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Rabinowitz JA, Thomas N, Strickland JC, Meredith JJ, Hung I, Cupertino RB, Felton JW, Gelino B, Stone B, Maher BS, Dick D, Yi R, Flores‐Ocampo V, García‐Marín LM, Rentería ME, Palmer AA, Sanchez‐Roige S. Genetic Propensity for Delay Discounting and Educational Attainment in Adults Are Associated With Delay Discounting in Preadolescents: Findings From the Adolescent Brain Cognitive Development Study. GENES, BRAIN, AND BEHAVIOR 2025; 24:e70020. [PMID: 40147852 PMCID: PMC11949538 DOI: 10.1111/gbb.70020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2024] [Revised: 02/13/2025] [Accepted: 03/04/2025] [Indexed: 03/29/2025]
Abstract
Higher delay discounting (DD) (i.e., propensity to devalue larger, delayed rewards over immediate, smaller rewards) is a transdiagnostic marker underpinning multiple health behaviors. Although genetic influences account for some of the variability in DD among adults, less is known about the genetic contributors to DD among preadolescents. We examined whether polygenic scores (PGS) for DD, educational attainment, and behavioral traits (i.e., impulsivity, inhibition, and externalizing behavior) were associated with phenotypic DD among preadolescents. Participants included youth (N = 8982, 53% male) from the Adolescent Brain Cognitive Development Study who completed an Adjusting Delay Discounting Task at the 1-year follow-up and had valid genetic data. PGS for DD, educational attainment, impulsivity, inhibition, and externalizing behaviors were created based on the largest GWAS available. Separate linear mixed effects models were conducted in individuals most genetically similar to European (EUR; n = 4972), African (AFR; n = 1769), and Admixed American (AMR; n = 2241) reference panels. After adjusting for age, sex, income, and the top ten genetic ancestry principal components, greater PGS for DD and lower educational attainment (but not impulsivity, inhibition, or externalizing) were associated with higher rates of DD (i.e., preference for sooner, smaller rewards) in participants most genetically similar to EUR reference panels. Findings provide insight into the influence of genetic propensity for DD and educational attainment on the discounting tendencies of preadolescents, particularly those most genetically similar to European reference samples, thereby advancing our understanding of the etiology of choice behaviors in this population.
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Affiliation(s)
- Jill A. Rabinowitz
- Department of PsychiatryRobert Wood Johnson Medical School, Rutgers UniversityPiscatawayNew JerseyUSA
| | - Nathaniel Thomas
- Department of PsychiatryRobert Wood Johnson Medical School, Rutgers UniversityPiscatawayNew JerseyUSA
| | - Justin C. Strickland
- Department of Psychiatry and Behavioral SciencesJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - John J. Meredith
- Department of PsychiatryUniversity of California San DiegoLa JollaCaliforniaUSA
| | - I‐Tzu Hung
- Department of PsychiatryRobert Wood Johnson Medical School, Rutgers UniversityPiscatawayNew JerseyUSA
| | | | - Julia W. Felton
- Center for Health Policy & Health Services ResearchHenry Ford HealthDetroitMichiganUSA
| | - Brett Gelino
- Department of PsychiatryRobert Wood Johnson Medical School, Rutgers UniversityPiscatawayNew JerseyUSA
| | - Bryant Stone
- Department of Mental HealthJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Brion S. Maher
- Department of Mental HealthJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Danielle Dick
- Department of PsychiatryRobert Wood Johnson Medical School, Rutgers UniversityPiscatawayNew JerseyUSA
| | - Richard Yi
- Department of PsychologyUniversity of KentuckyLawrenceKansasUSA
| | - Victor Flores‐Ocampo
- Brain and Mental Health ProgramQIMR Berghofer Medical Research InstituteBrisbaneAustralia
- School of Biomedical Sciences, Faculty of Health, Medicine and Behavioural SciencesThe University of QueenslandBrisbaneAustralia
| | - Luis M. García‐Marín
- Brain and Mental Health ProgramQIMR Berghofer Medical Research InstituteBrisbaneAustralia
- School of Biomedical Sciences, Faculty of Health, Medicine and Behavioural SciencesThe University of QueenslandBrisbaneAustralia
| | - Miguel E. Rentería
- Brain and Mental Health ProgramQIMR Berghofer Medical Research InstituteBrisbaneAustralia
- School of Biomedical Sciences, Faculty of Health, Medicine and Behavioural SciencesThe University of QueenslandBrisbaneAustralia
| | - Abraham A. Palmer
- Department of PsychiatryUniversity of California San DiegoLa JollaCaliforniaUSA
- Institute for Genomics Medicine, University of California san DiegoLa JollaCaliforniaUSA
| | - Sandra Sanchez‐Roige
- Department of PsychiatryUniversity of California San DiegoLa JollaCaliforniaUSA
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
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Dygrýn J, Brazo-Sayavera J, Cruz J, Gebremariam MK, Ribeiro JC, Capranica L, MacDonncha C, Netz Y. Definitions of determinants of physical activity behaviour: process and outcome of consensus from the DE-PASS expert group. Int J Behav Nutr Phys Act 2025; 22:34. [PMID: 40102955 PMCID: PMC11921651 DOI: 10.1186/s12966-025-01728-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2024] [Accepted: 02/28/2025] [Indexed: 03/20/2025] Open
Abstract
BACKGROUND Despite extensive research on physical activity behaviour (PAB), consensus is lacking on related terms and definitions, thereby hindering the ability to compare findings between studies and to develop reliable assessment tools. This study therefore aimed to establish consensus on the definitions of key PAB determinants. METHODS First, an international expert steering committee was established, comprising members of the European Cooperation in Science and Technology (COST) action "DEterminants of Physical ActivitieS in Settings" (DE-PASS). Recently published review-level studies were used to identify key determinants of PAB. Two independent reviewers systematically reviewed the literature to catalogue the range of definitions used for key determinants of PAB (steps 1-2). A two-round modified Delphi survey was conducted online from February to September 2023, to determine the optimal definition for each determinant. In round 1, experts selected the most suitable definition for each of the 41 initially identified determinants. In round 2, experts ranked the appropriateness of the definition selected from round 1 on a 5-point Likert scale. Consensus was defined a priori as ≥ 75% agreement on the definition (i.e., ratings of ≥ 4 points). A professional English language expert ensured concise, coherent wording and high-quality editing of the definitions (steps 3-6). RESULTS Eighty-five experts in PAB research participated in round 1, and sixty-nine experts in round 2. Consensus of definitions was achieved for 39 of the 41 determinants (88.4%-98.6% agreement). The consensus threshold was not achieved for two determinants: genetic profile and regulation (69.6%) and backyard access/size (73.9%). CONCLUSIONS The findings of this study offer a consensus-based set of definitions for 39 key determinants of PAB. These definitions can be used homogenously in academic research on physical activity.
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Affiliation(s)
- Jan Dygrýn
- Faculty of Physical Culture, Palacký University Olomouc, třída Míru 117, Olomouc, 779 00, Olomouc, Czech Republic.
| | - Javier Brazo-Sayavera
- Department of Sports and Computer Science, Universidad Pablo de Olavide, Seville, Spain
| | - Joana Cruz
- Center for Innovative Care and Health Technology (ciTechCare), School of Health Sciences of the Polytechnic University of Leiria, Leiria, Portugal
| | - Mekdes Kebede Gebremariam
- Department of Community Medicine and Global Health, Institute of Health and Society, University of Oslo, Oslo, Norway
- Sustainable Health Unit, University of Oslo, Oslo, Norway
| | - José Carlos Ribeiro
- Research Centre in Physical Activity, Health and Leisure, Faculty of Sport, University of Porto, Porto, Portugal
- Laboratory for Integrative and Translational Research in Population Health, Porto, Portugal
| | - Laura Capranica
- Department of Movement, Human and Health Sciences, University of Rome Foro Italico, Rome, Italy
| | - Ciaran MacDonncha
- Department of Physical Education and Sport Sciences and Health Research Institute, University of Limerick, Limerick, Ireland
| | - Yael Netz
- Levinsky-Wingate Academic College, Wingate Campus, Netanya, Israel
- Department of Health Promotion and Rehabilitation, Lithuanian Sports University, Kaunas, Lithuania
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Reed ZE, Thomas R, Boyd A, Griffith GJ, Morris TT, Rai D, Manley D, Davey Smith G, Davis OS. Mapping associations of polygenic scores with autistic and ADHD traits in a single city region. J Child Psychol Psychiatry 2025; 66:202-213. [PMID: 39143033 PMCID: PMC7616875 DOI: 10.1111/jcpp.14047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/10/2024] [Indexed: 08/16/2024]
Abstract
BACKGROUND The genetic and environmental aetiology of autistic and Attention Deficit Hyperactivity Disorder (ADHD) traits is known to vary spatially, but does this translate into variation in the association of specific common genetic variants? METHODS We mapped associations between polygenic scores for autism and ADHD and their respective traits in the Avon Longitudinal Study of Parents and Children (N = 4,255-6,165) across the area surrounding Bristol, UK, and compared them to maps of environments associated with the prevalence of autism and ADHD. RESULTS Our results suggest genetic associations vary spatially, with consistent patterns for autistic traits across polygenic scores constructed at different p-value thresholds. Patterns for ADHD traits were more variable across thresholds. We found that the spatial distributions often correlated with known environmental influences. CONCLUSIONS These findings shed light on the factors that contribute to the complex interplay between the environment and genetic influences in autistic and ADHD traits.
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Affiliation(s)
- Zoe E. Reed
- MRC Integrative Epidemiology UnitUniversity of BristolBristolUK
- School of Psychological ScienceUniversity of BristolBristolUK
| | - Richard Thomas
- Department of Population Health SciencesBristol Medical SchoolUniversity of BristolBristolUK
| | - Andy Boyd
- Department of Population Health SciencesBristol Medical SchoolUniversity of BristolBristolUK
- Department of Population Health Sciences, ALSPACBristol Medical SchoolUniversity of BristolBristolUK
| | - Gareth J. Griffith
- MRC Integrative Epidemiology UnitUniversity of BristolBristolUK
- Department of Population Health SciencesBristol Medical SchoolUniversity of BristolBristolUK
| | - Tim T. Morris
- Centre for Longitudinal StudiesSocial Research InstituteUniversity College LondonLondonUK
| | - Dheeraj Rai
- Department of Population Health SciencesBristol Medical SchoolUniversity of BristolBristolUK
- National Institute for Health Research Biomedical Research CentreUniversity Hospitals Bristol NHS Foundation Trust and the University of BristolBristolUK
- Avon and Wiltshire Partnership NHS Mental Health TrustBathUK
| | - David Manley
- School of Geographical SciencesUniversity of BristolBristolUK
- Department of UrbanismDelft University of TechnologyDelftThe Netherlands
| | - George Davey Smith
- MRC Integrative Epidemiology UnitUniversity of BristolBristolUK
- Department of Population Health SciencesBristol Medical SchoolUniversity of BristolBristolUK
| | - Oliver S.P. Davis
- MRC Integrative Epidemiology UnitUniversity of BristolBristolUK
- Department of Population Health SciencesBristol Medical SchoolUniversity of BristolBristolUK
- National Institute for Health Research Biomedical Research CentreUniversity Hospitals Bristol NHS Foundation Trust and the University of BristolBristolUK
- Alan Turing InstituteLondonUK
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Bagheri M, Bombin A, Shi M, Murthy VL, Shah R, Mosley JD, Ferguson JF. Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations. Front Genet 2024; 15:1392622. [PMID: 38812968 PMCID: PMC11133605 DOI: 10.3389/fgene.2024.1392622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 05/03/2024] [Indexed: 05/31/2024] Open
Abstract
Introduction: Circulating metabolites act as biomarkers of dysregulated metabolism and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. Methods: We examined the association between polygenic scores for 724 metabolites with 1,247 clinical phenotypes in the BioVU DNA biobank, comprising 57,735 European ancestry and 15,754 African ancestry participants. We applied Mendelian randomization (MR) to probe significant relationships and validated significant MR associations using independent GWAS of candidate phenotypes. Results and Discussion: We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes in African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolitephenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p < 0.05). These included associations between bilirubin and X-21796 with cholelithiasis, phosphatidylcholine (16:0/22:5n3,18:1/20:4) and arachidonate with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.
