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Your search keyword '"Angelantonio, Emanuele"' showing total 12 results
Start Over Author "Angelantonio, Emanuele" Publisher escholarship, university of california
12 results on '"Angelantonio, Emanuele"'

1. South Asian medical cohorts reveal strong founder effects and high rates of homozygosity

Author

Wall, Jeffrey D, Sathirapongsasuti, J Fah, Gupta, Ravi, Rasheed, Asif, Venkatesan, Radha, Belsare, Saurabh, Menon, Ramesh, Phalke, Sameer, Mittal, Anuradha, Fang, John, Tanneeru, Deepak, Deshmukh, Manjari, Bassi, Akshi, Robinson, Jacqueline, Chaudhary, Ruchi, Murugan, Sakthivel, ul-Asar, Zameer, Saleem, Imran, Ishtiaq, Unzila, Fatima, Areej, Sheikh, Saqib Shafi, Hameed, Shahid, Ishaq, Mohammad, Rasheed, Syed Zahed, Memon, Fazal-ur-Rehman, Jalal, Anjum, Abbas, Shahid, Frossard, Philippe, Fuchsberger, Christian, Forer, Lukas, Schoenherr, Sebastian, Bei, Qixin, Bhangale, Tushar, Tom, Jennifer, Gadde, Santosh Gopi Krishna, B V, Priya, Naik, Naveen Kumar, Wang, Minxian, Kwok, Pui-Yan, Khera, Amit V, Lakshmi, BR, Butterworth, Adam S, Chowdhury, Rajiv, Danesh, John, di Angelantonio, Emanuele, Naheed, Aliya, Goyal, Vinay, Kandadai, Rukmini M, Kumar, Hrishikesh, Borgohain, Rupam, Mukherjee, Adreesh, Wadia, Pettarusp M, Yadav, Ravi, Desai, Soaham, Kumar, Niraj, Biswas, Atanu, Pal, Pramod Kumar, Muthane, Uday B, Das, Shymal K, Ramprasad, Vedam L, Kukkle, Prashanth L, Seshagiri, Somasekar, Kathiresan, Sekar, Ghosh, Arkasubhra, Mohan, V, Saleheen, Danish, Stawiski, Eric W, and Peterson, Andrew S

Subjects
Genetics, Biotechnology, Clinical Research, Human Genome, Generic health relevance, Good Health and Well Being, Humans, Asian People, Bangladesh, Founder Effect, Homozygote, India, Pakistan, South Asian People
Abstract

The benefits of large-scale genetic studies for healthcare of the populations studied are well documented, but these genetic studies have traditionally ignored people from some parts of the world, such as South Asia. Here we describe whole genome sequence (WGS) data from 4806 individuals recruited from the healthcare delivery systems of Pakistan, India and Bangladesh, combined with WGS from 927 individuals from isolated South Asian populations. We characterize population structure in South Asia and describe a genotyping array (SARGAM) and imputation reference panel that are optimized for South Asian genomes. We find evidence for high rates of reproductive isolation, endogamy and consanguinity that vary across the subcontinent and that lead to levels of rare homozygotes that reach 100 times that seen in outbred populations. Founder effects increase the power to associate functional variants with disease processes and make South Asia a uniquely powerful place for population-scale genetic studies.

Published
2023

2. Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP.

Author

Matsushita, Kunihiro, Kaptoge, Stephen, Hageman, Steven, Sang, Yingying, Ballew, Shoshana, Grams, Morgan, Surapaneni, Aditya, Sun, Luanluan, Arnlov, Johan, Bozic, Milica, Brenner, Hermann, Brunskill, Nigel, Chang, Alex, Chinnadurai, Rajkumar, Cirillo, Massimo, Correa, Adolfo, Ebert, Natalie, Eckardt, Kai-Uwe, Gansevoort, Ron, Gutierrez, Orlando, Hadaegh, Farzad, He, Jiang, Hwang, Shih-Jen, Jafar, Tazeen, Jassal, Simerjot, Kayama, Takamasa, Kovesdy, Csaba, Landman, Gijs, Levey, Andrew, Lloyd-Jones, Donald, Major, Rupert, Miura, Katsuyuki, Muntner, Paul, Nadkarni, Girish, Nowak, Christoph, Ohkubo, Takayoshi, Pena, Michelle, Polkinghorne, Kevan, Sairenchi, Toshimi, Schaeffner, Elke, Schneider, Markus, Shalev, Varda, Shlipak, Michael, Solbu, Marit, Stempniewicz, Nikita, Tollitt, James, Valdivielso, José, van der Leeuw, Joep, Wang, Angela, Wen, Chi-Pang, Woodward, Mark, Yamagishi, Kazumasa, Yatsuya, Hiroshi, Zhang, Luxia, Dorresteijn, Jannick, Di Angelantonio, Emanuele, Visseren, Frank, Pennells, Lisa, and Coresh, Josef

