501
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Abstract
Human genetic studies have been the driving force in bringing to light the underlying biology of psychiatric conditions. As these studies fill in the gaps in our knowledge of the mechanisms at play, we will be better equipped to design therapies in rational and targeted ways, or repurpose existing therapies in previously unanticipated ways. This review is intended for those unfamiliar with psychiatric genetics as a field and provides a primer on different modes of genetic variation, the technologies currently used to probe them, and concepts that provide context for interpreting the gene-phenotype relationship. Like other subfields in human genetics, psychiatric genetics is moving from microarray technology to sequencing-based approaches as barriers of cost and expertise are removed, and the ramifications of this transition are discussed here. A summary is then given of recent genetic discoveries in a number of neuropsychiatric conditions, with particular emphasis on neurodevelopmental conditions. The general impact of genetics on drug development has been to underscore the extensive etiological heterogeneity in seemingly cohesive diagnostic categories. Consequently, the path forward is not in therapies hoping to reach large swaths of patients sharing a clinically defined diagnosis, but rather in targeting patients belonging to specific "biotypes" defined through a combination of objective, quantifiable data, including genotype.
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Affiliation(s)
- Jacob J Michaelson
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA.
- Department of Biomedical Engineering, University of Iowa College of Engineering, Iowa City, IA, USA.
- Department of Communication Sciences and Disorders, University of Iowa College of Liberal Arts and Sciences, Iowa City, IA, USA.
- Iowa Institute of Human Genetics, University of Iowa, Iowa City, IA, USA.
- Genetics Cluster Initiative, University of Iowa, Iowa City, IA, USA.
- The DeLTA Center, University of Iowa, Iowa City, IA, USA.
- University of Iowa Informatics Initiative, University of Iowa, Iowa City, IA, USA.
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502
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Genetics: Implications for Prevention and Management of Coronary Artery Disease. J Am Coll Cardiol 2017; 68:2797-2818. [PMID: 28007143 DOI: 10.1016/j.jacc.2016.10.039] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 10/12/2016] [Accepted: 10/24/2016] [Indexed: 12/21/2022]
Abstract
An exciting new era has dawned for the prevention and management of coronary artery disease (CAD) utilizing genetic risk variants. The recent identification of over 60 susceptibility loci for CAD confirms not only the importance of established risk factors, but also the existence of many novel causal pathways that are expected to improve our understanding of the genetic basis of CAD and facilitate the development of new therapeutic agents over time. Concurrently, Mendelian randomization studies have provided intriguing insights on the causal relationship between CAD-related traits, and highlight the potential benefits of long-term modifications of risk factors. Last, genetic risk scores of CAD may serve not only as prognostic, but also as predictive markers, and carry the potential to considerably improve the delivery of established prevention strategies. This review will summarize the evolution and discovery of genetic risk variants for CAD and their current and future clinical applications.
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503
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Liu Z, Lin X. Multiple phenotype association tests using summary statistics in genome-wide association studies. Biometrics 2017; 74:165-175. [PMID: 28653391 DOI: 10.1111/biom.12735] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2016] [Revised: 05/01/2017] [Accepted: 05/01/2017] [Indexed: 12/13/2022]
Abstract
We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis.
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Affiliation(s)
- Zhonghua Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston 02115, U.S.A
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston 02115, U.S.A
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504
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Waldron JS, Malone SM, McGue M, Iacono WG. Genetic and environmental sources of covariation between early drinking and adult functioning. PSYCHOLOGY OF ADDICTIVE BEHAVIORS 2017; 31:589-600. [PMID: 28594187 DOI: 10.1037/adb0000283] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The vast majority of individuals initiate alcohol consumption for the first time in adolescence. Given the widespread nature of its use and evidence that adolescents may be especially vulnerable to its effects, there is concern about the long-term detrimental impact of adolescent drinking on adult functioning. While some researchers have suggested that genetic processes may confound the relationship, the mechanisms linking drinking and later adjustment remain unclear. The current study utilized a genetically informed sample and biometric modeling to examine the nature of the familial influences on this association and identify the potential for genetic confounding. The sample was drawn from the Minnesota Twin Family Study (MTFS), a longitudinal study consisting of 2,764 twins assessed in 2 cohorts at regular follow-ups from age 17 to age 29 (older cohort) or age 11 to age 29 (younger cohort). A broad range of adult measures was included assessing substance use, antisocial behavior, personality, socioeconomic status, and social functioning. A bivariate Cholesky decomposition was used to examine the common genetic and environmental influences on adolescent drinking and each of the measures of adult adjustment. The results revealed that genetic factors and nonshared environmental influences were generally most important in explaining the relationship between adolescent drinking and later functioning. While the presence of nonshared environmental influences on the association are not inconsistent with a causal impact of adolescent drinking, the findings suggest that many of the adjustment issues associated with adolescent alcohol consumption are best understood as genetically influenced vulnerabilities. (PsycINFO Database Record
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Affiliation(s)
| | | | - Matt McGue
- Department of Psychology, University of Minnesota
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505
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Alharbi O, El-Sohemy A. Lactose Intolerance ( LCT-13910C>T) Genotype Is Associated with Plasma 25-Hydroxyvitamin D Concentrations in Caucasians: A Mendelian Randomization Study. J Nutr 2017; 147:1063-1069. [PMID: 28446633 DOI: 10.3945/jn.116.246108] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 01/11/2017] [Accepted: 03/24/2017] [Indexed: 11/14/2022] Open
Abstract
Background: The LCT-13910C>T gene variant is associated with lactose intolerance (LI) in different ethnic groups. Individuals with LI often limit or avoid dairy consumption, a major dietary source of vitamin D in North America, which may lead to inadequate vitamin D intake.Objective: The objective was to determine the prevalence of genotypes predictive of LI in different ethnic groups living in Canada and to determine whether the LCT genotype is associated with plasma 25(OH)D concentrations.Methods: Blood samples were drawn from a total of 1495 men and women aged 20-29 y from the Toronto Nutrigenomics and Health Study for genotyping and plasma 25(OH)D analysis. Intakes of dairy were assessed by using a 196-item food frequency questionnaire. The prevalence of LCT-13910C>T genotypes was compared by using χ2 analysis. Using a Mendelian randomization approach, we examined the association between LCT genotypes and 25(OH)D concentrations.Results: Approximately 32% of Caucasians, 99% of East Asians, 74% of South Asians, and 59% of those with other or mixed ethnicities had the CC genotype associated with LI. Compared with those with the TT genotype, those with the CC genotype had a lower mean ± SE total dairy intake (2.15 ± 0.09 compared with 2.67 ± 0.12 servings/d, P = 0.003), a lower skim-milk intake (0.20 ± 0.03 compared with 0.46 ± 0.06 servings/d, P = 0.0004), and a lower plasma 25(OH)D concentration (63 ± 1.9 compared with 75.8 ± 2.4 nmol/L, P < 0.0001). The CT and CC genotypes were associated with a 50% and a 2-fold increased risk, respectively, of a suboptimal plasma 25(OH)D concentration (<75 nmol/L).Conclusions: In Caucasians, the CC genotype that predicts LI is associated with a lower plasma 25(OH)D concentration, which is attributable at least in part to a lower intake of dairy, particularly skim milk. Increased risk of suboptimal concentrations of vitamin D was also observed among those with the CT genotype, suggesting an intermediate effect of the heterozygous genotype.
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Affiliation(s)
- Ohood Alharbi
- Department of Nutritional Sciences, University of Toronto, Toronto, Canada
| | - Ahmed El-Sohemy
- Department of Nutritional Sciences, University of Toronto, Toronto, Canada
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506
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Arnar DO, Palsson R. Genetics of common complex diseases: a view from Iceland. Eur J Intern Med 2017; 41:3-9. [PMID: 28433481 DOI: 10.1016/j.ejim.2017.03.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 03/22/2017] [Accepted: 03/24/2017] [Indexed: 12/21/2022]
Abstract
In the past decade, large scale genotyping has led to discoveries of numerous sequence variants that confer increased risk of many common complex diseases. Interestingly, a substantial proportion of pioneering genetic work has originated from the small nation of Iceland and has been facilitated by an extensive genealogy database. We provide examples of relevant observations made so far in several major disease categories central to internal medicine practice. Some of these findings offer new mechanistic clues into the pathophysiology of common disorders and may suggest novel approaches in diagnosis and drug therapy. However, a number of unresolved issues remain that will be subject of future research, driven by recent advances in high-throughput sequencing of the genome. At the same time, we are ready to begin transforming the abundant existing genetic data into practical clinical knowledge with the aim of improving the delivery of medical care. The era of precision medicine has arrived.
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Affiliation(s)
- David O Arnar
- Division of Cardiology, Internal Medicine Services, Landspitali - The National University Hospital of Iceland, Reykjavik, Iceland; Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
| | - Runolfur Palsson
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland; Division of Nephrology, Internal Medicine Services, Landspitali - The National University Hospital of Iceland, Reykjavik, Iceland
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507
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Genetic evidence for role of integration of fast and slow neurotransmission in schizophrenia. Mol Psychiatry 2017; 22:792-801. [PMID: 28348379 PMCID: PMC5495879 DOI: 10.1038/mp.2017.33] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 01/05/2017] [Accepted: 01/17/2017] [Indexed: 12/12/2022]
Abstract
The most recent genome-wide association studies (GWAS) of schizophrenia (SCZ) identified hundreds of risk variants potentially implicated in the disease. Further, novel statistical methodology designed for polygenic architecture revealed more potential risk variants. This can provide a link between individual genetic factors and the mechanistic underpinnings of SCZ. Intriguingly, a large number of genes coding for ionotropic and metabotropic receptors for various neurotransmitters-glutamate, γ-aminobutyric acid (GABA), dopamine, serotonin, acetylcholine and opioids-and numerous ion channels were associated with SCZ. Here, we review these findings from the standpoint of classical neurobiological knowledge of neuronal synaptic transmission and regulation of electrical excitability. We show that a substantial proportion of the identified genes are involved in intracellular cascades known to integrate 'slow' (G-protein-coupled receptors) and 'fast' (ionotropic receptors) neurotransmission converging on the protein DARPP-32. Inspection of the Human Brain Transcriptome Project database confirms that that these genes are indeed expressed in the brain, with the expression profile following specific developmental trajectories, underscoring their relevance to brain organization and function. These findings extend the existing pathophysiology hypothesis by suggesting a unifying role of dysregulation in neuronal excitability and synaptic integration in SCZ. This emergent model supports the concept of SCZ as an 'associative' disorder-a breakdown in the communication across different slow and fast neurotransmitter systems through intracellular signaling pathways-and may unify a number of currently competing hypotheses of SCZ pathophysiology.
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508
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Roda F, Walter GM, Nipper R, Ortiz‐Barrientos D. Genomic clustering of adaptive loci during parallel evolution of an Australian wildflower. Mol Ecol 2017; 26:3687-3699. [DOI: 10.1111/mec.14150] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Revised: 03/07/2017] [Accepted: 04/03/2017] [Indexed: 12/11/2022]
Affiliation(s)
- Federico Roda
- School of Biological Sciences The University of Queensland St. Lucia QLD Australia
- Harvard University Boston MA USA
| | - Greg M. Walter
- School of Biological Sciences The University of Queensland St. Lucia QLD Australia
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509
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Smeland OB, Wang Y, Lo MT, Li W, Frei O, Witoelar A, Tesli M, Hinds DA, Tung JY, Djurovic S, Chen CH, Dale AM, Andreassen OA. Identification of genetic loci shared between schizophrenia and the Big Five personality traits. Sci Rep 2017; 7:2222. [PMID: 28533504 PMCID: PMC5440373 DOI: 10.1038/s41598-017-02346-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Accepted: 04/10/2017] [Indexed: 11/25/2022] Open
Abstract
Schizophrenia is associated with differences in personality traits, and recent studies suggest that personality traits and schizophrenia share a genetic basis. Here we aimed to identify specific genetic loci shared between schizophrenia and the Big Five personality traits using a Bayesian statistical framework. Using summary statistics from genome-wide association studies (GWAS) on personality traits in the 23andMe cohort (n = 59,225) and schizophrenia in the Psychiatric Genomics Consortium cohort (n = 82,315), we evaluated overlap in common genetic variants. The Big Five personality traits neuroticism, extraversion, openness, agreeableness and conscientiousness were measured using a web implementation of the Big Five Inventory. Applying the conditional false discovery rate approach, we increased discovery of genetic loci and identified two loci shared between neuroticism and schizophrenia and six loci shared between openness and schizophrenia. The study provides new insights into the relationship between personality traits and schizophrenia by highlighting genetic loci involved in their common genetic etiology.
