Large-scale pharmacogenomic study of sulfonylureas and the QT, JT, and QRS intervals: CHARGE Pharmacogenomics Working Group

[1]  D. Nathan.  Diabetes: Advances in Diagnosis and Treatment. , 2015, JAMA.

[2]  G. Ruaño,et al.  Pharmacogenetic association study of warfarin safety endpoints in Puerto Ricans. , 2014, Puerto Rico health sciences journal.

[3]  M. Borggrefe,et al.  Clinical characteristics and treatment of short QT syndrome , 2005, Expert review of cardiovascular therapy.

[4]  K. Lunetta,et al.  Methods in Genetics and Clinical Interpretation Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Design of Prospective Meta-Analyses of Genome-Wide Association Studies From 5 Cohorts , 2010 .

[5]  Thomas Lumley,et al.  Generalized estimating equations for genome‐wide association studies using longitudinal phenotype data , 2015, Statistics in medicine.

[6]  M. Peters,et al.  Systematic identification of trans eQTLs as putative drivers of known disease associations , 2013, Nature Genetics.

[7]  A. Hattersley,et al.  Loss‐of‐Function CYP2C9 Variants Improve Therapeutic Response to Sulfonylureas in Type 2 Diabetes: A Go‐DARTS Study , 2010, Clinical pharmacology and therapeutics.

[8]  D. Thomas,et al.  Gene–environment-wide association studies: emerging approaches , 2010, Nature Reviews Genetics.

[9]  D. Levy,et al.  QT interval is a heritable quantitative trait with evidence of linkage to chromosome 3 in a genome-wide linkage analysis: The Framingham Heart Study. , 2005, Heart rhythm.

[10]  P. Stang,et al.  Short QT in a Cohort of 1.7 Million Persons: Prevalence, Correlates, and Prognosis , 2014, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[11]  J. Hanley,et al.  Statistical analysis of correlated data using generalized estimating equations: an orientation. , 2003, American journal of epidemiology.

[12]  I. Tkáč,et al.  KCNJ11 gene E23K variant and therapeutic response to sulfonylureas. , 2012, European journal of internal medicine.

[13]  M. Olivier.  A haplotype map of the human genome , 2003, Nature.

[14]  S. Wolfe,et al.  Timing of new black box warnings and withdrawals for prescription medications. , 2002, JAMA.

[15]  C. Meinert,et al.  A study of the effects of hypoglycemic agents on vascular complications in patients with adult-onset diabetes. II. Mortality results. , 1970, Diabetes.

[16]  H. Völzke,et al.  Genome-wide association study of chronic periodontitis in a general German population. , 2013, Journal of clinical periodontology.

[17]  R. Shah.  Drugs, QTc Interval Prolongation and Final ICH E14 Guideline , 2005, Drug safety.

[18]  A. Hofman,et al.  Common variation in the NOS1AP gene is associated with reduced glucose-lowering effect and with increased mortality in users of sulfonylurea , 2008, Pharmacogenetics and genomics.

[19]  Alan Bernjak,et al.  Risk of Cardiac Arrhythmias During Hypoglycemia in Patients With Type 2 Diabetes and Cardiovascular Risk , 2014, Diabetes.

[20]  M. Stumvoll,et al.  The E23K Variant of KCNJ11 and the Risk for Severe Sulfonylurea-induced Hypoglycemia in Patients with Type 2 Diabetes , 2009, Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme.

[21]  J. Molnar,et al.  Differential effect of glyburide (glibenclamide) and metformin on QT dispersion: a potential adenosine triphosphate sensitive K+ channel effect. , 2002, The American journal of cardiology.

[22]  Emmanouil Collab.  A map of human genome variation from population-scale sequencing , 2011, Nature.

[23]  Peggy Hall,et al.  The NHGRI GWAS Catalog, a curated resource of SNP-trait associations , 2013, Nucleic Acids Res..

[24]  M. Pirmohamed,et al.  Influence of CYP2C9 and VKORC1 on Patient Response to Warfarin: A Systematic Review and Meta-Analysis , 2012, PloS one.

[25]  E. Michelson,et al.  Considerations for assessing the potential effects of antidiabetes drugs on cardiac ventricular repolarization: A report from the Cardiac Safety Research Consortium. , 2015, American heart journal.

[26]  Tom H. Pringle,et al.  The human genome browser at UCSC. , 2002, Genome research.

[27]  Derek J Van Booven,et al.  Cytochrome P450 2C9-CYP2C9. , 2010, Pharmacogenetics and genomics.

[28]  David C. Nickle,et al.  Lung eQTLs to Help Reveal the Molecular Underpinnings of Asthma , 2012, PLoS genetics.

