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1. Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting

Author

Chauhan, Ganesh, Adams, Hieab HH, Satizabal, Claudia L, Bis, Joshua C, Teumer, Alexander, Sargurupremraj, Muralidharan, Hofer, Edith, Trompet, Stella, Hilal, Saima, Smith, Albert Vernon, Jian, Xueqiu, Malik, Rainer, Traylor, Matthew, Pulit, Sara L, Amouyel, Philippe, Mazoyer, Bernard, Zhu, Yi-Cheng, Kaffashian, Sara, Schilling, Sabrina, Beecham, Gary W, Montine, Thomas J, Schellenberg, Gerard D, Kjartansson, Olafur, Guðnason, Vilmundur, Knopman, David S, Griswold, Michael E, Windham, B Gwen, Gottesman, Rebecca F, Mosley, Thomas H, Schmidt, Reinhold, Saba, Yasaman, Schmidt, Helena, Takeuchi, Fumihiko, Yamaguchi, Shuhei, Nabika, Toru, Kato, Norihiro, Rajan, Kumar B, Aggarwal, Neelum T, De Jager, Philip L, Evans, Denis A, Psaty, Bruce M, Rotter, Jerome I, Rice, Kenneth, Lopez, Oscar L, Liao, Jiemin, Chen, Christopher, Cheng, Ching-Yu, Wong, Tien Y, Ikram, Mohammad K, van der Lee, Sven J, Amin, Najaf, Chouraki, Vincent, DeStefano, Anita L, Aparicio, Hugo J, Romero, Jose R, Maillard, Pauline, DeCarli, Charles, Wardlaw, Joanna M, del C. Valdés Hernández, Maria, Luciano, Michelle, Liewald, David, Deary, Ian J, Starr, John M, Bastin, Mark E, Maniega, Susana Muñoz, Slagboom, P Eline, Beekman, Marian, Deelen, Joris, Uh, Hae-Won, Lemmens, Robin, Brodaty, Henry, Wright, Margaret J, Ames, David, Boncoraglio, Giorgio B, Hopewell, Jemma C, Beecham, Ashley H, Blanton, Susan H, Wright, Clinton B, Sacco, Ralph L, Wen, Wei, Thalamuthu, Anbupalam, Armstrong, Nicola J, Chong, Elizabeth, Schofield, Peter R, Kwok, John B, van der Grond, Jeroen, Stott, David J, Ford, Ian, Jukema, J Wouter, Vernooij, Meike W, Hofman, Albert, Uitterlinden, André G, van der Lugt, Aad, Wittfeld, Katharina, Grabe, Hans J, Hosten, Norbert, von Sarnowski, Bettina, Völker, Uwe, Levi, Christopher, and Jimenez-Conde, Jordi

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Stroke, Brain Disorders, Human Genome, Cardiovascular, Aging, Prevention, Genetics, Cerebrovascular, 2.1 Biological and endogenous factors, Good Health and Well Being, Stroke Genetics Network (SiGN), the International Stroke Genetics Consortium (ISGC), METASTROKE, Alzheimer's Disease Genetics Consortium (ADGC), and the Neurology Working Group of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, Cognitive Sciences, Neurology & Neurosurgery, Clinical sciences
Abstract

ObjectiveTo explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts.MethodsWe performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI.ResultsThe mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p [BI] = 4.4 × 10-10; p [SSBI] = 1.2 × 10-4), diabetes (p [BI] = 1.7 × 10-8; p [SSBI] = 2.8 × 10-3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy.ConclusionIn this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.

Published
2019

2. Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting.

Author

Chauhan, Ganesh, Adams, Hieab HH, Satizabal, Claudia L, Bis, Joshua C, Teumer, Alexander, Sargurupremraj, Muralidharan, Hofer, Edith, Trompet, Stella, Hilal, Saima, Smith, Albert Vernon, Jian, Xueqiu, Malik, Rainer, Traylor, Matthew, Pulit, Sara L, Amouyel, Philippe, Mazoyer, Bernard, Zhu, Yi-Cheng, Kaffashian, Sara, Schilling, Sabrina, Beecham, Gary W, Montine, Thomas J, Schellenberg, Gerard D, Kjartansson, Olafur, Guðnason, Vilmundur, Knopman, David S, Griswold, Michael E, Windham, B Gwen, Gottesman, Rebecca F, Mosley, Thomas H, Schmidt, Reinhold, Saba, Yasaman, Schmidt, Helena, Takeuchi, Fumihiko, Yamaguchi, Shuhei, Nabika, Toru, Kato, Norihiro, Rajan, Kumar B, Aggarwal, Neelum T, De Jager, Philip L, Evans, Denis A, Psaty, Bruce M, Rotter, Jerome I, Rice, Kenneth, Lopez, Oscar L, Liao, Jiemin, Chen, Christopher, Cheng, Ching-Yu, Wong, Tien Y, Ikram, Mohammad K, van der Lee, Sven J, Amin, Najaf, Chouraki, Vincent, DeStefano, Anita L, Aparicio, Hugo J, Romero, Jose R, Maillard, Pauline, DeCarli, Charles, Wardlaw, Joanna M, Hernández, Maria Del C Valdés, Luciano, Michelle, Liewald, David, Deary, Ian J, Starr, John M, Bastin, Mark E, Muñoz Maniega, Susana, Slagboom, P Eline, Beekman, Marian, Deelen, Joris, Uh, Hae-Won, Lemmens, Robin, Brodaty, Henry, Wright, Margaret J, Ames, David, Boncoraglio, Giorgio B, Hopewell, Jemma C, Beecham, Ashley H, Blanton, Susan H, Wright, Clinton B, Sacco, Ralph L, Wen, Wei, Thalamuthu, Anbupalam, Armstrong, Nicola J, Chong, Elizabeth, Schofield, Peter R, Kwok, John B, van der Grond, Jeroen, Stott, David J, Ford, Ian, Jukema, J Wouter, Vernooij, Meike W, Hofman, Albert, Uitterlinden, André G, van der Lugt, Aad, Wittfeld, Katharina, Grabe, Hans J, Hosten, Norbert, von Sarnowski, Bettina, Völker, Uwe, Levi, Christopher, and Jimenez-Conde, Jordi