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Affiliation(s)
- Minoo Bagheri
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Andrei Bombin
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Mingjian Shi
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Venkatesh L. Murthy
- Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Ravi Shah
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jonathan D. Mosley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jane F. Ferguson
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
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Elam KK, Bountress KE, Ha T, Shaw DS, Wilson MN, Aliev F, Dick DM, Lemery-Chalfant K. Developmental genetic effects on externalizing behavior and alcohol use: Examination across two longitudinal samples. Dev Psychopathol 2024; 36:82-91. [PMID: 35983793 PMCID: PMC9938843 DOI: 10.1017/s0954579422000980] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Externalizing behavior in early adolescence is associated with alcohol use in adolescence and early adulthood and these behaviors often emerge as part of a developmental sequence. This pattern can be the result of heterotypic continuity, in which different behaviors emerge over time based on an underlying shared etiology. In particular, there is largely a shared genetic etiology underlying externalizing and substance use behaviors. We examined whether polygenic risk for alcohol use disorder predicted (1) externalizing behavior in early adolescence and alcohol use in adolescence in the Early Steps Multisite sample and (2) externalizing behavior in adolescence and alcohol use in early adulthood in the Project Alliance 1 (PAL1) sample. We examined associations separately for African Americans and European Americans. When examining European Americans in the Early Steps sample, greater polygenic risk was associated with externalizing behavior in early adolescence. In European Americans in PAL1, we found greater polygenic risk was associated with alcohol use in early adulthood. Effects were largely absent in African Americans in both samples. Results imply that genetic predisposition for alcohol use disorder may increase risk for externalizing and alcohol use as these behaviors emerge developmentally.
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Affiliation(s)
- Kit K. Elam
- Department of Applied Health Science, Indiana University, 1025 E. 7 St., Suite 116, Bloomington, IN 47405
| | - Kaitlin E. Bountress
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University
| | - Thao Ha
- Department of Psychology, Arizona State University
| | | | | | - Fazil Aliev
- Department of Psychiatry, Rutgers Robert Wood Johnson Medical School
| | - Danielle M. Dick
- Department of Psychiatry, Rutgers Robert Wood Johnson Medical School
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Keller M, Svensson SIA, Rohde-Zimmermann K, Kovacs P, Böttcher Y. Genetics and Epigenetics in Obesity: What Do We Know so Far? Curr Obes Rep 2023; 12:482-501. [PMID: 37819541 DOI: 10.1007/s13679-023-00526-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/07/2023] [Indexed: 10/13/2023]
Abstract
PURPOSE OF REVIEW Enormous progress has been made in understanding the genetic architecture of obesity and the correlation of epigenetic marks with obesity and related traits. This review highlights current research and its challenges in genetics and epigenetics of obesity. RECENT FINDINGS Recent progress in genetics of polygenic traits, particularly represented by genome-wide association studies, led to the discovery of hundreds of genetic variants associated with obesity, which allows constructing polygenic risk scores (PGS). In addition, epigenome-wide association studies helped identifying novel targets and methylation sites being important in the pathophysiology of obesity and which are essential for the generation of methylation risk scores (MRS). Despite their great potential for predicting the individual risk for obesity, the use of PGS and MRS remains challenging. Future research will likely discover more loci being involved in obesity, which will contribute to better understanding of the complex etiology of human obesity. The ultimate goal from a clinical perspective will be generating highly robust and accurate prediction scores allowing clinicians to predict obesity as well as individual responses to body weight loss-specific life-style interventions.
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Affiliation(s)
- Maria Keller
- Medical Department III-Endocrinology, Nephrology, Rheumatology, Medical Center, University of Leipzig, 04103, Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Center Munich at the University of Leipzig, University Hospital Leipzig, 04103, Leipzig, Germany
| | - Stina Ingrid Alice Svensson
- EpiGen, Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, 0316, Oslo, Norway
| | - Kerstin Rohde-Zimmermann
- Medical Department III-Endocrinology, Nephrology, Rheumatology, Medical Center, University of Leipzig, 04103, Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Center Munich at the University of Leipzig, University Hospital Leipzig, 04103, Leipzig, Germany
| | - Peter Kovacs
- Medical Department III-Endocrinology, Nephrology, Rheumatology, Medical Center, University of Leipzig, 04103, Leipzig, Germany
| | - Yvonne Böttcher
- EpiGen, Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, 0316, Oslo, Norway.
- EpiGen, Medical Division, Akershus University Hospital, 1478, Lørenskog, Norway.
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Bowling F, Badrick T. Methods for determining clinical utility. Clin Biochem 2023; 121-122:110674. [PMID: 37844681 DOI: 10.1016/j.clinbiochem.2023.110674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/02/2023] [Accepted: 10/11/2023] [Indexed: 10/18/2023]
Abstract
Measuring the clinical utility of a diagnostic test involves evaluating its impact on patient outcomes, clinical decision-making, and healthcare resource utilization. Determining clinical utility requires accessing patient medical history and outcomes data. These studies involve enrolling patients undergoing diagnostic tests and tracking their clinical outcomes. Researchers can determine the test's clinical utility by comparing the outcomes of patients who receive the diagnostic test to those who do not. These outcomes include benefits and harm. The highest level of evidence to support clinical utility determinations may be obtained from clinical trials. However, clinical laboratories are often not involved in clinical trials, and laboratory specialists may not be experienced in conducting such trials. Many established laboratory tests have never had clinical utility determined. Prospective studies assessing a diagnostic test's impact on clinical outcomes may require long-term patient monitoring, which is problematic. This paper presents methods that may be used to assess clinical utility.
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Affiliation(s)
- Francis Bowling
- Walter and Eliza Hall Institute, University of Melbourne, Australia
| | - Tony Badrick
- Royal College of Pathologists of Australasia Quality Assurance Programs, St Leonards, Sydney, Australia.
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Osterman MD, Song YE, Lynn A, Miskimen K, Adams LD, Laux RA, Caywood LJ, Prough MB, Clouse JE, Herington SD, Slifer SH, Fuzzell SL, Hochstetler SD, Main LR, Dorfsman DA, Zaman AF, Ogrocki P, Lerner AJ, Vance JM, Cuccaro ML, Scott WK, Pericak-Vance MA, Haines JL. Founder population-specific weights yield improvements in performance of polygenic risk scores for Alzheimer disease in the Midwestern Amish. HGG ADVANCES 2023; 4:100241. [PMID: 37742071 PMCID: PMC10565871 DOI: 10.1016/j.xhgg.2023.100241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 09/16/2023] [Accepted: 09/16/2023] [Indexed: 09/25/2023] Open
Abstract
Alzheimer disease (AD) is the most common type of dementia and is estimated to affect 6 million Americans. Risk for AD is multifactorial, including both genetic and environmental risk factors. AD genomic research has generally focused on identification of risk variants. Using this information, polygenic risk scores (PRSs) can be calculated to quantify an individual's relative disease risk due to genetic factors. The Amish are a founder population descended from German and Swiss Anabaptist immigrants. They experienced a genetic bottleneck after arrival in the United States, making their genetic architecture different from the broader European ancestry population. Prior work has demonstrated the lack of transferability of PRSs across populations. Here, we compared the performance of PRSs derived from genome-wide association studies (GWASs) of Amish individuals to those derived from a large European ancestry GWAS. Participants were screened for cognitive impairment with further evaluation for AD. Genotype data were imputed after collection via Illumina genotyping arrays. The Amish individuals were split into two groups based on the primary site of recruitment. For each group, GWAS was conducted with account for relatedness and adjustment for covariates. PRSs were then calculated using weights from the other Amish group. PRS models were evaluated with and without covariates. The Amish-derived PRSs distinguished between dementia status better than the European-derived PRS in our Amish populations and demonstrated performance improvements despite a smaller training sample size. This work highlighted considerations for AD PRS usage in populations that cannot be adequately described by basic race/ethnicity or ancestry classifications.
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Affiliation(s)
- Michael D Osterman
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA; Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.
| | - Yeunjoo E Song
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA; Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Audrey Lynn
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA; Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Kristy Miskimen
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Larry D Adams
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Renee A Laux
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Laura J Caywood
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael B Prough
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jason E Clouse
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sharlene D Herington
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Susan H Slifer
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sarada L Fuzzell
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Sherri D Hochstetler
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Leighanne R Main
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Daniel A Dorfsman
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Andrew F Zaman
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Paula Ogrocki
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Alan J Lerner
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Jeffery M Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael L Cuccaro
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - William K Scott
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Margaret A Pericak-Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jonathan L Haines
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA; Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.
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Bagheri M, Bombin A, Shi M, Murthy VL, Shah R, Mosley JD, Ferguson JF. Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations. RESEARCH SQUARE 2023:rs.3.rs-3222588. [PMID: 37790512 PMCID: PMC10543429 DOI: 10.21203/rs.3.rs-3222588/v2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Circulating metabolites act as biomarkers of dysregulated metabolism, and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. We examined the association between polygenic scores for 726 metabolites (derived from OMICSPRED) with 1,247 clinical phenotypes in 57,735 European ancestry and 15,754 African ancestry participants from the BioVU DNA Biobank. We probed significant relationships through Mendelian randomization (MR) using genetic instruments constructed from the METSIM Study, and validated significant MR associations using independent GWAS of candidate phenotypes. We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes among African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolite-phenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p<0.05). Validated findings included the metabolites bilirubin and X-21796 with cholelithiasis, phosphatidylcholine(16:0/22:5n3,18:1/20:4) and arachidonate(20:4n6) with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.