Subjects
Cardiovascular disease, Chronic kidney disease, Meta-analysis, Risk prediction, Humans, Aged, Aged, 80 and over, Cardiovascular Diseases, Risk Factors, Creatinine, Renal Insufficiency, Chronic, Albuminuria, Glomerular Filtration Rate, Heart Disease Risk Factors
Abstract

AIMS: The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an Add-on to incorporate CKD measures into these algorithms, using a validated approach. METHODS: In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP. We validated C-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,997 719 participants from 34 different datasets. RESULTS: In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved C-statistic by 0.006 (95%CI 0.004-0.008) and 0.016 (0.010-0.023), respectively, for SCORE2 and 0.012 (0.009-0.015) and 0.024 (0.014-0.035), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57 485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI [e.g. 0.100 (0.062-0.138) for SCORE2] compared to the qualitative approach in the ESC guideline. CONCLUSION: Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD.

Published
2023

3. Gene Sequencing Identifies Perturbation in Nitric Oxide Signaling as a Nonlipid Molecular Subtype of Coronary Artery Disease

Author

Khera, Amit V, Wang, Minxian, Chaffin, Mark, Emdin, Connor A, Samani, Nilesh J, Schunkert, Heribert, Watkins, Hugh, McPherson, Ruth, Erdmann, Jeanette, Elosua, Roberto, Boerwinkle, Eric, Ardissino, Diego, Butterworth, Adam S, Di Angelantonio, Emanuele, Naheed, Aliya, Danesh, John, Chowdhury, Rajiv, Krumholz, Harlan M, Sheu, Wayne H-H, Rich, Stephen S, Rotter, Jerome I, Chen, Yii-der Ida, Gabriel, Stacey, Lander, Eric S, Saleheen, Danish, and Kathiresan, Sekar

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Heart Disease - Coronary Heart Disease, Genetics, Prevention, Cardiovascular, Atherosclerosis, Heart Disease, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Humans, Coronary Artery Disease, Polymorphism, Genetic, Nitric Oxide, Cholesterol, Hypercholesterolemia, atherosclerosis, coronary artery disease, genetic association studies, nitric oxide synthase type III, precision medicine, Medical Biotechnology, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundA key goal of precision medicine is to disaggregate common, complex diseases into discrete molecular subtypes. Rare coding variants in the low-density lipoprotein receptor gene (LDLR) are identified in 1% to 2% of coronary artery disease (CAD) patients, defining a molecular subtype with risk driven by hypercholesterolemia.MethodsTo search for additional subtypes, we compared the frequency of rare, predicted loss-of-function and damaging missense variants aggregated within a given gene in 41 081 CAD cases versus 217 115 controls.ResultsRare variants in LDLR were most strongly associated with CAD, present in 1% of cases and associated with 4.4-fold increased CAD risk. A second subtype was characterized by variants in endothelial nitric oxide synthase gene (NOS3), a key enzyme regulating vascular tone, endothelial function, and platelet aggregation. A rare predicted loss-of-function or damaging missense variants in NOS3 was present in 0.6% of cases and associated with 2.42-fold increased risk of CAD (95% CI, 1.80-3.26; P=5.50×10-9). These variants were associated with higher systolic blood pressure (+3.25 mm Hg; [95% CI, 1.86-4.65]; P=5.00×10-6) and increased risk of hypertension (adjusted odds ratio 1.31; [95% CI, 1.14-1.51]; P=2.00×10-4) but not circulating cholesterol concentrations, suggesting that, beyond lipid pathways, nitric oxide synthesis is a key nonlipid driver of CAD risk.ConclusionsBeyond LDLR, we identified an additional nonlipid molecular subtype of CAD characterized by rare variants in the NOS3 gene.