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Affiliation(s)
- Olav B Smeland
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway.
| | - Yunpeng Wang
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
- Department of Neurosciences, University of California San Diego, La Jolla, CA, 92093, United States of America
| | - Min-Tzu Lo
- Department of Radiology, University of California, San Diego, La Jolla, CA, 92093, United States of America
| | - Wen Li
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Aree Witoelar
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Martin Tesli
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
- Lovisenberg Diakonale Hospital, 0456, Oslo, Norway
| | - David A Hinds
- 23andMe, Inc., Mountain View, CA, 94041, United States of America
| | - Joyce Y Tung
- 23andMe, Inc., Mountain View, CA, 94041, United States of America
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Chi-Hua Chen
- Department of Radiology, University of California, San Diego, La Jolla, CA, 92093, United States of America
| | - Anders M Dale
- Department of Neurosciences, University of California San Diego, La Jolla, CA, 92093, United States of America
- Department of Radiology, University of California, San Diego, La Jolla, CA, 92093, United States of America
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway.
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510
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Karnes JH, Bastarache L, Shaffer CM, Gaudieri S, Xu Y, Glazer AM, Mosley JD, Zhao S, Raychaudhuri S, Mallal S, Ye Z, Mayer JG, Brilliant MH, Hebbring SJ, Roden DM, Phillips EJ, Denny JC. Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants. Sci Transl Med 2017; 9:eaai8708. [PMID: 28490672 PMCID: PMC5563969 DOI: 10.1126/scitranslmed.aai8708] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 03/27/2017] [Indexed: 12/22/2022]
Abstract
Although many phenotypes have been associated with variants in human leukocyte antigen (HLA) genes, the full phenotypic impact of HLA variants across all diseases is unknown. We imputed HLA genomic variation from two populations of 28,839 and 8431 European ancestry individuals and tested association of HLA variation with 1368 phenotypes. A total of 104 four-digit and 92 two-digit HLA allele phenotype associations were significant in both discovery and replication cohorts, the strongest being HLA-DQB1*03:02 and type 1 diabetes. Four previously unidentified associations were identified across the spectrum of disease with two- and four-digit HLA alleles and 10 with nonsynonymous variants. Some conditions associated with multiple HLA variants and stronger associations with more severe disease manifestations were identified. A comprehensive, publicly available catalog of clinical phenotypes associated with HLA variation is provided. Examining HLA variant disease associations in this large data set allows comprehensive definition of disease associations to drive further mechanistic insights.
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Affiliation(s)
- Jason H Karnes
- Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tucson, AZ 85721, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Christian M Shaffer
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Silvana Gaudieri
- School of Anatomy, Physiology and Human Biology, University of Western Australia, Nedlands, Western Australia, Australia
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
| | - Yaomin Xu
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Andrew M Glazer
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jonathan D Mosley
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Shilin Zhao
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Soumya Raychaudhuri
- Division of Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA 02115, USA
- Institute of Inflammation and Repair, University of Manchester, Manchester, UK
- Department of Medicine, Karolinska Institutet and Karolinska University Hospital Solna, Stockholm, Sweden
| | - Simon Mallal
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Zhan Ye
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - John G Mayer
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Murray H Brilliant
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Scott J Hebbring
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Dan M Roden
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Elizabeth J Phillips
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
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511
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Ombrello MJ, Arthur VL, Remmers EF, Hinks A, Tachmazidou I, Grom AA, Foell D, Martini A, Gattorno M, Özen S, Prahalad S, Zeft AS, Bohnsack JF, Ilowite NT, Mellins ED, Russo R, Len C, Hilario MOE, Oliveira S, Yeung RSM, Rosenberg AM, Wedderburn LR, Anton J, Haas JP, Rosen-Wolff A, Minden K, Tenbrock K, Demirkaya E, Cobb J, Baskin E, Signa S, Shuldiner E, Duerr RH, Achkar JP, Kamboh MI, Kaufman KM, Kottyan LC, Pinto D, Scherer SW, Alarcón-Riquelme ME, Docampo E, Estivill X, Gül A, Langefeld CD, Thompson S, Zeggini E, Kastner DL, Woo P, Thomson W. Genetic architecture distinguishes systemic juvenile idiopathic arthritis from other forms of juvenile idiopathic arthritis: clinical and therapeutic implications. Ann Rheum Dis 2017; 76:906-913. [PMID: 27927641 PMCID: PMC5530341 DOI: 10.1136/annrheumdis-2016-210324] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 10/27/2016] [Accepted: 11/12/2016] [Indexed: 01/14/2023]
Abstract
OBJECTIVES Juvenile idiopathic arthritis (JIA) is a heterogeneous group of conditions unified by the presence of chronic childhood arthritis without an identifiable cause. Systemic JIA (sJIA) is a rare form of JIA characterised by systemic inflammation. sJIA is distinguished from other forms of JIA by unique clinical features and treatment responses that are similar to autoinflammatory diseases. However, approximately half of children with sJIA develop destructive, long-standing arthritis that appears similar to other forms of JIA. Using genomic approaches, we sought to gain novel insights into the pathophysiology of sJIA and its relationship with other forms of JIA. METHODS We performed a genome-wide association study of 770 children with sJIA collected in nine countries by the International Childhood Arthritis Genetics Consortium. Single nucleotide polymorphisms were tested for association with sJIA. Weighted genetic risk scores were used to compare the genetic architecture of sJIA with other JIA subtypes. RESULTS The major histocompatibility complex locus and a locus on chromosome 1 each showed association with sJIA exceeding the threshold for genome-wide significance, while 23 other novel loci were suggestive of association with sJIA. Using a combination of genetic and statistical approaches, we found no evidence of shared genetic architecture between sJIA and other common JIA subtypes. CONCLUSIONS The lack of shared genetic risk factors between sJIA and other JIA subtypes supports the hypothesis that sJIA is a unique disease process and argues for a different classification framework. Research to improve sJIA therapy should target its unique genetics and specific pathophysiological pathways.
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Affiliation(s)
- Michael J Ombrello
- Translational Genetics and Genomics Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health,US Department of Health and Human Services, Bethesda, Maryland, USA
| | - Victoria L Arthur
- Translational Genetics and Genomics Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health,US Department of Health and Human Services, Bethesda, Maryland, USA
| | - Elaine F Remmers
- Inflammatory Disease Section, National Human Genome Research Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, USA
| | - Anne Hinks
- Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, Manchester, UK
| | | | - Alexei A Grom
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Dirk Foell
- Department of Pediatric Rheumatology and Immunology, University Hospital Münster, Münster, Germany
| | - Alberto Martini
- Department of Pediatrics, University of Genova, Genoa, Italy
- Pediatrics II Unit, Giannina Gaslini Institute, Genoa, Italy
| | - Marco Gattorno
- Pediatrics II Unit, Giannina Gaslini Institute, Genoa, Italy
| | - Seza Özen
- Department of Pediatric Rheumatology, Hacettepe University, Ankara, Turkey
| | - Sampath Prahalad
- Departments of Pediatrics and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
- Children's Healthcare of Atlanta, Atlanta, Georgia, USA
| | - Andrew S Zeft
- Department of Pediatrics, Cleveland Clinic, Cleveland, Ohio, USA
| | - John F Bohnsack
- Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
| | - Norman T Ilowite
- Department of Pediatrics, Albert Einstein College of Medicine and Children's Hospital at Montefiore, Bronx, New York, USA
| | | | - Ricardo Russo
- Service of Immunology and Rheumatology, Hospital de Pediatria Garrahan, Buenos Aires, Argentina
| | - Claudio Len
- Department of Pediatrics, Universidade Federal de São Paulo, São Paulo, Brazil
| | | | - Sheila Oliveira
- Universidade Federal de Rio de Janeiro, Rio de Janeiro, Brazil
| | - Rae S M Yeung
- Department of Pediatrics, University of Toronto, Toronto, Canada
- Department of Immunology, University of Toronto, Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Alan M Rosenberg
- Department of Pediatrics, University of Saskatchewan, Saskatoon, Canada
| | - Lucy R Wedderburn
- Institute of Child Health, University College London, London, UK
- Center of Paediatric and Adolescent Rheumatology, University College London, London, UK
| | - Jordi Anton
- Pediatric Rheumatology Unit, Hospital Sant Joan de Déu, Universitat de Barcelona, Barcelona, Spain
| | - Johannes-Peter Haas
- German Center for Pediatric and Adolescent Rheumatology, Garmisch-Partenkirchen, Germany
| | | | - Kirsten Minden
- Department of Rheumatology and Clinical Immunology, Charité -University Medicine, Berlin, Germany
- Epidemiology Unit, German Rheumatism Research Centre, Berlin, Germany
| | - Klaus Tenbrock
- Department of Pediatrics, RWTH Aachen University, Aachen, Germany
| | - Erkan Demirkaya
- Department of Pediatric Rheumatology, Hacettepe University, Ankara, Turkey
| | - Joanna Cobb
- Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, Manchester, UK
- National Institute for Health Research Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Elizabeth Baskin
- Translational Genetics and Genomics Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health,US Department of Health and Human Services, Bethesda, Maryland, USA
| | - Sara Signa
- Department of Pediatrics, University of Genova, Genoa, Italy
| | - Emily Shuldiner
- Translational Genetics and Genomics Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health,US Department of Health and Human Services, Bethesda, Maryland, USA
| | - Richard H Duerr
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Jean-Paul Achkar
- Department of Gastroenterology and Hepatology, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Pathobiology, Cleveland Clinic, Cleveland, Ohio, USA
| | - M Ilyas Kamboh
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Kenneth M Kaufman
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Leah C Kottyan
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Dalila Pinto
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Stephen W Scherer
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Marta E Alarcón-Riquelme
- Center for Genomics and Oncological Research, Pfizer-University of Granada-Andalusian Government, Granada, Spain
- Unit of Chronic Inflammatory Diseases, Institute for Environmental Medicine, Karolinska Institutet, Solna, Sweden
| | - Elisa Docampo
- Interdisciplinary Cluster for Applied Genoproteomics-Université de Liège, Liège, Belgium
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, and Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Xavier Estivill
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, and Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Sidra Medical and Research Centre, Doha, Qatar
| | - Ahmet Gül
- Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | | | - Carl D Langefeld
- Department of Biostatistical Sciences, Wake Forest University Health Sciences, Winston-Salem, North Carolina, USA
| | - Susan Thompson
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | | | - Daniel L Kastner
- Inflammatory Disease Section, National Human Genome Research Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, USA
| | - Patricia Woo
- Center of Paediatric and Adolescent Rheumatology, University College London, London, UK
| | - Wendy Thomson
- Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, Manchester, UK
- National Institute for Health Research Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
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512
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Gordon D, Londono D, Patel P, Kim W, Finch SJ, Heiman GA. An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance. Hum Hered 2017; 81:194-209. [PMID: 28315880 DOI: 10.1159/000457135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Accepted: 01/20/2017] [Indexed: 01/14/2023] Open
Abstract
Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes.
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Affiliation(s)
- Derek Gordon
- Department of Genetics, The State University of New Jersey, Piscataway, NJ, USA
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513
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Ruffieux H, Davison AC, Hager J, Irincheeva I. Efficient inference for genetic association studies with multiple outcomes. Biostatistics 2017; 18:618-636. [DOI: 10.1093/biostatistics/kxx007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 02/06/2017] [Indexed: 02/04/2023] Open
Abstract
SUMMARY
Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson and others (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes.
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Affiliation(s)
- Helene Ruffieux
- Nestlé Institute of Health Sciences SA, EPFL Innovation Park, 1015 Lausanne, Switzerland Ecole Polytechnique Fédérale de Lausanne, EPFL SB MATH STAT, Station 8, 1015 Lausanne, Switzerland
| | - Anthony C. Davison
- Ecole Polytechnique Fédérale de Lausanne, EPFL SB MATH STAT, Station 8, 1015 Lausanne, Switzerland
| | - Jorg Hager
- Nestlé Institute of Health Sciences SA, EPFL Innovation Park, 1015 Lausanne, Switzerland
| | - Irina Irincheeva
- Nestlé Institute of Health Sciences SA, EPFL Innovation Park, 1015 Lausanne, Switzerland
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514
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Porter HF, O’Reilly PF. Multivariate simulation framework reveals performance of multi-trait GWAS methods. Sci Rep 2017; 7:38837. [PMID: 28287610 PMCID: PMC5347376 DOI: 10.1038/srep38837] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 10/19/2016] [Indexed: 01/22/2023] Open
Abstract
Burgeoning availability of genome-wide association study (GWAS) results and national biobank data has led to growing interest in performing multi-trait genetic analyses. Numerous multi-trait GWAS methods that exploit either summary statistics or individual-level data have been developed, but their relative performance is unclear. Here we develop a simulation framework to model the complex networks underlying multivariate genetic epidemiology, enabling the vast model space of genetic effects on multiple correlated traits to be explored systematically. We perform a comprehensive comparison of the leading multi-trait GWAS methods, finding: (1) method performance is highly sensitive to the specific combination of genetic effects and phenotypic correlations, (2) most of the current multivariate methods have remarkably similar statistical power, and (3) multivariate methods may offer a substantial increase in the discovery of genetic variants over the standard univariate approach. We believe our findings offer the clearest picture to date of the relative performance of multi-trait GWAS methods and act as a guide for method selection. We provide a web application and open-source software program implementing our simulation framework, for: (i) further benchmarking of multivariate GWAS methods, (ii) power calculations for multivariate genetic studies, and (iii) generating data for testing any multivariate method in genetic epidemiology.
