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Pagoni P, Higgins JPT, Lawlor DA, Stergiakouli E, Warrington NM, Morris TT, Tilling K. Meta-regression of genome-wide association studies to estimate age-varying genetic effects. Eur J Epidemiol 2024; 39:257-270. [PMID: 38183607 PMCID: PMC10995067 DOI: 10.1007/s10654-023-01086-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 11/15/2023] [Indexed: 01/08/2024]
Abstract
Fixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age (or other characteristics), and not allowing for this in a GWAS might lead to bias. Meta-regression models between study heterogeneity and allows effect modification of the genetic effects to be explored. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes. With simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of the FTO SNP rs9939609 on Body Mass Index (BMI). Fixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both main genetic effects and age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. Our findings challenge the assumption that genetic effects are consistent across all ages and provide a method for exploring this. GWAS consortia should be encouraged to use meta-regression to explore age-varying genetic effects.
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Affiliation(s)
- Panagiota Pagoni
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Julian P T Higgins
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Evie Stergiakouli
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nicole M Warrington
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Frazer Institute, University of Queensland, Brisbane, QLD, Australia
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tim T Morris
- Centre for Longitudinal Studies, Social Research Institute, University College London, London, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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2
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Yang Y, Knol MJ, Wang R, Mishra A, Liu D, Luciano M, Teumer A, Armstrong N, Bis JC, Jhun MA, Li S, Adams HHH, Aziz NA, Bastin ME, Bourgey M, Brody JA, Frenzel S, Gottesman RF, Hosten N, Hou L, Kardia SLR, Lohner V, Marquis P, Maniega SM, Satizabal CL, Sorond FA, Valdés Hernández MC, van Duijn CM, Vernooij MW, Wittfeld K, Yang Q, Zhao W, Boerwinkle E, Levy D, Deary IJ, Jiang J, Mather KA, Mosley TH, Psaty BM, Sachdev PS, Smith JA, Sotoodehnia N, DeCarli CS, Breteler MMB, Ikram MA, Grabe HJ, Wardlaw J, Longstreth WT, Launer LJ, Seshadri S, Debette S, Fornage M. Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI. Brain 2023; 146:492-506. [PMID: 35943854 PMCID: PMC9924914 DOI: 10.1093/brain/awac290] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/23/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Cerebral white matter hyperintensities on MRI are markers of cerebral small vessel disease, a major risk factor for dementia and stroke. Despite the successful identification of multiple genetic variants associated with this highly heritable condition, its genetic architecture remains incompletely understood. More specifically, the role of DNA methylation has received little attention. We investigated the association between white matter hyperintensity burden and DNA methylation in blood at ∼450 000 cytosine-phosphate-guanine (CpG) sites in 9732 middle-aged to older adults from 14 community-based studies. Single CpG and region-based association analyses were carried out. Functional annotation and integrative cross-omics analyses were performed to identify novel genes underlying the relationship between DNA methylation and white matter hyperintensities. We identified 12 single CpG and 46 region-based DNA methylation associations with white matter hyperintensity burden. Our top discovery single CpG, cg24202936 (P = 7.6 × 10-8), was associated with F2 expression in blood (P = 6.4 × 10-5) and co-localized with FOLH1 expression in brain (posterior probability = 0.75). Our top differentially methylated regions were in PRMT1 and in CCDC144NL-AS1, which were also represented in single CpG associations (cg17417856 and cg06809326, respectively). Through Mendelian randomization analyses cg06809326 was putatively associated with white matter hyperintensity burden (P = 0.03) and expression of CCDC144NL-AS1 possibly mediated this association. Differentially methylated region analysis, joint epigenetic association analysis and multi-omics co-localization analysis consistently identified a role of DNA methylation near SH3PXD2A, a locus previously identified in genome-wide association studies of white matter hyperintensities. Gene set enrichment analyses revealed functions of the identified DNA methylation loci in the blood-brain barrier and in the immune response. Integrative cross-omics analysis identified 19 key regulatory genes in two networks related to extracellular matrix organization, and lipid and lipoprotein metabolism. A drug-repositioning analysis indicated antihyperlipidaemic agents, more specifically peroxisome proliferator-activated receptor-alpha, as possible target drugs for white matter hyperintensities. Our epigenome-wide association study and integrative cross-omics analyses implicate novel genes influencing white matter hyperintensity burden, which converged on pathways related to the immune response and to a compromised blood-brain barrier possibly due to disrupted cell-cell and cell-extracellular matrix interactions. The results also suggest that antihyperlipidaemic therapy may contribute to lowering risk for white matter hyperintensities possibly through protection against blood-brain barrier disruption.
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Affiliation(s)
- Yunju Yang
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science at Houston, Houston, TX 77030, USA
| | - Maria J Knol
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Ruiqi Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Aniket Mishra
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, F-33000 Bordeaux, France
| | - Dan Liu
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Michelle Luciano
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald 17475, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald 17475, Germany
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, 15-269, Poland
| | - Nicola Armstrong
- Mathematics and Statistics, Curtin University, 6845 Perth, Australia
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 02115, USA
| | - Min A Jhun
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA
| | - Shuo Li
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Hieab H H Adams
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Nasir Ahmad Aziz
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Neurology, Faculty of Medicine, University of Bonn, 53127 Bonn, Germany
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Mathieu Bourgey
- Canadian Centre for Computational Genomics, McGill University, Montréal, Quebec, Canada H3A 0G1
- Department for Human Genetics, McGill University Genome Centre, McGill University, Montréal, Quebec, Canada H3A 0G1
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 02115, USA
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald 17475, Germany
| | - Rebecca F Gottesman
- Stroke Branch, National Institutes of Neurological Disorders and Stroke, Bethesda, MD 20814, USA
| | - Norbert Hosten
- Department of Radiology and Neuroradiology, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA
| | - Valerie Lohner
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Pascale Marquis
- Canadian Centre for Computational Genomics, McGill University, Montréal, Quebec, Canada H3A 0G1
- Department for Human Genetics, McGill University Genome Centre, McGill University, Montréal, Quebec, Canada H3A 0G1
| | - Susana Muñoz Maniega
- Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases and Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USA
- The Framingham Heart Study, Framingham, MA 01701, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02115, USA
| | - Farzaneh A Sorond
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Maria C Valdés Hernández
- Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
- Nuffield Department of Population Health, Oxford University, Oxford, OX3 7LF, UK
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald 17475, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, 17475 Rostock, Germany
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
- The Framingham Heart Study, Framingham, MA 01701, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Science at Houston, Houston, TX 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daniel Levy
- The Framingham Heart Study, Framingham, MA 01701, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | - Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia
- Neuroscience Research Australia, Sydney, NSW 2031, Australia
| | - Thomas H Mosley
- The Memory Impairment Neurodegenerative Dementia (MIND) Research Center, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 02115, USA
- Department of Epidemiology, University of Washington, Seattle, WA 98104, USA
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia
- Neuropsychiatric Institute, The Prince of Wales Hospital, University of New South Wales, Randwick, NSW 2031, Australia
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 02115, USA
| | - Charles S DeCarli
- Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA 95816, USA
| | - Monique M B Breteler
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, 53127 Bonn, Germany
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald 17475, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, 17475 Rostock, Germany
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - W T Longstreth
- Department of Epidemiology, University of Washington, Seattle, WA 98104, USA
- Department of Neurology, University of Washington, Seattle, WA 98104, USA
| | - Lenore J Launer
- Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases and Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USA
- The Framingham Heart Study, Framingham, MA 01701, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02115, USA
| | - Stephanie Debette
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, F-33000 Bordeaux, France
- Department of Neurology, Boston University School of Medicine, Boston, MA 02115, USA
- CHU de Bordeaux, Department of Neurology, F-33000 Bordeaux, France
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science at Houston, Houston, TX 77030, USA
- Human Genetics Center, School of Public Health, University of Texas Health Science at Houston, Houston, TX 77030, USA
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Discerning asthma endotypes through comorbidity mapping. Nat Commun 2022; 13:6712. [PMID: 36344522 PMCID: PMC9640644 DOI: 10.1038/s41467-022-33628-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 09/27/2022] [Indexed: 11/09/2022] Open
Abstract
Asthma is a heterogeneous, complex syndrome, and identifying asthma endotypes has been challenging. We hypothesize that distinct endotypes of asthma arise in disparate genetic variation and life-time environmental exposure backgrounds, and that disease comorbidity patterns serve as a surrogate for such genetic and exposure variations. Here, we computationally discover 22 distinct comorbid disease patterns among individuals with asthma (asthma comorbidity subgroups) using diagnosis records for >151 M US residents, and re-identify 11 of the 22 subgroups in the much smaller UK Biobank. GWASs to discern asthma risk loci for individuals within each subgroup and in all subgroups combined reveal 109 independent risk loci, of which 52 are replicated in multi-ancestry meta-analysis across different ethnicity subsamples in UK Biobank, US BioVU, and BioBank Japan. Fourteen loci confer asthma risk in multiple subgroups and in all subgroups combined. Importantly, another six loci confer asthma risk in only one subgroup. The strength of association between asthma and each of 44 health-related phenotypes also varies dramatically across subgroups. This work reveals subpopulations of asthma patients distinguished by comorbidity patterns, asthma risk loci, gene expression, and health-related phenotypes, and so reveals different asthma endotypes.
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Joukhadar R, Daetwyler HD. Data Integration, Imputation, and Meta-analysis for Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:173-183. [PMID: 35641765 DOI: 10.1007/978-1-0716-2237-7_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Growing genomic and phenotypic datasets require different groups around the world to collaborate and integrate these valuable resources to maximize their benefit and increase reference population sizes for genomic prediction and genome-wide association studies (GWAS). However, different studies use different genotyping techniques which requires a synchronizing step for the genotyped variants called "imputation" before combining them. Optimally, different GWAS datasets can be analysed within a meta-analysis, which recruits summary statistics instead of actual data. This chapter describes the general principles for genotypic imputation and meta-GWAS analysis with a description of study designs and command lines required for such analyses.
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Affiliation(s)
- Reem Joukhadar
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Hans D Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia.
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia.
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Gholizadeh M, Esmaeili-Fard SM. Meta-analysis of genome-wide association studies for litter size in sheep. Theriogenology 2021; 180:103-112. [PMID: 34968818 DOI: 10.1016/j.theriogenology.2021.12.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 12/19/2021] [Accepted: 12/19/2021] [Indexed: 01/01/2023]
Abstract
Litter size and ovulation rate are important reproduction traits in sheep and have important impacts on the profitability of farm animals. To investigate the genetic architecture of litter size, we report the first meta-analysis of genome-wide association studies (GWAS) using 522 ewes and 564,377 SNPs from six sheep breeds. We identified 29 significant associations for litter size which 27 of which have not been reported in individual GWAS for each population. However, we could confirm the role of BMPR1B in prolificacy. Our gene set analysis discovered biological pathways related to cell signaling, communication, and adhesion. Functional clustering and enrichment using protein databases identified epidermal growth factor-like domain affecting litter size. Through analyzing protein-protein interaction data, we could identify hub genes like CASK, PLCB4, RPTOR, GRIA2, and PLCB1 that were enriched in most of the significant pathways. These genes have a role in cell proliferation, cell adhesion, cell growth and survival, and autophagy. Notably, identified SNPs were scattered on several different chromosomes implying different genetic mechanisms underlying variation of prolificacy in each breed. Given the different layers that make up the follicles and the need for communication and transfer of hormones and nutrients through these layers to the oocyte, the significance of pathways related to cell signaling and communication seems logical. Our results provide genetic insights into the litter size variation in different sheep breeds.
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Affiliation(s)
- Mohsen Gholizadeh
- Department of Animal Science, Faculty of Animal Science and Fisheries, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
| | - Seyed Mehdi Esmaeili-Fard
- Department of Animal Science, Faculty of Animal Science and Fisheries, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
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Makinde FL, Tchamga MSS, Jafali J, Fatumo S, Chimusa ER, Mulder N, Mazandu GK. Reviewing and assessing existing meta-analysis models and tools. Brief Bioinform 2021; 22:bbab324. [PMID: 34415019 PMCID: PMC8575034 DOI: 10.1093/bib/bbab324] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 07/07/2021] [Accepted: 07/25/2021] [Indexed: 01/03/2023] Open
Abstract
Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.
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Affiliation(s)
- Funmilayo L Makinde
- Computational Biology Division at University of Cape Town in collaboration with the African Institute for Mathematical Sciences (AIMS), South Africa
| | - Milaine S S Tchamga
- Division of Human Genetics at University of Cape in collaboration with the African Institute for Mathematical Sciences (AIMS), South Africa
| | - James Jafali
- Pathogen Biology Research Group, Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Malawi
| | - Segun Fatumo
- London School of Hygiene and Tropical Medicine, University of London, UK
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, University of Cape Town, South Africa
| | - Nicola Mulder
- Computational Biology Division at University of Cape Town, South Africa
| | - Gaston K Mazandu
- Division of Human Genetics, Department of Pathology at University of Cape Town, and Associate Researcher at the African Institute for Mathematical Sciences (AIMS), South Africa
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Jin Q, Shi G. Meta-Analysis of SNP-Environment Interaction With Overlapping Data. Front Genet 2020; 10:1400. [PMID: 32082364 PMCID: PMC7002557 DOI: 10.3389/fgene.2019.01400] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 12/23/2019] [Indexed: 11/19/2022] Open
Abstract
Meta-analysis, which combines the results of multiple studies, is an important analytical method in genome-wide association studies. In genome-wide association studies practice, studies employing meta-analysis may have overlapping data, which could yield false positive results. Recent studies have proposed models to handle the issue of overlapping data when testing the genetic main effect of single nucleotide polymorphism. However, there is still no meta-analysis method for testing gene-environment interaction when overlapping data exist. Inspired by the methods of testing the main effect of gene with overlapping data, we proposed an overlapping meta-regulation method to address the issue in testing the gene-environment interaction. We generalized the covariance matrices of the regular meta-regression model by employing Lin’s and Han’s correlation structures to incorporate the correlations introduced by the overlapping data. Based on our proposed models, we further provided statistical significance tests of the gene-environment interaction as well as joint effects of the gene main effect and the interaction. Through simulations, we examined type I errors and statistical powers of our proposed methods at different levels of data overlap among studies. We demonstrated that our method well controls the type I error and simultaneously achieves statistical power comparable with the method that removes overlapping samples a priori before the meta-analysis, i.e., the splitting method. On the other hand, ignoring overlapping data will inflate the type I error. Unlike the splitting method that requires individual-level genotype and phenotype data, our proposed method for testing gene-environment interaction handles the issue of overlapping data effectively and statistically efficiently at the meta-analysis level.
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Affiliation(s)
- Qinqin Jin
- State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China.,Applied Science College, Taiyuan University of Science and Technology, Taiyuan, China
| | - Gang Shi
- State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China
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Lan T, Yang B, Zhang X, Wang T, Lu Q. Statistical Methods and Software for Substance Use and Dependence Genetic Research. Curr Genomics 2019; 20:172-183. [PMID: 31929725 PMCID: PMC6935956 DOI: 10.2174/1389202920666190617094930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 05/16/2019] [Accepted: 05/24/2019] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Substantial substance use disorders and related health conditions emerged dur-ing the mid-20th century and continue to represent a remarkable 21st century global burden of disease. This burden is largely driven by the substance-dependence process, which is a complex process and is influenced by both genetic and environmental factors. During the past few decades, a great deal of pro-gress has been made in identifying genetic variants associated with Substance Use and Dependence (SUD) through linkage, candidate gene association, genome-wide association and sequencing studies. METHODS Various statistical methods and software have been employed in different types of SUD ge-netic studies, facilitating the identification of new SUD-related variants. CONCLUSION In this article, we review statistical methods and software that are currently available for SUD genetic studies, and discuss their strengths and limitations.
