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333 results on '"Heather J, Cordell"'

1. Network proximity analysis as a theoretical model for identifying potential novel therapies in primary sclerosing cholangitis

Author

Jessica Leighton, David E. J. Jones, Jessica K. Dyson, and Heather J. Cordell

Subjects
Network proximity analysis, Genome-wide association studies, Therapeutics, Autoimmune liver disease, Primary sclerosing cholangitis, Hepatology, Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract Primary Sclerosing Cholangitis (PSC) is a progressive cholestatic liver disease with no licensed therapies. Previous Genome Wide Association Studies (GWAS) have identified genes that correlate significantly with PSC, and these were identified by systematic review. Here we use novel Network Proximity Analysis (NPA) methods to identify already licensed candidate drugs that may have an effect on the genetically coded aspects of PSC pathophysiology. Over 2000 agents were identified as significantly linked to genes implicated in PSC by this method. The most significant results include previously researched agents such as metronidazole, as well as biological agents such as basiliximab, abatacept and belatacept. This in silico analysis could potentially serve as a basis for developing novel clinical trials in this rare disease.

Published
2024
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2. How far back do we need to look to capture diagnoses in electronic health records? A retrospective observational study of hospital electronic health record data

Author

Tom Marshall, Elizabeth Sapey, Miles D Witham, Steve Harris, Rachel Cooper, Chris Plummer, Felicity Evison, James Wason, Heather J Cordell, Suzy Gallier, Fiona E Matthews, Ewan Pearson, Avan A Sayer, Mervyn Singer, Joanne Field, Mohammed Osman, Sian Robinson, Victoria Bartle, Thomas Scharf, Jadene Lewis, Rominique Doal, Peta le Roux, Ray Holding, and Paolo Missier

Subjects
Medicine
Abstract

Objectives Analysis of routinely collected electronic health data is a key tool for long-term condition research and practice for hospitalised patients. This requires accurate and complete ascertainment of a broad range of diagnoses, something not always recorded on an admission document at a single point in time. This study aimed to ascertain how far back in time electronic hospital records need to be interrogated to capture long-term condition diagnoses.Design Retrospective observational study of routinely collected hospital electronic health record data.Setting Queen Elizabeth Hospital Birmingham (UK)-linked data held by the PIONEER acute care data hub.Participants Patients whose first recorded admission for chronic obstructive pulmonary disease (COPD) exacerbation (n=560) or acute stroke (n=2142) was between January and December 2018 and who had a minimum of 10 years of data prior to the index date.Outcome measures We identified the most common International Classification of Diseases version 10-coded diagnoses received by patients with COPD and acute stroke separately. For each diagnosis, we derived the number of patients with the diagnosis recorded at least once over the full 10-year lookback period, and then compared this with shorter lookback periods from 1 year to 9 years prior to the index admission.Results Seven of the top 10 most common diagnoses in the COPD dataset reached >90% completeness by 6 years of lookback. Atrial fibrillation and diabetes were >90% coded with 2–3 years of lookback, but hypertension and asthma completeness continued to rise all the way out to 10 years of lookback. For stroke, 4 of the top 10 reached 90% completeness by 5 years of lookback; angina pectoris was >90% coded at 7 years and previous transient ischaemic attack completeness continued to rise out to 10 years of lookback.Conclusion A 7-year lookback captures most, but not all, common diagnoses. Lookback duration should be tailored to the conditions being studied.

Published
2024
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3. Multitrait genome-wide analyses identify new susceptibility loci and candidate drugs to primary sclerosing cholangitis

Author

Younghun Han, Jinyoung Byun, Catherine Zhu, Ryan Sun, Julia Y. Roh, Heather J. Cordell, Hyun-Sung Lee, Vikram R. Shaw, Sung Wook Kang, Javad Razjouyan, Matthew A. Cooley, Manal M. Hassan, Katherine A. Siminovitch, Trine Folseraas, David Ellinghaus, Annika Bergquist, Simon M. Rushbrook, Andre Franke, Tom H. Karlsen, Konstantinos N. Lazaridis, The International PSC Study Group, Katherine A. McGlynn, Lewis R. Roberts, and Christopher I. Amos

Subjects
Science
Abstract

The genetic basis of primary sclerosing cholangitis has only been partially uncovered. Here, the authors perform a multitrait genome-wide association study to provide insight into the genetic etiology of primary sclerosing cholangitis risk and possible therapeutic drug targets.

Published
2023
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4. Comprehensive genetic and functional analyses of Fc gamma receptors influence on response to rituximab therapy for autoimmunityResearch in context

Author

James I. Robinson, Md Yuzaiful Md Yusof, Vinny Davies, Dawn Wild, Michael Morgan, John C. Taylor, Yasser El-Sherbiny, David L. Morris, Lu Liu, Andy C. Rawstron, Maya H. Buch, Darren Plant, Heather J. Cordell, John D. Isaacs, Ian N. Bruce, Paul Emery, Anne Barton, Timothy J. Vyse, Jennifer H. Barrett, Edward M. Vital, and Ann W. Morgan

Subjects
Autoimmune diseases, B-lymphocytes, Genetics, Rheumatoid arthritis, Rituximab, Systemic lupus erythematosus, Medicine, Medicine (General), R5-920
Abstract

Summary: Background: Rituximab is widely used to treat autoimmunity but clinical response varies. Efficacy is determined by the efficiency of B-cell depletion, which may depend on various Fc gamma receptor (FcγR)-dependent mechanisms. Study of FcγR is challenging due to the complexity of the FCGR genetic locus. We sought to assess the effect of FCGR variants on clinical response, B-cell depletion and NK-cell-mediated killing in rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). Methods: A longitudinal cohort study was conducted in 835 patients [RA = 573; SLE = 262]. Clinical outcome measures were two-component disease activity score in 28-joints (2C-DAS28CRP) for RA and British Isles Lupus Assessment Group (BILAG)-2004 major clinical response (MCR) for SLE at 6 months. B-cells were evaluated by highly-sensitive flow cytometry. Single nucleotide polymorphism and copy number variation for genes encoding five FcγRs were measured using multiplex ligation-dependent probe amplification. Ex vivo studies assessed NK-cell antibody-dependent cellular cytotoxicity (ADCC) and FcγR expression. Findings: In RA, carriage of FCGR3A-158V and increased FCGR3A-158V copies were associated with greater 2C-DAS28CRP response (adjusted for baseline 2C-DAS28CRP). In SLE, MCR was associated with increased FCGR3A-158V, OR 1.64 (95% CI 1.12–2.41) and FCGR2C-ORF OR 1.93 (95% CI 1.09–3.40) copies. 236/413 (57%) patients with B-cell data achieved complete depletion. Homozygosity for FCGR3A-158V and increased FCGR3A-158V copies were associated with complete depletion in combined analyses. FCGR3A genotype was associated with rituximab-induced ADCC, and increased NK-cell FcγRIIIa expression was associated with improved clinical response and depletion in vivo. Furthermore, disease status and concomitant therapies impacted both NK-cell FcγRIIIa expression and ADCC. Interpretation: FcγRIIIa is the major low affinity FcγR associated with rituximab response. Increased copies of the FCGR3A-158V allele (higher affinity for IgG1), influences clinical and biological responses to rituximab in autoimmunity. Enhancing FcγR-effector functions could improve the next generation of CD20-depleting therapies and genotyping may stratify patients for optimal treatment protocols. Funding: Medical Research Council, National Institute for Health and Care Research, Versus Arthritis.

Published
2022
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5. Genome-wide association study identifies susceptibility loci for acute myeloid leukemia

Author

Wei-Yu Lin, Sarah E. Fordham, Eric Hungate, Nicola J. Sunter, Claire Elstob, Yaobo Xu, Catherine Park, Anne Quante, Konstantin Strauch, Christian Gieger, Andrew Skol, Thahira Rahman, Lara Sucheston-Campbell, Junke Wang, Theresa Hahn, Alyssa I. Clay-Gilmour, Gail L. Jones, Helen J. Marr, Graham H. Jackson, Tobias Menne, Mathew Collin, Adam Ivey, Robert K. Hills, Alan K. Burnett, Nigel H. Russell, Jude Fitzgibbon, Richard A. Larson, Michelle M. Le Beau, Wendy Stock, Olaf Heidenreich, Abrar Alharbi, David J. Allsup, Richard S. Houlston, Jean Norden, Anne M. Dickinson, Elisabeth Douglas, Clare Lendrem, Ann K. Daly, Louise Palm, Kim Piechocki, Sally Jeffries, Martin Bornhäuser, Christoph Röllig, Heidi Altmann, Leo Ruhnke, Desiree Kunadt, Lisa Wagenführ, Heather J. Cordell, Rebecca Darlay, Mette K. Andersen, Maria C. Fontana, Giovanni Martinelli, Giovani Marconi, Miguel A. Sanz, José Cervera, Inés Gómez-Seguí, Thomas Cluzeau, Chimène Moreilhon, Sophie Raynaud, Heinz Sill, Maria Teresa Voso, Francesco Lo-Coco, Hervé Dombret, Meyling Cheok, Claude Preudhomme, Rosemary E. Gale, David Linch, Julia Gaal-Wesinger, Andras Masszi, Daniel Nowak, Wolf-Karsten Hofmann, Amanda Gilkes, Kimmo Porkka, Jelena D. Milosevic Feenstra, Robert Kralovics, David Grimwade, Manja Meggendorfer, Torsten Haferlach, Szilvia Krizsán, Csaba Bödör, Friedrich Stölzel, Kenan Onel, and James M. Allan

Subjects
Science
Abstract

Genome wide association studies in cancer are used to understand the heritable genetic contribution to disease risk. Here, the authors perform a genome wide association study in European patients with acute myeloid leukemia and identify loci associated with risk of developing the disease.

Published
2021
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6. Confirmation of the superior performance of the causal Graphical Analysis Using Genetics (cGAUGE) pipeline in comparison to various competing alternatives [version 1; peer review: 2 approved]

Author

Heather J. Cordell and Richard Howey

Subjects
Causal inference, Mendelian randomization, Bayesian networks, eng, Medicine, Science
Abstract

Various methods exist that utilise information from genetic predictors to help identify potential causal relationships between measured biological or clinical traits. Here we conduct computer simulations to investigate the performance of a recently proposed causal Graphical Analysis Using Genetics (cGAUGE) pipeline, used as a precursor to Mendelian randomization analysis, in comparison to our previously proposed Bayesian Network approach for addressing this problem. We use the same simulation (and analysis) code as was used by the developers of cGAUGE, adding in a comparison with the Bayesian Network approach. Overall, we find the optimal method (in terms of giving high power and low false discovery rate) is the cGAUGE pipeline followed by subsequent analysis using the MR-PRESSO Mendelian randomization approach.

Published
2022
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8. Increased serum miR-193a-5p during non-alcoholic fatty liver disease progression: Diagnostic and mechanistic relevance

Author

Katherine Johnson, Peter J. Leary, Olivier Govaere, Matthew J. Barter, Sarah H. Charlton, Simon J. Cockell, Dina Tiniakos, Michalina Zatorska, Pierre Bedossa, M. Julia Brosnan, Jeremy F. Cobbold, Mattias Ekstedt, Guruprasad P. Aithal, Karine Clément, Jörn M. Schattenberg, Jerome Boursier, Vlad Ratziu, Elisabetta Bugianesi, Quentin M. Anstee, Ann K. Daly, James Clark, Heather J. Cordell, Rebecca Darlay, Christopher P. Day, Tim Hardy, Yang-Lin Liu, Fiona Oakley, Jeremy Palmer, Rachel Queen, Kristy Wonders, Patrick M. Bossuyt, Adriaan G. Holleboom, Hadi Zafarmand, Yasaman Vali, Jenny Lee, Karine Clement, Raluca Pais, Detlef Schuppan, Michael Allison, Sergio Rodriguez Cuenca, Vanessa Pellegrinelli, Michele Vacca, Antonio Vidal-Puig, Tuulia Hyötyläinen, Aidan McGlinchey, Matej Orešič, Partho Sen, Jose Mato, Óscar Millet, Jean-Francois Dufour, Stephen Harrison, Stefan Neubauer, Michael Pavlides, Ferenc Mozes, Salma Akhtar, Rajarshi Banerjee, Matt Kelly, Elizabeth Shumbayawonda, Andrea Dennis, Charlotte Erpicum, Manuel Romero-Gomez, Rocío Gallego-Durán, Isabel Fernández, Morten Karsdal, Diana Leeming, Mette Juul Fisker, Elisabeth Erhardtsen, Daniel Rasmussen, Per Qvist, Antonia Sinisi, Estelle Sandt, Maria Manuela Tonini, Maurizio Parola, Chiara Rosso, Fabio Marra, Amalia Gastaldelli, Sven Francque, Stergios Kechagias, Hannele Yki-Järvinen, Kimmo Porthan, Saskia van Mil, George Papatheodoridis, Helena Cortez-Pinto, Luca Valenti, Salvatore Petta, Luca Miele, Andreas Geier, Christian Trautwein, Paul Hockings, Phil Newsome, David Wenn, Cecília Maria Pereira Rodrigues, Rémy Hanf, Pierre Chaumat, Christian Rosenquist, Aldo Trylesinski, Pablo Ortiz, Kevin Duffin, Carla Yunis, Melissa Miller, Theresa Tuthill, Judith Ertle, Ramy Younes, Leigh Alexander, Rachel Ostroff, Mette Skalshøi Kjær, Lars Friis Mikkelsen, Clifford Brass, Lori Jennings, Maria-Magdalena Balp, Miljen Martic, Guido Hanauer, Sudha Shankar, Richard Torstenson, Céline Fournier, Richard Ehman, Michael Kalutkiewicz, Kay Pepin, Joel Myers, Diane Shevell, Gideon Ho, Henrik Landgren, Rob Myers, Lynda Doward, Diane Whalley, and James Twiss

Subjects
MicroRNA, Non-alcoholic fatty liver disease, Biomarker, Sequencing, Diseases of the digestive system. Gastroenterology, RC799-869
Abstract

Background & Aims: Serum microRNA (miRNA) levels are known to change in non-alcoholic fatty liver disease (NAFLD) and may serve as useful biomarkers. This study aimed to profile miRNAs comprehensively at all NAFLD stages. Methods: We profiled 2,083 serum miRNAs in a discovery cohort (183 cases with NAFLD representing the complete NAFLD spectrum and 10 population controls). miRNA libraries generated by HTG EdgeSeq were sequenced by Illumina NextSeq. Selected serum miRNAs were profiled in 372 additional cases with NAFLD and 15 population controls by quantitative reverse transcriptase PCR. Results: Levels of 275 miRNAs differed between cases and population controls. Fewer differences were seen within individual NAFLD stages, but miR-193a-5p consistently showed increased levels in all comparisons. Relative to NAFL/non-alcoholic steatohepatitis (NASH) with mild fibrosis (stage 0/1), 3 miRNAs (miR-193a-5p, miR-378d, and miR378d) were increased in cases with NASH and clinically significant fibrosis (stages 2–4), 7 (miR193a-5p, miR-378d, miR-378e, miR-320b, miR-320c, miR-320d, and miR-320e) increased in cases with NAFLD activity score (NAS) 5–8 compared with lower NAS, and 3 (miR-193a-5p, miR-378d, and miR-378e) increased but 1 (miR-19b-3p) decreased in steatosis, activity, and fibrosis (SAF) activity score 2–4 compared with lower SAF activity. The significant findings for miR-193a-5p were replicated in the additional cohort with NAFLD. Studies in Hep G2 cells showed that following palmitic acid treatment, miR-193a-5p expression decreased significantly. Gene targets for miR-193a-5p were investigated in liver RNAseq data for a case subgroup (n = 80); liver GPX8 levels correlated positively with serum miR-193a-5p. Conclusions: Serum miR-193a-5p levels correlate strongly with NAFLD activity grade and fibrosis stage. MiR-193a-5p may have a role in the hepatic response to oxidative stress and is a potential clinically tractable circulating biomarker for progressive NAFLD. Lay summary: MicroRNAs (miRNAs) are small pieces of nucleic acid that may turn expression of genes on or off. These molecules can be detected in the blood circulation, and their levels in blood may change in liver disease including non-alcoholic fatty liver disease (NAFLD). To see if we could detect specific miRNA associated with advanced stages of NAFLD, we carried out miRNA sequencing in a group of 183 patients with NAFLD of varying severity together with 10 population controls. We found that a number of miRNAs showed changes, mainly increases, in serum levels but that 1 particular miRNA miR-193a-5p consistently increased. We confirmed this increase in a second group of cases with NAFLD. Measuring this miRNA in a blood sample may be a useful way to determine whether a patient has advanced NAFLD without an invasive liver biopsy.

Published
2022
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9. A Bayesian network approach incorporating imputation of missing data enables exploratory analysis of complex causal biological relationships.

Author

Richard Howey, Alexander D Clark, Najib Naamane, Louise N Reynard, Arthur G Pratt, and Heather J Cordell

Subjects
Genetics, QH426-470
Abstract

Bayesian networks can be used to identify possible causal relationships between variables based on their conditional dependencies and independencies, which can be particularly useful in complex biological scenarios with many measured variables. Here we propose two improvements to an existing method for Bayesian network analysis, designed to increase the power to detect potential causal relationships between variables (including potentially a mixture of both discrete and continuous variables). Our first improvement relates to the treatment of missing data. When there is missing data, the standard approach is to remove every individual with any missing data before performing analysis. This can be wasteful and undesirable when there are many individuals with missing data, perhaps with only one or a few variables missing. This motivates the use of imputation. We present a new imputation method that uses a version of nearest neighbour imputation, whereby missing data from one individual is replaced with data from another individual, their nearest neighbour. For each individual with missing data, the subsets of variables to be used to select the nearest neighbour are chosen by sampling without replacement the complete data and estimating a best fit Bayesian network. We show that this approach leads to marked improvements in the recall and precision of directed edges in the final network identified, and we illustrate the approach through application to data from a recent study investigating the causal relationship between methylation and gene expression in early inflammatory arthritis patients. We also describe a second improvement in the form of a pseudo-Bayesian approach for upweighting certain network edges, which can be useful when there is prior evidence concerning their directions.

