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9 results on '"Staley, James R"'

1. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations

Author

Kamat, Mihir A, Blackshaw, James A, Young, Robin, Surendran, Praveen, Burgess, Stephen, Danesh, John, Butterworth, Adam S, and Staley, James R

Subjects
Genome, Phenotype, Genotype, Humans, Polymorphism, Single Nucleotide, Genetic Association Studies, Linkage Disequilibrium, Software, 3. Good health, Genome-Wide Association Study
Abstract

SUMMARY: PhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates 'phenome scans', where genetic variants are cross-referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner ('PhenoScanner V2'), including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants. AVAILABILITY AND IMPLEMENTATION: PhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.uk.

2. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

Author

Zheng, Jie, Haberland, Valeriia, Baird, Denis, Walker, Venexia, Haycock, Philip C, Hurle, Mark R, Gutteridge, Alex, Erola, Pau, Liu, Yi, Luo, Shan, Robinson, Jamie, Richardson, Tom G, Staley, James R, Elsworth, Benjamin, Burgess, Stephen, Sun, Benjamin B, Danesh, John, Runz, Heiko, Maranville, Joseph C, Martin, Hannah M, Yarmolinsky, James, Laurin, Charles, Holmes, Michael V, Liu, Jimmy Z, Estrada, Karol, Santos, Rita, McCarthy, Linda, Waterworth, Dawn, Nelson, Matthew R, Smith, George Davey, Butterworth, Adam S, Hemani, Gibran, Scott, Robert A, and Gaunt, Tom R

Subjects
Phenotype, Proteome, Humans, Genetic Predisposition to Disease, Blood Proteins, Mendelian Randomization Analysis, Polymorphism, Single Nucleotide, 3. Good health, Genome-Wide Association Study
Abstract

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.

3. A robust and efficient method for Mendelian randomization with hundreds of genetic variants

Author

Burgess, Stephen, Foley, Christopher N, Allara, Elias, Staley, James R, and Howson, Joanna M. M.

Subjects
631/443/592/2727, 692/53/2422, article, 631/208/205, 692/308/174, 3. Good health
Abstract

Funder: DH | National Institute for Health Research (NIHR); doi: https://doi.org/10.13039/501100000272, Mendelian randomization (MR) is an epidemiological technique that uses genetic variants to distinguish correlation from causation in observational data. The reliability of a MR investigation depends on the validity of the genetic variants as instrumental variables (IVs). We develop the contamination mixture method, a method for MR with two modalities. First, it identifies groups of genetic variants with similar causal estimates, which may represent distinct mechanisms by which the risk factor influences the outcome. Second, it performs MR robustly and efficiently in the presence of invalid IVs. Compared to other robust methods, it has the lowest mean squared error across a range of realistic scenarios. The method identifies 11 variants associated with increased high-density lipoprotein-cholesterol, decreased triglyceride levels, and decreased coronary heart disease risk that have the same directions of associations with various blood cell traits, suggesting a shared mechanism linking lipids and coronary heart disease risk mediated via platelet aggregation.

4. A robust and efficient method for Mendelian randomization with hundreds of genetic variants

Author

Burgess, Stephen, Foley, Christopher N, Allara, Elias, Staley, James R, and Howson, Joanna M M

Subjects
3. Good health
Abstract

Mendelian randomization (MR) is an epidemiological technique that uses genetic variants to distinguish correlation from causation in observational data. The reliability of a MR investigation depends on the validity of the genetic variants as instrumental variables (IVs). We develop the contamination mixture method, a method for MR with two modalities. First, it identifies groups of genetic variants with similar causal estimates, which may represent distinct mechanisms by which the risk factor influences the outcome. Second, it performs MR robustly and efficiently in the presence of invalid IVs. Compared to other robust methods, it has the lowest mean squared error across a range of realistic scenarios. The method identifies 11 variants associated with increased high-density lipoprotein-cholesterol, decreased triglyceride levels, and decreased coronary heart disease risk that have the same directions of associations with various blood cell traits, suggesting a shared mechanism linking lipids and coronary heart disease risk mediated via platelet aggregation.

5. Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals

Author

Surendran, Praveen, Feofanova, Elena V, Lahrouchi, Najim, Ntalla, Ioanna, Karthikeyan, Savita, Cook, James, Chen, Lingyan, Mifsud, Borbala, Yao, Chen, Kraja, Aldi T, Cartwright, James H, Hellwege, Jacklyn N, Giri, Ayush, Tragante, Vinicius, Thorleifsson, Gudmar, Liu, Dajiang J, Prins, Bram P, Stewart, Isobel D, Cabrera, Claudia P, Eales, James M, Akbarov, Artur, Auer, Paul L, Bielak, Lawrence F, Bis, Joshua C, Braithwaite, Vickie S, Brody, Jennifer A, Daw, E Warwick, Warren, Helen R, Drenos, Fotios, Nielsen, Sune Fallgaard, Faul, Jessica D, Fauman, Eric B, Fava, Cristiano, Ferreira, Teresa, Foley, Christopher N, Franceschini, Nora, Gao, He, Giannakopoulou, Olga, Giulianini, Franco, Gudbjartsson, Daniel F, Guo, Xiuqing, Harris, Sarah E, Havulinna, Aki S, Helgadottir, Anna, Huffman, Jennifer E, Hwang, Shih-Jen, Kanoni, Stavroula, Kontto, Jukka, Larson, Martin G, Li-Gao, Ruifang, Lindström, Jaana, Lotta, Luca A, Lu, Yingchang, Luan, Jian'an, Mahajan, Anubha, Malerba, Giovanni, Masca, Nicholas GD, Mei, Hao, Menni, Cristina, Mook-Kanamori, Dennis O, Mosen-Ansorena, David, Müller-Nurasyid, Martina, Paré, Guillaume, Paul, Dirk S, Perola, Markus, Poveda, Alaitz, Rauramaa, Rainer, Richard, Melissa, Richardson, Tom G, Sepúlveda, Nuno, Sim, Xueling, Smith, Albert V, Smith, Jennifer A, Staley, James R, Stanáková, Alena, Sulem, Patrick, Thériault, Sébastien, Thorsteinsdottir, Unnur, Trompet, Stella, Varga, Tibor V, Velez Edwards, Digna R, Veronesi, Giovanni, Weiss, Stefan, Willems, Sara M, Yao, Jie, Young, Robin, Yu, Bing, Zhang, Weihua, Zhao, Jing-Hua, Zhao, Wei, Evangelou, Evangelos, Aeschbacher, Stefanie, Asllanaj, Eralda, Blankenberg, Stefan, Bonnycastle, Lori L, Bork-Jensen, Jette, Brandslund, Ivan, Braund, Peter S, Burgess, Stephen, Cho, Kelly, Christensen, Cramer, Connell, John, Mutsert, Renée De, Dominiczak, Anna F, Dörr, Marcus, Eiriksdottir, Gudny, Farmaki, Aliki-Eleni, Gaziano, J Michael, Grarup, Niels, Grove, Megan L, Hallmans, Göran, Hansen, Torben, Have, Christian T, Heiss, Gerardo, Jørgensen, Marit E, Jousilahti, Pekka, Kajantie, Eero, Kamat, Mihir, Käräjämäki, AnneMari, Karpe, Fredrik, Koistinen, Heikki A, Kovesdy, Csaba P, Kuulasmaa, Kari, Laatikainen, Tiina, Lannfelt, Lars, Lee, I-Te, Lee, Wen-Jane, LifeLines Cohort Study, Linneberg, Allan, Martin, Lisa W, Moitry, Marie, Nadkarni, Girish, Neville, Matt J, Palmer, Colin NA, Papanicolaou, George J, Pedersen, Oluf, Peters, James, Poulter, Neil, Rasheed, Asif, Rasmussen, Katrine L, Rayner, N