[29]  M. Olivier.  A haplotype map of the human genome. , 2003, Nature.

[30]  E. Guallar,et al.  QT-interval duration and mortality rate: results from the Third National Health and Nutrition Examination Survey. , 2011, Archives of internal medicine.

[31]  Kristin Reiche,et al.  Dissecting the genetics of the human transcriptome identifies novel trait-related trans-eQTLs and corroborates the regulatory relevance of non-protein coding loci† , 2015, Human molecular genetics.

[32]  C. Rotimi,et al.  Pharmacogenomics, ancestry and clinical decision making for global populations , 2013, The Pharmacogenomics Journal.

[33]  Xiping Xu,et al.  Ser1369Ala Variant in Sulfonylurea Receptor Gene ABCC8 Is Associated With Antidiabetic Efficacy of Gliclazide in Chinese Type 2 Diabetic Patients , 2008, Diabetes Care.

[34]  Toshihiro Tanaka.  The International HapMap Project , 2003, Nature.

[35]  A. Janež,et al.  CYP2C9, KCNJ11 and ABCC8 polymorphisms and the response to sulphonylurea treatment in type 2 diabetes patients , 2014, European Journal of Clinical Pharmacology.

[36]  P. Neuvonen,et al.  Drug interactions with oral antidiabetic agents: pharmacokinetic mechanisms and clinical implications. , 2012, Trends in pharmacological sciences.

[37]  Soo-Youn Lee,et al.  Effect of genetic polymorphisms on the pharmacokinetics and efficacy of glimepiride in a Korean population. , 2011, Clinica chimica acta; international journal of clinical chemistry.

[38]  M. McCarthy,et al.  Variation in TCF7L2 Influences Therapeutic Response to Sulfonylureas , 2007, Diabetes.

[39]  M. Stumvoll,et al.  TCF7L2 and therapeutic response to sulfonylureas in patients with type 2 diabetes , 2011, BMC Medical Genetics.

[40]  S. Szeinbach,et al.  Market withdrawal of new molecular entities approved in the United States from 1980 to 2009 , 2011, Pharmacoepidemiology and drug safety.

[41]  S. Flores-Martínez,et al.  CYP2C9 and CYP2C19 Allele and Haplotype Distributions in Four Mestizo Populations from Western Mexico: An Interethnic Comparative Study. , 2016, Genetic testing and molecular biomarkers.

[42]  Saurabh Baheti,et al.  Comprehensively evaluating cis-regulatory variation in the human prostate transcriptome by using gene-level allele-specific expression. , 2015, American journal of human genetics.

[43]  Agostino Gnasso,et al.  The E23K variant of KCNJ11 encoding the pancreatic beta-cell adenosine 5'-triphosphate-sensitive potassium channel subunit Kir6.2 is associated with an increased risk of secondary failure to sulfonylurea in patients with type 2 diabetes. , 2006, The Journal of clinical endocrinology and metabolism.

[44]  G. Kempermann.  Faculty Opinions recommendation of Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. , 2015 .

[45]  Christian Gieger,et al.  Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium , 2009, Nature Genetics.

[46]  Juan Pablo Lewinger,et al.  Methodological Issues in Multistage Genome-wide Association Studies. , 2009, Statistical science : a review journal of the Institute of Mathematical Statistics.

[47]  Marco Perizzolo,et al.  Molecular Cloning of a Third Member of the Potassium-dependent Sodium-Calcium Exchanger Gene Family,NCKX3 * , 2001, The Journal of Biological Chemistry.

[48]  Z. Khan,et al.  Nuclear Localization of L-type Glutaminase in Mammalian Brain* , 2002, The Journal of Biological Chemistry.

[49]  R. Elston,et al.  Two-marker association tests yield new disease associations for coronary artery disease and hypertension , 2011, Human Genetics.

[50]  J. Adelman,et al.  Structure of the gating domain of a Ca2+-activated K+ channel complexed with Ca2+/calmodulin , 2001, Nature.

[51]  Yun Li,et al.  METAL: fast and efficient meta-analysis of genomewide association scans , 2010, Bioinform..

[52]  Christian Gieger,et al.  Multiple Loci Are Associated with White Blood Cell Phenotypes , 2011, PLoS genetics.

[53]  G. Ruaño,et al.  Pharmacogenetics of drug-metabolizing enzymes in US Hispanics , 2015, Drug metabolism and personalized therapy.

[54]  S. Flores-Martínez,et al.  CYP2C9 and CYP2C19 Allele and Haplotype Distributions in Four Mestizo Populations from Western Mexico: An Interethnic Comparative Study. , 2016 .