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Affiliation(s)
- Heather F. Porter
- MRC SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Paul F. O’Reilly
- MRC SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, UK
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515
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Tong P, Monahan J, Prendergast JGD. Shared regulatory sites are abundant in the human genome and shed light on genome evolution and disease pleiotropy. PLoS Genet 2017; 13:e1006673. [PMID: 28282383 PMCID: PMC5365138 DOI: 10.1371/journal.pgen.1006673] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 03/24/2017] [Accepted: 03/07/2017] [Indexed: 12/16/2022] Open
Abstract
Large-scale gene expression datasets are providing an increasing understanding of the location of cis-eQTLs in the human genome and their role in disease. However, little is currently known regarding the extent of regulatory site-sharing between genes. This is despite it having potentially wide-ranging implications, from the determination of the way in which genetic variants may shape multiple phenotypes to the understanding of the evolution of human gene order. By first identifying the location of non-redundant cis-eQTLs, we show that regulatory site-sharing is a relatively common phenomenon in the human genome, with over 10% of non-redundant regulatory variants linked to the expression of multiple nearby genes. We show that these shared, local regulatory sites are linked to high levels of chromatin looping between the regulatory sites and their associated genes. In addition, these co-regulated gene modules are found to be strongly conserved across mammalian species, suggesting that shared regulatory sites have played an important role in shaping human gene order. The association of these shared cis-eQTLs with multiple genes means they also appear to be unusually important in understanding the genetics of human phenotypes and pleiotropy, with shared regulatory sites more often linked to multiple human phenotypes than other regulatory variants. This study shows that regulatory site-sharing is likely an underappreciated aspect of gene regulation and has important implications for the understanding of various biological phenomena, including how the two and three dimensional structures of the genome have been shaped and the potential causes of disease pleiotropy outside coding regions.
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Affiliation(s)
- Pin Tong
- Wellcome Trust Centre for Cell Biology and Institute of Cell Biology, School of Biological Sciences, The University of Edinburgh, Max Born Crescent, Edinburgh, Scotland, United Kingdom
| | - Jack Monahan
- The European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - James G. D. Prendergast
- The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, Scotland, United Kingdom
- * E-mail:
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516
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Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types. Nat Genet 2017; 49:600-605. [PMID: 28218759 PMCID: PMC5374036 DOI: 10.1038/ng.3795] [Citation(s) in RCA: 149] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 01/26/2017] [Indexed: 12/13/2022]
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517
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Wangler MF, Hu Y, Shulman JM. Drosophila and genome-wide association studies: a review and resource for the functional dissection of human complex traits. Dis Model Mech 2017; 10:77-88. [PMID: 28151408 PMCID: PMC5312009 DOI: 10.1242/dmm.027680] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Human genome-wide association studies (GWAS) have successfully identified thousands of susceptibility loci for common diseases with complex genetic etiologies. Although the susceptibility variants identified by GWAS usually have only modest effects on individual disease risk, they contribute to a substantial burden of trait variation in the overall population. GWAS also offer valuable clues to disease mechanisms that have long proven to be elusive. These insights could lead the way to breakthrough treatments; however, several challenges hinder progress, making innovative approaches to accelerate the follow-up of results from GWAS an urgent priority. Here, we discuss the largely untapped potential of the fruit fly, Drosophila melanogaster, for functional investigation of findings from human GWAS. We highlight selected examples where strong genomic conservation with humans along with the rapid and powerful genetic tools available for flies have already facilitated fine mapping of association signals, elucidated gene mechanisms, and revealed novel disease-relevant biology. We emphasize current research opportunities in this rapidly advancing field, and present bioinformatic analyses that systematically explore the applicability of Drosophila for interrogation of susceptibility signals implicated in more than 1000 human traits, based on all GWAS completed to date. Thus, our discussion is targeted at both human geneticists seeking innovative strategies for experimental validation of findings from GWAS, as well as the Drosophila research community, by whom ongoing investigations of the implicated genes will powerfully inform our understanding of human disease.
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Affiliation(s)
- Michael F Wangler
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
- Program in Developmental Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yanhui Hu
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Joshua M Shulman
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
- Program in Developmental Biology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
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518
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Rodríguez JA, Marigorta UM, Hughes DA, Spataro N, Bosch E, Navarro A. Antagonistic pleiotropy and mutation accumulation influence human senescence and disease. Nat Ecol Evol 2017; 1:55. [DOI: 10.1038/s41559-016-0055] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 12/14/2016] [Indexed: 11/10/2022]
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519
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A systematic SNP selection approach to identify mechanisms underlying disease aetiology: linking height to post-menopausal breast and colorectal cancer risk. Sci Rep 2017; 7:41034. [PMID: 28117334 PMCID: PMC5259777 DOI: 10.1038/srep41034] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 12/15/2016] [Indexed: 01/28/2023] Open
Abstract
Data from GWAS suggest that SNPs associated with complex diseases or traits tend to co-segregate in regions of low recombination, harbouring functionally linked gene clusters. This phenomenon allows for selecting a limited number of SNPs from GWAS repositories for large-scale studies investigating shared mechanisms between diseases. For example, we were interested in shared mechanisms between adult-attained height and post-menopausal breast cancer (BC) and colorectal cancer (CRC) risk, because height is a risk factor for these cancers, though likely not a causal factor. Using SNPs from public GWAS repositories at p-values < 1 × 10−5 and a genomic sliding window of 1 mega base pair, we identified SNP clusters including at least one SNP associated with height and one SNP associated with either post-menopausal BC or CRC risk (or both). SNPs were annotated to genes using HapMap and GRAIL and analysed for significantly overrepresented pathways using ConsensuspathDB. Twelve clusters including 56 SNPs annotated to 26 genes were prioritised because these included at least one height- and one BC risk- or CRC risk-associated SNP annotated to the same gene. Annotated genes were involved in Indian hedgehog signalling (p-value = 7.78 × 10−7) and several cancer site-specific pathways. This systematic approach identified a limited number of clustered SNPs, which pinpoint potential shared mechanisms linking together the complex phenotypes height, post-menopausal BC and CRC.
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520
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Identifying Pleiotropic Genes in Genome-Wide Association Studies for Multivariate Phenotypes with Mixed Measurement Scales. PLoS One 2017; 12:e0169893. [PMID: 28081206 PMCID: PMC5231271 DOI: 10.1371/journal.pone.0169893] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Accepted: 12/22/2016] [Indexed: 11/30/2022] Open
Abstract
We propose a multivariate genome-wide association test for mixed continuous, binary, and ordinal phenotypes. A latent response model is used to estimate the correlation between phenotypes with different measurement scales so that the empirical distribution of the Fisher’s combination statistic under the null hypothesis is estimated efficiently. The simulation study shows that our proposed correlation estimation methods have high levels of accuracy. More importantly, our approach conservatively estimates the variance of the test statistic so that the type I error rate is controlled. The simulation also shows that the proposed test maintains the power at the level very close to that of the ideal analysis based on known latent phenotypes while controlling the type I error. In contrast, conventional approaches–dichotomizing all observed phenotypes or treating them as continuous variables–could either reduce the power or employ a linear regression model unfit for the data. Furthermore, the statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that conducting a multivariate test on multiple phenotypes can increase the power of identifying markers that may not be, otherwise, chosen using marginal tests. The proposed method also offers a new approach to analyzing the Fagerström Test for Nicotine Dependence as multivariate phenotypes in genome-wide association studies.
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521
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Ittisoponpisan S, Alhuzimi E, Sternberg MJE, David A. Landscape of Pleiotropic Proteins Causing Human Disease: Structural and System Biology Insights. Hum Mutat 2017; 38:289-296. [PMID: 27957775 DOI: 10.1002/humu.23155] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Accepted: 12/03/2016] [Indexed: 12/13/2022]
Abstract
Pleiotropy is the phenomenon by which the same gene can result in multiple phenotypes. Pleiotropic proteins are emerging as important contributors to rare and common disorders. Nevertheless, little is known on the mechanisms underlying pleiotropy and the characteristic of pleiotropic proteins. We analyzed disease-causing proteins reported in UniProt and observed that 12% are pleiotropic (variants in the same protein cause more than one disease). Pleiotropic proteins were enriched in deleterious and rare variants, but not in common variants. Pleiotropic proteins were more likely to be involved in the pathogenesis of neoplasms, neurological, and circulatory diseases and congenital malformations, whereas non-pleiotropic proteins in endocrine and metabolic disorders. Pleiotropic proteins were more essential and had a higher number of interacting partners compared with non-pleiotropic proteins. Significantly more pleiotropic than non-pleiotropic proteins contained at least one intrinsically long disordered region (P < 0.001). Deleterious variants occurring in structurally disordered regions were more commonly found in pleiotropic, rather than non-pleiotropic proteins. In conclusion, pleiotropic proteins are an important contributor to human disease. They represent a biologically different class of proteins compared with non-pleiotropic proteins and a better understanding of their characteristics and genetic variants can greatly aid in the interpretation of genetic studies and drug design.
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Affiliation(s)
- Sirawit Ittisoponpisan
- Structural Bioinformatics Group, Department of Life Sciences, Imperial College London, London, UK
| | - Eman Alhuzimi
- Structural Bioinformatics Group, Department of Life Sciences, Imperial College London, London, UK
| | - Michael J E Sternberg
- Structural Bioinformatics Group, Department of Life Sciences, Imperial College London, London, UK
| | - Alessia David
- Structural Bioinformatics Group, Department of Life Sciences, Imperial College London, London, UK
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522
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Mägi R, Suleimanov YV, Clarke GM, Kaakinen M, Fischer K, Prokopenko I, Morris AP. SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes. BMC Bioinformatics 2017; 18:25. [PMID: 28077070 PMCID: PMC5225593 DOI: 10.1186/s12859-016-1437-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2016] [Accepted: 12/17/2016] [Indexed: 11/10/2022] Open
Abstract
Background Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits. Results We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements “reverse regression” methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10−8), which has not been reported in previous large-scale GWAS of lipid traits. Conclusions The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach.
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Affiliation(s)
- Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Yury V Suleimanov
- Computation-based Science and Technology Research Center, Cyprus Institute, Nicosia, Cyprus.,Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Geraldine M Clarke
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - Krista Fischer
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | | | - Andrew P Morris
- Estonian Genome Center, University of Tartu, Tartu, Estonia. .,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. .,Department of Biostatistics, University of Liverpool, Liverpool, UK.
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523
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Wu B, Pankow JS. Genome-wide association test of multiple continuous traits using imputed SNPs. STATISTICS AND ITS INTERFACE 2017; 10:379-386. [PMID: 28217245 PMCID: PMC5310616 DOI: 10.4310/sii.2017.v10.n3.a2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
More and more large cohort studies have conducted or are conducting genome-wide association studies (GWAS) to reveal the genetic components of many complex human diseases. These large cohort studies often collected a broad array of correlated phenotypes that reflect common physiological processes. By jointly analyzing these correlated traits, we can gain more power by aggregating multiple weak effects and shed light on the mechanisms underlying complex human diseases. The majority of existing multi-trait association test methods are based on jointly modeling the multivariate traits conditional on the genotype as covariate, and can readily accommodate the imputed SNPs by using their imputed dosage as a covariate. An alternative class of multi-trait association tests is based on the inverted regression, which models the distribution of genotypes conditional on the covariate and multivariate traits, and has been shown to have competitive performance. To our knowledge, all existing inverted regression approaches have implicitly used the "best-guess" genotypes, which is not efficient and known to lead to dramatic power loss, and there have not been any proposed methods of incorporating imputation uncertainty into inverted regressions. In this work, we propose a general and efficient framework that can account for the imputation uncertainty to further improve the association test power of inverted regression models for imputed SNPs. We demonstrate through extensive numerical studies that the proposed method has competitive performance. We further illustrate its usefulness by application to association test of diabetes-related glycemic traits in the Atherosclerosis Risk in Communities (ARIC) Study.