Published
2021
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10. A meta-analysis of genome-wide association studies identifies multiple longevity genes

Author

Joris Deelen, Daniel S. Evans, Dan E. Arking, Niccolò Tesi, Marianne Nygaard, Xiaomin Liu, Mary K. Wojczynski, Mary L. Biggs, Ashley van der Spek, Gil Atzmon, Erin B. Ware, Chloé Sarnowski, Albert V. Smith, Ilkka Seppälä, Heather J. Cordell, Janina Dose, Najaf Amin, Alice M. Arnold, Kristin L. Ayers, Nir Barzilai, Elizabeth J. Becker, Marian Beekman, Hélène Blanché, Kaare Christensen, Lene Christiansen, Joanna C. Collerton, Sarah Cubaynes, Steven R. Cummings, Karen Davies, Birgit Debrabant, Jean-François Deleuze, Rachel Duncan, Jessica D. Faul, Claudio Franceschi, Pilar Galan, Vilmundur Gudnason, Tamara B. Harris, Martijn Huisman, Mikko A. Hurme, Carol Jagger, Iris Jansen, Marja Jylhä, Mika Kähönen, David Karasik, Sharon L. R. Kardia, Andrew Kingston, Thomas B. L. Kirkwood, Lenore J. Launer, Terho Lehtimäki, Wolfgang Lieb, Leo-Pekka Lyytikäinen, Carmen Martin-Ruiz, Junxia Min, Almut Nebel, Anne B. Newman, Chao Nie, Ellen A. Nohr, Eric S. Orwoll, Thomas T. Perls, Michael A. Province, Bruce M. Psaty, Olli T. Raitakari, Marcel J. T. Reinders, Jean-Marie Robine, Jerome I. Rotter, Paola Sebastiani, Jennifer Smith, Thorkild I. A. Sørensen, Kent D. Taylor, André G. Uitterlinden, Wiesje van der Flier, Sven J. van der Lee, Cornelia M. van Duijn, Diana van Heemst, James W. Vaupel, David Weir, Kenny Ye, Yi Zeng, Wanlin Zheng, Henne Holstege, Douglas P. Kiel, Kathryn L. Lunetta, P. Eline Slagboom, and Joanne M. Murabito

Subjects
Science
Abstract

Genome-wide association studies have only revealed a handful of genetic loci for longevity. Here, in a case–control design based on phenotype definitions of individuals surviving at or beyond the age corresponding to the 90th and 99th survival percentile, the authors report two additional loci located in the APOE locus and near GPR78.

Published
2019
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11. Haplin power analysis: a software module for power and sample size calculations in genetic association analyses of family triads and unrelated controls

Author

Miriam Gjerdevik, Astanand Jugessur, Øystein A. Haaland, Julia Romanowska, Rolv T. Lie, Heather J. Cordell, and Håkon K. Gjessing

Subjects
Log-linear and multinomial models, Genome-wide association studies (GWAS), Statistical power estimation, Sample size estimation, Haplin, EMIM, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Log-linear and multinomial modeling offer a flexible framework for genetic association analyses of offspring (child), parent-of-origin and maternal effects, based on genotype data from a variety of child-parent configurations. Although the calculation of statistical power or sample size is an important first step in the planning of any scientific study, there is currently a lack of software for genetic power calculations in family-based study designs. Here, we address this shortcoming through new implementations of power calculations in the R package Haplin, which is a flexible and robust software for genetic epidemiological analyses. Power calculations in Haplin can be performed analytically using the asymptotic variance-covariance structure of the parameter estimator, or else by a straightforward simulation approach. Haplin performs power calculations for child, parent-of-origin and maternal effects, as well as for gene-environment interactions. The power can be calculated for both single SNPs and haplotypes, either autosomal or X-linked. Moreover, Haplin enables power calculations for different child-parent configurations, including (but not limited to) case-parent triads, case-mother dyads, and case-parent triads in combination with unrelated control-parent triads. Results We compared the asymptotic power approximations to the power of analysis attained with Haplin. For external validation, the results were further compared to the power of analysis attained by the EMIM software using data simulations from Haplin. Consistency observed between Haplin and EMIM across various genetic scenarios confirms the computational accuracy of the inference methods used in both programs. The results also demonstrate that power calculations in Haplin are applicable to genetic association studies using either log-linear or multinomial modeling approaches. Conclusions Haplin provides a robust and reliable framework for power calculations in genetic association analyses for a wide range of genetic effects and etiologic scenarios, based on genotype data from a variety of child-parent configurations.

Published
2019
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12. Genetic Risk Score for Intracranial Aneurysms: Prediction of Subarachnoid Hemorrhage and Role in Clinical Heterogeneity

Author

Mark K. Bakker, Jos P. Kanning, Gad Abraham, Amy E. Martinsen, Bendik S. Winsvold, John-Anker Zwart, Romain Bourcier, Tomonobu Sawada, Masaru Koido, Yoichiro Kamatani, Sandrine Morel, Philippe Amouyel, Stéphanie Debette, Philippe Bijlenga, Takiy Berrandou, Santhi K. Ganesh, Nabila Bouatia-Naji, Gregory Jones, Matthew Bown, Gabriel J.E. Rinkel, Jan H. Veldink, Ynte M. Ruigrok, Anne Hege Aamodt, Anne Heidi Skogholt, Ben M Brumpton, Cristen J Willer, Else C Sandset, Espen S Kristoffersen, Hanne Ellekjær, Ingrid Heuch, Jonas B Nielsen, Knut Hagen, Kristian Hveem, Lars G Fritsche, Laurent F Thomas, Linda M Pedersen, Maiken E Gabrielsen, Oddgeir L Holmen, Sigrid Børte, Wei Zhou, Shérine Abboud, Massimo Pandolfo, Vincent Thijs, Didier Leys, Marie Bodenant, Fabien Louillet, Emmanuel Touzé, Jean-Louis Mas, Yves Samson, Sara Leder, Anne Léger, Sandrine Deltour, Sophie Crozier, Isabelle Méresse, Sandrine Canaple, Olivier Godefroy, Maurice Giroud, Yannick Béjot, Pierre Decavel, Elizabeth Medeiros, Paola Montiel, Thierry Moulin, Fabrice Vuillier, Jean Dallongeville, Antti J Metso, Tiina Metso, Turgut Tatlisumak, Caspar Grond-Ginsbach, Christoph Lichy, Manja Kloss, Inge Werner, Marie-Luise Arnold, Michael Dos Santos, Armin Grau, Martin Dichgans, Constanze Thomas-Feles, Ralf Weber, Tobias Brandt, Alessandro Pezzini, Valeria De Giuli, Filomena Caria, Loris Poli, Alessandro Padovani, Anna Bersano, Silvia Lanfranconi, Simone Beretta, Carlo Ferrarese, Giacomo Giacolone, Stefano Paolucci, Philippe Lyrer, Stefan Engelter, Felix Fluri, Florian Hatz, Dominique Gisler, Leo Bonati, Henrik Gensicke, Margareth Amort, Hugh Markus, Jennifer Majersik, Bradford Worrall, Andrew Southerland, John Cole, Steven Kittner, Evangelos Evangelou, Helen R Warren, He Gao, Georgios Ntritsos, Niki Dimou, Tonu Esko, Reedik Mägi, Lili Milani, Peter Almgren, Thibaud Boutin, Jun Ding, Franco Giulianini, Elizabeth G Holliday, Anne U Jackson, Ruifang Li-Gao, Wei-Yu Lin, Jian’an Luan, Massimo Mangino, Christopher Oldmeadow, Bram Peter Prins, Yong Qian, Muralidharan Sargurupremraj, Nabi Shah, Praveen Surendran, Sébastien Thériault, Niek Verweij, Sara M Willems, Jing-Hua Zhao, John Connell, Renée de Mutsert, Alex SF Doney, Martin Farrall, Cristina Menni, Andrew D Morris, Raymond Noordam, Guillaume Paré, Neil R Poulter, Denis C Shields, Alice Stanton, Simon Thom, Gonçalo Abecasis, Najaf Amin, Dan E Arking, Kristin L Ayers, Caterina M Barbieri, Chiara Batini, Joshua C Bis, Tineka Blake, Murielle Bochud, Michael Boehnke, Eric Boerwinkle, Dorret I Boomsma, Erwin P Bottinger, Peter S Braund, Marco Brumat, Archie Campbell, Harry Campbell, Aravinda Chakravarti, John C Chambers, Ganesh Chauhan, Marina Ciullo, Massimiliano Cocca, Francis Collins, Heather J Cordell, Gail Davies, Martin H de Borst, Eco J de Geus, Ian J Deary, Joris Deelen, Fabiola Del Greco M, Cumhur Yusuf Demirkale, Marcus Dörr, Georg B Ehret, Roberto Elosua, Stefan Enroth, A Mesut Erzurumluoglu, Teresa Ferreira, Mattias Frånberg, Oscar H Franco, Ilaria Gandin, Paolo Gasparini, Vilmantas Giedraitis, Christian Gieger, Giorgia Girotto, Anuj Goel, Alan J Gow, Vilmundur Gudnason, Xiuqing Guo, Ulf Gyllensten, Anders Hamsten, Tamara B Harris, Sarah E Harris, Catharina A Hartman, Aki S Havulinna, Andrew A Hicks, Edith Hofer, Albert Hofman, Jouke-Jan Hottenga, Jennifer E Huffman, Shih-Jen Hwang, Erik Ingelsson, Alan James, Rick Jansen, Marjo-Riitta Jarvelin, Roby Joehanes, Åsa Johansson, Andrew D Johnson, Peter K Joshi, Pekka Jousilahti, J Wouter Jukema, Antti Jula, Mika Kähönen, Sekar Kathiresan, Bernard D Keavney, Kay-Tee Khaw, Paul Knekt, Joanne Knight, Ivana Kolcic, Jaspal S Kooner, Seppo Koskinen, Kati Kristiansson, Zoltan Kutalik, Maris Laan, Marty Larson, Lenore J Launer, Benjamin Lehne, Terho Lehtimäki, David CM Liewald, Li Lin, Lars Lind, Cecilia M Lindgren, YongMei Liu, Ruth JF Loos, Lorna M Lopez, Yingchang Lu, Leo-Pekka Lyytikäinen, Anubha Mahajan, Chrysovalanto Mamasoula, Jaume Marrugat, Jonathan Marten, Yuri Milaneschi, Anna Morgan, Andrew P Morris, Alanna C Morrison, Peter J Munson, Mike A Nalls, Priyanka Nandakumar, Christopher P Nelson, Teemu Niiranen, Ilja M Nolte, Teresa Nutile, Albertine J Oldehinkel, Ben A Oostra, Paul F O’Reilly, Elin Org, Sandosh Padmanabhan, Walter Palmas, Aarno Palotie, Alison Pattie, Brenda WJH Penninx, Markus Perola, Annette Peters, Ozren Polasek, Peter P Pramstaller, Quang Tri Nguyen, Olli T Raitakari, Rainer Rettig, Kenneth Rice, Paul M Ridker, Janina S Ried, Harriëtte Riese, Samuli Ripatti, Antonietta Robino, Lynda M Rose, Jerome I Rotter, Igor Rudan, Daniela Ruggiero, Yasaman Saba, Cinzia F Sala, Veikko Salomaa, Nilesh J Samani, Antti-Pekka Sarin, Reinhold Schmidt, Helena Schmidt, Nick Shrine, David Siscovick, Albert V Smith, Harold Snieder, Siim Sõber, Rossella Sorice, John M Starr, David J Stott, David P Strachan, Rona J Strawbridge, Johan Sundström, Morris A Swertz, Kent D Taylor, Alexander Teumer, Martin D Tobin, Maciej Tomaszewski, Daniela Toniolo, Michela Traglia, Stella Trompet, Jaakko Tuomilehto, Christophe Tzourio, André G Uitterlinden, Ahmad Vaez, Peter J van der Most, Cornelia M van Duijn, Germaine C Verwoert, Veronique Vitart, Uwe Völker, Peter Vollenweider, Dragana Vuckovic, Hugh Watkins, Sarah H Wild, Gonneke Willemsen, James F Wilson, Alan F Wright, Jie Yao, Tatijana Zemunik, Weihua Zhang, John R Attia, Adam S Butterworth, Daniel I Chasman, David Conen, Francesco Cucca, John Danesh, Caroline Hayward, Joanna MM Howson, Markku Laakso, Edward G Lakatta, Claudia Langenberg, Olle Melander, Dennis O Mook-Kanamori, Colin NA Palmer, Lorenz Risch, Robert A Scott, Rodney J Scott, Peter Sever, Tim D Spector, Pim van der Harst, Nicholas J Wareham, Eleftheria Zeggini, Daniel Levy, Patricia B Munroe, Christopher Newton-Cheh, Morris J Brown, Andres Metspalu, Bruce M. Psaty, Louise V Wain, Paul Elliott, Mark J Caulfield, Padhraig Gormley, Verneri Anttila, Priit Palta, Tune H Pers, Kai-How Farh, Ester Cuenca-Leon, Mikko Muona, Nicholas A Furlotte, Tobias Kurth, Andres Ingason, George McMahon, Lannie Ligthart, Gisela M Terwindt, Mikko Kallela, Tobias M Freilinger, Caroline Ran, Scott G Gordon, Anine H Stam, Stacy Steinberg, Guntram Borck, Markku Koiranen, Lydia Quaye, Hieab H H Adams, Juho Wedenoja, David A Hinds, Julie E Buring, Markus Schürks, Maria Gudlaug Hrafnsdottir, Hreinn Stefansson, Susan M Ring, Brenda W J H Penninx, Markus Färkkilä, Ville Artto, Mari Kaunisto, Salli Vepsäläinen, Rainer Malik, Andrew C Heath, Pamela A F Madden, Nicholas G Martin, Grant W Montgomery, Mitja I Kurki, Mart Kals, Kalle Pärn, Eija Hämäläinen, Hailiang Huang, Andrea E Byrnes, Lude Franke, Jie Huang, Evie Stergiakouli, Phil H Lee, Cynthia Sandor, Caleb Webber, Zameel Cader, Bertram Muller-Myhsok, Stefan Schreiber, Thomas Meitinger, Johan G Eriksson, Kauko Heikkilä, Elizabeth Loehrer, Andre G Uitterlinden, Lynn Cherkas, Audun Stubhaug, Christopher S Nielsen, Minna Männikkö, Evelin Mihailov, Hartmut Göbel, Ann-Louise Esserlind, Anne Francke Christensen, Thomas Folkmann Hansen, Thomas Werge, Jaakko Kaprio, Arpo J Aromaa, Olli Raitakari, M Arfan Ikram, Tim Spector, Marjo-Riitta Järvelin, Christian Kubisch, Michel D Ferrari, Andrea C Belin, Maija Wessman, Arn M J M van den Maagdenberg, George Davey Smith, Kari Stefansson, Nicholas Eriksson, Mark J Daly, Benjamin M Neale, Jes Olesen, Dale R Nyholt, Masato Akiyama, Varinder S. Alg, Joseph P. Broderick, Ben M. Brumpton, Jérôme Dauvillier, Hubert Desal, Christian Dina, Christoph M. Friedrich, Emília I. Gaál-Paavola, Jean-Christophe Gentric, Sven Hirsch, Isabel C. Hostettler, Henry Houlden, Juha E. Jääskeläinen, Marianne Bakke Johnsen, Liming Li, Kuang Lin, Antti Lindgren, Olivier Martin, Koichi Matsuda, Iona Y. Millwood, Olivier Naggara, Mika Niemelä, Joanna Pera, Richard Redon, Guy A. Rouleau, Marie Søfteland Sandvei, Sabine Schilling, Eimad Shotar, Agnieszka Slowik, Chikashi Terao, W. M. Monique Verschuren, Robin G. Walters, David J. Werring, Cristen J. Willer, Daniel Woo, Bradford B. Worrall, Sirui Zhou, Biological Psychology, Amsterdam Reproduction & Development, APH - Mental Health, APH - Methodology, AMS - Sports, AMS - Ageing & Vitality, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, Systems Ecology, Sociology and Social Gerontology, Bakker, Mark K., Kanning, Jos P., Abraham, Gad, Martinsen, Amy E., Winsvold, Bendik S., Zwart, John-Anker, Bourcier, Romain, Sawada, Tomonobu, Koido, Masaru, Kamatani, Yoichiro, Morel, Sandrine, Amouyel, Philippe, Debette, Stéphanie, Bijlenga, Philippe, Berrandou, Takiy, Ganesh, Santhi K., Bouatia-Naji, Nabila, Jones, Gregory, Bown, Matthew, Rinkel, Gabriel J. E., Veldink, Jan H., Ruigrok, Ynte M., Girotto, G., All-In Stroke, Hunt, Group, Cadisp, Consortium for Blood Pressure, International, Headache Genetics Consortium, International, Stroke Genetics Consortium (ISGC) Intracranial Aneurysm Working Group, International, Utrecht University [Utrecht], Baker Heart and Diabetes Institute (AUSTRALIA), University of Melbourne, University of Oslo (UiO), Norwegian University of Science and Technology (NTNU), Oslo University Hospital [Oslo], Centre hospitalier universitaire de Nantes (CHU Nantes), unité de recherche de l'institut du thorax UMR1087 UMR6291 (ITX), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Nantes Université - UFR de Médecine et des Techniques Médicales (Nantes Univ - UFR MEDECINE), Nantes Université - pôle Santé, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Santé, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), The University of Tokyo (UTokyo), RIKEN Center for Integrative Medical Sciences [Yokohama] (RIKEN IMS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Hôpital Universitaire de Genève = University Hospitals of Geneva (HUG), Université de Genève = University of Geneva (UNIGE), Excellence Laboratory LabEx DISTALZ, Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 (RID-AGE), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Bordeaux [Bordeaux], Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), University of Michigan Medical School [Ann Arbor], University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, University of Otago [Dunedin, Nouvelle-Zélande], University of Leicester, Laboratoire de Neurosciences Fonctionnelles et Pathologies - UR UPJV 4559 (LNFP), Université de Picardie Jules Verne (UPJV), CHU Amiens-Picardie, HUNT All-In Stroke, CADISP group, International Consortium for Blood Pressure, International Headache Genetics Consortium, International Stroke Genetics Consortium (ISGC) Intracranial Aneurysm Working Group: Anne Hege Aamodt, Anne Heidi Skogholt, Ben M Brumpton, Cristen J Willer, Else C Sandset, Espen S Kristoffersen, Hanne Ellekjær, Ingrid Heuch, Jonas B Nielsen, Knut Hagen, Kristian Hveem, Lars G Fritsche, Laurent F Thomas, Linda M Pedersen, Maiken E Gabrielsen, Oddgeir L Holmen, Sigrid Børte, Wei Zhou, Shérine Abboud, Massimo Pandolfo, Vincent Thijs, Didier Leys, Marie Bodenant, Fabien Louillet, Emmanuel Touzé, Jean-Louis Mas, Yves Samson, Sara Leder, Anne Léger, Sandrine Deltour, Sophie Crozier, Isabelle Méresse, Sandrine Canaple, Olivier Godefroy, Maurice Giroud, Yannick Béjot, Pierre Decavel, Elizabeth Medeiros, Paola Montiel, Thierry Moulin, Fabrice Vuillier, Jean Dallongeville, Antti J Metso, Tiina Metso, Turgut Tatlisumak, Caspar Grond-Ginsbach, Christoph Lichy, Manja Kloss, Inge Werner, Marie-Luise Arnold, Michael Dos Santos, Armin Grau, Martin Dichgans, Constanze Thomas-Feles, Ralf Weber, Tobias Brandt, Alessandro Pezzini, Valeria De Giuli, Filomena Caria, Loris Poli, Alessandro Padovani, Anna Bersano, Silvia Lanfranconi, Simone Beretta, Carlo Ferrarese, Giacomo Giacolone, Stefano Paolucci, Philippe Lyrer, Stefan Engelter, Felix Fluri, Florian Hatz, Dominique Gisler, Leo Bonati, Henrik Gensicke, Margareth Amort, Hugh Markus, Jennifer Majersik, Bradford Worrall, Andrew Southerland, John Cole, Steven Kittner, Evangelos Evangelou, Helen R Warren, He Gao, Georgios Ntritsos, Niki Dimou, Tonu Esko, Reedik Mägi, Lili Milani, Peter Almgren, Thibaud Boutin, Jun Ding, Franco Giulianini, Elizabeth G Holliday, Anne U Jackson, Ruifang Li-Gao, Wei-Yu Lin, Jian'an Luan, Massimo Mangino, Christopher Oldmeadow, Bram Peter Prins, Yong Qian, Muralidharan Sargurupremraj, Nabi Shah, Praveen Surendran, Sébastien Thériault, Niek Verweij, Sara M Willems, Jing-Hua Zhao, John Connell, Renée de Mutsert, Alex Sf Doney, Martin Farrall, Cristina Menni, Andrew D Morris, Raymond Noordam, Guillaume Paré, Neil R Poulter, Denis C Shields, Alice Stanton, Simon Thom, Gonçalo Abecasis, Najaf Amin, Dan E Arking, Kristin L Ayers, Caterina M Barbieri, Chiara Batini, Joshua C Bis, Tineka Blake, Murielle Bochud, Michael Boehnke, Eric Boerwinkle, Dorret I Boomsma, Erwin P Bottinger, Peter S Braund, Marco Brumat, Archie Campbell, Harry Campbell, Aravinda Chakravarti, John C Chambers, Ganesh Chauhan, Marina Ciullo, Massimiliano Cocca, Francis Collins, Heather J Cordell, Gail Davies, Martin H de Borst, Eco J de Geus, Ian J Deary, Joris Deelen, Fabiola Del Greco M, Cumhur Yusuf Demirkale, Marcus Dörr, Georg B Ehret, Roberto Elosua, Stefan Enroth, A Mesut Erzurumluoglu, Teresa Ferreira, Mattias Frånberg, Oscar H Franco, Ilaria Gandin, Paolo Gasparini, Vilmantas Giedraitis, Christian Gieger, Giorgia Girotto, Anuj Goel, Alan J Gow, Vilmundur Gudnason, Xiuqing Guo, Ulf Gyllensten, Anders Hamsten, Tamara B Harris, Sarah E Harris, Catharina A Hartman, Aki S Havulinna, Andrew A Hicks, Edith Hofer, Albert Hofman, Jouke-Jan Hottenga, Jennifer E Huffman, Shih-Jen Hwang, Erik Ingelsson, Alan James, Rick Jansen, Marjo-Riitta Jarvelin, Roby Joehanes, Åsa Johansson, Andrew D Johnson, Peter K Joshi, Pekka Jousilahti, J Wouter Jukema, Antti Jula, Mika Kähönen, Sekar Kathiresan, Bernard D Keavney, Kay-Tee Khaw, Paul Knekt, Joanne Knight, Ivana Kolcic, Jaspal S Kooner, Seppo Koskinen, Kati Kristiansson, Zoltan Kutalik, Maris Laan, Marty Larson, Lenore J Launer, Benjamin Lehne, Terho Lehtimäki, David Cm Liewald, Li Lin, Lars Lind, Cecilia M Lindgren, YongMei Liu, Ruth Jf Loos, Lorna M Lopez, Yingchang Lu, Leo-Pekka Lyytikäinen, Anubha Mahajan, Chrysovalanto Mamasoula, Jaume Marrugat, Jonathan Marten, Yuri Milaneschi, Anna Morgan, Andrew P Morris, Alanna C Morrison, Peter J Munson, Mike A Nalls, Priyanka Nandakumar, Christopher P Nelson, Teemu Niiranen, Ilja M Nolte, Teresa Nutile, Albertine J Oldehinkel, Ben A Oostra, Paul F O'Reilly, Elin Org, Sandosh Padmanabhan, Walter Palmas, Aarno Palotie, Alison Pattie, Brenda Wjh Penninx, Markus Perola, Annette Peters, Ozren Polasek, Peter P Pramstaller, Quang Tri Nguyen, Olli T Raitakari, Rainer Rettig, Kenneth Rice, Paul M Ridker, Janina S Ried, Harriëtte Riese, Samuli Ripatti, Antonietta Robino, Lynda M Rose, Jerome I Rotter, Igor Rudan, Daniela Ruggiero, Yasaman Saba, Cinzia F Sala, Veikko Salomaa, Nilesh J Samani, Antti-Pekka Sarin, Reinhold Schmidt, Helena Schmidt, Nick Shrine, David Siscovick, Albert V Smith, Harold Snieder, Siim Sõber, Rossella Sorice, John M Starr, David J Stott, David P Strachan, Rona J Strawbridge, Johan Sundström, Morris A Swertz, Kent D Taylor, Alexander Teumer, Martin D Tobin, Maciej Tomaszewski, Daniela Toniolo, Michela Traglia, Stella Trompet, Jaakko Tuomilehto, Christophe Tzourio, André G Uitterlinden, Ahmad Vaez, Peter J van der Most, Cornelia M van Duijn, Germaine C Verwoert, Veronique Vitart, Uwe Völker, Peter Vollenweider, Dragana Vuckovic, Hugh Watkins, Sarah H Wild, Gonneke Willemsen, James F Wilson, Alan F Wright, Jie Yao, Tatijana Zemunik, Weihua Zhang, John R Attia, Adam S Butterworth, Daniel I Chasman, David Conen, Francesco Cucca, John Danesh, Caroline Hayward, Joanna Mm Howson, Markku Laakso, Edward G Lakatta, Claudia Langenberg, Olle Melander, Dennis O Mook-Kanamori, Colin Na Palmer, Lorenz Risch, Robert A Scott, Rodney J Scott, Peter Sever, Tim D Spector, Pim van der Harst, Nicholas J Wareham, Eleftheria Zeggini, Daniel Levy, Patricia B Munroe, Christopher Newton-Cheh, Morris J Brown, Andres Metspalu, Bruce M Psaty, Louise V Wain, Paul Elliott, Mark J Caulfield, Padhraig Gormley, Verneri Anttila, Priit Palta, Tonu Esko, Tune H Pers, Kai-How Farh, Ester Cuenca-Leon, Mikko Muona, Nicholas A Furlotte, Tobias Kurth, Andres Ingason, George McMahon, Lannie Ligthart, Gisela M Terwindt, Mikko Kallela, Tobias M Freilinger, Caroline Ran, Scott G Gordon, Anine H Stam, Stacy Steinberg, Guntram Borck, Markku Koiranen, Lydia Quaye, Hieab H H Adams, Terho Lehtimäki, Antti-Pekka Sarin, Juho Wedenoja, David A Hinds, Julie E Buring, Markus Schürks, Paul M Ridker, Maria Gudlaug Hrafnsdottir, Hreinn Stefansson, Susan M Ring, Jouke-Jan Hottenga, Brenda W J H Penninx, Markus Färkkilä, Ville Artto, Mari Kaunisto, Salli Vepsäläinen, Rainer Malik, Andrew C Heath, Pamela A F Madden, Nicholas G Martin, Grant W Montgomery, Mitja I Kurki, Mart Kals, Reedik Mägi, Kalle Pärn, Eija Hämäläinen, Hailiang Huang, Andrea E Byrnes, Lude Franke, Jie Huang, Evie Stergiakouli, Phil H Lee, Cynthia Sandor, Caleb Webber, Zameel Cader, Bertram Muller-Myhsok, Stefan Schreiber, Thomas Meitinger, Johan G Eriksson, Veikko Salomaa, Kauko Heikkilä, Elizabeth Loehrer, Andre G Uitterlinden, Albert Hofman, Cornelia M van Duijn, Lynn Cherkas, Linda M Pedersen, Audun Stubhaug, Christopher S Nielsen, Minna Männikkö, Evelin Mihailov, Lili Milani, Hartmut Göbel, Ann-Louise Esserlind, Anne Francke Christensen, Thomas Folkmann Hansen, Thomas Werge, Jaakko Kaprio, Arpo J Aromaa, Olli Raitakari, M Arfan Ikram, Tim Spector, Marjo-Riitta Järvelin, Andres Metspalu, Christian Kubisch, David P Strachan, Michel D Ferrari, Andrea C Belin, Martin Dichgans, Maija Wessman, Arn M J M van den Maagdenberg, Dorret I Boomsma, George Davey Smith, Kari Stefansson, Nicholas Eriksson, Mark J Daly, Benjamin M Neale, Jes Olesen, Daniel I Chasman, Dale R Nyholt, Aarno Palotie, Masato Akiyama, Varinder S Alg, Sigrid Børte, Joseph P Broderick, Ben M Brumpton, Jérôme Dauvillier, Hubert Desal, Christian Dina, Christoph M Friedrich, Emília I Gaál-Paavola, Jean-Christophe Gentric, Sven Hirsch, Isabel C Hostettler, Henry Houlden, Kristian Hveem, Juha E Jääskeläinen, Marianne Bakke Johnsen, Liming Li, Kuang Lin, Antti Lindgren, Olivier Martin, Koichi Matsuda, Iona Y Millwood, Olivier Naggara, Mika Niemelä, Joanna Pera, Richard Redon, Guy A Rouleau, Marie Søfteland Sandvei, Sabine Schilling, Eimad Shotar, Agnieszka Slowik, Chikashi Terao, W M Monique Verschuren, Robin G Walters, David J Werring, Cristen J Willer, Daniel Woo, Bradford B Worrall, Sirui Zhou, Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, and Admin, Oskar

