Dörte Radke

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

Common variants at 10 genomic loci influence hemoglobin A1C levels via glycemic and nonglycemic pathways

OBJECTIVE Glycated hemoglobin (HbA1c), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA1c. We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA1c levels. RESEARCH DESIGN AND METHODS We studied associations with HbA1c in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA1c loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening. RESULTS Ten loci reached genome-wide significant association with HbA1c, including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10−26), HFE (rs1800562/P = 2.6 × 10−20), TMPRSS6 (rs855791/P = 2.7 × 10−14), ANK1 (rs4737009/P = 6.1 × 10−12), SPTA1 (rs2779116/P = 2.8 × 10−9) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10−9), and four known HbA1c loci: HK1 (rs16926246/P = 3.1 × 10−54), MTNR1B (rs1387153/P = 4.0 × 10−11), GCK (rs1799884/P = 1.5 × 10−20) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10−18). We show that associations with HbA1c are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA1c) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA1c. CONCLUSIONS GWAS identified 10 genetic loci reproducibly associated with HbA1c. Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA1c levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA1c.

Dynamic plastid redox signals integrate gene expression and metabolism to induce distinct metabolic states in photosynthetic acclimation in Arabidopsis

Plants possess acclimation responses in which structural reconfigurations adapt the photosynthetic apparatus to fluctuating illumination. Long-term acclimation involves changes in plastid and nuclear gene expression and is controlled by redox signals from photosynthesis. The kinetics of these signals and the adjustments of energetic and metabolic demands to the changes in the photosynthetic apparatus are currently poorly understood. Using a redox signaling system that preferentially excites either photosystem I or II, we measured the time-dependent impact of redox signals on the transcriptome and metabolome of Arabidopsis thaliana. We observed rapid and dynamic changes in nuclear transcript accumulation resulting in differential and specific expression patterns for genes associated with photosynthesis and metabolism. Metabolite pools also exhibited dynamic changes and indicate readjustments between distinct metabolic states depending on the respective illumination. These states reflect reallocation of energy resources in a defined and reversible manner, indicating that structural changes in the photosynthetic apparatus during long-term acclimation are additionally supported at the level of metabolism. We propose that photosynthesis can act as an environmental sensor, producing retrograde redox signals that trigger two parallel adjustment loops that coordinate photosynthesis and metabolism to adapt plant primary productivity to the environment.

Age-Specific Reference Ranges for Serum Testosterone and Androstenedione Concentrations in Women Measured by Liquid Chromatography-Tandem Mass Spectrometry

OBJECTIVEnRIA-based sex hormone measurements offer only limited precision and specificity in the low concentration range of women. Therefore, we aimed to establish age-specific reference ranges for serum sex hormone concentrations in women using mass spectrometry and quantile regression.nnnMETHODS AND RESULTSnData from 985 women aged 20-80 yr, recruited for the prospective Study of Health in Pomerania, were included in the analyses. Quantile regressions models were performed to calculate the age-specific 2.5th and 97.5th percentiles for sex hormone concentrations in women. Serum total testosterone (TT) and androstenedione (AD) concentrations were measured by liquid chromatography-tandem mass spectrometry. Measured concentrations of SHBG and TT were used to calculate free testosterone (free T). TT, AD, and free T concentrations showed a distinct age-related decline across 10-yr age groups (one way ANOVA P < 0.001). Sex hormone reference ranges for TT, AD, and free T were determined across each single year of age and for 10-yr age groups. Reference ranges over the whole age range of 20-80 yr were 0.35-1.97 nmol/liter for TT, 0.89-4.56 nmol/liter for AD, and 0.0025-0.0253 nmol/liter for free T. Separate reference ranges were provided for pre- and postmenopausal women as well as after inclusion of women using oral contraceptives or hormone therapy (n = 1357).nnnCONCLUSIONnThis is the first study to establish age-specific reference ranges for liquid chromatography-tandem mass spectrometry-measured TT and AD and calculated free T concentrations based on quantile regression analyses, accurately accounting for the observed low concentration range and the strong age dependency of these sex hormones in women.

Genome-wide association study identifies four genetic loci associated with thyroid volume and goiter risk.

Thyroid disorders such as goiters represent important diseases, especially in iodine-deficient areas. Sibling studies have demonstrated that genetic factors substantially contribute to the interindividual variation of thyroid volume. We performed a genome-wide association study of this phenotype by analyzing a discovery cohort consisting of 3620 participants of the Study of Health in Pomerania (SHIP). Four genetic loci were associated with thyroid volume on a genome-wide level of significance. Of these, two independent loci are located upstream of and within CAPZB, which encodes the β subunit of the barbed-end F-actin binding protein that modulates actin polymerization, a process crucial in the colloid engulfment during thyroglobulin mobilization in the thyroid. The third locus marks FGF7, which encodes fibroblast growth factor 7. Members of this protein family have been discussed as putative signal molecules involved in the regulation of thyroid development. The fourth locus represents a gene desert on chromosome 16q23, located directly downstream of the predicted coding sequence LOC440389, which, however, had already been removed from the NCBI database as a result of the standard genome annotation processing at the time that this study was initiated. Experimental proof of the formerly predicted mature mRNA, however, demonstrates that LOC440389 indeed represents a real gene. All four associations were replicated in an independent sample of 1290 participants of the KORA study. These results increase the knowledge about genetic factors and physiological mechanisms influencing thyroid volume.

Predictive modeling of health care costs: do cardiovascular risk markers improve prediction?

Background To investigate the ability of multiple cardiovascular disease (CVD) markers to predict future health care costs. CVD markers included traditional risk factors (smoking status, body mass index, waist circumference, alcohol intake, diabetes, total: high-density lipoprotein cholesterol ratio, actual hypertension, physical activity) and newer markers (carotid intima-media thickness, hemoglobin A1c, apolipoprotein B: apolipoprotein A-1 ratio, lipoprotein (a), leukocyte count, highsensitive C-reactive protein, plasma fibrinogen, estimated glomerular filtration rate, urinary albumin: creatinine ratio). Design and methods The study sample consisted of 2233 participants without history of myocardial infarction, stroke, heart failure, and angina pectoris at baseline (50.6% women; mean age 60.9 years; age range 45–81 years) from the cohort Study of Health in Pomerania, Germany (median follow-up 5 years). Results Predictive modeling revealed that a basic model with sex, age, years of school education, insurance status, and income explained 0.9% in baseline total cost variation and 1.5% in total cost variation at 5-year follow-up. The incorporation of a combination of significant CVD markers resulted in an increase in the R2 for total costs of 70% at baseline and 69% after 5 years, with a final R2 of 0.030 at baseline and an R2 of 0.048 at 5-year follow-up. Conclusion Our data suggest that for individuals without history of CVD, the simultaneous addition of several CVD risk markers improves predictive modeling of future health care costs beyond that of a model that is based on established health care predictors. Eur J Cardiovasc Prev Rehabil 17:355–362