Published
2022

4. Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations

Author

Chen, Ming-Huei, Raffield, Laura M, Mousas, Abdou, Sakaue, Saori, Huffman, Jennifer E, Moscati, Arden, Trivedi, Bhavi, Jiang, Tao, Akbari, Parsa, Vuckovic, Dragana, Bao, Erik L, Zhong, Xue, Manansala, Regina, Laplante, Véronique, Chen, Minhui, Lo, Ken Sin, Qian, Huijun, Lareau, Caleb A, Beaudoin, Mélissa, Hunt, Karen A, Akiyama, Masato, Bartz, Traci M, Ben-Shlomo, Yoav, Beswick, Andrew, Bork-Jensen, Jette, Bottinger, Erwin P, Brody, Jennifer A, van Rooij, Frank JA, Chitrala, Kumaraswamynaidu, Cho, Kelly, Choquet, Hélène, Correa, Adolfo, Danesh, John, Di Angelantonio, Emanuele, Dimou, Niki, Ding, Jingzhong, Elliott, Paul, Esko, Tõnu, Evans, Michele K, Floyd, James S, Broer, Linda, Grarup, Niels, Guo, Michael H, Greinacher, Andreas, Haessler, Jeff, Hansen, Torben, Howson, Joanna MM, Huang, Qin Qin, Huang, Wei, Jorgenson, Eric, Kacprowski, Tim, Kähönen, Mika, Kamatani, Yoichiro, Kanai, Masahiro, Karthikeyan, Savita, Koskeridis, Fotis, Lange, Leslie A, Lehtimäki, Terho, Lerch, Markus M, Linneberg, Allan, Liu, Yongmei, Lyytikäinen, Leo-Pekka, Manichaikul, Ani, Martin, Hilary C, Matsuda, Koichi, Mohlke, Karen L, Mononen, Nina, Murakami, Yoshinori, Nadkarni, Girish N, Nauck, Matthias, Nikus, Kjell, Ouwehand, Willem H, Pankratz, Nathan, Pedersen, Oluf, Preuss, Michael, Psaty, Bruce M, Raitakari, Olli T, Roberts, David J, Rich, Stephen S, Rodriguez, Benjamin AT, Rosen, Jonathan D, Rotter, Jerome I, Schubert, Petra, Spracklen, Cassandra N, Surendran, Praveen, Tang, Hua, Tardif, Jean-Claude, Trembath, Richard C, Ghanbari, Mohsen, Völker, Uwe, Völzke, Henry, Watkins, Nicholas A, Zonderman, Alan B, Program, VA Million Veteran, Wilson, Peter WF, Li, Yun, Butterworth, Adam S, Gauchat, Jean-François, Chiang, Charleston WK, and Li, Bingshan

Subjects
Genetics, Good Health and Well Being, Asian People, Genome-Wide Association Study, HEK293 Cells, Humans, Interleukin-7, Mutation, Missense, Phenotype, Polymorphism, Single Nucleotide, White People, VA Million Veteran Program, interleukin-7, genetic architecture, fine-mapping, selective sweeps, polygenic trait score, phenome-wide association study, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Most loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10-9, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.

Published
2020

5. Association of anthropometry and weight change with risk of dementia and its major subtypes: A meta‐analysis consisting 2.8 million adults with 57 294 cases of dementia

Author

Lee, Crystal ManYing, Woodward, Mark, Batty, G David, Beiser, Alexa S, Bell, Steven, Berr, Claudine, Bjertness, Espen, Chalmers, John, Clarke, Robert, Dartigues, Jean‐Francois, Davis‐Plourde, Kendra, Debette, Stéphanie, Di Angelantonio, Emanuele, Feart, Catherine, Frikke‐Schmidt, Ruth, Gregson, John, Haan, Mary N, Hassing, Linda B, Hayden, Kathleen M, Hoevenaar‐Blom, Marieke P, Kaprio, Jaakko, Kivimaki, Mika, Lappas, Georgios, Larson, Eric B, LeBlanc, Erin S, Lee, Anne, Lui, Li‐Yung, van Charante, Eric P Moll, Ninomiya, Toshiharu, Nordestgaard, Liv Tybjærg, Ohara, Tomoyuki, Ohkuma, Toshiaki, Palviainen, Teemu, Peres, Karine, Peters, Ruth, Qizilbash, Nawab, Richard, Edo, Rosengren, Annika, Seshadri, Sudha, Shipley, Martin, Singh‐Manoux, Archana, Strand, Bjorn Heine, Gool, Willem A, Vuoksimaa, Eero, Yaffe, Kristine, and Huxley, Rachel R

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Health Sciences, Neurosciences, Dementia, Acquired Cognitive Impairment, Clinical Research, Nutrition, Neurodegenerative, Rare Diseases, Brain Disorders, Prevention, Aging, Obesity, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), 2.1 Biological and endogenous factors, Aetiology, Neurological, Cardiovascular, Adult, Aged, Anthropometry, Body Mass Index, Body Size, Body Weight, Dementia, Vascular, Female, Humans, Male, Middle Aged, Risk Factors, Thinness, Waist Circumference, Weight Gain, Weight Loss, Medical and Health Sciences, Psychology and Cognitive Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