Subjects
Advanced and Specialized Nursing, Incidence, risk assessment, Smoking/epidemiology, intracranial aneurysm, genetic heterogeneity, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Risk Factors, Intracranial Aneurysm/epidemiology, Humans, Subarachnoid Hemorrhage/epidemiology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, genetics, Neurology (clinical), aneurysmal subarachnoid hemorrhage, genetic, Cardiology and Cardiovascular Medicine
Abstract

Background: Recently, common genetic risk factors for intracranial aneurysm (IA) and aneurysmal subarachnoid hemorrhage (ASAH) were found to explain a large amount of disease heritability and therefore have potential to be used for genetic risk prediction. We constructed a genetic risk score to (1) predict ASAH incidence and IA presence (combined set of unruptured IA and ASAH) and (2) assess its association with patient characteristics. Methods: A genetic risk score incorporating genetic association data for IA and 17 traits related to IA (so-called metaGRS) was created using 1161 IA cases and 407 392 controls from the UK Biobank population study. The metaGRS was validated in combination with risk factors blood pressure, sex, and smoking in 828 IA cases and 68 568 controls from the Nordic HUNT population study. Furthermore, we assessed association between the metaGRS and patient characteristics in a cohort of 5560 IA patients. Results: Per SD increase of metaGRS, the hazard ratio for ASAH incidence was 1.34 (95% CI, 1.20–1.51) and the odds ratio for IA presence 1.09 (95% CI, 1.01–1.18). Upon including the metaGRS on top of clinical risk factors, the concordance index to predict ASAH hazard increased from 0.63 (95% CI, 0.59–0.67) to 0.65 (95% CI, 0.62–0.69), while prediction of IA presence did not improve. The metaGRS was statistically significantly associated with age at ASAH (β=−4.82×10 −3 per year [95% CI, −6.49×10 −3 to −3.14×10 −3 ]; P =1.82×10 −8 ), and location of IA at the internal carotid artery (odds ratio=0.92 [95% CI, 0.86–0.98]; P =0.0041). Conclusions: The metaGRS was predictive of ASAH incidence, although with limited added value over clinical risk factors. The metaGRS was not predictive of IA presence. Therefore, we do not recommend using this metaGRS in daily clinical care. Genetic risk does partly explain the clinical heterogeneity of IA warranting prioritization of clinical heterogeneity in future genetic prediction studies of IA and ASAH.

Published
2023
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13. A genome-wide association study of mitochondrial DNA copy number in two population-based cohorts

Author

Anna L. Guyatt, Rebecca R. Brennan, Kimberley Burrows, Philip A. I. Guthrie, Raimondo Ascione, Susan M. Ring, Tom R. Gaunt, Angela Pyle, Heather J. Cordell, Debbie A. Lawlor, Patrick F. Chinnery, Gavin Hudson, and Santiago Rodriguez

Subjects
Mitochondrial DNA, ALSPAC, Genome-wide association study, Complex traits, Genetic epidemiology, Medicine, Genetics, QH426-470
Abstract

Abstract Background Mitochondrial DNA copy number (mtDNA CN) exhibits interindividual and intercellular variation, but few genome-wide association studies (GWAS) of directly assayed mtDNA CN exist. We undertook a GWAS of qPCR-assayed mtDNA CN in the Avon Longitudinal Study of Parents and Children (ALSPAC) and the UK Blood Service (UKBS) cohort. After validating and harmonising data, 5461 ALSPAC mothers (16–43 years at mtDNA CN assay) and 1338 UKBS females (17–69 years) were included in a meta-analysis. Sensitivity analyses restricted to females with white cell-extracted DNA and adjusted for estimated or assayed cell proportions. Associations were also explored in ALSPAC children and UKBS males. Results A neutrophil-associated locus approached genome-wide significance (rs709591 [MED24], β (change in SD units of mtDNA CN per allele) [SE] − 0.084 [0.016], p = 1.54e−07) in the main meta-analysis of adult females. This association was concordant in magnitude and direction in UKBS males and ALSPAC neonates. SNPs in and around ABHD8 were associated with mtDNA CN in ALSPAC neonates (rs10424198, β [SE] 0.262 [0.034], p = 1.40e−14), but not other study groups. In a meta-analysis of unrelated individuals (N = 11,253), we replicated a published association in TFAM (β [SE] 0.046 [0.017], p = 0.006), with an effect size much smaller than that observed in the replication analysis of a previous in silico GWAS. Conclusions In a hypothesis-generating GWAS, we confirm an association between TFAM and mtDNA CN and present putative loci requiring replication in much larger samples. We discuss the limitations of our work, in terms of measurement error and cellular heterogeneity, and highlight the need for larger studies to better understand nuclear genomic control of mtDNA copy number.

Published
2019
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14. Mutations in SPATA13/ASEF2 cause primary angle closure glaucoma.

Author

Naushin H Waseem, Sancy Low, Amna Z Shah, Deepa Avisetti, Pia Ostergaard, Michael Simpson, Katarzyna A Niemiec, Belen Martin-Martin, Hebah Aldehlawi, Saima Usman, Pak Sang Lee, Anthony P Khawaja, Jonathan B Ruddle, Ameet Shah, Ege Sackey, Alexander Day, Yuzhen Jiang, Geoff Swinfield, Ananth Viswanathan, Giovanna Alfano, Christina Chakarova, Heather J Cordell, David F Garway-Heath, Peng T Khaw, Shomi S Bhattacharya, Ahmad Waseem, and Paul J Foster

Subjects
Genetics, QH426-470
Abstract

Current estimates suggest 50% of glaucoma blindness worldwide is caused by primary angle-closure glaucoma (PACG) but the causative gene is not known. We used genetic linkage and whole genome sequencing to identify Spermatogenesis Associated Protein 13, SPATA13 (NM_001166271; NP_001159743, SPATA13 isoform I), also known as ASEF2 (Adenomatous polyposis coli-stimulated guanine nucleotide exchange factor 2), as the causal gene for PACG in a large seven-generation white British family showing variable expression and incomplete penetrance. The 9 bp deletion, c.1432_1440del; p.478_480del was present in all affected individuals with angle-closure disease. We show ubiquitous expression of this transcript in cell lines derived from human tissues and in iris, retina, retinal pigment and ciliary epithelia, cornea and lens. We also identified eight additional mutations in SPATA13 in a cohort of 189 unrelated PACS/PAC/PACG samples. This gene encodes a 1277 residue protein which localises to the nucleus with partial co-localisation with nuclear speckles. In cells undergoing mitosis SPATA13 isoform I becomes part of the kinetochore complex co-localising with two kinetochore markers, polo like kinase 1 (PLK-1) and centrosome-associated protein E (CENP-E). The 9 bp deletion reported in this study increases the RAC1-dependent guanine nucleotide exchange factors (GEF) activity. The increase in GEF activity was also observed in three other variants identified in this study. Taken together, our data suggest that SPATA13 is involved in the regulation of mitosis and the mutations dysregulate GEF activity affecting homeostasis in tissues where it is highly expressed, influencing PACG pathogenesis.

