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Your search keyword '"Sotoodehnia N."' showing total 24 results
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24 results on '"Sotoodehnia N."'

1. Genetic insights into resting heart rate and its role in cardiovascular disease.

Author

van de Vegte YJ, Eppinga RN, van der Ende MY, Hagemeijer YP, Mahendran Y, Salfati E, Smith AV, Tan VY, Arking DE, Ntalla I, Appel EV, Schurmann C, Brody JA, Rueedi R, Polasek O, Sveinbjornsson G, Lecoeur C, Ladenvall C, Zhao JH, Isaacs A, Wang L, Luan J, Hwang SJ, Mononen N, Auro K, Jackson AU, Bielak LF, Zeng L, Shah N, Nethander M, Campbell A, Rankinen T, Pechlivanis S, Qi L, Zhao W, Rizzi F, Tanaka T, Robino A, Cocca M, Lange L, Müller-Nurasyid M, Roselli C, Zhang W, Kleber ME, Guo X, Lin HJ, Pavani F, Galesloot TE, Noordam R, Milaneschi Y, Schraut KE, den Hoed M, Degenhardt F, Trompet S, van den Berg ME, Pistis G, Tham YC, Weiss S, Sim XS, Li HL, van der Most PJ, Nolte IM, Lyytikäinen LP, Said MA, Witte DR, Iribarren C, Launer L, Ring SM, de Vries PS, Sever P, Linneberg A, Bottinger EP, Padmanabhan S, Psaty BM, Sotoodehnia N, Kolcic I, Arnar DO, Gudbjartsson DF, Holm H, Balkau B, Silva CT, Newton-Cheh CH, Nikus K, Salo P, Mohlke KL, Peyser PA, Schunkert H, Lorentzon M, Lahti J, Rao DC, Cornelis MC, Faul JD, Smith JA, Stolarz-Skrzypek K, Bandinelli S, Concas MP, Sinagra G, Meitinger T, Waldenberger M, Sinner MF, Strauch K, Delgado GE, Taylor KD, Yao J, Foco L, Melander O, de Graaf J, de Mutsert R, de Geus EJC, Johansson Å, Joshi PK, Lind L, Franke A, Macfarlane PW, Tarasov KV, Tan N, Felix SB, Tai ES, Quek DQ, Snieder H, Ormel J, Ingelsson M, Lindgren C, Morris AP, Raitakari OT, Hansen T, Assimes T, Gudnason V, Timpson NJ, Morrison AC, Munroe PB, Strachan DP, Grarup N, Loos RJF, Heckbert SR, Vollenweider P, Hayward C, Stefansson K, Froguel P, Groop L, Wareham NJ, van Duijn CM, Feitosa MF, O'Donnell CJ, Kähönen M, Perola M, Boehnke M, Kardia SLR, Erdmann J, Palmer CNA, Ohlsson C, Porteous DJ, Eriksson JG, Bouchard C, Moebus S, Kraft P, Weir DR, Cusi D, Ferrucci L, Ulivi S, Girotto G, Correa A, Kääb S, Peters A, Chambers JC, Kooner JS, März W, Rotter JI, Hicks AA, Smith JG, Kiemeney LALM, Mook-Kanamori DO, Penninx BWJH, Gyllensten U, Wilson JF, Burgess S, Sundström J, Lieb W, Jukema JW, Eijgelsheim M, Lakatta ELM, Cheng CY, Dörr M, Wong TY, Sabanayagam C, Oldehinkel AJ, Riese H, Lehtimäki T, Verweij N, and van der Harst P

Subjects
Humans, Risk Factors, Heart Rate genetics, Genetic Predisposition to Disease, Mendelian Randomization Analysis methods, Genome-Wide Association Study methods, Polymorphism, Single Nucleotide, Cardiovascular Diseases genetics, Atrial Fibrillation
Abstract

Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development., (© 2023. The Author(s).)

Published
2023
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2. Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease.

Author

Young WJ, Haessler J, Benjamins JW, Repetto L, Yao J, Isaacs A, Harper AR, Ramirez J, Garnier S, van Duijvenboden S, Baldassari AR, Concas MP, Duong T, Foco L, Isaksen JL, Mei H, Noordam R, Nursyifa C, Richmond A, Santolalla ML, Sitlani CM, Soroush N, Thériault S, Trompet S, Aeschbacher S, Ahmadizar F, Alonso A, Brody JA, Campbell A, Correa A, Darbar D, De Luca A, Deleuze JF, Ellervik C, Fuchsberger C, Goel A, Grace C, Guo X, Hansen T, Heckbert SR, Jackson RD, Kors JA, Lima-Costa MF, Linneberg A, Macfarlane PW, Morrison AC, Navarro P, Porteous DJ, Pramstaller PP, Reiner AP, Risch L, Schotten U, Shen X, Sinagra G, Soliman EZ, Stoll M, Tarazona-Santos E, Tinker A, Trajanoska K, Villard E, Warren HR, Whitsel EA, Wiggins KL, Arking DE, Avery CL, Conen D, Girotto G, Grarup N, Hayward C, Jukema JW, Mook-Kanamori DO, Olesen MS, Padmanabhan S, Psaty BM, Pattaro C, Ribeiro ALP, Rotter JI, Stricker BH, van der Harst P, van Duijn CM, Verweij N, Wilson JG, Orini M, Charron P, Watkins H, Kooperberg C, Lin HJ, Wilson JF, Kanters JK, Sotoodehnia N, Mifsud B, Lambiase PD, Tereshchenko LG, and Munroe PB

Subjects
Humans, Genome-Wide Association Study, Risk Factors, Arrhythmias, Cardiac genetics, Electrocardiography methods, Biomarkers, Cardiovascular Diseases genetics, Atrioventricular Block
Abstract

The 3-dimensional spatial and 2-dimensional frontal QRS-T angles are measures derived from the vectorcardiogram. They are independent risk predictors for arrhythmia, but the underlying biology is unknown. Using multi-ancestry genome-wide association studies we identify 61 (58 previously unreported) loci for the spatial QRS-T angle (N = 118,780) and 11 for the frontal QRS-T angle (N = 159,715). Seven out of the 61 spatial QRS-T angle loci have not been reported for other electrocardiographic measures. Enrichments are observed in pathways related to cardiac and vascular development, muscle contraction, and hypertrophy. Pairwise genome-wide association studies with classical ECG traits identify shared genetic influences with PR interval and QRS duration. Phenome-wide scanning indicate associations with atrial fibrillation, atrioventricular block and arterial embolism and genetically determined QRS-T angle measures are associated with fascicular and bundle branch block (and also atrioventricular block for the frontal QRS-T angle). We identify potential biology involved in the QRS-T angle and their genetic relationships with cardiovascular traits and diseases, may inform future research and risk prediction., (© 2023. The Author(s).)

Published
2023
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3. Publisher Correction: Endophenotype effect sizes support variant pathogenicity in monogenic disease susceptibility genes.

Author

Halford JL, Morrill VN, Choi SH, Jurgens SJ, Melloni G, Marston NA, Weng LC, Nauffal V, Hall AW, Gunn S, Austin-Tse CA, Pirruccello JP, Khurshid S, Rehm HL, Benjamin EJ, Boerwinkle E, Brody JA, Correa A, Fornwalt BK, Gupta N, Haggerty CM, Harris S, Heckbert SR, Hong CC, Kooperberg C, Lin HJ, Loos RJF, Mitchell BD, Morrison AC, Post W, Psaty BM, Redline S, Rice KM, Rich SS, Rotter JI, Schnatz PF, Soliman EZ, Sotoodehnia N, Wong EK, Sabatine MS, Ruff CT, Lunetta KL, Ellinor PT, and Lubitz SA

Published
2022
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4. Clonal hematopoiesis of indeterminate potential, DNA methylation, and risk for coronary artery disease.

Author

Uddin MDM, Nguyen NQH, Yu B, Brody JA, Pampana A, Nakao T, Fornage M, Bressler J, Sotoodehnia N, Weinstock JS, Honigberg MC, Nachun D, Bhattacharya R, Griffin GK, Chander V, Gibbs RA, Rotter JI, Liu C, Baccarelli AA, Chasman DI, Whitsel EA, Kiel DP, Murabito JM, Boerwinkle E, Ebert BL, Jaiswal S, Floyd JS, Bick AG, Ballantyne CM, Psaty BM, Natarajan P, and Conneely KN

Subjects
DNA Methylation genetics, Hematopoiesis genetics, Hematopoietic Stem Cells, Humans, Clonal Hematopoiesis genetics, Coronary Artery Disease genetics
Abstract

Age-related changes to the genome-wide DNA methylation (DNAm) pattern observed in blood are well-documented. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by the age-related acquisition and expansion of leukemogenic mutations in hematopoietic stem cells (HSCs), is associated with blood cancer and coronary artery disease (CAD). Epigenetic regulators DNMT3A and TET2 are the two most frequently mutated CHIP genes. Here, we present results from an epigenome-wide association study for CHIP in 582 Cardiovascular Health Study (CHS) participants, with replication in 2655 Atherosclerosis Risk in Communities (ARIC) Study participants. We show that DNMT3A and TET2 CHIP have distinct and directionally opposing genome-wide DNAm association patterns consistent with their regulatory roles, albeit both promoting self-renewal of HSCs. Mendelian randomization analyses indicate that a subset of DNAm alterations associated with these two leading CHIP genes may promote the risk for CAD., (© 2022. The Author(s).)