Uncertainty exists regarding the relation of body size and weight change with dementia risk. As populations continue to age and the global obesity epidemic shows no sign of waning, reliable quantification of such associations is important. We examined the relationship of body mass index, waist circumference, and annual percent weight change with risk of dementia and its subtypes by pooling data from 19 prospective cohort studies and four clinical trials using meta-analysis. Compared with body mass index-defined lower-normal weight (18.5-22.4 kg/m2 ), the risk of all-cause dementia was higher among underweight individuals but lower among those with upper-normal (22.5-24.9 kg/m2 ) levels. Obesity was associated with higher risk in vascular dementia. Similarly, relative to the lowest fifth of waist circumference, those in the highest fifth had nonsignificant higher vascular dementia risk. Weight loss was associated with higher all-cause dementia risk relative to weight maintenance. Weight gain was weakly associated with higher vascular dementia risk. The relationship between body size, weight change, and dementia is complex and exhibits non-linear associations depending on dementia subtype under scrutiny. Weight loss was associated with an elevated risk most likely due to reverse causality and/or pathophysiological changes in the brain, although the latter remains speculative.

Published
2020

6. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution

Author

Justice, Anne E, Karaderi, Tugce, Highland, Heather M, Young, Kristin L, Graff, Mariaelisa, Lu, Yingchang, Turcot, Valérie, Auer, Paul L, Fine, Rebecca S, Guo, Xiuqing, Schurmann, Claudia, Lempradl, Adelheid, Marouli, Eirini, Mahajan, Anubha, Winkler, Thomas W, Locke, Adam E, Medina-Gomez, Carolina, Esko, Tõnu, Vedantam, Sailaja, Giri, Ayush, Lo, Ken Sin, Alfred, Tamuno, Mudgal, Poorva, Ng, Maggie CY, Heard-Costa, Nancy L, Feitosa, Mary F, Manning, Alisa K, Willems, Sara M, Sivapalaratnam, Suthesh, Abecasis, Goncalo, Alam, Dewan S, Allison, Matthew, Amouyel, Philippe, Arzumanyan, Zorayr, Balkau, Beverley, Bastarache, Lisa, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bork-Jensen, Jette, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Broer, Linda, Burt, Amber A, Butterworth, Adam S, Caulfield, Mark J, Cesana, Giancarlo, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der Ida, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Collins, Francis S, Cook, James P, Cox, Amanda J, Crosslin, David S, Danesh, John, de Bakker, Paul IW, Denus, Simon de, Mutsert, Renée de, Dedoussis, George, Demerath, Ellen W, Dennis, Joe G, Denny, Josh C, Di Angelantonio, Emanuele, Dörr, Marcus, Drenos, Fotios, Dubé, Marie-Pierre, Dunning, Alison M, Easton, Douglas F, Elliott, Paul, Evangelou, Evangelos, Farmaki, Aliki-Eleni, Feng, Shuang, Ferrannini, Ele, Ferrieres, Jean, Florez, Jose C, Fornage, Myriam, Fox, Caroline S, Franks, Paul W, Friedrich, Nele, Gan, Wei, Gandin, Ilaria, Gasparini, Paolo, Giedraitis, Vilmantas, Girotto, Giorgia, Gorski, Mathias, Grallert, Harald, Grarup, Niels, Grove, Megan L, Gustafsson, Stefan, Haessler, Jeff, Hansen, Torben, and Hattersley, Andrew T

Subjects
Clinical Research, Obesity, Genetics, Nutrition, Prevention, Biotechnology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Cardiovascular, Animals, Body Fat Distribution, Body Mass Index, Case-Control Studies, Drosophila, Exome, Female, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Homeostasis, Humans, Lipids, Male, Proteins, Risk Factors, Waist-Hip Ratio, CHD Exome+ Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, InterAct, ReproGen Consortium, T2D-Genes Consortium, MAGIC Investigators, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF

Published
2019

7. Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.