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Affiliation(s)
| | | | | | | | - Ravi Shah
- Vanderbilt University Medical Center
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10
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Bagheri M, Bombin A, Shi M, Murthy VL, Shah R, Mosley JD, Ferguson JF. Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations. RESEARCH SQUARE 2023:rs.3.rs-3222588. [PMID: 37790512 PMCID: PMC10543429 DOI: 10.21203/rs.3.rs-3222588/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Circulating metabolites act as biomarkers of dysregulated metabolism, and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. We examined the association between polygenic scores for 726 metabolites (derived from OMICSPRED) with 1,247 clinical phenotypes in 57,735 European ancestry and 15,754 African ancestry participants from the BioVU DNA Biobank. We probed significant relationships through Mendelian randomization (MR) using genetic instruments constructed from the METSIM Study, and validated significant MR associations using independent GWAS of candidate phenotypes. We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes among African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolite-phenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p<0.05). Validated findings included the metabolites bilirubin and X-21796 with cholelithiasis, phosphatidylcholine(16:0/22:5n3,18:1/20:4) and arachidonate(20:4n6) with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.
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Affiliation(s)
| | | | | | | | - Ravi Shah
- Vanderbilt University Medical Center
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Schulz CA, Weinhold L, Schmid M, Nöthen MM, Nöthlings U. Association between urinary iodine excretion, genetic disposition and fluid intelligence in children, adolescents and young adults: the DONALD study. Eur J Nutr 2023; 62:2375-2385. [PMID: 37103611 PMCID: PMC10421824 DOI: 10.1007/s00394-023-03152-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 04/13/2023] [Indexed: 04/28/2023]
Abstract
PURPOSE Iodine deficiency increases the risk of cognitive impairment and delayed physical development in children. It is also associated with cognitive impairment in adults. Cognitive abilities are among the most inheritable behavioural traits. However, little is known about the consequences of insufficient postnatal iodine intake and whether the individual genetic disposition modifies the association between iodine intake and fluid intelligence in children and young adults. METHODS The cultural fair intelligence test was used to assess fluid intelligence in the participants of the DONALD study (n = 238; mean age, 16.5 [SD = 7.7] years). Urinary iodine excretion, a surrogate iodine intake marker, was measured in 24-h urine. Individual genetic disposition (n = 162) was assessed using a polygenic score, associated with general cognitive function. Linear regression analyses were conducted to determine whether Urinary iodine excretion was associated with fluid intelligence and whether this association was modified by individual genetic disposition. RESULTS Urinary iodine excretion above the age-specific estimated average requirement was associated with a five-point higher fluid intelligence score than that below the estimated average requirement (P = 0.02). The polygenic score was positively associated with the fluid intelligence score (β = 2.3; P = 0.03). Participants with a higher polygenic score had a higher fluid intelligence score. CONCLUSION Urinary iodine excretion above the estimated average requirement in childhood and adolescence is beneficial for fluid intelligence. In adults, fluid intelligence was positively associated with a polygenic score for general cognitive function. No evidence showed that the individual genetic disposition modifies the association between Urinary iodine excretion and fluid intelligence.
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Affiliation(s)
| | - Leonie Weinhold
- Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Matthias Schmid
- Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, School of Medicine, University of Bonn, University Hospital Bonn, Bonn, Germany
| | - Ute Nöthlings
- Institute of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Bonn, Germany
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Clark R, Lee SSY, Du R, Wang Y, Kneepkens SCM, Charng J, Huang Y, Hunter ML, Jiang C, Tideman JWL, Melles RB, Klaver CCW, Mackey DA, Williams C, Choquet H, Ohno-Matsui K, Guggenheim JA. A new polygenic score for refractive error improves detection of children at risk of high myopia but not the prediction of those at risk of myopic macular degeneration. EBioMedicine 2023; 91:104551. [PMID: 37055258 PMCID: PMC10203044 DOI: 10.1016/j.ebiom.2023.104551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/17/2023] [Accepted: 03/17/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND High myopia (HM), defined as a spherical equivalent refractive error (SER) ≤ -6.00 diopters (D), is a leading cause of sight impairment, through myopic macular degeneration (MMD). We aimed to derive an improved polygenic score (PGS) for predicting children at risk of HM and to test if a PGS is predictive of MMD after accounting for SER. METHODS The PGS was derived from genome-wide association studies in participants of UK Biobank, CREAM Consortium, and Genetic Epidemiology Research on Adult Health and Aging. MMD severity was quantified by a deep learning algorithm. Prediction of HM was quantified as the area under the receiver operating curve (AUROC). Prediction of severe MMD was assessed by logistic regression. FINDINGS In independent samples of European, African, South Asian and East Asian ancestry, the PGS explained 19% (95% confidence interval 17-21%), 2% (1-3%), 8% (7-10%) and 6% (3-9%) of the variation in SER, respectively. The AUROC for HM in these samples was 0.78 (0.75-0.81), 0.58 (0.53-0.64), 0.71 (0.69-0.74) and 0.67 (0.62-0.72), respectively. The PGS was not associated with the risk of MMD after accounting for SER: OR = 1.07 (0.92-1.24). INTERPRETATION Performance of the PGS approached the level required for clinical utility in Europeans but not in other ancestries. A PGS for refractive error was not predictive of MMD risk once SER was accounted for. FUNDING Supported by the Welsh Government and Fight for Sight (24WG201).
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Affiliation(s)
- Rosie Clark
- School of Optometry & Vision Sciences, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Samantha Sze-Yee Lee
- University of Western Australia, Centre for Ophthalmology and Visual Science (incorporating the Lions Eye Institute), Perth, Western Australia, Australia
| | - Ran Du
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 1138510, Japan; Department of Ophthalmology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Yining Wang
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 1138510, Japan
| | - Sander C M Kneepkens
- Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands; Generation R Study Group, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jason Charng
- University of Western Australia, Centre for Ophthalmology and Visual Science (incorporating the Lions Eye Institute), Perth, Western Australia, Australia; Department of Optometry, School of Allied Health, University of Western Australia, Perth, Australia
| | - Yu Huang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Michael L Hunter
- Busselton Health Study Centre, Busselton Population Medical Research Institute, Busselton, Western Australia; School of Population and Global Health, University of Western Australia, Perth, Western Australia
| | - Chen Jiang
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - J Willem L Tideman
- Department of Ophthalmology, Martini Hospital, Groningen, the Netherlands; Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ronald B Melles
- Department of Ophthalmology Kaiser Permanente Northern California, Redwood City, CA, USA
| | - Caroline C W Klaver
- Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands; Generation R Study Group, Erasmus University Medical Center, Rotterdam, the Netherlands; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland; Department of Ophthalmology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - David A Mackey
- University of Western Australia, Centre for Ophthalmology and Visual Science (incorporating the Lions Eye Institute), Perth, Western Australia, Australia; Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, East Melbourne, Victoria, Australia; School of Medicine, Menzies Research Institute Tasmania, University of Tasmania, Hobart, Tasmania, Australia
| | - Cathy Williams
- Centre for Academic Child Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS81NU, UK
| | - Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Kyoko Ohno-Matsui
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 1138510, Japan
| | - Jeremy A Guggenheim
- School of Optometry & Vision Sciences, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK.
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Schulz CA, Weinhold L, Schmid M, Nöthen MM, Nöthlings U. Analysis of associations between dietary patterns, genetic disposition, and cognitive function in data from UK Biobank. Eur J Nutr 2023; 62:511-521. [PMID: 36152054 PMCID: PMC9899759 DOI: 10.1007/s00394-022-02976-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 07/29/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE Research suggests that diet influences cognitive function and the risk for neurodegenerative disease. The present study aimed to determine whether a recently developed diet score, based on recommendations for dietary priorities for cardio metabolic health, was associated with fluid intelligence, and whether these associations were modified by individual genetic disposition. METHODS This research has been conducted using the UK Biobank Resource. Analyses were performed using self-report data on diet and the results for the verbal-numerical reasoning test of fluid intelligence of 104,895 individuals (46% male: mean age at recruitment 57.1 years (range 40-70)). For each participant, a diet score and a polygenic score (PGS) were constructed, which evaluated predefined cut-offs for the intake of fruit, vegetables, fish, processed meat, unprocessed meat, whole grain, and refined grain, and ranged from 0 (unfavorable) to 7 (favorable). To investigate whether the diet score was associated with fluid intelligence, and whether the association was modified by PGS, linear regression analyses were performed. RESULTS The average diet score was 3.9 (SD 1.4). After adjustment for selected confounders, a positive association was found between baseline fluid intelligence and PGS (P < 0.001). No association was found between baseline fluid intelligence and diet score (P = 0.601), even after stratification for PGS, or in participants with longitudinal data available (n = 9,482). CONCLUSION In this middle-aged cohort, no evidence was found for an association between the investigated diet score and either baseline or longitudinal fluid intelligence. However, as in previous reports, fluid intelligence was strongly associated with a PGS for general cognitive function.
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Affiliation(s)
| | - Leonie Weinhold
- Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Matthias Schmid
- Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Ute Nöthlings
- Institute of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Bonn, Germany
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Uddin MJ, Hjorthøj C, Ahammed T, Nordentoft M, Ekstrøm CT. The use of polygenic risk scores as a covariate in psychological studies. METHODS IN PSYCHOLOGY 2022. [DOI: 10.1016/j.metip.2022.100099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
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Weber H, Maihofer AX, Jaksic N, Bojic EF, Kucukalic S, Dzananovic ES, Uka AG, Hoxha B, Haxhibeqiri V, Haxhibeqiri S, Kravic N, Umihanic MM, Franc AC, Babic R, Pavlovic M, Mehmedbasic AB, Aukst-Margetic B, Kucukalic A, Marjanovic D, Babic D, Bozina N, Jakovljevic M, Sinanovic O, Avdibegović E, Agani F, Warrings B, Domschke K, Nievergelt CM, Deckert J, Dzubur-Kulenovic A, Erhardt A. Association of polygenic risk scores, traumatic life events and coping strategies with war-related PTSD diagnosis and symptom severity in the South Eastern Europe (SEE)-PTSD cohort. J Neural Transm (Vienna) 2022; 129:661-674. [PMID: 34837533 PMCID: PMC9188618 DOI: 10.1007/s00702-021-02446-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 11/19/2021] [Indexed: 11/19/2022]
Abstract
OBJECTIVES Posttraumatic stress disorder (PTSD) is triggered by extremely stressful environmental events and characterized by high emotional distress, re-experiencing of trauma, avoidance and hypervigilance. The present study uses polygenic risk scores (PRS) derived from the UK Biobank (UKBB) mega-cohort analysis as part of the PGC PTSD GWAS effort to determine the heritable basis of PTSD in the South Eastern Europe (SEE)-PTSD cohort. We further analyzed the relation between PRS and additional disease-related variables, such as number and intensity of life events, coping, sex and age at war on PTSD and CAPS as outcome variables. METHODS Association of PRS, number and intensity of life events, coping, sex and age on PTSD were calculated using logistic regression in a total of 321 subjects with current and remitted PTSD and 337 controls previously subjected to traumatic events but not having PTSD. In addition, PRS and other disease-related variables were tested for association with PTSD symptom severity, measured by the Clinician Administrated PTSD Scale (CAPS) by liner regression. To assess the relationship between the main outcomes PTSD diagnosis and symptom severity, each of the examined variables was adjusted for all other PTSD related variables. RESULTS The categorical analysis showed significant polygenic risk in patients with remitted PTSD and the total sample, whereas no effects were found on symptom severity. Intensity of life events as well as the individual coping style were significantly associated with PTSD diagnosis in both current and remitted cases. The dimensional analyses showed as association of war-related frequency of trauma with symptom severity, whereas the intensity of trauma yielded significant results independently of trauma timing in current PTSD. CONCLUSIONS The present PRS application in the SEE-PTSD cohort confirms modest but significant polygenic risk for PTSD diagnosis. Environmental factors, mainly the intensity of traumatic life events and negative coping strategies, yielded associations with PTSD both categorically and dimensionally with more significant p-values. This suggests that, at least in the present cohort of war-related trauma, the association of environmental factors and current individual coping strategies with PTSD psychopathology was stronger than the polygenic risk.