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Affiliation(s)
| | | | | | - Tong Wang
- Address correspondence to these authors at the Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA; Tel/ Fax: ++1-517-353-8623; E-mails: ;
| | - Qing Lu
- Address correspondence to these authors at the Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA; Tel/ Fax: ++1-517-353-8623; E-mails: ;
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Genetic determinants of risk in pulmonary arterial hypertension: international genome-wide association studies and meta-analysis. THE LANCET. RESPIRATORY MEDICINE 2019; 7:227-238. [PMID: 30527956 PMCID: PMC6391516 DOI: 10.1016/s2213-2600(18)30409-0] [Citation(s) in RCA: 119] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 09/20/2018] [Accepted: 09/26/2018] [Indexed: 01/06/2023]
Abstract
BACKGROUND Rare genetic variants cause pulmonary arterial hypertension, but the contribution of common genetic variation to disease risk and natural history is poorly characterised. We tested for genome-wide association for pulmonary arterial hypertension in large international cohorts and assessed the contribution of associated regions to outcomes. METHODS We did two separate genome-wide association studies (GWAS) and a meta-analysis of pulmonary arterial hypertension. These GWAS used data from four international case-control studies across 11 744 individuals with European ancestry (including 2085 patients). One GWAS used genotypes from 5895 whole-genome sequences and the other GWAS used genotyping array data from an additional 5849 individuals. Cross-validation of loci reaching genome-wide significance was sought by meta-analysis. Conditional analysis corrected for the most significant variants at each locus was used to resolve signals for multiple associations. We functionally annotated associated variants and tested associations with duration of survival. All-cause mortality was the primary endpoint in survival analyses. FINDINGS A locus near SOX17 (rs10103692, odds ratio 1·80 [95% CI 1·55-2·08], p=5·13 × 10-15) and a second locus in HLA-DPA1 and HLA-DPB1 (collectively referred to as HLA-DPA1/DPB1 here; rs2856830, 1·56 [1·42-1·71], p=7·65 × 10-20) within the class II MHC region were associated with pulmonary arterial hypertension. The SOX17 locus had two independent signals associated with pulmonary arterial hypertension (rs13266183, 1·36 [1·25-1·48], p=1·69 × 10-12; and rs10103692). Functional and epigenomic data indicate that the risk variants near SOX17 alter gene regulation via an enhancer active in endothelial cells. Pulmonary arterial hypertension risk variants determined haplotype-specific enhancer activity, and CRISPR-mediated inhibition of the enhancer reduced SOX17 expression. The HLA-DPA1/DPB1 rs2856830 genotype was strongly associated with survival. Median survival from diagnosis in patients with pulmonary arterial hypertension with the C/C homozygous genotype was double (13·50 years [95% CI 12·07 to >13·50]) that of those with the T/T genotype (6·97 years [6·02-8·05]), despite similar baseline disease severity. INTERPRETATION This is the first study to report that common genetic variation at loci in an enhancer near SOX17 and in HLA-DPA1/DPB1 is associated with pulmonary arterial hypertension. Impairment of SOX17 function might be more common in pulmonary arterial hypertension than suggested by rare mutations in SOX17. Further studies are needed to confirm the association between HLA typing or rs2856830 genotyping and survival, and to determine whether HLA typing or rs2856830 genotyping improves risk stratification in clinical practice or trials. FUNDING UK NIHR, BHF, UK MRC, Dinosaur Trust, NIH/NHLBI, ERS, EMBO, Wellcome Trust, EU, AHA, ACClinPharm, Netherlands CVRI, Dutch Heart Foundation, Dutch Federation of UMC, Netherlands OHRD and RNAS, German DFG, German BMBF, APH Paris, INSERM, Université Paris-Sud, and French ANR.
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Lee CH, Cook S, Lee JS, Han B. Comparison of Two Meta-Analysis Methods: Inverse-Variance-Weighted Average and Weighted Sum of Z-Scores. Genomics Inform 2016; 14:173-180. [PMID: 28154508 PMCID: PMC5287121 DOI: 10.5808/gi.2016.14.4.173] [Citation(s) in RCA: 133] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 12/03/2016] [Accepted: 12/03/2016] [Indexed: 12/22/2022] Open
Abstract
The meta-analysis has become a widely used tool for many applications in bioinformatics, including genome-wide association studies. A commonly used approach for meta-analysis is the fixed effects model approach, for which there are two popular methods: the inverse variance-weighted average method and weighted sum of z-scores method. Although previous studies have shown that the two methods perform similarly, their characteristics and their relationship have not been thoroughly investigated. In this paper, we investigate the optimal characteristics of the two methods and show the connection between the two methods. We demonstrate that the each method is optimized for a unique goal, which gives us insight into the optimal weights for the weighted sum of z-scores method. We examine the connection between the two methods both analytically and empirically and show that their resulting statistics become equivalent under certain assumptions. Finally, we apply both methods to the Wellcome Trust Case Control Consortium data and demonstrate that the two methods can give distinct results in certain study designs.
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Affiliation(s)
- Cue Hyunkyu Lee
- Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea.; Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Seungho Cook
- Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea.; School of Systems Biomedical Science, Soongsil University, Seoul 06978, Korea
| | - Ji Sung Lee
- Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea.; Department of Medicine, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Buhm Han
- Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea.; Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul 05505, Korea
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11
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Power considerations for λ inflation factor in meta-analyses of genome-wide association studies. Genet Res (Camb) 2016; 98:e9. [PMID: 27193946 DOI: 10.1017/s0016672316000069] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The genomic control (GC) approach is extensively used to effectively control false positive signals due to population stratification in genome-wide association studies (GWAS). However, GC affects the statistical power of GWAS. The loss of power depends on the magnitude of the inflation factor (λ) that is used for GC. We simulated meta-analyses of different GWAS. Minor allele frequency (MAF) ranged from 0·001 to 0·5 and λ was sampled from two scenarios: (i) random scenario (empirically-derived distribution of real λ values) and (ii) selected scenario from simulation parameter modification. Adjustment for λ was considered under single correction (within study corrected standard errors) and double correction (additional λ corrected summary estimate). MAF was a pivotal determinant of observed power. In random λ scenario, double correction induced a symmetric power reduction in comparison to single correction. For MAF 1·2 and MAF >5%. Our results provide a quick but detailed index for power considerations of future meta-analyses of GWAS that enables a more flexible design from early steps based on the number of studies accumulated in different groups and the λ values observed in the single studies.
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12
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Timofeeva MN, Kinnersley B, Farrington SM, Whiffin N, Palles C, Svinti V, Lloyd A, Gorman M, Ooi LY, Hosking F, Barclay E, Zgaga L, Dobbins S, Martin L, Theodoratou E, Broderick P, Tenesa A, Smillie C, Grimes G, Hayward C, Campbell A, Porteous D, Deary IJ, Harris SE, Northwood EL, Barrett JH, Smith G, Wolf R, Forman D, Morreau H, Ruano D, Tops C, Wijnen J, Schrumpf M, Boot A, Vasen HFA, Hes FJ, van Wezel T, Franke A, Lieb W, Schafmayer C, Hampe J, Buch S, Propping P, Hemminki K, Försti A, Westers H, Hofstra R, Pinheiro M, Pinto C, Teixeira M, Ruiz-Ponte C, Fernández-Rozadilla C, Carracedo A, Castells A, Castellví-Bel S, Campbell H, Bishop DT, Tomlinson IPM, Dunlop MG, Houlston RS. Recurrent Coding Sequence Variation Explains Only A Small Fraction of the Genetic Architecture of Colorectal Cancer. Sci Rep 2015; 5:16286. [PMID: 26553438 PMCID: PMC4639776 DOI: 10.1038/srep16286] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 09/21/2015] [Indexed: 12/21/2022] Open
Abstract
Whilst common genetic variation in many non-coding genomic regulatory regions are known to impart risk of colorectal cancer (CRC), much of the heritability of CRC remains unexplained. To examine the role of recurrent coding sequence variation in CRC aetiology, we genotyped 12,638 CRCs cases and 29,045 controls from six European populations. Single-variant analysis identified a coding variant (rs3184504) in SH2B3 (12q24) associated with CRC risk (OR = 1.08, P = 3.9 × 10(-7)), and novel damaging coding variants in 3 genes previously tagged by GWAS efforts; rs16888728 (8q24) in UTP23 (OR = 1.15, P = 1.4 × 10(-7)); rs6580742 and rs12303082 (12q13) in FAM186A (OR = 1.11, P = 1.2 × 10(-7) and OR = 1.09, P = 7.4 × 10(-8)); rs1129406 (12q13) in ATF1 (OR = 1.11, P = 8.3 × 10(-9)), all reaching exome-wide significance levels. Gene based tests identified associations between CRC and PCDHGA genes (P < 2.90 × 10(-6)). We found an excess of rare, damaging variants in base-excision (P = 2.4 × 10(-4)) and DNA mismatch repair genes (P = 6.1 × 10(-4)) consistent with a recessive mode of inheritance. This study comprehensively explores the contribution of coding sequence variation to CRC risk, identifying associations with coding variation in 4 genes and PCDHG gene cluster and several candidate recessive alleles. However, these findings suggest that recurrent, low-frequency coding variants account for a minority of the unexplained heritability of CRC.
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Affiliation(s)
- Maria N. Timofeeva
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Ben Kinnersley
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
| | - Susan M. Farrington
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Nicola Whiffin
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
| | - Claire Palles
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Victoria Svinti
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Amy Lloyd
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
| | - Maggie Gorman
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Li-Yin Ooi
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Fay Hosking
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
| | - Ella Barclay
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Lina Zgaga
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Sara Dobbins
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
| | - Lynn Martin
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Evropi Theodoratou
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
- Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, United Kingdom
| | - Peter Broderick
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
| | - Albert Tenesa
- Roslin Institute, University of Edinburgh, Easter Bush, Roslin EH25 9RG, United Kingdom
- Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Claire Smillie
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Graeme Grimes
- Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Caroline Hayward
- Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Archie Campbell
- Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
- Generation Scotland, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - David Porteous
- Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
- Generation Scotland, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Ian J. Deary
- University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
| | - Sarah E. Harris
- Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
- University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
| | - Emma L. Northwood
- Section of Epidemiology & Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, St James’s University Hospital, Leeds, UK
| | - Jennifer H. Barrett
- Section of Epidemiology & Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, St James’s University Hospital, Leeds, UK
| | - Gillian Smith
- Medical Research Institute, University of Dundee, Dundee, UK
| | - Roland Wolf
- Medical Research Institute, University of Dundee, Dundee, UK
| | | | - Hans Morreau
- Department of Pathology, Leiden University Medical Center, The Netherlands
| | - Dina Ruano
- Department of Pathology, Leiden University Medical Center, The Netherlands
| | - Carli Tops
- Department of Clinical Genetics, Leiden University Medical Center, The Netherlands
| | - Juul Wijnen
- Department of Human Genetics, Leiden University Medical Center, The Netherlands
| | - Melanie Schrumpf
- Department of Pathology, Leiden University Medical Center, The Netherlands
| | - Arnoud Boot
- Department of Pathology, Leiden University Medical Center, The Netherlands
| | - Hans F A Vasen
- Department of Gastroenterology, Leiden University Medical Center, The Netherlands
| | - Frederik J. Hes
- Department of Clinical Genetics, Leiden University Medical Center, The Netherlands
| | - Tom van Wezel
- Department of Pathology, Leiden University Medical Center, The Netherlands
| | - Andre Franke
- Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Wolgang Lieb
- Institute of Epidemiology, Christian-Albrechts-University Kiel, Kiel
| | - Clemens Schafmayer
- Department of General and Thoracic Surgery, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Jochen Hampe
- Medical Department 1, University Hospital Dresden, TU Dresden, Dresden, Germany
| | - Stephan Buch
- Medical Department 1, University Hospital Dresden, TU Dresden, Dresden, Germany
| | - Peter Propping
- Institute of Human Genetics, University Hospital Bonn, Bonn, Germany
| | - Kari Hemminki
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany
- Center for Primary Health Care Research, Lund University, 205 02 Malmö, Sweden
| | - Asta Försti
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany
- Center for Primary Health Care Research, Lund University, 205 02 Malmö, Sweden
| | - Helga Westers
- University of Groningen, University Medical Centre Groningen, Department of Genetics, PO Box 30001, 9700 RB Groningen, the Netherlands
| | - Robert Hofstra
- University of Groningen, University Medical Centre Groningen, Department of Genetics, PO Box 30001, 9700 RB Groningen, the Netherlands
- Department of Clinical Genetics, Erasmus Medical Center, Dr. Molewaterplein 50, 3015 GE Rotterdam, the Netherlands
| | - Manuela Pinheiro
- Department of Genetics, Portuguese Oncology Institute and Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Carla Pinto
- Department of Genetics, Portuguese Oncology Institute and Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Manuel Teixeira
- Department of Genetics, Portuguese Oncology Institute and Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Clara Ruiz-Ponte
- Fundación Pública Galega de Medicina Xenómica (FPGMX), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Genomics Medicine Group, Hospital Clínico, 15706 Santiago de Compostela, University of Santiago de Compostela, Galicia, Spain
| | - Ceres Fernández-Rozadilla
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
- Fundación Pública Galega de Medicina Xenómica (FPGMX), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Genomics Medicine Group, Hospital Clínico, 15706 Santiago de Compostela, University of Santiago de Compostela, Galicia, Spain
| | - Angel Carracedo
- Fundación Pública Galega de Medicina Xenómica (FPGMX), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Genomics Medicine Group, Hospital Clínico, 15706 Santiago de Compostela, University of Santiago de Compostela, Galicia, Spain
| | - Antoni Castells
- Servei de Gastroenterologia, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, 08036 Barcelona, Catalonia, Spain
| | - Sergi Castellví-Bel
- Servei de Gastroenterologia, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, 08036 Barcelona, Catalonia, Spain
| | - Harry Campbell
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
- Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, United Kingdom
| | - D. Timothy Bishop
- Section of Epidemiology & Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, St James’s University Hospital, Leeds, UK
| | - Ian P M Tomlinson
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Malcolm G. Dunlop
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Western General Hospital Edinburgh, Crewe Road, Edinburgh, EH4 2XU, United Kingdom
| | - Richard S. Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, United Kingdom
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Hancock DB, Levy JL, Gaddis NC, Glasheen C, Saccone NL, Page GP, Bierut LJ, Kral AH, Johnson EO. Replication of ZNF804A gene variant associations with risk of heroin addiction. GENES, BRAIN, AND BEHAVIOR 2015; 14:635-40. [PMID: 26382569 PMCID: PMC4715582 DOI: 10.1111/gbb.12254] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Revised: 08/28/2015] [Accepted: 09/14/2015] [Indexed: 12/19/2022]
Abstract
Heroin addiction is heritable, but few specific genetic variants have been reproducibly associated with this disease. The zinc finger protein 804A (ZNF804A) gene is a biologically plausible susceptibility gene for heroin addiction, given its function as a transcription factor in human brain. Novel associations of two common ZNF804A single nucleotide polymorphisms (SNPs), rs7597593 and rs1344706, with heroin addiction have been reported in Han Chinese. Both SNPs have also been implicated for regulating ZNF804A expression in human brain, including the addiction-relevant dorsolateral prefrontal cortex. In this independent replication study, we tested the rs7597593 and rs1344706 SNP genotypes and their corresponding haplotypes for association with heroin addiction using cases drawn from the Urban Health Study and population controls: total N = 10 757 [7095 European Americans (EAs) and 3662 African Americans (AAs)]. We independently replicated both ZNF804A SNP associations in EAs: the rs7597593-T (P = 0.016) and rs1344706-A (P = 0.029) alleles both being associated with increased risk of heroin addiction, consistent with the prior report. Neither SNP was associated in AAs alone, but meta-analysis across both ancestry groups resulted in significant associations for rs1344706-A [P = 0.016, odds ratio (95% confidence interval) = 1.13 (1.02-1.25)] and its haplotype with rs7597593-T [P = 0.0067, odds ratio (95% confidence interval) = 1.16 (1.04-1.29)]. By showing consistent associations across independent studies and diverse ancestry groups, our study provides evidence that these two ZNF804A SNPs and their risk haplotype are among the few replicable genetic associations with heroin addiction.