Subjects
Stroke Genetics Network (SiGN), the International Stroke Genetics Consortium (ISGC), METASTROKE, Alzheimer's Disease Genetics Consortium (ADGC), and the Neurology Working Group of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, Cardiovascular, Aging, Human Genome, Prevention, Stroke, Genetics, 2.1 Biological and endogenous factors, Neurology & Neurosurgery, Clinical Sciences, Neurosciences, Cognitive Sciences
Abstract

OBJECTIVE:To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. METHODS:We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. RESULTS:The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p [BI] = 4.4 × 10-10; p [SSBI] = 1.2 × 10-4), diabetes (p [BI] = 1.7 × 10-8; p [SSBI] = 2.8 × 10-3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. CONCLUSION:In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.

Published
2019

3. GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes.

Author

Franceschini, Nora, Giambartolomei, Claudia, de Vries, Paul S, Finan, Chris, Bis, Joshua C, Huntley, Rachael P, Lovering, Ruth C, Tajuddin, Salman M, Winkler, Thomas W, Graff, Misa, Kavousi, Maryam, Dale, Caroline, Smith, Albert V, Hofer, Edith, van Leeuwen, Elisabeth M, Nolte, Ilja M, Lu, Lingyi, Scholz, Markus, Sargurupremraj, Muralidharan, Pitkänen, Niina, Franzén, Oscar, Joshi, Peter K, Noordam, Raymond, Marioni, Riccardo E, Hwang, Shih-Jen, Musani, Solomon K, Schminke, Ulf, Palmas, Walter, Isaacs, Aaron, Correa, Adolfo, Zonderman, Alan B, Hofman, Albert, Teumer, Alexander, Cox, Amanda J, Uitterlinden, André G, Wong, Andrew, Smit, Andries J, Newman, Anne B, Britton, Annie, Ruusalepp, Arno, Sennblad, Bengt, Hedblad, Bo, Pasaniuc, Bogdan, Penninx, Brenda W, Langefeld, Carl D, Wassel, Christina L, Tzourio, Christophe, Fava, Cristiano, Baldassarre, Damiano, O'Leary, Daniel H, Teupser, Daniel, Kuh, Diana, Tremoli, Elena, Mannarino, Elmo, Grossi, Enzo, Boerwinkle, Eric, Schadt, Eric E, Ingelsson, Erik, Veglia, Fabrizio, Rivadeneira, Fernando, Beutner, Frank, Chauhan, Ganesh, Heiss, Gerardo, Snieder, Harold, Campbell, Harry, Völzke, Henry, Markus, Hugh S, Deary, Ian J, Jukema, J Wouter, de Graaf, Jacqueline, Price, Jacqueline, Pott, Janne, Hopewell, Jemma C, Liang, Jingjing, Thiery, Joachim, Engmann, Jorgen, Gertow, Karl, Rice, Kenneth, Taylor, Kent D, Dhana, Klodian, Kiemeney, Lambertus ALM, Lind, Lars, Raffield, Laura M, Launer, Lenore J, Holdt, Lesca M, Dörr, Marcus, Dichgans, Martin, Traylor, Matthew, Sitzer, Matthias, Kumari, Meena, Kivimaki, Mika, Nalls, Mike A, Melander, Olle, Raitakari, Olli, Franco, Oscar H, Rueda-Ochoa, Oscar L, Roussos, Panos, Whincup, Peter H, Amouyel, Philippe, and Giral, Philippe

Subjects
MEGASTROKE Consortium, Humans, Coronary Disease, Genetic Predisposition to Disease, Amino Acid Oxidoreductases, Protein-Lysine 6-Oxidase, Risk Factors, Lod Score, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study, Plaque, Atherosclerotic, Carotid Intima-Media Thickness, ADAMTS9 Protein, Plaque, Atherosclerotic, Polymorphism, Single Nucleotide
Abstract

Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans.