William, Mägi, Reedik, Renström, Frida, Rettig, Rainer, Rossouw, Jacques, Schreiner, Pamela J, Sever, Peter S, Sigurdsson, Emil L, Skaaby, Tea, Sun, Yan V, Sundstrom, Johan, Thorgeirsson, Gudmundur, Esko, Tõnu, Trabetti, Elisabetta, Tsao, Philip S, Tuomi, Tiinamaija, Turner, Stephen T, Tzoulaki, Ioanna, Vaartjes, Ilonca, Vergnaud, Anne-Claire, Willer, Cristen J, Wilson, Peter WF, Witte, Daniel R, Yonova-Doing, Ekaterina, Zhang, He, Aliya, Naheed, Almgren, Peter, Amouyel, Philippe, Asselbergs, Folkert W, Barnes, Michael R, Blakemore, Alexandra I, Boehnke, Michael, Bots, Michiel L, Bottinger, Erwin P, Buring, Julie E, Chambers, John C, Chen, Yii-Der Ida, Chowdhury, Rajiv, Conen, David, Correa, Adolfo, Davey Smith, George, Boer, Rudolf A De, Deary, Ian J, Dedoussis, George, Deloukas, Panos, Di Angelantonio, Emanuele, Elliott, Paul, EPIC-CVD, EPIC-InterAct, Felix, Stephan B, Ferrières, Jean, Ford, Ian, Fornage, Myriam, Franks, Paul W, Franks, Stephen, Frossard, Philippe, Gambaro, Giovanni, Gaunt, Tom R, Groop, Leif, Gudnason, Vilmundur, Harris, Tamara B, Hayward, Caroline, Hennig, Branwen J, Herzig, Karl-Heinz, Ingelsson, Erik, Tuomilehto, Jaakko, Järvelin, Marjo-Riitta, Jukema, J Wouter, Kardia, Sharon LR, Kee, Frank, Kooner, Jaspal S, Kooperberg, Charles, Launer, Lenore J, Lind, Lars, Loos, Ruth JF, Majumder, Abdulla Al Shafi, Laakso, Markku, McCarthy, Mark I, Melander, Olle, Mohlke, Karen L, Murray, Alison D, Nordestgaard, Børge Grønne, Orho-Melander, Marju, Packard, Chris J, Padmanabhan, Sandosh, Palmas, Walter, Polasek, Ozren, Porteous, David J, Prentice, Andrew M, Province, Michael A, Relton, Caroline L, Rice, Kenneth, Ridker, Paul M, Rolandsson, Olov, Rosendaal, Frits R, Rotter, Jerome I, Rudan, Igor, Salomaa, Veikko, Samani, Nilesh J, Sattar, Naveed, Sheu, Wayne H-H, Smith, Blair H, Soranzo, Nicole, Spector, Timothy D, Starr, John M, Sebert, Sylvain, Taylor, Kent D, Lakka, Timo A, Timpson, Nicholas J, Tobin, Martin D, Understanding Society Scientific Group, Van Der Harst, Pim, Van Der Meer, Peter, Ramachandran, Vasan S, Verweij, Niek, Virtamo, Jarmo, Völker, Uwe, Weir, David R, Zeggini, Eleftheria, Charchar, Fadi J, Million Veteran Program, Wareham, Nicholas J, Langenberg, Claudia, Tomaszewski, Maciej, Butterworth, Adam S, Caulfield, Mark J, Danesh, John, Edwards, Todd L, Holm, Hilma, Hung, Adriana M, Lindgren, Cecilia M, Liu, Chunyu, Manning, Alisa K, Morris, Andrew P, Morrison, Alanna C, O'Donnell, Christopher J, Psaty, Bruce M, Saleheen, Danish, Stefansson, Kari, Boerwinkle, Eric, Chasman, Daniel I, Levy, Daniel, Newton-Cheh, Christopher, Munroe, Patricia B, and Howson, Joanna MM