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Affiliation(s)
- Dana B. Hancock
- Behavioral and Urban Health Program, Behavioral Health and Criminal Justice Division, Research Triangle Institute (RTI) International, Research Triangle Park, North Carolina, USA
| | - Joshua L. Levy
- Research Computing Division, RTI International, Research Triangle Park, North Carolina, USA
| | - Nathan C. Gaddis
- Research Computing Division, RTI International, Research Triangle Park, North Carolina, USA
| | - Cristie Glasheen
- Behavioral and Urban Health Program, Behavioral Health and Criminal Justice Division, Research Triangle Institute (RTI) International, Research Triangle Park, North Carolina, USA
| | - Nancy L. Saccone
- Department of Genetics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Grier P. Page
- Fellow Program, Center for Genomics in Public Health and Medicine Genomics, and Statistical Genetics, and Environmental Research Program, RTI International, Atlanta, Georgia, USA
| | - Laura J. Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Alex H. Kral
- Behavioral and Urban Health Program, Behavioral Health and Criminal Justice Division, San Francisco, California, USA
| | - Eric O. Johnson
- Fellow Program and Behavioral Health and Criminal Justice Division, RTI International, Research Triangle Park, North Carolina, USA
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Statistical and Computational Methods for Genetic Diseases: An Overview. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:954598. [PMID: 26106440 PMCID: PMC4464008 DOI: 10.1155/2015/954598] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Accepted: 04/23/2015] [Indexed: 12/19/2022]
Abstract
The identification of causes of genetic diseases has been carried out by several approaches with increasing complexity. Innovation of genetic methodologies leads to the production of large amounts of data that needs the support of statistical and computational methods to be correctly processed. The aim of the paper is to provide an overview of statistical and computational methods paying attention to methods for the sequence analysis and complex diseases.
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Verhaaren BF, Debette S, Bis JC, Smith JA, Ikram MK, Adams HH, Beecham AH, Rajan KB, Lopez LM, Barral S, van Buchem MA, van der Grond J, Smith AV, Hegenscheid K, Aggarwal NT, de Andrade M, Atkinson EJ, Beekman M, Beiser AS, Blanton SH, Boerwinkle E, Brickman AM, Bryan RN, Chauhan G, Chen CP, Chouraki V, de Craen AJ, Crivello F, Deary IJ, Deelen J, De Jager PL, Dufouil C, Elkind MS, Evans DA, Freudenberger P, Gottesman RF, Guðnason V, Habes M, Heckbert SR, Heiss G, Hilal S, Hofer E, Hofman A, Ibrahim-Verbaas CA, Knopman DS, Lewis CE, Liao J, Liewald DC, Luciano M, van der Lugt A, Martinez OO, Mayeux R, Mazoyer B, Nalls M, Nauck M, Niessen WJ, Oostra BA, Psaty BM, Rice KM, Rotter JI, von Sarnowski B, Schmidt H, Schreiner PJ, Schuur M, Sidney SS, Sigurdsson S, Slagboom PE, Stott DJ, van Swieten JC, Teumer A, Töglhofer AM, Traylor M, Trompet S, Turner ST, Tzourio C, Uh HW, Uitterlinden AG, Vernooij MW, Wang JJ, Wong TY, Wardlaw JM, Windham BG, Wittfeld K, Wolf C, Wright CB, Yang Q, Zhao W, Zijdenbos A, Jukema JW, Sacco RL, Kardia SL, Amouyel P, Mosley TH, Longstreth WT, DeCarli CC, van Duijn CM, Schmidt R, Launer LJ, Grabe HJ, Seshadri SS, Ikram MA, Fornage M. Multiethnic genome-wide association study of cerebral white matter hyperintensities on MRI. CIRCULATION. CARDIOVASCULAR GENETICS 2015; 8:398-409. [PMID: 25663218 PMCID: PMC4427240 DOI: 10.1161/circgenetics.114.000858] [Citation(s) in RCA: 132] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2014] [Accepted: 01/23/2015] [Indexed: 12/11/2022]
Abstract
BACKGROUND The burden of cerebral white matter hyperintensities (WMH) is associated with an increased risk of stroke, dementia, and death. WMH are highly heritable, but their genetic underpinnings are incompletely characterized. To identify novel genetic variants influencing WMH burden, we conducted a meta-analysis of multiethnic genome-wide association studies. METHODS AND RESULTS We included 21 079 middle-aged to elderly individuals from 29 population-based cohorts, who were free of dementia and stroke and were of European (n=17 936), African (n=1943), Hispanic (n=795), and Asian (n=405) descent. WMH burden was quantified on MRI either by a validated automated segmentation method or a validated visual grading scale. Genotype data in each study were imputed to the 1000 Genomes reference. Within each ethnic group, we investigated the relationship between each single-nucleotide polymorphism and WMH burden using a linear regression model adjusted for age, sex, intracranial volume, and principal components of ancestry. A meta-analysis was conducted for each ethnicity separately and for the combined sample. In the European descent samples, we confirmed a previously known locus on chr17q25 (P=2.7×10(-19)) and identified novel loci on chr10q24 (P=1.6×10(-9)) and chr2p21 (P=4.4×10(-8)). In the multiethnic meta-analysis, we identified 2 additional loci, on chr1q22 (P=2.0×10(-8)) and chr2p16 (P=1.5×10(-8)). The novel loci contained genes that have been implicated in Alzheimer disease (chr2p21 and chr10q24), intracerebral hemorrhage (chr1q22), neuroinflammatory diseases (chr2p21), and glioma (chr10q24 and chr2p16). CONCLUSIONS We identified 4 novel genetic loci that implicate inflammatory and glial proliferative pathways in the development of WMH in addition to previously proposed ischemic mechanisms.
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Affiliation(s)
- Benjamin F.J. Verhaaren
- Dept of Epidemiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
- Dept of Radiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
| | - Stéphanie Debette
- Inserm U897, Université Bordeaux Segalen, Bordeaux
- Dept of Neurology, Lariboisière Hospital, Paris
- Inserm U1191, Montpellier, France
- Dept of Neurology, Boston Univ School of Medicine, Boston, MA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Dept of Medicine, Seattle, WA
| | - Jennifer A. Smith
- Dept of Epidemiology, School of Public Health, Univ of Michigan, Ann Arbor, MI
| | - M. Kamran Ikram
- Singapore Eye Research Institute, Singapore National Eye Centre, National Univ of Singapore & National Univ Health System, Singapore
- Dept of Ophthalmology, National Univ of Singapore & National Univ Health System, Singapore
- Memory Aging & Cognition Centre, National Univ of Singapore, Singapore
| | - Hieab H. Adams
- Dept of Epidemiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
- Dept of Radiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
| | - Ashley H. Beecham
- John P. Hussman Institute for Human Genomics, Univ of Miami, Miller School of Medicine, Miami, FL
| | | | - Lorna M. Lopez
- Centre for Cognitive Ageing & Cognitive Epidemiology, Psychology, Univ of Edinburgh, United Kingdom
| | - Sandra Barral
- Dept of Neurology, Columbia Univ Medical Ctr, New York, NY
| | | | | | | | - Katrin Hegenscheid
- Dept of Diagnostic Radiology & Neuroradiology, Univ Medicine Greifswald, Greifswald, Germany
| | | | - Mariza de Andrade
- Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN
| | | | - Marian Beekman
- Dept of Molecular Epidemiology, Leiden Univ Medical Ctr, Leiden, the Netherlands
| | - Alexa S. Beiser
- Dept of Neurology, Boston Univ School of Medicine, Boston, MA
- National Heart, Lung, & Blood Institute's Framingham Heart Study, Framingham
- Dept of Biostatistics, Boston Univ School of Public Health, Boston, MA
| | - Susan H. Blanton
- John P. Hussman Institute for Human Genomics, Univ of Miami, Miller School of Medicine, Miami, FL
- Dr. John T. Macdonald Foundation Dept of Human Genetics, Univ of Miami, Miller School of Medicine, Miami, FL
- Neuroscience Program, Univ of Miami, Miller School of Medicine, Miami, FL
| | - Eric Boerwinkle
- Human Genetics Ctr, Univ of Texas Health Science Ctr at Houston, Houston, TX
| | - Adam M. Brickman
- G.H. Sergievsky Ctr, Taub Institute for Research on Alzheimer’s Disease & Aging Brain, Columbia Univ Medical Ctr, New York, NY
| | - R. Nick Bryan
- Dept of Radiology, Perelman School of Medicine, Univ of Pennsylvania Health System, Philadelphia, PA
| | - Ganesh Chauhan
- Inserm U897, Université Bordeaux Segalen, Bordeaux
- Inserm U1191, Montpellier, France
| | | | - Vincent Chouraki
- Dept of Neurology, Boston Univ School of Medicine, Boston, MA
- National Heart, Lung, & Blood Institute's Framingham Heart Study, Framingham
| | - Anton J.M. de Craen
- Dept of Gerontology & Geriatrics, Leiden Univ Medical Ctr, Leiden, the Netherlands
| | | | - Ian J. Deary
- Centre for Cognitive Ageing & Cognitive Epidemiology, Psychology, Univ of Edinburgh, United Kingdom
| | - Joris Deelen
- Dept of Molecular Epidemiology, Leiden Univ Medical Ctr, Leiden, the Netherlands
| | | | | | - Mitchell S.V. Elkind
- Dept of Neurology, College of Physicians & Surgeons, Dept of Epidemiology, Mailman School of Public Health, Columbia Univ, New York, NY
| | | | - Paul Freudenberger
- Institute of Molecular Biology & Biochemistry, Medical Univ Graz, Graz, Austria
| | | | - Vilmundur Guðnason
- The Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, Univ of Iceland, Reykjavik, Iceland
| | - Mohamad Habes
- Dept of Radiology, Perelman School of Medicine, Univ of Pennsylvania Health System, Philadelphia, PA
- Institutes for Community Medicine, Univ Medicine Greifswald, Greifswald, Germany
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit, Dept of Medicine, Seattle, WA
- Cardiovascular Health Research Unit, Dept of Medicine Epidemiology, Univ of Washington, Seattle, WA
| | - Gerardo Heiss
- Dept of Epidemiology, Univ of North Carolina at Chapel Hill School of Public Health, Chapel Hill, NC
| | - Saima Hilal
- Memory Aging & Cognition Centre, National Univ of Singapore, Singapore
| | - Edith Hofer
- Dept of Neurology, Clinical Division of Neurogeriatrics, Medical Univ Graz, Graz, Austria
- Institute for Medical Informatics, Statistics & Documentation, Medical Univ Graz, Graz, Austria
| | - Albert Hofman
- Dept of Epidemiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
| | - Carla A. Ibrahim-Verbaas
- Dept of Epidemiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
- Dept of Neurology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
| | | | - Cora E. Lewis
- Division of Preventive Medicine, Univ of Alabama, Birmingham, AL
| | - Jiemin Liao
- Singapore Eye Research Institute, Singapore National Eye Centre, National Univ of Singapore & National Univ Health System, Singapore
- Dept of Ophthalmology, National Univ of Singapore & National Univ Health System, Singapore
| | - David C.M. Liewald
- Centre for Cognitive Ageing & Cognitive Epidemiology, Psychology, Univ of Edinburgh, United Kingdom
| | - Michelle Luciano
- Centre for Cognitive Ageing & Cognitive Epidemiology, Psychology, Univ of Edinburgh, United Kingdom
| | - Aad van der Lugt
- Dept of Radiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
| | - Oliver O. Martinez
- Alzheimer's Disease Ctr, Imaging of Dementia & Aging (IdeA) Laboratory, Dept of Neurology, Ctr for Neuroscience, Univ of California, Davis, CA
| | - Richard Mayeux
- G.H. Sergievsky Ctr, Taub Institute for Research on Alzheimer’s Disease & Aging Brain, Columbia Univ Medical Ctr, New York, NY
| | | | - Mike Nalls
- Laboratory of Neurogenetics, The National Institutes of Health, Bethesda, MD
| | - Matthias Nauck
- Clinical Chemistry & Laboratory Medicine, Univ Medicine Greifswald, Greifswald, Germany
| | - Wiro J. Niessen
- Dept of Radiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
- Dept of Medical Informatics, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
- Faculty of Applied Sciences, Delft Univ of Technology, Delft, the Netherlands
| | - Ben A. Oostra
- Dept of Epidemiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Dept of Medicine, Seattle, WA
- Cardiovascular Health Research Unit, Dept of Medicine Epidemiology, Univ of Washington, Seattle, WA
- Cardiovascular Health Research Unit, Dept of Medicine Health Services, Univ of Washington, Seattle, WA
- Group Health Research Institute, Group Health Cooperative, Seattle, WA
| | - Kenneth M. Rice
- Cardiovascular Health Research Unit, Dept of Biostatistics, Univ of Washington, Seattle, WA
| | - Jerome I. Rotter
- Institute for Translational Genomics & Population Sciences, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Ctr, Torrance, CA
- Dept of Neurology, Univ Medicine of Greifswald, Greifswald, Germany
| | | | - Helena Schmidt
- Dept of Neurology, Johns Hopkins Univ School of Medicine, Baltimore, MD
| | - Pamela J. Schreiner
- Division of Epidemiology & Community Health, Univ of Minnesota, Minneapolis, MN
| | - Maaike Schuur
- Dept of Epidemiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
- Dept of Neurology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
| | | | | | - P. Eline Slagboom
- Dept of Molecular Epidemiology, Leiden Univ Medical Ctr, Leiden, the Netherlands
| | - David J.M. Stott
- Institute of Cardiovascular and Medical Sciences, Faculty of Medicine, Univ of Glasgow, Glasgow
| | | | - Alexander Teumer
- Interfaculty Institute for Genetics & Functional Genomics, Univ Medicine Greifswald, Greifswald, Germany
| | | | - Matthew Traylor
- Research Centre for Stroke & Dementia, St. George's, Univ of London, London, United Kingdom
| | - Stella Trompet
- Dept of Cardiology, Leiden Univ Medical Ctr, Leiden, the Netherlands
- Interuniversity Cardiology Institute of the Netherlands, Utrecht, the Netherlands
| | | | | | - Hae-Won Uh
- Dept of Medical Statistics & Bioinformatics, Leiden Univ Medical Ctr, Leiden, the Netherlands
| | - André G. Uitterlinden
- Dept of Epidemiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
- Dept of Internal Medicine, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
- Dept of Clinical Chemistry, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
| | - Meike W. Vernooij
- Dept of Epidemiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
- Dept of Radiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
| | - Jing J. Wang
- National Heart, Lung, & Blood Institute's Framingham Heart Study, Framingham
- Dept of Biostatistics, Boston Univ School of Public Health, Boston, MA
| | - Tien Y. Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, National Univ of Singapore & National Univ Health System, Singapore
- Dept of Ophthalmology, National Univ of Singapore & National Univ Health System, Singapore
| | - Joanna M. Wardlaw
- Centre for Cognitive Ageing & Cognitive Epidemiology, Psychology, Univ of Edinburgh, United Kingdom
- Brain Research Imaging Centre, SINAPSE Collaboration, Centre for Clinical Brain Sciences, Univ of Edinburgh, United Kingdom
| | - B. Gwen Windham
- Division of Geriatrics/ Gerontology, Univ of Mississippi Medical Ctr, Jackson, MS
| | - Katharina Wittfeld
- German Ctr for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Germany
| | | | - Clinton B. Wright
- Neuroscience Program, Univ of Miami, Miller School of Medicine, Miami, FL
- Dept of Epidemiology & Public Health Sciences, Univ of Miami, Miller School of Medicine, Miami, FL
- Dept of Neurology, Univ of Miami, Miller School of Medicine, Miami, FL
- Evelyn F. McKnight Brain Institute, Univ of Miami, Miller School of Medicine, Miami, FL
| | - Qiong Yang
- National Heart, Lung, & Blood Institute's Framingham Heart Study, Framingham
- Dept of Biostatistics, Boston Univ School of Public Health, Boston, MA
| | - Wei Zhao
- Dept of Epidemiology, School of Public Health, Univ of Michigan, Ann Arbor, MI
| | | | - J. Wouter Jukema
- Dept of Cardiology, Leiden Univ Medical Ctr, Leiden, the Netherlands
- Interuniversity Cardiology Institute of the Netherlands, Utrecht, the Netherlands
| | - Ralph L. Sacco
- Dept of Epidemiology & Public Health Sciences, Univ of Miami, Miller School of Medicine, Miami, FL
- Dept of Neurology, Univ of Miami, Miller School of Medicine, Miami, FL
- Evelyn F. McKnight Brain Institute, Univ of Miami, Miller School of Medicine, Miami, FL
| | - Sharon L.R. Kardia
- Dept of Epidemiology, School of Public Health, Univ of Michigan, Ann Arbor, MI
| | - Philippe Amouyel
- Inserm, U744, Lille, France
- Université Lille 2, Lille, France
- Institut Pasteur de Lille, Lille, France
- Ctr Hospitalier Régional Universitaire de Lille, Lille, France
| | | | - W. T. Longstreth
- Cardiovascular Health Research Unit, Dept of Medicine Epidemiology, Univ of Washington, Seattle, WA
- Cardiovascular Health Research Unit, Dept of Neurology, Univ of Washington, Seattle, WA
| | - Charles C. DeCarli
- Alzheimer's Disease Ctr, Imaging of Dementia & Aging (IdeA) Laboratory, Dept of Neurology, Ctr for Neuroscience, Univ of California, Davis, CA
| | | | - Reinhold Schmidt
- Dept of Neurology, Clinical Division of Neurogeriatrics, Medical Univ Graz, Graz, Austria
| | - Lenore J. Launer
- Laboratory of Epidemiology, Demography, & Biometry, National Institute of Aging, The National Institutes of Health, Bethesda, MD
| | - Hans J. Grabe
- Dept of Psychiatry & Psychotherapy, Univ Medicine Greifswald, Greifswald, Germany
| | - Sudha S. Seshadri
- Dept of Neurology, Boston Univ School of Medicine, Boston, MA
- National Heart, Lung, & Blood Institute's Framingham Heart Study, Framingham
| | - M. Arfan Ikram
- Dept of Epidemiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
- Dept of Radiology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
- Dept of Neurology, Erasmus MC Univ Medical Ctr, Rotterdam, the Netherlands
| | - Myriam Fornage
- Human Genetics Ctr, Univ of Texas Health Science Ctr at Houston, Houston, TX
- Institute of Molecular Medicine, Univ of Texas Health Science Ctr at Houston, Houston, TX
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16
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Strike LT, Couvy-Duchesne B, Hansell NK, Cuellar-Partida G, Medland SE, Wright MJ. Genetics and Brain Morphology. Neuropsychol Rev 2015; 25:63-96. [DOI: 10.1007/s11065-015-9281-1] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Accepted: 02/08/2015] [Indexed: 12/17/2022]
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17
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Gusareva ES, Van Steen K. Practical aspects of genome-wide association interaction analysis. Hum Genet 2014; 133:1343-58. [DOI: 10.1007/s00439-014-1480-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Accepted: 08/18/2014] [Indexed: 12/31/2022]
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18
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Sung YJ, Schwander K, Arnett DK, Kardia SLR, Rankinen T, Bouchard C, Boerwinkle E, Hunt SC, Rao DC. An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions. Genet Epidemiol 2014; 38:369-78. [PMID: 24719363 DOI: 10.1002/gepi.21800] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2013] [Revised: 02/10/2014] [Accepted: 02/28/2014] [Indexed: 11/11/2022]
Abstract
For analysis of the main effects of SNPs, meta-analysis of summary results from individual studies has been shown to provide comparable results as "mega-analysis" that jointly analyzes the pooled participant data from the available studies. This fact revolutionized the genetic analysis of complex traits through large GWAS consortia. Investigations of gene-environment (G×E) interactions are on the rise since they can potentially explain a part of the missing heritability and identify individuals at high risk for disease. However, for analysis of gene-environment interactions, it is not known whether these methods yield comparable results. In this empirical study, we report that the results from both methods were largely consistent for all four tests; the standard 1 degree of freedom (df) test of main effect only, the 1 df test of the main effect (in the presence of interaction effect), the 1 df test of the interaction effect, and the joint 2 df test of main and interaction effects. They provided similar effect size and standard error estimates, leading to comparable P-values. The genomic inflation factors and the number of SNPs with various thresholds were also comparable between the two approaches. Mega-analysis is not always feasible especially in very large and diverse consortia since pooling of raw data may be limited by the terms of the informed consent. Our study illustrates that meta-analysis can be an effective approach also for identifying interactions. To our knowledge, this is the first report investigating meta-versus mega-analyses for interactions.
