Matthias Nauck

Systematic identification of trans eQTLs as putative drivers of known disease associations

Identifying the downstream effects of disease-associated SNPs is challenging. To help overcome this problem, we performed expression quantitative trait locus (eQTL) meta-analysis in non-transformed peripheral blood samples from 5,311 individuals with replication in 2,775 individuals. We identified and replicated trans eQTLs for 233 SNPs (reflecting 103 independent loci) that were previously associated with complex traits at genome-wide significance. Some of these SNPs affect multiple genes in trans that are known to be altered in individuals with disease: rs4917014, previously associated with systemic lupus erythematosus (SLE), altered gene expression of C1QB and five type I interferon response genes, both hallmarks of SLE. DeepSAGE RNA sequencing showed that rs4917014 strongly alters the 3′ UTR levels of IKZF1 in cis, and chromatin immunoprecipitation and sequencing analysis of the trans-regulated genes implicated IKZF1 as the causal gene. Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.

Cohort Profile: The Study of Health in Pomerania

Henry Volzke, y Dietrich Alte,1y Carsten Oliver Schmidt, Dorte Radke, Roberto Lorbeer, Nele Friedrich, Nicole Aumann, Katharina Lau, Michael Piontek, Gabriele Born, Christoph Havemann, Till Ittermann, Sabine Schipf, Robin Haring, Sebastian E Baumeister, Henri Wallaschofski, Matthias Nauck, Stephanie Frick, Andreas Arnold, Michael Junger, Julia Mayerle, Matthias Kraft, Markus M Lerch, Marcus Dorr, Thorsten Reffelmann, Klaus Empen, Stephan B Felix, Anne Obst, Beate Koch, Sven Glaser, Ralf Ewert, Ingo Fietze, Thomas Penzel, Martina Doren, Wolfgang Rathmann, Johannes Haerting, Mario Hannemann, Jurgen Ropcke, Ulf Schminke, Clemens Jurgens, Frank Tost, Rainer Rettig, Jan A Kors, Saskia Ungerer, Katrin Hegenscheid, Jens-Peter Kuhn, Julia Kuhn, Norbert Hosten, Ralf Puls, Jorg Henke, Oliver Gloger, Alexander Teumer, Georg Homuth, Uwe Volker, Christian Schwahn, Birte Holtfreter, Ines Polzer, Thomas Kohlmann, Hans J Grabe, Dieter Rosskopf, Heyo K Kroemer, Thomas Kocher, Reiner Biffar,17,y Ulrich John20y and Wolfgang Hoffmann1y

Meta-Analysis of 28,141 Individuals Identifies Common Variants within Five New Loci That Influence Uric Acid Concentrations

Elevated serum uric acid levels cause gout and are a risk factor for cardiovascular disease and diabetes. To investigate the polygenetic basis of serum uric acid levels, we conducted a meta-analysis of genome-wide association scans from 14 studies totalling 28,141 participants of European descent, resulting in identification of 954 SNPs distributed across nine loci that exceeded the threshold of genome-wide significance, five of which are novel. Overall, the common variants associated with serum uric acid levels fall in the following nine regions: SLC2A9 (p = 5.2×10−201), ABCG2 (p = 3.1×10−26), SLC17A1 (p = 3.0×10−14), SLC22A11 (p = 6.7×10−14), SLC22A12 (p = 2.0×10−9), SLC16A9 (p = 1.1×10−8), GCKR (p = 1.4×10−9), LRRC16A (p = 8.5×10−9), and near PDZK1 (p = 2.7×10−9). Identified variants were analyzed for gender differences. We found that the minor allele for rs734553 in SLC2A9 has greater influence in lowering uric acid levels in women and the minor allele of rs2231142 in ABCG2 elevates uric acid levels more strongly in men compared to women. To further characterize the identified variants, we analyzed their association with a panel of metabolites. rs12356193 within SLC16A9 was associated with DL-carnitine (p = 4.0×10−26) and propionyl-L-carnitine (p = 5.0×10−8) concentrations, which in turn were associated with serum UA levels (p = 1.4×10−57 and p = 8.1×10−54, respectively), forming a triangle between SNP, metabolites, and UA levels. Taken together, these associations highlight additional pathways that are important in the regulation of serum uric acid levels and point toward novel potential targets for pharmacological intervention to prevent or treat hyperuricemia. In addition, these findings strongly support the hypothesis that transport proteins are key in regulating serum uric acid levels.