Published
2018

4. GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes

Author

Franceschini, Nora, Giambartolomei, Claudia, de Vries, Paul S, Finan, Chris, Bis, Joshua C, Huntley, Rachael P, Lovering, Ruth C, Tajuddin, Salman M, Winkler, Thomas W, Graff, Misa, Kavousi, Maryam, Dale, Caroline, Smith, Albert V, Hofer, Edith, van Leeuwen, Elisabeth M, Nolte, Ilja M, Lu, Lingyi, Scholz, Markus, Sargurupremraj, Muralidharan, Pitkänen, Niina, Franzén, Oscar, Joshi, Peter K, Noordam, Raymond, Marioni, Riccardo E, Hwang, Shih-Jen, Musani, Solomon K, Schminke, Ulf, Palmas, Walter, Isaacs, Aaron, Correa, Adolfo, Zonderman, Alan B, Hofman, Albert, Teumer, Alexander, Cox, Amanda J, Uitterlinden, André G, Wong, Andrew, Smit, Andries J, Newman, Anne B, Britton, Annie, Ruusalepp, Arno, Sennblad, Bengt, Hedblad, Bo, Pasaniuc, Bogdan, Penninx, Brenda W, Langefeld, Carl D, Wassel, Christina L, Tzourio, Christophe, Fava, Cristiano, Baldassarre, Damiano, O’Leary, Daniel H, Teupser, Daniel, Kuh, Diana, Tremoli, Elena, Mannarino, Elmo, Grossi, Enzo, Boerwinkle, Eric, Schadt, Eric E, Ingelsson, Erik, Veglia, Fabrizio, Rivadeneira, Fernando, Beutner, Frank, Chauhan, Ganesh, Heiss, Gerardo, Snieder, Harold, Campbell, Harry, Völzke, Henry, Markus, Hugh S, Deary, Ian J, Jukema, J Wouter, de Graaf, Jacqueline, Price, Jacqueline, Pott, Janne, Hopewell, Jemma C, Liang, Jingjing, Thiery, Joachim, Engmann, Jorgen, Gertow, Karl, Rice, Kenneth, Taylor, Kent D, Dhana, Klodian, Kiemeney, Lambertus ALM, Lind, Lars, Raffield, Laura M, Launer, Lenore J, Holdt, Lesca M, Dörr, Marcus, Dichgans, Martin, Traylor, Matthew, Sitzer, Matthias, Kumari, Meena, Kivimaki, Mika, Nalls, Mike A, Melander, Olle, Raitakari, Olli, Franco, Oscar H, Rueda-Ochoa, Oscar L, Roussos, Panos, Whincup, Peter H, Amouyel, Philippe, and Giral, Philippe

Subjects
Biological Sciences, Genetics, Epidemiology, Engineering, Health Sciences, Biomedical Engineering, Heart Disease - Coronary Heart Disease, Aging, Heart Disease, Cardiovascular, Human Genome, Brain Disorders, Stroke, Atherosclerosis, 2.1 Biological and endogenous factors, Aetiology, ADAMTS9 Protein, Amino Acid Oxidoreductases, Carotid Intima-Media Thickness, Coronary Disease, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Lod Score, Plaque, Atherosclerotic, Polymorphism, Single Nucleotide, Protein-Lysine 6-Oxidase, Quantitative Trait Loci, Risk Factors, MEGASTROKE Consortium
Abstract

Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans.

Published
2018

5. Causal Effect of Plasminogen Activator Inhibitor Type 1 on Coronary Heart Disease

Author

Song, Ci, Burgess, Stephen, Eicher, John D, O'Donnell, Christopher J, Johnson, Andrew D, Huang, Jie, Sabater‐Lleal, Maria, Asselbergs, Folkert W, Tregouet, David, Shin, So‐Youn, Ding, Jingzhong, Baumert, Jens, Oudot‐Mellakh, Tiphaine, Folkersen, Lasse, Smith, Nicholas L, Williams, Scott M, Ikram, Mohammad A, Kleber, Marcus E, Becker, Diane M, Truong, Vinh, Mychaleckyj, Josyf C, Tang, Weihong, Yang, Qiong, Sennblad, Bengt, Moore, Jason H, Williams, Frances MK, Dehghan, Abbas, Silbernagel, Günther, Schrijvers, Elisabeth MC, Smith, Shelly, Karakas, Mahir, Tofler, Geoffrey H, Silveira, Angela, Navis, Gerjan J, Lohman, Kurt, Chen, Ming‐Huei, Peters, Annette, Goel, Anuj, Hopewell, Jemma C, Chambers, John C, Saleheen, Danish, Lundmark, Per, Psaty, Bruce M, Strawbridge, Rona J, Boehm, Bernhard O, Carter, Angela M, Meisinger, Christa, Peden, John F, Bis, Joshua C, McKnight, Barbara, Öhrvik, John, Taylor, Kent, Franzosi, Maria Grazia, Seedorf, Udo, Collins, Rory, Franco‐Cereceda, Anders, Syvänen, Ann‐Christine, Goodall, Alison H, Yanek, Lisa R, Cushman, Mary, Müller‐Nurasyid, Martina, Folsom, Aaron R, Basu, Saonli, Matijevic, Nena, van Gilst, Wiek H, Kooner, Jaspal S, Hofman, Albert, Danesh, John, Clarke, Robert, Meigs, James B, Kathiresan, Sekar, Reilly, Muredach P, Klopp, Norman, Harris, Tamara B, Winkelmann, Bernhard R, Grant, Peter J, Hillege, Hans L, Watkins, Hugh, Spector, Timothy D, Becker, Lewis C, Tracy, Russell P, März, Winfried, Uitterlinden, Andre G, Eriksson, Per, Cambien, Francois, Morange, Pierre‐Emmanuel, Koenig, Wolfgang, Soranzo, Nicole, van der Harst, Pim, Liu, Yongmei, Hamsten, Anders, Ehret, Georg B, Munroe, Patricia B, Rice, Kenneth M, Bochud, Murielle, Chasman, Daniel I, Smith, Albert V, Tobin, Martin D, and Verwoert, Germaine C