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Affiliation(s)
- Heike Weber
- Department of Psychiatry, Psychosomatics and Psychotherapy, Centre of Mental Health, Julius-Maximilians-University, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany.
| | - Adam X Maihofer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Nenad Jaksic
- Department of Psychiatry and Psychological Medicine, University Hospital Center Zagreb, Zagreb, Croatia
| | - Elma Feric Bojic
- Department for Genetic and Biotechnology, International Burch University, Sarajevo, Bosnia and Herzegovina
| | - Sabina Kucukalic
- Department of Psychiatry, University Clinical Center, Sarajevo, Bosnia and Herzegovina
| | | | - Aferdita Goci Uka
- Department of Psychiatry, University Clinical Center of Kosovo, Prishtina, Kosovo
| | - Blerina Hoxha
- Department of Psychiatry, University Clinical Center of Kosovo, Prishtina, Kosovo
| | - Valdete Haxhibeqiri
- Department of Medical Biochemistry, University Clinical Center of Kosovo, Prishtina, Kosovo
| | - Shpend Haxhibeqiri
- Institute of Kosovo Forensic Psychiatry, University Clinical Center of Kosovo, Prishtina, Kosovo
| | - Nermina Kravic
- Department of Psychiatry, University Clinical Center of Tuzla, Tuzla, Bosnia and Herzegovina
| | | | - Ana Cima Franc
- Department of Psychiatry and Psychological Medicine, University Hospital Center Zagreb, Zagreb, Croatia
| | - Romana Babic
- Department of Psychiatry, University Clinical Center of Mostar, Mostar, Bosnia and Herzegovina
| | - Marko Pavlovic
- Department of Psychiatry, University Clinical Center of Mostar, Mostar, Bosnia and Herzegovina
| | | | | | - Abdulah Kucukalic
- Department of Psychiatry, University Clinical Center, Sarajevo, Bosnia and Herzegovina
| | - Damir Marjanovic
- Department for Genetic and Biotechnology, International Burch University, Sarajevo, Bosnia and Herzegovina
- Center for Applied Bioanthropology, Institute for Anthropological Researches, Zagreb, Croatia
| | - Dragan Babic
- Department of Psychiatry, University Clinical Center of Mostar, Mostar, Bosnia and Herzegovina
| | - Nada Bozina
- Department of Laboratory Diagnostics, University Hospital Center Zagreb, Zagreb, Croatia
| | - Miro Jakovljevic
- Department of Psychiatry and Psychological Medicine, University Hospital Center Zagreb, Zagreb, Croatia
| | - Osman Sinanovic
- Department of Neurology, University Clinical Center of Tuzla, Tuzla, Bosnia and Herzegovina
| | - Esmina Avdibegović
- Department of Psychiatry, University Clinical Center of Tuzla, Tuzla, Bosnia and Herzegovina
| | - Ferid Agani
- Faculty of Medicine, University Hasan Prishtina, Prishtina, Kosovo
| | - Bodo Warrings
- Department of Psychiatry, Psychosomatics and Psychotherapy, Centre of Mental Health, Julius-Maximilians-University, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | - Jürgen Deckert
- Department of Psychiatry, Psychosomatics and Psychotherapy, Centre of Mental Health, Julius-Maximilians-University, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
| | - Alma Dzubur-Kulenovic
- Department of Psychiatry, University Clinical Center, Sarajevo, Bosnia and Herzegovina
| | - Angelika Erhardt
- Department of Psychiatry, Psychosomatics and Psychotherapy, Centre of Mental Health, Julius-Maximilians-University, Margarete-Höppel-Platz 1, 97080, Würzburg, Germany
- Department of Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
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Can adult polygenic scores improve prediction of body mass index in childhood? Int J Obes (Lond) 2022; 46:1375-1383. [PMID: 35505076 DOI: 10.1038/s41366-022-01130-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 04/20/2022] [Accepted: 04/22/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND/OBJECTIVES Modelling genetic pre-disposition may identify children at risk of obesity. However, most polygenic scores (PGSs) have been derived in adults, and lack validation during childhood. This study compared the utility of existing large-scale adult-derived PGSs to predict common anthropometric traits (body mass index (BMI), waist circumference, and body fat) in children and adults, and examined whether childhood BMI prediction could be improved by combining PGSs and non-genetic factors (maternal and earlier child BMI). SUBJECTS/METHODS Participants (n = 1365 children, and n = 2094 adults made up of their parents) were drawn from the Longitudinal Study of Australian Children. Children were weighed and measured every two years from 0-1 to 12-13 years, and adults were measured or self-reported measurements were obtained concurrently (average analysed). Participants were genotyped from blood or oral samples, and PGSs were derived based on published genome-wide association studies. We used linear regression to compare the relative utility of these PGSs to predict their respective traits at different ages. RESULTS BMI PGSs explained up to 12% of child BMI z-score variance in 10-13 year olds, compared with up to 15% in adults. PGSs for waist circumference and body fat explained less variance (up to 8%). An interaction between BMI PGSs and puberty (p = 0.001-0.002) suggests the effect of some variants may differ across the life course. Individual BMI measures across childhood predicted 10-60% of the variance in BMI at 12-13 years, and maternal BMI and BMI PGS each added 1-9% above this. CONCLUSION Adult-derived PGSs for BMI, particularly those derived by modelling between-variant interactions, may be useful for predicting BMI during adolescence with similar accuracy to that obtained in adulthood. The level of precision presented here to predict BMI during childhood may be relevant to public health, but is likely to be less useful for individual clinical purposes.
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Rabinowitz JA, Campos AI, Ong JS, García-Marín LM, Alcauter S, Mitchell BL, Grasby KL, Cuéllar-Partida G, Gillespie NA, Huhn AS, Martin NG, Thompson PM, Medland SE, Maher BS, Rentería ME. Shared Genetic Etiology between Cortical Brain Morphology and Tobacco, Alcohol, and Cannabis Use. Cereb Cortex 2022; 32:796-807. [PMID: 34379727 PMCID: PMC8841600 DOI: 10.1093/cercor/bhab243] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 06/23/2021] [Accepted: 06/24/2021] [Indexed: 12/13/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified genetic variants associated with brain morphology and substance use behaviors (SUB). However, the genetic overlap between brain structure and SUB has not been well characterized. We leveraged GWAS summary data of 71 brain imaging measures and alcohol, tobacco, and cannabis use to investigate their genetic overlap using linkage disequilibrium score regression. We used genomic structural equation modeling to model a "common SUB genetic factor" and investigated its genetic overlap with brain structure. Furthermore, we estimated SUB polygenic risk scores (PRS) and examined whether they predicted brain imaging traits using the Adolescent Behavior and Cognitive Development (ABCD) study. We identified 8 significant negative genetic correlations, including between (1) alcoholic drinks per week and average cortical thickness, and (2) intracranial volume with age of smoking initiation. We observed 5 positive genetic correlations, including those between (1) insula surface area and lifetime cannabis use, and (2) the common SUB genetic factor and pericalcarine surface area. SUB PRS were associated with brain structure variation in ABCD. Our findings highlight a shared genetic etiology between cortical brain morphology and SUB and suggest that genetic variants associated with SUB may be causally related to brain structure differences.
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Affiliation(s)
- Jill A Rabinowitz
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Adrian I Campos
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Jue-Sheng Ong
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Luis M García-Marín
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Sarael Alcauter
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, México
| | - Brittany L Mitchell
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
- School of Biomedical Science, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Queensland 4059, Australia
| | - Katrina L Grasby
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Gabriel Cuéllar-Partida
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Queensland 4102, Australia
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Andrew S Huhn
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Baltimore, MD 21205, USA
| | - Nicholas G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90007, USA
| | - Sarah E Medland
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Brion S Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Miguel E Rentería
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4072, Australia
- School of Biomedical Science, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Queensland 4059, Australia
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Reporting methodological issues of the mendelian randomization studies in health and medical research: a systematic review. BMC Med Res Methodol 2022; 22:21. [PMID: 35034628 PMCID: PMC8761268 DOI: 10.1186/s12874-022-01504-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 01/03/2022] [Indexed: 01/03/2023] Open
Abstract
Background Mendelian randomization (MR) studies using Genetic risk scores (GRS) as an instrumental variable (IV) have increasingly been used to control for unmeasured confounding in observational healthcare databases. However, proper reporting of methodological issues is sparse in these studies. We aimed to review published papers related to MR studies and identify reporting problems. Methods We conducted a systematic review using the clinical articles published between 2009 and 2019. We searched PubMed, Scopus, and Embase databases. We retrieved information from every MR study, including the tests performed to evaluate assumptions and the modelling approach used for estimation. Using our inclusion/exclusion criteria, finally, we identified 97 studies to conduct the review according to the PRISMA statement. Results Only 66 (68%) of the studies empirically verified the first assumption (Relevance assumption), and 40 (41.2%) studies reported the appropriate tests (e.g., R2, F-test) to investigate the association. A total of 35.1% clearly stated and discussed theoretical justifications for the second and third assumptions. 30.9% of the studies used a two-stage least square, and 11.3% used the Wald estimator method for estimating IV. Also, 44.3% of the studies conducted a sensitivity analysis to illuminate the robustness of estimates for violations of the untestable assumptions. Conclusions We found that incompleteness of the justification of the assumptions for the instrumental variable in MR studies was a common problem in our selected studies. This may misdirect the findings of the studies. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01504-0.