A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium.

The number and volume of cells in the blood affect a wide range of disorders including cancer and cardiovascular, metabolic, infectious and immune conditions. We consider here the genetic variation in eight clinically relevant hematological parameters, including hemoglobin levels, red and white blood cell counts and platelet counts and volume. We describe common variants within 22 genetic loci reproducibly associated with these hematological parameters in 13,943 samples from six European population-based studies, including 6 associated with red blood cell parameters, 15 associated with platelet parameters and 1 associated with total white blood cell count. We further identified a long-range haplotype at 12q24 associated with coronary artery disease and myocardial infarction in 9,479 cases and 10,527 controls. We show that this haplotype demonstrates extensive disease pleiotropy, as it contains known risk loci for type 1 diabetes, hypertension and celiac disease and has been spread by a selective sweep specific to European and geographically nearby populations.

Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium

Measurements of erythrocytes within the blood are important clinical traits and can indicate various hematological disorders. We report here genome-wide association studies (GWAS) for six erythrocyte traits, including hemoglobin concentration (Hb), hematocrit (Hct), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC) and red blood cell count (RBC). We performed an initial GWAS in cohorts of the CHARGE Consortium totaling 24,167 individuals of European ancestry and replication in additional independent cohorts of the HaemGen Consortium totaling 9,456 individuals. We identified 23 loci significantly associated with these traits in a meta-analysis of the discovery and replication cohorts (combined P values ranging from 5 × 10−8 to 7 × 10−86). Our findings include loci previously associated with these traits (HBS1L-MYB, HFE, TMPRSS6, TFR2, SPTA1) as well as new associations (EPO, TFRC, SH2B3 and 15 other loci). This study has identified new determinants of erythrocyte traits, offering insight into common variants underlying variation in erythrocyte measures.

The Predictive Value of Different Measures of Obesity for Incident Cardiovascular Events and Mortality

CONTEXT To date, it is unclear which measure of obesity is the most appropriate for risk stratification. OBJECTIVE The aim of the study was to compare the associations of various measures of obesity with incident cardiovascular events and mortality. DESIGN AND SETTING We analyzed two German cohort studies, the DETECT study and SHIP, including primary care and general population. PARTICIPANTS A total of 6355 (mean follow-up, 3.3 yr) and 4297 (mean follow-up, 8.5 yr) individuals participated in DETECT and SHIP, respectively. INTERVENTIONS We measured body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), and waist-to-hip ratio (WHR) and assessed cardiovascular and all-cause mortality and the composite endpoint of incident stroke, myocardial infarction, or cardiovascular death. RESULTS In both studies, we found a positive association of the composite endpoint with WHtR but not with BMI. There was no heterogeneity among studies. The relative risks in the highest versus the lowest sex- and age-specific quartile of WHtR, WC, WHR, and BMI after adjustment for multiple confounders were as follows in the pooled data: cardiovascular mortality, 2.75 (95% confidence interval, 1.31-5.77), 1.74 (0.84-3.6), 1.71 (0.91-3.22), and 0.74 (0.35-1.57), respectively; all-cause mortality, 1.86 (1.25-2.76), 1.62 (1.22-2.38), 1.36 (0.93-1.69), and 0.77 (0.53-1.13), respectively; and composite endpoint, 2.16 (1.39-3.35), 1.59 (1.04-2.44), 1.49 (1.07-2.07), and 0.57 (0.37-0.89), respectively. Separate analyses of sex and age groups yielded comparable results. Receiver operating characteristics analysis yielded the highest areas under the curve for WHtR for predicting these endpoints. CONCLUSIONS WHtR represents the best predictor of cardiovascular risk and mortality, followed by WC and WHR. Our results discourage the use of the BMI.

Low serum testosterone levels are associated with increased risk of mortality in a population-based cohort of men aged 20–79