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Affiliation(s)
- Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America
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19
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Pei YF, Zhang L, Papasian CJ, Wang YP, Deng HW. On individual genome-wide association studies and their meta-analysis. Hum Genet 2014; 133:265-79. [PMID: 24114349 PMCID: PMC4127980 DOI: 10.1007/s00439-013-1366-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2013] [Accepted: 09/22/2013] [Indexed: 01/07/2023]
Abstract
Individual genome-wide association (GWA) studies and their meta-analyses represent two approaches for identifying genetic loci associated with complex diseases/traits. Inconsistent findings and non-replicability between individual GWA studies and meta-analyses are commonly observed, hence posing the critical question as to how to interpret their respective results properly. In this study, we performed a series of simulation studies to investigate and compare the statistical properties of the two approaches. Our results show that (1) as expected, meta-analysis of larger sample size is more powerful than individual GWA studies under the ideal setting of population homogeneity among individual studies; (2) under the realistic setting of heterogeneity among individual studies, detection of heterogeneity is usually difficult and meta-analysis (even with the random-effects model) may introduce elevated false positive and/or negative rates; (3) despite relatively small sample size, well-designed individual GWA study has the capacity to identify novel loci for complex traits; (4) replicability between meta-analysis and independent individual studies or between independent meta-analyses is limited, and thus inconsistent findings are not unexpected.
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Affiliation(s)
- Yu-Fang Pei
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China,
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20
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Multivariate meta-analysis of the association of G-protein beta 3 gene (GNB3) haplotypes with cardiovascular phenotypes. Mol Biol Rep 2014; 41:3113-25. [PMID: 24477587 DOI: 10.1007/s11033-014-3171-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2013] [Accepted: 01/16/2014] [Indexed: 10/25/2022]
Abstract
The objective of the present study was to review previous investigations on the association of haplotypes in the G-protein β3 subunit (GNB3) gene with representative cardiovascular risk factors/phenotypes: hypertension, overweight, and variation in the systolic and diastolic blood pressures (SBP and DBP, respectively) and as well as body mass index (BMI). A comprehensive literature search was undertaken in Pubmed, Web of Science, EMBASE, Biological Abstracts, LILACS and Google Scholar to identify potentially relevant articles published up to April 2011. Six genetic association studies encompassing 16,068 participants were identified. Individual participant data were obtained for all studies. The three most investigated GNB3 polymorphisms (G-350A, C825T and C1429T) were considered. Expectation-maximization and generalized linear models were employed to estimate haplotypic effects from data with uncertain phase while adjusting for covariates. Study-specific results were combined through a random-effects multivariate meta-analysis. After carefully adjustments for relevant confounding factors, our analysis failed to support a role for GNB3 haplotypes in any of the investigated phenotypes. Sensitivity analyses excluding studies violating Hardy-Weinberg expectations, considering gender-specific effects or more extreme phenotypes (e.g. obesity only) as well as a fixed-effects "pooled" analysis also did not disclose a significant influence of GNB3 haplotypes on cardiovascular phenotypes. We conclude that the previous cumulative evidence does not support the proposal that haplotypes formed by common GNB3 polymorphisms might contribute either to the development of hypertension and obesity, or to the variation in the SBP, DBP and BMI.
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21
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Goh CL, Saunders EJ, Leongamornlert DA, Tymrakiewicz M, Thomas K, Selvadurai ED, Woode-Amissah R, Dadaev T, Mahmud N, Castro E, Olmos D, Guy M, Govindasami K, O'Brien LT, Hall AL, Wilkinson RA, Sawyer EJ, Al Olama AA, Easton DF, Kote-Jarai Z, Parker CC, Eeles RA. Clinical implications of family history of prostate cancer and genetic risk single nucleotide polymorphism (SNP) profiles in an active surveillance cohort. BJU Int 2013; 112:666-73. [PMID: 23320731 PMCID: PMC3633604 DOI: 10.1111/j.1464-410x.2012.11648.x] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To explore the potential prognostic role of family history (FH) of prostate cancer and prostate cancer risk single nucleotide polymorphisms (SNPs) in patients undergoing active surveillance (AS) for prostate cancer. This is the first study to date, which has investigated the potential prognostic role of SNP profiles in an AS cohort PATIENTS AND METHODS FH data were collected from patients in the Royal Marsden Hospital AS study. In all, 39 prostate cancer-risk SNPs identified from published genome wide association studies (GWAS) were genotyped using the Sequenom Platform and TaqMan™ assays from available DNA. The cumulative genetic-risk scores for each patient were then calculated using the weighted effect estimated from previous GWAS (log-additive model). FH status and the genetic-risk scores were assessed against adverse outcomes in AS, time to treatment and adverse histology on repeat biopsy, using univariable and multivariable Cox regression models to address time to treatment; and binary logistic regression to address biopsy upgrade. RESULTS Of 471 patients, 55 (13.6%) had adverse histology on repeat biopsies and 145 (30.8%) had deferred treatment. On univariate analysis, there was no significant relationship between FH of prostate cancer in any degree of relation, and adverse histology or time to treatment. For risk score analyses, 386 patients' DNA was studied; and there was also no relationship found between the calculated genetic risk scores and adverse histology or time to treatment (P = 0.573 and P = 0.965, respectively). The retrospective study design and the few events were the main limitation of the study. CONCLUSIONS There is currently insufficient data to support the use of FH status or prostate cancer SNP profile risk scores as prognostic factors in AS and these should not be used to influence management decisions. As more genetic variants are discovered this may change and should be reassessed in multicentre AS cohorts.
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Affiliation(s)
- Chee L Goh
- Oncogenetics team, The Institute of Cancer Research
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22
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Zheng HF, Duncan E, Yerges-Armstrong LM, Eriksson J, Bergström U, Leo PJ, Leslie WD, Goltzman D, Blangero J, Hanley DA, Carless MA, Streeten EA, Lorentzon M, Brown MA, Spector TD, Pettersson-Kymmer U, Ohlsson C, Mitchell BD, Richards JB. Meta-analysis of genome-wide studies identifies MEF2C SNPs associated with bone mineral density at forearm. J Med Genet 2013; 50:473-8. [PMID: 23572186 PMCID: PMC3769185 DOI: 10.1136/jmedgenet-2012-101287] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BACKGROUND Forearm fractures affect 1.7 million individuals worldwide each year and most occur earlier in life than hip fractures. While the heritability of forearm bone mineral density (BMD) and fracture is high, their genetic determinants are largely unknown. AIM To identify genetic variants associated with forearm BMD and forearm fractures. METHODS BMD at distal radius, measured by dual-energy x-ray absorptiometry, was tested for association with common genetic variants. We conducted a meta-analysis of genome-wide association studies for BMD in 5866 subjects of European descent and then selected the variants for replication in 715 Mexican American samples. Gene-based association was carried out to supplement the single-nucleotide polymorphism (SNP) association test. We then tested the BMD-associated SNPs for association with forearm fracture in 2023 cases and 3740 controls. RESULTS We found that five SNPs in the introns of MEF2C were associated with forearm BMD at a genome-wide significance level (p<5×10(-8)) in meta-analysis (lead SNP, rs11951031[T] -0.20 SDs per allele, p=9.01×10(-9)). The gene-based association test suggested an association between MEF2C and forearm BMD (p=0.003). The association between MEF2C variants and risk of fracture did not achieve statistical significance (SNP rs12521522[A]: OR=1.14 (95% CI 0.92 to 1.35), p=0.14). Meta-analysis also revealed two genome-wide suggestive loci at CTNNA2 and 6q23.2. CONCLUSIONS These findings demonstrate that variants at MEF2C were associated with forearm BMD, implicating this gene in the determination of BMD at forearm.
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Affiliation(s)
- Hou-Feng Zheng
- Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, H3T 1E2 Canada
| | - Emma Duncan
- Human Genetics Group, University of Queensland Diamantina Institute, University of Queensland, Princess Alexandra Hospital, Brisbane, 4102, Australia
- Endocrinology, Royal Brisbane and Women's Hospital, Brisbane, 4029, Australia
| | - Laura M. Yerges-Armstrong
- Department of Medicine; Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Joel Eriksson
- Center for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, 41345, Sweden
| | - Ulrica Bergström
- Surgical and Perioperative Sciences, Umeå University, Umeå, S-90187, Sweden
| | - Paul J. Leo
- Human Genetics Group, University of Queensland Diamantina Institute, University of Queensland, Princess Alexandra Hospital, Brisbane, 4102, Australia
| | - William D. Leslie
- Department of Internal Medicine, University of Manitoba, Winnipeg, Canada
| | - David Goltzman
- Department of Medicine, McGill University, Montreal, Canada
| | - John Blangero
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, USA
| | - David A. Hanley
- Department of Medicine, University of Calgary, Calgary, AB, Canada
| | - Melanie A. Carless
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, USA
| | - Elizabeth A. Streeten
- Department of Medicine; Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Geriatric Research and Education Clinical Center (GRECC), Veterans Administration Medical Center, Baltimore, MD, 21201, USA
| | - Mattias Lorentzon
- Center for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, 41345, Sweden
| | - Matthew A. Brown
- Human Genetics Group, University of Queensland Diamantina Institute, University of Queensland, Princess Alexandra Hospital, Brisbane, 4102, Australia
| | - Tim D. Spector
- Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Ulrika Pettersson-Kymmer
- Pharmacology and Neuroscience, Umeå University, Umeå, S-90187, Sweden
- Public Health and Clinical Medicine, Umeå Unviersity, Umeå, S-90187, Sweden
| | - Claes Ohlsson
- Center for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, 41345, Sweden
| | - Braxton D. Mitchell
- Department of Medicine; Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - J. Brent Richards
- Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, H3T 1E2 Canada
- Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
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Panagiotou OA, Willer CJ, Hirschhorn JN, Ioannidis JPA. The power of meta-analysis in genome-wide association studies. Annu Rev Genomics Hum Genet 2013; 14:441-65. [PMID: 23724904 DOI: 10.1146/annurev-genom-091212-153520] [Citation(s) in RCA: 90] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Meta-analysis of multiple genome-wide association (GWA) studies has become common practice over the past few years. The main advantage of this technique is the maximization of power to detect subtle genetic effects for common traits. Moreover, one can use meta-analysis to probe and identify heterogeneity in the effect sizes across the combined studies. In this review, we systematically appraise and evaluate the characteristics of GWA meta-analyses with 10,000 or more subjects published up to June 2012. We provide an overview of the current landscape of variants discovered by GWA meta-analyses, and we discuss and assess with extrapolations from empirical data the value of larger meta-analyses for the discovery of additional genetic associations and new biology in the future. Finally, we discuss some emerging logistical and practical issues related to the conduct of meta-analysis of GWA studies.
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Affiliation(s)
- Orestis A Panagiotou
- Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina 45110, Greece;
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24
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Evangelou E, Ioannidis JPA. Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet 2013; 14:379-89. [PMID: 23657481 DOI: 10.1038/nrg3472] [Citation(s) in RCA: 382] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia.
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Affiliation(s)
- Evangelos Evangelou
- Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece
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Chen DT, Jiang X, Akula N, Shugart YY, Wendland JR, Steele CJM, Kassem L, Park JH, Chatterjee N, Jamain S, Cheng A, Leboyer M, Muglia P, Schulze TG, Cichon S, Nöthen MM, Rietschel M, McMahon FJ, Farmer A, McGuffin P, Craig I, Lewis C, Hosang G, Cohen-Woods S, Vincent JB, Kennedy JL, Strauss J. Genome-wide association study meta-analysis of European and Asian-ancestry samples identifies three novel loci associated with bipolar disorder. Mol Psychiatry 2013; 18:195-205. [PMID: 22182935 DOI: 10.1038/mp.2011.157] [Citation(s) in RCA: 157] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Meta-analyses of bipolar disorder (BD) genome-wide association studies (GWAS) have identified several genome-wide significant signals in European-ancestry samples, but so far account for little of the inherited risk. We performed a meta-analysis of ∼750,000 high-quality genetic markers on a combined sample of ∼14,000 subjects of European and Asian-ancestry (phase I). The most significant findings were further tested in an extended sample of ∼17,700 cases and controls (phase II). The results suggest novel association findings near the genes TRANK1 (LBA1), LMAN2L and PTGFR. In phase I, the most significant single nucleotide polymorphism (SNP), rs9834970 near TRANK1, was significant at the P=2.4 × 10(-11) level, with no heterogeneity. Supportive evidence for prior association findings near ANK3 and a locus on chromosome 3p21.1 was also observed. The phase II results were similar, although the heterogeneity test became significant for several SNPs. On the basis of these results and other established risk loci, we used the method developed by Park et al. to estimate the number, and the effect size distribution, of BD risk loci that could still be found by GWAS methods. We estimate that >63,000 case-control samples would be needed to identify the ∼105 BD risk loci discoverable by GWAS, and that these will together explain <6% of the inherited risk. These results support previous GWAS findings and identify three new candidate genes for BD. Further studies are needed to replicate these findings and may potentially lead to identification of functional variants. Sample size will remain a limiting factor in the discovery of common alleles associated with BD.