Published
2020
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15. Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data.

Author

Richard Howey, So-Youn Shin, Caroline Relton, George Davey Smith, and Heather J Cordell

Subjects
Genetics, QH426-470
Abstract

Mendelian randomization (MR) implemented through instrumental variables analysis is an increasingly popular causal inference tool used in genetic epidemiology. But it can have limitations for evaluating simultaneous causal relationships in complex data sets that include, for example, multiple genetic predictors and multiple potential risk factors associated with the same genetic variant. Here we use real and simulated data to investigate Bayesian network analysis (BN) with the incorporation of directed arcs, representing genetic anchors, as an alternative approach. A Bayesian network describes the conditional dependencies/independencies of variables using a graphical model (a directed acyclic graph) with an accompanying joint probability. In real data, we found BN could be used to infer simultaneous causal relationships that confirmed the individual causal relationships suggested by bi-directional MR, while allowing for the existence of potential horizontal pleiotropy (that would violate MR assumptions). In simulated data, BN with two directional anchors (mimicking genetic instruments) had greater power for a fixed type 1 error than bi-directional MR, while BN with a single directional anchor performed better than or as well as bi-directional MR. Both BN and MR could be adversely affected by violations of their underlying assumptions (such as genetic confounding due to unmeasured horizontal pleiotropy). BN with no directional anchor generated inference that was no better than by chance, emphasizing the importance of directional anchors in BN (as in MR). Under highly pleiotropic simulated scenarios, BN outperformed both MR (and its recent extensions) and two recently-proposed alternative approaches: a multi-SNP mediation intersection-union test (SMUT) and a latent causal variable (LCV) test. We conclude that BN incorporating genetic anchors is a useful complementary method to conventional MR for exploring causal relationships in complex data sets such as those generated from modern "omics" technologies.

Published
2020
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16. Using penalized regression to predict phenotype from SNP data

Author

Svetlana Cherlin, Richard A. J. Howey, and Heather J. Cordell

Subjects
Medicine, Science
Abstract

Abstract Background In a typical genome-enabled prediction problem there are many more predictor variables than response variables. This prohibits the application of multiple linear regression, because the unique ordinary least squares estimators of the regression coefficients are not defined. To overcome this problem, penalized regression methods have been proposed, aiming at shrinking the coefficients toward zero. Methods We explore prediction of phenotype from single nucleotide polymorphism (SNP) data in the GAW20 data set using a penalized regression approach (LASSO [least absolute shrinkage and selection operator] regression). We use 10-fold cross-validation to assess predictive performance and 10-fold nested cross-validation to specify a penalty parameter. Results By analyzing approximately 600,000 SNPs we find that, when the sample size comprises a few hundred individuals, SNP effects are heavily penalized, resulting in a poor predictive performance. Increasing the sample size to a few thousand individuals results in a much smaller penalization of the true effects, thus greatly improving the prediction. Conclusions LASSO regression results in a heavy shrinkage of the regression coefficients, and also requires large sample sizes (several thousand individuals) to achieve good prediction.

Published
2018
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17. Application of Bayesian networks to GAW20 genetic and blood lipid data

Author

Richard A. J. Howey and Heather J. Cordell

Subjects
Medicine, Science
Abstract

Abstract Background Bayesian networks have been proposed as a way to identify possible causal relationships between measured variables based on their conditional dependencies and independencies. We explored the use of Bayesian network analyses applied to the GAW20 data to identify possible causal relationships between differential methylation of cytosine-phosphate-guanine dinucleotides (CpGs), single-nucleotide polymorphisms (SNPs), and blood lipid trait (triglycerides [TGs]). Methods After initial exploratory linear regression analyses, 2 Bayesian networks analyses were performed. First, we used the real data and modeled the effects of 4 CpGs previously found to be associated with TGs in the Genetics of Lipid Lowering Drugs and Diet Network Study (GOLDN). Second, we used the simulated data and modeled the effect of a fictional lipid modifying drug with 5 known causal SNPs and 5 corresponding CpGs. Results In the real data we show that relationships are present between the CpGs, TGs, and other variables—age, sex, and center. In the simulated data, we show, using linear regression, that no CpGs and only 1 SNP were associated with a change in TG levels, and, using Bayesian network analysis, that relationships are present between the change in TG levels and most SNPs, but not with CpGs. Conclusions Even when the causal relationships between variables are known, as with the simulated data, if the relationships are not strong then it is challenging to reproduce them in a Bayesian network.

Published
2018
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18. Author Correction: Identifying genetic factors that contribute to the increased risk of congenital heart defects in infants with Down syndrome

Author

Cristina E. Trevino, Aaron M. Holleman, Holly Corbitt, Cheryl L. Maslen, Tracie C. Rosser, David J. Cutler, H. Richard Johnston, Benjamin L. Rambo-Martin, Jai Oberoi, Kenneth J. Dooley, George T. Capone, Roger H. Reeves, Heather J. Cordell, Bernard D. Keavney, A. J. Agopian, Elizabeth Goldmuntz, Peter J. Gruber, James E. O’Brien, Douglas C. Bittel, Lalita Wadhwa, Clifford L. Cua, Ivan P. Moskowitz, Jennifer G. Mulle, Michael P. Epstein, Stephanie L. Sherman, and Michael E. Zwick

Subjects
Medicine, Science
Published
2021
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19. Author Correction: Genome-wide association study identifies susceptibility loci for acute myeloid leukemia

Author

Wei-Yu Lin, Sarah E. Fordham, Eric Hungate, Nicola J. Sunter, Claire Elstob, Yaobo Xu, Catherine Park, Anne Quante, Konstantin Strauch, Christian Gieger, Andrew Skol, Thahira Rahman, Lara Sucheston-Campbell, Junke Wang, Theresa Hahn, Alyssa I. Clay-Gilmour, Gail L. Jones, Helen J. Marr, Graham H. Jackson, Tobias Menne, Mathew Collin, Adam Ivey, Robert K. Hills, Alan K. Burnett, Nigel H. Russell, Jude Fitzgibbon, Richard A. Larson, Michelle M. Le Beau, Wendy Stock, Olaf Heidenreich, Abrar Alharbi, David J. Allsup, Richard S. Houlston, Jean Norden, Anne M. Dickinson, Elisabeth Douglas, Clare Lendrem, Ann K. Daly, Louise Palm, Kim Piechocki, Sally Jeffries, Martin Bornhäuser, Christoph Röllig, Heidi Altmann, Leo Ruhnke, Desiree Kunadt, Lisa Wagenführ, Heather J. Cordell, Rebecca Darlay, Mette K. Andersen, Maria C. Fontana, Giovanni Martinelli, Giovanni Marconi, Miguel A. Sanz, José Cervera, Inés Gómez-Seguí, Thomas Cluzeau, Chimène Moreilhon, Sophie Raynaud, Heinz Sill, Maria Teresa Voso, Francesco Lo-Coco, Hervé Dombret, Meyling Cheok, Claude Preudhomme, Rosemary E. Gale, David Linch, Julia Gaal-Wesinger, Andras Masszi, Daniel Nowak, Wolf-Karsten Hofmann, Amanda Gilkes, Kimmo Porkka, Jelena D. Milosevic Feenstra, Robert Kralovics, David Grimwade, Manja Meggendorfer, Torsten Haferlach, Szilvia Krizsán, Csaba Bödör, Friedrich Stölzel, Kenan Onel, and James M. Allan

Subjects
Science
Published
2022
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20. Protein-altering and regulatory genetic variants near GATA4 implicated in bicuspid aortic valve

Author

Bo Yang, Wei Zhou, Jiao Jiao, Jonas B. Nielsen, Michael R. Mathis, Mahyar Heydarpour, Guillaume Lettre, Lasse Folkersen, Siddharth Prakash, Claudia Schurmann, Lars Fritsche, Gregory A. Farnum, Maoxuan Lin, Mohammad Othman, Whitney Hornsby, Anisa Driscoll, Alexandra Levasseur, Marc Thomas, Linda Farhat, Marie-Pierre Dubé, Eric M. Isselbacher, Anders Franco-Cereceda, Dong-chuan Guo, Erwin P. Bottinger, G. Michael Deeb, Anna Booher, Sachin Kheterpal, Y. Eugene Chen, Hyun Min Kang, Jacob Kitzman, Heather J. Cordell, Bernard D. Keavney, Judith A. Goodship, Santhi K. Ganesh, Gonçalo Abecasis, Kim A. Eagle, Alan P. Boyle, Ruth J. F. Loos, Per Eriksson, Jean-Claude Tardif, Chad M. Brummett, Dianna M. Milewicz, Simon C. Body, and Cristen J. Willer

Subjects
Science
Abstract

Bicuspid aortic valve (BAV) is the most common human congenital cardiovascular malformation. Here, the authors perform a genome-wide association study for BAV and identify risk variants in the gene region of cardiac-specific transcription factor GATA4 and implicate GATA4 in heart valve development.

Published
2017
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21. Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits

Author

James J. Fryett, Andrew P. Morris, and Heather J. Cordell

Subjects
Multifactorial Inheritance, Models, Genetic, Genotype, Epidemiology, Humans, CpG Islands, DNA Methylation, Transcriptome, Polymorphism, Single Nucleotide, Genetics (clinical), Genome-Wide Association Study
Abstract

As popularised by PrediXcan (and related methods), transcriptome-wide association studies (TWAS), in which gene expression is imputed from single-nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being under genetic regulation, may be imputable from SNP genotypes. Here, we investigate prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. We start by examining how well CpG methylation can be predicted from SNP genotypes, comparing three penalised regression approaches and examining whether changing the window size improves prediction accuracy. Although methylation at most CpG sites cannot be accurately predicted from SNP genotypes, for a subset it can be predicted well. We next apply our methylation prediction models (trained using the optimal method and window size) to carry out a methylome-wide association study (MWAS) of primary biliary cholangitis. We intersect the regions identified via MWAS with those identified via TWAS, providing insight into the interplay between CpG methylation, gene expression and disease status. We conclude that MWAS has the potential to improve understanding of biological mechanisms in complex traits.

Published
2022
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22. The Plight of Muntaser Ibrahim.

Author

Giorgio Sirugo, Scott M Williams, Sarah A Tishkoff, Heather J Cordell, Jonathan Marchini, Gregory S Barsh, and Gregory P Copenhaver

Subjects
Genetics, QH426-470
Published
2019
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23. Publisher Correction: A meta-analysis of genome-wide association studies identifies multiple longevity genes

Author

Joris Deelen, Daniel S. Evans, Dan E. Arking, Niccolò Tesi, Marianne Nygaard, Xiaomin Liu, Mary K. Wojczynski, Mary L. Biggs, Ashley van der Spek, Gil Atzmon, Erin B. Ware, Chloé Sarnowski, Albert V. Smith, Ilkka Seppälä, Heather J. Cordell, Janina Dose, Najaf Amin, Alice M. Arnold, Kristin L. Ayers, Nir Barzilai, Elizabeth J. Becker, Marian Beekman, Hélène Blanché, Kaare Christensen, Lene Christiansen, Joanna C. Collerton, Sarah Cubaynes, Steven R. Cummings, Karen Davies, Birgit Debrabant, Jean-François Deleuze, Rachel Duncan, Jessica D. Faul, Claudio Franceschi, Pilar Galan, Vilmundur Gudnason, Tamara B. Harris, Martijn Huisman, Mikko A. Hurme, Carol Jagger, Iris Jansen, Marja Jylhä, Mika Kähönen, David Karasik, Sharon L. R. Kardia, Andrew Kingston, Thomas B. L. Kirkwood, Lenore J. Launer, Terho Lehtimäki, Wolfgang Lieb, Leo-Pekka Lyytikäinen, Carmen Martin-Ruiz, Junxia Min, Almut Nebel, Anne B. Newman, Chao Nie, Ellen A. Nohr, Eric S. Orwoll, Thomas T. Perls, Michael A. Province, Bruce M. Psaty, Olli T. Raitakari, Marcel J. T. Reinders, Jean-Marie Robine, Jerome I. Rotter, Paola Sebastiani, Jennifer Smith, Thorkild I. A. Sørensen, Kent D. Taylor, André G. Uitterlinden, Wiesje van der Flier, Sven J. van der Lee, Cornelia M. van Duijn, Diana van Heemst, James W. Vaupel, David Weir, Kenny Ye, Yi Zeng, Wanlin Zheng, Henne Holstege, Douglas P. Kiel, Kathryn L. Lunetta, P. Eline Slagboom, and Joanne M. Murabito

Subjects
Science
Abstract

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22613-2

Published
2021
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24. Rare complement factor I variants associated with reduced macular thickness and age-related macular degeneration in the UK Biobank

Author

Nikolaos Tzoumas, David Kavanagh, Heather J Cordell, Andrew J Lotery, Praveen J Patel, and David H Steel

Subjects
Genotype, Fibrinogen, General Medicine, Polymorphism, Single Nucleotide, United Kingdom, eye diseases, Macular Degeneration, Complement Factor I, Complement Factor H, Genetics, Humans, sense organs, Molecular Biology, Genetics (clinical), Biological Specimen Banks
Abstract

To evaluate potential diagnostic and therapeutic biomarkers for age-related macular degeneration (AMD), we identified 8433 UK Biobank participants with rare complement Factor I gene (CFI) variants, 579 with optical coherence tomography-derived macular thickness data. We stratified these variants by predicted gene expression and measured their association with retinal pigment epithelium-Bruch’s membrane (RPE-BM) complex and retinal thicknesses at nine macular subfields, as well as AMD risk, using multivariable regression models adjusted for the common complement Factor H gene (CFH) p.Y402H and age-related maculopathy susceptibility protein 2 gene (ARMS2) p.A69S risk genotypes. CFI variants associated with low Factor I levels predicted a thinner mean RPE-BM (95% confidence interval [CI] −1.66 to −0.37 μm, P = 0.002) and retina (95% CI −5.88 to −0.13 μm, P = 0.04) and a higher AMD risk (odds ratio [OR] = 2.26, 95% CI 1.56 to 3.27, P

Published
2022
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25. Amino acid residues in five separate HLA genes can explain most of the known associations between the MHC and primary biliary cholangitis.

Author

Rebecca Darlay, Kristin L Ayers, George F Mells, Lynsey S Hall, Jimmy Z Liu, Mohamed A Almarri, Graeme J Alexander, David E Jones, Richard N Sandford, Carl A Anderson, and Heather J Cordell

Subjects
Genetics, QH426-470
Abstract

Primary Biliary Cholangitis (PBC) is a chronic autoimmune liver disease characterised by progressive destruction of intrahepatic bile ducts. The strongest genetic association is with HLA-DQA1*04:01, but at least three additional independent HLA haplotypes contribute to susceptibility. We used dense single nucleotide polymorphism (SNP) data in 2861 PBC cases and 8514 controls to impute classical HLA alleles and amino acid polymorphisms using state-of-the-art methodologies. We then demonstrated through stepwise regression that association in the HLA region can be largely explained by variation at five separate amino acid positions. Three-dimensional modelling of protein structures and calculation of electrostatic potentials for the implicated HLA alleles/amino acid substitutions demonstrated a correlation between the electrostatic potential of pocket P6 in HLA-DP molecules and the HLA-DPB1 alleles/amino acid substitutions conferring PBC susceptibility/protection, highlighting potential new avenues for future functional investigation.

Published
2018
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26. Rationale, Design, and the Baseline Characteristics of the RHDGen (The Genetics of Rheumatic Heart Disease) Network Study†

Author

Tafadzwa, Machipisa, Chishala, Chishala, Gasnat, Shaboodien, Liesl J, Zühlke, Babu, Muhamed, Shahiemah, Pandie, Jantina de, Vries, Nakita, Laing, Alexia, Joachim, Rezeen, Daniels, Mpiko, Ntsekhe, Christopher T, Hugo-Hamman, Bernard, Gitura, Stephen, Ogendo, Peter, Lwabi, Emmy, Okello, Albertino, Damasceno, Celia, Novela, Ana O, Mocumbi, Geoffrey, Madeira, John, Musuku, Agnes, Mtaja, Ahmed, ElSayed, Huda H M, Alhassan, Fidelia, Bode-Thomas, Christopher, Yilgwan, Ganiyu, Amusa, Esin, Nkereuwem, Nicola, Mulder, Raj, Ramesar, Maia, Lesosky, Heather J, Cordell, Michael, Chong, Bernard, Keavney, Guillaume, Paré, Mark E, Engel, and C, Durojaiye-Amodu

Subjects
General Medicine
Abstract

Background: The genetics of rheumatic heart disease (RHDGen) Network was developed to assist the discovery and validation of genetic variations and biomarkers of risk for rheumatic heart disease (RHD) in continental Africans, as a part of the global fight to control and eradicate rheumatic fever/RHD. Thus, we describe the rationale and design of the RHDGen study, comprising participants from 8 African countries. Methods: RHDGen screened potential participants using echocardiography, thereafter enrolling RHD cases and ethnically-matched controls for whom case characteristics were documented. Biological samples were collected for conducting genetic analyses, including a discovery case-control genome-wide association study (GWAS) and a replication trio family study. Additional biological samples were also collected, and processed, for the measurement of biomarker analytes and the biomarker analyses are underway. Results: Participants were enrolled into RHDGen between December 2012 and March 2018. For GWAS, 2548 RHD cases and 2261 controls (3301 women [69%]; mean age [SD], 37 [16.3] years) were available. RHD cases were predominantly Black (66%), Admixed (24%), and other ethnicities (10%). Among RHD cases, 34% were asymptomatic, 26% had prior valve surgery, and 23% had atrial fibrillation. The trio family replication arm included 116 RHD trio probands and 232 parents. Conclusions: RHDGen presents a rare opportunity to identify relevant patterns of genetic factors and biomarkers in Africans that may be associated with differential RHD risk. Furthermore, the RHDGen Network provides a platform for further work on fully elucidating the causes and mechanisms associated with RHD susceptibility and development.