Published
2022
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5. Genetic analyses of the electrocardiographic QT interval and its components identify additional loci and pathways.

Author

Young WJ, Lahrouchi N, Isaacs A, Duong T, Foco L, Ahmed F, Brody JA, Salman R, Noordam R, Benjamins JW, Haessler J, Lyytikäinen LP, Repetto L, Concas MP, van den Berg ME, Weiss S, Baldassari AR, Bartz TM, Cook JP, Evans DS, Freudling R, Hines O, Isaksen JL, Lin H, Mei H, Moscati A, Müller-Nurasyid M, Nursyifa C, Qian Y, Richmond A, Roselli C, Ryan KA, Tarazona-Santos E, Thériault S, van Duijvenboden S, Warren HR, Yao J, Raza D, Aeschbacher S, Ahlberg G, Alonso A, Andreasen L, Bis JC, Boerwinkle E, Campbell A, Catamo E, Cocca M, Cutler MJ, Darbar D, De Grandi A, De Luca A, Ding J, Ellervik C, Ellinor PT, Felix SB, Froguel P, Fuchsberger C, Gögele M, Graff C, Graff M, Guo X, Hansen T, Heckbert SR, Huang PL, Huikuri HV, Hutri-Kähönen N, Ikram MA, Jackson RD, Junttila J, Kavousi M, Kors JA, Leal TP, Lemaitre RN, Lin HJ, Lind L, Linneberg A, Liu S, MacFarlane PW, Mangino M, Meitinger T, Mezzavilla M, Mishra PP, Mitchell RN, Mononen N, Montasser ME, Morrison AC, Nauck M, Nauffal V, Navarro P, Nikus K, Pare G, Patton KK, Pelliccione G, Pittman A, Porteous DJ, Pramstaller PP, Preuss MH, Raitakari OT, Reiner AP, Ribeiro ALP, Rice KM, Risch L, Schlessinger D, Schotten U, Schurmann C, Shen X, Shoemaker MB, Sinagra G, Sinner MF, Soliman EZ, Stoll M, Strauch K, Tarasov K, Taylor KD, Tinker A, Trompet S, Uitterlinden A, Völker U, Völzke H, Waldenberger M, Weng LC, Whitsel EA, Wilson JG, Avery CL, Conen D, Correa A, Cucca F, Dörr M, Gharib SA, Girotto G, Grarup N, Hayward C, Jamshidi Y, Järvelin MR, Jukema JW, Kääb S, Kähönen M, Kanters JK, Kooperberg C, Lehtimäki T, Lima-Costa MF, Liu Y, Loos RJF, Lubitz SA, Mook-Kanamori DO, Morris AP, O'Connell JR, Olesen MS, Orini M, Padmanabhan S, Pattaro C, Peters A, Psaty BM, Rotter JI, Stricker B, van der Harst P, van Duijn CM, Verweij N, Wilson JF, Arking DE, Ramirez J, Lambiase PD, Sotoodehnia N, Mifsud B, Newton-Cheh C, and Munroe PB

Subjects
Death, Sudden, Cardiac, Genetic Testing, Humans, Male, Arrhythmias, Cardiac genetics, Electrocardiography methods
Abstract

The QT interval is an electrocardiographic measure representing the sum of ventricular depolarization and repolarization, estimated by QRS duration and JT interval, respectively. QT interval abnormalities are associated with potentially fatal ventricular arrhythmia. Using genome-wide multi-ancestry analyses (>250,000 individuals) we identify 177, 156 and 121 independent loci for QT, JT and QRS, respectively, including a male-specific X-chromosome locus. Using gene-based rare-variant methods, we identify associations with Mendelian disease genes. Enrichments are observed in established pathways for QT and JT, and previously unreported genes indicated in insulin-receptor signalling and cardiac energy metabolism. In contrast for QRS, connective tissue components and processes for cell growth and extracellular matrix interactions are significantly enriched. We demonstrate polygenic risk score associations with atrial fibrillation, conduction disease and sudden cardiac death. Prioritization of druggable genes highlight potential therapeutic targets for arrhythmia. Together, these results substantially advance our understanding of the genetic architecture of ventricular depolarization and repolarization., (© 2022. The Author(s).)

Published
2022
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6. Endophenotype effect sizes support variant pathogenicity in monogenic disease susceptibility genes.

Author

Halford JL, Morrill VN, Choi SH, Jurgens SJ, Melloni G, Marston NA, Weng LC, Nauffal V, Hall AW, Gunn S, Austin-Tse CA, Pirruccello JP, Khurshid S, Rehm HL, Benjamin EJ, Boerwinkle E, Brody JA, Correa A, Fornwalt BK, Gupta N, Haggerty CM, Harris S, Heckbert SR, Hong CC, Kooperberg C, Lin HJ, Loos RJF, Mitchell BD, Morrison AC, Post W, Psaty BM, Redline S, Rice KM, Rich SS, Rotter JI, Schnatz PF, Soliman EZ, Sotoodehnia N, Wong EK, Sabatine MS, Ruff CT, Lunetta KL, Ellinor PT, and Lubitz SA

Subjects
Disease Susceptibility, Humans, Virulence, Endophenotypes, Long QT Syndrome
Abstract

Accurate and efficient classification of variant pathogenicity is critical for research and clinical care. Using data from three large studies, we demonstrate that population-based associations between rare variants and quantitative endophenotypes for three monogenic diseases (low-density-lipoprotein cholesterol for familial hypercholesterolemia, electrocardiographic QTc interval for long QT syndrome, and glycosylated hemoglobin for maturity-onset diabetes of the young) provide evidence for variant pathogenicity. Effect sizes are associated with pathogenic ClinVar assertions (P < 0.001 for each trait) and discriminate pathogenic from non-pathogenic variants (area under the curve 0.82-0.84 across endophenotypes). An effect size threshold of ≥ 0.5 times the endophenotype standard deviation nominates up to 35% of rare variants of uncertain significance or not in ClinVar in disease susceptibility genes with pathogenic potential. We propose that variant associations with quantitative endophenotypes for monogenic diseases can provide evidence supporting pathogenicity., (© 2022. The Author(s).)

Published
2022
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7. DNA methylation signature of chronic low-grade inflammation and its role in cardio-respiratory diseases.

Author

Wielscher M, Mandaviya PR, Kuehnel B, Joehanes R, Mustafa R, Robinson O, Zhang Y, Bodinier B, Walton E, Mishra PP, Schlosser P, Wilson R, Tsai PC, Palaniswamy S, Marioni RE, Fiorito G, Cugliari G, Karhunen V, Ghanbari M, Psaty BM, Loh M, Bis JC, Lehne B, Sotoodehnia N, Deary IJ, Chadeau-Hyam M, Brody JA, Cardona A, Selvin E, Smith AK, Miller AH, Torres MA, Marouli E, Gào X, van Meurs JBJ, Graf-Schindler J, Rathmann W, Koenig W, Peters A, Weninger W, Farlik M, Zhang T, Chen W, Xia Y, Teumer A, Nauck M, Grabe HJ, Doerr M, Lehtimäki T, Guan W, Milani L, Tanaka T, Fisher K, Waite LL, Kasela S, Vineis P, Verweij N, van der Harst P, Iacoviello L, Sacerdote C, Panico S, Krogh V, Tumino R, Tzala E, Matullo G, Hurme MA, Raitakari OT, Colicino E, Baccarelli AA, Kähönen M, Herzig KH, Li S, Conneely KN, Kooner JS, Köttgen A, Heijmans BT, Deloukas P, Relton C, Ong KK, Bell JT, Boerwinkle E, Elliott P, Brenner H, Beekman M, Levy D, Waldenberger M, Chambers JC, Dehghan A, and Järvelin MR

Subjects
C-Reactive Protein genetics, CpG Islands genetics, Humans, Nucleotide Motifs, DNA Methylation genetics, Inflammation genetics
Abstract

We performed a multi-ethnic Epigenome Wide Association study on 22,774 individuals to describe the DNA methylation signature of chronic low-grade inflammation as measured by C-Reactive protein (CRP). We find 1,511 independent differentially methylated loci associated with CRP. These CpG sites show correlation structures across chromosomes, and are primarily situated in euchromatin, depleted in CpG islands. These genomic loci are predominantly situated in transcription factor binding sites and genomic enhancer regions. Mendelian randomization analysis suggests altered CpG methylation is a consequence of increased blood CRP levels. Mediation analysis reveals obesity and smoking as important underlying driving factors for changed CpG methylation. Finally, we find that an activated CpG signature significantly increases the risk for cardiometabolic diseases and COPD., (© 2022. The Author(s).)

