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1. Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study

Author

de Vries, Paul S, Sabater-Lleal, Maria, Chasman, Daniel I, Trompet, Stella, Ahluwalia, Tarunveer S, Teumer, Alexander, Kleber, Marcus E, Chen, Ming-Huei, Wang, Jie Jin, Attia, John R, Marioni, Riccardo E, Steri, Maristella, Weng, Lu-Chen, Pool, Rene, Grossmann, Vera, Brody, Jennifer A, Venturini, Cristina, Tanaka, Toshiko, Rose, Lynda M, Oldmeadow, Christopher, Mazur, Johanna, Basu, Saonli, Frånberg, Mattias, Yang, Qiong, Ligthart, Symen, Hottenga, Jouke J, Rumley, Ann, Mulas, Antonella, de Craen, Anton JM, Grotevendt, Anne, Taylor, Kent D, Delgado, Graciela E, Kifley, Annette, Lopez, Lorna M, Berentzen, Tina L, Mangino, Massimo, Bandinelli, Stefania, Morrison, Alanna C, Hamsten, Anders, Tofler, Geoffrey, de Maat, Moniek PM, Draisma, Harmen HM, Lowe, Gordon D, Zoledziewska, Magdalena, Sattar, Naveed, Lackner, Karl J, Völker, Uwe, McKnight, Barbara, Huang, Jie, Holliday, Elizabeth G, McEvoy, Mark A, Starr, John M, Hysi, Pirro G, Hernandez, Dena G, Guan, Weihua, Rivadeneira, Fernando, McArdle, Wendy L, Slagboom, P Eline, Zeller, Tanja, Psaty, Bruce M, Uitterlinden, André G, de Geus, Eco JC, Stott, David J, Binder, Harald, Hofman, Albert, Franco, Oscar H, Rotter, Jerome I, Ferrucci, Luigi, Spector, Tim D, Deary, Ian J, März, Winfried, Greinacher, Andreas, Wild, Philipp S, Cucca, Francesco, Boomsma, Dorret I, Watkins, Hugh, Tang, Weihong, Ridker, Paul M, Jukema, Jan W, Scott, Rodney J, Mitchell, Paul, Hansen, Torben, O'Donnell, Christopher J, Smith, Nicholas L, Strachan, David P, and Dehghan, Abbas

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Genome-Wide Association Study, HapMap Project, Humans, General Science & Technology
Abstract

An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.

Published
2017

2. A meta-analysis of 120 246 individuals identifies 18 new loci for fibrinogen concentration

Author

de Vries, Paul S, Chasman, Daniel I, Sabater-Lleal, Maria, Chen, Ming-Huei, Huffman, Jennifer E, Steri, Maristella, Tang, Weihong, Teumer, Alexander, Marioni, Riccardo E, Grossmann, Vera, Hottenga, Jouke J, Trompet, Stella, Müller-Nurasyid, Martina, Zhao, Jing Hua, Brody, Jennifer A, Kleber, Marcus E, Guo, Xiuqing, Wang, Jie Jin, Auer, Paul L, Attia, John R, Yanek, Lisa R, Ahluwalia, Tarunveer S, Lahti, Jari, Venturini, Cristina, Tanaka, Toshiko, Bielak, Lawrence F, Joshi, Peter K, Rocanin-Arjo, Ares, Kolcic, Ivana, Navarro, Pau, Rose, Lynda M, Oldmeadow, Christopher, Riess, Helene, Mazur, Johanna, Basu, Saonli, Goel, Anuj, Yang, Qiong, Ghanbari, Mohsen, Willemsen, Gonneke, Rumley, Ann, Fiorillo, Edoardo, de Craen, Anton JM, Grotevendt, Anne, Scott, Robert, Taylor, Kent D, Delgado, Graciela E, Yao, Jie, Kifley, Annette, Kooperberg, Charles, Qayyum, Rehan, Lopez, Lorna M, Berentzen, Tina L, Räikkönen, Katri, Mangino, Massimo, Bandinelli, Stefania, Peyser, Patricia A, Wild, Sarah, Trégouët, David-Alexandre, Wright, Alan F, Marten, Jonathan, Zemunik, Tatijana, Morrison, Alanna C, Sennblad, Bengt, Tofler, Geoffrey, de Maat, Moniek PM, de Geus, Eco JC, Lowe, Gordon D, Zoledziewska, Magdalena, Sattar, Naveed, Binder, Harald, Völker, Uwe, Waldenberger, Melanie, Khaw, Kay-Tee, Mcknight, Barbara, Huang, Jie, Jenny, Nancy S, Holliday, Elizabeth G, Qi, Lihong, Mcevoy, Mark G, Becker, Diane M, Starr, John M, Sarin, Antti-Pekka, Hysi, Pirro G, Hernandez, Dena G, Jhun, Min A, Campbell, Harry, Hamsten, Anders, Rivadeneira, Fernando, Mcardle, Wendy L, Slagboom, P Eline, Zeller, Tanja, Koenig, Wolfgang, Psaty, Bruce M, Haritunians, Talin, Liu, Jingmin, Palotie, Aarno, Uitterlinden, André G, Stott, David J, Hofman, Albert, and Franco, Oscar H

Subjects
Biological Sciences, Genetics, Human Genome, Adult, Aged, Aged, 80 and over, Female, Fibrinogen, Genetic Loci, Genome-Wide Association Study, Humans, INDEL Mutation, Male, Middle Aged, Polymorphism, Single Nucleotide, White People, Medical and Health Sciences, Genetics & Heredity
Abstract

Genome-wide association studies have previously identified 23 genetic loci associated with circulating fibrinogen concentration. These studies used HapMap imputation and did not examine the X-chromosome. 1000 Genomes imputation provides better coverage of uncommon variants, and includes indels. We conducted a genome-wide association analysis of 34 studies imputed to the 1000 Genomes Project reference panel and including ∼120 000 participants of European ancestry (95 806 participants with data on the X-chromosome). Approximately 10.7 million single-nucleotide polymorphisms and 1.2 million indels were examined. We identified 41 genome-wide significant fibrinogen loci; of which, 18 were newly identified. There were no genome-wide significant signals on the X-chromosome. The lead variants of five significant loci were indels. We further identified six additional independent signals, including three rare variants, at two previously characterized loci: FGB and IRF1. Together the 41 loci explain 3% of the variance in plasma fibrinogen concentration.

Published
2016

5. Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study.

Author

Paul S de Vries, Maria Sabater-Lleal, Daniel I Chasman, Stella Trompet, Tarunveer S Ahluwalia, Alexander Teumer, Marcus E Kleber, Ming-Huei Chen, Jie Jin Wang, John R Attia, Riccardo E Marioni, Maristella Steri, Lu-Chen Weng, Rene Pool, Vera Grossmann, Jennifer A Brody, Cristina Venturini, Toshiko Tanaka, Lynda M Rose, Christopher Oldmeadow, Johanna Mazur, Saonli Basu, Mattias Frånberg, Qiong Yang, Symen Ligthart, Jouke J Hottenga, Ann Rumley, Antonella Mulas, Anton J M de Craen, Anne Grotevendt, Kent D Taylor, Graciela E Delgado, Annette Kifley, Lorna M Lopez, Tina L Berentzen, Massimo Mangino, Stefania Bandinelli, Alanna C Morrison, Anders Hamsten, Geoffrey Tofler, Moniek P M de Maat, Harmen H M Draisma, Gordon D Lowe, Magdalena Zoledziewska, Naveed Sattar, Karl J Lackner, Uwe Völker, Barbara McKnight, Jie Huang, Elizabeth G Holliday, Mark A McEvoy, John M Starr, Pirro G Hysi, Dena G Hernandez, Weihua Guan, Fernando Rivadeneira, Wendy L McArdle, P Eline Slagboom, Tanja Zeller, Bruce M Psaty, André G Uitterlinden, Eco J C de Geus, David J Stott, Harald Binder, Albert Hofman, Oscar H Franco, Jerome I Rotter, Luigi Ferrucci, Tim D Spector, Ian J Deary, Winfried März, Andreas Greinacher, Philipp S Wild, Francesco Cucca, Dorret I Boomsma, Hugh Watkins, Weihong Tang, Paul M Ridker, Jan W Jukema, Rodney J Scott, Paul Mitchell, Torben Hansen, Christopher J O'Donnell, Nicholas L Smith, David P Strachan, and Abbas Dehghan

Subjects
Medicine, Science
Abstract

An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.

Published
2017
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6. Modulation of genetic associations with serum urate levels by body-mass-index in humans.

Author

Jennifer E Huffman, Eva Albrecht, Alexander Teumer, Massimo Mangino, Karen Kapur, Toby Johnson, Zoltán Kutalik, Nicola Pirastu, Giorgio Pistis, Lorna M Lopez, Toomas Haller, Perttu Salo, Anuj Goel, Man Li, Toshiko Tanaka, Abbas Dehghan, Daniela Ruggiero, Giovanni Malerba, Albert V Smith, Ilja M Nolte, Laura Portas, Amanda Phipps-Green, Lora Boteva, Pau Navarro, Asa Johansson, Andrew A Hicks, Ozren Polasek, Tõnu Esko, John F Peden, Sarah E Harris, Federico Murgia, Sarah H Wild, Albert Tenesa, Adrienne Tin, Evelin Mihailov, Anne Grotevendt, Gauti K Gislason, Josef Coresh, Pio D'Adamo, Sheila Ulivi, Peter Vollenweider, Gerard Waeber, Susan Campbell, Ivana Kolcic, Krista Fisher, Margus Viigimaa, Jeffrey E Metter, Corrado Masciullo, Elisabetta Trabetti, Cristina Bombieri, Rossella Sorice, Angela Döring, Eva Reischl, Konstantin Strauch, Albert Hofman, Andre G Uitterlinden, Melanie Waldenberger, H-Erich Wichmann, Gail Davies, Alan J Gow, Nicola Dalbeth, Lisa Stamp, Johannes H Smit, Mirna Kirin, Ramaiah Nagaraja, Matthias Nauck, Claudia Schurmann, Kathrin Budde, Susan M Farrington, Evropi Theodoratou, Antti Jula, Veikko Salomaa, Cinzia Sala, Christian Hengstenberg, Michel Burnier, Reedik Mägi, Norman Klopp, Stefan Kloiber, Sabine Schipf, Samuli Ripatti, Stefano Cabras, Nicole Soranzo, Georg Homuth, Teresa Nutile, Patricia B Munroe, Nicholas Hastie, Harry Campbell, Igor Rudan, Claudia Cabrera, Chris Haley, Oscar H Franco, Tony R Merriman, Vilmundur Gudnason, Mario Pirastu, Brenda W Penninx, Harold Snieder, Andres Metspalu, Marina Ciullo, Peter P Pramstaller, Cornelia M van Duijn, Luigi Ferrucci, Giovanni Gambaro, Ian J Deary, Malcolm G Dunlop, James F Wilson, Paolo Gasparini, Ulf Gyllensten, Tim D Spector, Alan F Wright, Caroline Hayward, Hugh Watkins, Markus Perola, Murielle Bochud, W H Linda Kao, Mark Caulfield, Daniela Toniolo, Henry Völzke, Christian Gieger, Anna Köttgen, and Veronique Vitart

Subjects
Medicine, Science
Abstract

We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, Pinter= 2.6 x 10-8). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDARADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 x 10-8), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight= 9.1 x 10-8), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (Pdifflean-obese= 2 x 10-4). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obesogenic environment.

Published
2015
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10. No evidence for genome-wide interactions on plasma fibrinogen by smoking, alcohol consumption and body mass index: results from meta-analyses of 80,607 subjects.

Author

Jens Baumert, Jie Huang, Barbara McKnight, Maria Sabater-Lleal, Maristella Steri, Audrey Y Chu, Stella Trompet, Lorna M Lopez, Myriam Fornage, Alexander Teumer, Weihong Tang, Alicja R Rudnicka, Anders Mälarstig, Jouke-Jan Hottenga, Maryam Kavousi, Jari Lahti, Toshiko Tanaka, Caroline Hayward, Jennifer E Huffman, Pierre-Emmanuel Morange, Lynda M Rose, Saonli Basu, Ann Rumley, David J Stott, Brendan M Buckley, Anton J M de Craen, Serena Sanna, Marco Masala, Reiner Biffar, Georg Homuth, Angela Silveira, Bengt Sennblad, Anuj Goel, Hugh Watkins, Martina Müller-Nurasyid, Regina Rückerl, Kent Taylor, Ming-Huei Chen, Eco J C de Geus, Albert Hofman, Jacqueline C M Witteman, Moniek P M de Maat, Aarno Palotie, Gail Davies, David S Siscovick, Ivana Kolcic, Sarah H Wild, Jaejoon Song, Wendy L McArdle, Ian Ford, Naveed Sattar, David Schlessinger, Anne Grotevendt, Maria Grazia Franzosi, Thomas Illig, Melanie Waldenberger, Thomas Lumley, Geoffrey H Tofler, Gonneke Willemsen, André G Uitterlinden, Fernando Rivadeneira, Katri Räikkönen, Daniel I Chasman, Aaron R Folsom, Gordon D Lowe, Rudi G J Westendorp, P Eline Slagboom, Francesco Cucca, Henri Wallaschofski, Rona J Strawbridge, Udo Seedorf, Wolfgang Koenig, Joshua C Bis, Kenneth J Mukamal, Jenny van Dongen, Elisabeth Widen, Oscar H Franco, John M Starr, Kiang Liu, Luigi Ferrucci, Ozren Polasek, James F Wilson, Tiphaine Oudot-Mellakh, Harry Campbell, Pau Navarro, Stefania Bandinelli, Johan Eriksson, Dorret I Boomsma, Abbas Dehghan, Robert Clarke, Anders Hamsten, Eric Boerwinkle, J Wouter Jukema, Silvia Naitza, Paul M Ridker, Henry Völzke, Ian J Deary, Alexander P Reiner, David-Alexandre Trégouët, Christopher J O'Donnell, David P Strachan, Annette Peters, and Nicholas L Smith

Subjects
Medicine, Science
Abstract

Plasma fibrinogen is an acute phase protein playing an important role in the blood coagulation cascade having strong associations with smoking, alcohol consumption and body mass index (BMI). Genome-wide association studies (GWAS) have identified a variety of gene regions associated with elevated plasma fibrinogen concentrations. However, little is yet known about how associations between environmental factors and fibrinogen might be modified by genetic variation. Therefore, we conducted large-scale meta-analyses of genome-wide interaction studies to identify possible interactions of genetic variants and smoking status, alcohol consumption or BMI on fibrinogen concentration. The present study included 80,607 subjects of European ancestry from 22 studies. Genome-wide interaction analyses were performed separately in each study for about 2.6 million single nucleotide polymorphisms (SNPs) across the 22 autosomal chromosomes. For each SNP and risk factor, we performed a linear regression under an additive genetic model including an interaction term between SNP and risk factor. Interaction estimates were meta-analysed using a fixed-effects model. No genome-wide significant interaction with smoking status, alcohol consumption or BMI was observed in the meta-analyses. The most suggestive interaction was found for smoking and rs10519203, located in the LOC123688 region on chromosome 15, with a p value of 6.2 × 10(-8). This large genome-wide interaction study including 80,607 participants found no strong evidence of interaction between genetic variants and smoking status, alcohol consumption or BMI on fibrinogen concentrations. Further studies are needed to yield deeper insight in the interplay between environmental factors and gene variants on the regulation of fibrinogen concentrations.

Published
2014
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15. Clowns in Paediatric Surgery: Less Anxiety and More Oxytocin? A Pilot Study

Author

Dorothea Hoeppner, Winfried Barthlen, Tabea Scheel, and Anne Grotevendt

Subjects
Parents, medicine.medical_specialty, Adolescent, Psychological intervention, Pilot Projects, Anxiety, Oxytocin, 03 medical and health sciences, 0302 clinical medicine, Intervention (counseling), medicine, Humans, 0501 psychology and cognitive sciences, Laughter Therapy, 050102 behavioral science & comparative psychology, Child, Psychiatry, Paediatric surgery, Health professionals, business.industry, 05 social sciences, Child, Preschool, Psychological well-being, Pediatrics, Perinatology and Child Health, Oxytocin Measurement, medicine.symptom, business, Child, Hospitalized, Stress, Psychological, 030217 neurology & neurosurgery, medicine.drug
Abstract

Background Hospital stays and medical interventions are accompanied by worries and anxiety in children and parents. Recent studies show that hospital clowns may reduce anxiety and enhance well-being. However, so far studies are based solely on subjective measures and clowns are usually not integrated in medical routine. With this pilot study, we aim to provide both psychological and physiological evidence of positive effects of clowns’ interventions in hospitalized children. Patients/Method In a consecutive randomized intervention-control group design with 31 children aged 4 to 13 years, 17 patients were accompanied by a clown prior to surgery or during ward round (intervention group) and 14 were not (control group). Saliva samples for oxytocin measurement were taken from all patients before hospitalization (T1) and prior to surgery or after ward round (T2). Self- and parents-reports were obtained at T1, T2 as well as at time of discharge from hospital (T3) regarding children’s anxiety (STAI), worries and well-being. Clowns evaluated their success in cheering up the child. Health professionals were asked for their acceptance of clowns in hospitals. Results Children in the intervention group had lower anxiety ratings and a higher oxytocin concentration at T2 as compared with T1; the control group showed no changes. Parents rated the well-being of their children higher if their child had clown’s contact and were more willing to recommend the hospital. The staff judged the clowns as helpful for patients. Discussion Consistent psychological and physiological results suggest the positive impact of a clown’s intervention in hospitalized children.