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Affiliation(s)
- Baolin Wu
- Division of Biostatistics, University of Minnesota
| | - James S. Pankow
- Division of Epidemiology and Community Health School of Public Health, University of Minnesota
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524
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Gong C, Du Q, Xie J, Quan M, Chen B, Zhang D. Dissection of Insertion-Deletion Variants within Differentially Expressed Genes Involved in Wood Formation in Populus. FRONTIERS IN PLANT SCIENCE 2017; 8:2199. [PMID: 29403506 PMCID: PMC5778123 DOI: 10.3389/fpls.2017.02199] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Accepted: 12/14/2017] [Indexed: 05/02/2023]
Abstract
Short insertions and deletions (InDels) are one of the major genetic variants and are distributed widely across the genome; however, few investigations of InDels have been conducted in long-lived perennial plants. Here, we employed a combination of RNA-seq and population resequencing to identify InDels within differentially expressed (DE) genes underlying wood formation in a natural population of Populus tomentosa (435 individuals) and utilized InDel-based association mapping to detect the causal variants under additive, dominance, and epistasis underlying growth and wood properties. In the present paper, 5,482 InDels detected from 629 DE genes showed uneven distributions throughout all 19 chromosomes, and 95.9% of these loci were diallelic InDels. Seventy-four InDels (positive false discovery rate q ≤ 0.10) from 68 genes exhibited significant additive/dominant effects on 10 growth and wood-properties, with an average of 14.7% phenotypic variance explained. Potential pleiotropy was observed in one-third of the InDels (representing 24 genes). Seven genes exhibited significantly differential expression among the genotypic classes of associated InDels, indicating possible important roles for these InDels. Epistasis analysis showed that overlapping interacting genes formed unique interconnected networks for each trait, supporting the putative biochemical links that control quantitative traits. Therefore, the identification and utilization of InDels in trees will be recognized as an effective marker system for molecular marker-assisted breeding applications, and further facilitate our understanding of quantitative genomics.
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Affiliation(s)
- Chenrui Gong
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- College of Forestry, Henan Agricultural University, Zhengzhou, China
| | - Qingzhang Du
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China
| | - Jianbo Xie
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Mingyang Quan
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Beibei Chen
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Deqiang Zhang
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- *Correspondence: Deqiang Zhang,
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525
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Abstract
For over a decade, genome-wide association studies (GWAS) have been a major tool for detecting genetic variants underlying complex traits. Recent studies have demonstrated that the same variant or gene can be associated with multiple traits, and such associations are termed cross-phenotype (CP) associations. CP association analysis can improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this chapter, we discuss existing statistical methods for analyzing association between a single marker and multivariate phenotypes, we introduce a general approach, CPASSOC, to detect the CP associations, and explain how to conduct the analysis in practice.
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526
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More than Meets the Eye: A Primer for "Timing of Locomotor Recovery from Anoxia Modulated by the white Gene in Drosophila melanogaster". Genetics 2016; 204:1369-1375. [PMID: 27927903 DOI: 10.1534/genetics.116.196519] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
SummaryA single gene might have several functions within an organism, and so mutational loss of that gene has multiple effects across different physiological systems in the organism. Though the white gene in Drosophila melanogaster was identified originally for its effect on fly eye color, an article by Xiao and Robertson in the June 2016 issue of GENETICS describes a function for the white gene in the response of Drosophila to oxygen deprivation. This Primer article provides background information on the white gene, the phenomenon of pleiotropy, and the molecular and genetic approaches used in the study to demonstrate a new behavioral function for the white gene.
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527
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Chiu CY, Jung J, Wang Y, Weeks DE, Wilson AF, Bailey-Wilson JE, Amos CI, Mills JL, Boehnke M, Xiong M, Fan R. A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing. Genet Epidemiol 2016; 41:18-34. [PMID: 27917525 DOI: 10.1002/gepi.22014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Revised: 09/01/2016] [Accepted: 09/19/2016] [Indexed: 01/23/2023]
Abstract
In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models that perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods.
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Affiliation(s)
- Chi-Yang Chiu
- Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Jeesun Jung
- Laboratory of Epidemiology and Biometry, National Institute on Alcohol, Abuse and Alcoholism, NIH, Bethesda, MD, USA
| | - Yifan Wang
- Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA
| | - Daniel E Weeks
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alexander F Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD, USA
| | - Joan E Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD, USA
| | - Christopher I Amos
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - James L Mills
- Epidemiology Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Michael Boehnke
- Department of Biostatistics, School of Public Health, The University of Michigan, Ann Arbor, MI, USA
| | - Momiao Xiong
- Human Genetics Center, University of Texas-Houston, Houston, TX, USA
| | - Ruzong Fan
- Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, MD, USA
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528
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Bysani M, Perfilyev A, de Mello VD, Rönn T, Nilsson E, Pihlajamäki J, Ling C. Epigenetic alterations in blood mirror age-associated DNA methylation and gene expression changes in human liver. Epigenomics 2016; 9:105-122. [PMID: 27911095 DOI: 10.2217/epi-2016-0087] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
AIM To study the impact of aging on DNA methylation and mRNA expression in human liver. EXPERIMENTAL PROCEDURES We analysed genome-wide DNA methylation and gene expression in human liver samples using Illumina 450K and HumanHT12 expression BeadChip arrays. RESULTS DNA methylation analysis of ∼455,000 CpG sites in human liver revealed that age was significantly associated with altered DNA methylation of 20,396 CpG sites. Comparison of liver methylation data with published methylation data in other tissues showed that vast majority of the age-associated significant CpG sites overlapped between liver and blood, whereas a smaller overlap was found between liver and pancreatic islets or adipose tissue, respectively. We identified 151 genes whose liver expression also correlated with age. CONCLUSIONS We identified age-associated DNA methylation and expression changes in human liver that are partly reflected by epigenetic alterations in blood.
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Affiliation(s)
- Madhusudhan Bysani
- Epigenetics & Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Malmö, Sweden
| | - Alexander Perfilyev
- Epigenetics & Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Malmö, Sweden
| | - Vanessa D de Mello
- Department of Clinical Nutrition, Institute of Public Health & Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Tina Rönn
- Epigenetics & Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Malmö, Sweden
| | - Emma Nilsson
- Epigenetics & Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Malmö, Sweden
| | - Jussi Pihlajamäki
- Department of Clinical Nutrition, Institute of Public Health & Clinical Nutrition, University of Eastern Finland, Kuopio, Finland.,Clinical Nutrition & Obesity Center, Kuopio University Hospital, Kuopio, Finland
| | - Charlotte Ling
- Epigenetics & Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Malmö, Sweden
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529
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Marioni RE, Ritchie SJ, Joshi PK, Hagenaars SP, Okbay A, Fischer K, Adams MJ, Hill WD, Davies G, Nagy R, Amador C, Läll K, Metspalu A, Liewald DC, Campbell A, Wilson JF, Hayward C, Esko T, Porteous DJ, Gale CR, Deary IJ. Genetic variants linked to education predict longevity. Proc Natl Acad Sci U S A 2016; 113:13366-13371. [PMID: 27799538 PMCID: PMC5127357 DOI: 10.1073/pnas.1605334113] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Educational attainment is associated with many health outcomes, including longevity. It is also known to be substantially heritable. Here, we used data from three large genetic epidemiology cohort studies (Generation Scotland, n = ∼17,000; UK Biobank, n = ∼115,000; and the Estonian Biobank, n = ∼6,000) to test whether education-linked genetic variants can predict lifespan length. We did so by using cohort members' polygenic profile score for education to predict their parents' longevity. Across the three cohorts, meta-analysis showed that a 1 SD higher polygenic education score was associated with ∼2.7% lower mortality risk for both mothers (total ndeaths = 79,702) and ∼2.4% lower risk for fathers (total ndeaths = 97,630). On average, the parents of offspring in the upper third of the polygenic score distribution lived 0.55 y longer compared with those of offspring in the lower third. Overall, these results indicate that the genetic contributions to educational attainment are useful in the prediction of human longevity.
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Affiliation(s)
- Riccardo E Marioni
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom;
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Stuart J Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
| | - Peter K Joshi
- Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh EH16 4UX, United Kingdom
| | - Saskia P Hagenaars
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
- Division of Psychiatry, University of Edinburgh, Edinburgh EH8 9YL, United Kingdom
| | - Aysu Okbay
- Department of Applied Economics, Erasmus School of Economics, Erasmus University, 3062 PA Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, 3015 CE Rotterdam, The Netherlands
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam 3062 PA, The Netherlands
| | - Krista Fischer
- Estonian Genome Center, University of Tartu, Tartu 51010, Estonia
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh EH8 9YL, United Kingdom
| | - W David Hill
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
| | - Gail Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
| | - Reka Nagy
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Carmen Amador
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Kristi Läll
- Estonian Genome Center, University of Tartu, Tartu 51010, Estonia
- Institute of Mathematical Statistics, University of Tartu, Tartu 50409, Estonia
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu, Tartu 51010, Estonia
| | - David C Liewald
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
| | - Archie Campbell
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - James F Wilson
- Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh EH16 4UX, United Kingdom
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu 51010, Estonia
- Institute of Mathematical Statistics, University of Tartu, Tartu 50409, Estonia
- Broad Institute, Cambridge, MA 02142
- Department of Endocrinology, Children's Hospital Boston, Boston, MA 02115
| | - David J Porteous
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Catharine R Gale
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
- Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
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530
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Ross-Adams H, Ball S, Lawrenson K, Halim S, Russell R, Wells C, Strand SH, Ørntoft TF, Larson M, Armasu S, Massie CE, Asim M, Mortensen MM, Borre M, Woodfine K, Warren AY, Lamb AD, Kay J, Whitaker H, Ramos-Montoya A, Murrell A, Sørensen KD, Fridley BL, Goode EL, Gayther SA, Masters J, Neal DE, Mills IG. HNF1B variants associate with promoter methylation and regulate gene networks activated in prostate and ovarian cancer. Oncotarget 2016; 7:74734-74746. [PMID: 27732966 PMCID: PMC5342698 DOI: 10.18632/oncotarget.12543] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Accepted: 09/26/2016] [Indexed: 12/21/2022] Open
Abstract
Two independent regions within HNF1B are consistently identified in prostate and ovarian cancer genome-wide association studies (GWAS); their functional roles are unclear. We link prostate cancer (PC) risk SNPs rs11649743 and rs3760511 with elevated HNF1B gene expression and allele-specific epigenetic silencing, and outline a mechanism by which common risk variants could effect functional changes that increase disease risk: functional assays suggest that HNF1B is a pro-differentiation factor that suppresses epithelial-to-mesenchymal transition (EMT) in unmethylated, healthy tissues. This tumor-suppressor activity is lost when HNF1B is silenced by promoter methylation in the progression to PC. Epigenetic inactivation of HNF1B in ovarian cancer also associates with known risk SNPs, with a similar impact on EMT. This represents one of the first comprehensive studies into the pleiotropic role of a GWAS-associated transcription factor across distinct cancer types, and is the first to describe a conserved role for a multi-cancer genetic risk factor.
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Affiliation(s)
- Helen Ross-Adams
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Stephen Ball
- Prostate Cancer Research Centre, University College London, London, UK
| | - Kate Lawrenson
- Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | - Silvia Halim
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Roslin Russell
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Claire Wells
- Division of Cancer Studies, King's College London, London, UK
| | - Siri H. Strand
- Department of Molecular Medicine, Aarhus University Hospital, Denmark
| | - Torben F. Ørntoft
- Department of Molecular Medicine, Aarhus University Hospital, Denmark
| | | | | | - Charles E. Massie
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Mohammad Asim
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | | | - Michael Borre
- Department of Urology, Aarhus University Hospital, Aarhus, Denmark
| | - Kathryn Woodfine
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Anne Y. Warren
- Department of Pathology, Addenbrooke's Hospital, Cambridge, UK
| | - Alastair D. Lamb
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Urology, Addenbrooke's Hospital, Cambridge, UK
| | - Jonathan Kay
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Molecular Diagnostics and Therapeutics Group, University College London, London, UK
| | - Hayley Whitaker
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Molecular Diagnostics and Therapeutics Group, University College London, London, UK
| | | | - Adele Murrell
- Department of Biology and Biochemistry, University of Bath, Centre for Regenerative Medicine, Claverton Down, Bath, UK
| | | | - Brooke L. Fridley
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA
| | | | - Simon A. Gayther
- Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | - John Masters
- Prostate Cancer Research Centre, University College London, London, UK
| | - David E. Neal
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Urology, Addenbrooke's Hospital, Cambridge, UK
| | - Ian G. Mills
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Prostate Cancer Research Group, Centre for Molecular Medicine Norway, Nordic EMBL Partnership, University of Oslo and Oslo University Hospital, Oslo, Norway
- Departments of Cancer Prevention and Urology, Institute of Cancer Research and Department of Urology, Oslo University Hospital, Oslo, Norway
- Prostate Cancer UK/Movember Centre of Excellence for Prostate Cancer Research, Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, UK
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531
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Pouladi N, Achour I, Li H, Berghout J, Kenost C, Gonzalez-Garay ML, Lussier YA. Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records. Yearb Med Inform 2016; 25:194-206. [PMID: 27830251 PMCID: PMC5171562 DOI: 10.15265/iy-2016-040] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVES Disease comorbidity is a pervasive phenomenon impacting patients' health outcomes, disease management, and clinical decisions. This review presents past, current and future research directions leveraging both phenotypic and molecular information to uncover disease similarity underpinning the biology and etiology of disease comorbidity. METHODS We retrieved ~130 publications and retained 59, ranging from 2006 to 2015, that comprise a minimum number of five diseases and at least one type of biomolecule. We surveyed their methods, disease similarity metrics, and calculation of comorbidities in the electronic health records, if present. RESULTS Among the surveyed studies, 44% generated or validated disease similarity metrics in context of comorbidity, with 60% being published in the last two years. As inputs, 87% of studies utilized intragenic loci and proteins while 13% employed RNA (mRNA, LncRNA or miRNA). Network modeling was predominantly used (35%) followed by statistics (28%) to impute similarity between these biomolecules and diseases. Studies with large numbers of biomolecules and diseases used network models or naïve overlap of disease-molecule associations, while machine learning, statistics, and information retrieval were utilized in smaller and moderate sized studies. Multiscale computations comprising shared function, network topology, and phenotypes were performed exclusively on proteins. CONCLUSION This review highlighted the growing methods for identifying the molecular mechanisms underpinning comorbidities that leverage multiscale molecular information and patterns from electronic health records. The survey unveiled that intergenic polymorphisms have been overlooked for similarity imputation compared to their intragenic counterparts, offering new opportunities to bridge the mechanistic and similarity gaps of comorbidity.