Published
2022
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8. Meta-analyses identify DNA methylation associated with kidney function and damage.

Author

Schlosser P, Tin A, Matias-Garcia PR, Thio CHL, Joehanes R, Liu H, Weihs A, Yu Z, Hoppmann A, Grundner-Culemann F, Min JL, Adeyemo AA, Agyemang C, Ärnlöv J, Aziz NA, Baccarelli A, Bochud M, Brenner H, Breteler MMB, Carmeli C, Chaker L, Chambers JC, Cole SA, Coresh J, Corre T, Correa A, Cox SR, de Klein N, Delgado GE, Domingo-Relloso A, Eckardt KU, Ekici AB, Endlich K, Evans KL, Floyd JS, Fornage M, Franke L, Fraszczyk E, Gao X, Gào X, Ghanbari M, Ghasemi S, Gieger C, Greenland P, Grove ML, Harris SE, Hemani G, Henneman P, Herder C, Horvath S, Hou L, Hurme MA, Hwang SJ, Jarvelin MR, Kardia SLR, Kasela S, Kleber ME, Koenig W, Kooner JS, Kramer H, Kronenberg F, Kühnel B, Lehtimäki T, Lind L, Liu D, Liu Y, Lloyd-Jones DM, Lohman K, Lorkowski S, Lu AT, Marioni RE, März W, McCartney DL, Meeks KAC, Milani L, Mishra PP, Nauck M, Navas-Acien A, Nowak C, Peters A, Prokisch H, Psaty BM, Raitakari OT, Ratliff SM, Reiner AP, Rosas SE, Schöttker B, Schwartz J, Sedaghat S, Smith JA, Sotoodehnia N, Stocker HR, Stringhini S, Sundström J, Swenson BR, Tellez-Plaza M, van Meurs JBJ, van Vliet-Ostaptchouk JV, Venema A, Verweij N, Walker RM, Wielscher M, Winkelmann J, Wolffenbuttel BHR, Zhao W, Zheng Y, Loh M, Snieder H, Levy D, Waldenberger M, Susztak K, Köttgen A, and Teumer A

Subjects
Adult, Aged, CpG Islands, Female, Glomerular Filtration Rate, Humans, Interferon Regulatory Factors genetics, Interferon Regulatory Factors metabolism, Kidney metabolism, Kidney physiopathology, Kidney Function Tests, LIM Domain Proteins genetics, LIM Domain Proteins metabolism, Male, Membrane Proteins genetics, Membrane Proteins metabolism, Middle Aged, Renal Insufficiency, Chronic metabolism, Renal Insufficiency, Chronic physiopathology, Transcription Factors genetics, Transcription Factors metabolism, DNA Methylation, Renal Insufficiency, Chronic genetics
Abstract

Chronic kidney disease is a major public health burden. Elevated urinary albumin-to-creatinine ratio is a measure of kidney damage, and used to diagnose and stage chronic kidney disease. To extend the knowledge on regulatory mechanisms related to kidney function and disease, we conducted a blood-based epigenome-wide association study for estimated glomerular filtration rate (n = 33,605) and urinary albumin-to-creatinine ratio (n = 15,068) and detected 69 and seven CpG sites where DNA methylation was associated with the respective trait. The majority of these findings showed directionally consistent associations with the respective clinical outcomes chronic kidney disease and moderately increased albuminuria. Associations of DNA methylation with kidney function, such as CpGs at JAZF1, PELI1 and CHD2 were validated in kidney tissue. Methylation at PHRF1, LDB2, CSRNP1 and IRF5 indicated causal effects on kidney function. Enrichment analyses revealed pathways related to hemostasis and blood cell migration for estimated glomerular filtration rate, and immune cell activation and response for urinary albumin-to-creatinineratio-associated CpGs., (© 2021. The Author(s).)

Published
2021
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9. Epigenome-wide association study of serum urate reveals insights into urate co-regulation and the SLC2A9 locus.

Author

Tin A, Schlosser P, Matias-Garcia PR, Thio CHL, Joehanes R, Liu H, Yu Z, Weihs A, Hoppmann A, Grundner-Culemann F, Min JL, Kuhns VLH, Adeyemo AA, Agyemang C, Ärnlöv J, Aziz NA, Baccarelli A, Bochud M, Brenner H, Bressler J, Breteler MMB, Carmeli C, Chaker L, Coresh J, Corre T, Correa A, Cox SR, Delgado GE, Eckardt KU, Ekici AB, Endlich K, Floyd JS, Fraszczyk E, Gao X, Gào X, Gelber AC, Ghanbari M, Ghasemi S, Gieger C, Greenland P, Grove ML, Harris SE, Hemani G, Henneman P, Herder C, Horvath S, Hou L, Hurme MA, Hwang SJ, Kardia SLR, Kasela S, Kleber ME, Koenig W, Kooner JS, Kronenberg F, Kühnel B, Ladd-Acosta C, Lehtimäki T, Lind L, Liu D, Lloyd-Jones DM, Lorkowski S, Lu AT, Marioni RE, März W, McCartney DL, Meeks KAC, Milani L, Mishra PP, Nauck M, Nowak C, Peters A, Prokisch H, Psaty BM, Raitakari OT, Ratliff SM, Reiner AP, Schöttker B, Schwartz J, Sedaghat S, Smith JA, Sotoodehnia N, Stocker HR, Stringhini S, Sundström J, Swenson BR, van Meurs JBJ, van Vliet-Ostaptchouk JV, Venema A, Völker U, Winkelmann J, Wolffenbuttel BHR, Zhao W, Zheng Y, Loh M, Snieder H, Waldenberger M, Levy D, Akilesh S, Woodward OM, Susztak K, Teumer A, and Köttgen A

Subjects
Amino Acid Transport System y+ genetics, Cohort Studies, CpG Islands, DNA Methylation, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Glucose Transport Proteins, Facilitative metabolism, Gout blood, Humans, Male, Epigenome, Glucose Transport Proteins, Facilitative genetics, Gout genetics, Uric Acid blood
Abstract

Elevated serum urate levels, a complex trait and major risk factor for incident gout, are correlated with cardiometabolic traits via incompletely understood mechanisms. DNA methylation in whole blood captures genetic and environmental influences and is assessed in transethnic meta-analysis of epigenome-wide association studies (EWAS) of serum urate (discovery, n = 12,474, replication, n = 5522). The 100 replicated, epigenome-wide significant (p < 1.1E-7) CpGs explain 11.6% of the serum urate variance. At SLC2A9, the serum urate locus with the largest effect in genome-wide association studies (GWAS), five CpGs are associated with SLC2A9 gene expression. Four CpGs at SLC2A9 have significant causal effects on serum urate levels and/or gout, and two of these partly mediate the effects of urate-associated GWAS variants. In other genes, including SLC7A11 and PHGDH, 17 urate-associated CpGs are associated with conditions defining metabolic syndrome, suggesting that these CpGs may represent a blood DNA methylation signature of cardiometabolic risk factors. This study demonstrates that EWAS can provide new insights into GWAS loci and the correlation of serum urate with other complex traits., (© 2021. The Author(s).)

Published
2021
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10. Publisher Correction: A multi-ethnic epigenome-wide association study of leukocyte DNA methylation and blood lipids.

Author

Jhun MA, Mendelson M, Wilson R, Gondalia R, Joehanes R, Salfati E, Zhao X, Braun KVE, Do AN, Hedman ÅK, Zhang T, Carnero-Montoro E, Shen J, Bartz TM, Brody JA, Montasser ME, O'Connell JR, Yao C, Xia R, Boerwinkle E, Grove M, Guan W, Liliane P, Singmann P, Müller-Nurasyid M, Meitinger T, Gieger C, Peters A, Zhao W, Ware EB, Smith JA, Dhana K, van Meurs J, Uitterlinden A, Ikram MA, Ghanbari M, Zhi D, Gustafsson S, Lind L, Li S, Sun D, Spector TD, Chen YI, Damcott C, Shuldiner AR, Absher DM, Horvath S, Tsao PS, Kardia S, Psaty BM, Sotoodehnia N, Bell JT, Ingelsson E, Chen W, Dehghan A, Arnett DK, Waldenberger M, Hou L, Whitsel EA, Baccarelli A, Levy D, Fornage M, Irvin MR, and Assimes TL

Published
2021
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11. A multi-ethnic epigenome-wide association study of leukocyte DNA methylation and blood lipids.