Published
2017

16. Associations of aldosterone and renin concentrations with inflammation—the Study of Health in Pomerania and the German Conn’s Registry

Author

Martin Reincke, Wolfgang Hoffmann, Rainer Rettig, Anne Grotevendt, Marcus Quinkler, Christian Adolf, Henri Wallaschofski, Anke Hannemann, and M. Nauck

Subjects
Adult, Male, medicine.medical_specialty, Endocrinology, Diabetes and Metabolism, Population, 030209 endocrinology & metabolism, 030204 cardiovascular system & hematology, Fibrinogen, Plasma renin activity, Cohort Studies, Leukocyte Count, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Endocrinology, Primary aldosteronism, Reference Values, Germany, Internal medicine, Hyperaldosteronism, Renin, Renin–angiotensin system, medicine, Humans, Longitudinal Studies, Registries, education, Aldosterone, Inflammation, education.field_of_study, biology, business.industry, C-reactive protein, Middle Aged, medicine.disease, C-Reactive Protein, Socioeconomic Factors, chemistry, biology.protein, Female, business, Biomarkers, medicine.drug
Abstract

Chronic inflammation is an age-independent and body mass index-independent contributor to the development of multi-morbidity. Alterations of the renin-angiotensin-aldosterone system are observed within the context of proinflammatory states. We assessed circulating aldosterone, renin, and inflammatory biomarker concentrations in healthy, normotensive subjects and patients with primary aldosteronism. We included 1177 normotensive individuals from the population-based Study of Health in Pomerania (first follow-up, Study of Health in Pomerania-1) and 103 primary aldosteronism patients from the German Conn’s Registry. A 1:1 matching for sex, age, body mass index, smoking status, diabetes mellitus, and the estimated glomerular filtration rate was performed to determine whether primary aldosteronism patients exhibit higher inflammatory biomarker concentrations than normotensive controls. The associations of plasma aldosterone concentration or plasma renin concentration with circulating fibrinogen concentrations, white blood cell count, and high sensitive C-reactive protein concentrations in the normotensive sample were determined with multivariable linear and logistic regression analyses. 1:1 matched primary aldosteronism patients demonstrated significantly (p

Published
2017

17. Analytical robustness of nine common assays: frequency of outliers and extreme differences identified by a large number of duplicate measurements

Author

Anne Grotevendt, Stefanie Neubig, Matthias Nauck, Astrid Petersmann, and Anders Kallner

Subjects
Quality Control, 030213 general clinical medicine, Quality Assurance, Health Care, Clinical Biochemistry, clinical chemistry tests, 030204 cardiovascular system & hematology, Biology, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Reference Values, Robustness (computer science), Statistics, Humans, Alanine aminotransferase, analytical quality, Creatinine, Plasma samples, Clinical Laboratory Techniques, Diagnostic Tests, Routine, Biochemistry (medical), Original Papers, chemistry, humans, reproducibility of results, quality control, Outlier, Biological Assay, Biomarkers
Abstract

Introduction: Duplicate measurements can be used to describe the performance and analytical robustness of assays and to identify outliers. We performed about 235,000 duplicate measurements of nine routinely measured quantities and evaluated the observed differences between the replicates to develop new markers for analytical performance and robustness. Materials and methods: Catalytic activity concentrations of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and concentrations of calcium, cholesterol, creatinine, C-reactive protein (CRP), lactate, triglycerides and thyroid-stimulating hormone (TSH) in 237,261 patient plasma samples were measured in replicates using routine methods. The performance of duplicate measurements was evaluated in scatterplots with a variable and symmetrical zone of acceptance (A-zone) around the equal line. Two quality markers were established: 1) AZ95: the width of an A-zone at which 95 % of all duplicate measurements were within this zone; and 2) OPM (outliers per mille): the relative number of outliers if an A-zone width of 5 % was applied. Results: The AZ95 ranges from 3.2% for calcium to 11.5% for CRP and the OPM from 5 (calcium) to 250 (creatinine). Calcium, TSH and cholesterol have an AZ95 of less than 5% and an OPM of less than 50. Conclusions: Duplicate measurements of a large number of patient samples identify even low frequencies of extreme differences and thereof defined outliers. We suggest two additional quality markers, AZ95 and OPM, to complement description of assay performance and robustness. This approach can aid the selection process of measurement procedures in view of clinical needs.

Published
2017

18. Angiopoietin-2, its soluble receptor Tie-2, and metabolic syndrome components in a population-based sample

Author

Anne Grotevendt, Ramachandran S. Vasan, Marcus Dörr, Hans-Jörgen Grabe, Wolfgang Lieb, Sabrina Schlesinger, Henry Völzke, Henri Wallaschofski, Alexander Teumer, Sebastian E. Baumeister, Matthias Nauck, and Roberto Lorbeer

Subjects
0301 basic medicine, Percentile, medicine.medical_specialty, Endocrinology, Diabetes and Metabolism, Population, Medicine (miscellaneous), Disease, 030204 cardiovascular system & hematology, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Endocrinology, Internal medicine, medicine, education, Receptor, Subclinical infection, education.field_of_study, Nutrition and Dietetics, Cholesterol, business.industry, medicine.disease, 030104 developmental biology, chemistry, Study of Health in Pomerania, Metabolic syndrome, business
Abstract

Objective: Since angiopoietin-2 (Ang-2) levels strongly correlate with cardiovascular mortality and subclinical cardiovascular disease, it was hypothesized that levels of Ang-2 and its soluble receptor (sTie-2) were associated with the metabolic syndrome (MetS) and individual MetS components. Methods: Within the population-based Study of Health in Pomerania, two sets of analyses were performed. First, Ang-2 and sTie-2 were related to the prevalence of MetS and its components cross-sectionally (n=3,205). Second, the association between baseline Ang-2 and sTie-2 and incident MetS or longitudinal changes in its components in 1,295 individuals was investigated. Results: High Ang-2 levels (90th percentile), compared with low Ang-2 levels (10th percentile), were positively associated with MetS (OR: 1.78) and with the following MetS criteria: increased triglycerides, lower HDL cholesterol, and higher non-fasting glucose. Furthermore, high sTie-2 levels (90th percentile), compared with low levels (10th percentile), were positively related to MetS (OR: 1.58) and most of its components. However, Ang-2 and sTie-2 levels were not associated with incident MetS or longitudinal change in components of MetS. Conclusions: Ang-2 and sTie-2 levels were cross-sectionally associated with MetS and several of its components. However, Ang-2 and sTie-2 levels were not associated with incident MetS or changes in individual MetS components during follow-up.

Published
2016

19. Longitudinal effects of systemic inflammation markers on periodontitis

Author

Marcello Ricardo Paulista Markus, Anne Grotevendt, Marcus Dörr, Christiane Pink, Matthias Nauck, Peter Meisel, Lukasz Jablonowski, Thomas Kocher, and Birte Holtfreter

Subjects
Inflammation, Periodontitis, business.industry, medicine.disease, Fibrinogen, Systemic inflammation, Article, Leukocyte Count, Tooth Loss, medicine.anatomical_structure, Clinical attachment loss, White blood cell, Diabetes mellitus, Study of Health in Pomerania, Periodontal Attachment Loss, Immunology, Humans, Periodontics, Medicine, medicine.symptom, business, Biomarkers, medicine.drug
Abstract

Aim Systemic low-grade inflammation represents a central hallmark of chronic diseases and has been proposed as the underlying mechanism linking factors like obesity or diabetes with periodontitis. However, the impact of inflammatory markers on periodontitis has not yet been investigated. Materials and Methods The study population comprised 1784 subjects from the Study of Health in Pomerania with complete 11-year follow-up. Fibrinogen and white blood cell (WBC) counts were measured as markers of inflammation. Periodontitis was assessed by probing depth (PD), clinical attachment loss (CAL) and the CDC/AAP case definition. Results Multilevel regression analyses revealed significant coefficients for the impact of both inflammation markers on the percentage of sites with PD/CAL ≥ 3 mm. Increases in fibrinogen about 1 g/l were associated with 3.0% and 2.7% more sites with PD/CAL ≥ 3 mm respectively. Consistent associations were found using mean values of PD/CAL but not using missing teeth or caries. Regarding the CDC/AAP case definition, 11-year changes in fibrinogen and WBC counts were significantly associated with ≥1 category progression (OR: 1.36 and 1.11). Conclusions Fibrinogen levels and WBC counts showed consistent long-term associations with PD, CAL and the CDC/AAP case definition. Results indicate that systemic low-grade inflammation might indeed represent one possible pathway for effects of obesity, diabetes or other chronic inflammatory conditions on periodontitis.

Published
2015

20. Comparison of HapMap and 1000 genomes reference panels in a large-scale genome-wide association study

Author

Graciela E. Delgado, Alanna C. Morrison, Jie Huang, Toshiko Tanaka, Karl J. Lackner, Torben Hansen, Magdalena Zoledziewska, Jan W. Jukema, Antonella Mulas, Gordon D.O. Lowe, Philipp S. Wild, Maria Sabater-Lleal, Weihua Guan, David P. Strachan, Johanna Mazur, Paul Mitchell, Stefania Bandinelli, Cristina Venturini, Albert Hofman, Daniel I. Chasman, Paul M. Ridker, Pirro G. Hysi, Fernando Rivadeneira, Kent D. Taylor, P. Eline Slagboom, Massimo Mangino, Jouke J. Hottenga, Vera Grossmann, Maristella Steri, Ian J. Deary, Paul S. de Vries, Ann Rumley, Mark McEvoy, Marcus E. Kleber, Tarunveer S. Ahluwalia, Christopher Oldmeadow, Riccardo E. Marioni, Lynda M. Rose, Harald Binder, Naveed Sattar, Anton J. M. de Craen, René Pool, Francesco Cucca, Saonli Basu, Jie Jin Wang, Oscar H. Franco, Elizabeth G. Holliday, Stella Trompet, Rodney J. Scott, Moniek P.M. de Maat, Winfried März, Annette Kifley, Wendy L. McArdle, Alexander Teumer, Nicholas L. Smith, Eco J. C. de Geus, John M. Starr, Christopher J. O'Donnell, John Attia, Bruce M. Psaty, Mattias Frånberg, Uwe Völker, Tim D. Spector, Harmen H.M. Draisma, Tanja Zeller, Symen Ligthart, Dorret I. Boomsma, Anders Hamsten, Qiong Yang, Barbara McKnight, André G. Uitterlinden, Lu-Chen Weng, Weihong Tang, Geoffrey H. Tofler, Hugh Watkins, Tina L. Berentzen, David J. Stott, Jerome I. Rotter, Anne Grotevendt, Jennifer A. Brody, Lorna M. Lopez, Abbas Dehghan, Luigi Ferrucci, Andreas Greinacher, Dena G. Hernandez, Ming-Huei Chen, Epidemiology, Hematology, Internal Medicine, Biological Psychology, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, APH - Methodology, and Yao, Yong-Gang

Subjects
Netherlands Twin Register (NTR), 0301 basic medicine, Glycobiology, Social Sciences, lcsh:Medicine, Genome-wide association study, 030105 genetics & heredity, Biochemistry, Mathematical and Statistical Techniques, Sociology, Cell Signaling, Consortia, GENETIC-VARIANTS, Medicine and Health Sciences, IMPUTATION, International HapMap Project, lcsh:Science, Genetics, Multidisciplinary, COMMON VARIANTS, Genomics, Multidisciplinary Sciences, INSIGHTS, CARDIOVASCULAR-DISEASE, Physical Sciences, symbols, Science & Technology - Other Topics, Health Services Research, Genomic Signal Processing, Statistics (Mathematics), Research Article, Signal Transduction, Genotyping, SUSCEPTIBILITY LOCI, General Science & Technology, BIOLOGY, Single-nucleotide polymorphism, HapMap Project, Computational biology, PRESSURE, Biology, Research and Analysis Methods, 03 medical and health sciences, symbols.namesake, MD Multidisciplinary, Genome-Wide Association Studies, Journal Article, Humans, Statistical Methods, 1000 Genomes Project, Molecular Biology Techniques, Molecular Biology, METAANALYSIS, Glycoproteins, Science & Technology, lcsh:R, Human Genome, CONSORTIUM, Biology and Life Sciences, Computational Biology, Fibrinogen, Human Genetics, Cell Biology, Comparative Genomics, Genome Analysis, Health Care, 030104 developmental biology, Bonferroni correction, lcsh:Q, Haplotype estimation, Mathematics, Imputation (genetics), Meta-Analysis, Genome-Wide Association Study
Abstract

An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.

Published
2017

21. Association between serum vitamin D concentrations and inflammatory markers in the general adult population

Author

Matthias Nauck, Liesa Mellenthin, Henri Wallaschofski, Henry Völzke, Anke Hannemann, and Anne Grotevendt

Subjects
Adult, Male, medicine.medical_specialty, Endocrinology, Diabetes and Metabolism, Population, vitamin D deficiency, Endocrinology, Germany, Internal medicine, medicine, Vitamin D and neurology, Humans, Vitamin D, education, Aged, Aged, 80 and over, Inflammation, education.field_of_study, biology, business.industry, C-reactive protein, Middle Aged, medicine.disease, Study of Health in Pomerania, biology.protein, Population study, Female, business, Biomarkers, Dyslipidemia, Blood sampling
Abstract

In recent years links among vitamin D deficiency, inflammation and cardio-metabolic disease were proposed. As information regarding the associations between vitamin D and inflammatory markers in the general population is sparse, we investigated the associations of 25-hydroxy vitamin D [25(OH)D] with high-sensitivity C-reactive protein (hs-CRP), fibrinogen and white blood cell count (WBC).The study population comprised 2723 men and women aged 25-88 years from the first follow-up of the Study of Health in Pomerania. Analyses of variance, linear and logistic regressions were performed to assess the associations between 25(OH)D and the three inflammatory markers. The models were adjusted for age, sex, waist circumference, diabetes mellitus, dyslipidemia, anti-inflammatory medication and month of blood sampling. The association between 25(OH)D and WBC was assessed separately in smokers (n = 718) and non-smokers (n = 2005) as effect modification was observed.We detected a U-shaped association between 25(OH)D and hs-CRP with a nadir of 21-25 ng/ml in fully-adjusted linear regression models with restricted cubic splines (p0.01; p'0.01). We further detected an inverse association between 25(OH)D and fibrinogen (p0.01). In addition, there was an inverse association between 25(OH)D and WBC in smokers (p = 0.02) but no association in non-smokers (p = 0.73).Our study confirms a potential role of 25(OH)D in chronic inflammation. Yet, different inflammatory biomarkers are differently associated with 25(OH)D. Beneficial effects of increasing 25(OH)D were observed for fibrinogen and WBC (in smokers only). In contrast, the U-shaped association between 25(OH)D and hs-CRP indicates that ever-increasing 25(OH)D concentrations may also be related to proinflammatory states.

Published
2014

22. Abdominal obesity modifies long-term associations between periodontitis and markers of systemic inflammation

Author

Lukasz Jablonowski, Birte Holtfreter, Christiane Gocke, Anne Grotevendt, Thomas Kocher, Marcello Ricardo Paulista Markus, Peter Meisel, and Matthias Nauck

Subjects
Adult, Male, medicine.medical_specialty, Pathology, Population, Inflammation, Fibrinogen, Systemic inflammation, Gastroenterology, Risk Factors, Germany, Internal medicine, Humans, Medicine, Longitudinal Studies, Periodontitis, education, Abdominal obesity, education.field_of_study, business.industry, Middle Aged, Atherosclerosis, medicine.disease, Cardiovascular Diseases, Obesity, Abdominal, Study of Health in Pomerania, Female, medicine.symptom, Cardiology and Cardiovascular Medicine, business, Cohort study, medicine.drug
Abstract

Periodontitis is considered to promote atherosclerosis and cardiovascular diseases through increased low-grade systemic inflammation. However, there is no information on the long-term impact of periodontitis on systemic inflammation from cohort studies. Thus, this study aims to assess the impact of periodontitis on systemic inflammation (fibrinogen and white blood cells (WBC)) in a population-based longitudinal survey in north-eastern Germany.The study sample comprised 2622 subjects from the Study of Health in Pomerania with complete 5- and 11-year follow-ups. Periodontitis was assessed by probing depth and clinical attachment level. Multilevel regression analyses were applied to evaluate associations between periodontitis measures and i) fibrinogen/WBC count using 11-year follow-up data and ii) respective z-scores of fibrinogen/WBC count using 5- and 11-year follow-up data. We adjusted for common cardiovascular risk factors and stratified analyses by abdominal obesity (P for interaction0.10).In lean subjects, beta-coefficients of mean probing depth were B = 0.13 (0.08-0.019; P 0.001) for fibrinogen and B = 0.50 (0.37-0.64; P 0.001) for WBC count using 11-year follow-up data only. For lean subjects, models using z-scores confirmed that increased mean probing depths were associated with increased fibrinogen z-scores (B = 0.14 (0.09-0.18; P 0.001)) and increased WBC z-scores (B = 0.16 (0.11-0.20; P 0.001)). Consistent results were found for mean clinical attachment levels. For abdominally obese subjects, relations between periodontitis measures and levels of inflammation markers were less pronounced or non-significant.Modified by abdominal obesity, periodontitis affected systemic inflammation in a significant dose-dependent manner. Results contribute to the discussion on how periodontitis is linked to atherosclerosis and cardiovascular diseases.

Published
2014

23. Cross-sectional and longitudinal relation of IGF1 and IGF-binding protein 3 with lipid metabolism

Author

Henry Völzke, Henri Wallaschofski, Matthias Nauck, Stefanie Samietz, Marie-Luise Eggert, Nele Friedrich, and Anne Grotevendt

Subjects
Adult, Male, medicine.medical_specialty, Cross-sectional study, Endocrinology, Diabetes and Metabolism, Population, IGFBP3, Young Adult, chemistry.chemical_compound, Endocrinology, Internal medicine, medicine, Humans, Insulin-Like Growth Factor I, Risk factor, education, Aged, education.field_of_study, Cholesterol, business.industry, Lipid metabolism, General Medicine, Middle Aged, Lipid Metabolism, Cross-Sectional Studies, Insulin-Like Growth Factor Binding Protein 3, chemistry, Study of Health in Pomerania, Female, Analysis of variance, business
Abstract

BackgroundPrevious intervention studies in patients with GH disorders suggested an impact of IGF1 and IGF-binding protein 3 (IGFBP3) on lipid metabolism, whereas population-based studies revealed conflicting results. Therefore, we aimed to assess the cross-sectional and longitudinal associations between IGF1 or IGFBP3 serum levels and lipids (total, LDL, or HDL cholesterol and triglycerides) in a large-scale study.MethodsData of 2935 subjects (1356 women) from the population-based Study of Health in Pomerania (SHIP) were used. ANOVA, quantile regression, and logistic regression models adjusted for age, waist circumference, physical activity, and alcohol consumption were performed.ResultsIn cross-sectional analyses, we detected that IGF1 and IGFBP3 levels were positively related to total and LDL cholesterol and inversely related to HDL cholesterol in both sexes. Furthermore, IGFBP3 levels showed a positive relationship to triglycerides. In total, IGFBP3 levels were more strongly associated to lipids than IGF1. In longitudinal analysis, we found no influence of baseline IGF1 or IGFBP3 serum concentration on incidentally elevated or reduced lipid levels. However, the positive relationship between IGFBP3 and incidentally elevated triglycerides barely missed statistical significance in women.ConclusionThe present study showed strong cross-sectional associations between IGF1 or IGFBP3 and lipids, whereas no longitudinal relationships were revealed. Therefore, our findings suggest IGF1 and IGFBP3 as a risk marker rather than a risk factor for alterations in lipid metabolism. Further studies are needed to elucidate the mechanisms underlying the association between the GH/IGF axis and lipid metabolism.