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Affiliation(s)
| | | | | | | | | | | | - Y A Lussier
- Dr. Yves A. Lussier, The University of Arizona, Bio5 Building, 1657 East Helen Street, Tucson, AZ 85721, USA, Fax: +1 520 626 4824, E-Mail:
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532
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Liang J, Cade BE, Wang H, Chen H, Gleason KJ, Larkin EK, Saxena R, Lin X, Redline S, Zhu X. Comparison of Heritability Estimation and Linkage Analysis for Multiple Traits Using Principal Component Analyses. Genet Epidemiol 2016; 40:222-32. [PMID: 27027516 DOI: 10.1002/gepi.21957] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Revised: 11/30/2015] [Accepted: 12/14/2015] [Indexed: 12/16/2022]
Abstract
A disease trait often can be characterized by multiple phenotypic measurements that can provide complementary information on disease etiology, physiology, or clinical manifestations. Given that multiple phenotypes may be correlated and reflect common underlying genetic mechanisms, the use of multivariate analysis of multiple traits may improve statistical power to detect genes and variants underlying complex traits. The literature, however, has been unclear as to the optimal approach for analyzing multiple correlated traits. In this study, heritability and linkage analysis was performed for six obstructive sleep apnea hypopnea syndrome (OSAHS) related phenotypes, as well as principal components of the phenotypes and principal components of the heritability (PCHs) using the data from Cleveland Family Study, which include both African and European American families. Our study demonstrates that principal components generally result in higher heritability and linkage evidence than individual traits. Furthermore, the PCHs can be transferred across populations, strongly suggesting that these PCHs reflect traits with common underlying genetic mechanisms for OSAHS across populations. Thus, PCHs can provide useful traits for using data on multiple phenotypes and for genetic studies of trans-ethnic populations.
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Affiliation(s)
- Jingjing Liang
- Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.,Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Heming Wang
- Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Han Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Kevin J Gleason
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.,Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Emma K Larkin
- Department of Medicine, Division of Allergy, Pulmonary and Critical Care, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Richa Saxena
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.,Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, United States of America.,Center for Human Genetic Research and Department of Anesthesia, Pain, and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.,Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, United States of America.,Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
| | - Xiaofeng Zhu
- Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
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533
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Molecular Genetic Contributions to Social Deprivation and Household Income in UK Biobank. Curr Biol 2016; 26:3083-3089. [PMID: 27818178 PMCID: PMC5130721 DOI: 10.1016/j.cub.2016.09.035] [Citation(s) in RCA: 94] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 07/13/2016] [Accepted: 09/19/2016] [Indexed: 11/20/2022]
Abstract
Individuals with lower socio-economic status (SES) are at increased risk of physical and mental illnesses and tend to die at an earlier age [1, 2, 3]. Explanations for the association between SES and health typically focus on factors that are environmental in origin [4]. However, common SNPs have been found collectively to explain around 18% of the phenotypic variance of an area-based social deprivation measure of SES [5]. Molecular genetic studies have also shown that common physical and psychiatric diseases are partly heritable [6]. It is possible that phenotypic associations between SES and health arise partly due to a shared genetic etiology. We conducted a genome-wide association study (GWAS) on social deprivation and on household income using 112,151 participants of UK Biobank. We find that common SNPs explain 21% of the variation in social deprivation and 11% of household income. Two independent loci attained genome-wide significance for household income, with the most significant SNP in each of these loci being rs187848990 on chromosome 2 and rs8100891 on chromosome 19. Genes in the regions of these SNPs have been associated with intellectual disabilities, schizophrenia, and synaptic plasticity. Extensive genetic correlations were found between both measures of SES and illnesses, anthropometric variables, psychiatric disorders, and cognitive ability. These findings suggest that some SNPs associated with SES are involved in the brain and central nervous system. The genetic associations with SES obviously do not reflect direct causal effects and are probably mediated via other partly heritable variables, including cognitive ability, personality, and health. Common SNPs explain 21% of social deprivation and 11% of household income Two loci attained genome-wide significance for household income Genes in these loci have been linked to synaptic plasticity Genetic correlations were found between both measures of SES and many other traits
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534
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Hagenaars SP, Harris SE, Davies G, Hill WD, Liewald DCM, Ritchie SJ, Marioni RE, Fawns-Ritchie C, Cullen B, Malik R, Worrall BB, Sudlow CLM, Wardlaw JM, Gallacher J, Pell J, McIntosh AM, Smith DJ, Gale CR, Deary IJ. Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N=112 151) and 24 GWAS consortia. Mol Psychiatry 2016; 21:1624-1632. [PMID: 26809841 PMCID: PMC5078856 DOI: 10.1038/mp.2015.225] [Citation(s) in RCA: 235] [Impact Index Per Article: 29.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 11/19/2015] [Accepted: 12/07/2015] [Indexed: 12/23/2022]
Abstract
Causes of the well-documented association between low levels of cognitive functioning and many adverse neuropsychiatric outcomes, poorer physical health and earlier death remain unknown. We used linkage disequilibrium regression and polygenic profile scoring to test for shared genetic aetiology between cognitive functions and neuropsychiatric disorders and physical health. Using information provided by many published genome-wide association study consortia, we created polygenic profile scores for 24 vascular-metabolic, neuropsychiatric, physiological-anthropometric and cognitive traits in the participants of UK Biobank, a very large population-based sample (N=112 151). Pleiotropy between cognitive and health traits was quantified by deriving genetic correlations using summary genome-wide association study statistics and to the method of linkage disequilibrium score regression. Substantial and significant genetic correlations were observed between cognitive test scores in the UK Biobank sample and many of the mental and physical health-related traits and disorders assessed here. In addition, highly significant associations were observed between the cognitive test scores in the UK Biobank sample and many polygenic profile scores, including coronary artery disease, stroke, Alzheimer's disease, schizophrenia, autism, major depressive disorder, body mass index, intracranial volume, infant head circumference and childhood cognitive ability. Where disease diagnosis was available for UK Biobank participants, we were able to show that these results were not confounded by those who had the relevant disease. These findings indicate that a substantial level of pleiotropy exists between cognitive abilities and many human mental and physical health disorders and traits and that it can be used to predict phenotypic variance across samples.
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Affiliation(s)
- S P Hagenaars
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - S E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - G Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - W D Hill
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - D C M Liewald
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - S J Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - R E Marioni
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - C Fawns-Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - B Cullen
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - R Malik
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany
| | - METASTROKE Consortium, International Consortium for Blood Pressure GWAS
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Neurology, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - SpiroMeta Consortium
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Neurology, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - CHARGE Consortium Pulmonary Group, CHARGE Consortium Aging and Longevity Group
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Neurology, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - B B Worrall
- Department of Neurology, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - C L M Sudlow
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - J M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - J Gallacher
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - J Pell
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - A M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - D J Smith
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - C R Gale
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - I J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
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535
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Gizer IR. Molecular genetic approaches to understanding the comorbidity of psychiatric disorders. Dev Psychopathol 2016; 28:1089-1101. [PMID: 27739393 PMCID: PMC5079621 DOI: 10.1017/s0954579416000717] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Epidemiologic studies demonstrating high rates of co-occurrence among psychiatric disorders at the population level have contributed to large literatures focused on identifying the causal mechanisms underlying the patterns of co-occurrence among these disorders. Such efforts have long represented a core focus of developmental psychopathologists and have more recently been supported by the Research Domain Criteria initiative developed by the NIMH, which provides a further framework for how the hypothesized mechanisms can be studied at different levels of analysis. The present overview focuses on molecular genetic approaches that are being used currently to study the etiology of psychiatric disorders, and how these approaches have been applied in efforts to understand the biological mechanisms that give rise to comorbid conditions. The present report begins with a review of molecular genetic approaches used to identify individual variants that confer risk for multiple disorders and the intervening biological mechanisms that contribute to their comorbidity. This is followed by a review of molecular genetic approaches that use genetic data in aggregate to examine these questions, and concludes with a discussion of how developmental psychopathologists are uniquely positioned to apply these methods in a way that will further our understanding of the causal factors that contribute to the development of comorbid conditions.
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536
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The implications of the shared genetics of psychiatric disorders. Nat Med 2016; 22:1214-1219. [DOI: 10.1038/nm.4196] [Citation(s) in RCA: 107] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 09/08/2016] [Indexed: 12/14/2022]
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537
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He L, Kernogitski Y, Kulminskaya I, Loika Y, Arbeev KG, Loiko E, Bagley O, Duan M, Yashkin A, Ukraintseva SV, Kovtun M, Yashin AI, Kulminski AM. Pleiotropic Meta-Analyses of Longitudinal Studies Discover Novel Genetic Variants Associated with Age-Related Diseases. Front Genet 2016; 7:179. [PMID: 27790247 PMCID: PMC5061751 DOI: 10.3389/fgene.2016.00179] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 09/21/2016] [Indexed: 01/31/2023] Open
Abstract
Age-related diseases may result from shared biological mechanisms in intrinsic processes of aging. Genetic effects on age-related diseases are often modulated by environmental factors due to their little contribution to fitness or are mediated through certain endophenotypes. Identification of genetic variants with pleiotropic effects on both common complex diseases and endophenotypes may reveal potential conflicting evolutionary pressures and deliver new insights into shared genetic contribution to healthspan and lifespan. Here, we performed pleiotropic meta-analyses of genetic variants using five NIH-funded datasets by integrating univariate summary statistics for age-related diseases and endophenotypes. We investigated three groups of traits: (1) endophenotypes such as blood glucose, blood pressure, lipids, hematocrit, and body mass index, (2) time-to-event outcomes such as the age-at-onset of diabetes mellitus (DM), cancer, cardiovascular diseases (CVDs) and neurodegenerative diseases (NDs), and (3) both combined. In addition to replicating previous findings, we identify seven novel genome-wide significant loci (< 5e-08), out of which five are low-frequency variants. Specifically, from Group 2, we find rs7632505 on 3q21.1 in SEMA5B, rs460976 on 21q22.3 (1 kb from TMPRSS2) and rs12420422 on 11q24.1 predominantly associated with a variety of CVDs, rs4905014 in ITPK1 associated with stroke and heart failure, rs7081476 on 10p12.1 in ANKRD26 associated with multiple diseases including DM, CVDs, and NDs. From Group 3, we find rs8082812 on 18p11.22 and rs1869717 on 4q31.3 associated with both endophenotypes and CVDs. Our follow-up analyses show that rs7632505, rs4905014, and rs8082812 have age-dependent effects on coronary heart disease or stroke. Functional annotation suggests that most of these SNPs are within regulatory regions or DNase clusters and in linkage disequilibrium with expression quantitative trait loci, implying their potential regulatory influence on the expression of nearby genes. Our mediation analyses suggest that the effects of some SNPs are mediated by specific endophenotypes. In conclusion, these findings indicate that loci with pleiotropic effects on age-related disorders tend to be enriched in genes involved in underlying mechanisms potentially related to nervous, cardiovascular and immune system functions, stress resistance, inflammation, ion channels and hematopoiesis, supporting the hypothesis of shared pathological role of infection, and inflammation in chronic age-related diseases.