Subjects
Gene Frequency, Genotype, GATA5 Transcription Factor, Hypertension, Mutation, Phospholipase C beta, Humans, Blood Pressure, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, 3. Good health, Genome-Wide Association Study
Abstract

Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency > 0.05). In a meta-analysis of up to ~1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequency ≤ 0.01) variant BP associations (P < 5 × 10-8), of which 32 were in new BP-associated loci and 55 were independent BP-associated single-nucleotide variants within known BP-associated regions. Average effects of rare variants (44% coding) were ~8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (for example, GATA5 and PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare-variant analyses for identifying candidate genes and the results highlight potential therapeutic targets.

6. A robust and efficient method for Mendelian randomization with hundreds of genetic variants

Author

Burgess, Stephen, Foley, Christopher N, Allara, Elias, Staley, James R, and Howson, Joanna MM

Subjects
Molecular Epidemiology, Models, Genetic, Cholesterol, HDL, Genetic Variation, Reproducibility of Results, Coronary Disease, Genetic Pleiotropy, Cholesterol, LDL, Mendelian Randomization Analysis, 3. Good health, Body Mass Index, Phenotype, Diabetes Mellitus, Type 2, Research Design, Risk Factors, Humans, Genetic Predisposition to Disease, Genetic Association Studies, Triglycerides
Abstract

Mendelian randomization (MR) is an epidemiological technique that uses genetic variants to distinguish correlation from causation in observational data. The reliability of a MR investigation depends on the validity of the genetic variants as instrumental variables (IVs). We develop the contamination mixture method, a method for MR with two modalities. First, it identifies groups of genetic variants with similar causal estimates, which may represent distinct mechanisms by which the risk factor influences the outcome. Second, it performs MR robustly and efficiently in the presence of invalid IVs. Compared to other robust methods, it has the lowest mean squared error across a range of realistic scenarios. The method identifies 11 variants associated with increased high-density lipoprotein-cholesterol, decreased triglyceride levels, and decreased coronary heart disease risk that have the same directions of associations with various blood cell traits, suggesting a shared mechanism linking lipids and coronary heart disease risk mediated via platelet aggregation.

7. Body mass index and all cause mortality in HUNT and UK Biobank studies: linear and non-linear mendelian randomisation analyses

Author

Sun, Yi-Qian, Burgess, Stephen, Staley, James R, Wood, Angela, Bell, Steven, Kaptoge, Stephen, Guo, Qi, Bolton, Thomas, Mason, Amy, Butterworth, Adam, Di Angelantonio, Emanuele, Vie, Gunnhild Å, Bjørngaard, Johan H, Kinge, Jonas Minet, Chen, Yue, and Mai, Xiao-Mei

Subjects
2. Zero hunger, Adult, Male, Norway, nutritional and metabolic diseases, Middle Aged, Mendelian Randomization Analysis, United Kingdom, 3. Good health, Body Mass Index, Thinness, Cardiovascular Diseases, Risk Factors, Neoplasms, Cause of Death, Humans, Female, Obesity, Sex Distribution, Aged
Abstract

Objective: To investigate the shape of the causal relationship between body mass index (BMI) and mortality. Design: Linear and non-linear Mendelian randomization analyses of two prospective population-based cohorts. Setting: Middle-aged to early late-aged individuals of European descent. Participants: 56,150 participants from the Nord-Trøndelag Health Study (HUNT) in Norway and 366,385 participants from UK Biobank recruited by postal invitation. Main outcome measures: All-cause and cause-specific (cardiovascular, cancer, and non-cardiovascular non-cancer) mortality. Results: In total 12,015 and 10,344 participants died during a median of 18.5 and 7.0 years’ follow-up in the HUNT and UK Biobank studies respectively. Linear Mendelian randomization analyses indicated an overall positive association between genetically-predicted BMI and all-cause mortality risk. A 1 kg/m2 increase in genetically-predicted BMI led to a 5% (95% confidence interval [CI]: 1%, 8%) higher mortality risk in overweight individuals (BMI 25.0-29.9), and a 9% (95% CI: 4%, 14%) higher mortality risk in obese individuals (BMI ≥ 30.0), but 34% (95% CI: 16%, 48%), lower risk in underweight (BMI < 18.5) and 14% (95% CI: -1%, 27%) lower risk in low normal weight individuals (BMI 18.5-19.9). Non-linear Mendelian randomization indicated a J-shaped relationship between genetically-predicted BMI and all-cause mortality risk, with the nadir of risk at BMI about 22-25 kg/m2 for the overall sample. Increased risk of all-cause mortality in overweight and obese individuals was more evident in women. However, subgroup analyses by smoking status suggested a monotone increasing relationship of BMI with mortality in never-smokers, but a non-monotone relationship in ever-smokers. Conclusion: The previously observed J-shaped relationship between BMI and mortality appears to have a causal basis, but subgroup analyses by smoking status revealed the BMI—mortality relationship is likely comprised of at least two distinct curves, rather than a single J-shaped relationship. Increased mortality risk for being underweight was only evident in ever-smokers., YQS and this work were supported by The Norwegian Cancer Society (project ID 5769155-2015) and The Research Council of Norway “Gaveforsterkning”. GÅV and JMK were supported by The Research Council of Norway (grant number 250335). S Burgess is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 204623/Z/16/Z). The funders had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication

8. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension

Author

Surendran, Praveen, Drenos, Fotios, Young, Robin, Warren, Helen, Cook, James P, Manning, Alisa K, Grarup, Niels, Sim, Xueling, Barnes, Daniel R, Witkowska, Kate, Staley, James R, Tragante, Vinicius, Tukiainen, Taru, Yaghootkar, Hanieh, Masca, Nicholas, Freitag, Daniel F, Ferreira, Teresa, Giannakopoulou, Olga, Tinker, Andrew, Harakalova, Magdalena, Mihailov, Evelin, Liu, Chunyu, Kraja, Aldi T, Fallgaard Nielsen, Sune, Rasheed, Asif, Samuel, Maria, Zhao, Wei, Bonnycastle, Lori L, Jackson, Anne U, Narisu, Narisu, Swift, Amy J, Southam, Lorraine, Marten, Jonathan, Huyghe, Jeroen R, Stančáková, Alena, Fava, Cristiano, Ohlsson, Therese, Matchan, Angela, Stirrups, Kathleen E, Bork-Jensen, Jette, Gjesing, Anette P, Kontto, Jukka, Perola, Markus, Shaw-Hawkins, Susan, Havulinna, Aki S, Zhang, He, Donnelly, Louise A, Groves, Christopher J, Rayner, N William, Neville, Matt J, Robertson, Neil R, Yiorkas, Andrianos M, Herzig, Karl-Heinz, Kajantie, Eero, Zhang, Weihua, Willems, Sara M, Lannfelt, Lars, Malerba, Giovanni, Soranzo, Nicole, Trabetti, Elisabetta, Verweij, Niek, Evangelou, Evangelos, Moayyeri, Alireza, Vergnaud, Anne-Claire, Nelson, Christopher P, Poveda, Alaitz, Varga, Tibor V, Caslake, Muriel, De Craen, Anton Jm, Trompet, Stella, Luan, Jian'an, Scott, Robert A, Harris, Sarah E, Liewald, David Cm, Marioni, Riccardo, Menni, Cristina, Farmaki, Aliki-Eleni, Hallmans, Göran, Renström, Frida, Huffman, Jennifer E, Hassinen, Maija, Burgess, Stephen, Vasan, Ramachandran S, Felix, Janine F, CHARGE-Heart Failure Consortium, Uria-Nickelsen, Maria, Malarstig, Anders, Reily, Dermot F, Hoek, Maarten, Vogt, Thomas, Lin, Honghuang, Lieb, Wolfgang, EchoGen Consortium, Traylor, Matthew, Markus, Hugh F, METASTROKE Consortium, Highland, Heather M, Justice, Anne E, Marouli, Eirini, GIANT Consortium, Lindström, Jaana, Uusitupa, Matti, Komulainen, Pirjo, Lakka, Timo A, Rauramaa, Rainer, Polasek, Ozren, Rudan, Igor, Rolandsson, Olov, Franks, Paul W, Dedoussis, George, Spector, Timothy D, EPIC-InterAct Consortium, Jousilahti, Pekka, Männistö, Satu, Deary, Ian J, Starr, John M, Langenberg, Claudia, Wareham, Nick J, Brown, Morris J, Dominiczak, Anna F, Connell, John M, Jukema, J Wouter, Sattar, Naveed, Ford, Ian, Packard, Chris J, Esko, Tõnu, Mägi, Reedik, Metspalu, Andres, De Boer, Rudolf A, Van Der Meer, Peter, Van Der Harst, Pim, Lifelines Cohort Study, Gambaro, Giovanni, Ingelsson, Erik, Lind, Lars, De Bakker, Paul Iw, Numans, Mattijs E, Brandslund, Ivan, Christensen, Cramer, Petersen, Eva Rb, Korpi-Hyövälti, Eeva, Oksa, Heikki, Chambers, John C, Kooner, Jaspal S, Blakemore, Alexandra If, Franks, Steve, Jarvelin, Marjo-Riitta, Husemoen, Lise L, Linneberg, Allan, Skaaby, Tea, Thuesen, Betina, Karpe, Fredrik, Tuomilehto, Jaakko, Doney, Alex Sf, Morris, Andrew D, Palmer, Colin Na, Holmen, Oddgeir Lingaas, Hveem, Kristian, Willer, Cristen J, Tuomi, Tiinamaija, Groop, Leif, Käräjämäki, AnneMari, Palotie, Aarno, Ripatti, Samuli, Salomaa, Veikko, Alam, Dewan S, Shafi Majumder, Abdulla Al, Di Angelantonio, Emanuele, Chowdhury, Rajiv, McCarthy, Mark I, Poulter, Neil, Stanton, Alice V, Sever, Peter, Amouyel, Philippe, Arveiler, Dominique, Blankenberg, Stefan, Ferrières, Jean, Kee, Frank, Kuulasmaa, Kari, Müller-Nurasyid, Martina, Veronesi, Giovanni, Virtamo, Jarmo, Deloukas, Panos, Wellcome Trust Case Control Consortium, Elliott, Paul, Understanding Society Scientific Group, Zeggini, Eleftheria, Kathiresan, Sekar, Melander, Olle, Kuusisto, Johanna, Laakso, Markku, Padmanabhan, Sandosh, Porteous, David, Hayward, Caroline, Scotland, Generation, Collins, Francis S, Mohlke, Karen L, Hansen, Torben, Pedersen, Oluf, Boehnke, Michael, Stringham, Heather M, EPIC-CVD Consortium, Frossard, Philippe, Newton-Cheh, Christopher, CHARGE+ Exome Chip Blood Pressure Consortium, Tobin, Martin D, Nordestgaard, Børge Grønne, T2D-GENES Consortium, GoT2DGenes Consortium, ExomeBP Consortium, CHD Exome+ Consortium, Caulfield, Mark J, Mahajan, Anubha, Morris, Andrew P, Tomaszewski, Maciej, Samani, Nilesh J, Saleheen, Danish, Asselbergs, Folkert W, Lindgren, Cecilia M, Danesh, John, Wain, Louise V, Butterworth, Adam S, Howson, Joanna Mm, and Munroe, Patricia B