Published
2014

24. Modulation of Genetic Associations with Serum Urate Levels by Body-Mass-Index in Humans

Author

Huffman, JE, Albrecht, E, Teumer, A, Mangino, M, Kapur, K, Johnson, T, Kutalik, Z, Pirastu, N, Pistis, G, Lopez, LM, Haller, T, Salo, P, Goel, A, Li, M, Tanaka, T, Dehghan, Abbas, Ruggiero, D, Malerba, G, Smith, AV, Nolte, IM (Ilja), Portas, L, Phipps-Green, A, Boteva, L, Navarro, P, Johansson, A, Hicks, AA, Polasek, O, Esko, T, Peden, JF, Harris, SE, Murgia, F, Wild, SH, Tenesa, A, Tin, A, Mihailov, E, Grotevendt, A, Gislason, GK, Coresh, J, d'Adamo, P, Ulivi, S, Vollenweider, P, Waeber, G, Campbell, S, Kolcic, I, Fisher, K, Viigimaa, M, Metter, JE, Masciullo, C, Trabetti, E, Bombieri, C, Sorice, R, Doring, A, Reischl, E, Strauch, K, Hofman, Bert, Uitterlinden, André, Waldenberger, M, Wichmann, HE, Davies, G, Gow, AJ, Dalbeth, N, Stamp, L, Smit, JH, Kirin, M, Nagaraja, R, Nauck, M, Schurmann, C, Budde, K, Farrington, SM, Theodoratou, E, Jula, A, Salomaa, V, Sala, C, Hengstenberg, C, Burnier, M, Magi, R, Klopp, N, Kloiber, S, Schipf, S, Ripatti, S, Cabras, S, Soranzo, N, Homuth, G, Nutile, T, Munroe, PB, Hastie, N, Campbell, H, Rudan, I, Cabrera, C, Haley, C, Franco Duran, OH, Merriman, TR, Gudnason, V, Pirastu, M, Penninx, BW, Snieder, H, Metspalu, A, Ciullo, M, Pramstaller, PP, Duijn, Cornelia, Ferrucci, L, Gambaro, G, Deary, IJ, Dunlop, MG, Wilson, JF, Gasparini, P, Gyllensten, U, Spector, TD, Wright, AF, Hayward, C, Watkins, H, Perola, M, Bochud, M, Kao, WHL, Caulfield, M, Toniolo, D, Volzke, H, Gieger, C, Kottgen, A, Vitart, V, Huffman, Jennifer E., Albrecht, Eva, Teumer, Alexander, Mangino, Massimo, Kapur, Karen, Johnson, Toby, Kutalik, Zoltán, Pirastu, Nicola, Pistis, Giorgio, Lopez, Lorna M., Haller, Tooma, Salo, Perttu, Goel, Anuj, Li, Man, Tanaka, Toshiko, Dehghan, Abba, Ruggiero, Daniela, Malerba, Giovanni, Smith, Albert V., Nolte, Ilja M., Portas, Laura, Phipps Green, Amanda, Boteva, Lora, Navarro, Pau, Johansson, Asa, Hicks, Andrew A., Polasek, Ozren, Esko, Tõnu, Peden, John F., Harris, Sarah E., Murgia, Federico, Wild, Sarah H., Tenesa, Albert, Tin, Adrienne, Mihailov, Evelin, Grotevendt, Anne, Gislason, Gauti K., Coresh, Josef, D'Adamo, ADAMO PIO, Ulivi, Sheila, Vollenweider, Peter, Waeber, Gerard, Campbell, Susan, Kolcic, Ivana, Fisher, Krista, Viigimaa, Margu, Metter, Jeffrey E., Masciullo, Corrado, Trabetti, Elisabetta, Bombieri, Cristina, Sorice, Rossella, Döring, Angela, Reischl, Eva, Strauch, Konstantin, Hofman, Albert, Uitterlinden, Andre G., Waldenberger, Melanie, Wichmann, H. Erich, Davies, Gail, Gow, Alan J., Dalbeth, Nicola, Stamp, Lisa, Smit, Johannes H., Kirin, Mirna, Nagaraja, Ramaiah, Nauck, Matthia, Schurmann, Claudia, Budde, Kathrin, Farrington, Susan M., Theodoratou, Evropi, Jula, Antti, Salomaa, Veikko, Sala, Cinzia, Hengstenberg, Christian, Burnier, Michel, Mägi, Reedik, Klopp, Norman, Kloiber, Stefan, Schipf, Sabine, Ripatti, Samuli, Cabras, Stefano, Soranzo, Nicole, Homuth, Georg, Nutile, Teresa, Munroe, Patricia B., Hastie, Nichola, Campbell, Harry, Rudan, Igor, Cabrera, Claudia, Haley, Chri, Franco, Oscar H., Merriman, Tony R., Gudnason, Vilmundur, Pirastu, Mario, Penninx, Brenda W., Snieder, Harold, Metspalu, Andre, Ciullo, Marina, Pramstaller, Peter P., Van Duijn, Cornelia M., Ferrucci, Luigi, Gambaro, Giovanni, Deary, Ian J., Dunlop, Malcolm G., Wilson, James F., Gasparini, Paolo, Gyllensten, Ulf, Spector, Tim D., Wright, Alan F., Hayward, Caroline, Watkins, Hugh, Perola, Marku, Bochud, Murielle, Linda Kao, W. H., Caulfield, Mark, Toniolo, Daniela, Völzke, Henry, Gieger, Christian, Köttgen, Anna, Vitart, Veronique, Life Course Epidemiology (LCE), Institute for Molecular Medicine Finland, Samuli Olli Ripatti / Principal Investigator, Biostatistics Helsinki, Quantitative Genetics, Complex Disease Genetics, Epidemiology, Public Health, and Internal Medicine

Subjects
Genetics and Molecular Biology (all), Male, Gout, lcsh:Medicine, Biochemistry, Body Mass Index, Risk Factors, GWAS, ATP Binding Cassette Transporter, Subfamily G, Member 2, Settore MED/14 - NEFROLOGIA, Oxidoreductases Acting on Sulfur Group Donors, lcsh:Science, METABOLIC SYNDROME, INSULIN-RESISTANCE, Membrane Glycoproteins, PLASMA N-GLYCANS, Edar Receptor, Medicine (all), Antigens, Nuclear, Neoplasm Proteins, Female, Medical Genetics, Research Article, Genotype, Nerve Tissue Proteins, Polymorphism, Single Nucleotide, BMI, SDG 3 - Good Health and Well-being, Humans, Agricultural and Biological Sciences (all), Biochemistry, Genetics and Molecular Biology (all), Obesity, GENOME-WIDE ASSOCIATION, Medicinsk genetik, ENVIRONMENT, lcsh:R, TRANSPORTER, serum urate levels, genetic associations, Overweight, Uric Acid, ALKALINE-PHOSPHATASE, Genetic Loci, RISK-FACTORS, Linear Models, lcsh:Q, ATP-Binding Cassette Transporters, 3111 Biomedicine, URIC-ACID LEVELS, Genome-Wide Association Study
Abstract

We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, P-inter= 2.6 x 10(-8)). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDAR-ADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 x 10(-8)), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight= 9.1 x 10(-8)), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (Pdifflean-obese= 2 x 10(-4)). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obesogenic environment.

Published
2015

25. Meta-analysis identifies multiple loci associated with kidney function-related traits in east Asian populations

Author

Okada, Y, Sim, X, Go, Mj, Wu, Jy, Gu, D, Takeuchi, F, Takahashi, A, Maeda, S, Tsunoda, T, Chen, P, Lim, Sc, Wong, Ty, Liu, J, Young, Tl, Aung, T, Seielstad, M, Teo, Yy, Kim, Yj, Lee, Jy, Han, Bg, Kang, D, Chen, Ch, Tsai, Fj, Chang, Lc, Fann, Sj, Mei, H, Rao, Dc, Hixson, Je, Chen, S, Katsuya, T, Isono, M, Ogihara, T, Chambers, Jc, Zhang, W, Kooner, Js, Kidneygen, Consortium, Ckdgen, Consortium, Albrecht, E, Gugc, Consortium, Yamamoto, K, Kubo, M, Nakamura, Y, Kamatani, N, Kato, N, He, J, Chen, Yt, Cho, Ys, Tai, Es, Tanaka, T., Lord, Gm, van der Harst, P, Lawlor, Da, Sehmi, Js, Gale, Dp, Wass, Mn, Ahmadi, Kr, Bakker, Sj, Beckmann, J, Bilo, Hj, Bochud, M, Brown, Mj, Caulfield, Mj, Connell, Jm, Cook, Ht, Cotlarciuc, I, Smith, Gd, de Silva, R, Deng, G, Devuyst, O, Dikkeschei, Ld, Dimkovic, N, Dockrell, M, Dominiczak, A, Ebrahim, S, Eggermann, T, Farrall, M, Ferrucci, L, Floege, J, Forouhi, Ng, Gansevoort, Rt, Han, X, Hedblad, B, van der Heide JJ, Hepkema, Bg, Hernandez Fuentes, M, Hypponen, E, Johnson, T, de Jong PE, Kleefstra, N, Lagou, V, Lapsley, M, Li, Y, Loos, Rj, Luan, J, Luttropp, K, Maréchal, C, Melander, O, Munroe, Pb, Nordfors, L, Parsa, A, Peltonen, L, Penninx, Bw, Perucha, E, Pouta, A, Prokopenko, I, Roderick, Pj, Ruokonen, A, Samani, Nj, Sanna, S, Schalling, M, Schlessinger, D, Schlieper, G, Seelen, Ma, Shuldiner, Ar, Sjögren, M, Smit, Jh, Snieder, H, Soranzo, N, Spector, Td, Stenvinkel, P, Sternberg, Mj, Swaminathan, R, Tanaka, T, Ubink Veltmaat LJ, Uda, M, Vollenweider, P, Wallace, C, Waterworth, D, Zerres, K, Waeber, G, Wareham, Nj, Maxwell, Ph, Mccarthy, Mi, Jarvelin, Mr, Mooser, V, Abecasis, Gr, Lightstone, L, Scott, J, Navis, G, Elliott, P, Köttgen, A, Pattaro, C, Böger, Ca, Fuchsberger, C, Olden, M, Glazer, Nl, Gao, X, Yang, Q, Smith, Av, O'Connell, Jr, Li, M, Schmidt, H, Isaacs, A, Ketkar, S, Hwang, Sj, Johnson, Ad, Dehghan, A, Teumer, A, Paré, G, Atkinson, Ej, Zeller, T, Lohman, K, Cornelis, Mc, Probst Hensch NM, Kronenberg, F, Tönjes, A, Hayward, C, Aspelund, T, Eiriksdottir, G, Launer, Lj, Harris, Tb, Rampersaud, E, Mitchell, Bd, Arking, De, Boerwinkle, E, Struchalin, M, Cavalieri, M, Singleton, A, Giallauria, F, Metter, J, de Boer IH, Haritunians, T, Lumley, T, Siscovick, D, Psaty, Bm, Zillikens, Mc, Oostra, Ba, Feitosa, M, Province, M, de Andrade, M, Turner, St, Schillert, A, Ziegler, A, Wild, Ps, Schnabel, Rb, Wilde, S, Munzel, Tf, Leak, Ts, Illig, T, Klopp, N, Meisinger, C, Wichmann, He, Koenig, W, Zgaga, L, Zemunik, T, Kolcic, I, Minelli, C, Hu, Fb, Johansson, Å, Igl, W, Zaboli, G, Wild, Sh, Wright, Af, Campbell, H, Ellinghaus, D, Schreiber, S, Aulchenko, Ys, Felix, Jf, Rivadeneira, F, Uitterlinden, Ag, Hofman, A, Imboden, M, Nitsch, D, Brandstätter, A, Kollerits, B, Kedenko, L, Mägi, R, Stumvoll, M, Kovacs, P, Boban, M, Campbell, S, Endlich, K, Völzke, H, Kroemer, Hk, Nauck, M, Völker, U, Polasek, O, Vitart, V, Badola, S, Parker, An, Ridker, Pm, Kardia, Sl, Blankenberg, S, Liu, Y, Curhan, Gc, Franke, A, Rochat, T, Paulweber, B, Wang, W, Gudnason, V, Coresh, J, Schmidt, R, Shlipak, Mg, van Duijn CM, Borecki, I, Krämer, Bk, Rudan, I, Gyllensten, U, Wilson, Jf, Witteman, Jc, Pramstaller, Pp, Rettig, R, Hastie, N, Chasman, Di, Kao, Wh, Heid, Im, Fox, Cs, Krumsiek, J, Hundertmark, C, Pistis, G, Ruggiero, D, O'Seaghdha, M, Haller, T, Kutalik, Z, Shi, J, Middelberg, Ps, Gaffo, Al, Pirastu, N, Li, G, Huffman, J, Yengo, L, Zhao, Jh, Demirkan, A, Feitosa, Mf, Liu, X, Malerba, Giovanni, Lopez, Lm, Li, X, Kleber, Me, Hicks, Aa, Nolte, Im, Johansson, A, Murgia, F, Peden, Jf, Steri, M, Tenesa, A, Salo, P, Mangino, M, Rose, Lm, Lehtimäki, T, Woodward, Om, Tin, A, Müller, C, Oldmeadow, C, Putku, M, Czamara, D, Kraft, P, Frogheri, L, Thun, Ga, Grotevendt, A, Gislason, Gk, Mcardle, P, Schallert, M, Martin, Ng, Montgomery, Gw, Jacobs DR Jr, Liu, K, D'Adamo, P, Ulivi, S, Rotter, Ji, Navaro, P, Balkau, B, Froguel, P, Esko, T, Salumets, A, Khaw, Kt, Langenberg, C, Kraja, A, Zhang, Q, Scott, Rj, Holliday, Eg, Org, E, Viigimaa, M, Bandinelli, S, Metter, Je, Lupo, Antonio, Trabetti, Elisabetta, Sorice, R, Döring, A, Lattka, E, Strauch, K, Theis, F, Waldenberger, M, Davies, G, Gow, Aj, Bruinenberg, M, Stolk, Rp, Winkelmann, Br, Boehm, Bo, Lucae, S, Curhan, G, Mudgal, P, Plenge, Rm, Portas, L, Persico, I, Kirin, M, Mateo Leach, I, van Gilst WH, Goel, A, Ongen, H, von Eckardstein, A, Cucca, F, Nagaraja, R, Piras, Mg, Schurmann, C, Budde, K, Ernst, F, Farrington, Sm, Theodoratou, E, Jula, A, Perola, M, Salomaa, V, Shin, Sy, Sala, C, Kähönen, M, Viikari, J, Hengstenberg, C, Nelson, Cp, Meschia, Jf, Nalls, Ma, Sharma, P, Singleton, Ab, Burnier, M, Attia, J, Laan, M, Hillege, Hl, Kloiber, S, Choi, H, Pirastu, M, Tore, S, Whitfield, Jb, Fornage, M, Gasparini, P, Siscovick, Ds, Bouatia Naji, N, Metspalu, A, Borecki, Ib, Gambaro, G, Deary, Ij, Wolffenbuttel, Bh, März, W, Watkins, H, Schipf, S, Dunlop, Mg, Ripatti, S, Toniolo, D, Raitakari, O, Ciullo, M, Caulfield, M, Gieger, C., Okada, Y, Sim, X, Go, Mj, Wu, Jy, Gu, D, Takeuchi, F, Takahashi, A, Maeda, S, Tsunoda, T, Chen, P, Lim, Sc, Wong, Ty, Liu, J, Young, Tl, Aung, T, Seielstad, M, Teo, Yy, Kim, Yj, Lee, Jy, Han, Bg, Kang, D, Chen, Ch, Tsai, Fj, Chang, Lc, Fann, Sj, Mei, H, Rao, Dc, Hixson, Je, Chen, S, Katsuya, T, Isono, M, Ogihara, T, Chambers, Jc, Zhang, W, Kooner, J, Albrecht, E, Yamamoto, K, Kubo, M, Nakamura, Y, Kamatani, N, Kato, N, He, J, Chen, Yt, Cho, Y, Tai, E, Tanaka, T, Lord, Gm, van der Harst, P, Lawlor, Da, Sehmi, J, Gale, Dp, Wass, Mn, Ahmadi, Kr, Bakker, Sj, Beckmann, J, Bilo, Hj, Bochud, M, Brown, Mj, Caulfield, Mj, Connell, Jm, Cook, Ht, Cotlarciuc, I, Smith, Gd, de Silva, R, Deng, G, Devuyst, O, Dikkeschei, Ld, Dimkovic, N, Dockrell, M, Dominiczak, A, Ebrahim, S, Eggermann, T, Farrall, M, Ferrucci, L, Floege, J, Forouhi, Ng, Gansevoort, Rt, Han, X, Hedblad, B, van der Heide, Jj, Hepkema, Bg, Hernandez Fuentes, M, Hypponen, E, Johnson, T, de Jong, Pe, Kleefstra, N, Lagou, V, Lapsley, M, Li, Y, Loos, Rj, Luan, J, 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D.P., Wass, M.N., Ahmadi, K.R., Bakker, S.J., Beckmann, J., Bilo, H.J., Bochud, M., Brown, M.J., Caulfield, M.J., Connell, J.M., Cook, H.T., Cotlarciuc, I., Smith, G.D., de Silva, R., Deng, G., Devuyst, O., Dikkeschei, L.D., Dimkovic, N., Dockrell, M., Dominiczak, A., Ebrahim, S., Eggermann, T., Farrall, M., Ferrucci, L., Floege, J., Forouhi, N.G., Gansevoort, R.T., Han, X., Hedblad, B., van der Heide, J.J., Hepkema, B.G., Hernandez-Fuentes, M., Hypponen, E., Johnson, T., de Jong, P.E., Kleefstra, N., Lagou, V., Lapsley, M., Li, Y., Loos, R.J., Luan, J., Luttropp, K., Maréchal, C., Melander, O., Munroe, P.B., Nordfors, L., Parsa, A., Peltonen, L., Penninx, B.W., Perucha, E., Pouta, A., Prokopenko, I., Roderick, P.J., Ruokonen, A., Samani, N.J., Sanna, S., Schalling, M., Schlessinger, D., Schlieper, G., Seelen, M.A., Shuldiner, A.R., Sjögren, M., Smit, J.H., Snieder, H., Soranzo, N., Spector, T.D., Stenvinkel, P., Sternberg, M.J., Swaminathan, R., Tanaka, T., Ubink-Veltmaat, L.J., Uda, M., Vollenweider, P., Wallace, C., Waterworth, D., Zerres, K., Waeber, G., Wareham, N.J., Maxwell, P.H., McCarthy, M.I., Jarvelin, M.R., Mooser, V., Abecasis, G.R., Lightstone, L., Scott, J., Navis, G., Elliott, P., Kooner, J.S., Köttgen, A., Pattaro, C., Böger, C.A., Fuchsberger, C., Olden, M., Glazer, N.L., Gao, X., Yang, Q., Smith, A.V., O'Connell, J.R., Li, M., Schmidt, H., Isaacs, A., Ketkar, S., Hwang, S.J., Johnson, A.D., Dehghan, A., Teumer, A., Paré, G., Atkinson, E.J., Zeller, T., Lohman, K., Cornelis, M.C., Probst-Hensch, N.M., Kronenberg, F., Tönjes, A., Hayward, C., Aspelund, T., Eiriksdottir, G., Launer, L.J., Harris, T.B., Rampersaud, E., Mitchell, B.D., Arking, D.E., Boerwinkle, E., Struchalin, M., Cavalieri, M., Singleton, A., Giallauria, F., Metter, J., de Boer, I.H., Haritunians, T., Lumley, T., Siscovick, D., Psaty, B.M., Zillikens, M.C., Oostra, B.A., Feitosa, M., Province, M., de Andrade, M., Turner, S.T., Schillert, A., Ziegler, A., Wild, P.S., Schnabel, R.B., Wilde, S., Munzel, T.F., Leak, T.S., Illig, T., Klopp, N., Meisinger, C., Wichmann, H.E., Koenig, W., Zgaga, L., Zemunik, T., Kolcic, I., Minelli, C., Hu, F.B., Johansson, Å., Igl, W., Zaboli, G., Wild, S.H., Wright, A.F., Campbell, H., Ellinghaus, D., Schreiber, S., Aulchenko, Y.S., Felix, J.F., Rivadeneira, F., Uitterlinden, A.G., Hofman, A., Imboden, M., Nitsch, D., Brandstätter, A., Kollerits, B., Kedenko, L., Mägi, R., Stumvoll, M., Kovacs, P., Boban, M., Campbell, S., Endlich, K., Völzke, H., Kroemer, H.K., Nauck, M., Völker, U., Polasek, O., Vitart, V., Badola, S., Parker, A.N., Ridker, P.M., Kardia, S.L., Blankenberg, S., Liu, Y., Curhan, G.C., Franke, A., Rochat, T., Paulweber, B., Wang, W., Gudnason, V., Coresh, J., Schmidt, R., Shlipak, M.G., van Duijn, C.M., Borecki, I., Krämer, B.K., Rudan, I., Gyllensten, U., Wilson, J.F., Witteman, J.C., Pramstaller, P.P., Rettig, R., Hastie, N., Chasman, D.I., Kao, W.H., Heid, I.M., Fox, C.S., Albrecht, E., Krumsiek, J., Hundertmark, C., Pistis, G., Ruggiero, D., O'Seaghdha, M., Haller, T., Kutalik, Z., Shi, J., Middelberg, P.S., Gaffo, A.L., Pirastu, N., Li, G., Huffman, J., Yengo, L., Zhao, J.H., Demirkan, A., Feitosa, M.F., Liu, X., Malerba, G., Lopez, L.M., Li, X., Kleber, M.E., Hicks, A.A., Nolte, I.M., Johansson, A., Murgia, F., Peden, J.F., Steri, M., Tenesa, A., Salo, P., Mangino, M., Rose, L.M., Lehtimäki, T., Woodward, O.M., Okada, Y., Tin, A., Müller, C., Oldmeadow, C., Putku, M., Czamara, D., Kraft, P., Frogheri, L., Thun, G.A., Grotevendt, A., Gislason, G.K., McArdle, P., Schallert, M., Martin, N.G., Montgomery, G.W., Kubo, M., Nakamura, Y., Jacobs, D.R., Liu, K., D'Adamo, P., Ulivi, S., Rotter, J.I., Navaro, P., Balkau, B., Froguel, P., Esko, T., Salumets, A., Khaw, K.T., Langenberg, C., Kraja, A., Zhang, Q., Scott, R.J., Holliday, E.G., Org, E., Viigimaa, M., Bandinelli, S., Metter, J.E., Lupo, A., Trabetti, E., Sorice, R., Döring, A., Lattka, E., Strauch, K., Theis, F., Waldenberger, M., Davies, G., 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Subjects
Asian Continental Ancestry Group, kidney, Population, Renal function, Genome-wide association study, Biology, Kidney, Polymorphism, Single Nucleotide, Article, Blood Urea Nitrogen, Cohort Studies, chemistry.chemical_compound, SDG 3 - Good Health and Well-being, Asian People, loci, Asian, medicine, Humans, genetics, Genetic Predisposition to Disease, Renal Insufficiency, Chronic, education, meta-analysis, Genome-Wide Association Study, Genetic association, Genetics, Creatinine, education.field_of_study, ta3121, medicine.disease, Uric Acid, Asian Continental Ancestry Group/genetics, Creatinine/blood, Glomerular Filtration Rate/genetics, Kidney/physiology, Renal Insufficiency, Chronic/genetics, Uric Acid/blood, medicine.anatomical_structure, chemistry, Genetic epidemiology, Cohort Studie, Human, Kidney disease, Glomerular Filtration Rate
Abstract