Author

Turcot, Valérie, Lu, Yingchang, Highland, Heather M, Schurmann, Claudia, Justice, Anne E, Fine, Rebecca S, Bradfield, Jonathan P, Esko, Tõnu, Giri, Ayush, Graff, Mariaelisa, Guo, Xiuqing, Hendricks, Audrey E, Karaderi, Tugce, Lempradl, Adelheid, Locke, Adam E, Mahajan, Anubha, Marouli, Eirini, Sivapalaratnam, Suthesh, Young, Kristin L, Alfred, Tamuno, Feitosa, Mary F, Masca, Nicholas GD, Manning, Alisa K, Medina-Gomez, Carolina, Mudgal, Poorva, Ng, Maggie CY, Reiner, Alex P, Vedantam, Sailaja, Willems, Sara M, Winkler, Thomas W, Abecasis, Gonçalo, Aben, Katja K, Alam, Dewan S, Alharthi, Sameer E, Allison, Matthew, Amouyel, Philippe, Asselbergs, Folkert W, Auer, Paul L, Balkau, Beverley, Bang, Lia E, Barroso, Inês, Bastarache, Lisa, Benn, Marianne, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bork-Jensen, Jette, Bots, Michiel L, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Breen, Gerome, Brilliant, Murray H, Broer, Linda, Brumat, Marco, Burt, Amber A, Butterworth, Adam S, Campbell, Peter T, Cappellani, Stefania, Carey, David J, Catamo, Eulalia, Caulfield, Mark J, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der I, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Cocca, Massimiliano, Collins, Francis S, Cook, James P, Corley, Janie, Corominas Galbany, Jordi, Cox, Amanda J, Crosslin, David S, Cuellar-Partida, Gabriel, D'Eustacchio, Angela, Danesh, John, Davies, Gail, Bakker, Paul IW, Groot, Mark CH, Mutsert, Renée, Deary, Ian J, Dedoussis, George, Demerath, Ellen W, Heijer, Martin, Hollander, Anneke I, Ruijter, Hester M, Dennis, Joe G, Denny, Josh C, Di Angelantonio, Emanuele, Drenos, Fotios, Du, Mengmeng, Dubé, Marie-Pierre, Dunning, Alison M, and Easton, Douglas F

Subjects
CHD Exome+ Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, EPIC InterAct Consortium, INTERVAL Study, ReproGen Consortium, T2D-Genes Consortium, MAGIC Investigators, Understanding Society Scientific Group, Nutrition, Obesity, Developmental Biology, Medical and Health Sciences, Biological Sciences
Abstract

In the version of this article originally published, one of the two authors with the name Wei Zhao was omitted from the author list and the affiliations for both authors were assigned to the single Wei Zhao in the author list. In addition, the ORCID for Wei Zhao (Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA) was incorrectly assigned to author Wei Zhou. The errors have been corrected in the HTML and PDF versions of the article.

Published
2018

8. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

Author

Turcot, Valérie, Lu, Yingchang, Highland, Heather M, Schurmann, Claudia, Justice, Anne E, Fine, Rebecca S, Bradfield, Jonathan P, Esko, Tõnu, Giri, Ayush, Graff, Mariaelisa, Guo, Xiuqing, Hendricks, Audrey E, Karaderi, Tugce, Lempradl, Adelheid, Locke, Adam E, Mahajan, Anubha, Marouli, Eirini, Sivapalaratnam, Suthesh, Young, Kristin L, Alfred, Tamuno, Feitosa, Mary F, Masca, Nicholas GD, Manning, Alisa K, Medina-Gomez, Carolina, Mudgal, Poorva, Ng, Maggie CY, Reiner, Alex P, Vedantam, Sailaja, Willems, Sara M, Winkler, Thomas W, Abecasis, Gonçalo, Aben, Katja K, Alam, Dewan S, Alharthi, Sameer E, Allison, Matthew, Amouyel, Philippe, Asselbergs, Folkert W, Auer, Paul L, Balkau, Beverley, Bang, Lia E, Barroso, Inês, Bastarache, Lisa, Benn, Marianne, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bork-Jensen, Jette, Bots, Michiel L, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Breen, Gerome, Brilliant, Murray H, Broer, Linda, Brumat, Marco, Burt, Amber A, Butterworth, Adam S, Campbell, Peter T, Cappellani, Stefania, Carey, David J, Catamo, Eulalia, Caulfield, Mark J, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der I, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Cocca, Massimiliano, Collins, Francis S, Cook, James P, Corley, Janie, Corominas Galbany, Jordi, Cox, Amanda J, Crosslin, David S, Cuellar-Partida, Gabriel, D’Eustacchio, Angela, Danesh, John, Davies, Gail, Bakker, Paul IW, Groot, Mark CH, Mutsert, Renée, Deary, Ian J, Dedoussis, George, Demerath, Ellen W, Heijer, Martin, Hollander, Anneke I, Ruijter, Hester M, Dennis, Joe G, Denny, Josh C, Di Angelantonio, Emanuele, Drenos, Fotios, Du, Mengmeng, Dubé, Marie-Pierre, Dunning, Alison M, and Easton, Douglas F

Subjects
Genetics, Biotechnology, Nutrition, Obesity, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Cancer, Stroke, Metabolic and endocrine, Cardiovascular, Oral and gastrointestinal, Adult, Animals, Body Mass Index, Drosophila, Energy Intake, Energy Metabolism, Female, Gene Frequency, Genetic Variation, Humans, Male, Proteins, Syndrome, CHD Exome+ Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, EPIC InterAct Consortium, INTERVAL Study, ReproGen Consortium, T2D-Genes Consortium, MAGIC Investigators, Understanding Society Scientific Group, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

