatibility complex, there was no evidence of genetic risk loci shared between psoriasis and cardiometabolic traits. Our findings suggest that psoriasis, in particular severe psoriasis, increases the risk for T2D and MI, and that the genetic architecture of psoriasis and cardiometabolic traits is largely distinct.

Human metabolic individuality in biomedical and pharmaceutical research.

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

Automatic scoring and quality assessment using accuracy bounds for FP-TDI SNP genotyping data.

BACKGROUND: Human diversity, namely single nucleotide polymorphisms (SNPs), is becoming a focus of biomedical research. Despite the binary nature of SNP determination, the majority of genotyping assay data need a critical evaluation for genotype calling. We applied statistical models to improve the automated analysis of 2-dimensional SNP data. METHODS: We derived several quantities in the framework of Gaussian mixture models that provide figures of merit to objectively measure the data quality. The accuracy of individual observations is scored as the probability of belonging to a certain genotype cluster, while the assay quality is measured by the overlap between the genotype clusters. RESULTS: The approach was extensively tested with a dataset of 438 nonredundant SNP assays comprising >150,000 datapoints. The performance of our automatic scoring method was compared with manual assignments. The agreement for the overall assay quality is remarkably good, and individual observations were scored differently by man and machine in 2.6% of cases, when applying stringent probability threshold values. CONCLUSION: Our definition of bounds for the accuracy for complete assays in terms of misclassification probabilities goes beyond other proposed analysis methods. We expect the scoring method to minimise human intervention and provide a more objective error estimate in genotype calling.