Chronic kidney disease (CKD), impairment of kidney function, is a serious public health problem, and the assessment of genetic factors influencing kidney function has substantial clinical relevance. Here, we report a meta-analysis of genomewide association studies for kidney function-related traits, including 71,149 east Asian individuals from 18 studies in 11 population-, hospital- or family-based cohorts, conducted as part of the Asian Genetic Epidemiology Network (AGEN). Our meta-analysis identified 17 loci newly associated with kidney function-related traits, including the concentrations of blood urea nitrogen, uric acid and serum creatinine and estimated glomerular filtration rate based on serum creatinine levels (eGFRcrea) (P < 5.0 x 10(-8)). We further examined these loci with in silico replication in individuals of European ancestry from the KidneyGen, CKDGen and GUGC consortia, including a combined total of similar to 110,347 individuals. We identify pleiotropic associations among these loci with kidney function-related traits and risk of CKD. These findings provide new insights into the genetics of kidney function.

Published
2012

26. Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study

Author

de Vries, Paul S., Sabater-Lleal, Maria, Chasman, Daniel I., Trompet, Stella, Ahluwalia, Tarunveer S., Teumer, Alexander, Kleber, Marcus E., Chen, Ming-Huei, Wang, Jie Jin, Attia, John R., Marioni, Riccardo E., Steri, Maristella, Weng, Lu-Chen, Pool, Rene, Grossmann, Vera, Brody, Jennifer A., Venturini, Cristina, Tanaka, Toshiko, Rose, Lynda M., Oldmeadow, Christopher, Mazur, Johanna, Basu, Saonli, Frånberg, Mattias, Yang, Qiong, Ligthart, Symen, Hottenga, Jouke J., Rumley, Ann, Mulas, Antonella, de Craen, Anton J. M., Grotevendt, Anne, Taylor, Kent D., Delgado, Graciela E., Kifley, Annette, Lopez, Lorna M., Berentzen, Tina L., Mangino, Massimo, Bandinelli, Stefania, Morrison, Alanna C., Hamsten, Anders, Tofler, Geoffrey, de Maat, Moniek P. M., Draisma, Harmen H. M., Lowe, Gordon D., Zoledziewska, Magdalena, Sattar, Naveed, Lackner, Karl J., Voelker, Uwe, McKnight, Barbara, Huang, Jie, Holliday, Elizabeth G., McEvoy, Mark A., Starr, John M., Hysi, Pirro G., Hernandez, Dena G., Guan, Weihua, Rivadeneira, Fernando, McArdle, Wendy L., Slagboom, P. Eline, Zeller, Tanja, Psaty, Bruce M., Uitterlinden, Andre G., de Geus, Eco J. C., Stott, David J., Binder, Harald, Hofman, Albert, Franco, Oscar H., Rotter, Jerome I., Ferrucci, Luigi, Spector, Tim D., Deary, Ian J., Maerz, Winfried, Greinacher, Andreas, Wild, Philipp S., Cucca, Francesco, Boomsma, Dorret I., Watkins, Hugh, Tang, Weihong, Ridker, Paul M., Jukema, Jan W., Scott, Rodney J., Mitchell, Paul, Hansen, Torben, O'Donnell, Christopher J., Smith, Nicholas L., Strachan, David P., Dehghan, Abbas, de Vries, Paul S., Sabater-Lleal, Maria, Chasman, Daniel I., Trompet, Stella, Ahluwalia, Tarunveer S., Teumer, Alexander, Kleber, Marcus E., Chen, Ming-Huei, Wang, Jie Jin, Attia, John R., Marioni, Riccardo E., Steri, Maristella, Weng, Lu-Chen, Pool, Rene, Grossmann, Vera, Brody, Jennifer A., Venturini, Cristina, Tanaka, Toshiko, Rose, Lynda M., Oldmeadow, Christopher, Mazur, Johanna, Basu, Saonli, Frånberg, Mattias, Yang, Qiong, Ligthart, Symen, Hottenga, Jouke J., Rumley, Ann, Mulas, Antonella, de Craen, Anton J. M., Grotevendt, Anne, Taylor, Kent D., Delgado, Graciela E., Kifley, Annette, Lopez, Lorna M., Berentzen, Tina L., Mangino, Massimo, Bandinelli, Stefania, Morrison, Alanna C., Hamsten, Anders, Tofler, Geoffrey, de Maat, Moniek P. M., Draisma, Harmen H. M., Lowe, Gordon D., Zoledziewska, Magdalena, Sattar, Naveed, Lackner, Karl J., Voelker, Uwe, McKnight, Barbara, Huang, Jie, Holliday, Elizabeth G., McEvoy, Mark A., Starr, John M., Hysi, Pirro G., Hernandez, Dena G., Guan, Weihua, Rivadeneira, Fernando, McArdle, Wendy L., Slagboom, P. Eline, Zeller, Tanja, Psaty, Bruce M., Uitterlinden, Andre G., de Geus, Eco J. C., Stott, David J., Binder, Harald, Hofman, Albert, Franco, Oscar H., Rotter, Jerome I., Ferrucci, Luigi, Spector, Tim D., Deary, Ian J., Maerz, Winfried, Greinacher, Andreas, Wild, Philipp S., Cucca, Francesco, Boomsma, Dorret I., Watkins, Hugh, Tang, Weihong, Ridker, Paul M., Jukema, Jan W., Scott, Rodney J., Mitchell, Paul, Hansen, Torben, O'Donnell, Christopher J., Smith, Nicholas L., Strachan, David P., and Dehghan, Abbas

Abstract

An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5x10(-8) is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5x10(-8)), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.

Published
2017
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27. Comparison of HapMap and 1000 genomes reference panels in a large-scale genome-wide association study

Author

Vries, P.S. (Paul) de, Sabater-Lleal, M. (Maria), Chasman, D.I. (Daniel), Trompet, S. (Stella), Ahluwalia, T.S. (Tarunveer Singh), Teumer, A. (Alexander), Kleber, M.E. (Marcus), Chen, M.-H. (Ming-Huei), Wang, J.J. (Jie Jin), Attia, J. (John), Marioni, R.E. (Riccardo), Steri, M. (Maristella), Weng, L.-C. (Lu-Chen), Pool, R. (Reńe), Grossmann, V. (Vera), Brody, J.A. (Jennifer A.), Venturini, C. (Cristina), Tanaka, T. (Toshiko), Rose, L.M. (Lynda), Oldmeadow, C. (Christopher), Mazur, J. (Johanna), Basu, S. (Saonli), Frånberg, M. (Mattias), Yang, Q. (Qiong), Ligthart, S. (Symen), Hottenga, J.J. (Jouke Jan), Rumley, A. (Ann), Mulas, A. (Antonella), Craen, A.J. (Anton) de, Grotevendt, A. (Anne), Taylor, K.D. (Kent D.), Delgado, G., Kifley, A. (Annette), Lopez, L.M. (Lorna), Berentzen, T.L. (Tina L.), Mangino, M. (Massimo), Bandinelli, S. (Stefania), Morrison, A.C. (Alanna C.), Hamsten, A. (Anders), Tofler, G.H. (Geoffrey), Maat, M.P.M. (Moniek) de, Draisma, G. (Gerrit), Lowe, G.D. (Gordon D.), Zoledziewska, M. (Magdalena), Sattar, N. (Naveed), Lackner, K.J. (Karl J.), Völker, U. (Uwe), McKnight, B. (Barbara), Huang, J. (Jian), Holliday, E.G. (Elizabeth), McEvoy, M.A. (Mark A.), Starr, J.M. (John), Hysi, P.G. (Pirro), Hernandez, D.G. (Dena), Guan, W. (Weihua), Rivadeneira Ramirez, F. (Fernando), McArdle, W.L. (Wendy), Slagboom, P.E. (Eline), Zeller, T. (Tanja), Psaty, B.M. (Bruce), Uitterlinden, A.G. (André), Geus, E.J.C. (Eco) de, Stott, D.J. (David J.), Binder, H. (Harald), Hofman, A. (Albert), Franco, O.H. (Oscar), Rotter, J.I. (Jerome I.), Ferrucci, L. (Luigi), Spector, T.D. (Tim D.), Deary, I.J. (Ian), März, W. (Winfried), Greinacher, A. (Andreas), Wild, P.S. (Philipp S.), Cucca, F. (Francesco), Boomsma, D.I. (Dorret), Watkins, H. (Hugh), Tang, W. (Weihong), Ridker, P.M. (Paul), Jukema, J.W., Scott, R.J. (Rodney J.), Mitchell, P. (Paul), Hansen, T. (T.), O'Donnell, C.J. (Christopher J.), Smith, N.L. (Nicholas L.), Strachan, D.P. (David P.), Dehghan, A. (Abbas), Vries, P.S. (Paul) de, Sabater-Lleal, M. (Maria), Chasman, D.I. (Daniel), Trompet, S. (Stella), Ahluwalia, T.S. (Tarunveer Singh), Teumer, A. (Alexander), Kleber, M.E. (Marcus), Chen, M.-H. (Ming-Huei), Wang, J.J. (Jie Jin), Attia, J. (John), Marioni, R.E. (Riccardo), Steri, M. (Maristella), Weng, L.-C. (Lu-Chen), Pool, R. (Reńe), Grossmann, V. (Vera), Brody, J.A. (Jennifer A.), Venturini, C. (Cristina), Tanaka, T. (Toshiko), Rose, L.M. (Lynda), Oldmeadow, C. (Christopher), Mazur, J. (Johanna), Basu, S. (Saonli), Frånberg, M. (Mattias), Yang, Q. (Qiong), Ligthart, S. (Symen), Hottenga, J.J. (Jouke Jan), Rumley, A. (Ann), Mulas, A. (Antonella), Craen, A.J. (Anton) de, Grotevendt, A. (Anne), Taylor, K.D. (Kent D.), Delgado, G., Kifley, A. (Annette), Lopez, L.M. (Lorna), Berentzen, T.L. (Tina L.), Mangino, M. (Massimo), Bandinelli, S. (Stefania), Morrison, A.C. (Alanna C.), Hamsten, A. (Anders), Tofler, G.H. (Geoffrey), Maat, M.P.M. (Moniek) de, Draisma, G. (Gerrit), Lowe, G.D. (Gordon D.), Zoledziewska, M. (Magdalena), Sattar, N. (Naveed), Lackner, K.J. (Karl J.), Völker, U. (Uwe), McKnight, B. (Barbara), Huang, J. (Jian), Holliday, E.G. (Elizabeth), McEvoy, M.A. (Mark A.), Starr, J.M. (John), Hysi, P.G. (Pirro), Hernandez, D.G. (Dena), Guan, W. (Weihua), Rivadeneira Ramirez, F. (Fernando), McArdle, W.L. (Wendy), Slagboom, P.E. (Eline), Zeller, T. (Tanja), Psaty, B.M. (Bruce), Uitterlinden, A.G. (André), Geus, E.J.C. (Eco) de, Stott, D.J. (David J.), Binder, H. (Harald), Hofman, A. (Albert), Franco, O.H. (Oscar), Rotter, J.I. (Jerome I.), Ferrucci, L. (Luigi), Spector, T.D. (Tim D.), Deary, I.J. (Ian), März, W. (Winfried), Greinacher, A. (Andreas), Wild, P.S. (Philipp S.), Cucca, F. (Francesco), Boomsma, D.I. (Dorret), Watkins, H. (Hugh), Tang, W. (Weihong), Ridker, P.M. (Paul), Jukema, J.W., Scott, R.J. (Rodney J.), Mitchell, P. (Paul), Hansen, T. (T.), O'Donnell, C.J. (Christopher J.), Smith, N.L. (Nicholas L.), Strachan, D.P. (David P.), and Dehghan, A. (Abbas)

Abstract

An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.