Published
2018

9. Identification of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary heart disease

Author

Zhao, Wei, Rasheed, Asif, Tikkanen, Emmi, Lee, Jung-Jin, Butterworth, Adam S, Howson, Joanna MM, Assimes, Themistocles L, Chowdhury, Rajiv, Orho-Melander, Marju, Damrauer, Scott, Small, Aeron, Asma, Senay, Imamura, Minako, Yamauch, Toshimasa, Chambers, John C, Chen, Peng, Sapkota, Bishwa R, Shah, Nabi, Jabeen, Sehrish, Surendran, Praveen, Lu, Yingchang, Zhang, Weihua, Imran, Atif, Abbas, Shahid, Majeed, Faisal, Trindade, Kevin, Qamar, Nadeem, Mallick, Nadeem Hayyat, Yaqoob, Zia, Saghir, Tahir, Rizvi, Syed Nadeem Hasan, Memon, Anis, Rasheed, Syed Zahed, Memon, Fazal-ur-Rehman, Mehmood, Khalid, Ahmed, Naveeduddin, Qureshi, Irshad Hussain, Tanveer-us-Salam, Iqbal, Wasim, Malik, Uzma, Mehra, Narinder, Kuo, Jane Z, Sheu, Wayne H-H, Guo, Xiuqing, Hsiung, Chao A, Juang, Jyh-Ming J, Taylor, Kent D, Hung, Yi-Jen, Lee, Wen-Jane, Quertermous, Thomas, Lee, I-Te, Hsu, Chih-Cheng, Bottinger, Erwin P, Ralhan, Sarju, Teo, Yik Ying, Wang, Tzung-Dau, Alam, Dewan S, Di Angelantonio, Emanuele, Epstein, Steve, Nielsen, Sune F, Nordestgaard, Børge G, Tybjaerg-Hansen, Anne, Young, Robin, Benn, Marianne, Frikke-Schmidt, Ruth, Kamstrup, Pia R, Jukema, J Wouter, Sattar, Naveed, Smit, Roelof, Chung, Ren-Hua, Liang, Kae-Woei, Anand, Sonia, Sanghera, Dharambir K, Ripatti, Samuli, Loos, Ruth JF, Kooner, Jaspal S, Tai, E Shyong, Rotter, Jerome I, Chen, Yii-Der Ida, Frossard, Philippe, Maeda, Shiro, Kadowaki, Takashi, Reilly, Muredach, Pare, Guillaume, Melander, Olle, Salomaa, Veikko, Rader, Daniel J, Danesh, John, Voight, Benjamin F, and Saleheen, Danish

Subjects
Biological Sciences, Genetics, Obesity, Human Genome, Heart Disease - Coronary Heart Disease, Clinical Research, Heart Disease, Diabetes, Cardiovascular, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Asia, Asian People, Biomarkers, Comorbidity, Coronary Disease, Diabetes Mellitus, Type 2, Europe, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, HLA-DRB5 Chains, Humans, Metabolic Networks and Pathways, Metabolic Syndrome, Molecular Targeted Therapy, Mutation, Missense, Polymorphism, Single Nucleotide, Risk Factors, White People, CHD Exome+ Consortium, EPIC-CVD Consortium, EPIC-Interact Consortium, Michigan Biobank, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

To evaluate the shared genetic etiology of type 2 diabetes (T2D) and coronary heart disease (CHD), we conducted a genome-wide, multi-ancestry study of genetic variation for both diseases in up to 265,678 subjects for T2D and 260,365 subjects for CHD. We identify 16 previously unreported loci for T2D and 1 locus for CHD, including a new T2D association at a missense variant in HLA-DRB5 (odds ratio (OR) = 1.29). We show that genetically mediated increase in T2D risk also confers higher CHD risk. Joint T2D-CHD analysis identified eight variants-two of which are coding-where T2D and CHD associations appear to colocalize, including a new joint T2D-CHD association at the CCDC92 locus that also replicated for T2D. The variants associated with both outcomes implicate new pathways as well as targets of existing drugs, including icosapent ethyl and adipocyte fatty-acid-binding protein.

