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Circulating oxytocin is genetically determined and associated with obesity and impaired glucose tolerance
Genetical Statistics and Systems Biology

Abstract (Expand)

CONTEXT Despite the emerging evidence on the role of oxytocin (OXT) in metabolic diseases, there is a lack of well powered studies addressing the relationship of circulating OXT with obesity and diabetes. …. OBJECTIVES AND DESIGN Here, we measured OXT in a study cohort (n=721; 396 women, 325 men; mean age\pmSD - 47.7\pm15.2 years) with sub-phenotypes related to obesity including anthropometric traits such as body mass index (BMI; mean\pmSD - 47.7\pm15.2 kg/m2), waist-to-hip-ratio (WHR; 0.88\pm0.09), blood parameters (glucose - 5.32\pm0.50 mmol/l, insulin - 5.3\pm3.3 µU/ml, lipids) and oral glucose tolerance test (OGTT) to clarify the association with OXT. We also tested in a genome-wide association study (GWAS) whether the inter-individual variation in OXT serum levels might be explained by genetic variation. RESULTS The OXT concentration was increased in subjects with elevated BMI and positively correlated with WHR, waist circumference and triglyceride levels. The OXT concentration in subjects with BMI\textless25 kg/m2 was significantly lower (n=256; 78.6 pg/ml) than in subjects with a BMI between 25-30 kg/m2 (n=314; 98.5 pg/ml, p=6x10-6) and with BMI\textgreater30 kg/m2 (n=137; 106.4 pg/ml, p=8x10-6). OXT levels were also positively correlated with plasma glucose and insulin and were elevated in subjects with impaired glucose tolerance (p=4.6x10-3). Heritability of OXT was estimated to 12.8%. In a GWAS, two hits in linkage disequilibrium close (19kb) to the OXT reached genome-wide significant association (top-hit rs12625893, p=3.1x10-8, explained variance 3%). CONCLUSIONS Our data show that OXT is genetically affected by a variant in OXT and is associated with obesity and impaired glucose tolerance.

Authors: Mark Florian Joachim Weingarten, Markus Scholz, Tobias Wohland, Katrin Horn, Michael Stumvoll, Peter Kovacs, Anke Tönjes

Date Published: 1st Nov 2019

Publication Type: Journal article

Common variants in glyoxalase I do not increase chronic pancreatitis risk
Genetical Statistics and Systems Biology

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INTRODUCTION Chronic pancreatitis (CP) may be caused by oxidative stress. An important source of reactive oxygen species (ROS) is the methylglyoxal-derived formation of advanced glycation endproducts …s (AGE). Methylglyoxal is detoxified by Glyoxalase I (GLO1). A reduction in GLO1 activity results in increased ROS. Single nucleotide polymorphisms (SNPs) of GLO1 have been linked to various inflammatory diseases. Here, we analyzed whether common GLO1 variants are associated with alcoholic (ACP) and non-alcoholic CP (NACP). METHODS Using melting curve analysis, we genotyped a screening cohort of 223 ACP, 218 NACP patients, and 328 controls for 11 tagging SNPs defined by the SNPinfo LD TAG SNP Selection tool and the functionally relevant variant rs4746. For selected variants the cohorts were extended to up to 1,441 patient samples. RESULTS In the ACP cohort, comparison of genotypes for rs1937780 between patients and controls displayed an ambiguous result in the screening cohort (p = 0.08). However, in the extended cohort of 1,441 patients no statistically significant association was found for the comparison of genotypes (p = 0.11), nor in logistic regression analysis (p = 0.214, OR 1.072, 95% CI 0.961-1.196). In the NACP screening cohort SNPs rs937662, rs1699012, and rs4746 displayed an ambiguous result when patients were compared to controls in the recessive or dominant model (p = 0.08, 0.08, and 0.07, respectively). Again, these associations were not confirmed in the extended cohorts (rs937662, dominant model: p = 0.07, logistic regression: p = 0.07, OR 1.207, 95% CI 0.985-1.480) or in the replication cohorts for rs4746 (Germany, p = 0.42, OR 1.080, 95% CI 0.673-1.124; France, p = 0.19, OR 0.90, 95% CI 0.76-1.06; China, p = 0.24, OR 1.18, 95% CI 0.90-1.54) and rs1699012 (Germany, Munich; p = 0.279, OR 0.903, 95% CI 0.750-1.087). CONCLUSIONS Common GLO1 variants do not increase chronic pancreatitis risk.