Published
2017
Full Text
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28. Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study

Author

Yao, Yong-Gang, de Vries, Paul S., Sabater-Lleal, Maria, Chasman, Daniel I., Trompet, Stella, Ahluwalia, Tarunveer S., Teumer, Alexander, Kleber, Marcus E., Chen, Ming-Huei, Wang, Jie Jin, Attia, John R., Marioni, Riccardo E., Steri, Maristella, Weng, Lu-Chen, Pool, Rene, Grossmann, Vera, Brody, Jennifer A., Venturini, Cristina, Tanaka, Toshiko, Rose, Lynda M., Oldmeadow, Christopher, Mazur, Johanna, Basu, Saonli, Frånberg, Mattias, Yang, Qiong, Ligthart, Symen, Hottenga, Jouke J., Rumley, Ann, Mulas, Antonella, de Craen, Anton J. M., Grotevendt, Anne, Taylor, Kent D., Delgado, Graciela E., Kifley, Annette, Lopez, Lorna M., Berentzen, Tina L., Mangino, Massimo, Bandinelli, Stefania, Morrison, Alanna C., Hamsten, Anders, Tofler, Geoffrey, de Maat, Moniek P. M., Draisma, Harmen H. M., Lowe, Gordon D., Zoledziewska, Magdalena, Sattar, Naveed, Lackner, Karl J., Völker, Uwe, McKnight, Barbara, Huang, Jie, Holliday, Elizabeth G., McEvoy, Mark A., Starr, John M., Hysi, Pirro G., Hernandez, Dena G., Guan, Weihua, Rivadeneira, Fernando, McArdle, Wendy L., Slagboom, P. Eline, Zeller, Tanja, Psaty, Bruce M., Uitterlinden, André G., de Geus, Eco J. C., Stott, David J., Binder, Harald, Hofman, Albert, Franco, Oscar H., Rotter, Jerome I., Ferrucci, Luigi, Spector, Tim D., Deary, Ian J., März, Winfried, Greinacher, Andreas, Wild, Philipp S., Cucca, Francesco, Boomsma, Dorret I., Watkins, Hugh, Tang, Weihong, Ridker, Paul M., Jukema, Jan W., Scott, Rodney J., Mitchell, Paul, Hansen, Torben, O'Donnell, Christopher J., Smith, Nicholas L., Strachan, David P., Dehghan, Abbas, Yao, Yong-Gang, de Vries, Paul S., Sabater-Lleal, Maria, Chasman, Daniel I., Trompet, Stella, Ahluwalia, Tarunveer S., Teumer, Alexander, Kleber, Marcus E., Chen, Ming-Huei, Wang, Jie Jin, Attia, John R., Marioni, Riccardo E., Steri, Maristella, Weng, Lu-Chen, Pool, Rene, Grossmann, Vera, Brody, Jennifer A., Venturini, Cristina, Tanaka, Toshiko, Rose, Lynda M., Oldmeadow, Christopher, Mazur, Johanna, Basu, Saonli, Frånberg, Mattias, Yang, Qiong, Ligthart, Symen, Hottenga, Jouke J., Rumley, Ann, Mulas, Antonella, de Craen, Anton J. M., Grotevendt, Anne, Taylor, Kent D., Delgado, Graciela E., Kifley, Annette, Lopez, Lorna M., Berentzen, Tina L., Mangino, Massimo, Bandinelli, Stefania, Morrison, Alanna C., Hamsten, Anders, Tofler, Geoffrey, de Maat, Moniek P. M., Draisma, Harmen H. M., Lowe, Gordon D., Zoledziewska, Magdalena, Sattar, Naveed, Lackner, Karl J., Völker, Uwe, McKnight, Barbara, Huang, Jie, Holliday, Elizabeth G., McEvoy, Mark A., Starr, John M., Hysi, Pirro G., Hernandez, Dena G., Guan, Weihua, Rivadeneira, Fernando, McArdle, Wendy L., Slagboom, P. Eline, Zeller, Tanja, Psaty, Bruce M., Uitterlinden, André G., de Geus, Eco J. C., Stott, David J., Binder, Harald, Hofman, Albert, Franco, Oscar H., Rotter, Jerome I., Ferrucci, Luigi, Spector, Tim D., Deary, Ian J., März, Winfried, Greinacher, Andreas, Wild, Philipp S., Cucca, Francesco, Boomsma, Dorret I., Watkins, Hugh, Tang, Weihong, Ridker, Paul M., Jukema, Jan W., Scott, Rodney J., Mitchell, Paul, Hansen, Torben, O'Donnell, Christopher J., Smith, Nicholas L., Strachan, David P., and Dehghan, Abbas

Abstract

An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10−8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10−8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.

Published
2017

29. Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study

Author

Vries, PS, Sabater-Lleal, M, Chasman, DI, Trompet, S, Ahluwalia, TS, Teumer, A, Kleber, ME, Chen, MH, Wang, JJ, Attia, JR, Marioni, RE, Steri, M, Weng, LC, Pool, R, Grossmann, V, Brody, JA, Venturini, C, Tanaka, T, Rose, LM, Oldmeadow, C, Mazur, J, Basu, S, Franberg, M, Yang, Q, Ligthart, Symen, Hottenga, JJ, Rumley, A, Mulas, A, de Craen, AJM, Grotevendt, A, Taylor, KD, Delgado, GE, Kifley, A, Lopez, LM, Berentzen, TL, Mangino, M, Bandinelli, S, Morrison, AC, Hamsten, A, Tofler, G, de Maat, Moniek, Draisma, HHM, Lowe, GD, Zoledziewska, M, Sattar, N, Lackner, KJ, Volker, U, McKnight, B, Huang, J, Holliday, EG, McEvoy, M A, Starr, JM, Hysi, PG, Hernandez, DG, Guan, WH, Rivadeneira, Fernando, McArdle, WL, Slagboom, PE (Eline), Zeller, T, Psaty, BM, Uitterlinden, André, de Geus, EJC, Stott, DJ, Binder, H, Hofman, Bert, Franco Duran, OH, Rotter, JI, Ferrucci, L, Spector, TD, Deary, IJ, Marz, W, Greinacher, A, Wild, PS, Cucca, F, Boomsma, DI, Watkins, H, Tang, WH, Ridker, PM, Jukema, JW, Scott, RJ, Mitchell, P, Hansen, T, O'Donnell, CJ, Smith, NL, Strachan, DP, Dehghan, Abbas, Vries, PS, Sabater-Lleal, M, Chasman, DI, Trompet, S, Ahluwalia, TS, Teumer, A, Kleber, ME, Chen, MH, Wang, JJ, Attia, JR, Marioni, RE, Steri, M, Weng, LC, Pool, R, Grossmann, V, Brody, JA, Venturini, C, Tanaka, T, Rose, LM, Oldmeadow, C, Mazur, J, Basu, S, Franberg, M, Yang, Q, Ligthart, Symen, Hottenga, JJ, Rumley, A, Mulas, A, de Craen, AJM, Grotevendt, A, Taylor, KD, Delgado, GE, Kifley, A, Lopez, LM, Berentzen, TL, Mangino, M, Bandinelli, S, Morrison, AC, Hamsten, A, Tofler, G, de Maat, Moniek, Draisma, HHM, Lowe, GD, Zoledziewska, M, Sattar, N, Lackner, KJ, Volker, U, McKnight, B, Huang, J, Holliday, EG, McEvoy, M A, Starr, JM, Hysi, PG, Hernandez, DG, Guan, WH, Rivadeneira, Fernando, McArdle, WL, Slagboom, PE (Eline), Zeller, T, Psaty, BM, Uitterlinden, André, de Geus, EJC, Stott, DJ, Binder, H, Hofman, Bert, Franco Duran, OH, Rotter, JI, Ferrucci, L, Spector, TD, Deary, IJ, Marz, W, Greinacher, A, Wild, PS, Cucca, F, Boomsma, DI, Watkins, H, Tang, WH, Ridker, PM, Jukema, JW, Scott, RJ, Mitchell, P, Hansen, T, O'Donnell, CJ, Smith, NL, Strachan, DP, and Dehghan, Abbas

Published
2017

30. Analytical robustness of nine common assays: frequency of outliers and extreme differences identified by a large number of duplicate measurements

Author

Stefanie Neubig, Anne Grotevendt, Anders Kallner, Matthias Nauck, Astrid Petersmann, Stefanie Neubig, Anne Grotevendt, Anders Kallner, Matthias Nauck, and Astrid Petersmann

Abstract

Introduction: Duplicate measurements can be used to describe the performance and analytical robustness of assays and to identify outliers. We performed about 235,000 duplicate measurements of nine routinely measured quantities and evaluated the observed differences between the replicates to develop new markers for analytical performance and robustness. Materials and methods: Catalytic activity concentrations of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and concentrations of calcium, cholesterol, creatinine, C-reactive protein (CRP), lactate, triglycerides and thyroid-stimulating hormone (TSH) in 237,261 patient plasma samples were measured in replicates using routine methods. The performance of duplicate measurements was evaluated in scatterplots with a variable and symmetrical zone of acceptance (A-zone) around the equal line. Two quality markers were established: 1) AZ95: the width of an A-zone at which 95 % of all duplicate measurements were within this zone; and 2) OPM (outliers per mille): the relative number of outliers if an A-zone width of 5 % was applied. Results: The AZ95 ranges from 3.2% for calcium to 11.5% for CRP and the OPM from 5 (calcium) to 250 (creatinine). Calcium, TSH and cholesterol have an AZ95 of less than 5% and an OPM of less than 50. Conclusions: Duplicate measurements of a large number of patient samples identify even low frequencies of extreme differences and thereof defined outliers. We suggest two additional quality markers, AZ95 and OPM, to complement description of assay performance and robustness. This approach can aid the selection process of measurement procedures in view of clinical needs.

Published
2017

31. Angiopoietin-2, its soluble receptor Tie-2, and metabolic syndrome components in a population-based sample

Author

Roberto, Lorbeer, Sebastian E, Baumeister, Marcus, Dörr, Matthias, Nauck, Anne, Grotevendt, Sabrina, Schlesinger, Alexander, Teumer, Henry, Völzke, Hans-Jörgen, Grabe, Henri, Wallaschofski, Ramachandran S, Vasan, and Wolfgang, Lieb

Subjects
Adult, Angiopoietin-2, Male, Metabolic Syndrome, Cardiovascular Diseases, Prevalence, Humans, Female, Insulin Resistance, Middle Aged, Receptor, TIE-2, Biomarkers
Abstract

Since angiopoietin-2 (Ang-2) levels strongly correlate with cardiovascular mortality and subclinical cardiovascular disease, it was hypothesized that levels of Ang-2 and its soluble receptor (sTie-2) were associated with the metabolic syndrome (MetS) and individual MetS components.Within the population-based Study of Health in Pomerania, two sets of analyses were performed. First, Ang-2 and sTie-2 were related to the prevalence of MetS and its components cross-sectionally (n = 3,205). Second, the association between baseline Ang-2 and sTie-2 and incident MetS or longitudinal changes in its components in 1,295 individuals was investigated.High Ang-2 levels (90th percentile), compared with low Ang-2 levels (10th percentile), were positively associated with MetS (OR: 1.78) and with the following MetS criteria: increased triglycerides, lower HDL cholesterol, and higher non-fasting glucose. Furthermore, high sTie-2 levels (90th percentile), compared with low levels (10th percentile), were positively related to MetS (OR: 1.58) and most of its components. However, Ang-2 and sTie-2 levels were not associated with incident MetS or longitudinal change in components of MetS.Ang-2 and sTie-2 levels were cross-sectionally associated with MetS and several of its components. However, Ang-2 and sTie-2 levels were not associated with incident MetS or changes in individual MetS components during follow-up.

Published
2016

32. A meta-analysis of 120 246 individuals identifies 18 new loci for fibrinogen concentration

Author

Riccardo E. Marioni, Christopher J. O'Donnell, Oscar H. Franco, Johan G. Eriksson, Francesco Cucca, Jonathan Marten, Alexander P. Reiner, Tim D. Spector, Philipp S. Wild, Paul Mitchell, Wolfgang Koenig, Barbara McKnight, J. Wouter Jukema, Alan F. Wright, Maria Sabater-Lleal, Charles Kooperberg, Albert Hofman, Talin Haritunians, Martina Müller-Nurasyid, Jie Huang, Jie Yao, Marcus E. Kleber, Pau Navarro, Nicholas L. Smith, Jennifer E. Huffman, Lynda M. Rose, Russell P. Tracy, Antti-Pekka Sarin, Stefania Bandinelli, Ian J. Deary, Aarno Palotie, Saonli Basu, Caroline Hayward, Kent D. Taylor, Winfried März, Aaron R. Folsom, Anne Grotevendt, Lawrence F. Bielak, Tarunveer S. Ahluwalia, Ming-Huei Chen, Alexander Teumer, Graciela E. Delgado, Sharon L.R. Kardia, Peter K. Joshi, Wendy L. McArdle, Toshiko Tanaka, Jing Hua Zhao, Diane M. Becker, Pierre-Emmanuel Morange, Alanna C. Morrison, Annette Kifley, Igor Rudan, Lorna M. Lopez, Kay-Tee Khaw, Nicholas J. Wareham, Eco J. C. de Geus, Mohsen Ghanbari, Jari Lahti, Tatijana Zemunik, John M. Starr, Magdalena Zoledziewska, Anuj Goel, Dena G. Hernandez, Abbas Dehghan, Ozren Polasek, Melanie Waldenberger, Helene Riess, Rodney J. Scott, Qiong Yang, Robert A. Scott, Nancy S. Jenny, Johanna Mazur, Weihong Tang, Paul M. Ridker, Torben Hansen, Anders Hamsten, James F. Wilson, Jouke J. Hottenga, Cristina Venturini, Paul L. Auer, Geoffrey H. Tofler, Pirro G. Hysi, Mark G. Mcevoy, Ann Rumley, Fernando Rivadeneira, Naveed Sattar, Ivana Kolcic, Jie Jin Wang, Jingmin Liu, Stella Trompet, Elizabeth G. Holliday, Sarah H. Wild, Maristella Steri, Uwe Völker, Tanja Zeller, Patricia A. Peyser, P. Eline Slagboom, Massimo Mangino, André G. Uitterlinden, Paul S. de Vries, Dorret I. Boomsma, Harry Campbell, Vera Grossmann, Gonneke Willemsen, Katri Räikkönen, Edoardo Fiorillo, Ares Rocanin-Arjo, Gordon D.O. Lowe, David P. Strachan, Anton J. M. de Craen, Moniek P.M. de Maat, David-Alexandre Trégouët, Jennifer A. Brody, Rehan Qayyum, Lisa R. Yanek, Luigi Ferrucci, Andreas Greinacher, Lewis C. Becker, Hugh Watkins, David J. Stott, Min A. Jhun, Tina L. Berentzen, Annette Peters, Bengt Sennblad, Daniel I. Chasman, Lihong Qi, Christopher Oldmeadow, Harald Binder, Xiuqing Guo, John Attia, Bruce M. Psaty, Nutrition, obésité et risque thrombotique (NORT), Institut National de la Recherche Agronomique (INRA)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Biological Psychology, EMGO+ - Quality of Care, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Aix Marseille Université (AMU)-Institut National de la Recherche Agronomique (INRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Epidemiology, Hematology, and Internal Medicine

Subjects
Adult, Male, 0301 basic medicine, Netherlands Twin Register (NTR), Single-nucleotide polymorphism, Genome-wide association study, Biology, Polymorphism, Single Nucleotide, White People, 03 medical and health sciences, INDEL Mutation, Genetics, Humans, 1000 Genomes Project, International HapMap Project, Indel, Molecular Biology, Genetics (clinical), ComputingMilieux_MISCELLANEOUS, Aged, Genetic association, Aged, 80 and over, Association Studies Articles, Fibrinogen, [SDV.MHEP.HEM]Life Sciences [q-bio]/Human health and pathology/Hematology, General Medicine, Middle Aged, Genetic architecture, 030104 developmental biology, Genetic Loci, Female, GENOME-WIDE ASSOCIATION, C-REACTIVE PROTEIN, CARDIOVASCULAR-DISEASE, CIRCULATING FIBRINOGEN, GENETIC ARCHITECTURE, VARIANTS, DESIGN, HEMOSTASIS, RESOURCE, HEALTH, Imputation (genetics), Genome-Wide Association Study
Abstract

Genome-wide association studies have previously identified 23 genetic loci associated with circulating fibrinogen concentration. These studies used HapMap imputation and did not examine the X-chromosome. 1000 Genomes imputation provides better coverage of uncommon variants, and includes indels. We conducted a genome-wide association analysis of 34 studies imputed to the 1000 Genomes Project reference panel and including similar to 120 000 participants of European ancestry (95 806 participants with data on the X-chromosome). Approximately 10.7 million single-nucleotide polymorphisms and 1.2 million indels were examined. We identified 41 genome-wide significant fibrinogen loci ; of which, 18 were newly identified. There were no genome-wide significant signals on the X-chromosome. The lead variants of five significant loci were indels. We further identified six additional independent signals, including three rare variants, at two previously characterized loci: FGB and IRF1. Together the 41 loci explain 3% of the variance in plasma fibrinogen concentration.

Published
2016

33. Ethylene-Promoted versus Ethylene-Free Enyne Metathesis

Author

Anne G. D. Grotevendt, Deryn E. Fogg, Melanie L. Mastronardi, and Justin A. M. Lummiss

Subjects
Ethylene, Enyne, Context (language use), General Chemistry, Metathesis, Enyne metathesis, Biochemistry, Medicinal chemistry, Catalysis, chemistry.chemical_compound, Colloid and Surface Chemistry, chemistry, Organic chemistry, Reactivity (chemistry), Inert gas
Abstract

The role of ethylene in promoting metathesis of acetylenic enynes is probed within the context of ring-closing enyne metathesis, using first- and second-generation Grubbs catalysts. Under inert atmosphere, rapid catalyst deactivation is observed by calibrated GC-FID analysis for substrates with minimal propargylic bulk. MALDI-TOF mass spectra reveal a Ru(enyne)(2) derivative that exhibits very low reactivity toward both enyne and ethylene. Under ethylene, formation of this species is suppressed. Enynes with bulky propargylic groups are not susceptible to this catalyst deactivation pathway, even under N(2) atmosphere.

Published
2011

34. A Practical Palladium-Catalyzed Telomerization for the Synthesis of Functionalized Alcohols

Author

Ralf Jackstell, Anne Grotevendt, Mario Gomez Andreu, and Matthias Beller

Subjects
chemistry.chemical_classification, Organic Chemistry, High selectivity, chemistry.chemical_element, Polymer, Catalysis, chemistry.chemical_compound, chemistry, Yield (chemistry), Telomerization, Organic chemistry, Physical and Theoretical Chemistry, Ethylene glycol, Palladium
Abstract

The synthesis of octadienyl-substituted alcohols via palladium-catalyzed telomerization is described. 2-N-Methylaminoethanol, ethylene glycol, and 1,2-propylenediol react smoothly with 1,3-butadiene to give valuable intermediates for speciality polymers in high selectivity and quantitative yield on multi-100 g-scale.