AIMS Although the association of low serum testosterone levels with mortality has gained strength in recent research, there are few population-based studies on this issue. This study examined whether low serum testosterone levels are a risk factor for all-cause or cause-specific mortality in a population-based sample of men aged 20-79. METHODS AND RESULTS We used data from 1954 men recruited for the prospective population-based Study of Health in Pomerania, with measured serum testosterone levels at baseline and 195 deaths during an average 7.2-year follow-up. A total serum testosterone level of less than 8.7 nmol/L (250 ng/dL) was classified as low. The relationships of low serum testosterone levels with all-cause and cause-specific mortality were analysed by Cox proportional hazards regression models. Men with low serum testosterone levels had a significantly higher mortality from all causes than men with higher serum testosterone levels (HR 2.24; 95% CI 1.41-3.57). After adjusting for waist circumference, smoking habits, high-risk alcohol use, physical activity, renal insufficiency, and levels of dehydroepiandrosterone sulfate, low serum testosterone levels continued to be associated with increased mortality (HR 2.32; 95% CI 1.38-3.89). In cause-specific analyses, low serum testosterone levels predicted increased risk of death from cardiovascular disease (CVD) (HR 2.56; 95% CI 1.15-6.52) and cancer (HR 3.46; 95% CI 1.68-6.68), but not from respiratory diseases or other causes. CONCLUSION Low serum testosterone levels were associated with an increased risk of all-cause mortality independent of numerous risk factors. As serum testosterone levels are inversely related to mortality due to CVD and cancer, it may be used as a predictive marker.

A genome-wide association study of metabolic traits in human urine

We present a genome-wide association study of metabolic traits in human urine, designed to investigate the detoxification capacity of the human body. Using NMR spectroscopy, we tested for associations between 59 metabolites in urine from 862 male participants in the population-based SHIP study. We replicated the results using 1,039 additional samples of the same study, including a 5-year follow-up, and 992 samples from the independent KORA study. We report five loci with joint P values of association from 3.2 × 10−19 to 2.1 × 10−182. Variants at three of these loci have previously been linked with important clinical outcomes: SLC7A9 is a risk locus for chronic kidney disease, NAT2 for coronary artery disease and genotype-dependent response to drug toxicity, and SLC6A20 for iminoglycinuria. Moreover, we identify rs37369 in AGXT2 as the genetic basis of hyper-β-aminoisobutyric aciduria.

Type 2 Diabetes Whole-Genome Association Study in Four Populations: The DiaGen Consortium

Type 2 diabetes (T2D) is a common, polygenic chronic disease with high heritability. The purpose of this whole-genome association study was to discover novel T2D-associated genes. We genotyped 500 familial cases and 497 controls with >300,000 HapMap-derived tagging single-nucleotide-polymorphism (SNP) markers. When a stringent statistical correction for multiple testing was used, the only significant SNP was at TCF7L2, which has already been discovered and confirmed as a T2D-susceptibility gene. For a replication study, we selected 10 SNPs in six chromosomal regions with the strongest association (singly or as part of a haplotype) for retesting in an independent case-control set including 2,573 T2D cases and 2,776 controls. The most significant replicated result was found at the AHI1-LOC441171 gene region.

Ultrasonographic hepatic steatosis increases prediction of mortality risk from elevated serum gamma-glutamyl transpeptidase levels†‡

The aim of the present study was to investigate the association of serum gamma‐glutamyltransferase (GGT) levels with all‐cause mortality and to assess the impact of ultrasonographic findings of hepatic hyperechogenicity in that association. We used data from 4,160 subjects (2,044 men and 2,116 women) recruited for the population‐based Study of Health in Pomerania (SHIP) without baseline hepatitis B and C infections or liver cirrhosis. GGT was divided into age‐ and sex‐dependent quintiles to calculate overall and sex‐specific crude incidence mortality rates. Hepatic steatosis was defined by elevated GGT levels (>80%) and the presence of hyperechogenic liver ultrasound. We used multiple‐adjusted Cox proportional hazards regression models, first, to assess the direct effect of GGT on all‐cause mortality, second, to stratify according to the ultrasonographic finding, and third, to investigate potential mediating effects of cardiometabolic risk factors. During 29,810 person‐years (7.3 years, median) of follow‐up, 307 individuals (7.5%) died, resulting in a death rate of 0.86 deaths per 1000 person‐years. Elevated GGT levels were associated with increased risk of mortality in men (hazard ratio [HR] 1.49; 95% confidence interval [CI], 1.08–2.05), but not in women (HR 1.30; 95% CI, 0.80–2.12). This association was even stronger in men with hepatic steatosis (HR 1.98; 95% CI, 1.21–3.27). Cause‐specific mortality analysis by cardiovascular disease deaths confirmed the sex‐specific association. Adjustment for cardiometabolic risk factors did not affect the estimates. Conclusion: In the case of increased GGT levels, liver ultrasound should be performed, not only for diagnosis, but also for further risk stratification. (HEPATOLOGY 2009.)