Authors: Tom Kaune, Marcus Hollenbach, Bettina Keil, Jian-Min Chen, Emmanuelle Masson, Carla Becker, Marko Damm, Claudia Ruffert, Robert Grützmann, Albrecht Hoffmeister, Rene H. M. Te Morsche, Giulia Martina Cavestro, Raffaella Alessia Zuppardo, Adrian Saftoiu, Ewa Malecka-Panas, Stanislaw Głuszek, Peter Bugert, Markus M. Lerch, Frank Ulrich Weiss, Wen-Bin Zou, Zhuan Liao, Peter Hegyi, Joost Ph Drenth, Jan Riedel, Claude Férec, Markus Scholz, Holger Kirsten, Andrea Tóth, Maren Ewers, Heiko Witt, Heidi Griesmann, Patrick Michl, Jonas Rosendahl

Date Published: 29th Oct 2019

Publication Type: Journal article

Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels
Genetical Statistics and Systems Biology

Abstract (Expand)

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 … individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.

Authors: Adrienne Tin, Jonathan Marten, Victoria L. Halperin Kuhns, Yong Li, Matthias Wuttke, Holger Kirsten, Karsten B. Sieber, Chengxiang Qiu, Mathias Gorski, Zhi Yu, Ayush Giri, Gardar Sveinbjornsson, Man Li, Audrey Y. Chu, Anselm Hoppmann, Luke J. O’Connor, Bram Prins, Teresa Nutile, Damia Noce, Masato Akiyama, Massimiliano Cocca, Sahar Ghasemi, Peter J. van der Most, Katrin Horn, Yizhe Xu, Christian Fuchsberger, Sanaz Sedaghat, Saima Afaq, Najaf Amin, Johan Ärnlöv, Stephan J. L. Bakker, Nisha Bansal, Daniela Baptista, Sven Bergmann, Mary L. Biggs, Ginevra Biino, Eric Boerwinkle, Erwin P. Bottinger, Thibaud S. Boutin, Marco Brumat, Ralph Burkhardt, Eric Campana, Archie Campbell, Harry Campbell, Robert J. Carroll, Eulalia Catamo, John C. Chambers, Marina Ciullo, Maria Pina Concas, Josef Coresh, Tanguy Corre, Daniele Cusi, Sala Cinzia Felicita, Martin H. de Borst, Alessandro de Grandi, Renée de Mutsert, Aiko P. J. de Vries, Graciela Delgado, Ayşe Demirkan, Olivier Devuyst, Katalin Dittrich, Kai-Uwe Eckardt, Georg Ehret, Karlhans Endlich, Michele K. Evans, Ron T. Gansevoort, Paolo Gasparini, Vilmantas Giedraitis, Christian Gieger, Giorgia Girotto, Martin Gögele, Scott D. Gordon, Daniel F. Gudbjartsson, Vilmundur Gudnason, Toomas Haller, Pavel Hamet, Tamara B. Harris, Caroline Hayward, Andrew A. Hicks, Edith Hofer, Hilma Holm, Wei Huang, Nina Hutri-Kähönen, Shih-Jen Hwang, M. Arfan Ikram, Raychel M. Lewis, Erik Ingelsson, Johanna Jakobsdottir, Ingileif Jonsdottir, Helgi Jonsson, Peter K. Joshi, Navya Shilpa Josyula, Bettina Jung, Mika Kähönen, Yoichiro Kamatani, Masahiro Kanai, Shona M. Kerr, Wieland Kiess, Marcus E. Kleber, Wolfgang Koenig, Jaspal S. Kooner, Antje Körner, Peter Kovacs, Bernhard K. Krämer, Florian Kronenberg, Michiaki Kubo, Brigitte Kühnel, Martina La Bianca, Leslie A. Lange, Benjamin Lehne, Terho Lehtimäki, Jun Liu, Markus Loeffler, Ruth J. F. Loos, Leo-Pekka Lyytikäinen, Reedik Magi, Anubha Mahajan, Nicholas G. Martin, Winfried März, Deborah Mascalzoni, Koichi Matsuda, Christa Meisinger, Thomas Meitinger, Andres Metspalu, Yuri Milaneschi, Christopher J. O’Donnell, Otis D. Wilson, J. Michael Gaziano, Pashupati P. Mishra, Karen L. Mohlke, Nina Mononen, Grant W. Montgomery, Dennis O. Mook-Kanamori, Martina Müller-Nurasyid, Girish N. Nadkarni, Mike A. Nalls, Matthias Nauck, Kjell Nikus, Boting Ning, Ilja M. Nolte, Raymond Noordam, Jeffrey R. O’Connell, Isleifur Olafsson, Sandosh Padmanabhan, Brenda W. J. H. Penninx, Thomas Perls, Annette Peters, Mario Pirastu, Nicola Pirastu, Giorgio Pistis, Ozren Polasek, Belen Ponte, David J. Porteous, Tanja Poulain, Michael H. Preuss, Ton J. Rabelink, Laura M. Raffield, Olli T. Raitakari, Rainer Rettig, Myriam Rheinberger, Kenneth M. Rice, Federica Rizzi, Antonietta Robino, Igor Rudan, Alena Krajcoviechova, Renata Cifkova, Rico Rueedi, Daniela Ruggiero, Kathleen A. Ryan, Yasaman Saba, Erika Salvi, Helena Schmidt, Reinhold Schmidt, Christian M. Shaffer, Albert V. Smith, Blair H. Smith, Cassandra N. Spracklen, Konstantin Strauch, Michael Stumvoll, Patrick Sulem, Salman M. Tajuddin, Andrej Teren, Joachim Thiery, Chris H. L. Thio, Unnur Thorsteinsdottir, Daniela Toniolo, Anke Tönjes, Johanne Tremblay, André G. Uitterlinden, Simona Vaccargiu, Pim van der Harst, Cornelia M. van Duijn, Niek Verweij, Uwe Völker, Peter Vollenweider, Gerard Waeber, Melanie Waldenberger, John B. Whitfield, Sarah H. Wild, James F. Wilson, Qiong Yang, Weihua Zhang, Alan B. Zonderman, Murielle Bochud, James G. Wilson, Sarah A. Pendergrass, Kevin Ho, Afshin Parsa, Peter P. Pramstaller, Bruce M. Psaty, Carsten A. Böger, Harold Snieder, Adam S. Butterworth, Yukinori Okada, Todd L. Edwards, Kari Stefansson, Katalin Susztak, Markus Scholz, Iris M. Heid, Adriana M. Hung, Alexander Teumer, Cristian Pattaro, Owen M. Woodward, Veronique Vitart, Anna Köttgen