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Rabinowitz JA, Jin J, Kahn G, Kuo SIC, Campos A, Rentería M, Benke K, Wilcox H, Ialongo NS, Maher BS, Kertes D, Eaton W, Uhl G, Wagner BM, Cohen D. Genetic propensity for risky behavior and depression and risk of lifetime suicide attempt among urban African Americans in adolescence and young adulthood. Am J Med Genet B Neuropsychiatr Genet 2021; 186:456-468. [PMID: 34231309 PMCID: PMC9976552 DOI: 10.1002/ajmg.b.32866] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 05/04/2021] [Accepted: 05/14/2021] [Indexed: 01/02/2023]
Abstract
Suicide attempts (SA) among African Americans have increased at a greater rate than any other racial/ethnic group. Research in European ancestry populations has indicated that SA are genetically influenced; however, less is known about the genetic contributors that underpin SA among African Americans. We examined whether genetic propensity for depression and risky behaviors (assessed via polygenic risk scores; PRS) independently and jointly are associated with SA among urban, African Americans and whether sex differences exist in these relations. Participants (N = 1,157, 45.0% male) were originally recruited as part of two first grade universal school-based prevention trials. Participants reported in adolescence and young adulthood on whether they ever attempted suicide in their life. Depression and risky behaviors PRS were created based on large-scale genome-wide association studies conducted by Howard et al. (2019) and Karlson Línner et al. (2019), respectively. There was a significant interaction between the risky behavior PRS and depression PRS such that the combination of high risky behavior polygenic risk and low/moderate polygenic risk for depression was associated with greater risk for lifetime SA among the whole sample and African American males specifically. In addition, the risky behavior PRS was significantly positively associated with lifetime SA among African American males. These findings provide preliminary evidence regarding the importance of examining risky behavior and depression polygenic risk in relation to SA among African Americans, though replication of our findings in other African American samples is needed.
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Affiliation(s)
- Jill A. Rabinowitz
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Jin Jin
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Geoffrey Kahn
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Sally I.-Chun Kuo
- Department of Psychology, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Adrian Campos
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Miguel Rentería
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Kelly Benke
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Holly Wilcox
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Nicholas S. Ialongo
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Brion S. Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Darlene Kertes
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - William Eaton
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - George Uhl
- New Mexico VA Health Care System, Las Vegas, Nevada, USA
| | - Barry M. Wagner
- Department of Psychology, Catholic University, Washington, District of Columbia, USA
| | - Daniel Cohen
- College of Education, The University of Alabama College of Education, Tuscaloosa, Alabama, USA
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20
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Neale ZE, Kuo SIC, Dick DM. A systematic review of gene-by-intervention studies of alcohol and other substance use. Dev Psychopathol 2021; 33:1410-1427. [PMID: 32602428 PMCID: PMC7772257 DOI: 10.1017/s0954579420000590] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Alcohol and other substance use problems are common, and the efficacy of current prevention and intervention programs is limited. Genetics may contribute to differential effectiveness of psychosocial prevention and intervention programs. This paper reviews gene-by-intervention (G×I) studies of alcohol and other substance use, and implications for integrating genetics into prevention science. Systematic review yielded 17 studies for inclusion. Most studies focused on youth substance prevention, alcohol was the most common outcome, and measures of genotype were heterogeneous. All studies reported at least one significant G×I interaction. We discuss these findings in the context of the history and current state of genetics, and provide recommendations for future G×I research. These include the integration of genome-wide polygenic scores into prevention studies, broad outcome measurement, recruitment of underrepresented populations, testing mediators of G×I effects, and addressing ethical implications. Integrating genetic research into prevention science, and training researchers to work fluidly across these fields, will enhance our ability to determine the best intervention for each individual across development. With growing public interest in obtaining personalized genetic information, we anticipate that the integration of genetics and prevention science will become increasingly important as we move into the era of precision medicine.
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Affiliation(s)
- Zoe E. Neale
- Department of Psychology, Virginia Commonwealth University
| | | | - Danielle M. Dick
- Department of Psychology, Virginia Commonwealth University
- Department of Human and Molecular Genetics, Virginia Commonwealth University
- College Behavioral and Emotional Health Institute, Virginia Commonwealth University
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21
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Neri de Souza Reis V, Tahira AC, Daguano Gastaldi V, Mari P, Portolese J, Feio dos Santos AC, Lisboa B, Mari J, Caetano SC, Brunoni D, Bordini D, Silvestre de Paula C, Vêncio RZN, Quackenbush J, Brentani H. Environmental Influences Measured by Epigenetic Clock and Vulnerability Components at Birth Impact Clinical ASD Heterogeneity. Genes (Basel) 2021; 12:genes12091433. [PMID: 34573415 PMCID: PMC8467464 DOI: 10.3390/genes12091433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/03/2021] [Accepted: 09/08/2021] [Indexed: 11/16/2022] Open
Abstract
Although Autism Spectrum Disorders (ASD) is recognized as being heavily influenced by genetic factors, the role of epigenetic and environmental factors is still being established. This study aimed to identify ASD vulnerability components based on familial history and intrauterine environmental stress exposure, explore possible vulnerability subgroups, access DNA methylation age acceleration (AA) as a proxy of stress exposure during life, and evaluate the association of ASD vulnerability components and AA to phenotypic severity measures. Principal Component Analysis (PCA) was used to search the vulnerability components from 67 mothers of autistic children. We found that PC1 had a higher correlation with psychosocial stress (maternal stress, maternal education, and social class), and PC2 had a higher correlation with biological factors (psychiatric family history and gestational complications). Comparing the methylome between above and below PC1 average subgroups we found 11,879 statistically significant differentially methylated probes (DMPs, p < 0.05). DMPs CpG sites were enriched in variably methylated regions (VMRs), most showing environmental and genetic influences. Hypermethylated probes presented higher rates in different regulatory regions associated with functional SNPs, indicating that the subgroups may have different affected regulatory regions and their liability to disease explained by common variations. Vulnerability components score moderated by epigenetic clock AA was associated with Vineland Total score (p = 0.0036, adjR2 = 0.31), suggesting risk factors with stress burden can influence ASD phenotype.
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Affiliation(s)
- Viviane Neri de Souza Reis
- Departamento & Instituto de Psiquiatria, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo 05403-903, SP, Brazil; (V.N.d.S.R.); (A.C.T.); (V.D.G.); (P.M.); (J.P.); (A.C.F.d.S.); (B.L.)
| | - Ana Carolina Tahira
- Departamento & Instituto de Psiquiatria, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo 05403-903, SP, Brazil; (V.N.d.S.R.); (A.C.T.); (V.D.G.); (P.M.); (J.P.); (A.C.F.d.S.); (B.L.)
- Instituto Butantan, São Paulo 05503-900, SP, Brazil
| | - Vinícius Daguano Gastaldi
- Departamento & Instituto de Psiquiatria, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo 05403-903, SP, Brazil; (V.N.d.S.R.); (A.C.T.); (V.D.G.); (P.M.); (J.P.); (A.C.F.d.S.); (B.L.)
| | - Paula Mari
- Departamento & Instituto de Psiquiatria, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo 05403-903, SP, Brazil; (V.N.d.S.R.); (A.C.T.); (V.D.G.); (P.M.); (J.P.); (A.C.F.d.S.); (B.L.)
| | - Joana Portolese
- Departamento & Instituto de Psiquiatria, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo 05403-903, SP, Brazil; (V.N.d.S.R.); (A.C.T.); (V.D.G.); (P.M.); (J.P.); (A.C.F.d.S.); (B.L.)
| | - Ana Cecilia Feio dos Santos
- Departamento & Instituto de Psiquiatria, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo 05403-903, SP, Brazil; (V.N.d.S.R.); (A.C.T.); (V.D.G.); (P.M.); (J.P.); (A.C.F.d.S.); (B.L.)
- Laboratório de Pesquisas Básicas em Malária—Entomologia, Seção de Parasitologia—Instituto Evandro Chagas/SVS/MS, Ananindeua 66093-020, PA, Brazil
| | - Bianca Lisboa
- Departamento & Instituto de Psiquiatria, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo 05403-903, SP, Brazil; (V.N.d.S.R.); (A.C.T.); (V.D.G.); (P.M.); (J.P.); (A.C.F.d.S.); (B.L.)
| | - Jair Mari
- Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo 04023-062, SP, Brazil; (J.M.); (S.C.C.); (D.B.); (C.S.d.P.)
| | - Sheila C. Caetano
- Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo 04023-062, SP, Brazil; (J.M.); (S.C.C.); (D.B.); (C.S.d.P.)
| | - Décio Brunoni
- Centro de Ciências Biológicas e da Saúde, Universidade Presbiteriana Mackenzie (UPM), São Paulo 01302-907, SP, Brazil;
| | - Daniela Bordini
- Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo 04023-062, SP, Brazil; (J.M.); (S.C.C.); (D.B.); (C.S.d.P.)
| | - Cristiane Silvestre de Paula
- Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo 04023-062, SP, Brazil; (J.M.); (S.C.C.); (D.B.); (C.S.d.P.)
- Centro de Ciências Biológicas e da Saúde, Universidade Presbiteriana Mackenzie (UPM), São Paulo 01302-907, SP, Brazil;
| | - Ricardo Z. N. Vêncio
- Departamento de Computação e Matemática FFCLRP-USP, Universidade de São Paulo, Ribeirão Preto 14040-901, SP, Brazil;
| | - John Quackenbush
- Center for Cancer Computational Biology, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA; or
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Helena Brentani
- Departamento & Instituto de Psiquiatria, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo 05403-903, SP, Brazil; (V.N.d.S.R.); (A.C.T.); (V.D.G.); (P.M.); (J.P.); (A.C.F.d.S.); (B.L.)
- Correspondence: ; Tel.: +55-(11)-99-931-4349
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22
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West BT, Little RJ, Andridge RR, Boonstra PS, Ware EB, Pandit A, Alvarado-Leiton F. ASSESSING SELECTION BIAS IN REGRESSION COEFFICIENTS ESTIMATED FROM NONPROBABILITY SAMPLES WITH APPLICATIONS TO GENETICS AND DEMOGRAPHIC SURVEYS. Ann Appl Stat 2021; 15:1556-1581. [PMID: 35237377 PMCID: PMC8887878 DOI: 10.1214/21-aoas1453] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Selection bias is a serious potential problem for inference about relationships of scientific interest based on samples without well-defined probability sampling mechanisms. Motivated by the potential for selection bias in: (a) estimated relationships of polygenic scores (PGSs) with phenotypes in genetic studies of volunteers and (b) estimated differences in subgroup means in surveys of smartphone users, we derive novel measures of selection bias for estimates of the coefficients in linear and probit regression models fitted to nonprobability samples, when aggregate-level auxiliary data are available for the selected sample and the target population. The measures arise from normal pattern-mixture models that allow analysts to examine the sensitivity of their inferences to assumptions about nonignorable selection in these samples. We examine the effectiveness of the proposed measures in a simulation study and then use them to quantify the selection bias in: (a) estimated PGS-phenotype relationships in a large study of volunteers recruited via Facebook and (b) estimated subgroup differences in mean past-year employment duration in a nonprobability sample of low-educated smartphone users. We evaluate the performance of the measures in these applications using benchmark estimates from large probability samples.