Published
2009

37. Efficient catalysts for telomerization of butadiene with amines

Author

Matthias Beller, Ralf Jackstell, Maribel Bartolome, Kingsley J. Cavell, Luis A. Oro, David J. Nielsen, Anke Spannenberg, and Anne Grotevendt

Subjects
Chemistry, Organic Chemistry, chemistry.chemical_element, 1,3-Butadiene, Homogeneous catalysis, Biochemistry, Catalysis, chemistry.chemical_compound, Telomerization (dimerization), Yield (chemistry), Drug Discovery, Organic chemistry, Selectivity, Carbene, Palladium
Abstract

In situ-generated N-heterocyclic carbene (NHC) palladium catalysts and isolated NHC-palladium complexes have been tested for the telomerization reaction of 1,3-butadiene with primary and secondary amines. Superior catalyst activity (TON up to 400.000) and selectivity are obtained. Applying optimized conditions a variety of octa-2,7-dienylamines were prepared in high yield and excellent selectivity.

Published
2007

38. Telomerization and dimerization of isoprene by in situ generated palladium–carbene catalysts

Author

Ralf Jackstell, Anne Grotevendt, Dirk Michalik, Larbi El Firdoussi, and Matthias Beller

Subjects
Organic Chemistry, chemistry.chemical_element, Homogeneous catalysis, Biochemistry, Chloride, Catalysis, Inorganic Chemistry, chemistry.chemical_compound, Telomerization (dimerization), chemistry, Polymer chemistry, Materials Chemistry, medicine, Organic chemistry, Physical and Theoretical Chemistry, Selectivity, Carbene, Isoprene, Palladium, medicine.drug
Abstract

The palladium-catalyzed telomerization of isoprene with methanol and dimerization of isoprene have been studied in presence of in situ generated palladium–carbene catalysts. Unprecedented catalyst productivity has been observed for these two reactions. A selectivity switch from the telomer to the dimer product occurred by using different substituted carbene ligands. Among the imidazolium salts tested 1,3-dimesitylimidazolium mesylate ( 1) , 1,3-dimesityl-4,5-dihydroimidazolium chloride ( 3) gave the best yields for telomerization reaction whereas 1,3-bis-(2,6-diisopropylphenyl)-4,5-dihydroimidazolium tetrafluoroborate ( 5) and 1,3-bis-(2,6-diisopropylphenyl)-4,5-dimethyl-4,5-dihydroimidazolium chloride ( 9) form dimers in high yield and good selectivity.

Published
2007

39. Partially desulfated heparin modulates the interaction between anti-protamine/heparin antibodies and platelets

Author

Sven Brandt, Anne Grotevendt, Jan Wesche, Ulrich J. Sachs, Tamam Bakchoul, Heike Zöllner, Rabie Jouni, Ahmad Khadour, Mihaela Delcea, Krystin Krauel, Hansjörg Schwertz, and Andreas Greinacher

Subjects
0301 basic medicine, Blood Platelets, Male, Cell Survival, Mice, SCID, 030204 cardiovascular system & hematology, Pharmacology, Platelet Factor 4, Antibodies, 03 medical and health sciences, Mice, 0302 clinical medicine, In vivo, Mice, Inbred NOD, Medicine, Animals, Humans, Platelet, Platelet activation, Protamines, biology, Dose-Response Relationship, Drug, business.industry, Heparin, food and beverages, Anticoagulants, Hematology, Platelet Activation, Protamine, Thrombocytopenia, In vitro, 030104 developmental biology, Immunoglobulin G, Immunology, Models, Animal, biology.protein, Female, Antibody, business, Platelet factor 4, medicine.drug
Abstract

SummaryProtamine (PRT) is the standard drug to neutralise heparin. PRT/heparin complexes induce an immune response similar to that observed in heparin-induced thrombocytopenia (HIT). Partially desulfated heparin (ODSH) was shown to interfere with anti-platelet factor 4/heparin antibodies (Abs), which are responsible for HIT. In this study, we analyse the impact of ODSH on the interaction between anti-PRT/heparin Abs and platelets. The ability of ODSH to prevent anti-PRT/heparin Ab-induced platelet destruction in vivo was investigated using the NOD/ SCID mouse model. ODSH improved platelet survival in the presence of PRT, heparin and anti-PRT/heparin Abs (median platelet survival after 300 minutes (min) with 20 μg/ml ODSH: 75 %, range 70–81 % vs without ODSH: 49%, range 44–59%, p=0.006). Furthermore, when ODSH was applied 60 min after Ab injection platelet survival was improved (median platelet survival after 300 min with ODSH: 83 %, range 77–93 % vs without ODSH: 59 %, range 29–61 %, p=0.02). In in vitro experiments ODSH inhibited platelet activation at concentrations > 16 μg/mL (p< 0.001), as well as PRT/heparin complex binding to platelets (mean fluorescence intensity [MFI] without ODSH: 85 ± 14 vs with ODSH: 15 ± 0.6, p=0.013). ODSH also displaced pre-bound complexes from the platelet surface (MFI without ODSH: 324 ± 43 vs with 32 μg/ml ODSH: 53 ± 9, p< 0.001). While interfering with platelet activation by anti-PRT/heparin Abs, up to a concentration of 16 μg/ml, ODSH had only minimal impact on neutralisation of heparin by PRT. In conclusion, our study shows that ODSH is able to inhibit platelet activation and destruction suggesting a potential clinical use to reduce anti-PRT/heparin Ab-mediated adverse effects.

Published
2015

40. Genome-wide association analyses identify 18 new loci associated with serum urate concentrations

Author

Jacqueline C.M. Witteman, Nicole Probst-Hensch, Gail Davies, Anna Köttgen, Qunyuan Zhang, Pio D'Adamo, Nicholas G. Martin, Silvia Tore, Stephan J. L. Bakker, Gian Andri Thun, Cinzia Sala, Peter P. Pramstaller, Tamara B. Harris, Aaron Isaacs, Sheila Ulivi, Rossella Sorice, Xuan Liu, Andrew A. Hicks, Henry Völzke, Daniela Ruggiero, Arnold von Eckardstein, Yukinori Okada, Andres Metspalu, Jeffrey Metter, Serena Sanna, Jennifer E. Huffman, Philipp S. Wild, Florian Ernst, Eva Albrecht, Mary F. Feitosa, Claudia Schurmann, Kathrin Budde, Veikko Salomaa, Darina Czamara, Elizabeth G. Holliday, Marcel Bruinenberg, Laura Portas, Maris Laan, Alan J. Gow, Caroline Hayward, Tim D. Spector, Ozren Polasek, Ilja M. Nolte, Rodney J. Scott, Massimo Mangino, David S. Siscovick, Nilesh J. Samani, Toomas Haller, Laura Frogheri, A. Jula, Mika Kähönen, Bruce H. R. Wolffenbuttel, Caroline S. Fox, David R. Jacobs, Julia Shi, Yusuke Nakamura, Daniel I. Chasman, Fabian J. Theis, Alan F. Wright, Ayse Demirkan, Francesco Cucca, Paolo Gasparini, Paul M. Ridker, Olli T. Raitakari, Daniela Toniolo, Maristella Steri, Mario Pirastu, Susan Campbell, Maksim Struchalin, So-Youn Shin, Mark J. Caulfield, H.-Erich Wichmann, Grant W. Montgomery, Åsa Johansson, Alexander Teumer, Ulf Gyllensten, Robert M. Plenge, Michel Burnier, Michael Schallert, Jan Krumsiek, Jing Hua Zhao, Andres Salumets, Poorva Mudgal, Anke Tönjes, James F. Meschia, Gauti Kjartan Gislason, Perttu Salo, Angela Döring, Afshin Parsa, Vilmundur Gudnason, Patrick F. McArdle, Rita P. S. Middelberg, W. H. Linda Kao, Aldi Kraja, Christopher Oldmeadow, Adrienne Tin, Loic Yengo, Murielle Bochud, Gerjan Navis, Christian Gieger, Toshiko Tanaka, Cornelia M. van Duijn, Samuli Ripatti, Terho Lehtimäki, Claudia Langenberg, Bernhard O. Boehm, Conall M. O'Seaghdha, Eric Boerwinkle, Nicole Soranzo, Tõnu Esko, Susan M. Farrington, Mike A. Nalls, John F. Peden, Hyon K. Choi, Anuj Goel, Andrew D. Johnson, Norman Klopp, Anne Grotevendt, Halit Ongen, Marcus E. Kleber, Myriam Fornage, Malcolm G. Dunlop, Igor Rudan, Federico Murgia, Beverley Balkau, Jorma Viikari, Christopher P. Nelson, Gérard Waeber, Lenore J. Launer, Ivana Persico, Inga Prokopenko, Guo Li, Tatijana Zemunik, Elisabetta Trabetti, Tanja Zeller, Hugh Watkins, Qiong Yang, James F. Wilson, Peter Vollenweider, Veronique Vitart, Markus Perola, Nicola Pirastu, Olivier Devuyst, Irene Mateo Leach, John Attia, Bruce M. Psaty, Nicholas J. Wareham, Susanne Lucae, Josef Coresh, Ingrid B. Borecki, Claudia Hundertmark, Weihua Zhang, Luigi Ferrucci, Jaspal S. Kooner, Stefan Kloiber, Lynda M. Rose, Naoyuki Kamatani, Zoltán Kutalik, Giovanni Malerba, Ian J. Deary, Albert V. Smith, Helena Schmidt, Eva Lattka, Philippe Froguel, Harry Campbell, Medea Imboden, Antonio Lupo, Angelo L. Gaffo, Vasiliki Lagou, Alan R. Shuldiner, John Whitfield, Abbas Dehghan, Winfried März, Elin Org, Albert Hofman, Margus Viigimaa, Konstantin Strauch, Toshihiro Tanaka, Andrew B. Singleton, Stefania Bandinelli, Christian Hengstenberg, Xinzhong Li, Wiek H. van Gilst, Pim van der Harst, Giorgio Pistis, Jerome I. Rotter, Maria Grazia Piras, Gary C. Curhan, Brenda W.J.H. Penninx, Ramaiah Nagaraja, Nabila Bouatia-Naji, Christian Müller, Ronald P. Stolk, Pau Navarro, Lorna M. Lopez, Ruth J. F. Loos, Albert Tenesa, Johannes H. Smit, Peter Kraft, Giovanni Gambaro, Sarah H. Wild, Patricia B. Munroe, André G. Uitterlinden, Ivana Kolcic, Bernhard R. Winkelmann, Pankaj Sharma, John C. Chambers, Nicholas D. Hastie, Matthias Nauck, Harold Snieder, Michael Stumvoll, Reinhold Schmidt, Hans L. Hillege, Kiang Liu, Fernando Rivadeneira, Owen M. Woodward, Morris J. Brown, Margus Putku, Sabine Schipf, Mirna Kirin, Kay-Tee Khaw, Melanie Waldenberger, Evropi Theodoratou, Marina Ciullo, Michiaki Kubo, Köttgen, A, Albrecht, E, Teumer, A, Vitart, V, Krumsiek, J, Hundertmark, C, Pistis, G, Ruggiero, D, O'Seaghdha, Cm, Haller, T, Yang, Q, Tanaka, T, Johnson, Ad, Kutalik, Z, Smith, Av, Shi, J, Struchalin, M, Middelberg, Rp, Brown, Mj, Gaffo, Al, Pirastu, Nicola, Li, G, Hayward, C, Zemunik, T, Huffman, J, Yengo, L, Zhao, Jh, Demirkan, A, Feitosa, Mf, Liu, X, Malerba, G, Lopez, Lm, van der Harst, P, Li, X, Kleber, Me, Hicks, Aa, Nolte, Im, Johansson, A, Murgia, F, Wild, Sh, Bakker, Sj, Peden, Jf, Dehghan, A, Steri, M, Tenesa, A, Lagou, V, Salo, P, Mangino, M, Rose, Lm, Lehtimäki, T, Woodward, Om, Okada, Y, Tin, A, Müller, C, Oldmeadow, C, Putku, M, Czamara, D, Kraft, P, Frogheri, L, Thun, Ga, Grotevendt, A, Gislason, Gk, Harris, Tb, Launer, Lj, Mcardle, P, Shuldiner, Ar, Boerwinkle, E, Coresh, J, Schmidt, H, Schallert, M, Martin, Ng, Montgomery, Gw, Kubo, M, Nakamura, Y, Munroe, Pb, Samani, Nj, Jacobs DR, Jr, Liu, K, D'Adamo, ADAMO PIO, Ulivi, S, Rotter, Ji, Psaty, Bm, Vollenweider, P, Waeber, G, Campbell, S, Devuyst, O, Navarro, P, Kolcic, I, Hastie, N, Balkau, B, Froguel, P, Esko, T, Salumets, A, Khaw, Kt, Langenberg, C, Wareham, Nj, Isaacs, A, Kraja, A, Zhang, Q, Wild, P, Scott, Rj, Holliday, Eg, Org, E, Viigimaa, M, Bandinelli, S, Metter, Je, Lupo, A, Trabetti, E, Sorice, R, Döring, A, Lattka, E, Strauch, K, Theis, F, Waldenberger, M, Wichmann, He, Davies, G, Gow, Aj, Bruinenberg, M, Stolk, Rp, Kooner, J, Zhang, W, Winkelmann, Br, Boehm, Bo, Lucae, S, Penninx, Bw, Smit, Jh, Curhan, G, Mudgal, P, Plenge, Rm, Portas, L, Persico, I, Kirin, M, Wilson, Jf, Leach, Im, van Gilst, Wh, Goel, A, Ongen, H, Hofman, A, Rivadeneira, F, Uitterlinden, Ag, Imboden, M, von Eckardstein, A, Cucca, F, Nagaraja, R, Piras, Mg, Nauck, M, Schurmann, C, Budde, K, Ernst, F, Farrington, Sm, Theodoratou, E, Prokopenko, I, Stumvoll, M, Jula, A, Perola, M, Salomaa, V, Shin, Sy, Spector, Td, Sala, C, Ridker, Pm, Kähönen, M, Viikari, J, Hengstenberg, C, Nelson, Cp, Meschia, Jf, Nalls, Ma, Sharma, P, Singleton, Ab, Kamatani, N, Zeller, T, Burnier, M, Attia, J, Laan, M, Klopp, N, Hillege, Hl, Kloiber, S, Choi, H, Pirastu, M, Tore, S, Probst Hensch, Nm, Völzke, H, Gudnason, V, Parsa, A, Schmidt, R, Whitfield, Jb, Fornage, M, Gasparini, Paolo, Siscovick, D, Polašek, O, Campbell, H, Rudan, I, Bouatia Naji, N, Metspalu, A, Loos, Rj, van Duijn, Cm, Borecki, Ib, Ferrucci, L, Gambaro, G, Deary, Ij, Wolffenbuttel, Bh, Chambers, Jc, März, W, Pramstaller, Pp, Snieder, H, Gyllensten, U, Wright, Af, Navis, G, Watkins, H, Witteman, Jc, Sanna, S, Schipf, S, Dunlop, Mg, Tönjes, A, Ripatti, S, Soranzo, N, Toniolo, D, Chasman, Di, Raitakari, O, Kao, Wh, Ciullo, M, Fox, C, Caulfield, M, Bochud, M, Gieger, C, LifeLines Cohort, Study, Cardiogram, Consortium, Diagram, Consortium, Icbp, Consortium, Magic, Consortium, Cardiovascular Centre (CVC), Life Course Epidemiology (LCE), Groningen Institute for Organ Transplantation (GIOT), Lifestyle Medicine (LM), Groningen Kidney Center (GKC), Vascular Ageing Programme (VAP), Center for Liver, Digestive and Metabolic Diseases (CLDM), Psychiatry, EMGO - Mental health, EMGO+ - Mental Health, LifeLines Cohort Study, CARDIoGRAM Consortium, DIAGRAM Consortium, ICBP Consortium, MAGIC Consortium, Epidemiology, Public Health, and Internal Medicine

Subjects
Candidate gene, Inhibins/genetics, Genome-wide association study, GENETIC-LOCI, chemistry.chemical_compound, 0302 clinical medicine, serum urate, Gene Frequency, Gout/blood, association analysis, serum urate, Glucose/metabolism, Settore MED/14 - NEFROLOGIA, Hyperuricemia, serum, urate, gene, POPULATION, METABOLIC SYNDROME, Genetics, 0303 health sciences, education.field_of_study, biology, Polymorphism, Single Nucleotide/genetics, 3. Good health, HYPERURICEMIA, Genetic Loci/genetics, SLC22A12, Single Nucleotide/genetics, SNPs, Signal Transduction, MOLECULAR PHYSIOLOGY, serum urate concentrations, gout, genome-wide meta-analysis, European Continental Ancestry Group, Population, Polymorphism, Single Nucleotide, White People, Uric Acid/blood, serum urate concentrations, genome-wide meta-analysis, 03 medical and health sciences, SDG 3 - Good Health and Well-being, uric acid, medicine, Humans, Inhibins, Polymorphism, education, 030304 developmental biology, 030203 arthritis & rheumatology, Analysis of Variance, GOUT, IDENTIFICATION, TRANSPORTER, CARDIOVASCULAR-DISEASE RISK, ta3121, medicine.disease, association analysis, Gout, meta-analysis, Glucose, chemistry, Genetic Loci, genome-wide association studies, biology.protein, Signal Transduction/genetics, Uric acid, URIC-ACID LEVELS, Genome-Wide Association Study, SLC2A9
Abstract

Elevated serum urate concentrations can cause gout, a prevalent and painful inflammatory arthritis. By combining data from >140,000 individuals of European ancestry within the Global Urate Genetics Consortium (GUGC), we identified and replicated 28 genome-wide significant loci in association with serum urate concentrations (18 new regions in or near TRIM46, INHBB, SFMBT1, TMEM171, VEGFA, BAZ1B, PRKAG2, STC1, HNF4G, A1CF, ATXN2, UBE2Q2, IGF1R, NFAT5, MAF, HLF, ACVR1B-ACVRL1 and B3GNT4). Associations for many of the loci were of similar magnitude in individuals of non-European ancestry. We further characterized these loci for associations with gout, transcript expression and the fractional excretion of urate. Network analyses implicate the inhibins-activins signaling pathways and glucose metabolism in systemic urate control. New candidate genes for serum urate concentration highlight the importance of metabolic control of urate production and excretion, which may have implications for the treatment and prevention of gout. © 2013 Nature America, Inc. All rights reserved.