Date Published: 1st Oct 2019

Publication Type: Journal article

Der Leipzig Health Atlas
LHA - Leipzig Health Atlas

Abstract (Expand)

Die Notwendigkeit des Managements von Forschungsdaten ist von der Forschungscommunity erkannt – Sponsoren, Gesetzgeber, Verlage erwarten und fördern die Einhaltung der guten wissenschaftlichen Praxis, … was nicht nur die Archivierung umfasst, sondern auch die Verfügbarkeit von Forschungsdaten- und ergebnissen im Sinne der FAIR-Prinzipien. Der Leipzig Health Atlas (LHA) ist ein Projekt zur Präsentation und zum Austausch eines breiten Spektrums von Publikationen, (bio) medizinischen Daten (z.B. klinisch, epidemiologisch, molekular), Modellen und Tools z.B. zur Risikoberechnung in der Gesundheitsforschung. Die Verbundpartner decken hierbei einen breiten Bereich wissenschaftlicher Disziplinen ab, beginnend von medizinischer Systembiologie über klinische und epidemiologische Forschung bis zu ontologischer und dynamischer Modellierung. Derzeit sind 18 Forschungskonsortien beteiligt (u.a. zu den Domänen Lymphome, Gliome, Sepsis, Erblicher Darm- und Brustkrebs), die Daten aus klinischen Studien, Patientenkohorten, epidemiologischen Kohorten, teilweise mit umfangreichen molekularen und genetischen Profilen, sammeln. Die Modellierung umfasst algorithmische Phänotypklassifizierung, Risikovorhersage und Krankheitsdynamik. Wir konnten in einer ersten Entwicklungsphase zeigen, dass unsere webbasierte Plattform geeignet ist, um (1) Methoden zur Verfügung zu stellen, um individuelle Patientendaten aus Publikationen für eine Weiternutzung zugänglich zu machen, (2) algorithmische Werkzeuge zur Phänotypisierung und Risikoprofilerstellung zu präsentieren, (3) Werkzeuge zur Durchführung dynamischer Krankheits- und Therapiemodelle interaktiv verfügbar zu machen und (4) strukturierte Metadaten zu quantitativen und qualitativen Merkmalen bereit zu stellen. Die semantische Datenintegration liefert hierzu die Technologien (Ontologien und Datamining Werkzeuge) für die (semantische) Datenintegration und Wissensanreicherung. Darüber hinaus stellt sie Werkzeuge zur Verknüpfung eigener Daten, Analyseergebnisse, öffentlich zugänglicher Daten- und Metadaten-Repositorien sowie zur Verdichtung komplexer Daten zur Verfügung. Eine Arbeitsgruppe zur Applikationsentwicklung und –validierung entwickelt innovative paradigmatische Anwendungen für (1) die klinische Entscheidungsfindung für Krebsstudien, die genetische Beratung, für Risikovorhersagemodelle sowie Gewebe- und Krankheitsmodelle und (2) Anwendungen (sog. Apps), die sich auf die Charakterisierung neuer Phänotypen (z.B. ‚omics‘-Merkmale, Körpertypen, Referenzwerte) aus epidemiologischen Studien konzentrieren. Diese Anwendungen werden gemeinsam mit klinischen Experten, Genetikern, Systembiologen, Biometrikern und Bioinformatikern spezifiziert. Der LHA stellt Integrationstechnologie bereit und implementiert die Anwendungen für die User Communities unter Verwendung verschiedener Präsentationswerkzeuge bzw. Technologien (z.B. R-Shiny, i2b2, Kubernetes, SEEK). Dazu ist es erforderlich, die Daten und Metadaten vor dem Hochladen zu kuratieren, Erlaubnisse der Datenbesitzer einzuholen, die erforderlichen Datenschutzkriterien zu berücksichtigen und semantische Annotationen zu überprüfen. Zudem werden