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Affiliation(s)
- Liang He
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke UniversityDurham, NC, USA
| | | | | | | | | | | | | | | | | | | | | | | | - Alexander M. Kulminski
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke UniversityDurham, NC, USA
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538
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Multivariate Analysis of Anthropometric Traits Using Summary Statistics of Genome-Wide Association Studies from GIANT Consortium. PLoS One 2016; 11:e0163912. [PMID: 27701450 PMCID: PMC5049793 DOI: 10.1371/journal.pone.0163912] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 09/17/2016] [Indexed: 11/19/2022] Open
Abstract
Meta-analysis of single trait for multiple cohorts has been used for increasing statistical power in genome-wide association studies (GWASs). Although hundreds of variants have been identified by GWAS, these variants only explain a small fraction of phenotypic variation. Cross-phenotype association analysis (CPASSOC) can further improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this study, we performed CPASSOC analysis on the summary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) consortium using a novel method recently developed by our group. Sex-specific meta-analysis data for height, body mass index (BMI), and waist-to-hip ratio adjusted for BMI (WHRadjBMI) from discovery phase of the GIANT consortium study were combined using CPASSOC for each trait as well as 3 traits together. The conventional meta-analysis results from the discovery phase data of GIANT consortium studies were used to compare with that from CPASSOC analysis. The CPASSOC analysis was able to identify 17 loci associated with anthropometric traits that were missed by conventional meta-analysis. Among these loci, 16 have been reported in literature by including additional samples and 1 is novel. We also demonstrated that CPASSOC is able to detect pleiotropic effects when analyzing multiple traits.
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539
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Zheng T, Xie W, Xu L, He X, Zhang Y, You M, Yang G, Chen Y. A machine learning-based framework to identify type 2 diabetes through electronic health records. Int J Med Inform 2016; 97:120-127. [PMID: 27919371 DOI: 10.1016/j.ijmedinf.2016.09.014] [Citation(s) in RCA: 118] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 09/27/2016] [Accepted: 09/30/2016] [Indexed: 01/19/2023]
Abstract
OBJECTIVE To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g., via Electronic Health Records (EHR)). However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate. MATERIALS AND METHODS We propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely-used machine learning models within our framework, including k-Nearest-Neighbors, Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects), randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014. RESULTS We apply top-performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state-of-the-art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances (∼0.98 in average AUC), which are much higher than the state-of-the-art algorithm (0.71 in AUC). DISCUSSION Expert algorithm-based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature engineering to loosen such selection criteria to achieve a high identification rate of cases and controls. CONCLUSIONS Our proposed framework demonstrates a more accurate and efficient approach for identifying subjects with and without T2DM from EHR.
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Affiliation(s)
- Tao Zheng
- Institute of Image Communication and Networking, Shanghai Jiao Tong University, Shanghai, China; Tongren Hospital Shanghai Jiao Tong University, Shanghai, China
| | - Wei Xie
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Liling Xu
- Tongren Hospital Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoying He
- Department of Endocrinology, the First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ya Zhang
- Institute of Image Communication and Networking, Shanghai Jiao Tong University, Shanghai, China
| | - Mingrong You
- Division of Epidemiology, Vanderbilt University, Nashville, TN, USA
| | - Gong Yang
- Division of Epidemiology, Vanderbilt University, Nashville, TN, USA
| | - You Chen
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
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540
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Richmond RC, Hemani G, Tilling K, Davey Smith G, Relton CL. Challenges and novel approaches for investigating molecular mediation. Hum Mol Genet 2016; 25:R149-R156. [PMID: 27439390 PMCID: PMC5036871 DOI: 10.1093/hmg/ddw197] [Citation(s) in RCA: 83] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 06/06/2016] [Accepted: 06/20/2016] [Indexed: 11/12/2022] Open
Abstract
Understanding mediation is useful for identifying intermediates lying between an exposure and an outcome which, when intervened upon, will block (some or all of) the causal pathway between the exposure and outcome. Mediation approaches used in conventional epidemiology have been adapted to understanding the role of molecular intermediates in situations of high-dimensional omics data with varying degrees of success. In particular, the limitations of observational epidemiological study including confounding, reverse causation and measurement error can afflict conventional mediation approaches and may lead to incorrect conclusions regarding causal effects. Solutions to analysing mediation which overcome these problems include the use of instrumental variable methods such as Mendelian randomization, which may be applied to evaluate causality in increasingly complex networks of omics data.
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Affiliation(s)
- R C Richmond
- MRC Integrative Epidemiology Unit, University of Bristol, UK School of Social and Community Medicine, University of Bristol, UK
| | - G Hemani
- MRC Integrative Epidemiology Unit, University of Bristol, UK School of Social and Community Medicine, University of Bristol, UK
| | - K Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, UK School of Social and Community Medicine, University of Bristol, UK
| | - G Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, UK School of Social and Community Medicine, University of Bristol, UK
| | - C L Relton
- MRC Integrative Epidemiology Unit, University of Bristol, UK School of Social and Community Medicine, University of Bristol, UK
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541
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Latvala A, Kuja-Halkola R, D'Onofrio BM, Larsson H, Lichtenstein P. Cognitive ability and risk for substance misuse in men: genetic and environmental correlations in a longitudinal nation-wide family study. Addiction 2016; 111:1814-22. [PMID: 27106532 DOI: 10.1111/add.13440] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2015] [Revised: 01/08/2016] [Accepted: 04/20/2016] [Indexed: 01/21/2023]
Abstract
AIMS To investigate the association in males between cognitive ability in late adolescence and subsequent substance misuse-related events, and to study the underlying genetic and environmental correlations. DESIGN A population-based longitudinal study with three different family-based designs. Cox proportional hazards models were conducted to investigate the association at the individual level. Bivariate quantitative genetic modelling in (1) full brothers and maternal half-brothers, (2) full brothers reared together and apart and (3) monozygotic and dizygotic twin brothers was used to estimate genetic and environmental correlations. SETTING Register-based study in Sweden. PARTICIPANTS The full sample included 1 402 333 Swedish men born 1958-91 and conscripted at mean age 18.2 [standard deviation (SD) = 0.5] years. A total of 1 361 066 men who had no substance misuse events before cognitive assessment at mandatory military conscription were included in the Cox regression models, with a follow-up time of up to 35.6 years. MEASURES Cognitive ability was assessed at conscription with the Swedish Enlistment Battery. Substance misuse events included alcohol- and drug-related court convictions, medical treatments and deaths, available from governmental registries. FINDINGS Lower cognitive ability in late adolescence predicted an increased risk for substance misuse events [hazard ratio (HR) for a 1-stanine unit decrease in cognitive ability: 1.29, 95% confidence interval (CI) = 1.29-1.30]. The association was somewhat attenuated within clusters of full brothers (HR = 1.21, 95% CI = 1.20-1.23). Quantitative genetic analyses indicated that the association was due primarily to genetic influences; the genetic correlations ranged between -0.39 (95% CI = -0.45, -0.34) and -0.52 (95% CI -0.55, -0.48) in the three different designs. CONCLUSIONS Shared genetic influences appear to underlie the association between low cognitive ability and subsequent risk for substance misuse events among Swedish men.
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Affiliation(s)
- Antti Latvala
- Department of Public Health, University of Helsinki, Finland. .,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Ralf Kuja-Halkola
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Brian M D'Onofrio
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Henrik Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Medical Sciences, Örebro University, Örebro, Sweden
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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542
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Luciano M. Commentary on Latvala et al. (2016): What can genetic cognitive epidemiology tell us about substance misuse and addiction? Addiction 2016; 111:1823-4. [PMID: 27605082 DOI: 10.1111/add.13456] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 05/16/2016] [Indexed: 11/29/2022]
Affiliation(s)
- Michelle Luciano
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK.
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543
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Shamseldin HE, Masuho I, Alenizi A, Alyamani S, Patil DN, Ibrahim N, Martemyanov KA, Alkuraya FS. GNB5 mutation causes a novel neuropsychiatric disorder featuring attention deficit hyperactivity disorder, severely impaired language development and normal cognition. Genome Biol 2016; 17:195. [PMID: 27677260 PMCID: PMC5037613 DOI: 10.1186/s13059-016-1061-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 09/12/2016] [Indexed: 11/23/2022] Open
Abstract
Background Neuropsychiatric disorders are common forms of disability in humans. Despite recent progress in deciphering the genetics of these disorders, their phenotypic complexity continues to be a major challenge. Mendelian neuropsychiatric disorders are rare but their study has the potential to unravel novel mechanisms that are relevant to their complex counterparts. Results In an extended consanguineous family, we identified a novel neuropsychiatric phenotype characterized by severe speech impairment, variable expressivity of attention deficit hyperactivity disorder (ADHD), and motor delay. We identified the disease locus through linkage analysis on 15q21.2, and exome sequencing revealed a novel missense variant in GNB5. GNB5 encodes an atypical β subunit of the heterotrimeric GTP-binding proteins (Gβ5). Gβ5 is enriched in the central nervous system where it forms constitutive complexes with members of the regulator of G protein signaling family of proteins to modulate neurotransmitter signaling that affects a number of neurobehavioral outcomes. Here, we show that the S81L mutant form of Gβ5 has significantly impaired activity in terminating responses that are elicited by dopamine. Conclusions We demonstrate that these deficits originate from the impaired expression of the mutant Gβ5 protein, resulting in the decreased ability to stabilize regulator of G protein signaling complexes. Our data suggest that this novel neuropsychiatric phenotype is the human equivalent of Gnb5 deficiency in mice, which manifest motor deficits and hyperactivity, and highlight a critical role of Gβ5 in normal behavior as well as language and motor development in humans. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1061-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hanan E Shamseldin
- Department of Genetics, King Faisal Specialist Hospital and Research Center, MBC-03, PO Box 3354, Riyadh, 11211, Saudi Arabia
| | - Ikuo Masuho
- Department of Neuroscience, The Scripps Research Institute, 130 Scripps Way, #3C2, Jupiter, FL, 33458, USA
| | - Ahmed Alenizi
- Department of Pediatrics, King Saud Medical City, Riyadh, Saudi Arabia
| | - Suad Alyamani
- Department of Neurosciences, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Dipak N Patil
- Department of Neuroscience, The Scripps Research Institute, 130 Scripps Way, #3C2, Jupiter, FL, 33458, USA
| | - Niema Ibrahim
- Department of Genetics, King Faisal Specialist Hospital and Research Center, MBC-03, PO Box 3354, Riyadh, 11211, Saudi Arabia
| | - Kirill A Martemyanov
- Department of Neuroscience, The Scripps Research Institute, 130 Scripps Way, #3C2, Jupiter, FL, 33458, USA.