Published
2017

10. Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms

Author

Howson, Joanna MM, Zhao, Wei, Barnes, Daniel R, Ho, Weang-Kee, Young, Robin, Paul, Dirk S, Waite, Lindsay L, Freitag, Daniel F, Fauman, Eric B, Salfati, Elias L, Sun, Benjamin B, Eicher, John D, Johnson, Andrew D, Sheu, Wayne HH, Nielsen, Sune F, Lin, Wei-Yu, Surendran, Praveen, Malarstig, Anders, Wilk, Jemma B, Tybjærg-Hansen, Anne, Rasmussen, Katrine L, Kamstrup, Pia R, Deloukas, Panos, Erdmann, Jeanette, Kathiresan, Sekar, Samani, Nilesh J, Schunkert, Heribert, Watkins, Hugh, Do, Ron, Rader, Daniel J, Johnson, Julie A, Hazen, Stanley L, Quyyumi, Arshed A, Spertus, John A, Pepine, Carl J, Franceschini, Nora, Justice, Anne, Reiner, Alex P, Buyske, Steven, Hindorff, Lucia A, Carty, Cara L, North, Kari E, Kooperberg, Charles, Boerwinkle, Eric, Young, Kristin, Graff, Mariaelisa, Peters, Ulrike, Absher, Devin, Hsiung, Chao A, Lee, Wen-Jane, Taylor, Kent D, Chen, Ying-Hsiang, Lee, I-Te, Guo, Xiuqing, Chung, Ren-Hua, Hung, Yi-Jen, Rotter, Jerome I, Juang, Jyh-Ming J, Quertermous, Thomas, Wang, Tzung-Dau, Rasheed, Asif, Frossard, Philippe, Alam, Dewan S, Majumder, Abdulla al Shafi, Di Angelantonio, Emanuele, Chowdhury, Rajiv, Chen, Yii-Der Ida, Nordestgaard, Børge G, Assimes, Themistocles L, Danesh, John, Butterworth, Adam S, and Saleheen, Danish

Subjects
Biological Sciences, Genetics, Cardiovascular, Heart Disease, Human Genome, Clinical Research, Aging, Atherosclerosis, Heart Disease - Coronary Heart Disease, 2.1 Biological and endogenous factors, Arteries, Cell Adhesion, Chemotaxis, Leukocyte, Coronary Artery Disease, Energy Metabolism, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Histone Code, Humans, Male, Muscle, Smooth, Vascular, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Risk Factors, CARDIoGRAMplusC4D, EPIC-CVD, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Although 58 genomic regions have been associated with CAD thus far, most of the heritability is unexplained, indicating that additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and performed meta-analysis of results with 194,427 participants previously genotyped, totaling 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD associations (P < 5 × 10-8, in fixed-effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leukocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 (p.Ser219Gly)) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell-type-specific gene expression and plasma protein levels sheds light on potential disease mechanisms.

Published
2017

11. Rare and low-frequency coding variants alter human adult height

Author

Marouli, Eirini, Graff, Mariaelisa, Medina-Gomez, Carolina, Lo, Ken Sin, Wood, Andrew R, Kjaer, Troels R, Fine, Rebecca S, Lu, Yingchang, Schurmann, Claudia, Highland, Heather M, Rüeger, Sina, Thorleifsson, Gudmar, Justice, Anne E, Lamparter, David, Stirrups, Kathleen E, Turcot, Valérie, Young, Kristin L, Winkler, Thomas W, Esko, Tõnu, Karaderi, Tugce, Locke, Adam E, Masca, Nicholas GD, Ng, Maggie CY, Mudgal, Poorva, Rivas, Manuel A, Vedantam, Sailaja, Mahajan, Anubha, Guo, Xiuqing, Abecasis, Goncalo, Aben, Katja K, Adair, Linda S, Alam, Dewan S, Albrecht, Eva, Allin, Kristine H, Allison, Matthew, Amouyel, Philippe, Appel, Emil V, Arveiler, Dominique, Asselbergs, Folkert W, Auer, Paul L, Balkau, Beverley, Banas, Bernhard, Bang, Lia E, Benn, Marianne, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bonnycastle, Lori L, Bork-Jensen, Jette, Bots, Michiel L, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Breen, Gerome, Brilliant, Murray H, Broer, Linda, Burt, Amber A, Butterworth, Adam S, Carey, David J, Caulfield, Mark J, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der Ida, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Cocca, Massimiliano, Collins, Francis S, Cook, James P, Corley, Janie, Galbany, Jordi Corominas, Cox, Amanda J, Cuellar-Partida, Gabriel, Danesh, John, Davies, Gail, de Bakker, Paul IW, de Borst, Gert J, de Denus, Simon, de Groot, Mark CH, de Mutsert, Renée, Deary, Ian J, Dedoussis, George, Demerath, Ellen W, den Hollander, Anneke I, Dennis, Joe G, Di Angelantonio, Emanuele, Drenos, Fotios, Du, Mengmeng, Dunning, Alison M, Easton, Douglas F, Ebeling, Tapani, Edwards, Todd L, Ellinor, Patrick T, Elliott, Paul, Evangelou, Evangelos, Farmaki, Aliki-Eleni, and Faul, Jessica D