die zugelieferten Modellalgorithmen in einer qualitätsgesicherten Weise aufbereitet und, soweit anwendbar, online interaktiv zur Verfügung gestellt. Der LHA richtet sich insbesondere an die Zielgruppen Kliniker, Epidemiologen, Molekulargenetiker, Humangenetiker, Pathologen, Biostatistiker und Modellierer ist aber unter www.healthatlas.de öffentlich zugänglich – aus rechtlichen Gründen erfordert der Zugriff auf bestimmte Applikationen und Datensätze zusätzliche Autorisierung. Das Projekt wird über das BMBF Programm i:DSem (Integrative Datensemantik für die Systemmedizin, Förderkennzeichen 031L0026) gefördert.

Authors: F. A. Meineke, Sebastian Stäubert, Matthias Löbe, C. Beger, René Hänsel, A. Uciteli, H. Binder, T. Kirsten, M. Scholz, H. Herre, C. Engel, Markus Löffler

Date Published: 19th Sep 2019

Publication Type: Misc

Association of Birth Weight With Type 2 Diabetes and Glycemic Traits: A Mendelian Randomization Study
Genetical Statistics and Systems Biology

Abstract (Expand)

Importance Observational studies have shown associations of birth weight with type 2 diabetes (T2D) and glycemic traits, but it remains unclear whether these associations represent causal associations. …. Objective To test the association of birth weight with T2D and glycemic traits using a mendelian randomization analysis. Design, Setting, and Participants This mendelian randomization study used a genetic risk score for birth weight that was constructed with 7 genome-wide significant single-nucleotide polymorphisms. The associations of this score with birth weight and T2D were tested in a mendelian randomization analysis using study-level data. The association of birth weight with T2D was tested using both study-level data (7 single-nucleotide polymorphisms were used as an instrumental variable) and summary-level data from the consortia (43 single-nucleotide polymorphisms were used as an instrumental variable). Data from 180 056 participants from 49 studies were included. Main Outcomes and Measures Type 2 diabetes and glycemic traits. Results This mendelian randomization analysis included 49 studies with 41 155 patients with T2D and 80 008 control participants from study-level data and 34 840 patients with T2D and 114 981 control participants from summary-level data. Study-level data showed that a 1-SD decrease in birth weight due to the genetic risk score was associated with higher risk of T2D among all participants (odds ratio [OR], 2.10; 95% CI, 1.69-2.61; P = 4.03 \times 10-5), among European participants (OR, 1.96; 95% CI, 1.42-2.71; P = .04), and among East Asian participants (OR, 1.39; 95% CI, 1.18-1.62; P = .04). Similar results were observed from summary-level analyses. In addition, each 1-SD lower birth weight was associated with 0.189 SD higher fasting glucose concentration (\textgreekb = 0.189; SE = 0.060; P = .002), but not with fasting insulin, 2-hour glucose, or hemoglobin A1c concentration. Conclusions and Relevance In this study, a genetic predisposition to lower birth weight was associated with increased risk of T2D and higher fasting glucose concentration, suggesting genetic effects on retarded fetal growth and increased diabetes risk that either are independent of each other or operate through alterations of integrated biological mechanisms.