Published
2022

27. Defining the nuclear genetic architecture of a common maternally inherited mitochondrial disorder

Author

Róisín M. Boggan, Yi Shiau Ng, Imogen G. Franklin, Charlotte L. Alston, Emma L. Blakely, Boriana Büchner, Enrico Bugiardini, Kevin Colclough, Catherine Feeney, Michael G. Hanna, Andrew T. Hattersley, Thomas Klopstock, Cornelia Kornblum, Michelangelo Mancuso, Kashyap A. Patel, Robert D. S. Pitceathly, Chiara Pizzamiglio, Holger Prokisch, Jochen Schäfer, Andrew M. Schaefer, Maggie H. Shepherd, Annemarie Thaele, Rhys H Thomas, Doug M. Turnbull, Cathy E. Woodward, Gráinne S. Gorman, Robert McFarland, Robert W. Taylor, Heather J. Cordell, and Sarah J. Pickett

Abstract

Maternally inherited mitochondrial diseases are caused by pathogenic mitochondrial (mt)DNA variants. Affecting individuals at any age, they are often multi-systemic and manifest extreme clinical variability. We have limited understanding of the cause of this heterogeneity, which makes disease diagnosis and prognosis exceptionally challenging. This is clearly demonstrated by disease caused by m.3243A>G, the most common pathogenic mtDNA variant. m.3243A>G can cause a severe syndrome characterised by mitochondrial encephalomyopathy lactic acidosis and stroke-like episodes (MELAS), but individuals who carry m.3243A>G may be asymptomatic or manifest with any number of a range of phenotypes. There is strong evidence for the presence of nuclear factors that modify phenotype; we set out to characterise the nature of this nuclear involvement using genetic linkage analysis.We assembled a multi-centre cohort of well-characterised patients and their maternal relatives, comprising 76 pedigrees, and characterised the nuclear genetic landscape of m.3243A>G- related disease phenotypes using non-parametric genetic linkage analysis. We considered eight of the most common m.3243A>G-related phenotypes, accounted for known risk factors using logistic regression, and determined empirical significance using simulation to identify regions of the nuclear genome most likely to contain disease modifying variants.We identified significant genetic linkage to encephalopathy on chromosome 7q22, and suggestive regions for encephalopathy, stroke-like episodes and psychiatric involvement on chromosomes 1, 5, 6, 11 and 13. These findings suggest that these neurological features are likely to be influenced by a small number of nuclear factors with a relatively large effect size. In contrast, no linkage regions were identified for cerebellar ataxia, migraine, diabetes mellitus, hearing impairment or chronic progressive external ophthalmoplegia.The genetic architecture of the nuclear factors influencing disease related to m.3243A>G differs between phenotypes. Severe and cardinal neurological features of MELAS are likely to be strongly influenced by a small number of nuclear genes, whereas the nuclear influence over other phenotypic presentations is more likely to be polygenic and complex in nature, composed of a larger number of factors that each exert a small effect. These results will inform strategies for future studies to identify the genes and pathways that influence clinical heterogeneity in m.3243A>G-related disease, with the ultimate aim of better understanding disease development and progression.

Published
2022
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28. Further investigations of the W-test for pairwise epistasis testing [version 1; referees: 2 approved]

Author

Richard Howey and Heather J. Cordell

Subjects
Bioinformatics, Genomics, Statistical Methodologies & Health Informatics, Medicine, Science
Abstract

Background: In a recent paper, a novel W-test for pairwise epistasis testing was proposed that appeared, in computer simulations, to have higher power than competing alternatives. Application to genome-wide bipolar data detected significant epistasis between SNPs in genes of relevant biological function. Network analysis indicated that the implicated genes formed two separate interaction networks, each containing genes highly related to autism and neurodegenerative disorders. Methods: Here we investigate further the properties and performance of the W-test via theoretical evaluation, computer simulations and application to real data. Results: We demonstrate that, for common variants, the W-test is closely related to several existing tests of association allowing for interaction, including logistic regression on 8 degrees of freedom, although logistic regression can show inflated type I error for low minor allele frequencies, whereas the W-test shows good/conservative type I error control. Although in some situations the W-test can show higher power, logistic regression is not limited to tests on 8 degrees of freedom but can instead be taylored to impose greater structure on the assumed alternative hypothesis, offering a power advantage when the imposed structure matches the true structure. Conclusions: The W-test is a potentially useful method for testing for association - without necessarily implying interaction - between genetic variants disease, particularly when one or more of the genetic variants are rare. For common variants, the advantages of the W-test are less clear, and, indeed, there are situations where existing methods perform better. In our investigations, we further uncover a number of problems with the practical implementation and application of the W-test (to bipolar disorder) previously described, apparently due to inadequate use of standard data quality-control procedures. This observation leads us to urge caution in interpretation of the previously-presented results, most of which we consider are highly likely to be artefacts.

Published
2017
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29. Biallelic variants in CEP164 cause a motile ciliopathy-like syndrome

Author

Laura A, Devlin, Janice, Coles, Claire L, Jackson, Miguel, Barroso-Gil, Ben, Green, Woolf T, Walker, N Simon, Thomas, James, Thompson, Simon A, Rock, Ruxandra, Neatu, Laura, Powell, Elisa, Molinari, Ian J, Wilson, Heather J, Cordell, Eric, Olinger, Colin G, Miles, John A, Sayer, Gabrielle, Wheway, and Jane S, Lucas

Subjects
Genetics, Genetics (clinical)
Abstract

Ciliopathies may be classed as primary or motile depending on the underlying ciliary defect and are usually considered distinct clinical entities. Primary ciliopathies are associated with multisystem syndromes typically affecting the brain, kidney, and eye, as well as other organ systems such as the liver, skeleton, auditory system, and metabolism. Motile ciliopathies are a heterogenous group of disorders with defects in specialised motile ciliated tissues found within the lung, brain, and reproductive system, and are associated with primary ciliary dyskinesia, bronchiectasis, infertility and rarely hydrocephalus. Primary and motile cilia share defined core ultra-structures with an overlapping proteome, and human disease phenotypes can reflect both primary and motile ciliopathies. CEP164 encodes a centrosomal distal appendage protein vital for primary ciliogenesis. Human CEP164 mutations are typically described in patients with nephronophthisis-related primary ciliopathies but have also been implicated in motile ciliary dysfunction. Here we describe a patient with an atypical motile ciliopathy phenotype and biallelic CEP164 variants. This work provides further evidence that CEP164 mutations can contribute to both primary and motile ciliopathy syndromes, supporting their functional and clinical overlap, and informs the investigation and management of CEP164 ciliopathy patients.

Published
2022

30. Common and Rare Genetic Variants That Could Contribute to Severe Otitis Media in an Australian Aboriginal Population

Author

Sarra E. Jamieson, Richard W. Francis, Timo Lassmann, Elizabeth S. H. Scaman, Jenefer M. Blackwell, Heather J. Cordell, Dave Tang, Genevieve Syn, Harvey Coates, Michaela Fakiola, and Denise Anderson

Subjects
Microbiology (medical), Genome-wide association study, LMNA, CDH23, Genetic predisposition, Medicine, Humans, Exome, Cell projection, Genetics, business.industry, Racial Groups, NREP neuronal regeneration related protein, Australia, otitis media, Phenotype, Cilium assembly, stereociliary bundles, Major Articles and Commentaries, Infectious Diseases, AcademicSubjects/MED00290, NR3C1 glucocorticoid receptor, Trans-Activators, business, cilium assembly, genetic susceptibility
Abstract

Background Our goal was to identify genetic risk factors for severe otitis media (OM) in Aboriginal Australians. Methods Illumina® Omni2.5 BeadChip and imputed data were compared between 21 children with severe OM (multiple episodes chronic suppurative OM and/or perforations or tympanic sclerosis) and 370 individuals without this phenotype, followed by FUnctional Mapping and Annotation (FUMA). Exome data filtered for common (EXaC_all ≥ 0.1) putative deleterious variants influencing protein coding (CADD-scaled scores ≥15] were used to compare 15 severe OM cases with 9 mild cases (single episode of acute OM recorded over ≥3 consecutive years). Rare (ExAC_all ≤ 0.01) such variants were filtered for those present only in severe OM. Enrichr was used to determine enrichment of genes contributing to pathways/processes relevant to OM. Results FUMA analysis identified 2 plausible genetic risk loci for severe OM: NR3C1 (Pimputed_1000G = 3.62 × 10−6) encoding the glucocorticoid receptor, and NREP (Pimputed_1000G = 3.67 × 10−6) encoding neuronal regeneration-related protein. Exome analysis showed: (i) association of severe OM with variants influencing protein coding (CADD-scaled ≥ 15) in a gene-set (GRXCR1, CDH23, LRP2, FAT4, ARSA, EYA4) enriched for Mammalian Phenotype Level 4 abnormal hair cell stereociliary bundle morphology and related phenotypes; (ii) rare variants influencing protein coding only seen in severe OM provided gene-sets enriched for “abnormal ear” (LMNA, CDH23, LRP2, MYO7A, FGFR1), integrin interactions, transforming growth factor signaling, and cell projection phenotypes including hair cell stereociliary bundles and cilium assembly. Conclusions This study highlights interacting genes and pathways related to cilium structure and function that may contribute to extreme susceptibility to OM in Aboriginal Australian children., Genome-wide analysis identifies common noncoding putative regulatory, as well as common and rare potentially deleterious variants directly influencing protein coding, in genes and pathways related to cilium structure and function that may contribute to severe otitis media in Aboriginal Australians.

Published
2021

31. Early B-cell Factor 3–Related Genetic Disease Can Mimic Urofacial Syndrome

Author

Sangeet Sidhu, J. Robert Harkness, John A. Sayer, Katherine A. Wood, Helen M. Stuart, Keng Wee Teik, Heather J. Cordell, Huw B. Thomas, Adrian S. Woolf, Glenda M. Beaman, and William G. Newman

Subjects
Nephrology, Early B-Cell Factor, business.industry, Urofacial syndrome, MEDLINE, Medicine, Disease, Nephrology Rounds, business, medicine.disease, Bioinformatics, lcsh:Diseases of the genitourinary system. Urology, lcsh:RC870-923
Published
2020

32. Obesity, Diabetes, Coffee, Tea, and Cannabis Use Alter Risk for Alcohol-Related Cirrhosis in 2 Large Cohorts of High-Risk Drinkers

Author

Florian Eyer, Suthat Liangpunsakul, Pascal Perney, Dermot Gleeson, John Whitfield, Timothy R. Morgan, Guruprasad P. Aithal, Pierre Nahon, Devanshi Seth, Felix Stickel, Steven Masson, Sylvie Naveau, Romain Moirand, Helmut K. Seitz, Lawrence Lumeng, Michael Soyka, Beat Muellhaupt, Philippe Mathurin, Paul S. Haber, Heather J. Cordell, Sebastian Mueller, Andrew Thompson, Bertrand Nalpas, Tatiana Foroud, Christopher P. Day, Munir Pirmohamed, Ann K. Daly, Jean-Marc Jacquet, QIMR Berghofer Medical Research Institute, Newcastle University [Newcastle], Indiana University System, Heidelberg University, University of Nottingham, UK (UON), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Royal Hallamshire Hospital, University of Liverpool, University hospital of Zurich [Zurich], University-Hospital Munich-Großhadern [München], Indiana University School of Medicine, Hôpital Universitaire Carémeau [Nîmes] (CHU Nîmes), Centre Hospitalier Universitaire de Nîmes (CHU Nîmes), Nutrition, Métabolismes et Cancer (NuMeCan), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), CHU Pontchaillou [Rennes], Génomique Fonctionnelle des Tumeurs Solides (U1162), Université Paris 13 (UP13)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Hôpital Jean Verdier [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), AP-HP - Hôpital Antoine Béclère [Clamart], The University of Sydney, Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), California State University [Long Beach] (CSULB ), University of California (UC), Jonchère, Laurent, Technical University of Munich (TUM), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of California, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)

Subjects
Male, medicine.medical_specialty, Cirrhosis, Alcohol Drinking, [SDV]Life Sciences [q-bio], Wine, Coffee, 03 medical and health sciences, Liver disease, 0302 clinical medicine, Liver Cirrhosis, Alcoholic, Risk Factors, Weight loss, Germany, Diabetes mellitus, Internal medicine, Diabetes Mellitus, medicine, Humans, Obesity, Medical History Taking, 2. Zero hunger, Tea, Hepatology, business.industry, Alcoholic Beverages, Smoking, Australia, Gastroenterology, Case-control study, Odds ratio, Middle Aged, medicine.disease, United Kingdom, United States, 3. Good health, [SDV] Life Sciences [q-bio], Logistic Models, Case-Control Studies, 030220 oncology & carcinogenesis, Female, Marijuana Use, 030211 gastroenterology & hepatology, France, medicine.symptom, business, Body mass index, Switzerland
Abstract

International audience; Introduction - Sustained high alcohol intake is necessary but not sufficient to produce alcohol-related cirrhosis. Identification of risk factors, apart from lifetime alcohol exposure, would assist in discovery of mechanisms and prediction of risk. Methods - We conducted a multicenter case-control study (GenomALC) comparing 1,293 cases (with alcohol-related cirrhosis, 75.6% male) and 754 controls (with equivalent alcohol exposure but no evidence of liver disease, 73.6% male). Information confirming or excluding cirrhosis, and on alcohol intake and other potential risk factors, was obtained from clinical records and by interview. Case-control differences in risk factors discovered in the GenomALC participants were validated using similar data from 407 cases and 6,573 controls from UK Biobank. Results - The GenomALC case and control groups reported similar lifetime alcohol intake (1,374 vs 1,412 kg). Cases had a higher prevalence of diabetes (20.5% (262/1,288) vs 6.5% (48/734), P = 2.27 × 10) and higher premorbid body mass index (26.37 ± 0.16 kg/m) than controls (24.44 ± 0.18 kg/m, P = 5.77 × 10). Controls were significantly more likely to have been wine drinkers, coffee drinkers, smokers, and cannabis users than cases. Cases reported a higher proportion of parents who died of liver disease than controls (odds ratio 2.25 95% confidence interval 1.55-3.26). Data from UK Biobank confirmed these findings for diabetes, body mass index, proportion of alcohol as wine, and coffee consumption. Discussion - If these relationships are causal, measures such as weight loss, intensive treatment of diabetes or prediabetic states, and coffee consumption should reduce the risk of alcohol-related cirrhosis.

Published
2020
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33. A Genome-wide Association Study Identifies SERPINB10, CRLF3, STX7, LAMP3, IFNG-AS1, and KRT80 As Risk Loci Contributing to Cutaneous Leishmaniasis in Brazil

Author

Jenefer M. Blackwell, Andréa Teixeira-Carvalho, Svetlana Cherlin, Lucas Almeida, Natalia S. Alves, Walderez O. Dutra, Léa Cristina Castellucci, Katia L.P. Morais, Tainã Souza do Lago, Clara M Cavalcanti, Amanda B. Figueiredo, Richard W. Francis, Michaela Fakiola, Edgar M. Carvalho, Kenneth J Gollob, Anadilton Santos da Hora, and Heather J. Cordell

Subjects
0301 basic medicine, Microbiology (medical), Keratins, Type II, Leishmaniasis, Cutaneous, Genome-wide association study, Polymorphism, Single Nucleotide, 03 medical and health sciences, Interferon-gamma, 0302 clinical medicine, interferon-γ, Genotype, Medicine, GWAS, Humans, Interferon gamma, Genetic Predisposition to Disease, Receptors, Cytokine, Enhancer, Online Only Articles, Gene, Genotyping, Serpins, Leishmania, IFNG-AS1, business.industry, Lysosome-Associated Membrane Glycoproteins, Molecular biology, Chromatin, Neoplasm Proteins, Major Articles and Commentaries, 030104 developmental biology, Infectious Diseases, AcademicSubjects/MED00290, post-GWAS integrated analysis, Expression quantitative trait loci, business, 030217 neurology & neurosurgery, Brazil, medicine.drug, Genome-Wide Association Study
Abstract

Background Our goal was to identify genetic risk factors for cutaneous leishmaniasis (CL) caused by Leishmania braziliensis. Methods Genotyping 2066 CL cases and 2046 controls using Illumina HumanCoreExomeBeadChips provided data for 4 498 586 imputed single-nucleotide variants (SNVs). A genome-wide association study (GWAS) using linear mixed models took account of genetic diversity/ethnicity/admixture. Post-GWAS positional, expression quantitative trait locus (eQTL) and chromatin interaction mapping was performed in Functional Mapping and Annotation (FUMA). Transcriptional data were compared between lesions and normal skin, and cytokines measured using flow cytometry and Bioplex assay. Results Positional mapping identified 32 genomic loci associated with CL, none achieving genome-wide significance (P < 5 × 10−8). Lead SNVs at 23 loci occurred at protein coding or noncoding RNA genes, 15 with eQTLs for functionally relevant cells/tissues and/or showing differential expression in lesions. Of these, the 6 most plausible genetic risk loci were SERPINB10 (Pimputed_1000G = 2.67 × 10−6), CRLF3 (Pimputed_1000G = 5.12 × 10−6), STX7 (Pimputed_1000G = 6.06 × 10−6), KRT80 (Pimputed_1000G = 6.58 × 10−6), LAMP3 (Pimputed_1000G = 6.54 × 10−6), and IFNG-AS1 (Pimputed_1000G = 1.32 × 10−5). LAMP3 (Padjusted = 9.25 × 10−12; +6-fold), STX7 (Padjusted = 7.62 × 10−3; +1.3-fold), and CRLF3 (Padjusted = 9.19 × 10−9; +1.97-fold) were expressed more highly in CL biopsies compared to normal skin; KRT80 (Padjusted = 3.07 × 10−8; −3-fold) was lower. Multiple cis-eQTLs across SERPINB10 mapped to chromatin interaction regions of transcriptional/enhancer activity in neutrophils, monocytes, B cells, and hematopoietic stem cells. Those at IFNG-AS1 mapped to transcriptional/enhancer regions in T, natural killer, and B cells. The percentage of peripheral blood CD3+ T cells making antigen-specific interferon-γ differed significantly by IFNG-AS1 genotype. Conclusions This first GWAS for CL identified multiple genetic risk loci including a novel lead to understanding CL pathogenesis through regulation of interferon-γ by IFNG antisense RNA 1., Genome-wide analysis of 2066 cases and 2046 controls, together with genotypic differences in antigen-specific interferon-γ made by CD3+ T cells, identifies IFNG-AS1 as a genetic risk factor for cutaneous leishmaniasis caused by Leishmania braziliensis.