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Affiliation(s)
- Brady T. West
- Survey Research Center, Institute for Social Research, University of Michigan,
| | - Roderick J. Little
- Department of Biostatistics, School of Public Health, University of Michigan,
| | | | - Philip S. Boonstra
- Department of Biostatistics, School of Public Health, University of Michigan,
| | - Erin B. Ware
- Survey Research Center, Institute for Social Research, University of Michigan,
| | - Anita Pandit
- Department of Biostatistics, School of Public Health, University of Michigan,
| | - Fernanda Alvarado-Leiton
- Michigan Program in Survey and Data Science, Institute for Social Research, University of Michigan
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23
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Machipisa T, Chong M, Muhamed B, Chishala C, Shaboodien G, Pandie S, de Vries J, Laing N, Joachim A, Daniels R, Ntsekhe M, Hugo-Hamman CT, Gitura B, Ogendo S, Lwabi P, Okello E, Damasceno A, Novela C, Mocumbi AO, Madeira G, Musuku J, Mtaja A, ElSayed A, Elhassan HHM, Bode-Thomas F, Okeahialam BN, Zühlke LJ, Mulder N, Ramesar R, Lesosky M, Parks T, Cordell HJ, Keavney B, Engel ME, Paré G. Association of Novel Locus With Rheumatic Heart Disease in Black African Individuals: Findings From the RHDGen Study. JAMA Cardiol 2021; 6:1000-1011. [PMID: 34106200 PMCID: PMC8190704 DOI: 10.1001/jamacardio.2021.1627] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/25/2021] [Indexed: 01/02/2023]
Abstract
Importance Rheumatic heart disease (RHD), a sequela of rheumatic fever characterized by permanent heart valve damage, is the leading cause of cardiac surgery in Africa. However, its pathophysiologic characteristics and genetics are poorly understood. Understanding genetic susceptibility may aid in prevention, control, and interventions to eliminate RHD. Objective To identify common genetic loci associated with RHD susceptibility in Black African individuals. Design, Setting, and Participants This multicenter case-control genome-wide association study (GWAS), the Genetics of Rheumatic Heart Disease, examined more than 7 million genotyped and imputed single-nucleotide variations. The 4809 GWAS participants and 116 independent trio families were enrolled from 8 African countries between December 31, 2012, and March 31, 2018. All GWAS participants and trio probands were screened by use of echocardiography. Data analyses took place from May 15, 2017, until March 14, 2021. Main Outcomes and Measures Genetic associations with RHD. Results This study included 4809 African participants (2548 RHD cases and 2261 controls; 3301 women [69%]; mean [SD] age, 36.5 [16.3] years). The GWAS identified a single RHD risk locus, 11q24.1 (rs1219406 [odds ratio, 1.65; 95% CI, 1.48-1.82; P = 4.36 × 10-8]), which reached genome-wide significance in Black African individuals. Our meta-analysis of Black (n = 3179) and admixed (n = 1055) African individuals revealed several suggestive loci. The study also replicated a previously reported association in Pacific Islander individuals (rs11846409) at the immunoglobulin heavy chain locus, in the meta-analysis of Black and admixed African individuals (odds ratio, 1.16; 95% CI, 1.06-1.27; P = 1.19 × 10-3). The HLA (rs9272622) associations reported in Aboriginal Australian individuals could not be replicated. In support of the known polygenic architecture for RHD, overtransmission of a polygenic risk score from unaffected parents to affected probands was observed (polygenic transmission disequilibrium testing mean [SE], 0.27 [0.16] SDs; P = .04996), and the chip-based heritability was estimated to be high at 0.49 (SE = 0.12; P = 3.28 × 10-5) in Black African individuals. Conclusions and Relevance This study revealed a novel candidate susceptibility locus exclusive to Black African individuals and an important heritable component to RHD susceptibility in African individuals.
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Affiliation(s)
- Tafadzwa Machipisa
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Hatter Institute for Cardiovascular Diseases Research in Africa and Cape Heart Institute, Department of Medicine, University of Cape Town, Cape Town, South Africa
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada
| | - Michael Chong
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada
| | - Babu Muhamed
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Hatter Institute for Cardiovascular Diseases Research in Africa and Cape Heart Institute, Department of Medicine, University of Cape Town, Cape Town, South Africa
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada
| | - Chishala Chishala
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Hatter Institute for Cardiovascular Diseases Research in Africa and Cape Heart Institute, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Gasnat Shaboodien
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Hatter Institute for Cardiovascular Diseases Research in Africa and Cape Heart Institute, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Shahiemah Pandie
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Jantina de Vries
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Nakita Laing
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Alexia Joachim
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Rezeen Daniels
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Mpiko Ntsekhe
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Christopher T. Hugo-Hamman
- Rheumatic Heart Disease Clinic, Windhoek Central Hospital, Ministry of Health and Social Services, Windhoek, Republic of Namibia
| | - Bernard Gitura
- Cardiology Department of Medicine, Kenyatta National Hospital, University of Nairobi, Nairobi, Kenya
| | - Stephen Ogendo
- Cardiology Department of Medicine, Kenyatta National Hospital, University of Nairobi, Nairobi, Kenya
| | | | | | - Albertino Damasceno
- Faculty of Medicine, Eduardo Mondlane University/Nucleo de Investigaçao, Departamento de Medicina, Hospital Central de Maputo, Maputo, Mozambique
| | - Celia Novela
- Faculty of Medicine, Eduardo Mondlane University/Nucleo de Investigaçao, Departamento de Medicina, Hospital Central de Maputo, Maputo, Mozambique
| | - Ana O. Mocumbi
- Instituto Nacional de Saúde Ministério da Saúde, Maputo, Moçambique
| | - Goeffrey Madeira
- Emergency Department, World Health Organization Mozambique, Maputo, Mozambique
| | - John Musuku
- Department of Paediatrics and Child Health, University Teaching Hospital–Children’s Hospital, University of Zambia, Lusaka, Zambia
| | - Agnes Mtaja
- Department of Paediatrics and Child Health, University Teaching Hospital–Children’s Hospital, University of Zambia, Lusaka, Zambia
| | - Ahmed ElSayed
- Department of Cardiothoracic Surgery, Alshaab Teaching Hospital, Alazhari Health Research Center, Alzaiem Alazhari University, Khartoum, Sudan
| | - Huda H. M. Elhassan
- Department of Cardiothoracic Surgery, Alshaab Teaching Hospital, Alazhari Health Research Center, Alzaiem Alazhari University, Khartoum, Sudan
| | - Fidelia Bode-Thomas
- Department of Paediatrics, Jos University Teaching Hospital and University of Jos, Jos, Plateau State Nigeria
| | - Basil N. Okeahialam
- Department of Paediatrics, Jos University Teaching Hospital and University of Jos, Jos, Plateau State Nigeria
| | - Liesl J. Zühlke
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Division of Paediatric Cardiology, Department of Paediatrics and Child Health, Red Cross War Memorial Children’s Hospital and University of Cape Town, South Africa
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Raj Ramesar
- Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Maia Lesosky
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Tom Parks
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Heather J. Cordell
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, International Centre for Life, Newcastle upon Tyne, United Kingdom
| | - Bernard Keavney
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, United Kingdom
- Manchester University National Health Service Foundation Trust, Manchester Academic Health Science CentreManchester, United Kingdom
| | - Mark E. Engel
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada
- Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton Ontario, Canada
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24
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Bagheri M, Wang C, Shi M, Manouchehri A, Murray KT, Murphy MB, Shaffer CM, Singh K, Davis LK, Jarvik GP, Stanaway IB, Hebbring S, Reilly MP, Gerszten RE, Wang TJ, Mosley JD, Ferguson JF. The genetic architecture of plasma kynurenine includes cardiometabolic disease mechanisms associated with the SH2B3 gene. Sci Rep 2021; 11:15652. [PMID: 34341450 PMCID: PMC8329184 DOI: 10.1038/s41598-021-95154-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 07/21/2021] [Indexed: 01/11/2023] Open
Abstract
Inflammation increases the risk of cardiometabolic disease. Delineating specific inflammatory pathways and biomarkers of their activity could identify the mechanistic underpinnings of the increased risk. Plasma levels of kynurenine, a metabolite involved in inflammation, associates with cardiometabolic disease risk. We used genetic approaches to identify inflammatory mechanisms associated with kynurenine variability and their relationship to cardiometabolic disease. We identified single-nucleotide polymorphisms (SNPs) previously associated with plasma kynurenine, including a missense-variant (rs3184504) in the inflammatory gene SH2B3/LNK. We examined the association between rs3184504 and plasma kynurenine in independent human samples, and measured kynurenine levels in SH2B3-knock-out mice and during human LPS-evoked endotoxemia. We conducted phenome scanning to identify clinical phenotypes associated with each kynurenine-related SNP and with a kynurenine polygenic score using the UK-Biobank (n = 456,422), BioVU (n = 62,303), and Electronic Medical Records and Genetics (n = 32,324) databases. The SH2B3 missense variant associated with plasma kynurenine levels and SH2B3-/- mice had significant tissue-specific differences in kynurenine levels.LPS, an acute inflammatory stimulus, increased plasma kynurenine in humans. Mendelian randomization showed increased waist-circumference, a marker of central obesity, associated with increased kynurenine, and increased kynurenine associated with C-reactive protein (CRP). We found 30 diagnoses associated (FDR q < 0.05) with the SH2B3 variant, but not with SNPs mapping to genes known to regulate tryptophan-kynurenine metabolism. Plasma kynurenine may be a biomarker of acute and chronic inflammation involving the SH2B3 pathways. Its regulation lies upstream of CRP, suggesting that kynurenine may be a biomarker of one inflammatory mechanism contributing to increased cardiometabolic disease risk.
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Affiliation(s)
- Minoo Bagheri
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, 2220 Pierce Ave, PRB 354B, Nashville, TN, 37232, USA
| | - Chuan Wang
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, 2220 Pierce Ave, PRB 354B, Nashville, TN, 37232, USA
| | - Mingjian Shi
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ali Manouchehri
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, 2220 Pierce Ave, PRB 354B, Nashville, TN, 37232, USA
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Katherine T Murray
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, 2220 Pierce Ave, PRB 354B, Nashville, TN, 37232, USA
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Matthew B Murphy
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christian M Shaffer
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kritika Singh
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lea K Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gail P Jarvik
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, WA, USA
| | - Ian B Stanaway
- Division of Nephrology, School of Medicine, Harborview Medical Center Kidney Research Institute, University of Washington, Seattle, WA, USA
| | - Scott Hebbring
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, USA
| | - Muredach P Reilly
- Irving Institute for Clinical and Translational Research and Division of Cardiology, Columbia University Medical Center, New York, NY, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Thomas J Wang
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, USA
| | - Jonathan D Mosley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jane F Ferguson
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, 2220 Pierce Ave, PRB 354B, Nashville, TN, 37232, USA.