Author

Jhun MA, Mendelson M, Wilson R, Gondalia R, Joehanes R, Salfati E, Zhao X, Braun KVE, Do AN, Hedman ÅK, Zhang T, Carnero-Montoro E, Shen J, Bartz TM, Brody JA, Montasser ME, O'Connell JR, Yao C, Xia R, Boerwinkle E, Grove M, Guan W, Liliane P, Singmann P, Müller-Nurasyid M, Meitinger T, Gieger C, Peters A, Zhao W, Ware EB, Smith JA, Dhana K, van Meurs J, Uitterlinden A, Ikram MA, Ghanbari M, Zhi D, Gustafsson S, Lind L, Li S, Sun D, Spector TD, Chen YI, Damcott C, Shuldiner AR, Absher DM, Horvath S, Tsao PS, Kardia S, Psaty BM, Sotoodehnia N, Bell JT, Ingelsson E, Chen W, Dehghan A, Arnett DK, Waldenberger M, Hou L, Whitsel EA, Baccarelli A, Levy D, Fornage M, Irvin MR, and Assimes TL

Subjects
Adult, Black or African American, Aged, Carnitine O-Palmitoyltransferase genetics, CpG Islands genetics, Epigenesis, Genetic, Epigenome genetics, Epigenomics, Female, Hispanic or Latino, Humans, Male, Middle Aged, Quantitative Trait Loci genetics, White People, DNA Methylation genetics, Leukocytes cytology, Lipids blood, Lipoproteins, HDL blood
Abstract

Here we examine the association between DNA methylation in circulating leukocytes and blood lipids in a multi-ethnic sample of 16,265 subjects. We identify 148, 35, and 4 novel associations among Europeans, African Americans, and Hispanics, respectively, and an additional 186 novel associations through a trans-ethnic meta-analysis. We observe a high concordance in the direction of effects across racial/ethnic groups, a high correlation of effect sizes between high-density lipoprotein and triglycerides, a modest overlap of associations with epigenome-wide association studies of other cardio-metabolic traits, and a largely non-overlap with lipid loci identified to date through genome-wide association studies. Thirty CpGs reached significance in at least 2 racial/ethnic groups including 7 that showed association with the expression of an annotated gene. CpGs annotated to CPT1A showed evidence of being influenced by triglycerides levels. DNA methylation levels of circulating leukocytes show robust and consistent association with blood lipid levels across multiple racial/ethnic groups.

Published
2021
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12. Epigenome-wide association meta-analysis of DNA methylation with coffee and tea consumption.

Author

Karabegović I, Portilla-Fernandez E, Li Y, Ma J, Maas SCE, Sun D, Hu EA, Kühnel B, Zhang Y, Ambatipudi S, Fiorito G, Huang J, Castillo-Fernandez JE, Wiggins KL, de Klein N, Grioni S, Swenson BR, Polidoro S, Treur JL, Cuenin C, Tsai PC, Costeira R, Chajes V, Braun K, Verweij N, Kretschmer A, Franke L, van Meurs JBJ, Uitterlinden AG, de Knegt RJ, Ikram MA, Dehghan A, Peters A, Schöttker B, Gharib SA, Sotoodehnia N, Bell JT, Elliott P, Vineis P, Relton C, Herceg Z, Brenner H, Waldenberger M, Rebholz CM, Voortman T, Pan Q, Fornage M, Levy D, Kayser M, and Ghanbari M

Subjects
Adult, Aged, Aged, 80 and over, Cohort Studies, CpG Islands, Epigenesis, Genetic, Female, Gene Knockdown Techniques, Genome-Wide Association Study, Humans, Liver enzymology, Male, Middle Aged, Phosphoglycerate Dehydrogenase antagonists & inhibitors, Phosphoglycerate Dehydrogenase genetics, Risk Factors, Coffee adverse effects, DNA Methylation, Epigenome, Tea adverse effects
Abstract

Coffee and tea are extensively consumed beverages worldwide which have received considerable attention regarding health. Intake of these beverages is consistently linked to, among others, reduced risk of diabetes and liver diseases; however, the mechanisms of action remain elusive. Epigenetics is suggested as a mechanism mediating the effects of dietary and lifestyle factors on disease onset. Here we report the results from epigenome-wide association studies (EWAS) on coffee and tea consumption in 15,789 participants of European and African-American ancestries from 15 cohorts. EWAS meta-analysis of coffee consumption reveals 11 CpGs surpassing the epigenome-wide significance threshold (P-value <1.1×10 -7 ), which annotated to the AHRR, F2RL3, FLJ43663, HDAC4, GFI1 and PHGDH genes. Among them, cg14476101 is significantly associated with expression of the PHGDH and risk of fatty liver disease. Knockdown of PHGDH expression in liver cells shows a correlation with expression levels of genes associated with circulating lipids, suggesting a role of PHGDH in hepatic-lipid metabolism. EWAS meta-analysis on tea consumption reveals no significant association, only two CpGs annotated to CACNA1A and PRDM16 genes show suggestive association (P-value <5.0×10 -6 ). These findings indicate that coffee-associated changes in DNA methylation levels may explain the mechanism of action of coffee consumption in conferring risk of diseases.

Published
2021
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13. Genome-wide meta-analysis of variant-by-diuretic interactions as modulators of lipid traits in persons of European and African ancestry.

Author

de las Fuentes L, Sung YJ, Sitlani CM, Avery CL, Bartz TM, Keyser C, Evans DS, Li X, Musani SK, Ruiter R, Smith AV, Sun F, Trompet S, Xu H, Arnett DK, Bis JC, Broeckel U, Busch EL, Chen YI, Correa A, Cummings SR, Floyd JS, Ford I, Guo X, Harris TB, Ikram MA, Lange L, Launer LJ, Reiner AP, Schwander K, Smith NL, Sotoodehnia N, Stewart JD, Stott DJ, Stürmer T, Taylor KD, Uitterlinden A, Vasan RS, Wiggins KL, Cupples LA, Gudnason V, Heckbert SR, Jukema JW, Liu Y, Psaty BM, Rao DC, Rotter JI, Stricker B, Wilson JG, and Whitsel EA

Subjects
Diuretics adverse effects, Genome-Wide Association Study, Humans, Hypertension drug therapy, Lipids blood, Black or African American genetics, Diuretics blood, Genetic Variation genetics, Hypertension blood, Hypertension genetics, White People genetics
Abstract

Hypertension (HTN) is a significant risk factor for cardiovascular morbidity and mortality. Metabolic abnormalities, including adverse cholesterol and triglycerides (TG) profiles, are frequent comorbid findings with HTN and contribute to cardiovascular disease. Diuretics, which are used to treat HTN and heart failure, have been associated with worsening of fasting lipid concentrations. Genome-wide meta-analyses with 39,710 European-ancestry (EA) individuals and 9925 African-ancestry (AA) individuals were performed to identify genetic variants that modify the effect of loop or thiazide diuretic use on blood lipid concentrations. Both longitudinal and cross sectional data were used to compute cohort-specific interaction results, which were then combined through meta-analysis in each ancestry. These ancestry-specific results were further combined through trans-ancestry meta-analysis. Analysis of EA data identified two genome-wide significant (p < 5 × 10 -8 ) loci with single nucleotide variant (SNV)-loop diuretic interaction on TG concentrations (including COL11A1). Analysis of AA data identified one genome-wide significant locus adjacent to BMP2 with SNV-loop diuretic interaction on TG concentrations. Trans-ancestry analysis strengthened evidence of association for SNV-loop diuretic interaction at two loci (KIAA1217 and BAALC). There were few significant SNV-thiazide diuretic interaction associations on TG concentrations and for either diuretic on cholesterol concentrations. Several promising loci were identified that may implicate biologic pathways that contribute to adverse metabolic side effects from diuretic therapy.

Published
2020
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14. Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction.

Author

Ntalla I, Weng LC, Cartwright JH, Hall AW, Sveinbjornsson G, Tucker NR, Choi SH, Chaffin MD, Roselli C, Barnes MR, Mifsud B, Warren HR, Hayward C, Marten J, Cranley JJ, Concas MP, Gasparini P, Boutin T, Kolcic I, Polasek O, Rudan I, Araujo NM, Lima-Costa MF, Ribeiro ALP, Souza RP, Tarazona-Santos E, Giedraitis V, Ingelsson E, Mahajan A, Morris AP, Del Greco M F, Foco L, Gögele M, Hicks AA, Cook JP, Lind L, Lindgren CM, Sundström J, Nelson CP, Riaz MB, Samani NJ, Sinagra G, Ulivi S, Kähönen M, Mishra PP, Mononen N, Nikus K, Caulfield MJ, Dominiczak A, Padmanabhan S, Montasser ME, O'Connell JR, Ryan K, Shuldiner AR, Aeschbacher S, Conen D, Risch L, Thériault S, Hutri-Kähönen N, Lehtimäki T, Lyytikäinen LP, Raitakari OT, Barnes CLK, Campbell H, Joshi PK, Wilson JF, Isaacs A, Kors JA, van Duijn CM, Huang PL, Gudnason V, Harris TB, Launer LJ, Smith AV, Bottinger EP, Loos RJF, Nadkarni GN, Preuss MH, Correa A, Mei H, Wilson J, Meitinger T, Müller-Nurasyid M, Peters A, Waldenberger M, Mangino M, Spector TD, Rienstra M, van de Vegte YJ, van der Harst P, Verweij N, Kääb S, Schramm K, Sinner MF, Strauch K, Cutler MJ, Fatkin D, London B, Olesen M, Roden DM, Benjamin Shoemaker M, Gustav Smith J, Biggs ML, Bis JC, Brody JA, Psaty BM, Rice K, Sotoodehnia N, De Grandi A, Fuchsberger C, Pattaro C, Pramstaller PP, Ford I, Wouter Jukema J, Macfarlane PW, Trompet S, Dörr M, Felix SB, Völker U, Weiss S, Havulinna AS, Jula A, Sääksjärvi K, Salomaa V, Guo X, Heckbert SR, Lin HJ, Rotter JI, Taylor KD, Yao J, de Mutsert R, Maan AC, Mook-Kanamori DO, Noordam R, Cucca F, Ding J, Lakatta EG, Qian Y, Tarasov KV, Levy D, Lin H, Newton-Cheh CH, Lunetta KL, Murray AD, Porteous DJ, Smith BH, Stricker BH, Uitterlinden A, van den Berg ME, Haessler J, Jackson RD, Kooperberg C, Peters U, Reiner AP, Whitsel EA, Alonso A, Arking DE, Boerwinkle E, Ehret GB, Soliman EZ, Avery CL, Gogarten SM, Kerr KF, Laurie CC, Seyerle AA, Stilp A, Assa S, Abdullah Said M, Yldau van der Ende M, Lambiase PD, Orini M, Ramirez J, Van Duijvenboden S, Arnar DO, Gudbjartsson DF, Holm H, Sulem P, Thorleifsson G, Thorolfsdottir RB, Thorsteinsdottir U, Benjamin EJ, Tinker A, Stefansson K, Ellinor PT, Jamshidi Y, Lubitz SA, and Munroe PB