Published
2020

34. Investigation of prediction accuracy and the impact of sample size, ancestry, and tissue in transcriptome-wide association studies

Author

James J. Fryett, Heather J. Cordell, and Andrew P. Morris

Subjects
Elastic net regularization, Mixed model, Genotype, Epidemiology, Bayesian probability, complex traits, 03 medical and health sciences, transcriptome‐wide association study, Statistics, Humans, Research Articles, Genetics (clinical), 030304 developmental biology, Mathematics, Genetic association, 0303 health sciences, Models, Statistical, Models, Genetic, 030305 genetics & heredity, Reproducibility of Results, Bayes Theorem, Statistical model, Regression, Expression (mathematics), Pedigree, Phenotype, Sample size determination, Sample Size, gene expression, Transcriptome, Research Article, Genome-Wide Association Study
Abstract

In transcriptome‐wide association studies (TWAS), gene expression values are predicted using genotype data and tested for association with a phenotype. The power of this approach to detect associations relies, at least in part, on the accuracy of the prediction. Here we compare the prediction accuracy of six different methods—LASSO, Ridge regression, Elastic net, Best Linear Unbiased Predictor, Bayesian Sparse Linear Mixed Model, and Random Forests—by performing cross‐validation using data from the Geuvadis Project. We also examine prediction accuracy (a) at different sample sizes, (b) when ancestry of the prediction model training and testing populations is different, and (c) when the tissue used to train the model is different from the tissue to be predicted. We find that, for most genes, the expression cannot be accurately predicted, but in general sparse statistical models tend to outperform polygenic models at prediction. Average prediction accuracy is reduced when the model training set size is reduced or when predicting across ancestries and is marginally reduced when predicting across tissues. We conclude that using sparse statistical models and the development of large reference panels across multiple ethnicities and tissues will lead to better prediction of gene expression, and thus may improve TWAS power.

Published
2020
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35. Corrigendum to: 'An international genome-wide meta-analysis of primary biliary cholangitis: Novel risk loci and candidate drugs' [J Hepatol 75 (2021) 572-581]

Author

Heather J. Cordell, James J. Fryett, Kazuko Ueno, Rebecca Darlay, Yoshihiro Aiba, Yuki Hitomi, Minae Kawashima, Nao Nishida, Seik-Soon Khor, Olivier Gervais, Yosuke Kawai, Masao Nagasaki, Katsushi Tokunaga, Ruqi Tang, Yongyong Shi, Zhiqiang Li, Brian D. Juran, Elizabeth J. Atkinson, Alessio Gerussi, Marco Carbone, Rosanna Asselta, Angela Cheung, Mariza de Andrade, Aris Baras, Julie Horowitz, Manuel A.R. Ferreira, Dylan Sun, David E. Jones, Steven Flack, Ann Spicer, Victoria L. Mulcahy, Jinyoung Byan, Younghun Han, Richard N. Sandford, Konstantinos N. Lazaridis, Christopher I. Amos, Gideon M. Hirschfield, Michael F. Seldin, Pietro Invernizzi, Katherine A. Siminovitch, Xiong Ma, Minoru Nakamura, and George F. Mells

Subjects
Hepatology
Published
2023
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36. Corrigendum to 'An international genome-wide meta-analysis of primary biliary cholangitis: Novel risk loci and candidate drugs' [J Hepatol 2021;75(3):572-581]

Author

Heather J, Cordell, James J, Fryett, Kazuko, Ueno, Rebecca, Darlay, Yoshihiro, Aiba, Yuki, Hitomi, Minae, Kawashima, Nao, Nishida, Seik-Soon, Khor, Olivier, Gervai, Yosuke, Kawai, Masao, Nagasaki, Katsushi, Tokunaga, Ruqi, Tang, Yongyong, Shi, Zhiqiang, Li, Brian D, Juran, Elizabeth J, Atkinson, Alessio, Gerussi, Marco, Carbone, Rosanna, Asselta, Angela, Cheung, Mariza, de Andrade, Aris, Bara, Julie, Horowitz, Manuel A R, Ferreira, Dylan, Sun, David E, Jone, Steven, Flack, Ann, Spicer, Victoria L, Mulcahy, Jinyoung, Byun, Younghun, Han, Richard N, Sandford, Konstantinos N, Lazaridi, Christopher I, Amo, Gideon M, Hirschfield, Michael F, Seldin, Pietro, Invernizzi, Katherine A, Siminovitch, Xiong, Ma, Minoru, Nakamura, George F, Mell, Siminovitch, Katherine A., Hirschfield, Gideon M., Mason, Andrew, Vincent, Catherine, Xie, Gang, Zhang, Jinyi, Tang, Ruqi, Ma, Xiong, Li, Zhiqiang, Shi, Yongyong, Affronti, Andrea, Almasio, Piero L., Alvaro, Domenico, Andreone, Pietro, Andriulli, Angelo, Azzaroli, Francesco, Battezzati, Pier Maria, Benedetti, Antonio, Bragazzi, Maria Consiglia, Brunetto, Maurizia, Bruno, Savino, Calvaruso, Vincenza, Cardinale, Vincenzo, Casella, Giovanni, Cazzagon, Nora, Ciaccio, Antonio, Coco, Barbara, Colli, Agostino, Colloredo, Guido, Colombo, Massimo, Colombo, Silvia, Cristoferi, Laura, Cursaro, Carmela, Saveria Crocè, Lory, Crosignani, Andrea, D’Amato, Daphne, Donato, Francesca, Elia, Gianfranco, Fabris, Luca, Fagiuoli, Stefano, Ferrari, Carlo, Floreani, Annarosa, Galli, Andrea, Giannini, Edoardo, Grattagliano, Ignazio, Lampertico, Pietro, Lleo, Ana, Malinverno, Federica, Mancuso, Clara, Marra, Fabio, Marzioni, Marco, Massironi, Sara, Mattalia, Alberto, Miele, Luca, Milani, Chiara, Morini, Lorenzo, Morisco, Filomena, Muratori, Luigi, Muratori, Paolo, Niro, Grazia A., O’Donnell, Sarah, Picciotto, Antonio, Portincasa, Piero, Rigamonti, Cristina, Ronca, Vincenzo, Rosina, Floriano, Spinzi, Giancarlo, Strazzabosco, Mario, Tarocchi, Mirko, Tiribelli, Claudio, Toniutto, Pierluigi, Valenti, Luca, Vinci, Maria, Zuin, Massimo, Consortium, Japan-PBC-GWAS, PBC Consortium, U, Consortium, UK-PBC, Luca, Miele (ORCID:0000-0003-3464-0068), Heather J, Cordell, James J, Fryett, Kazuko, Ueno, Rebecca, Darlay, Yoshihiro, Aiba, Yuki, Hitomi, Minae, Kawashima, Nao, Nishida, Seik-Soon, Khor, Olivier, Gervai, Yosuke, Kawai, Masao, Nagasaki, Katsushi, Tokunaga, Ruqi, Tang, Yongyong, Shi, Zhiqiang, Li, Brian D, Juran, Elizabeth J, Atkinson, Alessio, Gerussi, Marco, Carbone, Rosanna, Asselta, Angela, Cheung, Mariza, de Andrade, Aris, Bara, Julie, Horowitz, Manuel A R, Ferreira, Dylan, Sun, David E, Jone, Steven, Flack, Ann, Spicer, Victoria L, Mulcahy, Jinyoung, Byun, Younghun, Han, Richard N, Sandford, Konstantinos N, Lazaridi, Christopher I, Amo, Gideon M, Hirschfield, Michael F, Seldin, Pietro, Invernizzi, Katherine A, Siminovitch, Xiong, Ma, Minoru, Nakamura, George F, Mell, Siminovitch, Katherine A., Hirschfield, Gideon M., Mason, Andrew, Vincent, Catherine, Xie, Gang, Zhang, Jinyi, Tang, Ruqi, Ma, Xiong, Li, Zhiqiang, Shi, Yongyong, Affronti, Andrea, Almasio, Piero L., Alvaro, Domenico, Andreone, Pietro, Andriulli, Angelo, Azzaroli, Francesco, Battezzati, Pier Maria, Benedetti, Antonio, Bragazzi, Maria Consiglia, Brunetto, Maurizia, Bruno, Savino, Calvaruso, Vincenza, Cardinale, Vincenzo, Casella, Giovanni, Cazzagon, Nora, Ciaccio, Antonio, Coco, Barbara, Colli, Agostino, Colloredo, Guido, Colombo, Massimo, Colombo, Silvia, Cristoferi, Laura, Cursaro, Carmela, Saveria Crocè, Lory, Crosignani, Andrea, D’Amato, Daphne, Donato, Francesca, Elia, Gianfranco, Fabris, Luca, Fagiuoli, Stefano, Ferrari, Carlo, Floreani, Annarosa, Galli, Andrea, Giannini, Edoardo, Grattagliano, Ignazio, Lampertico, Pietro, Lleo, Ana, Malinverno, Federica, Mancuso, Clara, Marra, Fabio, Marzioni, Marco, Massironi, Sara, Mattalia, Alberto, Miele, Luca, Milani, Chiara, Morini, Lorenzo, Morisco, Filomena, Muratori, Luigi, Muratori, Paolo, Niro, Grazia A., O’Donnell, Sarah, Picciotto, Antonio, Portincasa, Piero, Rigamonti, Cristina, Ronca, Vincenzo, Rosina, Floriano, Spinzi, Giancarlo, Strazzabosco, Mario, Tarocchi, Mirko, Tiribelli, Claudio, Toniutto, Pierluigi, Valenti, Luca, Vinci, Maria, Zuin, Massimo, Consortium, Japan-PBC-GWAS, PBC Consortium, U, Consortium, UK-PBC, and Luca, Miele (ORCID:0000-0003-3464-0068)

Abstract

N/A

Published
2022

37. Compare and Contrast Meta Analysis (CCMA): A Method for Identification of Pleiotropic Loci in Genome-Wide Association Studies.

Author

Hansjörg Baurecht, Melanie Hotze, Elke Rodríguez, Judith Manz, Stephan Weidinger, Heather J Cordell, Thomas Augustin, and Konstantin Strauch

Subjects
Medicine, Science
Abstract

In recent years, genome-wide association studies (GWAS) have identified many loci that are shared among common disorders and this has raised interest in pleiotropy. For performing appropriate analysis, several methods have been proposed, e.g. conducting a look-up in external sources or exploiting GWAS results by meta-analysis based methods. We recently proposed the Compare & Contrast Meta-Analysis (CCMA) approach where significance thresholds were obtained by simulation. Here we present analytical formulae for the density and cumulative distribution function of the CCMA test statistic under the null hypothesis of no pleiotropy and no association, which, conveniently for practical reasons, turns out to be exponentially distributed. This allows researchers to apply the CCMA method without having to rely on simulations. Finally, we show that CCMA demonstrates power to detect disease-specific, agonistic and antagonistic loci comparable to the frequently used Subset-Based Meta-Analysis approach, while better controlling the type I error rate.

Published
2016
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38. Corrigendum to ‘An international genome-wide meta-analysis of primary biliary cholangitis: Novel risk loci and candidate drugs’ [J Hepatol 2021;75(3):572–581]

Author

Heather J. Cordell, James J. Fryett, Kazuko Ueno, Rebecca Darlay, Yoshihiro Aiba, Yuki Hitomi, Minae Kawashima, Nao Nishida, Seik-Soon Khor, Olivier Gervais, Yosuke Kawai, Masao Nagasaki, Katsushi Tokunaga, Ruqi Tang, Yongyong Shi, Zhiqiang Li, Brian D. Juran, Elizabeth J. Atkinson, Alessio Gerussi, Marco Carbone, Rosanna Asselta, Angela Cheung, Mariza de Andrade, Aris Baras, Julie Horowitz, Manuel A.R. Ferreira, Dylan Sun, David E. Jones, Steven Flack, Ann Spicer, Victoria L. Mulcahy, Jinyoung Byun, Younghun Han, Richard N. Sandford, Konstantinos N. Lazaridis, Christopher I. Amos, Gideon M. Hirschfield, Michael F. Seldin, Pietro Invernizzi, Katherine A. Siminovitch, Xiong Ma, Minoru Nakamura, George F. Mells, Andrew Mason, Catherine Vincent, Gang Xie, Jinyi Zhang, Andrea Affronti, Piero L. Almasio, Domenico Alvaro, Pietro Andreone, Angelo Andriulli, Francesco Azzaroli, Pier Maria Battezzati, Antonio Benedetti, Maria Consiglia Bragazzi, Maurizia Brunetto, Savino Bruno, Vincenza Calvaruso, Vincenzo Cardinale, Giovanni Casella, Nora Cazzagon, Antonio Ciaccio, Barbara Coco, Agostino Colli, Guido Colloredo, Massimo Colombo, Silvia Colombo, Laura Cristoferi, Carmela Cursaro, Lory Saveria Crocè, Andrea Crosignani, Daphne D’Amato, Francesca Donato, Gianfranco Elia, Luca Fabris, Stefano Fagiuoli, Carlo Ferrari, Annarosa Floreani, Andrea Galli, Edoardo Giannini, Ignazio Grattagliano, Pietro Lampertico, Ana Lleo, Federica Malinverno, Clara Mancuso, Fabio Marra, Marco Marzioni, Sara Massironi, Alberto Mattalia, Luca Miele, Chiara Milani, Lorenzo Morini, Filomena Morisco, Luigi Muratori, Paolo Muratori, Grazia A. Niro, Sarah O’Donnell, Antonio Picciotto, Piero Portincasa, Cristina Rigamonti, Vincenzo Ronca, Floriano Rosina, Giancarlo Spinzi, Mario Strazzabosco, Mirko Tarocchi, Claudio Tiribelli, Pierluigi Toniutto, Luca Valenti, Maria Vinci, Massimo Zuin, Hitomi Nakamura, Seigo Abiru, Shinya Nagaoka, Atsumasa Komori, Hiroshi Yatsuhashi, Hiromi Ishibashi, Masahiro Ito, Kiyoshi Migita, Hiromasa Ohira, Shinji Katsushima, Atsushi Naganuma, Kazuhiro Sugi, Tatsuji Komatsu, Tomohiko Mannami, Kouki Matsushita, Kaname Yoshizawa, Fujio Makita, Toshiki Nikami, Hideo Nishimura, Hiroshi Kouno, Hirotaka Kouno, Hajime Ota, Takuya Komura, Yoko Nakamura, Masaaki Shimada, Noboru Hirashima, Toshiki Komeda, Keisuke Ario, Makoto Nakamuta, Tsutomu Yamashita, Kiyoshi Furuta, Masahiro Kikuchi, Noriaki Naeshiro, Hironao Takahashi, Yutaka Mano, Seiji Tsunematsu, Iwao Yabuuchi, Yusuke Shimada, Kazuhiko Yamauchi, Rie Sugimoto, Hironori Sakai, Eiji Mita, Masaharu Koda, Satoru Tsuruta, Hiroshi Kamitsukasa, Takeaki Sato, Naohiko Masaki, Tatsuro Kobata, Nobuyoshi Fukushima, Yukio Ohara, Toyokichi Muro, Eiichi Takesaki, Hitoshi Takaki, Tetsuo Yamamoto, Michio Kato, Yuko Nagaoki, Shigeki Hayashi, Jinya Ishida, Yukio Watanabe, Masakazu Kobayashi, Michiaki Koga, Takeo Saoshiro, Michiyasu Yagura, Keisuke Hirata, Atsushu Tanaka, Hajime Takikawa, Mikio Zeniya, Masanori Abe, Morikazu Onji, Shuichi Kaneko, Masao Honda, Kuniaki Arai, Teruko Arinaga-Hino, Etsuko Hashimoto, Makiko Taniai, Takeji Umemura, Satoru Joshita, Kazuhiko Nakao, Tatsuki Ichikawa, Hidetaka Shibata, Satoshi Yamagiwa, Masataka Seike, Koichi Honda, Shotaro Sakisaka, Yasuaki Takeyama, Masaru Harada, Michio Senju, Osamu Yokosuka, Tatsuo Kanda, Yoshiyuki Ueno, Kentaro Kikuchi, Hirotoshi Ebinuma, Takashi Himoto, Michio Yasunami, Kazumoto Murata, Masashi Mizokami, Kazuhito Kawata, Shinji Shimoda, Yasuhiro Miyake, Akinobu Takaki, Kazuhide Yamamoto, Katsuji Hirano, Takafumi Ichida, Akio Ido, Hirohito Tsubouchi, Kazuaki Chayama, Kenichi Harada, Yasuni Nakanuma, Yoshihiko Maehara, Akinobu Taketomi, Ken Shirabe, Yuji Soejima, Akira Mori, Shintaro Yagi, Shinji Uemoto, Egawa H, Tomohiro Tanaka, Noriyo Yamashiki, Sumito Tamura, Yasuhiro Sugawara, Norihiro Kokudo, Naga Chalasani, Vel Luketic, Joseph Odin, Kapil Chopra, Goncalo Abecasis, Michael Cantor, Giovanni Coppola, Aris Economides, Luca A. Lotta, John D. Overton, Jeffrey G. Reid, Alan Shuldiner, Christina Beechert, Caitlin Forsythe, Erin D. Fuller, Zhenhua Gu, Michael Lattari, Alexander Lopez, Thomas D. Schleicher, Maria Sotiropoulos Padilla, Karina Toledo, Louis Widom, Sarah E. Wolf, Manasi Pradhan, Kia Manoochehri, Ricardo H. Ulloa, Xiaodong Bai, Suganthi Balasubramanian, Leland Barnard, Andrew Blumenfeld, Gisu Eom, Lukas Habegger, Alicia Hawes, Shareef Khalid, Evan K. Maxwell, William Salerno, Jeffrey C. Staples, Marcus B. Jones, Lyndon J. Mitnaul, Richard Sturgess, Christopher Healey, Andrew Yeoman, Anton V.J. Gunasekera, Paul Kooner, Kapil Kapur, V. Sathyanarayana, Yiannis Kallis, Javaid Subhani, Rory Harvey, Roger McCorry, Paul Rooney, David Ramanaden, Richard Evans, Thiriloganathan Mathialahan, Jaber Gasem, Christopher Shorrock, Mahesh Bhalme, Paul Southern, Jeremy A. Tibble, David A. Gorard, Susan Jones, George Mells, Victoria Mulcahy, Brijesh Srivastava, Matthew R. Foxton, Carole E. Collins, David Elphick, Mazn Karmo, Francisco Porras-Perez, Michael Mendall, Tom Yapp, Minesh Patel, Roland Ede, Joanne Sayer, James Jupp, Neil Fisher, Martyn J. Carter, Konrad Koss, Jayshri Shah, Andrzej Piotrowicz, Glyn Scott, Charles Grimley, Ian R. Gooding, Simon Williams, Judith Tidbury, Guan Lim, Kuldeep Cheent, Sass Levi, Dina Mansour, Matilda Beckley, Coral Hollywood, Terry Wong, Richard Marley, John Ramage, Harriet M. Gordon, Jo Ridpath, Theodore Ngatchu, Vijay Paul Bob Grover, Ray G. Shidrawi, George Abouda, L. Corless, Mark Narain, Ian Rees, Ashley Brown, Simon Taylor-Robinson, Joy Wilkins, Leonie Grellier, Paul Banim, Debasish Das, Michael A. Heneghan, Howard Curtis, Helen C. Matthews, Faiyaz Mohammed, Mark Aldersley, Raj Srirajaskanthan, Giles Walker, Alistair McNair, Amar Sharif, Sambit Sen, George Bird, Martin I. Prince, Geeta Prasad, Paul Kitchen, Adrian Barnardo, Chirag Oza, Nurani N. Sivaramakrishnan, Prakash Gupta, Amir Shah, Chris D.J. Evans, Subrata Saha, Katharine Pollock, Peter Bramley, Ashis Mukhopadhya, Stephen T. Barclay, Natasha McDonald, Andrew J. Bathgate, Kelvin Palmer, John F. Dillon, Simon M. Rushbrook, Robert Przemioslo, Chris McDonald, Andrew Millar, Cheh Tai, Stephen Mitchell, Jane Metcalf, Syed Shaukat, Mary Ninkovic, Udi Shmueli, Andrew Davis, Asifabbas Naqvi, Tom J.W. Lee, Stephen Ryder, Jane Collier, Howard Klass, Matthew E. Cramp, Nichols Sharer, Richard Aspinall, Deb Ghosh, Andrew C. Douds, Jonathan Booth, Earl Williams, Hyder Hussaini, John Christie, Steven Mann, Douglas Thorburn, Aileen Marshall, Imran Patanwala, Aftab Ala, Julia Maltby, Ray Matthew, Chris Corbett, Sam Vyas, Saket Singhal, Dermot Gleeson, Sharat Misra, Jeff Butterworth, Keith George, Tim Harding, Andrew Douglass, Harriet Mitchison, Simon Panter, Jeremy Shearman, Gary Bray, Michael Roberts, Graham Butcher, Daniel Forton, Zahid Mahmood, Matthew Cowan, Debashis Das, Chin Lye Ch'ng, Mesbah Rahman, Gregory C.A. Whatley, Emma Wesley, Aditya Mandal, Sanjiv Jain, Stephen P. Pereira, Mark Wright, Palak Trivedi, Fiona H. Gordon, Esther Unitt, Altaf Palejwala, Andrew Austin, Vishwaraj Vemala, Allister Grant, Andrew D. Higham, Alison Brind, Ray Mathew, Mark Cox, Subramaniam Ramakrishnan, Alistair King, Simon Whalley, Jocelyn Fraser, S.J. Thomson, Andrew Bell, Voi Shim Wong, Richard Kia, Ian Gee, Richard Keld, Rupert Ransford, James Gotto, and Charles Millson