| | - Fowzan S Alkuraya
- Department of Genetics, King Faisal Specialist Hospital and Research Center, MBC-03, PO Box 3354, Riyadh, 11211, Saudi Arabia. .,Department of Anatomy and Cell Biology, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
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544
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Kichaev G, Roytman M, Johnson R, Eskin E, Lindström S, Kraft P, Pasaniuc B. Improved methods for multi-trait fine mapping of pleiotropic risk loci. Bioinformatics 2016; 33:248-255. [PMID: 27663501 DOI: 10.1093/bioinformatics/btw615] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Revised: 08/28/2016] [Accepted: 09/17/2016] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Genome-wide association studies (GWAS) have identified thousands of regions in the genome that contain genetic variants that increase risk for complex traits and diseases. However, the variants uncovered in GWAS are typically not biologically causal, but rather, correlated to the true causal variant through linkage disequilibrium (LD). To discern the true causal variant(s), a variety of statistical fine-mapping methods have been proposed to prioritize variants for functional validation. RESULTS In this work we introduce a new approach, fastPAINTOR, that leverages evidence across correlated traits, as well as functional annotation data, to improve fine-mapping accuracy at pleiotropic risk loci. To improve computational efficiency, we describe an new importance sampling scheme to perform model inference. First, we demonstrate in simulations that by leveraging functional annotation data, fastPAINTOR increases fine-mapping resolution relative to existing methods. Next, we show that jointly modeling pleiotropic risk regions improves fine-mapping resolution compared to standard single trait and pleiotropic fine mapping strategies. We report a reduction in the number of SNPs required for follow-up in order to capture 90% of the causal variants from 23 SNPs per locus using a single trait to 12 SNPs when fine-mapping two traits simultaneously. Finally, we analyze summary association data from a large-scale GWAS of lipids and show that these improvements are largely sustained in real data. AVAILABILITY AND IMPLEMENTATION The fastPAINTOR framework is implemented in the PAINTOR v3.0 package which is publicly available to the research community http://bogdan.bioinformatics.ucla.edu/software/paintor CONTACT: gkichaev@ucla.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | | | - Eleazar Eskin
- Bioinformatics Interdepartmental Program.,Department of Human Genetics, David Geffen School of Medicine.,Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sara Lindström
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School T.H. Chan of Public Health, Boston, MA, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program.,Department of Human Genetics, David Geffen School of Medicine.,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90024, USA
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Kar SP, Beesley J, Amin Al Olama A, Michailidou K, Tyrer J, Kote-Jarai ZS, Lawrenson K, Lindstrom S, Ramus SJ, Thompson DJ, Kibel AS, Dansonka-Mieszkowska A, Michael A, Dieffenbach AK, Gentry-Maharaj A, Whittemore AS, Wolk A, Monteiro A, Peixoto A, Kierzek A, Cox A, Rudolph A, Gonzalez-Neira A, Wu AH, Lindblom A, Swerdlow A, Ziogas A, Ekici AB, Burwinkel B, Karlan BY, Nordestgaard BG, Blomqvist C, Phelan C, McLean C, Pearce CL, Vachon C, Cybulski C, Slavov C, Stegmaier C, Maier C, Ambrosone CB, Høgdall CK, Teerlink CC, Kang D, Tessier DC, Schaid DJ, Stram DO, Cramer DW, Neal DE, Eccles D, Flesch-Janys D, Edwards DRV, Wokozorczyk D, Levine DA, Yannoukakos D, Sawyer EJ, Bandera EV, Poole EM, Goode EL, Khusnutdinova E, Høgdall E, Song F, Bruinsma F, Heitz F, Modugno F, Hamdy FC, Wiklund F, Giles GG, Olsson H, Wildiers H, Ulmer HU, Pandha H, Risch HA, Darabi H, Salvesen HB, Nevanlinna H, Gronberg H, Brenner H, Brauch H, Anton-Culver H, Song H, Lim HY, McNeish I, Campbell I, Vergote I, Gronwald J, Lubiński J, Stanford JL, Benítez J, Doherty JA, Permuth JB, Chang-Claude J, Donovan JL, Dennis J, Schildkraut JM, Schleutker J, Hopper JL, Kupryjanczyk J, Park JY, Figueroa J, Clements JA, Knight JA, Peto J, Cunningham JM, Pow-Sang J, Batra J, Czene K, Lu KH, Herkommer K, Khaw KT, Matsuo K, Muir K, Offitt K, Chen K, Moysich KB, Aittomäki K, Odunsi K, Kiemeney LA, Massuger LFAG, Fitzgerald LM, Cook LS, Cannon-Albright L, Hooning MJ, Pike MC, Bolla MK, Luedeke M, Teixeira MR, Goodman MT, Schmidt MK, Riggan M, Aly M, Rossing MA, Beckmann MW, Moisse M, Sanderson M, Southey MC, Jones M, Lush M, Hildebrandt MAT, Hou MF, Schoemaker MJ, Garcia-Closas M, Bogdanova N, Rahman N, Le ND, Orr N, Wentzensen N, Pashayan N, Peterlongo P, Guénel P, Brennan P, Paulo P, Webb PM, Broberg P, Fasching PA, Devilee P, Wang Q, Cai Q, Li Q, Kaneva R, Butzow R, Kopperud RK, Schmutzler RK, Stephenson RA, MacInnis RJ, Hoover RN, Winqvist R, Ness R, Milne RL, Travis RC, Benlloch S, Olson SH, McDonnell SK, Tworoger SS, Maia S, Berndt S, Lee SC, Teo SH, Thibodeau SN, Bojesen SE, Gapstur SM, Kjær SK, Pejovic T, Tammela TLJ, Dörk T, Brüning T, Wahlfors T, Key TJ, Edwards TL, Menon U, Hamann U, Mitev V, Kosma VM, Setiawan VW, Kristensen V, Arndt V, Vogel W, Zheng W, Sieh W, Blot WJ, Kluzniak W, Shu XO, Gao YT, Schumacher F, Freedman ML, Berchuck A, Dunning AM, Simard J, Haiman CA, Spurdle A, Sellers TA, Hunter DJ, Henderson BE, Kraft P, Chanock SJ, Couch FJ, Hall P, Gayther SA, Easton DF, Chenevix-Trench G, Eeles R, Pharoah PDP, Lambrechts D. Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types. Cancer Discov 2016; 6:1052-67. [PMID: 27432226 PMCID: PMC5010513 DOI: 10.1158/2159-8290.cd-15-1227] [Citation(s) in RCA: 139] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2015] [Accepted: 06/07/2016] [Indexed: 02/02/2023]
Abstract
UNLABELLED Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10(-8) seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type-specific expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10(-5) in the three-cancer meta-analysis. SIGNIFICANCE We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 932.
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Affiliation(s)
- Siddhartha P Kar
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Jonathan Beesley
- Department of Genetics, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jonathan Tyrer
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | | | - Kate Lawrenson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Sara Lindstrom
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts
| | - Susan J Ramus
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Deborah J Thompson
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Adam S Kibel
- Division of Urologic Surgery, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Agnieszka Dansonka-Mieszkowska
- Department of Pathology and Laboratory Diagnostics, the Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland
| | | | - Aida K Dieffenbach
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Alice S Whittemore
- Department of Health Research and Policy - Epidemiology, Stanford University School of Medicine, Stanford, California
| | - Alicja Wolk
- Karolinska Institutet, Department of Environmental Medicine, Division of Nutritional Epidemiology, Stockholm, Sweden
| | - Alvaro Monteiro
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Ana Peixoto
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
| | | | - Angela Cox
- Sheffield Cancer Research, Department of Oncology, University of Sheffield, Sheffield, UK
| | - Anja Rudolph
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Anna Gonzalez-Neira
- Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO) and Spanish National Genotyping Center (CEGEN), Madrid, Spain
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Anthony Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK. Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
| | - Argyrios Ziogas
- Department of Epidemiology, UCI Center for Cancer Genetics Research and Prevention, School of Medicine, University of California, Irvine, Irvine, California
| | - Arif B Ekici
- University Hospital Erlangen, Institute of Human Genetics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Barbara Burwinkel
- Molecular Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany. Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Beth Y Karlan
- Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Børge G Nordestgaard
- Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Catherine Phelan
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Catriona McLean
- Anatomical Pathology, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Celeste Leigh Pearce
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Celine Vachon
- Department of Health Science Research, Division of Epidemiology, Mayo Clinic, Rochester, Minnesota
| | - Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Chavdar Slavov
- Department of Urology, Alexandrovska University Hospital, Medical University, Sofia, Bulgaria
| | | | | | | | - Claus K Høgdall
- The Juliane Marie Centre, Department of Gynecology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Craig C Teerlink
- Division of Genetic Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah
| | - Daehee Kang
- Cancer Research Institute, Seoul National University, Seoul, Korea. Departments of Preventive Medicine and Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Daniel C Tessier
- McGill University and Génome Québec Innovation Centre, Montréal, Canada
| | | | - Daniel O Stram
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Daniel W Cramer
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital, Boston, Massachusetts
| | - David E Neal
- Department of Oncology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK. Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Cambridge, UK
| | - Diana Eccles
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Dieter Flesch-Janys
- University Medical Center Hamburg-Eppendorf, Institute of Occupational Medicine and Maritime Medicine and Institute for Medical Biometrics and Epidemiology, Hamburg, Germany
| | - Digna R Velez Edwards
- Vanderbilt Epidemiology Center, Vanderbilt Genetics Institute, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Dominika Wokozorczyk
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Douglas A Levine
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Elinor J Sawyer
- Research Oncology, Guy's Hospital, King's College London, London, UK
| | - Elisa V Bandera
- Cancer Prevention and Control Program, Rutgers Cancer Institute of New Jersey, The State University of New Jersey, New Brunswick, New Jersey
| | - Elizabeth M Poole
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Ellen L Goode
- Department of Health Science Research, Division of Epidemiology, Mayo Clinic, Rochester, Minnesota
| | - Elza Khusnutdinova
- Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia. Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Ufa, Russia
| | - Estrid Høgdall
- Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark. Molecular Unit, Department of Pathology, Herlev Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Fengju Song
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin, National Clinical Research Center of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, P.R. China
| | - Fiona Bruinsma
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
| | - Florian Heitz
- Department of Gynecology and Gynecologic Oncology, Kliniken Essen-Mitte/Evang. Huyssens-Stiftung/Knappschaft GmbH, Essen, Germany. Department of Gynecology and Gynecologic Oncology, Dr. Horst Schmidt Kliniken Wiesbaden, Wiesbaden, Germany
| | - Francesmary Modugno
- Department of Obstetrics, Gynecology and Reproductive Sciences, Division of Gynecologic Oncology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania. Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania. Women's Cancer Research Program, Magee-Womens Research Institute and University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania
| | - Freddie C Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK. Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Graham G Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Håkan Olsson
- Departments of Cancer Epidemiology and Oncology, University Hospital, Lund, Sweden
| | - Hans Wildiers
- Department of General Medical Oncology, University Hospitals Leuven, Leuven Cancer Institute, Leuven, Belgium
| | | | | | - Harvey A Risch
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut
| | - Hatef Darabi
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Helga B Salvesen
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway. Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Henrik Gronberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. Division of Preventive Oncology, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hiltrud Brauch
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany. University of Tübingen, Tübingen, Germany
| | - Hoda Anton-Culver
- Department of Epidemiology, UCI Center for Cancer Genetics Research and Prevention, School of Medicine, University of California, Irvine, Irvine, California
| | - Honglin Song
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Hui-Yi Lim
- Biostatistics Program, Moffitt Cancer Center, Tampa, Florida
| | - Iain McNeish
- Institute of Cancer Sciences, University of Glasgow, Wolfson Wohl Cancer Research Centre, Beatson Institute for Cancer Research, Glasgow, UK
| | - Ian Campbell
- Cancer Genetics Laboratory, Research Division, Peter MacCallum Cancer Centre, St. Andrews Place, East Melbourne, Victoria, Australia. Department of Pathology, University of Melbourne, Parkville, Victoria, Australia
| | - Ignace Vergote
- Department of Gynaecologic Oncology, Leuven Cancer Institute, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Jacek Gronwald
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Jan Lubiński
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Janet L Stanford
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington. Department of Epidemiology, University of Washington, Seattle, Washington
| | - Javier Benítez
- Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO) and Spanish National Genotyping Center (CEGEN), Madrid, Spain
| | - Jennifer A Doherty
- Department of Epidemiology, The Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Jennifer B Permuth
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jenny L Donovan
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joellen M Schildkraut
- Department of Community and Family Medicine, Duke University Medical Center, Durham, North Carolina. Cancer Control and Population Sciences, Duke Cancer Institute, Durham, North Carolina
| | - Johanna Schleutker
- Department of Medical Biochemistry and Genetics Institute of Biomedicine, University of Turku, Turku, Finland. BioMediTech, University of Tampere and FimLab Laboratories, Tampere, Finland
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Jolanta Kupryjanczyk
- Department of Pathology and Laboratory Diagnostics, the Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland
| | - Jong Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Jonine Figueroa
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Judith A Clements
- Australian Prostate Cancer Research Centre, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Julia A Knight
- Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Canada. Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Julian Peto
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Julie M Cunningham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Julio Pow-Sang
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Karen H Lu
- Department of Gynecologic Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kathleen Herkommer
- Department of Urology, Klinikum rechts der Isar der Technischen Universitaet Muenchen, Munich, Germany
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge, UK
| | - Keitaro Matsuo
- Department of Preventive Medicine, Kyushu University Faculty of Medical Science, Nagoya, Aichi, Japan
| | - Kenneth Muir
- Institute of Population Health, University of Manchester, Manchester, UK. Warwick Medical School, University of Warwick, Coventry, UK
| | - Kenneth Offitt
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York. Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin, National Clinical Research Center of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, P.R. China
| | - Kirsten B Moysich
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, New York
| | - Kristiina Aittomäki
- Department of Clinical Genetics, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Kunle Odunsi
- Department of Gynecological Oncology, Roswell Park Cancer Institute, Buffalo, New York
| | - Lambertus A Kiemeney
- Radboud University Medical Centre, Radbond Institute for Health Sciences, Nijmegen, the Netherlands
| | - Leon F A G Massuger
- Department of Gynaecology, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | - Linda S Cook
- Division of Epidemiology and Biostatistics, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico
| | - Lisa Cannon-Albright
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, Utah
| | - Maartje J Hooning
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Malcolm C Pike
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Manuel Luedeke
- Department of Urology, University Hospital Ulm, Ulm, Germany
| | - Manuel R Teixeira
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal. Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Marc T Goodman
- Cancer Prevention and Control, Samuel Oschin Comprehensive Cancer Institute, and Community and Population Health Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Marjanka K Schmidt
- Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Marjorie Riggan
- Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, North Carolina
| | - Markus Aly
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden. Department of Clinical Sciences, Danderyds Hospital, Stockholm, Sweden
| | - Mary Anne Rossing
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington. Department of Epidemiology, University of Washington, Seattle, Washington