Subjects
Clinical Research, Heart Disease - Coronary Heart Disease, Cardiovascular, Heart Disease, Aging, Biomarkers, Blood Glucose, Coronary Disease, Fibrinolysis, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Incidence, Lipoproteins, HDL, Mendelian Randomization Analysis, Multivariate Analysis, Observational Studies as Topic, Odds Ratio, Plasminogen Activator Inhibitor 1, Polymorphism, Single Nucleotide, Risk Assessment, Risk Factors, coronary heart disease, genome-wide association study, Mendelian randomization, plasminogen activator inhibitor type 1, single nucleotide polymorphism, genome‐wide association study, Cardiorespiratory Medicine and Haematology
Abstract

Plasminogen activator inhibitor type 1 (PAI-1) plays an essential role in the fibrinolysis system and thrombosis. Population studies have reported that blood PAI-1 levels are associated with increased risk of coronary heart disease (CHD). However, it is unclear whether the association reflects a causal influence of PAI-1 on CHD risk. To evaluate the association between PAI-1 and CHD, we applied a 3-step strategy. First, we investigated the observational association between PAI-1 and CHD incidence using a systematic review based on a literature search for PAI-1 and CHD studies. Second, we explored the causal association between PAI-1 and CHD using a Mendelian randomization approach using summary statistics from large genome-wide association studies. Finally, we explored the causal effect of PAI-1 on cardiovascular risk factors including metabolic and subclinical atherosclerosis measures. In the systematic meta-analysis, the highest quantile of blood PAI-1 level was associated with higher CHD risk comparing with the lowest quantile (odds ratio=2.17; 95% CI: 1.53, 3.07) in an age- and sex-adjusted model. The effect size was reduced in studies using a multivariable-adjusted model (odds ratio=1.46; 95% CI: 1.13, 1.88). The Mendelian randomization analyses suggested a causal effect of increased PAI-1 level on CHD risk (odds ratio=1.22 per unit increase of log-transformed PAI-1; 95% CI: 1.01, 1.47). In addition, we also detected a causal effect of PAI-1 on elevating blood glucose and high-density lipoprotein cholesterol. Our study indicates a causal effect of elevated PAI-1 level on CHD risk, which may be mediated by glucose dysfunction.

Published
2017

6. A statin-dependent QTL for GATM expression is associated with statin-induced myopathy

Author

Mangravite, Lara M, Engelhardt, Barbara E, Medina, Marisa W, Smith, Joshua D, Brown, Christopher D, Chasman, Daniel I, Mecham, Brigham H, Howie, Bryan, Shim, Heejung, Naidoo, Devesh, Feng, QiPing, Rieder, Mark J, Chen, Yii-Der I, Rotter, Jerome I, Ridker, Paul M, Hopewell, Jemma C, Parish, Sarah, Armitage, Jane, Collins, Rory, Wilke, Russell A, Nickerson, Deborah A, Stephens, Matthew, and Krauss, Ronald M

Subjects
Genetics, 2.1 Biological and endogenous factors, Aetiology, Amidinotransferases, Cell Line, Cholesterol, Gene Expression Regulation, Gene Knockdown Techniques, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Lymphocytes, Muscular Diseases, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Simvastatin, Sterol Regulatory Element Binding Proteins, Transcription, Genetic, General Science & Technology
Abstract