[55]  Kenny Q. Ye,et al.  An integrated map of genetic variation from 1,092 human genomes , 2012, Nature.

[56]  D. Altshuler,et al.  A map of human genome variation from population-scale sequencing , 2010, Nature.

[57]  Changqing Zeng,et al.  Influence of CYP2C9 and VKORC1 genotypes on the risk of hemorrhagic complications in warfarin-treated patients: a systematic review and meta-analysis. , 2013, International journal of cardiology.

[58]  Andrew D. Johnson,et al.  Synthesis of 53 tissue and cell line expression QTL datasets reveals master eQTLs , 2014, BMC Genomics.

[59]  A. Morris,et al.  Transethnic Meta-Analysis of Genomewide Association Studies , 2011, Genetic epidemiology.

[60]  S. Tsai,et al.  Genetic variants associated with phenytoin-related severe cutaneous adverse reactions. , 2014, JAMA.

[61]  T. Ikeda.  QT prolongation in type 2 diabetes mellitus treated with glibenclamide. , 1994, Diabete & metabolisme.

[62]  Satterthwaite Fe.  An approximate distribution of estimates of variance components. , 1946 .

[63]  F. E. Satterthwaite.  An approximate distribution of estimates of variance components. , 1946, Biometrics.

[64]  PubMed Id,et al.  To replicate or not to replicate: the case of pharmacogenetic studies: Establishing validity of pharmacogenomic findings: from replication to triangulation. , 2013 .

[65]  Jun S. Liu,et al.  The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans , 2015, Science.

[66]  A. Hofman,et al.  Drug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval , 2013, The Pharmacogenomics Journal.

[67]  Yiyi Zhang,et al.  Electrocardiographic QT Interval and Mortality: A Meta-analysis , 2011, Epidemiology.

[68]  Jingyuan Fu,et al.  Common variants in 22 loci are associated with QRS duration and cardiac ventricular conduction , 2010, Nature Genetics.

[69]  B. Vandermeer,et al.  Mortality risk among sulfonylureas: a systematic review and network meta-analysis. , 2015, The lancet. Diabetes & endocrinology.

[70]  M. Malik,et al.  Drug induced shortening of the QT/QTc interval: an emerging safety issue warranting further modelling and evaluation in drug research and development? , 2009, Journal of pharmacological and toxicological methods.

[71]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[72]  D. Wysowski,et al.  Use of Antidiabetic Drugs in the U.S., 2003–2012 , 2014, Diabetes Care.

[73]  Martin Borggrefe,et al.  Short QT Syndrome: A Familial Cause of Sudden Death , 2003, Circulation.

[74]  M. Monami,et al.  Cardiovascular safety of sulfonylureas: a meta‐analysis of randomized clinical trials , 2013, Diabetes, obesity & metabolism.

[75]  C. D. dos Remedios,et al.  Genome-Wide Identification of Expression Quantitative Trait Loci (eQTLs) in Human Heart , 2014, PloS one.

[76]  C. Newton-Cheh,et al.  Gene-environment interaction between SCN5A-1103Y and hypokalemia influences QT interval prolongation in African Americans: the Jackson Heart Study. , 2014, American heart journal.

[77]  D. Lykken,et al.  Genetic factors in the electrocardiogram and heart rate of twins reared apart and together. , 1989, The American journal of cardiology.

[78]  Sharon R Grossman,et al.  Integrating common and rare genetic variation in diverse human populations , 2010, Nature.

[79]  Mary Brophy,et al.  Million Veteran Program: A mega-biobank to study genetic influences on health and disease. , 2016, Journal of clinical epidemiology.

[80]  G. Abecasis,et al.  Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies , 2006, Nature Genetics.

[81]  J. Brockmöller,et al.  Association between CYP2C9 slow metabolizer genotypes and severe hypoglycaemia on medication with sulphonylurea hypoglycaemic agents. , 2005, British journal of clinical pharmacology.

[82]  J. Burton,et al.  QT Interval Prolongation as Predictor of Sudden Death in Patients with Myocardial Infarction , 1978 .

[83]  P. Savage,et al.  Assessing the use of medications in the elderly: methods and initial experience in the Cardiovascular Health Study. The Cardiovascular Health Study Collaborative Research Group. , 1992, Journal of clinical epidemiology.

[84]  Michael J Ackerman,et al.  Nature Genetics Advance Online Publication Genetic Association Study of Qt Interval Highlights Role for Calcium Signaling Pathways in Myocardial Repolarization , 2022 .

[85]  Manolis Kellis,et al.  HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants , 2011, Nucleic Acids Res..

[86]  J. Ioannidis,et al.  The False-positive to False-negative Ratio in Epidemiologic Studies , 2011, Epidemiology.