Subjects
Arrhythmias, Cardiac physiopathology, Cardiovascular Diseases genetics, Cardiovascular Diseases physiopathology, Endophenotypes, Female, Gene Expression, Genetic Variation, Genome-Wide Association Study, Humans, Male, Multifactorial Inheritance, Quantitative Trait Loci genetics, Arrhythmias, Cardiac genetics, Electrocardiography, Genetic Loci genetics, Genetic Predisposition to Disease genetics
Abstract

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N = 293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease.

Published
2020
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15. Associations of autozygosity with a broad range of human phenotypes.

Author

Clark DW, Okada Y, Moore KHS, Mason D, Pirastu N, Gandin I, Mattsson H, Barnes CLK, Lin K, Zhao JH, Deelen P, Rohde R, Schurmann C, Guo X, Giulianini F, Zhang W, Medina-Gomez C, Karlsson R, Bao Y, Bartz TM, Baumbach C, Biino G, Bixley MJ, Brumat M, Chai JF, Corre T, Cousminer DL, Dekker AM, Eccles DA, van Eijk KR, Fuchsberger C, Gao H, Germain M, Gordon SD, de Haan HG, Harris SE, Hofer E, Huerta-Chagoya A, Igartua C, Jansen IE, Jia Y, Kacprowski T, Karlsson T, Kleber ME, Li SA, Li-Gao R, Mahajan A, Matsuda K, Meidtner K, Meng W, Montasser ME, van der Most PJ, Munz M, Nutile T, Palviainen T, Prasad G, Prasad RB, Priyanka TDS, Rizzi F, Salvi E, Sapkota BR, Shriner D, Skotte L, Smart MC, Smith AV, van der Spek A, Spracklen CN, Strawbridge RJ, Tajuddin SM, Trompet S, Turman C, Verweij N, Viberti C, Wang L, Warren HR, Wootton RE, Yanek LR, Yao J, Yousri NA, Zhao W, Adeyemo AA, Afaq S, Aguilar-Salinas CA, Akiyama M, Albert ML, Allison MA, Alver M, Aung T, Azizi F, Bentley AR, Boeing H, Boerwinkle E, Borja JB, de Borst GJ, Bottinger EP, Broer L, Campbell H, Chanock S, Chee ML, Chen G, Chen YI, Chen Z, Chiu YF, Cocca M, Collins FS, Concas MP, Corley J, Cugliari G, van Dam RM, Damulina A, Daneshpour MS, Day FR, Delgado GE, Dhana K, Doney ASF, Dörr M, Doumatey AP, Dzimiri N, Ebenesersdóttir SS, Elliott J, Elliott P, Ewert R, Felix JF, Fischer K, Freedman BI, Girotto G, Goel A, Gögele M, Goodarzi MO, Graff M, Granot-Hershkovitz E, Grodstein F, Guarrera S, Gudbjartsson DF, Guity K, Gunnarsson B, Guo Y, Hagenaars SP, Haiman CA, Halevy A, Harris TB, Hedayati M, van Heel DA, Hirata M, Höfer I, Hsiung CA, Huang J, Hung YJ, Ikram MA, Jagadeesan A, Jousilahti P, Kamatani Y, Kanai M, Kerrison ND, Kessler T, Khaw KT, Khor CC, de Kleijn DPV, Koh WP, Kolcic I, Kraft P, Krämer BK, Kutalik Z, Kuusisto J, Langenberg C, Launer LJ, Lawlor DA, Lee IT, Lee WJ, Lerch MM, Li L, Liu J, Loh M, London SJ, Loomis S, Lu Y, Luan J, Mägi R, Manichaikul AW, Manunta P, Másson G, Matoba N, Mei XW, Meisinger C, Meitinger T, Mezzavilla M, Milani L, Millwood IY, Momozawa Y, Moore A, Morange PE, Moreno-Macías H, Mori TA, Morrison AC, Muka T, Murakami Y, Murray AD, de Mutsert R, Mychaleckyj JC, Nalls MA, Nauck M, Neville MJ, Nolte IM, Ong KK, Orozco L, Padmanabhan S, Pálsson G, Pankow JS, Pattaro C, Pattie A, Polasek O, Poulter N, Pramstaller PP, Quintana-Murci L, Räikkönen K, Ralhan S, Rao DC, van Rheenen W, Rich SS, Ridker PM, Rietveld CA, Robino A, van Rooij FJA, Ruggiero D, Saba Y, Sabanayagam C, Sabater-Lleal M, Sala CF, Salomaa V, Sandow K, Schmidt H, Scott LJ, Scott WR, Sedaghati-Khayat B, Sennblad B, van Setten J, Sever PJ, Sheu WH, Shi Y, Shrestha S, Shukla SR, Sigurdsson JK, Sikka TT, Singh JR, Smith BH, Stančáková A, Stanton A, Starr JM, Stefansdottir L, Straker L, Sulem P, Sveinbjornsson G, Swertz MA, Taylor AM, Taylor KD, Terzikhan N, Tham YC, Thorleifsson G, Thorsteinsdottir U, Tillander A, Tracy RP, Tusié-Luna T, Tzoulaki I, Vaccargiu S, Vangipurapu J, Veldink JH, Vitart V, Völker U, Vuoksimaa E, Wakil SM, Waldenberger M, Wander GS, Wang YX, Wareham NJ, Wild S, Yajnik CS, Yuan JM, Zeng L, Zhang L, Zhou J, Amin N, Asselbergs FW, Bakker SJL, Becker DM, Lehne B, Bennett DA, van den Berg LH, Berndt SI, Bharadwaj D, Bielak LF, Bochud M, Boehnke M, Bouchard C, Bradfield JP, Brody JA, Campbell A, Carmi S, Caulfield MJ, Cesarini D, Chambers JC, Chandak GR, Cheng CY, Ciullo M, Cornelis M, Cusi D, Smith GD, Deary IJ, Dorajoo R, van Duijn CM, Ellinghaus D, Erdmann J, Eriksson JG, Evangelou E, Evans MK, Faul JD, Feenstra B, Feitosa M, Foisy S, Franke A, Friedlander Y, Gasparini P, Gieger C, Gonzalez C, Goyette P, Grant SFA, Griffiths LR, Groop L, Gudnason V, Gyllensten U, Hakonarson H, Hamsten A, van der Harst P, Heng CK, Hicks AA, Hochner H, Huikuri H, Hunt SC, Jaddoe VWV, De Jager PL, Johannesson M, Johansson Å, Jonas JB, Jukema JW, Junttila J, Kaprio J, Kardia SLR, Karpe F, Kumari M, Laakso M, van der Laan SW, Lahti J, Laudes M, Lea RA, Lieb W, Lumley T, Martin NG, März W, Matullo G, McCarthy MI, Medland SE, Merriman TR, Metspalu A, Meyer BF, Mohlke KL, Montgomery GW, Mook-Kanamori D, Munroe PB, North KE, Nyholt DR, O'connell JR, Ober C, Oldehinkel AJ, Palmas W, Palmer C, Pasterkamp GG, Patin E, Pennell CE, Perusse L, Peyser PA, Pirastu M, Polderman TJC, Porteous DJ, Posthuma D, Psaty BM, Rioux JD, Rivadeneira F, Rotimi C, Rotter JI, Rudan I, Den Ruijter HM, Sanghera DK, Sattar N, Schmidt R, Schulze MB, Schunkert H, Scott RA, Shuldiner AR, Sim X, Small N, Smith JA, Sotoodehnia N, Tai ES, Teumer A, Timpson NJ, Toniolo D, Tregouet DA, Tuomi T, Vollenweider P, Wang CA, Weir DR, Whitfield JB, Wijmenga C, Wong TY, Wright J, Yang J, Yu L, Zemel BS, Zonderman AB, Perola M, Magnusson PKE, Uitterlinden AG, Kooner JS, Chasman DI, Loos RJF, Franceschini N, Franke L, Haley CS, Hayward C, Walters RG, Perry JRB, Esko T, Helgason A, Stefansson K, Joshi PK, Kubo M, and Wilson JF

Subjects
Alleles, Haplotypes, Homozygote, Humans, Body Size genetics, Cognition, Consanguinity, Fertility genetics, Health Status, Inbreeding Depression genetics, Risk-Taking
Abstract

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (F ROH ) for >1.4 million individuals, we show that F ROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F ROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of F ROH are confirmed within full-sibling pairs, where the variation in F ROH is independent of all environmental confounding.