| | - Matthias W Beckmann
- Department of Gynaecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | | | - Maureen Sanderson
- Department of Family and Community Medicine, Meharry Medical College, Nashville, Tennessee
| | - Melissa C Southey
- Department of Pathology, University of Melbourne, Parkville, Victoria, Australia
| | - Michael Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | - Ming-Feng Hou
- Cancer Center and Department of Surgery, Chung-Ho Memorial Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Minouk J Schoemaker
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Montserrat Garcia-Closas
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Natalia Bogdanova
- Radiation Oncology Research Unit, Hannover Medical School, Hannover, Germany
| | - Nazneen Rahman
- Section of Cancer Genetics, The Institute of Cancer Research, London, UK
| | - Nhu D Le
- Cancer Control Research, British Columbia Cancer Agency, Vancouver, Canada
| | - Nick Orr
- Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Nora Pashayan
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK. Department of Applied Health Research, University College London, London, UK
| | | | - Pascal Guénel
- Environmental Epidemiology of Cancer, Center for Research in Epidemiology and Population Health, INSERM, Villejuif, France. University Paris-Sud, Villejuif, France
| | - Paul Brennan
- International Agency for Research on Cancer, Lyon, France
| | - Paula Paulo
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
| | - Penelope M Webb
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Per Broberg
- Department of Cancer Epidemiology, University Hospital, Lund, Sweden
| | - Peter A Fasching
- Department of Gynaecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Peter Devilee
- Departments of Human Genetics and of Pathology, Leiden University Medical Center, Leiden, the Netherlands
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Qiuyin Cai
- Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Qiyuan Li
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts. Medical College of Xiamen University, Xiamen, China
| | - Radka Kaneva
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University, Sofia, Bulgaria
| | - Ralf Butzow
- Department of Obstetrics and Gynecology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland. Department of Pathology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Reidun Kristin Kopperud
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway. Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Rita K Schmutzler
- Center for Integrated Oncology (CIO) and Center for Hereditary Breast and Ovarian Cancer, University Hospital of Cologne, Cologne, Germany. Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Robert A Stephenson
- Department of Surgery, University of Utah School of Medicine, Salt Lake City, Utah
| | - Robert J MacInnis
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Robert N Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Cancer Research and Translational Medicine, Biocenter Oulu, University of Oulu, and Northern Finland Laboratory Centre, Oulu, Finland
| | - Roberta Ness
- The University of Texas School of Public Health, Houston, Texas
| | - Roger L Milne
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Sara Benlloch
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Sara H Olson
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Shelley S Tworoger
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Sofia Maia
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
| | - Sonja Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Soo Chin Lee
- Department of Hematology-Oncology, National University Health System, Singapore. Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Soo-Hwang Teo
- Cancer Research Initiatives Foundation, Sime Darby Medical Centre, Subang Jaya, Malaysia. University of Malaya Cancer Research Institute, University Malaya Medical Centre, University of Malaya, Kuala Lumpur, Malaysia
| | | | - Stig E Bojesen
- Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark. Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Susan M Gapstur
- Epidemiology Research Program, American Cancer Society, Atlanta, Georgia
| | - Susanne Krüger Kjær
- Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark. Department of Gynaecology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Tanja Pejovic
- Department of Obstetrics and Gynecology, and Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon
| | - Teuvo L J Tammela
- Department of Urology, Tampere University Hospital and Medical School, University of Tampere, Tampere, Finland
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Thomas Brüning
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany
| | - Tiina Wahlfors
- Department of Medical Biochemistry and Genetics Institute of Biomedicine, University of Turku, Turku, Finland
| | - Tim J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Todd L Edwards
- Vanderbilt Epidemiology Center, Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee
| | - Usha Menon
- Women's Cancer, Institute for Women's Health, University College London, London, UK
| | - Ute Hamann
- Frauenklinik der Stadtklinik Baden-Baden, Baden-Baden, Germany
| | - Vanio Mitev
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University, Sofia, Bulgaria
| | - Veli-Matti Kosma
- Department of Pathology and Forensic Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland. Department of Pathology, Kuopio University Hospital, Kuopio, Finland
| | - Veronica Wendy Setiawan
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Vessela Kristensen
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway. K.G. Jebsen Center for Breast Cancer Research, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. Department of Clinical Molecular Biology, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Walther Vogel
- Institute of Human Genetics, University Hospital Ulm, Ulm, Germany
| | - Wei Zheng
- Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Weiva Sieh
- Department of Health Research and Policy - Epidemiology, Stanford University School of Medicine, Stanford, California
| | - William J Blot
- Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Wojciech Kluzniak
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Xiao-Ou Shu
- Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Yu-Tang Gao
- Shanghai Cancer Institute, Shanghai, P.R. China
| | - Fredrick Schumacher
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Matthew L Freedman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts. The Eli and Edythe L. Broad Institute, Cambridge, Massachusetts
| | - Andrew Berchuck
- Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, North Carolina
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec Research Center, Laval University, Québec City, Canada
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Amanda Spurdle
- Molecular Cancer Epidemiology Laboratory, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Thomas A Sellers
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - David J Hunter
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts
| | - Brian E Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Simon A Gayther
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Georgia Chenevix-Trench
- Department of Genetics, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Rosalind Eeles
- The Institute of Cancer Research, Sutton, UK. Royal Marsden National Health Service (NHS) Foundation Trust, London and Sutton, UK
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
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546
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Engelhardt LE, Mann FD, Briley DA, Church JA, Harden KP, Tucker-Drob EM. Strong genetic overlap between executive functions and intelligence. J Exp Psychol Gen 2016; 145:1141-59. [PMID: 27359131 PMCID: PMC5001920 DOI: 10.1037/xge0000195] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Executive functions (EFs) are cognitive processes that control, monitor, and coordinate more basic cognitive processes. EFs play instrumental roles in models of complex reasoning, learning, and decision making, and individual differences in EFs have been consistently linked with individual differences in intelligence. By middle childhood, genetic factors account for a moderate proportion of the variance in intelligence, and these effects increase in magnitude through adolescence. Genetic influences on EFs are very high, even in middle childhood, but the extent to which these genetic influences overlap with those on intelligence is unclear. We examined genetic and environmental overlap between EFs and intelligence in a racially and socioeconomically diverse sample of 811 twins ages 7 to 15 years (M = 10.91, SD = 1.74) from the Texas Twin Project. A general EF factor representing variance common to inhibition, switching, working memory, and updating domains accounted for substantial proportions of variance in intelligence, primarily via a genetic pathway. General EF continued to have a strong, genetically mediated association with intelligence even after controlling for processing speed. Residual variation in general intelligence was influenced only by shared and nonshared environmental factors, and there remained no genetic variance in general intelligence that was unique of EF. Genetic variance independent of EF did remain, however, in a more specific perceptual reasoning ability. These results provide evidence that genetic influences on general intelligence are highly overlapping with those on EF. (PsycINFO Database Record
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Affiliation(s)
| | - Frank D Mann
- Department of Psychology, University of Texas at Austin
| | - Daniel A Briley
- Department of Psychology, University of Illinois at Urbana-Champaign
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547
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Prieto ML, Ryu E, Jenkins GD, Batzler A, Nassan MM, Cuellar-Barboza AB, Pathak J, McElroy SL, Frye MA, Biernacka JM. Leveraging electronic health records to study pleiotropic effects on bipolar disorder and medical comorbidities. Transl Psychiatry 2016; 6:e870. [PMID: 27529678 PMCID: PMC5022084 DOI: 10.1038/tp.2016.138] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Revised: 05/13/2016] [Accepted: 06/15/2016] [Indexed: 01/27/2023] Open
Abstract
Patients with bipolar disorder (BD) have a high prevalence of comorbid medical illness. However, the mechanisms underlying these comorbidities with BD are not well known. Certain genetic variants may have pleiotropic effects, increasing the risk of BD and other medical illnesses simultaneously. In this study, we evaluated the association of BD-susceptibility genetic variants with various medical conditions that tend to co-exist with BD, using electronic health records (EHR) data linked to genome-wide single-nucleotide polymorphism (SNP) data. Data from 7316 Caucasian subjects were used to test the association of 19 EHR-derived phenotypes with 34 SNPs that were previously reported to be associated with BD. After Bonferroni multiple testing correction, P<7.7 × 10(-5) was considered statistically significant. The top association findings suggested that the BD risk alleles at SNP rs4765913 in CACNA1C gene and rs7042161 in SVEP1 may be associated with increased risk of 'cardiac dysrhythmias' (odds ratio (OR)=1.1, P=3.4 × 10(-3)) and 'essential hypertension' (OR=1.1, P=3.5 × 10(-3)), respectively. Although these associations are not statistically significant after multiple testing correction, both genes have been previously implicated with cardiovascular phenotypes. Moreover, we present additional evidence supporting these associations, particularly the association of the SVEP1 SNP with hypertension. This study shows the potential for EHR-based analyses of large cohorts to discover pleiotropic effects contributing to complex psychiatric traits and commonly co-occurring medical conditions.
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Affiliation(s)
- M L Prieto
- Department of Psychiatry and Psychology, Mayo Clinic College of Medicine, Rochester, MN, USA
- Universidad de los Andes, Facultad de Medicina, Departamento de Psiquiatría, Santiago, Chile
| | - E Ryu
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - G D Jenkins
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - A Batzler
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - M M Nassan
- Department of Psychiatry and Psychology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - A B Cuellar-Barboza
- Department of Psychiatry, Universidad Autónoma de Nuevo León, Nuevo León, Mexico
| | - J Pathak
- Division of Health Informatics, Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - S L McElroy
- Lindner Center of HOPE, Mason, OH, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - M A Frye
- Department of Psychiatry and Psychology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - J M Biernacka
- Department of Psychiatry and Psychology, Mayo Clinic College of Medicine, Rochester, MN, USA
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
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548
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Statistical Methods for Testing Genetic Pleiotropy. Genetics 2016; 204:483-497. [PMID: 27527515 DOI: 10.1534/genetics.116.189308] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Accepted: 08/11/2016] [Indexed: 12/28/2022] Open
Abstract
Genetic pleiotropy is when a single gene influences more than one trait. Detecting pleiotropy and understanding its causes can improve the biological understanding of a gene in multiple ways, yet current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or no traits are associated with a genetic variant. For the special case of two traits, one can construct this null hypothesis based on the intersection-union (IU) test, which rejects the null hypothesis only if the null hypotheses of no association for both traits are rejected. To allow for more than two traits, we developed a new likelihood-ratio test for pleiotropy. We then extended the testing framework to a sequential approach to test the null hypothesis that [Formula: see text] traits are associated, given that the null of k traits are associated was rejected. This provides a formal testing framework to determine the number of traits associated with a genetic variant, while accounting for correlations among the traits. By simulations, we illustrate the type I error rate and power of our new methods; describe how they are influenced by sample size, the number of traits, and the trait correlations; and apply the new methods to multivariate immune phenotypes in response to smallpox vaccination. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.
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549
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eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants. BMC Med Genomics 2016; 9 Suppl 1:32. [PMID: 27535653 PMCID: PMC4989894 DOI: 10.1186/s12920-016-0191-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND We explored premature stop-gain variants to test the hypothesis that variants, which are likely to have a consequence on protein structure and function, will reveal important insights with respect to the phenotypes associated with them. We performed a phenome-wide association study (PheWAS) exploring the association between a selected list of functional stop-gain genetic variants (variation resulting in truncated proteins or in nonsense-mediated decay) and an extensive group of diagnoses to identify novel associations and uncover potential pleiotropy. RESULTS In this study, we selected 25 stop-gain variants: 5 stop-gain variants with previously reported phenotypic associations, and a set of 20 putative stop-gain variants identified using dbSNP. For the PheWAS, we used data from the electronic MEdical Records and GEnomics (eMERGE) Network across 9 sites with a total of 41,057 unrelated patients. We divided all these samples into two datasets by equal proportion of eMERGE site, sex, race, and genotyping platform. We calculated single effect associations between these 25 stop-gain variants and ICD-9 defined case-control diagnoses. We also performed stratified analyses for samples of European and African ancestry. Associations were adjusted for sex, site, genotyping platform and the first three principal components to account for global ancestry. We identified previously known associations, such as variants in LPL associated with hyperglyceridemia indicating that our approach was robust. We also found a total of three significant associations with p < 0.01 in both datasets, with the most significant replicating result being LPL SNP rs328 and ICD-9 code 272.1 "Disorder of Lipoid metabolism" (pdiscovery = 2.59x10-6, preplicating = 2.7x10-4). The other two significant replicated associations identified by this study are: variant rs1137617 in KCNH2 gene associated with ICD-9 code category 244 "Acquired Hypothyroidism" (pdiscovery = 5.31x103, preplicating = 1.15x10-3) and variant rs12060879 in DPT gene associated with ICD-9 code category 996 "Complications peculiar to certain specified procedures" (pdiscovery = 8.65x103, preplicating = 4.16x10-3). CONCLUSION In conclusion, this PheWAS revealed novel associations of stop-gained variants with interesting phenotypes (ICD-9 codes) along with pleiotropic effects.
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550
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Hu JX, Thomas CE, Brunak S. Network biology concepts in complex disease comorbidities. Nat Rev Genet 2016; 17:615-29. [PMID: 27498692 DOI: 10.1038/nrg.2016.87] [Citation(s) in RCA: 201] [Impact Index Per Article: 25.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.
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Affiliation(s)
- Jessica Xin Hu
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen DK-2200, Denmark
| | - Cecilia Engel Thomas
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen DK-2200, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen DK-2200, Denmark.,Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, Copenhagen DK-2100, Denmark
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