Statins are prescribed widely to lower plasma low-density lipoprotein (LDL) concentrations and cardiovascular disease risk and have been shown to have beneficial effects in a broad range of patients. However, statins are associated with an increased risk, albeit small, of clinical myopathy and type 2 diabetes. Despite evidence for substantial genetic influence on LDL concentrations, pharmacogenomic trials have failed to identify genetic variations with large effects on either statin efficacy or toxicity, and have produced little information regarding mechanisms that modulate statin response. Here we identify a downstream target of statin treatment by screening for the effects of in vitro statin exposure on genetic associations with gene expression levels in lymphoblastoid cell lines derived from 480 participants of a clinical trial of simvastatin treatment. This analysis identified six expression quantitative trait loci (eQTLs) that interacted with simvastatin exposure, including rs9806699, a cis-eQTL for the gene glycine amidinotransferase (GATM) that encodes the rate-limiting enzyme in creatine synthesis. We found this locus to be associated with incidence of statin-induced myotoxicity in two separate populations (meta-analysis odds ratio = 0.60). Furthermore, we found that GATM knockdown in hepatocyte-derived cell lines attenuated transcriptional response to sterol depletion, demonstrating that GATM may act as a functional link between statin-mediated lowering of cholesterol and susceptibility to statin-induced myopathy.

Published
2013

7. A statin-dependent QTL for GATM expression is associated with statin-induced myopathy.

Author

Mangravite, Lara M, Engelhardt, Barbara E, Medina, Marisa W, Smith, Joshua D, Brown, Christopher D, Chasman, Daniel I, Mecham, Brigham H, Howie, Bryan, Shim, Heejung, Naidoo, Devesh, Feng, QiPing, Rieder, Mark J, Chen, Yii-Der I, Rotter, Jerome I, Ridker, Paul M, Hopewell, Jemma C, Parish, Sarah, Armitage, Jane, Collins, Rory, Wilke, Russell A, Nickerson, Deborah A, Stephens, Matthew, and Krauss, Ronald M

Subjects
Lymphocytes, Cell Line, Humans, Muscular Diseases, Simvastatin, Cholesterol, Amidinotransferases, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Transcription, Genetic, Gene Expression Regulation, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Sterol Regulatory Element Binding Proteins, Gene Knockdown Techniques, Biotechnology, Human Genome, Cardiovascular, Clinical Research, Genetics, Patient Safety, Heart Disease, 2.1 Biological and endogenous factors, General Science & Technology
Abstract

Statins are prescribed widely to lower plasma low-density lipoprotein (LDL) concentrations and cardiovascular disease risk and have been shown to have beneficial effects in a broad range of patients. However, statins are associated with an increased risk, albeit small, of clinical myopathy and type 2 diabetes. Despite evidence for substantial genetic influence on LDL concentrations, pharmacogenomic trials have failed to identify genetic variations with large effects on either statin efficacy or toxicity, and have produced little information regarding mechanisms that modulate statin response. Here we identify a downstream target of statin treatment by screening for the effects of in vitro statin exposure on genetic associations with gene expression levels in lymphoblastoid cell lines derived from 480 participants of a clinical trial of simvastatin treatment. This analysis identified six expression quantitative trait loci (eQTLs) that interacted with simvastatin exposure, including rs9806699, a cis-eQTL for the gene glycine amidinotransferase (GATM) that encodes the rate-limiting enzyme in creatine synthesis. We found this locus to be associated with incidence of statin-induced myotoxicity in two separate populations (meta-analysis odds ratio = 0.60). Furthermore, we found that GATM knockdown in hepatocyte-derived cell lines attenuated transcriptional response to sterol depletion, demonstrating that GATM may act as a functional link between statin-mediated lowering of cholesterol and susceptibility to statin-induced myopathy.

Published
2013

8. Large-Scale Gene-Centric Meta-analysis across 32 Studies Identifies Multiple Lipid Loci

Author

Asselbergs, Folkert W, Guo, Yiran, van Iperen, Erik PA, Sivapalaratnam, Suthesh, Tragante, Vinicius, Lanktree, Matthew B, Lange, Leslie A, Almoguera, Berta, Appelman, Yolande E, Barnard, John, Baumert, Jens, Beitelshees, Amber L, Bhangale, Tushar R, Chen, Yii-Der Ida, Gaunt, Tom R, Gong, Yan, Hopewell, Jemma C, Johnson, Toby, Kleber, Marcus E, Langaee, Taimour Y, Li, Mingyao, Li, Yun R, Liu, Kiang, McDonough, Caitrin W, Meijs, Matthijs FL, Middelberg, Rita PS, Musunuru, Kiran, Nelson, Christopher P, O’Connell, Jeffery R, Padmanabhan, Sandosh, Pankow, James S, Pankratz, Nathan, Rafelt, Suzanne, Rajagopalan, Ramakrishnan, Romaine, Simon PR, Schork, Nicholas J, Shaffer, Jonathan, Shen, Haiqing, Smith, Erin N, Tischfield, Sam E, van der Most, Peter J, van Vliet-Ostaptchouk, Jana V, Verweij, Niek, Volcik, Kelly A, Zhang, Li, Bailey, Kent R, Bailey, Kristian M, Bauer, Florianne, Boer, Jolanda MA, Braund, Peter S, Burt, Amber, Burton, Paul R, Buxbaum, Sarah G, Chen, Wei, Cooper-DeHoff, Rhonda M, Cupples, L Adrienne, deJong, Jonas S, Delles, Christian, Duggan, David, Fornage, Myriam, Furlong, Clement E, Glazer, Nicole, Gums, John G, Hastie, Claire, Holmes, Michael V, Illig, Thomas, Kirkland, Susan A, Kivimaki, Mika, Klein, Ronald, Klein, Barbara E, Kooperberg, Charles, Kottke-Marchant, Kandice, Kumari, Meena, LaCroix, Andrea Z, Mallela, Laya, Murugesan, Gurunathan, Ordovas, Jose, Ouwehand, Willem H, Post, Wendy S, Saxena, Richa, Scharnagl, Hubert, Schreiner, Pamela J, Shah, Tina, Shields, Denis C, Shimbo, Daichi, Srinivasan, Sathanur R, Stolk, Ronald P, Swerdlow, Daniel I, Taylor, Herman A, Topol, Eric J, Toskala, Elina, van Pelt, Joost L, van Setten, Jessica, Yusuf, Salim, Whittaker, John C, Zwinderman, AH, Study, LifeLines Cohort, Anand, Sonia S, Balmforth, Anthony J, and Berenson, Gerald S