Authors: Tao Huang, Tiange Wang, Yan Zheng, Christina Ellervik, Xiang Li, Meng Gao, Zhe Fang, Jin-Fang Chai, Tarun Veer S. Ahluwalia, Yujie Wang, Trudy Voortman, Raymond Noordam, Alexis Frazier-Wood, Markus Scholz, Emily Sonestedt, Masato Akiyama, Rajkumar Dorajoo, Ang Zhou, Tuomas O. Kilpeläinen, Marcus E. Kleber, Sarah R. Crozier, Keith M. Godfrey, Rozenn Lemaitre, Janine F. Felix, Yuan Shi, Preeti Gupta, Chiea-Chuen Khor, Terho Lehtimäki, Carol A. Wang, Carla M. T. Tiesler, Elisabeth Thiering, Marie Standl, Peter Rzehak, Eirini Marouli, Meian He, Cécile Lecoeur, Dolores Corella, Chao-Qiang Lai, Luis A. Moreno, Niina Pitkänen, Colin A. Boreham, Tao Zhang, Seang Mei Saw, Paul M. Ridker, Mariaelisa Graff, Frank J. A. van Rooij, Andre G. Uitterlinden, Albert Hofman, Diana van Heemst, Frits R. Rosendaal, Renée de Mutsert, Ralph Burkhardt, Christina-Alexandra Schulz, Ulrika Ericson, Yoichiro Kamatani, Jian-Min Yuan, Chris Power, Torben Hansen, Thorkild I. A. Sørensen, Anne Tjønneland, Kim Overvad, Graciela Delgado, Cyrus Cooper, Luc Djousse, Fernando Rivadeneira, Karen Jameson, Wanting Zhao, Jianjun Liu, Nanette R. Lee, Olli Raitakari, Mika Kähönen, Jorma Viikari, Veit Grote, Jean-Paul Langhendries, Berthold Koletzko, Joaquin Escribano, Elvira Verduci, George Dedoussis, Caizheng Yu, Yih Chung Tham, Blanche Lim, Sing Hui Lim, Philippe Froguel, Beverley Balkau, Nadia R. Fink, Rebecca K. Vinding, Astrid Sevelsted, Hans Bisgaard, Oscar Coltell, Jean Dallongeville, Frédéric Gottrand, Katja Pahkala, Harri Niinikoski, Elina Hyppönen, Oluf Pedersen, Winfried März, Hazel Inskip, Vincent W. V. Jaddoe, Elaine Dennison, Tien Yin Wong, Charumathi Sabanayagam, E-Shyong Tai, Karen L. Mohlke, David A. Mackey, Dariusz Gruszfeld, Panagiotis Deloukas, Katherine L. Tucker, Frédéric Fumeron, Klaus Bønnelykke, Peter Rossing, Ramon Estruch, Jose M. Ordovas, Donna K. Arnett, Aline Meirhaeghe, Philippe Amouyel, Ching-Yu Cheng, Xueling Sim, Yik Ying Teo, Rob M. van Dam, Woon-Puay Koh, Marju Orho-Melander, Markus Loeffler, Michiaki Kubo, Joachim Thiery, Dennis O. Mook-Kanamori, Dariush Mozaffarian, Bruce M. Psaty, Oscar H. Franco, Tangchun Wu, Kari E. North, George Davey Smith, Jorge E. Chavarro, Daniel I. Chasman, Lu Qi

Date Published: 4th Sep 2019

Publication Type: Journal article

Clinical and lifestyle related factors influencing whole blood metabolite levels -- A comparative analysis of three large cohorts
Genetical Statistics and Systems Biology

Abstract (Expand)