Subjects
Science & Technology, Hepatology, Gastroenterology & Hepatology, Italian PBC Study Group, Japan-PBC-GWAS Consortium, UK-PBC Consortium, Chinese PBC Consortium, 1103 Clinical Sciences, US PBC Consortium, Canadian PBC Consortium, Life Sciences & Biomedicine, PBC Consortia, 1117 Public Health and Health Services
Published
2021

39. Maternal filaggrin mutations increase the risk of atopic dermatitis in children: an effect independent of mutation inheritance.

Author

Jorge Esparza-Gordillo, Anja Matanovic, Ingo Marenholz, Anja Bauerfeind, Klaus Rohde, Katja Nemat, Min-Ae Lee-Kirsch, Magnus Nordenskjöld, Marten C G Winge, Thomas Keil, Renate Krüger, Susanne Lau, Kirsten Beyer, Birgit Kalb, Bodo Niggemann, Norbert Hübner, Heather J Cordell, Maria Bradley, and Young-Ae Lee

Subjects
Genetics, QH426-470
Abstract

Epidemiological studies suggest that allergy risk is preferentially transmitted through mothers. This can be due to genomic imprinting, where the phenotype effect of an allele depends on its parental origin, or due to maternal effects reflecting the maternal genome's influence on the child during prenatal development. Loss-of-function mutations in the filaggrin gene (FLG) cause skin barrier deficiency and strongly predispose to atopic dermatitis (AD). We investigated the 4 most prevalent European FLG mutations (c.2282del4, p.R501X, p.R2447X, and p.S3247X) in two samples including 759 and 450 AD families. We used the multinomial and maximum-likelihood approach implemented in the PREMIM/EMIM tool to model parent-of-origin effects. Beyond the known role of FLG inheritance in AD (R1meta-analysis = 2.4, P = 1.0 x 10-36), we observed a strong maternal FLG genotype effect that was consistent in both independent family sets and for all 4 mutations analysed. Overall, children of FLG-carrier mothers had a 1.5-fold increased AD risk (S1 = 1.50, Pmeta-analysis = 8.4 x 10-8). Our data point to two independent and additive effects of FLG mutations: i) carrying a mutation and ii) having a mutation carrier mother. The maternal genotype effect was independent of mutation inheritance and can be seen as a non-genetic transmission of a genetic effect. The FLG maternal effect was observed only when mothers had allergic sensitization (elevated allergen-specific IgE antibody plasma levels), suggesting that FLG mutation-induced systemic immune responses in the mother may influence AD risk in the child. Notably, the maternal effect reported here was stronger than most common genetic risk factors for AD recently identified through genome-wide association studies (GWAS). Our study highlights the power of family-based studies in the identification of new etiological mechanisms and reveals, for the first time, a direct influence of the maternal genotype on the offspring's susceptibility to a common human disease.

Published
2015
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40. Corrigendum to: ‘A genetic risk score and diabetes predict development of alcohol-related cirrhosis in drinkers’ [J Hepatol 2022 (76) 275–282]

Author

John B. Whitfield, Tae-Hwi Schwantes-An, Rebecca Darlay, Guruprasad P. Aithal, Stephen R. Atkinson, Ramon Bataller, Greg Botwin, Naga P. Chalasani, Heather J. Cordell, Ann K. Daly, Christopher P. Day, Florian Eyer, Tatiana Foroud, Dermot Gleeson, David Goldman, Paul S. Haber, Jean-Marc Jacquet, Tiebing Liang, Suthat Liangpunsakul, Steven Masson, Philippe Mathurin, Romain Moirand, Andrew McQuillin, Christophe Moreno, Marsha Y. Morgan, Sebastian Mueller, Beat Müllhaupt, Laura E. Nagy, Pierre Nahon, Bertrand Nalpas, Sylvie Naveau, Pascal Perney, Munir Pirmohamed, Helmut K. Seitz, Michael Soyka, Felix Stickel, Andrew Thompson, Mark R. Thursz, Eric Trépo, Timothy R. Morgan, and Devanshi Seth

Subjects
Hepatology
Published
2022
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41. Genome-wide association study identifies susceptibility loci for acute myeloid leukemia

Author

Anne M. Dickinson, Gail Jones, David C. Linch, Clare Lendrem, David Grimwade, Richard A. Larson, Andrew D. Skol, Yaobo Xu, Adam Ivey, Wei-Yu Lin, Manja Meggendorfer, Rosemary E. Gale, Inés Gómez-Seguí, Giovani Marconi, Jean Norden, Jude Fitzgibbon, Mette K. Andersen, M Bornhäuser, Sarah E. Fordham, Amanda F. Gilkes, Heinz Sill, Eric A. Hungate, José Cervera, Friedrich Stölzel, Julia Gaal-Wesinger, Kim Piechocki, Wendy Stock, Theresa Hahn, Konstantin Strauch, David Allsup, Kenan Onel, Claire Elstob, Alyssa I. Clay-Gilmour, Nicola J. Sunter, Jelena D. Milosevic Feenstra, Meyling Cheok, Abrar Alharbi, Ann K. Daly, Sally Jeffries, Lisa Wagenführ, Olaf Heidenreich, Robert Kralovics, Alan K. Burnett, Giovanni Martinelli, Desiree Kunadt, Christian Gieger, Francesco Lo-Coco, Leo Ruhnke, Maria Teresa Voso, Junke Wang, Catherine Park, Nigel H. Russell, Chimène Moreilhon, Robert Kerrin Hills, Claude Preudhomme, Graham Jackson, Daniel Nowak, Maria Chiara Fontana, James M. Allan, Heidi Altmann, Richard S. Houlston, Anne S. Quante, Michelle M. Le Beau, Thahira Rahman, Christoph Röllig, Rebecca Darlay, Sophie Raynaud, Helen Marr, Csaba Bödör, Louise Palm, Thomas Cluzeau, Szilvia Krizsán, Heather J. Cordell, Mathew Collin, Torsten Haferlach, Lara E. Sucheston-Campbell, Wolf-Karsten Hofmann, Kimmo Porkka, Andras Masszi, Hervé Dombret, Miguel A. Sanz, Elisabeth Douglas, Tobias Menne, HUS Comprehensive Cancer Center, University Management, Helsinki University Hospital Area, Department of Oncology, and Hematologian yksikkö

Subjects
Oncology, General Physics and Astronomy, Genome-wide association study, Disease, 0302 clinical medicine, AML, HLA Antigens, hemic and lymphatic diseases, Histone methylation, Cancer genomics, RISK, 0303 health sciences, Multidisciplinary, Myeloid leukemia, Middle Aged, CLONAL EVOLUTION, CANCER, 3. Good health, HLA, Leukemia, Myeloid, Acute, 030220 oncology & carcinogenesis, GENE-MUTATIONS, medicine.medical_specialty, Genotype, Science, Locus (genetics), HIF-1-ALPHA, Human leukocyte antigen, Polymorphism, Single Nucleotide, Article, Acute myeloid leukaemia, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Aldehyde Reductase, Internal medicine, medicine, Humans, Genetic Predisposition to Disease, 030304 developmental biology, Genetic association, OLDER PATIENTS, Whites, business.industry, HUMAN-LEUKOCYTE ANTIGEN, Reproducibility of Results, Cancer, General Chemistry, Settore MED/15, medicine.disease, IMMUNE ESCAPE, Risk factors, Case-Control Studies, 3111 Biomedicine, business, Genome-Wide Association Study
Abstract

Acute myeloid leukemia (AML) is a hematological malignancy with an undefined heritable risk. Here we perform a meta-analysis of three genome-wide association studies, with replication in a fourth study, incorporating a total of 4018 AML cases and 10488 controls. We identify a genome-wide significant risk locus for AML at 11q13.2 (rs4930561; P = 2.15 × 10−8; KMT5B). We also identify a genome-wide significant risk locus for the cytogenetically normal AML sub-group (N = 1287) at 6p21.32 (rs3916765; P = 1.51 × 10−10; HLA). Our results inform on AML etiology and identify putative functional genes operating in histone methylation (KMT5B) and immune function (HLA)., Genome wide association studies in cancer are used to understand the heritable genetic contribution to disease risk. Here, the authors perform a genome wide association study in European patients with acute myeloid leukemia and identify loci associated with risk of developing the disease.

Published
2021

42. A genetic risk score and diabetes predict development of alcohol-related cirrhosis in drinkers

Author

Munir Pirmohamed, Guruprasad P. Aithal, Felix Stickel, Mark Thursz, David Goldman, Paul S. Haber, John Whitfield, Romain Moirand, Bertrand Nalpas, Devanshi Seth, Christophe Moreno, Andrew Thompson, Eric Trepo, Stephen R. Atkinson, Rebecca Darlay, Beat Müllhaupt, Timothy R. Morgan, Dermot Gleeson, Sylvie Naveau, Ann K. Daly, Helmut K. Seitz, Michael Soyka, Christopher P. Day, Tatiana Foroud, Jean-Marc Jacquet, Laura E. Nagy, Naga Chalasani, Pascal Perney, Philippe Mathurin, Marsha Y. Morgan, Pierre Nahon, Sebastian Mueller, Tiebing Liang, Florian Eyer, Suthat Liangpunsakul, Heather J. Cordell, Steven Masson, Tae-Hwi Schwantes-An, Ramon Bataller, Greg Botwin, Andrew McQuillin, QIMR Berghofer Medical Research Institute, Indiana University School of Medicine, Indiana University System, Newcastle University [Newcastle], University of Nottingham, UK (UON), Imperial College London, University of Pittsburgh Medical Center [Pittsburgh, PA, États-Unis] (UPMC), California State University [Long Beach] (CSULB ), Indiana University - Purdue University Indianapolis (IUPUI), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institutes of Health [Bethesda] (NIH), The University of Sydney, Hôpital Universitaire Carémeau [Nîmes] (CHU Nîmes), Centre Hospitalier Universitaire de Nîmes (CHU Nîmes), Hôpital Claude Huriez [Lille], CHU Lille, Nutrition, Métabolismes et Cancer (NuMeCan), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University College of London [London] (UCL), Université libre de Bruxelles (ULB), University of Heidelberg, Medical Faculty, University hospital of Zurich [Zurich], Lerner Research Institute, Cleveland Clinic, Génomique Fonctionnelle des Tumeurs Solides (U1162), Université Paris 13 (UP13)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), AP-HP - Hôpital Antoine Béclère [Clamart], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, University of Liverpool, University-Hospital Munich-Großhadern [München], University of California, Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Lerner Research Institute [Cleveland, OH, USA], University of California (UC), Technical University of Munich (TUM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)

Subjects
Liver Cirrhosis, Male, Cirrhosis, Hepatocellular carcinoma, [SDV]Life Sciences [q-bio], Alcohol, Disease, risk stratification, Gastroenterology, Oral and gastrointestinal, Cohort Studies, Liver disease, Alcohol Use and Health, Substance Misuse, chemistry.chemical_compound, 0302 clinical medicine, Liver Cirrhosis, Alcoholic, single nucleotide polymorphism, Cancer, 2. Zero hunger, 0303 health sciences, Framingham Risk Score, genome wide association, Liver Disease, Single Nucleotide, Middle Aged, Alcoholic, 3. Good health, Alcoholism, Public Health and Health Services, 030211 gastroenterology & hepatology, Female, Risk assessment, Liver Cancer, Adult, medicine.medical_specialty, Alcohol Drinking, Chronic Liver Disease and Cirrhosis, Clinical Sciences, coffee, Polymorphism, Single Nucleotide, Risk Assessment, Article, 03 medical and health sciences, GenomALC Consortium, Rare Diseases, Clinical Research, Diabetes mellitus, Internal medicine, Genetics, Diabetes Mellitus, medicine, Humans, Genetic Predisposition to Disease, Genetic Testing, Polymorphism, Metabolic and endocrine, 030304 developmental biology, Gastroenterology & Hepatology, Hepatology, business.industry, [SDV.MHEP.HEG]Life Sciences [q-bio]/Human health and pathology/Hépatology and Gastroenterology, Odds ratio, medicine.disease, chronic alcohol use, Good Health and Well Being, chemistry, Case-Control Studies, genome-wide association, Digestive Diseases, business, Body mass index, Genome-Wide Association Study
Abstract

International audience; BACKGROUND and AIMS: Only a minority of excess alcohol drinkers develop cirrhosis. We developed and evaluated risk stratification scores to identify those at highest risk. METHODS: Three cohorts (GenomALC-1: n=1690, GenomALC-2: n=3037, UK Biobank: relevant n=6898) with a history of heavy alcohol consumption (≥80 g/day (men), ≥50 g/day (women), for ≥10 years) were included. Cases were participants with alcohol-related cirrhosis. Controls had a history of similar alcohol consumption but no evidence of liver disease. Risk scores were computed from up to eight genetic loci identified previously as associated with alcohol-related cirrhosis and three clinical risk factors. Score performance for the stratification of alcohol-related cirrhosis risk was assessed and compared across the alcohol-related liver disease spectrum, including hepatocellular carcinoma (HCC). RESULTS: A combination of three single nucleotide polymorphisms (SNPs) (PNPLA3:rs738409, SUGP1-TM6SF2:rs10401969, HSD17B13:rs6834314) and diabetes status best discriminated for cirrhosis risk. The odds ratio (OR) and 95% confidence intervals (CI) for the extreme score quintiles (Q1-Q5) of the 3-SNP score, based on independent allelic effect size estimates, were 5.99 (4.18;8.60) (GenomALC-1); 2.81 (2.03;3.89) (GenomALC-2); and 3.10 (2.32;4.14) (UK Biobank). Patients with diabetes and high-risk score, compared to those without diabetes and a low-risk score, had ORs increased to 14.7 (7.69;28.1) (GenomALC-1) and 17.1 (11.3;25.7) (UK Biobank). Patients with cirrhosis and HCC had significantly higher mean risk scores than patients with cirrhosis alone (0.76±0.06 versus 0.61±0.02, p=0.007). Score performance was not significantly enhanced by information on additional genetic risk variants, body mass index or coffee consumption. CONCLUSIONS: A risk score based on three genetic risk variants and diabetes status can provide meaningful risk stratification for cirrhosis in excess drinkers, allowing earlier prevention planning including intensive intervention. LAY SUMMARY: Excessive chronic drinking leads to liver cirrhosis in some people, but so far there is no way to identify those at high risk of developing this debilitating disease. Our study has developed a genetic risk score (GRS) test that can identify patients at high risk and shows that the risk of cirrhosis is increased >10-fold with just two risk factors - diabetes and high GRS. Risk assessment using this test has potential for early and personalised management of this disease in high-risk patients.