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Human Genome, Cardiovascular, Atherosclerosis, Aetiology, 2.1 Biological and endogenous factors, Cholesterol, HDL, Cholesterol, LDL, Female, Genome-Wide Association Study, Genotype, Humans, Lipids, Male, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Sex Factors, Triglycerides, White People, LifeLines Cohort Study, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Genome-wide association studies (GWASs) have identified many SNPs underlying variations in plasma-lipid levels. We explore whether additional loci associated with plasma-lipid phenotypes, such as high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TGs), can be identified by a dense gene-centric approach. Our meta-analysis of 32 studies in 66,240 individuals of European ancestry was based on the custom ∼50,000 SNP genotyping array (the ITMAT-Broad-CARe array) covering ∼2,000 candidate genes. SNP-lipid associations were replicated either in a cohort comprising an additional 24,736 samples or within the Global Lipid Genetic Consortium. We identified four, six, ten, and four unreported SNPs in established lipid genes for HDL-C, LDL-C, TC, and TGs, respectively. We also identified several lipid-related SNPs in previously unreported genes: DGAT2, HCAR2, GPIHBP1, PPARG, and FTO for HDL-C; SOCS3, APOH, SPTY2D1, BRCA2, and VLDLR for LDL-C; SOCS3, UGT1A1, BRCA2, UBE3B, FCGR2A, CHUK, and INSIG2 for TC; and SERPINF2, C4B, GCK, GATA4, INSR, and LPAL2 for TGs. The proportion of explained phenotypic variance in the subset of studies providing individual-level data was 9.9% for HDL-C, 9.5% for LDL-C, 10.3% for TC, and 8.0% for TGs. This large meta-analysis of lipid phenotypes with the use of a dense gene-centric approach identified multiple SNPs not previously described in established lipid genes and several previously unknown loci. The explained phenotypic variance from this approach was comparable to that from a meta-analysis of GWAS data, suggesting that a focused genotyping approach can further increase the understanding of heritability of plasma lipids.

Published
2012

9. Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci.

Author

Asselbergs, Folkert W, Guo, Yiran, van Iperen, Erik PA, Sivapalaratnam, Suthesh, Tragante, Vinicius, Lanktree, Matthew B, Lange, Leslie A, Almoguera, Berta, Appelman, Yolande E, Barnard, John, Baumert, Jens, Beitelshees, Amber L, Bhangale, Tushar R, Chen, Yii-Der Ida, Gaunt, Tom R, Gong, Yan, Hopewell, Jemma C, Johnson, Toby, Kleber, Marcus E, Langaee, Taimour Y, Li, Mingyao, Li, Yun R, Liu, Kiang, McDonough, Caitrin W, Meijs, Matthijs FL, Middelberg, Rita PS, Musunuru, Kiran, Nelson, Christopher P, O'Connell, Jeffery R, Padmanabhan, Sandosh, Pankow, James S, Pankratz, Nathan, Rafelt, Suzanne, Rajagopalan, Ramakrishnan, Romaine, Simon PR, Schork, Nicholas J, Shaffer, Jonathan, Shen, Haiqing, Smith, Erin N, Tischfield, Sam E, van der Most, Peter J, van Vliet-Ostaptchouk, Jana V, Verweij, Niek, Volcik, Kelly A, Zhang, Li, Bailey, Kent R, Bailey, Kristian M, Bauer, Florianne, Boer, Jolanda MA, Braund, Peter S, Burt, Amber, Burton, Paul R, Buxbaum, Sarah G, Chen, Wei, Cooper-Dehoff, Rhonda M, Cupples, L Adrienne, deJong, Jonas S, Delles, Christian, Duggan, David, Fornage, Myriam, Furlong, Clement E, Glazer, Nicole, Gums, John G, Hastie, Claire, Holmes, Michael V, Illig, Thomas, Kirkland, Susan A, Kivimaki, Mika, Klein, Ronald, Klein, Barbara E, Kooperberg, Charles, Kottke-Marchant, Kandice, Kumari, Meena, LaCroix, Andrea Z, Mallela, Laya, Murugesan, Gurunathan, Ordovas, Jose, Ouwehand, Willem H, Post, Wendy S, Saxena, Richa, Scharnagl, Hubert, Schreiner, Pamela J, Shah, Tina, Shields, Denis C, Shimbo, Daichi, Srinivasan, Sathanur R, Stolk, Ronald P, Swerdlow, Daniel I, Taylor, Herman A, Topol, Eric J, Toskala, Elina, van Pelt, Joost L, van Setten, Jessica, Yusuf, Salim, Whittaker, John C, Zwinderman, AH, LifeLines Cohort Study, Anand, Sonia S, Balmforth, Anthony J, and Berenson, Gerald S