Published
2019
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16. An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis.

Author

Liu J, Carnero-Montoro E, van Dongen J, Lent S, Nedeljkovic I, Ligthart S, Tsai PC, Martin TC, Mandaviya PR, Jansen R, Peters MJ, Duijts L, Jaddoe VWV, Tiemeier H, Felix JF, Willemsen G, de Geus EJC, Chu AY, Levy D, Hwang SJ, Bressler J, Gondalia R, Salfati EL, Herder C, Hidalgo BA, Tanaka T, Moore AZ, Lemaitre RN, Jhun MA, Smith JA, Sotoodehnia N, Bandinelli S, Ferrucci L, Arnett DK, Grallert H, Assimes TL, Hou L, Baccarelli A, Whitsel EA, van Dijk KW, Amin N, Uitterlinden AG, Sijbrands EJG, Franco OH, Dehghan A, Spector TD, Dupuis J, Hivert MF, Rotter JI, Meigs JB, Pankow JS, van Meurs JBJ, Isaacs A, Boomsma DI, Bell JT, Demirkan A, and van Duijn CM

Subjects
Adult, Aged, Aged, 80 and over, Computer Simulation, CpG Islands genetics, Diabetes Mellitus, Type 2 metabolism, Epigenesis, Genetic physiology, Epigenomics methods, Female, Gene Expression Profiling methods, Gene Expression Regulation genetics, Genome-Wide Association Study methods, Homeostasis genetics, Humans, Male, Metabolic Networks and Pathways genetics, Middle Aged, Obesity metabolism, Polymorphism, Single Nucleotide physiology, Young Adult, DNA Methylation physiology, Diabetes Mellitus, Type 2 genetics, Glucose metabolism, Insulin metabolism, Obesity genetics
Abstract

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.

Published
2019
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17. PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity.

Author

van Setten J, Brody JA, Jamshidi Y, Swenson BR, Butler AM, Campbell H, Del Greco FM, Evans DS, Gibson Q, Gudbjartsson DF, Kerr KF, Krijthe BP, Lyytikäinen LP, Müller C, Müller-Nurasyid M, Nolte IM, Padmanabhan S, Ritchie MD, Robino A, Smith AV, Steri M, Tanaka T, Teumer A, Trompet S, Ulivi S, Verweij N, Yin X, Arnar DO, Asselbergs FW, Bader JS, Barnard J, Bis J, Blankenberg S, Boerwinkle E, Bradford Y, Buckley BM, Chung MK, Crawford D, den Hoed M, Denny JC, Dominiczak AF, Ehret GB, Eijgelsheim M, Ellinor PT, Felix SB, Franco OH, Franke L, Harris TB, Holm H, Ilaria G, Iorio A, Kähönen M, Kolcic I, Kors JA, Lakatta EG, Launer LJ, Lin H, Lin HJ, Loos RJF, Lubitz SA, Macfarlane PW, Magnani JW, Leach IM, Meitinger T, Mitchell BD, Munzel T, Papanicolaou GJ, Peters A, Pfeufer A, Pramstaller PP, Raitakari OT, Rotter JI, Rudan I, Samani NJ, Schlessinger D, Silva Aldana CT, Sinner MF, Smith JD, Snieder H, Soliman EZ, Spector TD, Stott DJ, Strauch K, Tarasov KV, Thorsteinsdottir U, Uitterlinden AG, Van Wagoner DR, Völker U, Völzke H, Waldenberger M, Jan Westra H, Wild PS, Zeller T, Alonso A, Avery CL, Bandinelli S, Benjamin EJ, Cucca F, Dörr M, Ferrucci L, Gasparini P, Gudnason V, Hayward C, Heckbert SR, Hicks AA, Jukema JW, Kääb S, Lehtimäki T, Liu Y, Munroe PB, Parsa A, Polasek O, Psaty BM, Roden DM, Schnabel RB, Sinagra G, Stefansson K, Stricker BH, van der Harst P, van Duijn CM, Wilson JF, Gharib SA, de Bakker PIW, Isaacs A, Arking DE, and Sotoodehnia N

Subjects
Electrocardiography, Female, Humans, Linkage Disequilibrium genetics, Male, Mutation, Missense genetics, Risk Factors, Atrial Function physiology, Atrioventricular Node physiology, Electrophysiological Phenomena genetics, Genome-Wide Association Study
Abstract

Electrocardiographic PR interval measures atrio-ventricular depolarization and conduction, and abnormal PR interval is a risk factor for atrial fibrillation and heart block. Our genome-wide association study of over 92,000 European-descent individuals identifies 44 PR interval loci (34 novel). Examination of these loci reveals known and previously not-yet-reported biological processes involved in cardiac atrial electrical activity. Genes in these loci are over-represented in cardiac disease processes including heart block and atrial fibrillation. Variants in over half of the 44 loci were associated with atrial or blood transcript expression levels, or were in high linkage disequilibrium with missense variants. Six additional loci were identified either by meta-analysis of ~105,000 African and European-descent individuals and/or by pleiotropic analyses combining PR interval with heart rate, QRS interval, and atrial fibrillation. These findings implicate developmental pathways, and identify transcription factors, ion-channel genes, and cell-junction/cell-signaling proteins in atrio-ventricular conduction, identifying potential targets for drug development.

Published
2018
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18. Pharmacogenomics study of thiazide diuretics and QT interval in multi-ethnic populations: the cohorts for heart and aging research in genomic epidemiology.

Author

Seyerle AA, Sitlani CM, Noordam R, Gogarten SM, Li J, Li X, Evans DS, Sun F, Laaksonen MA, Isaacs A, Kristiansson K, Highland HM, Stewart JD, Harris TB, Trompet S, Bis JC, Peloso GM, Brody JA, Broer L, Busch EL, Duan Q, Stilp AM, O'Donnell CJ, Macfarlane PW, Floyd JS, Kors JA, Lin HJ, Li-Gao R, Sofer T, Méndez-Giráldez R, Cummings SR, Heckbert SR, Hofman A, Ford I, Li Y, Launer LJ, Porthan K, Newton-Cheh C, Napier MD, Kerr KF, Reiner AP, Rice KM, Roach J, Buckley BM, Soliman EZ, de Mutsert R, Sotoodehnia N, Uitterlinden AG, North KE, Lee CR, Gudnason V, Stürmer T, Rosendaal FR, Taylor KD, Wiggins KL, Wilson JG, Chen YD, Kaplan RC, Wilhelmsen K, Cupples LA, Salomaa V, van Duijn C, Jukema JW, Liu Y, Mook-Kanamori DO, Lange LA, Vasan RS, Smith AV, Stricker BH, Laurie CC, Rotter JI, Whitsel EA, Psaty BM, and Avery CL

Subjects
Adult, Aged, Aged, 80 and over, Aging drug effects, Aging ethnology, Cohort Studies, Electrocardiography drug effects, Electrocardiography trends, Female, Genomics methods, Heart Rate drug effects, Humans, Longitudinal Studies, Male, Middle Aged, Pharmacogenetics methods, Polymorphism, Single Nucleotide drug effects, Polymorphism, Single Nucleotide genetics, Aging genetics, Ethnicity genetics, Genomics trends, Heart Rate genetics, Pharmacogenetics trends, Sodium Chloride Symporter Inhibitors pharmacology
Abstract

Thiazide diuretics, commonly used antihypertensives, may cause QT interval (QT) prolongation, a risk factor for highly fatal and difficult to predict ventricular arrhythmias. We examined whether common single-nucleotide polymorphisms (SNPs) modified the association between thiazide use and QT or its component parts (QRS interval, JT interval) by performing ancestry-specific, trans-ethnic and cross-phenotype genome-wide analyses of European (66%), African American (15%) and Hispanic (19%) populations (N=78 199), leveraging longitudinal data, incorporating corrected standard errors to account for underestimation of interaction estimate variances and evaluating evidence for pathway enrichment. Although no loci achieved genome-wide significance (P<5 × 10 -8 ), we found suggestive evidence (P<5 × 10 -6 ) for SNPs modifying the thiazide-QT association at 22 loci, including ion transport loci (for example, NELL1, KCNQ3). The biologic plausibility of our suggestive results and simulations demonstrating modest power to detect interaction effects at genome-wide significant levels indicate that larger studies and innovative statistical methods are warranted in future efforts evaluating thiazide-SNP interactions.