Subjects
Genotype, Hypertension, Genetic Variation, Humans, Blood Pressure, Genetic Predisposition to Disease, 3. Good health, Genome-Wide Association Study
Abstract

High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to 192,763 individuals and used ∼155,063 samples for independent replication. We identified 30 new blood pressure- or hypertension-associated genetic regions in the general population, including 3 rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5 mm Hg/allele) than common variants. Multiple rare nonsense and missense variant associations were found in A2ML1, and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention.

9. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension

Author

Mägi, Reedik, Dedoussis, George, Ohlsson, Therese, Frossard, Philippe, Scott, Robert A., Komulainen, Pirjo, Warren, Helen, Nelson, Christopher P., Lind, Lars, Kuusisto, Johanna, Holmen, Oddgeir Lingaas, Langenberg, Claudia, Swift, Amy J., Herzig, Karl-Heinz, Burgess, Stephen, Alam, Dewan S., Moayyeri, Alireza, Traylor, Matthew, Cook, James P., Verweij, Niek, Young, Robin, Franks, Steve, Trompet, Stella, Van Der Meer, Peter, Gambaro, Giovanni, Linneberg, Allan, Boehnke, Michael, Lakka, Timo A., Stančáková, Alena, Surendran, Praveen, Malerba, Giovanni, Oksa, Heikki, Padmanabhan, Sandosh, Howson, Joanna M. M., Butterworth, Adam S., Zhang, He, Hansen, Torben, Karpe, Fredrik, De Boer, Rudolf A., Kathiresan, Sekar, Poveda, Alaitz, Collins, Francis S., Tuomilehto, Jaakko, Veronesi, Giovanni, Tukiainen, Taru, Lin, Honghuang, Majumder, Abdulla Al Shafi, Hallmans, Göran, Groop, Leif, Morris, Andrew D., Rudan, Igor, Varga, Tibor V, Brown, Morris J., Zeggini, Eleftheria, Mohlke, Karen L., Käräjämäki, Annemari, Christensen, Cramer, Huyghe, Jeroen R., Hoek, Maarten, Pedersen, Oluf, Donnelly, Louise A., Harris, Sarah E., Yiorkas, Andrianos M., Deloukas, Panos, Thuesen, Betina, Ford, Ian, Rauramaa, Rainer, Lindström, Jaana, Zhang, Weihua, Trabetti, Elisabetta, Vasan, Ramachandran S., Reilly, Dermot F., Nielsen, Sune Fallgaard, Deary, Ian J., Metspalu, Andres, Stirrups, Kathleen E., Witkowska, Kate, Perola, Markus, Palotie, Aarno, Tinker, Andrew, Amouyel, Philippe, Masca, Nicholas, Nordestgaard, Børge Grønne, Staley, James R., Ferreira, Teresa, Tuomi, Tiinamaija, Matchan, Angela, Hayward, Caroline, Marten, Jonathan, Chowdhury, Rajiv, Samuel, Maria, Markus, Hugh S., Kooner, Jaspal S., Farmaki, Aliki-Eleni, Wareham, Nick J., Kee, Frank, Southam, Lorraine, McCarthy, Mark I., Sever, Peter, Mahajan, Anubha, Starr, John M., Rasheed, Asif, Marioni, Riccardo, Gjesing, Anette