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Affiliation(s)
- D T Chen
- Human Genetics Branch, National Institute of Mental Health, Intramural Research Program, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD 20892, USA.
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Multilocus family-based association analysis of seven candidate polymorphisms with essential hypertension in an african-derived semi-isolated brazilian population. Int J Hypertens 2012; 2012:859219. [PMID: 23056922 PMCID: PMC3463917 DOI: 10.1155/2012/859219] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2012] [Accepted: 07/11/2012] [Indexed: 12/16/2022] Open
Abstract
Background. It has been widely suggested that analyses considering multilocus effects would be crucial to characterize the relationship between gene variability and essential hypertension (EH). Objective. To test for the presence of multilocus effects between/among seven polymorphisms (six genes) on blood pressure-related traits in African-derived semi-isolated Brazilian populations (quilombos). Methods. Analyses were carried out using a family-based design in a sample of 652 participants (97 families). Seven variants were investigated: ACE (rs1799752), AGT (rs669), ADD2 (rs3755351), NOS3 (rs1799983), GNB3 (rs5441 and rs5443), and GRK4 (rs1801058). Sensitivity analyses were further performed under a case-control design with unrelated participants only. Results. None of the investigated variants were associated individually with both systolic and diastolic BP levels (SBP and DBP, respectively) or EH (as a binary outcome). Multifactor dimensionality reduction-based techniques revealed a marginal association of the combined effect of both GNB3 variants on DBP levels in a family-based design (P = 0.040), whereas a putative NOS3-GRK4 interaction also in relation to DBP levels was observed in the case-control design only (P = 0.004). Conclusion. Our results provide limited support for the hypothesis of multilocus effects between/among the studied variants on blood pressure in quilombos. Further larger studies are needed to validate our findings.
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Zheng HF, Tobias JH, Duncan E, Evans DM, Eriksson J, Paternoster L, Yerges-Armstrong LM, Lehtimäki T, Bergström U, Kähönen M, Leo PJ, Raitakari O, Laaksonen M, Nicholson GC, Viikari J, Ladouceur M, Lyytikäinen LP, Medina-Gomez C, Rivadeneira F, Prince RL, Sievanen H, Leslie WD, Mellström D, Eisman JA, Movérare-Skrtic S, Goltzman D, Hanley DA, Jones G, St. Pourcain B, Xiao Y, Timpson NJ, Smith GD, Reid IR, Ring SM, Sambrook PN, Karlsson M, Dennison EM, Kemp JP, Danoy P, Sayers A, Wilson SG, Nethander M, McCloskey E, Vandenput L, Eastell R, Liu J, Spector T, Mitchell BD, Streeten EA, Brommage R, Pettersson-Kymmer U, Brown MA, Ohlsson C, Richards JB, Lorentzon M. WNT16 influences bone mineral density, cortical bone thickness, bone strength, and osteoporotic fracture risk. PLoS Genet 2012; 8:e1002745. [PMID: 22792071 PMCID: PMC3390364 DOI: 10.1371/journal.pgen.1002745] [Citation(s) in RCA: 206] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Accepted: 04/04/2012] [Indexed: 01/29/2023] Open
Abstract
We aimed to identify genetic variants associated with cortical bone thickness (CBT) and bone mineral density (BMD) by performing two separate genome-wide association study (GWAS) meta-analyses for CBT in 3 cohorts comprising 5,878 European subjects and for BMD in 5 cohorts comprising 5,672 individuals. We then assessed selected single-nucleotide polymorphisms (SNPs) for osteoporotic fracture in 2,023 cases and 3,740 controls. Association with CBT and forearm BMD was tested for ∼2.5 million SNPs in each cohort separately, and results were meta-analyzed using fixed effect meta-analysis. We identified a missense SNP (Thr>Ile; rs2707466) located in the WNT16 gene (7q31), associated with CBT (effect size of -0.11 standard deviations [SD] per C allele, P = 6.2 × 10(-9)). This SNP, as well as another nonsynonymous SNP rs2908004 (Gly>Arg), also had genome-wide significant association with forearm BMD (-0.14 SD per C allele, P = 2.3 × 10(-12), and -0.16 SD per G allele, P = 1.2 × 10(-15), respectively). Four genome-wide significant SNPs arising from BMD meta-analysis were tested for association with forearm fracture. SNP rs7776725 in FAM3C, a gene adjacent to WNT16, was associated with a genome-wide significant increased risk of forearm fracture (OR = 1.33, P = 7.3 × 10(-9)), with genome-wide suggestive signals from the two missense variants in WNT16 (rs2908004: OR = 1.22, P = 4.9 × 10(-6) and rs2707466: OR = 1.22, P = 7.2 × 10(-6)). We next generated a homozygous mouse with targeted disruption of Wnt16. Female Wnt16(-/-) mice had 27% (P<0.001) thinner cortical bones at the femur midshaft, and bone strength measures were reduced between 43%-61% (6.5 × 10(-13)<P<5.9 × 10(-4)) at both femur and tibia, compared with their wild-type littermates. Natural variation in humans and targeted disruption in mice demonstrate that WNT16 is an important determinant of CBT, BMD, bone strength, and risk of fracture.
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Affiliation(s)
- Hou-Feng Zheng
- Department of Medicine, Human Genetics, McGill University, Montreal, Canada
- Department of Epidemiology and Biostatistics, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada
| | - Jon H. Tobias
- Musculoskeletal Research Unit, School of Clinical Sciences, University of Bristol, Bristol, United Kingdom
| | - Emma Duncan
- Human Genetics Group, University of Queensland Diamantina Institute, Princess Alexandra Hospital, University of Queensland, Brisbane, Australia
- Endocrinology, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - David M. Evans
- Medical Research Council Centre for Causal Analyses in Translational Epidemiology, University of Bristol, Bristol, United Kingdom
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Joel Eriksson
- Center for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Lavinia Paternoster
- Medical Research Council Centre for Causal Analyses in Translational Epidemiology, University of Bristol, Bristol, United Kingdom
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Laura M. Yerges-Armstrong
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab, University of Tampere School of Medicine and Tampere University Hospital, Tampere, Finland
| | - Ulrica Bergström
- Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden
| | - Mika Kähönen
- Department of Clinical Physiology, University of Tampere School of Medicine and Tampere University Hospital, Tampere, Finland
| | - Paul J. Leo
- Human Genetics Group, University of Queensland Diamantina Institute, Princess Alexandra Hospital, University of Queensland, Brisbane, Australia
| | - Olli Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine and the Department of Clinical Physiology and Nuclear Medicine, University of Turku and Turku University Hospital, Turku, Finland
| | - Marika Laaksonen
- Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | | | - Jorma Viikari
- Department of Medicine, University of Turku and Turku University Hospital, Turku, Finland
| | | | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab, University of Tampere School of Medicine and Tampere University Hospital, Tampere, Finland
| | - Carolina Medina-Gomez
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Richard L. Prince
- Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Perth, Australia
- School of Medicine and Pharmacology, University of Western Australia, Perth, Australia
| | | | - William D. Leslie
- Department of Internal Medicine, University of Manitoba, Winnipeg, Canada
| | - Dan Mellström
- Center for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - John A. Eisman
- Garvan Institute of Medical Research, University of New South Wales, Sydney, Australia
| | - Sofia Movérare-Skrtic
- Center for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - David Goltzman
- Department of Medicine, McGill University, Montreal, Canada
| | - David A. Hanley
- Department of Medicine, University of Calgary, Calgary, Canada
| | - Graeme Jones
- Menzies Research Institute, University of Tasmania, Hobart, Australia
| | - Beate St. Pourcain
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Yongjun Xiao
- Centre for Bone and Periodontal Research, McGill University, Montreal, Canada
| | - Nicholas J. Timpson
- Medical Research Council Centre for Causal Analyses in Translational Epidemiology, University of Bristol, Bristol, United Kingdom
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- Medical Research Council Centre for Causal Analyses in Translational Epidemiology, University of Bristol, Bristol, United Kingdom
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Ian R. Reid
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Susan M. Ring
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Philip N. Sambrook
- Kolling Institute, Royal North Shore Hospital, University of Sydney, Sydney, Australia
| | - Magnus Karlsson
- Clinical and Molecular Osteoporosis Research Unit, Department of Orthopaedics, Skane University Hospital, Lund University, Malmö, Sweden
| | - Elaine M. Dennison
- Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
| | - John P. Kemp
- Medical Research Council Centre for Causal Analyses in Translational Epidemiology, University of Bristol, Bristol, United Kingdom
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Patrick Danoy
- Human Genetics Group, University of Queensland Diamantina Institute, Princess Alexandra Hospital, University of Queensland, Brisbane, Australia
| | - Adrian Sayers
- Musculoskeletal Research Unit, School of Clinical Sciences, University of Bristol, Bristol, United Kingdom
| | - Scott G. Wilson
- Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Perth, Australia
- School of Medicine and Pharmacology, University of Western Australia, Perth, Australia
- Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Maria Nethander
- Genomics Core Facility, University of Gothenburg, Gothenburg, Sweden
| | - Eugene McCloskey
- Academic Unit of Bone Metabolism, Metabolic Bone Centre, University of Sheffield, Sheffield, United Kingdom
- NIHR Musculoskeletal Biomedical Research Unit, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Liesbeth Vandenput
- Center for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Richard Eastell
- Academic Unit of Bone Metabolism, Metabolic Bone Centre, University of Sheffield, Sheffield, United Kingdom
| | - Jeff Liu
- Lexicon Pharmaceuticals, The Woodlands, Texas, United States of America
| | - Tim Spector
- Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Braxton D. Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Elizabeth A. Streeten
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- Geriatric Research and Education Clinical Center (GRECC), Veterans Administration Medical Center, Baltimore, Maryland, United States of America
| | - Robert Brommage
- Lexicon Pharmaceuticals, The Woodlands, Texas, United States of America
| | - Ulrika Pettersson-Kymmer
- Department of Pharmacology and Neuroscience, Umeå University, Umeå, Sweden
- Department of Public Health and Clinical Medicine, Umeå Unviersity, Umeå, Sweden
| | - Matthew A. Brown
- Human Genetics Group, University of Queensland Diamantina Institute, Princess Alexandra Hospital, University of Queensland, Brisbane, Australia
| | - Claes Ohlsson
- Center for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- * E-mail:
| | - J. Brent Richards
- Department of Medicine, Human Genetics, McGill University, Montreal, Canada
- Department of Epidemiology and Biostatistics, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada
- Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Mattias Lorentzon
- Center for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Aschard H, Lutz S, Maus B, Duell EJ, Fingerlin TE, Chatterjee N, Kraft P, Van Steen K. Challenges and opportunities in genome-wide environmental interaction (GWEI) studies. Hum Genet 2012; 131:1591-613. [PMID: 22760307 DOI: 10.1007/s00439-012-1192-0] [Citation(s) in RCA: 110] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2012] [Accepted: 06/11/2012] [Indexed: 02/03/2023]
Abstract
The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.
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Affiliation(s)
- Hugues Aschard
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
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29
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Abstract
In multi-cohort genetic association studies or meta-analysis, associations of genetic variants with complex traits across cohorts may be heterogeneous because of genuine genetic diversity or differential biases or errors. To detect the associations of genes with heterogeneous associations across cohorts, new global fixed-effect (FE) and random-effects (RE) meta-analytic methods have been recently proposed. These global methods had improved power over both traditional FE and RE methods under heterogeneity in limited simulation scenarios and data application, but their usefulness in a wide range of practical situations is not clear. We assessed the performance of these methods for both binary and quantitative traits in extensive simulations and applied them to a multi-cohort association study. We found that these new approaches have higher power to detect mostly the very small to small associations of common genetic variants when associations are highly heterogeneous across cohorts. They worked well when both the underlying and assumed genetic models are either multiplicative or dominant. But, they offered no clear advantage for less common variants unless heterogeneity was substantial. In conclusion, these new meta-analytic methods can be used to detect the association of genetic variants with high heterogeneity, which can then be subjected to further exploration, in multi-cohort association studies and meta-analyses.
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30
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Estrada K, Styrkarsdottir U, Evangelou E, Hsu YH, Duncan EL, Ntzani EE, Oei L, Albagha OME, Amin N, Kemp JP, Koller DL, Li G, Liu CT, Minster RL, Moayyeri A, Vandenput L, Willner D, Xiao SM, Yerges-Armstrong LM, Zheng HF, Alonso N, Eriksson J, Kammerer CM, Kaptoge SK, Leo PJ, Thorleifsson G, Wilson SG, Wilson JF, Aalto V, Alen M, Aragaki AK, Aspelund T, Center JR, Dailiana Z, Duggan DJ, Garcia M, Garcia-Giralt N, Giroux S, Hallmans G, Hocking LJ, Husted LB, Jameson KA, Khusainova R, Kim GS, Kooperberg C, Koromila T, Kruk M, Laaksonen M, Lacroix AZ, Lee SH, Leung PC, Lewis JR, Masi L, Mencej-Bedrac S, Nguyen TV, Nogues X, Patel MS, Prezelj J, Rose LM, Scollen S, Siggeirsdottir K, Smith AV, Svensson O, Trompet S, Trummer O, van Schoor NM, Woo J, Zhu K, Balcells S, Brandi ML, Buckley BM, Cheng S, Christiansen C, Cooper C, Dedoussis G, Ford I, Frost M, Goltzman D, González-Macías J, Kähönen M, Karlsson M, Khusnutdinova E, Koh JM, Kollia P, Langdahl BL, Leslie WD, Lips P, Ljunggren Ö, Lorenc RS, Marc J, Mellström D, Obermayer-Pietsch B, Olmos JM, Pettersson-Kymmer U, Reid DM, Riancho JA, Ridker PM, Rousseau F, Slagboom PE, Tang NLS, Urreizti R, Van Hul W, Viikari J, Zarrabeitia MT, Aulchenko YS, Castano-Betancourt M, Grundberg E, Herrera L, Ingvarsson T, Johannsdottir H, Kwan T, Li R, Luben R, Medina-Gómez C, Palsson ST, Reppe S, Rotter JI, Sigurdsson G, van Meurs JBJ, Verlaan D, Williams FMK, Wood AR, Zhou Y, Gautvik KM, Pastinen T, Raychaudhuri S, Cauley JA, Chasman DI, Clark GR, Cummings SR, Danoy P, Dennison EM, Eastell R, Eisman JA, Gudnason V, Hofman A, Jackson RD, Jones G, Jukema JW, Khaw KT, Lehtimäki T, Liu Y, Lorentzon M, McCloskey E, Mitchell BD, Nandakumar K, Nicholson GC, Oostra BA, Peacock M, Pols HAP, Prince RL, Raitakari O, Reid IR, Robbins J, Sambrook PN, Sham PC, Shuldiner AR, Tylavsky FA, van Duijn CM, Wareham NJ, Cupples LA, Econs MJ, Evans DM, Harris TB, Kung AWC, Psaty BM, Reeve J, Spector TD, Streeten EA, Zillikens MC, Thorsteinsdottir U, Ohlsson C, Karasik D, Richards JB, Brown MA, Stefansson K, Uitterlinden AG, Ralston SH, Ioannidis JPA, Kiel DP, Rivadeneira F. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet 2012; 44:491-501. [PMID: 22504420 PMCID: PMC3338864 DOI: 10.1038/ng.2249] [Citation(s) in RCA: 887] [Impact Index Per Article: 73.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2011] [Accepted: 03/16/2012] [Indexed: 12/15/2022]
Abstract
Bone mineral density (BMD) is the most widely used predictor of fracture risk. We performed the largest meta-analysis to date on lumbar spine and femoral neck BMD, including 17 genome-wide association studies and 32,961 individuals of European and east Asian ancestry. We tested the top BMD-associated markers for replication in 50,933 independent subjects and for association with risk of low-trauma fracture in 31,016 individuals with a history of fracture (cases) and 102,444 controls. We identified 56 loci (32 new) associated with BMD at genome-wide significance (P < 5 × 10(-8)). Several of these factors cluster within the RANK-RANKL-OPG, mesenchymal stem cell differentiation, endochondral ossification and Wnt signaling pathways. However, we also discovered loci that were localized to genes not known to have a role in bone biology. Fourteen BMD-associated loci were also associated with fracture risk (P < 5 × 10(-4), Bonferroni corrected), of which six reached P < 5 × 10(-8), including at 18p11.21 (FAM210A), 7q21.3 (SLC25A13), 11q13.2 (LRP5), 4q22.1 (MEPE), 2p16.2 (SPTBN1) and 10q21.1 (DKK1). These findings shed light on the genetic architecture and pathophysiological mechanisms underlying BMD variation and fracture susceptibility.