Published
2018
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19. Large-scale pharmacogenomic study of sulfonylureas and the QT, JT and QRS intervals: CHARGE Pharmacogenomics Working Group.

Author

Floyd JS, Sitlani CM, Avery CL, Noordam R, Li X, Smith AV, Gogarten SM, Li J, Broer L, Evans DS, Trompet S, Brody JA, Stewart JD, Eicher JD, Seyerle AA, Roach J, Lange LA, Lin HJ, Kors JA, Harris TB, Li-Gao R, Sattar N, Cummings SR, Wiggins KL, Napier MD, Stürmer T, Bis JC, Kerr KF, Uitterlinden AG, Taylor KD, Stott DJ, de Mutsert R, Launer LJ, Busch EL, Méndez-Giráldez R, Sotoodehnia N, Soliman EZ, Li Y, Duan Q, Rosendaal FR, Slagboom PE, Wilhelmsen KC, Reiner AP, Chen YD, Heckbert SR, Kaplan RC, Rice KM, Jukema JW, Johnson AD, Liu Y, Mook-Kanamori DO, Gudnason V, Wilson JG, Rotter JI, Laurie CC, Psaty BM, Whitsel EA, Cupples LA, and Stricker BH

Subjects
Aged, Cardiovascular Diseases chemically induced, Cardiovascular Diseases genetics, Cytochrome P-450 CYP2C9 genetics, Diabetes Mellitus, Type 2 drug therapy, Diabetes Mellitus, Type 2 genetics, Drug-Related Side Effects and Adverse Reactions genetics, Female, Genetic Variation drug effects, Genetic Variation genetics, Genome-Wide Association Study methods, Humans, Male, Middle Aged, Pharmacogenetics methods, Pharmacogenomic Testing methods, Sulfonylurea Compounds therapeutic use, Electrocardiography drug effects, Ethnicity genetics, Sulfonylurea Compounds adverse effects
Abstract

Sulfonylureas, a commonly used class of medication used to treat type 2 diabetes, have been associated with an increased risk of cardiovascular disease. Their effects on QT interval duration and related electrocardiographic phenotypes are potential mechanisms for this adverse effect. In 11 ethnically diverse cohorts that included 71 857 European, African-American and Hispanic/Latino ancestry individuals with repeated measures of medication use and electrocardiogram (ECG) measurements, we conducted a pharmacogenomic genome-wide association study of sulfonylurea use and three ECG phenotypes: QT, JT and QRS intervals. In ancestry-specific meta-analyses, eight novel pharmacogenomic loci met the threshold for genome-wide significance (P<5 × 10 -8 ), and a pharmacokinetic variant in CYP2C9 (rs1057910) that has been associated with sulfonylurea-related treatment effects and other adverse drug reactions in previous studies was replicated. Additional research is needed to replicate the novel findings and to understand their biological basis.

Published
2018
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20. Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity.

Author

Joshi PK, Pirastu N, Kentistou KA, Fischer K, Hofer E, Schraut KE, Clark DW, Nutile T, Barnes CLK, Timmers PRHJ, Shen X, Gandin I, McDaid AF, Hansen TF, Gordon SD, Giulianini F, Boutin TS, Abdellaoui A, Zhao W, Medina-Gomez C, Bartz TM, Trompet S, Lange LA, Raffield L, van der Spek A, Galesloot TE, Proitsi P, Yanek LR, Bielak LF, Payton A, Murgia F, Concas MP, Biino G, Tajuddin SM, Seppälä I, Amin N, Boerwinkle E, Børglum AD, Campbell A, Demerath EW, Demuth I, Faul JD, Ford I, Gialluisi A, Gögele M, Graff M, Hingorani A, Hottenga JJ, Hougaard DM, Hurme MA, Ikram MA, Jylhä M, Kuh D, Ligthart L, Lill CM, Lindenberger U, Lumley T, Mägi R, Marques-Vidal P, Medland SE, Milani L, Nagy R, Ollier WER, Peyser PA, Pramstaller PP, Ridker PM, Rivadeneira F, Ruggiero D, Saba Y, Schmidt R, Schmidt H, Slagboom PE, Smith BH, Smith JA, Sotoodehnia N, Steinhagen-Thiessen E, van Rooij FJA, Verbeek AL, Vermeulen SH, Vollenweider P, Wang Y, Werge T, Whitfield JB, Zonderman AB, Lehtimäki T, Evans MK, Pirastu M, Fuchsberger C, Bertram L, Pendleton N, Kardia SLR, Ciullo M, Becker DM, Wong A, Psaty BM, van Duijn CM, Wilson JG, Jukema JW, Kiemeney L, Uitterlinden AG, Franceschini N, North KE, Weir DR, Metspalu A, Boomsma DI, Hayward C, Chasman D, Martin NG, Sattar N, Campbell H, Esko T, Kutalik Z, and Wilson JF

Subjects
Alleles, Body Mass Index, Coronary Disease blood, Coronary Disease etiology, Education, Genetic Predisposition to Disease genetics, Genome-Wide Association Study, Humans, Insulin Resistance genetics, Lipoproteins, HDL blood, Lung Neoplasms blood, Lung Neoplasms genetics, Obesity complications, Obesity genetics, Polymorphism, Single Nucleotide, Smoking adverse effects, Socioeconomic Factors, HLA-DQ alpha-Chains genetics, HLA-DRB1 Chains genetics, Life Style, Lipoprotein(a) genetics, Longevity genetics
Abstract

Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.Variability in human longevity is genetically influenced. Using genetic data of parental lifespan, the authors identify associations at HLA-DQA/DRB1 and LPA and find that genetic variants that increase educational attainment have a positive effect on lifespan whereas increasing BMI negatively affects lifespan.

Published
2017
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21. Erratum: Genetic loci associated with heart rate variability and their effects on cardiac disease risk.

Author

Nolte IM, Munoz ML, Tragante V, Amare AT, Jansen R, Vaez A, von der Heyde B, Avery CL, Bis JC, Dierckx B, van Dongen J, Gogarten SM, Goyette P, Hernesniemi J, Huikari V, Hwang SJ, Jaju D, Kerr KF, Kluttig A, Krijthe BP, Kumar J, van der Laan SW, Lyytikäinen LP, Maihofer AX, Minassian A, van der Most PJ, Müller-Nurasyid M, Nivard M, Salvi E, Stewart JD, Thayer JF, Verweij N, Wong A, Zabaneh D, Zafarmand MH, Abdellaoui A, Albarwani S, Albert C, Alonso A, Ashar F, Auvinen J, Axelsson T, Baker DG, de Bakker PIW, Barcella M, Bayoumi R, Bieringa RJ, Boomsma D, Boucher G, Britton AR, Christophersen I, Dietrich A, Ehret GB, Ellinor PT, Eskola M, Felix JF, Floras JS, Franco OH, Friberg P, Gademan MGJ, Geyer MA, Giedraitis V, Hartman CA, Hemerich D, Hofman A, Hottenga JJ, Huikuri H, Hutri-Kähönen N, Jouven X, Junttila J, Juonala M, Kiviniemi AM, Kors JA, Kumari M, Kuznetsova T, Laurie CC, Lefrandt JD, Li Y, Li Y, Liao D, Limacher MC, Lin HJ, Lindgren CM, Lubitz SA, Mahajan A, McKnight B, Zu Schwabedissen HM, Milaneschi Y, Mononen N, Morris AP, Nalls MA, Navis G, Neijts M, Nikus K, North KE, O'Connor DT, Ormel J, Perz S, Peters A, Psaty BM, Raitakari OT, Risbrough VB, Sinner MF, Siscovick D, Smit JH, Smith NL, Soliman EZ, Sotoodehnia N, Staessen JA, Stein PK, Stilp AM, Stolarz-Skrzypek K, Strauch K, Sundström J, Swenne CA, Syvänen AC, Tardif JC, Taylor KD, Teumer A, Thornton TA, Tinker LE, Uitterlinden AG, van Setten J, Voss A, Waldenberger M, Wilhelmsen KC, Willemsen G, Wong Q, Zhang ZM, Zonderman AB, Cusi D, Evans MK, Greiser HK, van der Harst P, Hassan M, Ingelsson E, Järvelin MR, Kääb S, Kähönen M, Kivimaki M, Kooperberg C, Kuh D, Lehtimäki T, Lind L, Nievergelt CM, O'Donnell CJ, Oldehinkel AJ, Penninx B, Reiner AP, Riese H, van Roon AM, Rioux JD, Rotter JI, Sofer T, Stricker BH, Tiemeier H, Vrijkotte TGM, Asselbergs FW, Brundel BJJM, Heckbert SR, Whitsel EA, den Hoed M, Snieder H, and de Geus EJC

Abstract

This corrects the article DOI: 10.1038/ncomms15805.