Subjects
LifeLines Cohort Study, Humans, Lipids, Triglycerides, Sex Factors, Genotype, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, European Continental Ancestry Group, Female, Male, Cholesterol, LDL, Cholesterol, HDL, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Cholesterol, LDL, HDL, Genetics & Heredity, Biological Sciences, Medical and Health Sciences
Abstract

Genome-wide association studies (GWASs) have identified many SNPs underlying variations in plasma-lipid levels. We explore whether additional loci associated with plasma-lipid phenotypes, such as high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TGs), can be identified by a dense gene-centric approach. Our meta-analysis of 32 studies in 66,240 individuals of European ancestry was based on the custom ∼50,000 SNP genotyping array (the ITMAT-Broad-CARe array) covering ∼2,000 candidate genes. SNP-lipid associations were replicated either in a cohort comprising an additional 24,736 samples or within the Global Lipid Genetic Consortium. We identified four, six, ten, and four unreported SNPs in established lipid genes for HDL-C, LDL-C, TC, and TGs, respectively. We also identified several lipid-related SNPs in previously unreported genes: DGAT2, HCAR2, GPIHBP1, PPARG, and FTO for HDL-C; SOCS3, APOH, SPTY2D1, BRCA2, and VLDLR for LDL-C; SOCS3, UGT1A1, BRCA2, UBE3B, FCGR2A, CHUK, and INSIG2 for TC; and SERPINF2, C4B, GCK, GATA4, INSR, and LPAL2 for TGs. The proportion of explained phenotypic variance in the subset of studies providing individual-level data was 9.9% for HDL-C, 9.5% for LDL-C, 10.3% for TC, and 8.0% for TGs. This large meta-analysis of lipid phenotypes with the use of a dense gene-centric approach identified multiple SNPs not previously described in established lipid genes and several previously unknown loci. The explained phenotypic variance from this approach was comparable to that from a meta-analysis of GWAS data, suggesting that a focused genotyping approach can further increase the understanding of heritability of plasma lipids.

Published
2012

10. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes.

Author

Strawbridge, Rona J, Dupuis, Josée, Prokopenko, Inga, Barker, Adam, Ahlqvist, Emma, Rybin, Denis, Petrie, John R, Travers, Mary E, Bouatia-Naji, Nabila, Dimas, Antigone S, Nica, Alexandra, Wheeler, Eleanor, Chen, Han, Voight, Benjamin F, Taneera, Jalal, Kanoni, Stavroula, Peden, John F, Turrini, Fabiola, Gustafsson, Stefan, Zabena, Carina, Almgren, Peter, Barker, David JP, Barnes, Daniel, Dennison, Elaine M, Eriksson, Johan G, Eriksson, Per, Eury, Elodie, Folkersen, Lasse, Fox, Caroline S, Frayling, Timothy M, Goel, Anuj, Gu, Harvest F, Horikoshi, Momoko, Isomaa, Bo, Jackson, Anne U, Jameson, Karen A, Kajantie, Eero, Kerr-Conte, Julie, Kuulasmaa, Teemu, Kuusisto, Johanna, Loos, Ruth JF, Luan, Jian'an, Makrilakis, Konstantinos, Manning, Alisa K, Martínez-Larrad, María Teresa, Narisu, Narisu, Nastase Mannila, Maria, Ohrvik, John, Osmond, Clive, Pascoe, Laura, Payne, Felicity, Sayer, Avan A, Sennblad, Bengt, Silveira, Angela, Stancáková, Alena, Stirrups, Kathy, Swift, Amy J, Syvänen, Ann-Christine, Tuomi, Tiinamaija, van 't Hooft, Ferdinand M, Walker, Mark, Weedon, Michael N, Xie, Weijia, Zethelius, Björn, DIAGRAM Consortium, GIANT Consortium, MuTHER Consortium, CARDIoGRAM Consortium, C4D Consortium, Ongen, Halit, Mälarstig, Anders, Hopewell, Jemma C, Saleheen, Danish, Chambers, John, Parish, Sarah, Danesh, John, Kooner, Jaspal, Ostenson, Claes-Göran, Lind, Lars, Cooper, Cyrus C, Serrano-Ríos, Manuel, Ferrannini, Ele, Forsen, Tom J, Clarke, Robert, Franzosi, Maria Grazia, Seedorf, Udo, Watkins, Hugh, Froguel, Philippe, Johnson, Paul, Deloukas, Panos, Collins, Francis S, Laakso, Markku, Dermitzakis, Emmanouil T, Boehnke, Michael, McCarthy, Mark I, Wareham, Nicholas J, Groop, Leif, Pattou, François, Gloyn, Anna L, and Dedoussis, George V