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Affiliation(s)
- Karol Estrada
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands
| | | | - Evangelos Evangelou
- Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Greece
| | - Yi-Hsiang Hsu
- Institute for Aging Research, Hebrew SeniorLife, Boston, USA
- Department of Medicine, Harvard Medical School, Boston, USA
| | - Emma L Duncan
- Human Genetics Group, University of Queensland Diamantina Institute, Brisbane, Australia
- Department of Endocrinology, Royal Brisbane and Women’s Hospital, Brisbane, Australia
| | - Evangelia E Ntzani
- Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Greece
| | - Ling Oei
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands
| | - Omar M E Albagha
- Rheumatic Diseases Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Najaf Amin
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - John P Kemp
- Medical Research Council (MRC) Centre for Causal Analyses in Translational Epidemiology, University of Bristol, Bristol, UK
| | - Daniel L Koller
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, USA
| | - Guo Li
- Cardiovascular Health Research Unit, University of Washington, Seattle, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, USA
| | - Ryan L Minster
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alireza Moayyeri
- of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Liesbeth Vandenput
- Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Dana Willner
- Human Genetics Group, University of Queensland Diamantina Institute, Brisbane, Australia
- Australian Centre for Ecogenomics, University of Queensland, Brisbane, Australia
| | - Su-Mei Xiao
- Department of Medicine, The University of Hong Kong, Hong Kong, China
- Research Centre of Heart, Brain, Hormone and Healthy Aging, The University of Hong Kong, Hong Kong, China
| | - Laura M Yerges-Armstrong
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Hou-Feng Zheng
- Department of Human Genetics, Lady Davis Institute, McGill University, Montreal, Canada
| | - Nerea Alonso
- Rheumatic Diseases Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Joel Eriksson
- Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Candace M Kammerer
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Stephen K Kaptoge
- of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul J Leo
- Human Genetics Group, University of Queensland Diamantina Institute, Brisbane, Australia
| | | | - Scott G Wilson
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
- School of Medicine and Pharmacology, University of Western Australia, Perth, Australia
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Perth, Australia
| | - James F Wilson
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine at the University of Edinburgh, Edinburgh, UK
| | - Ville Aalto
- Department of Clinical Physiology, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Markku Alen
- Department of Medical Rehabilitation, Oulu University Hospital and Institute of Health Sciences, Oulu, Finland
| | - Aaron K Aragaki
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Thor Aspelund
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Jacqueline R Center
- Osteoporosis and Bone Biology Program, Garvan Institute of Medical Research, Sydney, Australia
- Department of Medicine, University of New South Wales, Sydney, Australia
- Department of Endocrinology, St Vincents Hospital, Sydney, Australia
| | - Zoe Dailiana
- Department of Orthopaedic Surgery, Medical School University of Thessalia, Larissa, Greece
| | | | - Melissa Garcia
- Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD, USA
| | - Natàlia Garcia-Giralt
- Department of Internal Medicine, Hospital del Mar, Instituto Municipal de Investigación Médica (IMIM), Red Temática de Investigación Cooperativa en Envejecimiento y Fragilidad (RETICEF), Universitat Autònoma de Barcelona (UAB), Barcelone, Spain
| | - Sylvie Giroux
- Unité de recherche en génétique humaine et moléculaire, Centre de recherche du Centre hospitalier universitaire de Québec - Hôpital St-François-d’Assise (CHUQ/HSFA), Québec City, Canada
| | - Göran Hallmans
- Department of Public Health and Clinical Medicine, Umeå Unviersity, Umeå, Sweden
| | - Lynne J Hocking
- Musculoskeletal Research Programme, Division of Applied Medicine, University of Aberdeen, Aberdeen, UK
| | - Lise Bjerre Husted
- Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus C, Denmark
| | - Karen A Jameson
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Rita Khusainova
- Ufa Scientific Centre of Russian Academy of Sciences, Institute of Biochemistry and Genetics, Ufa, Russia
- Biological Department, Bashkir State University, Ufa, Russia
| | - Ghi Su Kim
- Division of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Theodora Koromila
- Department of Genetics and Biotechnology, Faculty of Biology, University of Athens, Athens, Greece
| | - Marcin Kruk
- Department of Biochemistry and Experimental Medicine, The Children’s Memorial Health Institute, Warsaw, Poland
| | - Marika Laaksonen
- Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Andrea Z Lacroix
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Seung Hun Lee
- Division of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Ping C Leung
- Jockey Club Centre for Osteoporosis Care and Control, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Joshua R Lewis
- School of Medicine and Pharmacology, University of Western Australia, Perth, Australia
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Perth, Australia
| | - Laura Masi
- Department of Internal Medicine, University of Florence, Florence, Italy
| | - Simona Mencej-Bedrac
- Department of Clinical Biochemistry, University of Ljubljana, Ljubljana, Slovenia
| | - Tuan V Nguyen
- Osteoporosis and Bone Biology Program, Garvan Institute of Medical Research, Sydney, Australia
- Department of Medicine, University of New South Wales, Sydney, Australia
| | - Xavier Nogues
- Department of Internal Medicine, Hospital del Mar, Instituto Municipal de Investigación Médica (IMIM), Red Temática de Investigación Cooperativa en Envejecimiento y Fragilidad (RETICEF), Universitat Autònoma de Barcelona (UAB), Barcelone, Spain
| | - Millan S Patel
- Department of Medical Genetics, University of British Columbia, Vancouver, Canada
| | - Janez Prezelj
- Department of Endocrinology, University Medical Center, Ljubljana, Slovenia
| | - Lynda M Rose
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, USA
| | - Serena Scollen
- Department of Medicine, University of Cambridge, Cambridge, UK
| | | | - Albert V Smith
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Olle Svensson
- Department of Surgical and Perioperative Sciences, Umeå Unviersity, Umeå, Sweden
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Olivia Trummer
- Department of Internal Medicine, Division of Endocrinology and Metabolism, Medical University Graz, Graz, Austria
| | - Natasja M van Schoor
- Department of Epidemiology and Biostatistics, Extramuraal Geneeskundig Onderzoek (EMGO) Institute for Health and Care Research, Vrije Universiteit (VU) University Medical Center, Amsterdam, The Netherlands
| | - Jean Woo
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Kun Zhu
- School of Medicine and Pharmacology, University of Western Australia, Perth, Australia
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Perth, Australia
| | - Susana Balcells
- Department of Genetics, University of Barcelona, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelone, Spain
| | - Maria Luisa Brandi
- Department of Internal Medicine, University of Florence, Florence, Italy
| | - Brendan M Buckley
- Department of Pharmacology and Therapeutics, University College Cork, Cork, Ireland
| | - Sulin Cheng
- Department of Health Sciences, University of Jyväskylä, Jyväskylä, Finland
- Department of Orthopaedics and Traumatology, Kuopio University Hospital, Kuopio, Finland
| | | | - Cyrus Cooper
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - George Dedoussis
- Department of Nutrition and Dietetics, Harokopio University, Athens, Greece
| | - Ian Ford
- Robertson Center for Biostatistics, University of Glasgow, Glasgow, United Kingdom
| | - Morten Frost
- Department of Endocrinology, Odense University Hospital, Odense, Denmark
- Clinical Institute, University of Southern Denmark, Odense, Denmark
| | - David Goltzman
- Department of Medicine, McGill University, Montreal, Canada
| | - Jesús González-Macías
- Department of Medicine, University of Cantabria, Santander, Spain
- Department of Internal Medicine, Hospital Universitario Marqués de Valdecilla and Instituto de Formación e Investigación Marqués de Valdecilla (IFIMAV), Santander, Spain
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Department of Clinical Physiology, University of Tampere School of Medicine, Tampere, Finland
| | - Magnus Karlsson
- Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences and Department of Orthopaedics, Lund University, Malmö, Sweden
| | - Elza Khusnutdinova
- Ufa Scientific Centre of Russian Academy of Sciences, Institute of Biochemistry and Genetics, Ufa, Russia
- Biological Department, Bashkir State University, Ufa, Russia
| | - Jung-Min Koh
- Division of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Panagoula Kollia
- Department of Genetics and Biotechnology, Faculty of Biology, University of Athens, Athens, Greece
| | - Bente Lomholt Langdahl
- Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus C, Denmark
| | - William D Leslie
- Department of Internal Medicine, University of Manitoba, Winnipeg, Canada
| | - Paul Lips
- Department of Endocrinology, Vrije Universiteit (VU) University Medical Center, Amsterdam, The Netherlands
- Extramuraal Geneeskundig Onderzoek (EMGO) Institute for Health and Care Research, Vrije Universiteit (VU) University Medical Center, Amsterdam, The Netherlands
| | - Östen Ljunggren
- Department of Medical Sciences, University of Uppsala, Uppsala, Sweden
| | - Roman S Lorenc
- Department of Biochemistry and Experimental Medicine, The Children’s Memorial Health Institute, Warsaw, Poland
| | - Janja Marc
- Department of Clinical Biochemistry, University of Ljubljana, Ljubljana, Slovenia
| | - Dan Mellström
- Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Barbara Obermayer-Pietsch
- Department of Internal Medicine, Division of Endocrinology and Metabolism, Medical University Graz, Graz, Austria
| | - José M Olmos
- Department of Medicine, University of Cantabria, Santander, Spain
- Department of Internal Medicine, Hospital Universitario Marqués de Valdecilla and Instituto de Formación e Investigación Marqués de Valdecilla (IFIMAV), Santander, Spain
| | | | - David M Reid
- Musculoskeletal Research Programme, Division of Applied Medicine, University of Aberdeen, Aberdeen, UK
| | - José A Riancho
- Department of Medicine, University of Cantabria, Santander, Spain
- Department of Internal Medicine, Hospital Universitario Marqués de Valdecilla and Instituto de Formación e Investigación Marqués de Valdecilla (IFIMAV), Santander, Spain
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, USA
- Harvard Medical School, Boston, USA
| | - François Rousseau
- Unité de recherche en génétique humaine et moléculaire, Centre de recherche du Centre hospitalier universitaire de Québec - Hôpital St-François-d’Assise (CHUQ/HSFA), Québec City, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Université Laval, Québec City, Canada
- The APOGEE-Net/CanGèneTest Network on Genetic Health Services and Policy, Université Laval, Québec City, Canada
| | - P Eline Slagboom
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Nelson LS Tang
- Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Roser Urreizti
- Department of Genetics, University of Barcelona, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelone, Spain
| | - Wim Van Hul
- Department of Medical Genetics, University of Antwerp, Antwerp, Belgium
| | - Jorma Viikari
- Department of Medicine, Turku University Hospital, Turku, Finland
- Department of Medicine, University of Turku, Turku, Finland
| | | | - Yurii S Aulchenko
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Martha Castano-Betancourt
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands
| | - Elin Grundberg
- Department of Human Genetics, McGill University, Montreal, Canada
- McGill University and Genome Québec Innovation Centre, Montreal, Canada
- Wellcome Trust Sanger Institute, Hinxton, UK
| | - Lizbeth Herrera
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Thorvaldur Ingvarsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Orthopedic Surgery, Akureyri Hospital, Akureyri, Iceland
- Institution of Health Science, University Of Akureyri, Akureyri, Iceland
| | | | - Tony Kwan
- Department of Human Genetics, McGill University, Montreal, Canada
- McGill University and Genome Québec Innovation Centre, Montreal, Canada
| | - Rui Li
- Department of Epidemiology and Biostatistics, Lady Davis Institute, McGill University, Montreal, Canada
| | - Robert Luben
- of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Carolina Medina-Gómez
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Sjur Reppe
- Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway
| | - Jerome I Rotter
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Gunnar Sigurdsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Endocrinology and Metabolism, University Hospital, Reykjavik, Iceland
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands
| | - Dominique Verlaan
- Department of Human Genetics, McGill University, Montreal, Canada
- McGill University and Genome Québec Innovation Centre, Montreal, Canada
| | - Frances MK Williams
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Andrew R Wood
- Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, England
| | - Yanhua Zhou
- Department of Biostatistics, Boston University School of Public Health, Boston, USA
| | - Kaare M Gautvik
- Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway
- Department of Clinical Biochemistry, Lovisenberg Deacon Hospital, Oslo, Norway
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Tomi Pastinen
- Department of Human Genetics, McGill University, Montreal, Canada
- McGill University and Genome Québec Innovation Centre, Montreal, Canada
- Department of Medical Genetics, McGill University Health Centre, Montreal, Canada
| | - Soumya Raychaudhuri
- Division of Genetics and Rheumatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States
- Program in Medical and Population Genetics, Broad Institute, Cambridge, United States
| | - Jane A Cauley
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, USA
- Harvard Medical School, Boston, USA
| | - Graeme R Clark
- Human Genetics Group, University of Queensland Diamantina Institute, Brisbane, Australia
| | | | - Patrick Danoy
- Human Genetics Group, University of Queensland Diamantina Institute, Brisbane, Australia
| | - Elaine M Dennison
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Richard Eastell
- National Institute for Health and Research (NIHR) Musculoskeletal Biomedical Research Unit, University of Sheffield, Sheffield, UK
| | - John A Eisman
- Osteoporosis and Bone Biology Program, Garvan Institute of Medical Research, Sydney, Australia
- Department of Medicine, University of New South Wales, Sydney, Australia
- Department of Endocrinology, St Vincents Hospital, Sydney, Australia
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands
| | - Rebecca D Jackson
- Department of Internal Medicine, The Ohio State University, Columbus, USA
- Center for Clinical and Translational Science, The Ohio State University, Columbus, USA
| | - Graeme Jones
- Menzies Research Institute, University of Tasmania, Hobart, Australia
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Durrer Center for Cardiogenetic Research, Amsterdam, The Netherlands
- Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands
| | - Kay-Tee Khaw
- of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Tampere University Hospital, Tampere, Finland
- Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland
| | - Yongmei Liu
- Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Mattias Lorentzon
- Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Eugene McCloskey
- National Institute for Health and Research (NIHR) Musculoskeletal Biomedical Research Unit, University of Sheffield, Sheffield, UK
- Academic Unit of Bone Metabolism, Metabolic Bone Centre, University of Sheffield, Sheffield, UK
| | - Braxton D Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kannabiran Nandakumar
- Institute for Aging Research, Hebrew SeniorLife, Boston, USA
- Department of Medicine, Harvard Medical School, Boston, USA
| | | | - Ben A Oostra
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Munro Peacock
- Department of Medicine, Indiana University School of Medicine, Indianapolis, USA
| | - Huibert A P Pols
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Richard L Prince
- School of Medicine and Pharmacology, University of Western Australia, Perth, Australia
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Perth, Australia
| | - Olli Raitakari
- Department of Clinical Physiology, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Ian R Reid
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - John Robbins
- Department of Medicine, University of Davis, Sacramento, CA, USA
| | - Philip N Sambrook
- Kolling Institute, Royal North Shore Hospital, University of Sydney, Sydney, Australia
| | - Pak Chung Sham
- Department of Psychiatry, The University of Hong Kong, Hong Kong, China
- Centre for Reproduction, Development and Growth, The University of Hong Kong, Hong Kong, China
| | - Alan R Shuldiner
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatric Research and Education Clinical Center (GRECC), Veterans Administration Medical Center, Baltimore, MD, USA
| | - Frances A Tylavsky
- Department of Preventive Medicine, University of Tennessee College of Medicine, Memphis, TN, USA
| | | | - Nick J Wareham
- MRC Epidemiology Unit Box 285, Medical Research Council, Cambridge, UK
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, USA
- Framingham Heart Study, Framingham, USA
| | - Michael J Econs
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, USA
- Department of Medicine, Indiana University School of Medicine, Indianapolis, USA
| | - David M Evans
- Medical Research Council (MRC) Centre for Causal Analyses in Translational Epidemiology, University of Bristol, Bristol, UK
| | - Tamara B Harris
- Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD, USA
| | - Annie Wai Chee Kung
- Department of Medicine, The University of Hong Kong, Hong Kong, China
- Research Centre of Heart, Brain, Hormone and Healthy Aging, The University of Hong Kong, Hong Kong, China
| | - Bruce M Psaty
- Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, USA
- Group Health Research Institute, Group Health Cooperative, Seattle, USA
| | - Jonathan Reeve
- Medicine box 157, University of Cambridge, Cambridge, UK
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Elizabeth A Streeten
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatric Research and Education Clinical Center (GRECC), Veterans Administration Medical Center, Baltimore, MD, USA
| | - M Carola Zillikens
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Unnur Thorsteinsdottir
- deCODE Genetics, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Claes Ohlsson
- Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - David Karasik
- Institute for Aging Research, Hebrew SeniorLife, Boston, USA
- Department of Medicine, Harvard Medical School, Boston, USA
| | - J Brent Richards
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
- Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, Lady Davis Institute, McGill University, Montreal, Canada
| | - Matthew A Brown
- Human Genetics Group, University of Queensland Diamantina Institute, Brisbane, Australia
| | - Kari Stefansson
- deCODE Genetics, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands
| | - Stuart H Ralston
- Rheumatic Diseases Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - John P A Ioannidis
- Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Greece
- Stanford Prevention Research Center, Stanford University, Stanford, USA
| | - Douglas P Kiel
- Institute for Aging Research, Hebrew SeniorLife, Boston, USA
- Department of Medicine, Harvard Medical School, Boston, USA
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands
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Gögele M, Minelli C, Thakkinstian A, Yurkiewich A, Pattaro C, Pramstaller PP, Little J, Attia J, Thompson JR. Methods for meta-analyses of genome-wide association studies: critical assessment of empirical evidence. Am J Epidemiol 2012; 175:739-49. [PMID: 22427610 DOI: 10.1093/aje/kwr385] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
There has been a steep increase in the number of meta-analyses of genome-wide association (GWA) studies aimed at identifying genetic variants with increasingly smaller effects, but pressure to publish findings of new genetic associations has limited the time available for careful consideration of all of their methodological aspects. The authors surveyed the literature (2007-2010) to provide empirical evidence on the methods used in GWA meta-analyses, including their organization, requirements about the uniformity of methods used in primary studies, methods for data pooling, investigation of between-study heterogeneity, and quality of reporting. This review showed that a great variety of methods are being used, but the rationale for their choice is often unclear. It also highlights how important methodological aspects have received insufficient attention, potentially leading to missed opportunities for improving gene discovery and characterization. Evaluation of power to replicate findings was inadequate, and the number of variants selected for replication was not associated with replication sample size. A low proportion of GWA meta-analyses investigated the presence and magnitude of heterogeneity, even when there was little uniformity in methods used in primary studies. More methodological work is required before clear guidance can be offered as to optimal methods or tradeoffs between alternative methods. However, there is a clear need for guidelines for reporting the results of GWA meta-analyses.