Published
2017
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22. Genetic loci associated with heart rate variability and their effects on cardiac disease risk.

Author

Nolte IM, Munoz ML, Tragante V, Amare AT, Jansen R, Vaez A, von der Heyde B, Avery CL, Bis JC, Dierckx B, van Dongen J, Gogarten SM, Goyette P, Hernesniemi J, Huikari V, Hwang SJ, Jaju D, Kerr KF, Kluttig A, Krijthe BP, Kumar J, van der Laan SW, Lyytikäinen LP, Maihofer AX, Minassian A, van der Most PJ, Müller-Nurasyid M, Nivard M, Salvi E, Stewart JD, Thayer JF, Verweij N, Wong A, Zabaneh D, Zafarmand MH, Abdellaoui A, Albarwani S, Albert C, Alonso A, Ashar F, Auvinen J, Axelsson T, Baker DG, de Bakker PIW, Barcella M, Bayoumi R, Bieringa RJ, Boomsma D, Boucher G, Britton AR, Christophersen I, Dietrich A, Ehret GB, Ellinor PT, Eskola M, Felix JF, Floras JS, Franco OH, Friberg P, Gademan MGJ, Geyer MA, Giedraitis V, Hartman CA, Hemerich D, Hofman A, Hottenga JJ, Huikuri H, Hutri-Kähönen N, Jouven X, Junttila J, Juonala M, Kiviniemi AM, Kors JA, Kumari M, Kuznetsova T, Laurie CC, Lefrandt JD, Li Y, Li Y, Liao D, Limacher MC, Lin HJ, Lindgren CM, Lubitz SA, Mahajan A, McKnight B, Zu Schwabedissen HM, Milaneschi Y, Mononen N, Morris AP, Nalls MA, Navis G, Neijts M, Nikus K, North KE, O'Connor DT, Ormel J, Perz S, Peters A, Psaty BM, Raitakari OT, Risbrough VB, Sinner MF, Siscovick D, Smit JH, Smith NL, Soliman EZ, Sotoodehnia N, Staessen JA, Stein PK, Stilp AM, Stolarz-Skrzypek K, Strauch K, Sundström J, Swenne CA, Syvänen AC, Tardif JC, Taylor KD, Teumer A, Thornton TA, Tinker LE, Uitterlinden AG, van Setten J, Voss A, Waldenberger M, Wilhelmsen KC, Willemsen G, Wong Q, Zhang ZM, Zonderman AB, Cusi D, Evans MK, Greiser HK, van der Harst P, Hassan M, Ingelsson E, Järvelin MR, Kääb S, Kähönen M, Kivimaki M, Kooperberg C, Kuh D, Lehtimäki T, Lind L, Nievergelt CM, O'Donnell CJ, Oldehinkel AJ, Penninx B, Reiner AP, Riese H, van Roon AM, Rioux JD, Rotter JI, Sofer T, Stricker BH, Tiemeier H, Vrijkotte TGM, Asselbergs FW, Brundel BJJM, Heckbert SR, Whitsel EA, den Hoed M, Snieder H, and de Geus EJC

Subjects
Blood Pressure, Cohort Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Heart Diseases physiopathology, Humans, Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels genetics, Muscle Proteins genetics, Polymorphism, Single Nucleotide, Potassium Channels genetics, Quantitative Trait Loci, RGS Proteins genetics, Risk Factors, White People genetics, Heart Diseases genetics, Heart Rate
Abstract

Reduced cardiac vagal control reflected in low heart rate variability (HRV) is associated with greater risks for cardiac morbidity and mortality. In two-stage meta-analyses of genome-wide association studies for three HRV traits in up to 53,174 individuals of European ancestry, we detect 17 genome-wide significant SNPs in eight loci. HRV SNPs tag non-synonymous SNPs (in NDUFA11 and KIAA1755), expression quantitative trait loci (eQTLs) (influencing GNG11, RGS6 and NEO1), or are located in genes preferentially expressed in the sinoatrial node (GNG11, RGS6 and HCN4). Genetic risk scores account for 0.9 to 2.6% of the HRV variance. Significant genetic correlation is found for HRV with heart rate (-0.74g <-0.55) and blood pressure (-0.35g <-0.20). These findings provide clinically relevant biological insight into heritable variation in vagal heart rhythm regulation, with a key role for genetic variants (GNG11, RGS6) that influence G-protein heterotrimer action in GIRK-channel induced pacemaker membrane hyperpolarization.

Published
2017
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23. Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins.

Author

Postmus I, Trompet S, Deshmukh HA, Barnes MR, Li X, Warren HR, Chasman DI, Zhou K, Arsenault BJ, Donnelly LA, Wiggins KL, Avery CL, Griffin P, Feng Q, Taylor KD, Li G, Evans DS, Smith AV, de Keyser CE, Johnson AD, de Craen AJ, Stott DJ, Buckley BM, Ford I, Westendorp RG, Slagboom PE, Sattar N, Munroe PB, Sever P, Poulter N, Stanton A, Shields DC, O'Brien E, Shaw-Hawkins S, Chen YD, Nickerson DA, Smith JD, Dubé MP, Boekholdt SM, Hovingh GK, Kastelein JJ, McKeigue PM, Betteridge J, Neil A, Durrington PN, Doney A, Carr F, Morris A, McCarthy MI, Groop L, Ahlqvist E, Bis JC, Rice K, Smith NL, Lumley T, Whitsel EA, Stürmer T, Boerwinkle E, Ngwa JS, O'Donnell CJ, Vasan RS, Wei WQ, Wilke RA, Liu CT, Sun F, Guo X, Heckbert SR, Post W, Sotoodehnia N, Arnold AM, Stafford JM, Ding J, Herrington DM, Kritchevsky SB, Eiriksdottir G, Launer LJ, Harris TB, Chu AY, Giulianini F, MacFadyen JG, Barratt BJ, Nyberg F, Stricker BH, Uitterlinden AG, Hofman A, Rivadeneira F, Emilsson V, Franco OH, Ridker PM, Gudnason V, Liu Y, Denny JC, Ballantyne CM, Rotter JI, Adrienne Cupples L, Psaty BM, Palmer CN, Tardif JC, Colhoun HM, Hitman G, Krauss RM, Wouter Jukema J, and Caulfield MJ

Subjects
Humans, Pharmacogenetics, Polymorphism, Single Nucleotide genetics, Cholesterol, LDL genetics, Genome-Wide Association Study, Hydroxymethylglutaryl-CoA Reductase Inhibitors pharmacology
Abstract

Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response.

Published
2014
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24. Drug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval.

Author

Avery CL, Sitlani CM, Arking DE, Arnett DK, Bis JC, Boerwinkle E, Buckley BM, Ida Chen YD, de Craen AJ, Eijgelsheim M, Enquobahrie D, Evans DS, Ford I, Garcia ME, Gudnason V, Harris TB, Heckbert SR, Hochner H, Hofman A, Hsueh WC, Isaacs A, Jukema JW, Knekt P, Kors JA, Krijthe BP, Kristiansson K, Laaksonen M, Liu Y, Li X, Macfarlane PW, Newton-Cheh C, Nieminen MS, Oostra BA, Peloso GM, Porthan K, Rice K, Rivadeneira FF, Rotter JI, Salomaa V, Sattar N, Siscovick DS, Slagboom PE, Smith AV, Sotoodehnia N, Stott DJ, Stricker BH, Stürmer T, Trompet S, Uitterlinden AG, van Duijn C, Westendorp RG, Witteman JC, Whitsel EA, and Psaty BM

Subjects
Computer Simulation, Cross-Sectional Studies, Electrocardiography, Genome-Wide Association Study, Humans, Linear Models, Markov Chains, White People genetics, Drug-Related Side Effects and Adverse Reactions genetics, Gene-Environment Interaction, Long QT Syndrome genetics, Pharmacogenetics, Polymorphism, Single Nucleotide genetics, Quantitative Trait, Heritable
Abstract

Variability in response to drug use is common and heritable, suggesting that genome-wide pharmacogenomics studies may help explain the 'missing heritability' of complex traits. Here, we describe four independent analyses in 33 781 participants of European ancestry from 10 cohorts that were designed to identify genetic variants modifying the effects of drugs on QT interval duration (QT). Each analysis cross-sectionally examined four therapeutic classes: thiazide diuretics (prevalence of use=13.0%), tri/tetracyclic antidepressants (2.6%), sulfonylurea hypoglycemic agents (2.9%) and QT-prolonging drugs as classified by the University of Arizona Center for Education and Research on Therapeutics (4.4%). Drug-gene interactions were estimated using covariable-adjusted linear regression and results were combined with fixed-effects meta-analysis. Although drug-single-nucleotide polymorphism (SNP) interactions were biologically plausible and variables were well-measured, findings from the four cross-sectional meta-analyses were null (Pinteraction>5.0 × 10(-8)). Simulations suggested that additional efforts, including longitudinal modeling to increase statistical power, are likely needed to identify potentially important pharmacogenomic effects.

Published
2014
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