P., Manning, Alisa K., Liu, Chunyu, Ferrières, Jean, Kontto, Jukka, Stringham, Heather M., Morris, Andrew P., Newton-Cheh, Christopher, Korpi-Hyövälti, Eeva, Doney, Alex S. F., Laakso, Markku, Fava, Cristiano, Uria-Nickelsen, Maria, Palmer, Colin N. A., Arveiler, Dominique, Ripatti, Samuli, Connell, John M., Narisu, Narisu, Willer, Cristen J., Evangelou, Evangelos, Renström, Frida, Liewald, David C. M., Sim, Xueling, Menni, Cristina, Felix, Janine F., Jarvelin, Marjo-Riitta, Giannakopoulou, Olga, Mihailov, Evelin, Sattar, Naveed, Tobin, Martin D., Salomaa, Veikko, Wain, Louise V., Lieb, Wolfgang, Highland, Heather M., Saleheen, Danish, Di Angelantonio, Emanuele, Kajantie, Eero, Blakemore, Alexandra I. F., Virtamo, Jarmo, Skaaby, Tea, Chambers, John C., Männistö, Satu, Jousilahti, Pekka, Malarstig, Anders, Lindgren, Cecilia M., Bonnycastle, Lori L., Munroe, Patricia B., Melander, Olle, Asselbergs, Folkert W., Robertson, Neil R., Caulfield, Mark J., Uusitupa, Matti, Spector, Timothy D., Rolandsson, Olov, Tragante, Vinicius, Shaw-Hawkins, Susan, Van Der Harst, Pim, Marouli, Eirini, Freitag, Daniel F., Vogt, Thomas F., Porteous, David J., Samani, Nilesh J., Neville, Matt J., Jukema, J Wouter, Polasek, Ozren, Bork-Jensen, Jette, Franks, Paul W., Grarup, Niels, Brandslund, Ivan, Havulinna, Aki S., Esko, Tõnu, Blankenberg, Stefan, Vergnaud, Anne-Claire, Huffman, Jennifer E., Caslake, Muriel, Scotland, Generation, Rayner, N William, Jackson, Anne U., Packard, Chris J., Hveem, Kristian, Barnes, Daniel R., Soranzo, Nicole, Lannfelt, Lars, Harakalova, Magdalena, De Craen, Anton J. M., Tomaszewski, Maciej, Kuulasmaa, Kari, Elliott, Paul, Stanton, Alice V., Willems, Sara M., De Bakker, Paul I. W., Justice, Anne E., Husemoen, Lise L., Luan, Jian'An, Drenos, Fotios, Müller-Nurasyid, Martina, Petersen, Eva R. B., Zhao, Wei, Kraja, Aldi T., Dominiczak, Anna F., Ingelsson, Erik, Poulter, Neil, Hassinen, Maija, Groves, Christopher J., Danesh, John, Numans, Mattijs E., and Yaghootkar, Hanieh

Subjects
3. Good health
Abstract

High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to ~192,000 individuals, and used ~155,063 samples for independent replication. We identified 31 novel blood pressure or hypertension associated genetic regions in the general population, including three rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5mmHg/allele) than common variants. Multiple rare, nonsense and missense variant associations were found in A2ML1 and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention.

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