Published
2021
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43. A Bayesian network approach incorporating imputation of missing data enables exploratory analysis of complex causal biological relationships

Author

Louise N. Reynard, Alexander D. Clark, Arthur G. Pratt, Richard Howey, Najib Naamane, and Heather J. Cordell

Subjects
Cancer Research, B Cells, Gene Identification and Analysis, Gene Expression, 02 engineering and technology, Genetic Networks, QH426-470, computer.software_genre, Biochemistry, White Blood Cells, Animal Cells, 0202 electrical engineering, electronic engineering, information engineering, Medicine and Health Sciences, Imputation (statistics), Genetics (clinical), 0303 health sciences, DNA methylation, T Cells, Applied Mathematics, Simulation and Modeling, Chemical Reactions, Simple random sample, Chromatin, Nucleic acids, Chemistry, Data Interpretation, Statistical, Physical Sciences, 020201 artificial intelligence & image processing, Epigenetics, Data mining, Cellular Types, DNA modification, Algorithms, Chromatin modification, Network Analysis, Network analysis, Research Article, Chromosome biology, Cell biology, Computer and Information Sciences, Immune Cells, Immunology, Biology, Research and Analysis Methods, Methylation, 03 medical and health sciences, Genetics, Humans, Antibody-Producing Cells, Molecular Biology, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Blood Cells, Bayesian network, Biology and Life Sciences, Bayes Theorem, Exploratory analysis, DNA, Missing data, Precision and recall, computer, Mathematics, Data reduction
Abstract

Bayesian networks can be used to identify possible causal relationships between variables based on their conditional dependencies and independencies, which can be particularly useful in complex biological scenarios with many measured variables. Here we propose two improvements to an existing method for Bayesian network analysis, designed to increase the power to detect potential causal relationships between variables (including potentially a mixture of both discrete and continuous variables). Our first improvement relates to the treatment of missing data. When there is missing data, the standard approach is to remove every individual with any missing data before performing analysis. This can be wasteful and undesirable when there are many individuals with missing data, perhaps with only one or a few variables missing. This motivates the use of imputation. We present a new imputation method that uses a version of nearest neighbour imputation, whereby missing data from one individual is replaced with data from another individual, their nearest neighbour. For each individual with missing data, the subsets of variables to be used to select the nearest neighbour are chosen by sampling without replacement the complete data and estimating a best fit Bayesian network. We show that this approach leads to marked improvements in the recall and precision of directed edges in the final network identified, and we illustrate the approach through application to data from a recent study investigating the causal relationship between methylation and gene expression in early inflammatory arthritis patients. We also describe a second improvement in the form of a pseudo-Bayesian approach for upweighting certain network edges, which can be useful when there is prior evidence concerning their directions., Author summary Data analysis using Bayesian networks can help identify possible causal relationships between measured biological variables. Here we propose two improvements to an existing method for Bayesian network analysis. Our first improvement relates to the treatment of missing data. When there is missing data, the standard approach is to remove every individual with any missing data before performing analysis, even if only one or a few variables are missing. This is undesirable as it can reduce the ability of the approach to infer correct relationships. We propose a new method to instead fill in (impute) the missing data prior to analysis. We show through computer simulations that our method improves the reliability of the results obtained, and we illustrate the proposed approach by applying it to data from a recent study in early inflammatory arthritis. We also describe a second improvement involving the upweighting of certain network edges, which can be useful when there is prior evidence concerning their directions.

Published
2021

44. Comprehensive genetic and functional analysis of FcγRs in rituximab therapy for autoimmunity reveals a key role for FcγRIIIa on NK cells

Author

Anne Barton, Heather J. Cordell, Michael D Morgan, Ann W. Morgan, Vinny Davies, Edward M Vital, Andy C. Rawstron, Paul Emery, Maya H Buch, David L. Morris, Dawn Wild, Timothy J. Vyse, Darren Plant, Yasser M. El-Sherbiny, John D. Isaacs, John C. Taylor, James I. Robinson, Ian N. Bruce, Yuzaiful Md Yusof, Lu Liu, Masterplans Consortia, and Jennifer H. Barrett

Subjects
Autoimmune disease, Antibody-dependent cell-mediated cytotoxicity, CD20, biology, business.industry, FCGR3A, medicine.disease, medicine.disease_cause, Autoimmunity, Natural killer cell, medicine.anatomical_structure, Immunology, medicine, biology.protein, Rituximab, business, B cell, medicine.drug
Abstract

B cell depletion using rituximab is widely used to treat autoimmune diseases, but patient response varies. The efficacy of rituximab is limited by the efficiency of depletion. Strategies to improve response include altering rituximab dosing, switching anti-CD20-mAb, alternative B cell targets, or non-B cell targeted therapies. Implementing an appropriate strategy requires understanding of the mechanism(s) of resistance to depletion and, if this varies between individuals, a means to test for it. Rituximab kills B cells via a variety of Fcγ receptor (FcγR)-dependent mechanisms, including antibody-dependent cellular cytotoxicity (ADCC), as well as non-FcγR mechanisms. We conducted a longitudinal cohort study in rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) using two national registries. Qualitative and quantitative FCGR functional variants were measured using multiplexed ligation-dependent probe amplification, supplemented by novel FCGR2C assays.We provide consistent evidence that FCGR3A, specifically increased number of copies of the FCGR3A-158V allele, was the major FcγR gene associated with rituximab response, including clinical response in RA and SLE and depth of B cell depletion in the combined cohort. In SLE, we provide preliminary data suggesting increased FCGR2C ORF copies were also associated with improved clinical response. Furthermore, we demonstrated the impact of disease status and concomitant therapies on both natural killer cell FcγRIIIa expression and rituximab-induced ADCC; demonstrating increased FcγRIIIa expression and FCGR3A genotype were independently associated with clinical response and B cell depletion. Our findings highlight the importance of enhancing FcγR-effector functions, may help stratify patients, and support ongoing development of next-generation CD20 depleting therapeutics.One Sentence SummaryThe high affinity FcγRIIIa allotype on NK cells explains depth of B cell depletion and clinical response in rituximab therapy for autoimmune disease

Published
2021
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45. Linkage Analysis in Autoimmune Addison's Disease: NFATC1 as a Potential Novel Susceptibility Locus.

Author

Anna L Mitchell, Anette Bøe Wolff, Katie MacArthur, Jolanta U Weaver, Bijay Vaidya, Swedish Addision Registry Study Group, Sophie Bensing on behalf of The Swedish Addison Registry Study Group, Martina M Erichsen, Rebecca Darlay, Eystein S Husebye, Heather J Cordell, and Simon H S Pearce

Subjects
Medicine, Science
Abstract

BackgroundAutoimmune Addison's disease (AAD) is a rare, highly heritable autoimmune endocrinopathy. It is possible that there may be some highly penetrant variants which confer disease susceptibility that have yet to be discovered.MethodsDNA samples from 23 multiplex AAD pedigrees from the UK and Norway (50 cases, 67 controls) were genotyped on the Affymetrix SNP 6.0 array. Linkage analysis was performed using Merlin. EMMAX was used to carry out a genome-wide association analysis comparing the familial AAD cases to 2706 UK WTCCC controls. To explore some of the linkage findings further, a replication study was performed by genotyping 64 SNPs in two of the four linked regions (chromosomes 7 and 18), on the Sequenom iPlex platform in three European AAD case-control cohorts (1097 cases, 1117 controls). The data were analysed using a meta-analysis approach.ResultsIn a parametric analysis, applying a rare dominant model, loci on chromosomes 7, 9 and 18 had LOD scores >2.8. In a non-parametric analysis, a locus corresponding to the HLA region on chromosome 6, known to be associated with AAD, had a LOD score >3.0. In the genome-wide association analysis, a SNP cluster on chromosome 2 and a pair of SNPs on chromosome 6 were associated with AAD (P ConclusionThis linkage study has implicated a number of novel chromosomal regions in the pathogenesis of AAD in multiplex AAD families and adds further support to the role of HLA in AAD. The genome-wide association analysis has also identified a region of interest on chromosome 2. A replication study has demonstrated that the NFATC1 gene is worthy of future investigation, however each of the regions identified require further, systematic analysis.

Published
2015
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47. First genome-wide association study in an Australian aboriginal population provides insights into genetic risk factors for body mass index and type 2 diabetes.

Author

Denise Anderson, Heather J Cordell, Michaela Fakiola, Richard W Francis, Genevieve Syn, Elizabeth S H Scaman, Elizabeth Davis, Simon J Miles, Toby McLeay, Sarra E Jamieson, and Jenefer M Blackwell

Subjects
Medicine, Science
Abstract

A body mass index (BMI) >22kg/m2 is a risk factor for type 2 diabetes (T2D) in Aboriginal Australians. To identify loci associated with BMI and T2D we undertook a genome-wide association study using 1,075,436 quality-controlled single nucleotide polymorphisms (SNPs) genotyped (Illumina 2.5M Duo Beadchip) in 402 individuals in extended pedigrees from a Western Australian Aboriginal community. Imputation using the thousand genomes (1000G) reference panel extended the analysis to 6,724,284 post quality-control autosomal SNPs. No associations achieved genome-wide significance, commonly accepted as P45,000 years ago. The top hit (rs10868204 Pgenotyped = 1.50x10-6; rs11140653 Pimputed_1000G = 2.90x10-7) for BMI lies 5' of NTRK2, the type 2 neurotrophic tyrosine kinase receptor for brain-derived neurotrophic factor (BDNF) that regulates energy balance downstream of melanocortin-4 receptor (MC4R). PIK3C2G (rs12816270 Pgenotyped = 8.06x10-6; rs10841048 Pimputed_1000G = 6.28x10-7) was associated with BMI, but not with T2D as reported elsewhere. BMI also associated with CNTNAP2 (rs6960319 Pgenotyped = 4.65x10-5; rs13225016 Pimputed_1000G = 6.57x10-5), previously identified as the strongest gene-by-environment interaction for BMI in African-Americans. The top hit (rs11240074 Pgenotyped = 5.59x10-6, Pimputed_1000G = 5.73x10-6) for T2D lies 5' of BCL9 that, along with TCF7L2, promotes beta-catenin's transcriptional activity in the WNT signaling pathway. Additional hits occurred in genes affecting pancreatic (KCNJ6, KCNA1) and/or GABA (GABRR1, KCNA1) functions. Notable associations observed for genes previously identified at genome-wide significance in other populations included MC4R (Pgenotyped = 4.49x10-4) for BMI and IGF2BP2 Pimputed_1000G = 2.55x10-6) for T2D. Our results may provide novel functional leads in understanding disease pathogenesis in this Australian Aboriginal population.

Published
2015
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48. Statistical methods for genome-wide association studies

Author

Heather J. Cordell, Kristel Van Steen, and Maggie Haitian Wang

Subjects
0301 basic medicine, Cancer Research, Association test, Genotype, endocrine system diseases, Single-nucleotide polymorphism, Genome-wide association study, Computational biology, Biology, Polymorphism, Single Nucleotide, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Genetic Testing, Genetic association, Genetic testing, medicine.diagnostic_test, Genetic variants, nutritional and metabolic diseases, Validation methods, Phenotype, 030104 developmental biology, 030220 oncology & carcinogenesis, Data quality, Genome-Wide Association Study
Abstract

Genome-wide association studies (GWAS) detect common genetic variants associated with complex disorders. With their comprehensive coverage of common single nucleotide polymorphisms and comparatively low cost, GWAS are an attractive tool in the clinical and commercial genetic testing. This review introduces the pipeline of statistical methods used in GWAS analysis, from data quality control, association tests, population structure control, interaction effects and results visualization, through to post-GWAS validation methods and related issues.

Published
2019
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49. Comparison of methods to account for relatedness in genome-wide association studies with family-based data.

Author

Jakris Eu-Ahsunthornwattana, E Nancy Miller, Michaela Fakiola, Wellcome Trust Case Control Consortium, Selma M B Jeronimo, Jenefer M Blackwell, and Heather J Cordell

Subjects
Genetics, QH426-470
Abstract

Approaches based on linear mixed models (LMMs) have recently gained popularity for modelling population substructure and relatedness in genome-wide association studies. In the last few years, a bewildering variety of different LMM methods/software packages have been developed, but it is not always clear how (or indeed whether) any newly-proposed method differs from previously-proposed implementations. Here we compare the performance of several LMM approaches (and software implementations, including EMMAX, GenABEL, FaST-LMM, Mendel, GEMMA and MMM) via their application to a genome-wide association study of visceral leishmaniasis in 348 Brazilian families comprising 3626 individuals (1972 genotyped). The implementations differ in precise details of methodology implemented and through various user-chosen options such as the method and number of SNPs used to estimate the kinship (relatedness) matrix. We investigate sensitivity to these choices and the success (or otherwise) of the approaches in controlling the overall genome-wide error-rate for both real and simulated phenotypes. We compare the LMM results to those obtained using traditional family-based association tests (based on transmission of alleles within pedigrees) and to alternative approaches implemented in the software packages MQLS, ROADTRIPS and MASTOR. We find strong concordance between the results from different LMM approaches, and all are successful in controlling the genome-wide error rate (except for some approaches when applied naively to longitudinal data with many repeated measures). We also find high correlation between LMMs and alternative approaches (apart from transmission-based approaches when applied to SNPs with small or non-existent effects). We conclude that LMM approaches perform well in comparison to competing approaches. Given their strong concordance, in most applications, the choice of precise LMM implementation cannot be based on power/type I error considerations but must instead be based on considerations such as speed and ease-of-use.

Published
2014
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50. Genome-wide association study identifies susceptibility loci for acute myeloid leukemia

Author

Michelle M. Le Beau, Thahira Rahman, Yaobo Xu, Wendy Stock, Andrew D. Skol, Abrar Alharbi, David Allsup, Claire Elstob, Lara E. Sucheston-Campbell, Lisa Wagenführ, Olaf Heidenreich, Claude Preudhomme, Tobias Menne, Szilvia Krizsán, Rebecca Darlay, Jelena D. Milosevic Feenstra, David C. Linch, Sophie Raynaud, Helen Marr, Christian Gieger, Francesco Lo-Coco, David Grimwade, Maria Teresa Voso, Junke Wang, Christoph Röllig, Clare Lendrem, Wolf-Karsten Hofmann, Mathew Collin, Manja Meggendorfer, Friedrich Stölzel, Wei-Yu Lin, Ann K. Daly, Theresa Hahn, Torsten Haferlach, Sally Jeffries, Julia Gaal-Wesinger, Konstantin Strauch, Giovani Marconi, Amanda F. Gilkes, Chimène Moreilhon, Giovanni Martinelli, Anne M. Dickinson, Robert Kerrin Hills, Alan K. Burnett, Mette K. Andersen, Leo Ruhnke, Kimmo Porkka, Catherine Park, Desiree Kunadt, Nigel H. Russell, M Bornhäuser, Alyssa I. Clay-Gilmour, Hervé Dombret, Sarah E. Fordham, Eric A. Hungate, Miguel A. Sanz, Inés Gómez-Seguí, Csaba Bödör, Jean Norden, Elisabeth Douglas, Rosemary E. Gale, Heinz Sill, Kim Piechocki, Richard A. Larson, Robert Kralovics, Meyling Cheok, Heidi Altmann, Richard S. Houlston, Andras Masszi, Anne S. Quante, Louise Palm, Thomas Cluzeau, Heather J. Cordell, Nicola J. Sunter, Graham Jackson, Daniel Nowak, Maria Chiara Fontana, James M. Allan, José Cervera, Kenan Onel, Gail Jones, Adam Ivey, and Jude Fitzgibbon

Subjects
0303 health sciences, Myeloid leukemia, Locus (genetics), Genome-wide association study, Human leukocyte antigen, Biology, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Immune system, 030220 oncology & carcinogenesis, Histone methylation, Cancer research, Etiology, 030304 developmental biology, Genetic association
Abstract

Acute myeloid leukemia (AML) is a hematological malignancy with an undefined heritable risk. Here we performed a meta-analysis of three genome-wide association studies, with replication in a fourth study, incorporating a total of 4018 AML cases and 10488 controls. We identified a genome-wide significant risk locus for AML at 11q13.2 (rs4930561; P = 2.15 × 10-8; KMT5B). We also identified a genome-wide significant risk locus for the cytogenetically normal AML sub-group (N=1287) at 6p21.32 (rs3916765; P = 1.51 × 10-10; HLA). Our results inform on AML etiology by identifying putative functional genes operating in histone methylation (KMT5B) and immune function (HLA).

Published
2021
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