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25
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Abstract
More than 40% of the risk of developing prostate cancer (PCa) is from genetic factors. Genome-wide association studies have led to the discovery of more than 140 variants associated with PCa risk. Polygenic risk scores (PRS) generated using these variants show promise in identifying individuals at much higher (and lower) lifetime risk than the average man. PCa PRS also improve the predictive value of prostate-specific antigen screening, may inform the age for starting PCa screening, and are informative for development of more aggressive tumors. Despite the promise, few clinical trials have evaluated the benefit of PCa PRS for clinical care.
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26
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Wang SH, Huang SP, Pan YJ, Hsiao PC, Li CY, Chen LC, Yu CC, Huang CY, Lin VC, Lu TL, Bao BY. Association between the polygenic liabilities for prostate cancer and breast cancer with biochemical recurrence after radical prostatectomy for localized prostate cancer. Am J Cancer Res 2021; 11:2331-2342. [PMID: 34094689 PMCID: PMC8167673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 03/07/2021] [Indexed: 06/12/2023] Open
Abstract
Prostate and breast cancers are hormone-related malignancies and are characterized by a complex interplay of hundreds of susceptibility loci throughout the genome. Prostate cancer could be inhibited by eliminating androgens through castration or estrogen administration, thus facilitating long-term treatment of prostate cancer; however, the role of estrogen in prostate cancer remains unclear. This study aimed to determine whether polygenic risk scores (PRSs) comprising combinations of genome-wide susceptibility variants influence the clinical outcomes of prostate cancer patients. The study subjects were recruited from four medical centers in Taiwan, and genome-wide genotyping data were obtained from 643 prostate cancer patients. We derived the PRS for prostate cancer (PRS-PC) and for breast cancer (PRS-BC) for each patient. The association between the PRS-PC/PRS-BC at the age of prostate cancer onset and recurrence within seven years was evaluated using a regression model adjusted for population stratification components. A higher PRS-PC was associated with an earlier onset age for prostate cancer (beta in per SD increase in PRS = -0.89, P = 0.0008). In contrast, a higher PRS-BC was associated with an older onset age for prostate cancer (beta = 0.59, P = 0.02). PRS-PC was not associated with the risk of recurrence (hazard ratio = 1.03, P = 0.67), whereas a higher PRS-BC was associated with a low recurrence risk (hazard ratio = 0.86, P = 0.03). These results indicate that the genetic predisposition to breast cancer is associated with a low risk of prostate cancer recurrence. Further studies are warranted to explore the role of breast cancer susceptibility variants and estrogen signaling in prostate cancer progression.
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Affiliation(s)
- Shi-Heng Wang
- Department of Occupational Safety and Health, China Medical UniversityTaichung 404, Taiwan
- Department of Public Health, China Medical UniversityTaichung 404, Taiwan
| | - Shu-Pin Huang
- Department of Urology, Kaohsiung Medical University HospitalKaohsiung 807, Taiwan
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical UniversityKaohsiung 807, Taiwan
- Department of Urology, Faculty of Medicine, College of Medicine, Kaohsiung Medical UniversityKaohsiung 807, Taiwan
- Center for Cancer Research, Kaohsiung Medical UniversityKaohsiung 807, Taiwan
| | - Yi-Jiun Pan
- School of Medicine, China Medical UniversityTaichung 404, Taiwan
| | - Po-Chang Hsiao
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan UniversityTaipei 100, Taiwan
| | - Chia-Yang Li
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical UniversityKaohsiung 807, Taiwan
| | - Lih-Chyang Chen
- Department of Medicine, Mackay Medical CollegeNew Taipei 252, Taiwan
| | - Chia-Cheng Yu
- Division of Urology, Department of Surgery, Kaohsiung Veterans General HospitalKaohsiung 813, Taiwan
- Department of Urology, School of Medicine, National Yang Ming Chiao Tung UniversityTaipei 112, Taiwan
- Department of Pharmacy, College of Pharmacy and Health Care, Tajen UniversityPingtung 907, Taiwan
| | - Chao-Yuan Huang
- Department of Urology, National Taiwan University Hospital, College of Medicine, National Taiwan UniversityTaipei 100, Taiwan
| | - Victor C Lin
- Department of Urology, E-Da HospitalKaohsiung 824, Taiwan
- School of Medicine for International Students, I-Shou UniversityKaohsiung 840, Taiwan
| | - Te-Ling Lu
- Department of Pharmacy, China Medical UniversityTaichung 404, Taiwan
| | - Bo-Ying Bao
- Department of Pharmacy, China Medical UniversityTaichung 404, Taiwan
- Sex Hormone Research Center, China Medical University HospitalTaichung 404, Taiwan
- Department of Nursing, Asia UniversityTaichung 413, Taiwan
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27
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Rabinowitz JA, Campos AI, Benjet C, Su J, Macias-Kauffer L, Méndez E, Martinez-Levy GA, Cruz-Fuentes CS, Rentería ME. Depression polygenic scores are associated with major depressive disorder diagnosis and depressive episode in Mexican adolescents. JOURNAL OF AFFECTIVE DISORDERS REPORTS 2020. [DOI: 10.1016/j.jadr.2020.100028] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
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28
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Ware EB, Faul JD, Mitchell CM, Bakulski KM. Considering the APOE locus in Alzheimer's disease polygenic scores in the Health and Retirement Study: a longitudinal panel study. BMC Med Genomics 2020; 13:164. [PMID: 33143703 PMCID: PMC7607711 DOI: 10.1186/s12920-020-00815-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 10/25/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Polygenic scores are a strategy to aggregate the small, additive effects of single nucleotide polymorphisms across the genome. With phenotypes like Alzheimer's disease, which have a strong and well-established genomic locus (APOE), the cumulative effect of genetic variants outside of this area has not been well established in a population-representative sample. METHODS Here we examine the association between polygenic scores for Alzheimer's disease both with and without the APOE region (chr19: 45,384,477 to 45,432,606, build 37/hg 19) at different P value thresholds and dementia. We also investigate the addition of APOE-ε4 carrier status and its effect on the polygenic score-dementia association in the Health and Retirement Study using generalized linear models accounting for repeated measures by individual and use a binomial distribution, logit link, and unstructured correlation structure. RESULTS In a large sample of European ancestry participants of the Health and Retirement Study (n = 9872) with an average of 5.2 (standard deviation 1.8) visit spaced two years apart, we found that including the APOE region through weighted variants in a polygenic score was insufficient to capture the large amount of risk attributed to this region. We also found that a polygenic score with a P value threshold of 0.01 had the strongest association with the odds of dementia in this sample (odds ratio = 1.10 95%CI 1.0 to 1.2). CONCLUSION We recommend removing the APOE region from polygenic score calculation and treating the APOE locus as an independent covariate when modeling dementia. We also recommend using a moderately conservative P value threshold (e.g. 0.01) when creating polygenic scores for Alzheimer's disease on dementia. These recommendations may help elucidate relationships between polygenic scores and regions of strong significance for phenotypes similar to Alzheimer's disease.
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Affiliation(s)
- Erin B Ware
- Survey Research Center, Institute for Social Research, University of Michigan, 426 Thompson St., Rm. 3320 ISR-Thompson, Ann Arbor, MI, 48104, USA.
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, 426 Thompson St., Rm. 3320 ISR-Thompson, Ann Arbor, MI, 48104, USA
| | - Colter M Mitchell
- Survey Research Center, Institute for Social Research, University of Michigan, 426 Thompson St., Rm. 3320 ISR-Thompson, Ann Arbor, MI, 48104, USA
| | - Kelly M Bakulski
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
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29
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Lyngstad SH, Bettella F, Aminoff SR, Athanasiu L, Andreassen OA, Faerden A, Melle I. Associations between schizophrenia polygenic risk and apathy in schizophrenia spectrum disorders and healthy controls. Acta Psychiatr Scand 2020; 141:452-464. [PMID: 32091622 DOI: 10.1111/acps.13167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/16/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVE Apathy is a central predictor of a poor functional outcome in schizophrenia. Schizophrenia polygenic risk scores (PRSs) are used to detect genetic associations to key clinical phenotypes in schizophrenia. We explored the associations between schizophrenia PRS and apathy levels in schizophrenia spectrum disorders (n = 281) and matched healthy controls (n = 298), and further how schizophrenia PRS contributed in predicting apathy when added to premorbid and clinical factors in the patient sample. METHOD Schizophrenia PRSs were computed for each participant. Apathy was assessed with the Apathy Evaluation Scale. Bivariate correlation analyses were used to investigate associations between schizophrenia PRS and apathy, and between apathy and premorbid and clinical factors. Multiple hierarchical regression analyses were employed to evaluate the contributions of clinical variables and schizophrenia PRS to apathy levels. RESULTS We found no significant associations between schizophrenia PRS and apathy in patients and healthy controls. Several premorbid and clinical characteristics significantly predicted apathy in patients, but schizophrenia PRS did not. CONCLUSION Since the PRSs are based on common genetic variants, our results do not preclude associations to other types of genetic factors. The results could also indicate that environmentally based biological or psychological factors contribute to apathy levels in schizophrenia.