Objective Human blood metabolites are influenced by a number of lifestyle and environmental factors. Identification of these factors and the proper quantification of their relevance provides insights …s into human biological and metabolic disease processes, is key for standardized translation of metabolite biomarkers into clinical applications, and is a prerequisite for comparability of data between studies. However, so far only limited data exist from large and well-phenotyped human cohorts and current methods for analysis do not fully account for the characteristics of these data. The primary aim of this study was to identify, quantify and compare the impact of a comprehensive set of clinical and lifestyle related factors on metabolite levels in three large human cohorts. To achieve this goal, we improve current methodology by developing a principled analysis approach, which could be translated to other cohorts and metabolite panels. Methods 63 Metabolites (amino acids, acylcarnitines) were quantified by liquid chromatography tandem mass spectrometry in three cohorts (total N~=~16,222). Supported by a simulation study evaluating various analytical approaches, we developed an analysis pipeline including preprocessing, identification, and quantification of factors affecting metabolite levels. We comprehensively identified uni- and multivariable metabolite associations considering 29 environmental and clinical factors and performed metabolic pathway enrichment and network analyses. Results Inverse normal transformation of batch corrected and outlier removed metabolite levels accompanied by linear regression analysis proved to be the best suited method to deal with the metabolite data. Association analyses revealed numerous uni- and multivariable significant associations. 15 of the analyzed 29 factors explained {\textgreater}1{\%} of variance for at least one of the metabolites. Strongest factors are application of steroid hormones, reticulocytes, waist-to-hip ratio, sex, haematocrit, and age. Effect sizes of factors are comparable across studies. Conclusions We introduced a principled approach for the analysis of MS data allowing identification, and quantification of effects of clinical and lifestyle factors with metabolite levels. We detected a number of known and novel associations broadening our understanding of the regulation of the human metabolome. The large heterogeneity observed between cohorts could almost completely be explained by differences in the distribution of influencing factors emphasizing the necessity of a proper confounder analysis when interpreting metabolite associations.

Authors: Carl Beuchel, Susen Becker, Julia Dittrich, Holger Kirsten, Anke Toenjes, Michael Stumvoll, Markus Loeffler, Holger Thiele, Frank Beutner, Joachim Thiery, Uta Ceglarek, Markus Scholz

Date Published: 17th Aug 2019

Publication Type: Not specified

Genetic association study of eight steroid hormones and implications for sexual dimorphism of coronary artery disease
LIFE Adult, Genetical Statistics and Systems Biology, LIFE Heart

Abstract (Expand)

CONTEXT: Steroid hormones are important regulators of physiological processes in humans and are under genetic control. A link to coronary artery disease (CAD) is supposed. OBJECTIVE: Our main objective …e was to identify genetic loci influencing steroid hormone levels. As secondary aim, we searched for causal effects of steroid hormones on CAD. DESIGN: We conducted genome-wide meta-association studies for eight steroid hormones: cortisol, DHEA-S, estradiol and testosterone in two independent cohorts (LIFE-Adult, LIFE-Heart, max. n=7667), and progesterone, 17-hydroxyprogesterone, androstenedione and aldosterone in LIFE-Heart only (max. n=2070). All genome-wide significant loci were tested for sex interactions. Further, we tested if previously reported CAD SNPs were associated with our steroid hormone panel and investigated causal links between hormone levels and CAD status using Mendelian Randomization (MR) approaches. RESULTS: We discovered 15 novel associated loci for 17-hydroxyprogesterone, progesterone, DHEA-S, cortisol, androstenedione, and estradiol. Five of these loci relate to genes directly involved in steroid metabolism: CYP21A1, CYP11B1, CYP17A1, STS, and HSD17B12, almost completing the set of steroidogenic enzymes with genetic associations. Sexual dimorphisms were found for seven of the novel loci. Other loci correspond, e.g., to the WNT4/β-catenin pathway. MR revealed that cortisol, androstenedione, 17-hydroxyprogesterone and DHEA-S had causal effects on CAD. We also observed enrichment of cortisol and testosterone associations among known CAD hits. CONCLUSION: Our study greatly improves insight into genetic regulation of steroid hormones and their dependency on sex. These results could serve as a basis for analyzing sex-dimorphisms in other complex diseases.

Authors: J. Pott, YJ. Bae, K. Horn, A. Teren, Andreas Kühnapfel, H. Kirsten, U. Ceglarek, Markus Löffler, J. Thiery, J. Kratzsch, Markus Scholz

Date Published: 6th Jun 2019

Publication Type: Not specified

Human Diseases: coronary artery disease

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