Published
2013

41. Modulation of genetic associations with serum urate levels by body-mass-index in humans

Author

Huffman, Jennifer E., Albrecht, Eva, Teumer, Alexander, Mangino, Massimo, Kapur, Karen, Johnson, Toby, Kutalik, Zoltán, Pirastu, Nicola, Pistis, Giorgio, Lopez, Lorna M., Haller, Toomas, Salo, Perttu, Goel, Anuj, Li, Man, Tanaka, Toshiko, Dehghan, Abbas, Ruggiero, Daniela, Malerba, Giovanni, Smith, Albert V., Nolte, Ilja M., Portas, Laura, Phipps-Green, Amanda, Boteva, Lora, Navarro, Pau, Johansson, Asa, Hicks, Andrew A., Polasek, Ozren, Esko, Tõnu, Peden, John F., Harris, Sarah E., Murgia, Federico, Wild, Sarah H., Tenesa, Albert, Tin, Adrienne, Mihailov, Evelin, Grotevendt, Anne, Gislason, Gauti K., Coresh, Josef, D'Adamo, Pio, Ulivi, Sheila, Vollenweider, Peter, Waeber, Gerard, Campbell, Susan, Kolcic, Ivana, Fisher, Krista, Viigimaa, Margus, Metter, Jeffrey E., Masciullo, Corrado, Trabetti, Elisabetta, Bombieri, Cristina, Sorice, Rossella, Döring, Angela, Reischl, Eva, Strauch, Konstantin, Hofman, Albert, Uitterlinden, Andre G., Waldenberger, Melanie, Wichmann, H. Erich, Davies, Gail, Gow, Alan J., Dalbeth, Nicola, Stamp, Lisa, Smit, Johannes H., Kirin, Mirna, Nagaraja, Ramaiah, Nauck, Matthias, Schurmann, Claudia, Budde, Kathrin, Farrington, Susan M., Theodoratou, Evropi, Jula, Antti, Salomaa, Veikko, Sala, Cinzia, Hengstenberg, Christian, and Burnier, Michel

Subjects
glycan, obesity, gene locus, genetic association, effect size, lean body weight, adipocyte, genetic risk, major clinical study, body mass, regression analysis, carbohydrate synthesis, female, male, single nucleotide polymorphism, breast cancer resistance protein, estradiol, parasitic diseases, genetic variability, urate, controlled study, human, uric acid blood level
Abstract

We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, Pinter= 2.6 × 10-8). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDAR-ADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 × 10-8), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight = 9.1 × 10-8), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (P difflean-obese= 2 × 10-4). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obsogenic environment.

Published
2015

42. Modulation of Genetic Associations with Serum Urate Levels by Body-Mass-Index in Humans

Author

Crawford, Dana C, Huffman, Jennifer E., Albrecht, Eva, Teumer, Alexander, Mangino, Massimo, Kapur, Karen, Johnson, Toby, Kutalik, Zoltán, Pirastu, Nicola, Pistis, Giorgio, Lopez, Lorna M., Haller, Toomas, Salo, Perttu, Goel, Anuj, Li, Man, Tanaka, Toshiko, Dehghan, Abbas, Ruggiero, Daniela, Malerba, Giovanni, Smith, Albert V., Nolte, Ilja M., Portas, Laura, Phipps-Green, Amanda, Boteva, Lora, Navarro, Pau, Johansson, Asa, Hicks, Andrew A., Polasek, Ozren, Esko, Tõnu, Peden, John F., Harris, Sarah E., Murgia, Federico, Wild, Sarah H., Tenesa, Albert, Tin, Adrienne, Mihailov, Evelin, Grotevendt, Anne, Gislason, Gauti K., Coresh, Josef, D'Adamo, Pio, Ulivi, Sheila, Vollenweider, Peter, Waeber, Gerard, Campbell, Susan, Kolcic, Ivana, Fisher, Krista, Viigimaa, Margus, Metter, Jeffrey E., Masciullo, Corrado, Trabetti, Elisabetta, Bombieri, Cristina, Sorice, Rossella, Döring, Angela, Reischl, Eva, Strauch, Konstantin, Hofman, Albert, Uitterlinden, Andre G., Waldenberger, Melanie, Wichmann, H-Erich, Davies, Gail, Gow, Alan J., Dalbeth, Nicola, Stamp, Lisa, Smit, Johannes H., Kirin, Mirna, Nagaraja, Ramaiah, Nauck, Matthias, Schurmann, Claudia, Budde, Kathrin, Farrington, Susan M., Theodoratou, Evropi, Jula, Antti, Salomaa, Veikko, Sala, Cinzia, Hengstenberg, Christian, Burnier, Michel, Mägi, Reedik, Klopp, Norman, Kloiber, Stefan, Schipf, Sabine, Ripatti, Samuli, Cabras, Stefano, Soranzo, Nicole, Homuth, Georg, Nutile, Teresa, Munroe, Patricia B., Hastie, Nicholas, Campbell, Harry, Rudan, Igor, Cabrera, Claudia, Haley, Chris, Franco, Oscar H., Merriman, Tony R., Gudnason, Vilmundur, Pirastu, Mario, Penninx, Brenda W., Snieder, Harold, Metspalu, Andres, Ciullo, Marina, Pramstaller, Peter P., van Duijn, Cornelia M., Ferrucci, Luigi, Gambaro, Giovanni, Deary, Ian J., Dunlop, Malcolm G., Wilson, James F., Gasparini, Paolo, Gyllensten, Ulf, Spector, Tim D., Wright, Alan F., Hayward, Caroline, Watkins, Hugh, Perola, Markus, Bochud, Murielle, Kao, W. H. Linda, Caulfield, Mark, Toniolo, Daniela, Völzke, Henry, Gieger, Christian, Köttgen, Anna, and Vitart, Veronique

Abstract

We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, Pinter= 2.6 x 10-8). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDARADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 x 10-8), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight= 9.1 x 10-8), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (Pdifflean-obese= 2 x 10-4). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obesogenic environment.

Published
2015

43. No Evidence for Genome-Wide Interactions on Plasma Fibrinogen by Smoking, Alcohol Consumption and Body Mass Index: Results from Meta-Analyses of 80,607 Subjects

Author

Meyre, David, Baumert, Jens, Huang, Jie, McKnight, Barbara, Sabater-Lleal, Maria, Steri, Maristella, Chu, Audrey Y., Trompet, Stella, Lopez, Lorna M., Fornage, Myriam, Teumer, Alexander, Tang, Weihong, Rudnicka, Alicja R., Mälarstig, Anders, Hottenga, Jouke-Jan, Kavousi, Maryam, Lahti, Jari, Tanaka, Toshiko, Hayward, Caroline, Huffman, Jennifer E., Morange, Pierre-Emmanuel, Rose, Lynda M., Basu, Saonli, Rumley, Ann, Stott, David J., Buckley, Brendan M., de Craen, Anton J. M., Sanna, Serena, Masala, Marco, Biffar, Reiner, Homuth, Georg, Silveira, Angela, Sennblad, Bengt, Goel, Anuj, Watkins, Hugh, Müller-Nurasyid, Martina, Rückerl, Regina, Taylor, Kent, Chen, Ming-Huei, de Geus, Eco J. C., Hofman, Albert, Witteman, Jacqueline C. M., de Maat, Moniek P. M., Palotie, Aarno, Davies, Gail, Siscovick, David S., Kolcic, Ivana, Wild, Sarah H., Song, Jaejoon, McArdle, Wendy L., Ford, Ian, Sattar, Naveed, Schlessinger, David, Grotevendt, Anne, Franzosi, Maria Grazia, Illig, Thomas, Waldenberger, Melanie, Lumley, Thomas, Tofler, Geoffrey H., Willemsen, Gonneke, Uitterlinden, André G., Rivadeneira, Fernando, Räikkönen, Katri, Chasman, Daniel I., Folsom, Aaron R., Lowe, Gordon D., Westendorp, Rudi G. J., Slagboom, P. Eline, Cucca, Francesco, Wallaschofski, Henri, Strawbridge, Rona J., Seedorf, Udo, Koenig, Wolfgang, Bis, Joshua C., Mukamal, Kenneth J., van Dongen, Jenny, Widen, Elisabeth, Franco, Oscar H., Starr, John M., Liu, Kiang, Ferrucci, Luigi, Polasek, Ozren, Wilson, James F., Oudot-Mellakh, Tiphaine, Campbell, Harry, Navarro, Pau, Bandinelli, Stefania, Eriksson, Johan, Boomsma, Dorret I., Dehghan, Abbas, Clarke, Robert, Hamsten, Anders, Boerwinkle, Eric, Jukema, J. Wouter, Naitza, Silvia, Ridker, Paul M., Völzke, Henry, Deary, Ian J., Reiner, Alexander P., Trégouët, David-Alexandre, O'Donnell, Christopher J., Strachan, David P., Peters, Annette, Smith, Nicholas L., Biological Psychology, AIMMS, EMGO+ - Mental Health, Behavioural Sciences, Institute for Molecular Medicine Finland, Clinicum, Haartman Institute (-2014), Department of Medical and Clinical Genetics, Department of General Practice and Primary Health Care, Developmental Psychology Research Group, Genomics of Neurological and Neuropsychiatric Disorders, Genomic Discoveries and Clinical Translation, Epidemiology, Cell biology, Hematology, Internal Medicine, and Pediatrics

Subjects
Netherlands Twin Register (NTR), Epidemiology, LOCI, Inflammatory markers, lcsh:Medicine, Genome-wide association study, INFLAMMATORY MARKERS, 030204 cardiovascular system & hematology, Fibrinogen, Body Mass Index, 0302 clinical medicine, Cigarette smoking, Medicine and Health Sciences, Gene–environment interaction, lcsh:Science, 2. Zero hunger, Genetics, 0303 health sciences, INDIVIDUAL PARTICIPANT METAANALYSIS, Circulating fibrinogen, Multidisciplinary, Loci, Smoking, GENETIC-VARIATION, Genomics, Hematology, ASSOCIATION, 3. Good health, Lung cancer, Research Article, medicine.drug, medicine.medical_specialty, Alcohol Drinking, 515 Psychology, education, Cardiology, Single-nucleotide polymorphism, Association, 03 medical and health sciences, LUNG-CANCER, SDG 3 - Good Health and Well-being, Internal medicine, Genetic model, medicine, Humans, ddc:610, Genetic variation, Risk factor, 030304 developmental biology, Genetic association, Clinical Genetics, business.industry, lcsh:R, CARDIOVASCULAR-DISEASE RISK, Cardiovascular disease risk, Myocardial infarction, Endocrinology, MYOCARDIAL-INFARCTION, CIRCULATING FIBRINOGEN, 3121 General medicine, internal medicine and other clinical medicine, Individual participant metaanalysis, Gene-Environment Interaction, lcsh:Q, CIGARETTE-SMOKING, business, Body mass index
Abstract

Plasma fibrinogen is an acute phase protein playing an important role in the blood coagulation cascade having strong associations with smoking, alcohol consumption and body mass index (BMI). Genome-wide association studies (GWAS) have identified a variety of gene regions associated with elevated plasma fibrinogen concentrations. However, little is yet known about how associations between environmental factors and fibrinogen might be modified by genetic variation. Therefore, we conducted large-scale meta-analyses of genome-wide interaction studies to identify possible interactions of genetic variants and smoking status, alcohol consumption or BMI on fibrinogen concentration. The present study included 80,607 subjects of European ancestry from 22 studies. Genome-wide interaction analyses were performed separately in each study for about 2.6 million single nucleotide polymorphisms (SNPs) across the 22 autosomal chromosomes. For each SNP and risk factor, we performed a linear regression under an additive genetic model including an interaction term between SNP and risk factor. Interaction estimates were meta-analysed using a fixed-effects model. No genome-wide significant interaction with smoking status, alcohol consumption or BMI was observed in the meta-analyses. The most suggestive interaction was found for smoking and rs10519203, located in the LOC123688 region on chromosome 15, with a p value of 6.2×10-8. This large genome-wide interaction study including 80,607 participants found no strong evidence of interaction between genetic variants and smoking status, alcohol consumption or BMI on fibrinogen concentrations. Further studies are needed to yield deeper insight in the interplay between environmental factors and gene variants on the regulation of fibrinogen concentrations.

Published
2014
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44. A meta-analysis of 120 246 individuals identifies 18 new loci for fibrinogen concentration

Author

Vries, P.S. (Paul) de, Chasman, D.I. (Daniel), Sabater-Lleal, M. (Maria), Chen, M.-H. (Ming-Huei), Huffman, J.E. (Jennifer E.), Steri, M. (Maristella), Tang, W. (Weihong), Teumer, A. (Alexander), Marioni, R.E. (Riccardo), Grossmann, V. (Vera), Hottenga, J.J. (Jouke Jan), Trompet, S. (Stella), Müller-Nurasyid, M. (Martina), Zhao, J.H. (Jing Hua), Brody, J.A. (Jennifer A.), Kleber, M.E. (Marcus), Guo, X. (Xiuqing), Wang, J.J. (Jie Jin), Auer, P. (Paul), Attia, J. (John), Yanek, L.R. (Lisa), Ahluwalia, T.S. (Tarunveer Singh), Lahti, J. (Jari), Venturini, C. (Cristina), Tanaka, T. (Toshiko), Bielak, L.F. (Lawrence F.), Joshi, P.K. (Peter), Rocanin-Arjo, A. (Ares), Kolcic, I. (Ivana), Navarro, P. (Pau), Rose, L.M. (Lynda), Oldmeadow, C. (Christopher), Riess, H. (Helene), Mazur, J. (Johanna), Basu, S. (Saonli), Goel, A. (Anuj), Yang, Q. (Qiong), Ghanbari, M. (Mohsen), Willemsen, G. (Gonneke), Rumley, A. (Ann), Fiorillo, E. (Edoardo), Craen, A.J. (Anton) de, Grotevendt, A. (Anne), Scott, R.A. (Robert), Taylor, K.D. (Kent D.), Delgado, G.E. (Graciela E.), Yao, J. (Jie), Kifley, A. (Annette), Kooperberg, C. (Charles), Qayyum, Q. (Rehan), Lopez, L. (Lornam), Berentzen, T.L. (Tina L.), Räikkönen, K. (Katri), Mangino, M. (Massimo), Bandinelli, S. (Stefania), Peyser, P.A. (Patricia A.), Wild, S. (Sarah), Tregouet, D.-A. (David-Alexandre), Wright, A.F. (Alan), Marten, J. (Jonathan), Zemunik, T. (Tatijana), Morrison, A.C. (Alanna), Sennblad, B. (Bengt), Tofler, G.H. (Geoffrey), Maat, M.P.M. (Moniek) de, Geus, E.J.C. (Eco) de, Lowe, G.D. (Gordon D.), Zoledziewska, M. (Magdalena), Sattar, N. (Naveed), Binder, H. (Harald), Völker, U. (Uwe), Waldenberger, M. (Melanie), Khaw, K.-T. (Kay-Tee), McKnight, B. (Barbara), Huang, J. (Jian), Jenny, N.S. (Nancy), Holliday, E.G. (Elizabeth), Qi, L. (Lihong), Mcevoy, M.G. (Mark G.), Becker, D.M. (Diane), Starr, J.M. (John), Sarin, A.-P., Hysi, P.G. (Pirro), Hernandez, D.G. (Dena), Jhun, M.A. (Min A.), Campbell, H. (Harry), Hamsten, A. (Anders), Sarin, F. (Fernando), McArdle, W.L. (Wendy), Slagboom, P.E. (Eline), Zeller, T. (Tanja), Koenig, W. (Wolfgang), Psaty, B. (Brucem), Haritunians, T. (Talin), Liu, J. (Jingmin), Palotie, A. (Aarno), Uitterlinden, A.G. (André), Stott, D.J. (David J.), Hofman, A. (Albert), Franco, O.H. (Oscar), Polasek, O. (Ozren), Rudan, I. (Igor), Morange, P.-E. (P.), Wilson, J.F. (James F.), Kardia, S.L. (Sharon L.r), Ferrucci, L. (Luigi), Spector, T.D. (Timothy), Eriksson, J.G. (Johan G.), Hansen, T. (Torben), Deary, I.J. (Ian), Becker, L.C. (Lewis), Scott, R.J. (Rodney), Mitchell, P. (Paul), März, W. (Winfried), Wareham, N.J. (Nick J.), Peters, A. (Annette), Greinacher, A. (Andreas), Wild, P.S. (Philipp S.), Jukema, J.W. (Jan Wouter), Boomsma, D.I. (Dorret), Hayward, C. (Caroline), Cucca, F. (Francesco), Tracy, R.P. (Russell), Watkins, H. (Hugh), Reiner, A.P. (Alex P.), Folsom, A.R. (Aaron), Ridker, P.M. (Paul), O'Donnell, C.J. (Christopher J.), Smith, N.L. (Nicholas L.), Strachan, D.P. (David P.), Dehghan, A. (Abbas), Vries, P.S. (Paul) de, Chasman, D.I. (Daniel), Sabater-Lleal, M. (Maria), Chen, M.-H. (Ming-Huei), Huffman, J.E. (Jennifer E.), Steri, M. (Maristella), Tang, W. (Weihong), Teumer, A. (Alexander), Marioni, R.E. (Riccardo), Grossmann, V. (Vera), Hottenga, J.J. (Jouke Jan), Trompet, S. (Stella), Müller-Nurasyid, M. (Martina), Zhao, J.H. (Jing Hua), Brody, J.A. (Jennifer A.), Kleber, M.E. (Marcus), Guo, X. (Xiuqing), Wang, J.J. (Jie Jin), Auer, P. (Paul), Attia, J. (John), Yanek, L.R. (Lisa), Ahluwalia, T.S. (Tarunveer Singh), Lahti, J. (Jari), Venturini, C. (Cristina), Tanaka, T. (Toshiko), Bielak, L.F. (Lawrence F.), Joshi, P.K. (Peter), Rocanin-Arjo, A. (Ares), Kolcic, I. (Ivana), Navarro, P. (Pau), Rose, L.M. (Lynda), Oldmeadow, C. (Christopher), Riess, H. (Helene), Mazur, J. (Johanna), Basu, S. (Saonli), Goel, A. (Anuj), Yang, Q. (Qiong), Ghanbari, M. (Mohsen), Willemsen, G. (Gonneke), Rumley, A. (Ann), Fiorillo, E. (Edoardo), Craen, A.J. (Anton) de, Grotevendt, A. (Anne), Scott, R.A. (Robert), Taylor, K.D. (Kent D.), Delgado, G.E. (Graciela E.), Yao, J. (Jie), Kifley, A. (Annette), Kooperberg, C. (Charles), Qayyum, Q. (Rehan), Lopez, L. (Lornam), Berentzen, T.L. (Tina L.), Räikkönen, K. (Katri), Mangino, M. (Massimo), Bandinelli, S. (Stefania), Peyser, P.A. (Patricia A.), Wild, S. (Sarah), Tregouet, D.-A. (David-Alexandre), Wright, A.F. (Alan), Marten, J. (Jonathan), Zemunik, T. (Tatijana), Morrison, A.C. (Alanna), Sennblad, B. (Bengt), Tofler, G.H. (Geoffrey), Maat, M.P.M. (Moniek) de, Geus, E.J.C. (Eco) de, Lowe, G.D. (Gordon D.), Zoledziewska, M. (Magdalena), Sattar, N. (Naveed), Binder, H. (Harald), Völker, U. (Uwe), Waldenberger, M. (Melanie), Khaw, K.-T. (Kay-Tee), McKnight, B. (Barbara), Huang, J. (Jian), Jenny, N.S. (Nancy), Holliday, E.G. (Elizabeth), Qi, L. (Lihong), Mcevoy, M.G. (Mark G.), Becker, D.M. (Diane), Starr, J.M. (John), Sarin, A.-P., Hysi, P.G. (Pirro), Hernandez, D.G. (Dena), Jhun, M.A. (Min A.), Campbell, H. (Harry), Hamsten, A. (Anders), Sarin, F. (Fernando), McArdle, W.L. (Wendy), Slagboom, P.E. (Eline), Zeller, T. (Tanja), Koenig, W. (Wolfgang), Psaty, B. (Brucem), Haritunians, T. (Talin), Liu, J. (Jingmin), Palotie, A. (Aarno), Uitterlinden, A.G. (André), Stott, D.J. (David J.), Hofman, A. (Albert), Franco, O.H. (Oscar), Polasek, O. (Ozren), Rudan, I. (Igor), Morange, P.-E. (P.), Wilson, J.F. (James F.), Kardia, S.L. (Sharon L.r), Ferrucci, L. (Luigi), Spector, T.D. (Timothy), Eriksson, J.G. (Johan G.), Hansen, T. (Torben), Deary, I.J. (Ian), Becker, L.C. (Lewis), Scott, R.J. (Rodney), Mitchell, P. (Paul), März, W. (Winfried), Wareham, N.J. (Nick J.), Peters, A. (Annette), Greinacher, A. (Andreas), Wild, P.S. (Philipp S.), Jukema, J.W. (Jan Wouter), Boomsma, D.I. (Dorret), Hayward, C. (Caroline), Cucca, F. (Francesco), Tracy, R.P. (Russell), Watkins, H. (Hugh), Reiner, A.P. (Alex P.), Folsom, A.R. (Aaron), Ridker, P.M. (Paul), O'Donnell, C.J. (Christopher J.), Smith, N.L. (Nicholas L.), Strachan, D.P. (David P.), and Dehghan, A. (Abbas)