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Affiliation(s)
- Martin Gögele
- Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC), Viale Druso 1, 39100 Bolzano, Italy
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Interpreting meta-analyses of genome-wide association studies. PLoS Genet 2012; 8:e1002555. [PMID: 22396665 PMCID: PMC3291559 DOI: 10.1371/journal.pgen.1002555] [Citation(s) in RCA: 127] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2011] [Accepted: 01/10/2012] [Indexed: 11/19/2022] Open
Abstract
Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many factors. If heterogeneity is observed in the results of a meta-analysis, interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study. However, interpreting heterogeneous results is difficult. The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study. In this paper, we propose a framework facilitating the interpretation of the results of a meta-analysis. Our framework is based on a new statistic representing the posterior probability that the effect exists in each study, which is estimated utilizing cross-study information. Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect, the studies predicted to not have an effect, and the ambiguous studies that are underpowered. In addition to helping interpretation, the new framework also allows us to develop a new association testing procedure taking into account the existence of effect. Genome-wide association studies are an effective means of identifying genetic variants that are associated with diseases. Although many associated loci have been identified, those loci account for only a small fraction of the genetic contribution to the disease. The remaining contribution may be accounted by loci with very small effect sizes, so small that tens of thousands of samples are needed to identify them. Since it is costly to conduct a study collecting such a large sample, a practical alternative is to combine multiple independent studies in a single analysis called meta-analysis. However, many factors, such as genetic or environmental factors, can differ between the studies combined in a meta-analysis. These factors can cause the effect size of the causal variant to differ between the studies, a phenomenon called heterogeneity. If heterogeneity exists in the data of a meta-analysis, interpreting the meta-analysis results is an important but difficult task. In this paper, we propose a method that helps such interpretation, in addition to a new association testing procedure that is powerful when heterogeneity exists.
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Ntzani EE, Liberopoulos G, Manolio TA, Ioannidis JPA. Consistency of genome-wide associations across major ancestral groups. Hum Genet 2011; 131:1057-71. [PMID: 22183176 DOI: 10.1007/s00439-011-1124-4] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2011] [Accepted: 11/29/2011] [Indexed: 12/31/2022]
Abstract
It is not well known whether genetic markers identified through genome-wide association studies (GWAS) confer similar or different risks across people of different ancestry. We screened a regularly updated catalog of all published GWAS curated at the NHGRI website for GWAS-identified associations that had reached genome-wide significance (p ≤ 5 × 10(-8)) in at least one major ancestry group (European, Asian, African) and for which replication data were available for comparison in at least two different major ancestry groups. These groups were compared for the correlation between and differences in risk allele frequencies and genetic effects' estimates. Data on 108 eligible GWAS-identified associations with a total of 900 datasets (European, n = 624; Asian, n = 217; African, n = 60) were analyzed. Risk-allele frequencies were modestly correlated between ancestry groups, with >10% absolute differences in 75-89% of the three pairwise comparisons of ancestry groups. Genetic effect (odds ratio) point estimates between ancestry groups correlated modestly (pairwise comparisons' correlation coefficients: 0.20-0.33) and point estimates of risks were opposite in direction or differed more than twofold in 57%, 79%, and 89% of the European versus Asian, European versus African, and Asian versus African comparisons, respectively. The modest correlations, differing risk estimates, and considerable between-association heterogeneity suggest that differential ancestral effects can be anticipated and genomic risk markers may need separate further evaluation in different ancestry groups.
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Affiliation(s)
- Evangelia E Ntzani
- Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, 45110 Ioannina, Greece
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Panagiotou OA, Ioannidis JPA. What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations. Int J Epidemiol 2011; 41:273-86. [PMID: 22253303 DOI: 10.1093/ije/dyr178] [Citation(s) in RCA: 187] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Robust replication is a sine qua non for the rigorous documentation of proposed associations in the genome-wide association (GWA) setting. Currently, associations of common variants reaching P ≤ 5 × 10(-8) are considered replicated. However, there is some ambiguity about the most suitable threshold for claiming genome-wide significance. METHODS We defined as 'borderline' associations those with P > 5 × 10(-8) and P ≤ 1 × 10(-7). The eligible associations were retrieved using the 'Catalog of Published Genome-Wide Association Studies'. For each association we assessed whether it reached P ≤ 5 × 10(-8) with inclusion of additional data from subsequent GWA studies. RESULTS Thirty-four eligible genotype-phenotype associations were evaluated with data and clarifications contributed from diverse investigators. Replication data from subsequent GWA studies could be obtained for 26 of them. Of those, 19 associations (73%) reached P ≤ 5 × 10(-8) for the same or a related trait implicating either the exact same allele or one in very high linkage disequilibrium and 17 reached P < 10(-8). If the seven associations that did not reach P ≤ 5 × 10(-8) when additional data were considered are assumed to have been false-positives, the false-discovery rate for borderline associations is estimated to be 27% [95% confidence interval (CI) 12-48%]. For five associations, the current P-value is > 10(-6) [corresponding false-discovery rate 19% (95% CI 7-39%)]. CONCLUSION A substantial proportion, but not all, of the associations with borderline genome-wide significance represent replicable, possibly genuine associations. Our empirical evaluation suggests a possible relaxation in the current GWS threshold.
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Affiliation(s)
- Orestis A Panagiotou
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
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Patsopoulos NA, Esposito F, Reischl J, Lehr S, Bauer D, Heubach J, Sandbrink R, Pohl C, Edan G, Kappos L, Miller D, Montalbán J, Polman CH, Freedman MS, Hartung HP, Arnason BGW, Comi G, Cook S, Filippi M, Goodin DS, Jeffery D, O'Connor P, Ebers GC, Langdon D, Reder AT, Traboulsee A, Zipp F, Schimrigk S, Hillert J, Bahlo M, Booth DR, Broadley S, Brown MA, Browning BL, Browning SR, Butzkueven H, Carroll WM, Chapman C, Foote SJ, Griffiths L, Kermode AG, Kilpatrick TJ, Lechner-Scott J, Marriott M, Mason D, Moscato P, Heard RN, Pender MP, Perreau VM, Perera D, Rubio JP, Scott RJ, Slee M, Stankovich J, Stewart GJ, Taylor BV, Tubridy N, Willoughby E, Wiley J, Matthews P, Boneschi FM, Compston A, Haines J, Hauser SL, McCauley J, Ivinson A, Oksenberg JR, Pericak-Vance M, Sawcer SJ, De Jager PL, Hafler DA, de Bakker PIW. Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci. Ann Neurol 2011; 70:897-912. [PMID: 22190364 PMCID: PMC3247076 DOI: 10.1002/ana.22609] [Citation(s) in RCA: 260] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
OBJECTIVE To perform a 1-stage meta-analysis of genome-wide association studies (GWAS) of multiple sclerosis (MS) susceptibility and to explore functional consequences of new susceptibility loci. METHODS We synthesized 7 MS GWAS. Each data set was imputed using HapMap phase II, and a per single nucleotide polymorphism (SNP) meta-analysis was performed across the 7 data sets. We explored RNA expression data using a quantitative trait analysis in peripheral blood mononuclear cells (PBMCs) of 228 subjects with demyelinating disease. RESULTS We meta-analyzed 2,529,394 unique SNPs in 5,545 cases and 12,153 controls. We identified 3 novel susceptibility alleles: rs170934(T) at 3p24.1 (odds ratio [OR], 1.17; p = 1.6 × 10(-8)) near EOMES, rs2150702(G) in the second intron of MLANA on chromosome 9p24.1 (OR, 1.16; p = 3.3 × 10(-8)), and rs6718520(A) in an intergenic region on chromosome 2p21, with THADA as the nearest flanking gene (OR, 1.17; p = 3.4 × 10(-8)). The 3 new loci do not have a strong cis effect on RNA expression in PBMCs. Ten other susceptibility loci had a suggestive p < 1 × 10(-6) , some of these loci have evidence of association in other inflammatory diseases (ie, IL12B, TAGAP, PLEK, and ZMIZ1). INTERPRETATION We have performed a meta-analysis of GWAS in MS that more than doubles the size of previous gene discovery efforts and highlights 3 novel MS susceptibility loci. These and additional loci with suggestive evidence of association are excellent candidates for further investigations to refine and validate their role in the genetic architecture of MS.
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Affiliation(s)
- Nikolaos A Patsopoulos
- Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
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Chapman K, Ferreira T, Morris A, Asimit J, Zeggini E. Defining the power limits of genome-wide association scan meta-analyses. Genet Epidemiol 2011; 35:781-9. [PMID: 21922540 DOI: 10.1002/gepi.20627] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2011] [Revised: 06/22/2011] [Accepted: 07/22/2011] [Indexed: 11/06/2022]
Abstract
Large-scale meta-analyses of genome-wide association scans (GWAS) have been successful in discovering common risk variants with modest and small effects. The detection of lower frequency signals will undoubtedly require concerted efforts of at least similar scale. We investigate the sample size-dictated power limits of GWAS meta-analyses, in the presence and absence of modest levels of heterogeneity and across a range of different allelic architectures. We find that data combination through large-scale collaboration is vital in the quest for complex trait susceptibility loci, but that effect size heterogeneity across meta-analyzed studies drawn from similar populations does not appear to have a profound effect on sample size requirements.
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Affiliation(s)
- Kay Chapman
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, University of Oxford, Oxford, United Kingdom
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Pereira TV, Mingroni-Netto RC. A note on the use of the generalized odds ratio in meta-analysis of association studies involving bi- and tri-allelic polymorphisms. BMC Res Notes 2011; 4:172. [PMID: 21645382 PMCID: PMC3146434 DOI: 10.1186/1756-0500-4-172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2011] [Accepted: 06/06/2011] [Indexed: 11/16/2022] Open
Abstract
Background The generalized odds ratio (GOR) was recently suggested as a genetic model-free measure for association studies. However, its properties were not extensively investigated. We used Monte Carlo simulations to investigate type-I error rates, power and bias in both effect size and between-study variance estimates of meta-analyses using the GOR as a summary effect, and compared these results to those obtained by usual approaches of model specification. We further applied the GOR in a real meta-analysis of three genome-wide association studies in Alzheimer's disease. Findings For bi-allelic polymorphisms, the GOR performs virtually identical to a standard multiplicative model of analysis (e.g. per-allele odds ratio) for variants acting multiplicatively, but augments slightly the power to detect variants with a dominant mode of action, while reducing the probability to detect recessive variants. Although there were differences among the GOR and usual approaches in terms of bias and type-I error rates, both simulation- and real data-based results provided little indication that these differences will be substantial in practice for meta-analyses involving bi-allelic polymorphisms. However, the use of the GOR may be slightly more powerful for the synthesis of data from tri-allelic variants, particularly when susceptibility alleles are less common in the populations (≤10%). This gain in power may depend on knowledge of the direction of the effects. Conclusions For the synthesis of data from bi-allelic variants, the GOR may be regarded as a multiplicative-like model of analysis. The use of the GOR may be slightly more powerful in the tri-allelic case, particularly when susceptibility alleles are less common in the populations.
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Affiliation(s)
- Tiago V Pereira
- Centro de Estudos do Genoma Humano, Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo.
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Angeli CB, Kimura L, Auricchio MT, Vicente JP, Mattevi VS, Zembrzuski VM, Hutz MH, Pereira AC, Pereira TV, Mingroni-Netto RC. Multilocus analyses of seven candidate genes suggest interacting pathways for obesity-related traits in Brazilian populations. Obesity (Silver Spring) 2011; 19:1244-51. [PMID: 21233811 DOI: 10.1038/oby.2010.325] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
We investigated whether variants in major candidate genes for food intake and body weight regulation contribute to obesity-related traits under a multilocus perspective. We studied 375 Brazilian subjects from partially isolated African-derived populations (quilombos). Seven variants displaying conflicting results in previous reports and supposedly implicated in the susceptibility of obesity-related phenotypes were investigated: β2-adrenergic receptor (ADRB2) (Arg16Gly), insulin induced gene 2 (INSIG2) (rs7566605), leptin (LEP) (A19G), LEP receptor (LEPR) (Gln223Arg), perilipin (PLIN) (6209T > C), peroxisome proliferator-activated receptor-γ (PPARG) (Pro12Ala), and resistin (RETN) (-420 C > G). Regression models as well as generalized multifactor dimensionality reduction (GMDR) were employed to test the contribution of individual effects and higher-order interactions to BMI and waist-hip ratio (WHR) variation and risk of overweight/obesity. The best multilocus association signal identified in the quilombos was further examined in an independent sample of 334 Brazilian subjects of European ancestry. In quilombos, only the PPARG polymorphism displayed significant individual effects (WHR variation, P = 0.028). No association was observed either with the risk of overweight/obesity (BMI ≥ 25 kg/m2), risk of obesity alone (BMI ≥ 30 kg/m2) or BMI variation. However, GMDR analyses revealed an interaction between the LEPR and ADRB2 polymorphisms (P = 0.009) as well as a third-order effect involving the latter two variants plus INSIG2 (P = 0.034) with overweight/obesity. Assessment of the LEPR-ADRB2 interaction in the second sample indicated a marginally significant association (P = 0.0724), which was further verified to be limited to men (P = 0.0118). Together, our findings suggest evidence for a two-locus interaction between the LEPR Gln223Arg and ADRB2 Arg16Gly variants in the risk of overweight/obesity, and highlight further the importance of multilocus effects in the genetic component of obesity.