Subjects
Human Genome, Genetics, Aetiology, 2.1 Biological and endogenous factors, ADAMTS Proteins, Adult, Alleles, Body Height, Cell Adhesion Molecules, Female, Gene Frequency, Genetic Variation, Genome, Human, Glycoproteins, Glycosaminoglycans, Hedgehog Proteins, Humans, Intercellular Signaling Peptides and Proteins, Interferon Regulatory Factors, Interleukin-11 Receptor alpha Subunit, Male, Multifactorial Inheritance, NADPH Oxidase 4, NADPH Oxidases, Phenotype, Pregnancy-Associated Plasma Protein-A, Procollagen N-Endopeptidase, Proteoglycans, Proteolysis, Receptors, Androgen, Somatomedins, EPIC-InterAct Consortium, CHD Exome+ Consortium, ExomeBP Consortium, T2D-Genes Consortium, GoT2D Genes Consortium, Global Lipids Genetics Consortium, ReproGen Consortium, MAGIC Investigators, General Science & Technology
Abstract

Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.

Published
2017

12. Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms

Author

Howson, JMM, Zhao, W, Barnes, DR, Ho, W-K, Young, R, Paul, DS, Waite, LL, Freitag, DF, Fauman, EB, Salfati, EL, Sun, BB, Eicher, JD, Johnson, AD, Sheu, WHH, Nielsen, SF, Lin, W-Y, Surendran, P, Malarstig, A, Wilk, JB, Tybjærg-Hansen, A, Rasmussen, KL, Kamstrup, PR, Deloukas, P, Erdmann, J, Kathiresan, S, Samani, NJ, Schunkert, H, Watkins, H, CARDIoGRAMplusC4D, Do, R, Rader, DJ, Johnson, JA, Hazen, SL, Quyyumi, AA, Spertus, JA, Pepine, CJ, Franceschini, N, Justice, A, Reiner, AP, Buyske, S, Hindorff, LA, Carty, CL, North, KE, Kooperberg, C, Boerwinkle, E, Young, K, Graff, M, Peters, U, Absher, D, Hsiung, CA, Lee, W-J, Taylor, KD, Chen, Y-H, Lee, I-T, Guo, X, Chung, R-H, Hung, Y-J, Rotter, JI, Juang, J-MJ, Quertermous, T, Wang, T-D, Rasheed, A, Frossard, P, Alam, DS, Majumder, AAS, Di Angelantonio, E, Chowdhury, R, EPIC-CVD, Chen, Y-DI, Nordestgaard, BG, Assimes, TL, Danesh, J, Butterworth, AS, Saleheen, D, Howson, Joanna [0000-0001-7618-0050], Barnes, Daniel [0000-0002-3781-7570], Paul, Dirk [0000-0002-8230-0116], Sun, Ben [0000-0001-6347-2281], Surendran, Praveen [0000-0002-4911-6077], Di Angelantonio, Emanuele [0000-0001-8776-6719], Chowdhury, Rajiv [0000-0003-4881-5690], Danesh, John [0000-0003-1158-6791], Butterworth, Adam [0000-0002-6915-9015], and Apollo - University of Cambridge Repository

Subjects
Male, Genotype, Quantitative Trait Loci, Coronary Artery Disease, Cardiovascular, Medical and Health Sciences, Risk Factors, Vascular, Cell Adhesion, Genetics, Humans, 2.1 Biological and endogenous factors, Genetic Predisposition to Disease, Polymorphism, Aetiology, EPIC-CVD, Heart Disease - Coronary Heart Disease, screening and diagnosis, Chemotaxis, Human Genome, Arteries, Single Nucleotide, Leukocyte, Biological Sciences, Atherosclerosis, cardiovascular diseases, Histone Code, Detection, Heart Disease, genome-wide association studies, Muscle, Female, Smooth, Energy Metabolism, CARDIoGRAMplusC4D, Genome-Wide Association Study, Biotechnology, 4.2 Evaluation of markers and technologies, Developmental Biology
Abstract

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Although 58 genomic regions have been associated with CAD thus far, most of the heritability is unexplained, indicating that additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and performed meta-analysis of results with 194,427 participants previously genotyped, totaling 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD associations (P < 5 × 10(-8), in fixed-effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leukocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 (p.Ser219Gly)) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell-type-specific gene expression and plasma protein levels sheds light on potential disease mechanisms.

Published
2017

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