Abstract

Genome-wide association studies have previously identified 23 genetic loci associated with circulating fibrinogen concentration. These studies used HapMap imputation and did not examine the X-chromosome. 1000 Genomes imputation provides better coverage of uncommon variants, and includes indels.We conducted a genome-wide association analysis of 34 studies imputed to the 1000 Genomes Project reference panel and including ~120 000 participants of European ancestry (95 806 participants with data on the X-chromosome). Approximately 10.7 million single-nucleotide polymorphisms and 1.2 million indelswere examined.We identified 41 genome-wide significant fibrinogen loci; of which, 18were newly identified. Therewere no genome-wide significant signals on the X-chromosome. The lead variants of five significant loci were indels. We further identified six additional independent signals, including three rare variants, at two previously characterized loci: FGB and IRF1. Together the 41 loci explain 3% of the variance in plasma fibrinogen concentration.

Published
2016
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45. Acquired Hemophilia With Inhibitors Presenting as an Emergency

Author

Ariane Sümnig, Thomas Thiele, Antje Westphal, Anne Grotevendt, Andreas Greinacher, and Michael Fiene

Subjects
Rivaroxaban, medicine.medical_specialty, medicine.diagnostic_test, business.industry, Atrial fibrillation, General Medicine, Heparin, Thrombin time, medicine.disease, medicine, Onset of action, business, Intensive care medicine, circulatory and respiratory physiology, medicine.drug, Discovery and development of direct thrombin inhibitors, Partial thromboplastin time, Blood coagulation test
Abstract

New direct oral anticoagulants (DOACs) combine both direct thrombin inhibitors (1) and factor Xa antagonists (2). They are being increasingly used for long-term anticoagulation in patients with nonvalvular atrial fibrillation or venous thromboembolism (3). They have a number of advantages over traditional vitamin K antagonists. For example, monitoring and often costly dose adjustments at the beginning of treatment are no longer needed thanks to DOACs’ fast, reliable onset of action (4). Furthermore, DOACs are not overlapped with heparin until therapeutic effect is achieved. All the oral anticoagulants that are currently available can interfere with the commonly used tests for plasma clotting, such as the international normalized ratio (INR) and activated partial thromboplastin time (aPTT) (4). INR provides sufficiently reliable assessment of the potency of vitamin K antagonists for clinical practice. In contrast, INR and aPTT do not allow any precise conclusions to be drawn on the anticoagulant effect of DOACs (5, 6). In addition, different manufacturers’ test reagents have different levels of sensitivity to DOACs (4). This makes it difficult to interpret changes in INR and aPTT during the use of DOACs, particularly when more specific tests to determine blood levels, such as the anti-factor Xa assay for factor Xa antagonists or the diluted thrombin time for direct thrombin inhibitors, are not available. In the case described here, use of a DOAC led to misinterpretation of prolonged aPTT. This delayed diagnosis of acquired hemophilia with inhibitors in an emergency. The case demonstrates a problem with the interpretation of clotting values during emergency management of patients who have taken DOACs. It also highlights the fact that the cause of abnormal clotting values should be clarified before anticoagulant therapy is begun.

Published
2014

46. Optimizing centrifugation of coagulation samples in laboratory automation

Author

Anne Grotevendt, Jan Lüdemann, Anders Kallner, Juliane Suchsland, Matthias Nauck, Nele Friedrich, and Astrid Petersmann

Subjects
Automation, Laboratory, Prothrombin time, Hemostasis, Chromatography, medicine.diagnostic_test, Chemistry, Biochemistry (medical), Clinical Biochemistry, Centrifugation, General Medicine, Laboratory automation, medicine, Humans, Coagulation (water treatment), Platelet, Blood Coagulation, High acceleration, Partial thromboplastin time
Abstract

Background High acceleration centrifugation conditions are used in laboratory automation systems to reduce the turnaround time (TAT) of clinical chemistry samples, but not of coagulation samples. This often requires separate sample flows. The CLSI guideline and manufacturers recommendations for coagulation assays aim at reducing platelet counts. For measurement of prothrombin time (PT) and activated partial thromboplastin time (APTT) platelet counts (Plt) below 200×10(9)/L are recommended. Other coagulation assays may require even lower platelet counts, e.g., less than 10 × 10(9)/L. Unifying centrifugation conditions can facilitate the integration of coagulation samples in the overall workflow of a laboratory automation system. Methods We evaluated centrifugation conditions of coagulation samples by using high acceleration centrifugation conditions (5 min; 3280×g) in a single and two consecutive runs. RESULTS of coagulation assays [PT, APTT, coagulation factor VIII (F. VIII) and protein S] and platelet counts were compared after the first and second centrifugation. Results Platelet counts below 200×10(9)/L were obtained in all samples after the first centrifugation and less than 10 × 10(9)/L was obtained in 73% of the samples after a second centrifugation. Passing-Bablok regression analyses showed an equal performance of PT, APTT and F. VIII after first and second centrifugation whereas protein S measurements require a second centrifugation. Conclusions Coagulation samples can be integrated into the workflow of a laboratory automation system using high acceleration centrifugation. A single centrifugation was sufficient for PT, APTT and F. VIII whereas two successive centrifugations appear to be sufficient for protein S activity.

Published
2014

47. Acquired hemophilia with inhibitors presenting as an emergency: misinterpretation of clotting results during direct oral anticoagulation

Author

Ariane, Sümnig, Anne, Grotevendt, Antje, Westphal, Michael, Fiene, Andreas, Greinacher, and Thomas, Thiele

Subjects
Emergency Medical Services, Morpholines, Administration, Oral, Anticoagulants, Case Report, Thiophenes, Hemophilia A, Diagnosis, Differential, Treatment Outcome, Rivaroxaban, Humans, Female, Blood Coagulation Tests, Diagnostic Errors, Aged
Abstract

Direct oral anticoagulants (DOACs) were recently introduced and are being increasingly prescribed. Most DOACs alter the values of traditional coagulation tests, such as the international normalized ratio (INR) or the activated partial thromboplastin time (aPTT). Although vitamin K antagonists raise the INR value to an extent that mirrors their anticoagulant effect, DOACs do not, in general, alter standard clotting values in any consistent way. Thus, there is a risk that abnormal INR and aPTT values can be misinterpreted.A woman taking rivaroxaban, a DOAC, presented with ileus and was scheduled for urgent surgery. A prolonged aPTT was, at first, wrongly attributed to rivaroxaban, delaying the correct diagnosis of autoantibody-associated acquired hemophilia (a rare condition with incidence, 1.34-1.48 cases per million people per year). The patient had a history of unusually intense bleeding in the skin and mucous membranes during anticoagulant treatment. Her aPTT had been prolonged even before any anticoagulants were taken.The operation was delayed to await the elimination of rivaroxaban. The aPTT was still prolonged 24 hours later. The diagnosis of autoantibody-associated acquired hemophilia was suspected and then confirmed by the measurement of a factor VIII residual activity of 1% and the demonstration of factor VIII inhibition at an intensity of 9.2 Bethesda units per mL.The causes of abnormal clotting test results must be clarified before beginning anticoagulant therapy. Unusually intense bleeding during oral anticoagulation should arouse suspicion of a previously undiagnosed acquired coagulopathy, e.g., antibody-associated acquired hemophilia.

Published
2013

48. Angiopoietin-2, its soluble receptor Tie-2, and metabolic syndrome components in a population-based sample

Author

Lorbeer, Roberto, primary, Baumeister, Sebastian E., additional, Dörr, Marcus, additional, Nauck, Matthias, additional, Grotevendt, Anne, additional, Schlesinger, Sabrina, additional, Teumer, Alexander, additional, Völzke, Henry, additional, Grabe, Hans-Jörgen, additional, Wallaschofski, Henri, additional, Vasan, Ramachandran S., additional, and Lieb, Wolfgang, additional

Published
2016
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50. Modulation of genetic associations with serum urate levels by body-mass-index in humans

Author

Huffman, J.E. (Jennifer), Albrecht, E. (Eva), Teumer, A. (Alexander), Mangino, M. (Massimo), Kapur, K. (Karen), Johnson, T. (Toby), Kutalik, Z. (Zoltán), Pirastu, N. (Nicola), Pistis, G. (Giorgio), Lopez, L.M. (Lorna), Haller, T. (Toomas), Salo, P. (Perttu), Goel, A. (Anuj), Li, M. (Man), Tanaka, T. (Toshiko), Dehghan, A. (Abbas), Ruggiero, D., Malerba, G. (Giovanni), Smith, A.V. (Albert Vernon), Nolte, I.M. (Ilja M.), Portas, L. (Laura), Phipps-Green, A. (Amanda), Boteva, L. (Lora), Navarro, P. (Pau), Johansson, A. (Åsa), Hicks, A.A. (Andrew), Polasek, O. (Ozren), Esko, T. (Tõnu), Peden, J. (John), Harris, S.E. (Sarah), Murgia, D. (Daniela), Wild, S.H. (Sarah H.), Tenesa, A. (Albert), Tin, A. (Adrienne), Mihailov, E. (Evelin), Grotevendt, A. (Anne), Gislason, G.K., Coresh, J. (Josef), Adamo, P. (Pio) d', Ulivi, S. (Shelia), Vollenweider, P. (Peter), Waeber, G. (Gérard), Campbell, S. (Susan), Kolcic, I. (Ivana), Fisher, K. (Krista), Viigimaa, M. (Margus), Metter, J.E. (Jeffrey E.), Masciullo, C. (Corrado), Trabetti, E. (Elisabetta), Bombieri, C. (Cristina), Sorice, R., Döring, A. (Angela), Reischl, G. (Gunilla), Strauch, K. (Konstantin), Hofman, A. (Albert), Uitterlinden, A.G. (André), Waldenberger, M. (Melanie), Wichmann, H.E. (Heinz Erich), Davies, G. (Gail), Gow, A.J. (Alan J.), Dalbeth, N. (Nicola), Stamp, L. (Lisa), Smit, J.H. (Johannes H.), Kirin, M. (Mirna), Nagaraja, R. (Ramaiah), Nauck, M. (Matthias), Schurmann, C. (Claudia), Budde, K. (Klemens), Farrington, S.M. (Susan), Theodoratou, E. (Evropi), Jula, A. (Antti), Salomaa, V. (Veikko), Sala, C. (Cinzia), Hengstenberg, C. (Christian), Burnier, M. (Michel), Mägi, R. (Reedik), Klopp, N. (Norman), Kloiber, S. (Stefan), Schipf, S. (Sabine), Ripatti, S. (Samuli), Cabras, S. (Stefano), Soranzo, N. (Nicole), Homuth, G. (Georg), Nutile, T., Munroe, P. (Patricia), Hastie, N. (Nick), Campbell, H. (H.), Rudan, I. (Igor), Cabrera, C. (Claudia), Haley, C. (Chris), Franco, O.H. (Oscar), Merriman, T.R. (Tony R.), Gudnason, V. (Vilmundur), Pirastu, M. (Mario), Penninx, B.W.J.H. (Brenda), Snieder, H. (Harold), Metspalu, A. (Andres), Ciullo, M., Pramstaller, P.P. (Peter Paul), Duijn, C.M. (Cornelia) van, Ferrucci, L. (Luigi), Gambaro, G. (Giovanni), Deary, I.J. (Ian J.), Dunlop, M.G. (Malcolm), Wilson, J.F. (James F), Gasparini, P. (Paolo), Gyllensten, U. (Ulf), Spector, T.D. (Timothy), Wright, A.F. (Alan), Hayward, C. (Caroline), Watkins, H. (Hugh), Perola, M. (Markus), Bochud, M. (Murielle), Kao, W.H.L. (Wen), Caulfield, M. (Mark), Toniolo, D. (Daniela), Völzke, H. (Henry), Gieger, C. (Christian), Köttgen, A. (Anna), Vitart, V. (Veronique), Huffman, J.E. (Jennifer), Albrecht, E. (Eva), Teumer, A. (Alexander), Mangino, M. (Massimo), Kapur, K. (Karen), Johnson, T. (Toby), Kutalik, Z. (Zoltán), Pirastu, N. (Nicola), Pistis, G. (Giorgio), Lopez, L.M. (Lorna), Haller, T. (Toomas), Salo, P. (Perttu), Goel, A. (Anuj), Li, M. (Man), Tanaka, T. (Toshiko), Dehghan, A. (Abbas), Ruggiero, D., Malerba, G. (Giovanni), Smith, A.V. (Albert Vernon), Nolte, I.M. (Ilja M.), Portas, L. (Laura), Phipps-Green, A. (Amanda), Boteva, L. (Lora), Navarro, P. (Pau), Johansson, A. (Åsa), Hicks, A.A. (Andrew), Polasek, O. (Ozren), Esko, T. (Tõnu), Peden, J. (John), Harris, S.E. (Sarah), Murgia, D. (Daniela), Wild, S.H. (Sarah H.), Tenesa, A. (Albert), Tin, A. (Adrienne), Mihailov, E. (Evelin), Grotevendt, A. (Anne), Gislason, G.K., Coresh, J. (Josef), Adamo, P. (Pio) d', Ulivi, S. (Shelia), Vollenweider, P. (Peter), Waeber, G. (Gérard), Campbell, S. (Susan), Kolcic, I. (Ivana), Fisher, K. (Krista), Viigimaa, M. (Margus), Metter, J.E. (Jeffrey E.), Masciullo, C. (Corrado), Trabetti, E. (Elisabetta), Bombieri, C. (Cristina), Sorice, R., Döring, A. (Angela), Reischl, G. (Gunilla), Strauch, K. (Konstantin), Hofman, A. (Albert), Uitterlinden, A.G. (André), Waldenberger, M. (Melanie), Wichmann, H.E. (Heinz Erich), Davies, G. (Gail), Gow, A.J. (Alan J.), Dalbeth, N. (Nicola), Stamp, L. (Lisa), Smit, J.H. (Johannes H.), Kirin, M. (Mirna), Nagaraja, R. (Ramaiah), Nauck, M. (Matthias), Schurmann, C. (Claudia), Budde, K. (Klemens), Farrington, S.M. (Susan), Theodoratou, E. (Evropi), Jula, A. (Antti), Salomaa, V. (Veikko), Sala, C. (Cinzia), Hengstenberg, C. (Christian), Burnier, M. (Michel), Mägi, R. (Reedik), Klopp, N. (Norman), Kloiber, S. (Stefan), Schipf, S. (Sabine), Ripatti, S. (Samuli), Cabras, S. (Stefano), Soranzo, N. (Nicole), Homuth, G. (Georg), Nutile, T., Munroe, P. (Patricia), Hastie, N. (Nick), Campbell, H. (H.), Rudan, I. (Igor), Cabrera, C. (Claudia), Haley, C. (Chris), Franco, O.H. (Oscar), Merriman, T.R. (Tony R.), Gudnason, V. (Vilmundur), Pirastu, M. (Mario), Penninx, B.W.J.H. (Brenda), Snieder, H. (Harold), Metspalu, A. (Andres), Ciullo, M., Pramstaller, P.P. (Peter Paul), Duijn, C.M. (Cornelia) van, Ferrucci, L. (Luigi), Gambaro, G. (Giovanni), Deary, I.J. (Ian J.), Dunlop, M.G. (Malcolm), Wilson, J.F. (James F), Gasparini, P. (Paolo), Gyllensten, U. (Ulf), Spector, T.D. (Timothy), Wright, A.F. (Alan), Hayward, C. (Caroline), Watkins, H. (Hugh), Perola, M. (Markus), Bochud, M. (Murielle), Kao, W.H.L. (Wen), Caulfield, M. (Mark), Toniolo, D. (Daniela), Völzke, H. (Henry), Gieger, C. (Christian), Köttgen, A. (Anna), and Vitart, V. (Veronique)

Abstract

We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, Pinter= 2.6 × 10-8). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDAR-ADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 × 10-8), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight = 9.1 × 10-8), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (P difflean-obese= 2 × 10-4). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obsogenic environment.

Published
2015
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