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Affiliation(s)
- Cláudia B Angeli
- Centro de Estudos do Genoma Humano, Departamento de Genética e Biologia; Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo, Brazil
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Cornelis MC, Monda KL, Yu K, Paynter N, Azzato EM, Bennett SN, Berndt SI, Boerwinkle E, Chanock S, Chatterjee N, Couper D, Curhan G, Heiss G, Hu FB, Hunter DJ, Jacobs K, Jensen MK, Kraft P, Landi MT, Nettleton JA, Purdue MP, Rajaraman P, Rimm EB, Rose LM, Rothman N, Silverman D, Stolzenberg-Solomon R, Subar A, Yeager M, Chasman DI, van Dam RM, Caporaso NE. Genome-wide meta-analysis identifies regions on 7p21 (AHR) and 15q24 (CYP1A2) as determinants of habitual caffeine consumption. PLoS Genet 2011; 7:e1002033. [PMID: 21490707 PMCID: PMC3071630 DOI: 10.1371/journal.pgen.1002033] [Citation(s) in RCA: 161] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2010] [Accepted: 02/06/2011] [Indexed: 02/07/2023] Open
Abstract
We report the first genome-wide association study of habitual caffeine intake. We included 47,341 individuals of European descent based on five population-based studies within the United States. In a meta-analysis adjusted for age, sex, smoking, and eigenvectors of population variation, two loci achieved genome-wide significance: 7p21 (P = 2.4 × 10(-19)), near AHR, and 15q24 (P = 5.2 × 10(-14)), between CYP1A1 and CYP1A2. Both the AHR and CYP1A2 genes are biologically plausible candidates as CYP1A2 metabolizes caffeine and AHR regulates CYP1A2.
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Affiliation(s)
- Marilyn C. Cornelis
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Keri L. Monda
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Nina Paynter
- Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Elizabeth M. Azzato
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Siiri N. Bennett
- Collaborative Health Studies Coordinating Center, University of Washington, Seattle, Washington, United States of America
| | - Sonja I. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Eric Boerwinkle
- Division of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Nilanjan Chatterjee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - David Couper
- Department of Biostatistics, Collaborative Studies Coordinating Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Gary Curhan
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Gerardo Heiss
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Frank B. Hu
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - David J. Hunter
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Kevin Jacobs
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Majken K. Jensen
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Peter Kraft
- Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Jennifer A. Nettleton
- Division of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Mark P. Purdue
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Preetha Rajaraman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Eric B. Rimm
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Lynda M. Rose
- Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Debra Silverman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Rachael Stolzenberg-Solomon
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Amy Subar
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Daniel I. Chasman
- Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Rob M. van Dam
- Department of Epidemiology and Public Health and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Neil E. Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
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Pereira TV, Ioannidis JPA. Statistically significant meta-analyses of clinical trials have modest credibility and inflated effects. J Clin Epidemiol 2011; 64:1060-9. [PMID: 21454050 DOI: 10.1016/j.jclinepi.2010.12.012] [Citation(s) in RCA: 85] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2010] [Revised: 12/06/2010] [Accepted: 12/10/2010] [Indexed: 10/18/2022]
Abstract
OBJECTIVE To assess whether nominally statistically significant effects in meta-analyses of clinical trials are true and whether their magnitude is inflated. STUDY DESIGN AND SETTING Data from the Cochrane Database of Systematic Reviews 2005 (issue 4) and 2010 (issue 1) were used. We considered meta-analyses with binary outcomes and four or more trials in 2005 with P<0.05 for the random-effects odds ratio (OR). We examined whether any of these meta-analyses had updated counterparts in 2010. We estimated the credibility (true-positive probability) under different prior assumptions and inflation in OR estimates in 2005. RESULTS Four hundred sixty-one meta-analyses in 2005 were eligible, and 80 had additional trials included by 2010. The effect sizes (ORs) were smaller in the updating data (2005-2010) than in the respective meta-analyses in 2005 (median 0.85-fold, interquartile range [IQR]: 0.66-1.06), even more prominently for meta-analyses with less than 300 events in 2005 (median 0.67-fold, IQR: 0.54-0.96). Mean credibility of the 461 meta-analyses in 2005 was 63-84% depending on the assumptions made. Credibility estimates changed >20% in 19-31 (24-39%) of the 80 updated meta-analyses. CONCLUSIONS Most meta-analyses with nominally significant results pertain to truly nonnull effects, but exceptions are not uncommon. The magnitude of observed effects, especially in meta-analyses with limited evidence, is often inflated.
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Affiliation(s)
- Tiago V Pereira
- Department of Hygiene and Epidemiology, Clinical Trials and Evidence-Based Medicine Unit, University of Ioannina School of Medicine, Ioannina 45110, Greece
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Loizidou MA, Hadjisavvas A, Ioannidis JPA, Kyriacou K. Replication of genome-wide discovered breast cancer risk loci in the Cypriot population. Breast Cancer Res Treat 2011; 128:267-72. [PMID: 21210208 DOI: 10.1007/s10549-010-1319-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2010] [Accepted: 12/18/2010] [Indexed: 01/08/2023]
Abstract
Genome-wide association studies (GWAS) have identified associations with robust statistical support for influencing breast cancer susceptibility. Most GWAS and replications have been conducted in Northern European populations and to a lesser extent in Asians, and Ashkenazi Jews. It is important to evaluate whether these variants confer risk across different populations and also to assess the magnitude of risk conferred. The aim of this study was to evaluate previously GWAS-identified breast cancer risk variants in the Cypriot population. Eleven GWAS-discovered single nucleotide polymorphisms (SNPs) were analyzed for association with breast cancer in 1,109 Cypriot female breast cancer patients and 1,177 healthy female controls. Four of the 11 SNPs evaluated were found to be nominally significantly associated (P < 0.05) with breast cancer risk in the Cypriot population. Based on estimated power, five associations would be expected to be nominally significant. The correlation coefficient of effect sizes (per-allele odds ratio) between the Cypriot population and the original GWAS populations where these SNPs had been discovered was 0.58 (P = 0.064), while allele frequencies were very similar (r = 0.88, P < 0.001). Overall, we show modest concordance for breast cancer GWAS-discovered alleles and their effect sizes in the Cypriot population. The effects sizes of GWAS-discovered SNPs need to be verified separately in different populations.
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Affiliation(s)
- Maria A Loizidou
- Department of Electron Microscope / Molecular Pathology, The Cyprus Institute of Neurology and Genetics, Ayios Dometios, 23462, 1683, Nicosia, Cyprus
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Pereira TV, Patsopoulos NA, Pereira AC, Krieger JE. Strategies for genetic model specification in the screening of genome-wide meta-analysis signals for further replication. Int J Epidemiol 2010; 40:457-69. [PMID: 21149279 DOI: 10.1093/ije/dyq203] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Meta-analysis is increasingly being employed as a screening procedure in large-scale association studies to select promising variants for follow-up studies. However, standard methods for meta-analysis require the assumption of an underlying genetic model, which is typically unknown a priori. This drawback can introduce model misspecifications, causing power to be suboptimal, or the evaluation of multiple genetic models, which augments the number of false-positive associations, ultimately leading to waste of resources with fruitless replication studies. We used simulated meta-analyses of large genetic association studies to investigate naïve strategies of genetic model specification to optimize screenings of genome-wide meta-analysis signals for further replication. METHODS Different methods, meta-analytical models and strategies were compared in terms of power and type-I error. Simulations were carried out for a binary trait in a wide range of true genetic models, genome-wide thresholds, minor allele frequencies (MAFs), odds ratios and between-study heterogeneity (τ²). RESULTS Among the investigated strategies, a simple Bonferroni-corrected approach that fits both multiplicative and recessive models was found to be optimal in most examined scenarios, reducing the likelihood of false discoveries and enhancing power in scenarios with small MAFs either in the presence or in absence of heterogeneity. Nonetheless, this strategy is sensitive to τ² whenever the susceptibility allele is common (MAF ≥ 30%), resulting in an increased number of false-positive associations compared with an analysis that considers only the multiplicative model. CONCLUSION Invoking a simple Bonferroni adjustment and testing for both multiplicative and recessive models is fast and an optimal strategy in large meta-analysis-based screenings. However, care must be taken when examined variants are common, where specification of a multiplicative model alone may be preferable.
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Affiliation(s)
- Tiago V Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), São Paulo University Medical School, University of São Paulo, São Paulo, Brazil
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Ioannidis JPA, Castaldi P, Evangelou E. A compendium of genome-wide associations for cancer: critical synopsis and reappraisal. J Natl Cancer Inst 2010; 102:846-58. [PMID: 20505153 DOI: 10.1093/jnci/djq173] [Citation(s) in RCA: 62] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Since 2007, genome-wide association (GWA) studies have identified numerous well-supported, novel genetic risk loci for common cancers; however, there are concerns that this technology is reaching its limits. We provide an overview of GWA-identified genetic associations with solid tumors. We simulated the distribution of population risk alleles for colorectal, prostate, testicular, and thyroid cancers based on genetic variants identified in GWA studies. We also evaluated whether statistical power to detect typical genetic effects could be improved with studies performing GWA analyses of all available samples rather than multistage designs. Fifty-six eligible articles yielded 92 eligible associations between cancer phenotypes and genetic variants with a median per-allele odds ratio (OR) of 1.22 (interquartile range = 1.15-1.36). Half of the associations pertained to prostate, colorectal, or breast cancer. Individuals at the upper quartile of simulated risk had only 2.1- to 4.2-fold higher relative risk than those in the lower quartile. Comprehensive evaluation of currently available samples with GWA platforms would yield few additional variants with per-allele OR = 1.4, but many more variants with OR = 1.2 could be detected; statistical power to detect weak associations (OR = 1.07) would still be negligible. The GWA approach is effective in identifying common genetic variants with moderate effect; however, identifying loci with very small effects and rare variants will require major new efforts. At present, the utility of GWA-identified risk loci in risk stratification for cancer is limited.
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Affiliation(s)
- John P A Ioannidis
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina 45110, Greece.
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Cornelis MC, Agrawal A, Cole JW, Hansel NN, Barnes KC, Beaty TH, Bennett SN, Bierut LJ, Boerwinkle E, Doheny KF, Feenstra B, Feingold E, Fornage M, Haiman CA, Harris EL, Hayes MG, Heit JA, Hu FB, Kang JH, Laurie CC, Ling H, Manolio TA, Marazita ML, Mathias RA, Mirel DB, Paschall J, Pasquale LR, Pugh EW, Rice JP, Udren J, van Dam RM, Wang X, Wiggs JL, Williams K, Yu K. The Gene, Environment Association Studies consortium (GENEVA): maximizing the knowledge obtained from GWAS by collaboration across studies of multiple conditions. Genet Epidemiol 2010; 34:364-72. [PMID: 20091798 PMCID: PMC2860056 DOI: 10.1002/gepi.20492] [Citation(s) in RCA: 122] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Genome-wide association studies (GWAS) have emerged as powerful means for identifying genetic loci related to complex diseases. However, the role of environment and its potential to interact with key loci has not been adequately addressed in most GWAS. Networks of collaborative studies involving different study populations and multiple phenotypes provide a powerful approach for addressing the challenges in analysis and interpretation shared across studies. The Gene, Environment Association Studies (GENEVA) consortium was initiated to: identify genetic variants related to complex diseases; identify variations in gene-trait associations related to environmental exposures; and ensure rapid sharing of data through the database of Genotypes and Phenotypes. GENEVA consists of several academic institutions, including a coordinating center, two genotyping centers and 14 independently designed studies of various phenotypes, as well as several Institutes and Centers of the National Institutes of Health led by the National Human Genome Research Institute. Minimum detectable effect sizes include relative risks ranging from 1.24 to 1.57 and proportions of variance explained ranging from 0.0097 to 0.02. Given the large number of research participants (N>80,000), an important feature of GENEVA is harmonization of common variables, which allow analyses of additional traits. Environmental exposure information available from most studies also enables testing of gene-environment interactions. Facilitated by its sizeable infrastructure for promoting collaboration, GENEVA has established a unified framework for genotyping, data quality control, analysis and interpretation. By maximizing knowledge obtained through collaborative GWAS incorporating environmental exposure information, GENEVA aims to enhance our understanding of disease etiology, potentially identifying opportunities for intervention.
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Affiliation(s)
- Marilyn C Cornelis
- Department of Nutrition, Harvard School of Public Health, 665 Huntington Avenue, Boston,MA 02115.
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Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat Genet 2010; 42:441-7. [PMID: 20418890 DOI: 10.1038/ng.571] [Citation(s) in RCA: 838] [Impact Index Per Article: 59.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2009] [Accepted: 03/18/2010] [Indexed: 12/18/2022]
Abstract
Consistent but indirect evidence has implicated genetic factors in smoking behavior. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n > 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A], beta = 1.03, standard error (s.e.) = 0.053, P = 2.8 x 10(-73)). Two 10q25 SNPs (rs1329650[G], beta = 0.367, s.e. = 0.059, P = 5.7 x 10(-10); and rs1028936[A], beta = 0.446, s.e. = 0.074, P = 1.3 x 10(-9)) and one 9q13 SNP in EGLN2 (rs3733829[G], beta = 0.333, s.e. = 0.058, P = 1.0 x 10(-8)) also exceeded genome-wide significance for cigarettes per day. For smoking initiation, eight SNPs exceeded genome-wide significance, with the strongest association at a nonsynonymous SNP in BDNF on chromosome 11 (rs6265[C], odds ratio (OR) = 1.06, 95% confidence interval (Cl) 1.04-1.08, P = 1.8 x 10(-8)). One SNP located near DBH on chromosome 9 (rs3025343[G], OR = 1.12, 95% Cl 1.08-1.18, P = 3.6 x 10(-8)) was significantly associated with smoking cessation.
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Affiliation(s)
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- Department of Genetics, University of North Carolina, Chapel Hill, NC 27710, USA
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Genome-wide association studies--data generation, storage, interpretation, and bioinformatics. J Cardiovasc Transl Res 2010; 3:183-8. [PMID: 20560038 DOI: 10.1007/s12265-010-9181-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2010] [Accepted: 03/02/2010] [Indexed: 10/19/2022]
Abstract
Genome-wide association studies (GWAS) have had great success in identifying common genetic determinants of disease. One of the challenges posed by GWAS is the analysis of the large amount of data generated. This review aims to provide the non-geneticists with an overview of the different steps entailed in analysis of GWAS data, with an emphasis on popular bioinformatics tools available. GWAS data generation, analysis, and interpretation will be covered.
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Lanktree MB, Dichgans M, Hegele RA. Advances in genomic analysis of stroke: what have we learned and where are we headed? Stroke 2010; 41:825-32. [PMID: 20167918 DOI: 10.1161/strokeaha.109.570523] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
As a result of technological advances, the genomic analysis of stroke has shifted from candidate gene association studies to genome-wide association studies (GWAS). Agnostic GWAS evaluate up to 90% of common genetic variation in a single experiment, creating an improved framework for identifying novel genetic leads for biochemical and cellular mechanisms underlying stroke. Given the ubiquity of the GWAS approach, it has become essential for stroke researchers and clinicians to be able to interpret GWAS results. Thus, we review the basic elements of design, methods, presentation, and interpretation of GWAS in the context of stroke research. In 8 recent stroke GWAS reports, no single locus has been identified in 2 GWAS at a genome-wide level of significance. Additionally, no significant association signal between stroke and a locus with previous evidence from candidate gene studies of stroke has been identified yet. Some caveats of the approach and future directions for stroke genomics are discussed, including the use of intermediate phenotypes, Mendelian randomization, phenomics, and deep resequencing. Intelligent, appropriately powered, multidisciplinary studies incorporating knowledge from clinical medicine, epidemiology, genetics, and molecular biology will be required to fully characterize the genomic contributors to stroke.
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Affiliation(s)
- Matthew B Lanktree
- Robarts Research Institute and Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
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