Subjects
DIAGRAM Consortium, GIANT Consortium, MuTHER Consortium, CARDIoGRAM Consortium, C4D Consortium, Humans, Diabetes Mellitus, Type 2, Insulin, Proinsulin, Fasting, Genotype, Polymorphism, Single Nucleotide, Genome, Human, Adult, Female, Male, Genetic Variation, Diabetes Mellitus, Type 2, Polymorphism, Single Nucleotide, Genome, Human, Endocrinology & Metabolism, Medical and Health Sciences
Abstract

ObjectiveProinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology.Research design and methodsWe have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates.ResultsNine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10(-8)). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10(-4)), improved β-cell function (P = 1.1 × 10(-5)), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10(-6)). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets.ConclusionsWe have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.

Published
2011

11. Genome-Wide Association Identifies Nine Common Variants Associated With Fasting Proinsulin Levels and Provides New Insights Into the Pathophysiology of Type 2 Diabetes

Author

Strawbridge, Rona J, Dupuis, Josée, Prokopenko, Inga, Barker, Adam, Ahlqvist, Emma, Rybin, Denis, Petrie, John R, Travers, Mary E, Bouatia-Naji, Nabila, Dimas, Antigone S, Nica, Alexandra, Wheeler, Eleanor, Chen, Han, Voight, Benjamin F, Taneera, Jalal, Kanoni, Stavroula, Peden, John F, Turrini, Fabiola, Gustafsson, Stefan, Zabena, Carina, Almgren, Peter, Barker, David JP, Barnes, Daniel, Dennison, Elaine M, Eriksson, Johan G, Eriksson, Per, Eury, Elodie, Folkersen, Lasse, Fox, Caroline S, Frayling, Timothy M, Goel, Anuj, Gu, Harvest F, Horikoshi, Momoko, Isomaa, Bo, Jackson, Anne U, Jameson, Karen A, Kajantie, Eero, Kerr-Conte, Julie, Kuulasmaa, Teemu, Kuusisto, Johanna, Loos, Ruth JF, Luan, Jian'an, Makrilakis, Konstantinos, Manning, Alisa K, Martínez-Larrad, María Teresa, Narisu, Narisu, Nastase Mannila, Maria, Öhrvik, John, Osmond, Clive, Pascoe, Laura, Payne, Felicity, Sayer, Avan A, Sennblad, Bengt, Silveira, Angela, Stančáková, Alena, Stirrups, Kathy, Swift, Amy J, Syvänen, Ann-Christine, Tuomi, Tiinamaija, van 't Hooft, Ferdinand M, Walker, Mark, Weedon, Michael N, Xie, Weijia, Zethelius, Björn, Ongen, Halit, Mälarstig, Anders, Hopewell, Jemma C, Saleheen, Danish, Chambers, John, Parish, Sarah, Danesh, John, Kooner, Jaspal, Östenson, Claes-Göran, Lind, Lars, Cooper, Cyrus C, Serrano-Ríos, Manuel, Ferrannini, Ele, Forsen, Tom J, Clarke, Robert, Franzosi, Maria Grazia, Seedorf, Udo, Watkins, Hugh, Froguel, Philippe, Johnson, Paul, Deloukas, Panos, Collins, Francis S, Laakso, Markku, Dermitzakis, Emmanouil T, Boehnke, Michael, McCarthy, Mark I, Wareham, Nicholas J, Groop, Leif, Pattou, François, Gloyn, Anna L, Dedoussis, George V, Lyssenko, Valeriya, Meigs, James B, Barroso, Inês, Watanabe, Richard M, and Ingelsson, Erik

Subjects
Diabetes, Obesity, Human Genome, Genetics, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Adult, Diabetes Mellitus, Type 2, Fasting, Female, Genetic Variation, Genome, Human, Genotype, Humans, Insulin, Male, Polymorphism, Single Nucleotide, Proinsulin, DIAGRAM Consortium, GIANT Consortium, MuTHER Consortium, CARDIoGRAM Consortium, C4D Consortium, Medical and Health Sciences, Endocrinology & Metabolism
Abstract

ObjectiveProinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology.Research design and methodsWe have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates.ResultsNine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10(-8)). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10(-4)), improved β-cell function (P = 1.1 × 10(-5)), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10(-6)). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets.ConclusionsWe have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.

Published
2011

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