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Affiliation(s)
- S H Lyngstad
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - F Bettella
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - S R Aminoff
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Early Intervention in Psychosis Advisory Unit for South East Norway, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - L Athanasiu
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - O A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - A Faerden
- Division of Mental Health and Addiction, Department of Acute Psychiatry, Oslo University Hospital, Oslo, Norway
| | - I Melle
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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30
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Hickie IB, Scott EM, Cross SP, Iorfino F, Davenport TA, Guastella AJ, Naismith SL, Carpenter JS, Rohleder C, Crouse JJ, Hermens DF, Koethe D, Markus Leweke F, Tickell AM, Sawrikar V, Scott J. Right care, first time: a highly personalised and measurement-based care model to manage youth mental health. Med J Aust 2020; 211 Suppl 9:S3-S46. [PMID: 31679171 DOI: 10.5694/mja2.50383] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Mood and psychotic syndromes most often emerge during adolescence and young adulthood, a period characterised by major physical and social change. Consequently, the effects of adolescent-onset mood and psychotic syndromes can have long term consequences. A key clinical challenge for youth mental health is to develop and test new systems that align with current evidence for comorbid presentations and underlying neurobiology, and are useful for predicting outcomes and guiding decisions regarding the provision of appropriate and effective care. Our highly personalised and measurement-based care model includes three core concepts: ▶ A multidimensional assessment and outcomes framework that includes: social and occupational function; self-harm, suicidal thoughts and behaviour; alcohol or other substance misuse; physical health; and illness trajectory. ▶ Clinical stage. ▶ Three common illness subtypes (psychosis, anxious depression, bipolar spectrum) based on proposed pathophysiological mechanisms (neurodevelopmental, hyperarousal, circadian). The model explicitly aims to prevent progression to more complex and severe forms of illness and is better aligned to contemporary models of the patterns of emergence of psychopathology. Inherent within this highly personalised approach is the incorporation of other evidence-based processes, including real-time measurement-based care as well as utilisation of multidisciplinary teams of health professionals. Data-driven local system modelling and personalised health information technologies provide crucial infrastructure support to these processes for better access to, and higher quality, mental health care for young people. CHAPTER 1: MULTIDIMENSIONAL OUTCOMES IN YOUTH MENTAL HEALTH CARE: WHAT MATTERS AND WHY?: Mood and psychotic syndromes present one of the most serious public health challenges that we face in the 21st century. Factors including prevalence, age of onset, and chronicity contribute to substantial burden and secondary risks such as alcohol or other substance misuse. Mood and psychotic syndromes most often emerge during adolescence and young adulthood, a period characterised by major physical and social change; thus, effects can have long term consequences. We propose five key domains which make up a multidimensional outcomes framework that aims to address the specific needs of young people presenting to health services with emerging mental illness. These include social and occupational function; self-harm, suicidal thoughts and behaviours; alcohol or other substance misuse; physical health; and illness type, stage and trajectory. Impairment and concurrent morbidity are well established in young people by the time they present for mental health care. Despite this, services and health professionals tend to focus on only one aspect of the presentation - illness type, stage and trajectory - and are often at odds with the preferences of young people and their families. There is a need to address the disconnect between mental health, physical health and social services and interventions, to ensure that youth mental health care focuses on the outcomes that matter to young people. CHAPTER 2: COMBINING CLINICAL STAGE AND PATHOPHYSIOLOGICAL MECHANISMS TO UNDERSTAND ILLNESS TRAJECTORIES IN YOUNG PEOPLE WITH EMERGING MOOD AND PSYCHOTIC SYNDROMES: Traditional diagnostic classification systems for mental disorders map poorly onto the early stages of illness experienced by young people, and purport categorical distinctions that are not readily supported by research into genetic, environmental and neurobiological risk factors. Consequently, a key clinical challenge in youth mental health is to develop and test new classification systems that align with current evidence on comorbid presentations, are consistent with current understanding of underlying neurobiology, and provide utility for predicting outcomes and guiding decisions regarding the provision of appropriate and effective care. This chapter outlines a transdiagnostic framework for classifying common adolescent-onset mood and psychotic syndromes, combining two independent but complementary dimensions: clinical staging, and three proposed pathophysiological mechanisms. Clinical staging reflects the progression of mental disorders and is in line with the concept used in general medicine, where more advanced stages are associated with a poorer prognosis and a need for more intensive interventions with a higher risk-to-benefit ratio. The three proposed pathophysiological mechanisms are neurodevelopmental abnormalities, hyperarousal and circadian dysfunction, which, over time, have illness trajectories (or pathways) to psychosis, anxious depression and bipolar spectrum disorders, respectively. The transdiagnostic framework has been evaluated in young people presenting to youth mental health clinics of the University of Sydney's Brain and Mind Centre, alongside a range of clinical and objective measures. Our research to date provides support for this framework, and we are now exploring its application to the development of more personalised models of care. CHAPTER 3: A COMPREHENSIVE ASSESSMENT FRAMEWORK FOR YOUTH MENTAL HEALTH: GUIDING HIGHLY PERSONALISED AND MEASUREMENT-BASED CARE USING MULTIDIMENSIONAL AND OBJECTIVE MEASURES: There is an urgent need for improved care for young people with mental health problems, in particular those with subthreshold mental disorders that are not sufficiently severe to meet traditional diagnostic criteria. New comprehensive assessment frameworks are needed to capture the biopsychosocial profile of a young person to drive highly personalised and measurement-based mental health care. We present a range of multidimensional measures involving five key domains: social and occupational function; self-harm, suicidal thoughts and behaviours; alcohol or other substance misuse; physical health; and illness type, stage and trajectory. Objective measures include: neuropsychological function; sleep-wake behaviours and circadian rhythms; metabolic and immune markers; and brain structure and function. The recommended multidimensional measures facilitate the development of a comprehensive clinical picture. The objective measures help to further develop informative and novel insights into underlying pathophysiological mechanisms and illness trajectories to guide personalised care plans. A panel of specific multidimensional and objective measures are recommended as standard clinical practice, while others are recommended secondarily to provide deeper insights with the aim of revealing alternative clinical paths for targeted interventions and treatments matched to the clinical stage and proposed pathophysiological mechanisms of the young person. CHAPTER 4: PERSONALISING CARE OPTIONS IN YOUTH MENTAL HEALTH: USING MULTIDIMENSIONAL ASSESSMENT, CLINICAL STAGE, PATHOPHYSIOLOGICAL MECHANISMS, AND INDIVIDUAL ILLNESS TRAJECTORIES TO GUIDE TREATMENT SELECTION: New models of mental health care for young people require that interventions be matched to illness type, clinical stage, underlying pathophysiological mechanisms and individual illness trajectories. Narrow syndrome-focused classifications often direct clinical attention away from other key factors such as functional impairment, self-harm and suicidality, alcohol or other substance misuse, and poor physical health. By contrast, we outline a treatment selection guide for early intervention for adolescent-onset mood and psychotic syndromes (ie, active treatments and indicated and more specific secondary prevention strategies). This guide is based on experiences with the Brain and Mind Centre's highly personalised and measurement-based care model to manage youth mental health. The model incorporates three complementary core concepts: ▶A multidimensional assessment and outcomes framework including: social and occupational function; self-harm, suicidal thoughts and behaviours; alcohol or other substance misuse; physical health; and illness trajectory. ▶Clinical stage. ▶Three common illness subtypes (psychosis, anxious depression, bipolar spectrum) based on three underlying pathophysiological mechanisms (neurodevelopmental, hyperarousal, circadian). These core concepts are not mutually exclusive and together may facilitate improved outcomes through a clinical stage-appropriate and transdiagnostic framework that helps guide decisions regarding the provision of appropriate and effective care options. Given its emphasis on adolescent-onset mood and psychotic syndromes, the Brain and Mind Centre's model of care also respects a fundamental developmental perspective - categorising childhood problems (eg, anxiety and neurodevelopmental difficulties) as risk factors and respecting the fact that young people are in a period of major biological and social transition. Based on these factors, a range of social, psychological and pharmacological interventions are recommended, with an emphasis on balancing the personal benefit-to-cost ratio. CHAPTER 5: A SERVICE DELIVERY MODEL TO SUPPORT HIGHLY PERSONALISED AND MEASUREMENT-BASED CARE IN YOUTH MENTAL HEALTH: Over the past decade, we have seen a growing focus on creating mental health service delivery models that better meet the unique needs of young Australians. Recent policy directives from the Australian Government recommend the adoption of stepped-care services to improve the appropriateness of care, determined by severity of need. Here, we propose that a highly personalised approach enhances stepped-care models by incorporating clinical staging and a young person's current and multidimensional needs. It explicitly aims to prevent progression to more complex and severe forms of illness and is better aligned to contemporary models of the patterns of emergence of psychopathology. Inherent within a highly personalised approach is the incorporation of other evidence-based processes, including real-time measurement-based care and use of multidisciplinary teams of health professionals. Data-driven local system modelling and personalised health information technologies provide crucial infrastructure support to these processes for better access to, and higher quality of, mental health care for young people.
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Affiliation(s)
- Ian B Hickie
- Brain and Mind Centre, University of Sydney, Sydney, NSW
| | - Elizabeth M Scott
- Brain and Mind Centre, University of Sydney, Sydney, NSW.,University of Notre Dame Australia, Sydney, NSW
| | - Shane P Cross
- Brain and Mind Centre, University of Sydney, Sydney, NSW
| | - Frank Iorfino
- Brain and Mind Centre, University of Sydney, Sydney, NSW
| | | | | | | | | | | | - Jacob J Crouse
- Brain and Mind Centre, University of Sydney, Sydney, NSW
| | - Daniel F Hermens
- Brain and Mind Centre, University of Sydney, Sydney, NSW.,Sunshine Coast Mind and Neuroscience - Thompson Institute, University of the Sunshine Coast, Birtinya, QLD
| | - Dagmar Koethe
- Brain and Mind Centre, University of Sydney, Sydney, NSW
| | | | | | - Vilas Sawrikar
- Brain and Mind Centre, University of Sydney, Sydney, NSW.,University of Edinburgh, Edinburgh, UK
| | - Jan Scott
- Brain and Mind Centre, University of Sydney, Sydney, NSW.,Institute of Neuroscience, Newcastle University, Newcastle Upon Tyne, UK
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31
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Mallet J, Le Strat Y, Dubertret C, Gorwood P. Polygenic Risk Scores Shed Light on the Relationship between Schizophrenia and Cognitive Functioning: Review and Meta-Analysis. J Clin Med 2020; 9:E341. [PMID: 31991840 PMCID: PMC7074036 DOI: 10.3390/jcm9020341] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 01/14/2020] [Accepted: 01/23/2020] [Indexed: 12/26/2022] Open
Abstract
Schizophrenia is a multifactorial disease associated with widespread cognitive impairment. Although cognitive deficits are one of the factors most strongly associated with functional impairment in schizophrenia (SZ), current treatment strategies hardly tackle these impairments. To develop more efficient treatment strategies in patients, a better understanding of their pathogenesis is needed. Recent progress in genetics, driven by large genome-wide association studies (GWAS) and the use of polygenic risk scores (PRS), has provided new insights about the genetic architecture of complex human traits, including cognition and SZ. Here, we review the recent findings examining the genetic links between SZ and cognitive functions in population-based samples as well as in participants with SZ. The performed meta-analysis showed a negative correlation between the polygenetic risk score of schizophrenia and global cognition (p < 0.001) when the samples rely on general and healthy participants, while no significant correlation was detected when the three studies devoted to schizophrenia patients were meta-analysed (p > 0.05). Our review and meta-analysis therefore argues against universal pleiotropy for schizophrenia alleles and cognition, since cognition in SZ patients would be underpinned by the same genetic factors than in the general population, and substantially independent of common variant liability to the disorder.
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Affiliation(s)
- Jasmina Mallet
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Yann Le Strat
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Caroline Dubertret
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Philip Gorwood
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
- GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014 Paris, France
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32
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Moorman SM, Greenfield EA, Garcia S. School Context in Adolescence and Cognitive Functioning 50 Years Later. JOURNAL OF HEALTH AND SOCIAL BEHAVIOR 2019; 60:493-508. [PMID: 31912762 PMCID: PMC7007773 DOI: 10.1177/0022146519887354] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
To advance understanding of how social inequalities from childhood might contribute to cognitive aging, we examined the extent to which school context in adolescence was associated with individuals' cognitive performance more than 50 years later. Using data from 3,012 participants in the Wisconsin Longitudinal Study (WLS), we created an aggregate measure of school-level structural advantage, with indicators such as the proportion of teachers who had at least five years of teaching experience and spending per pupil. Multilevel models indicated that secondary school advantage was associated with small benefits in language/executive function at age 65 among older adults who had lower academic achievement in secondary school. Findings suggest that school advantage is a developmental context of adolescence that has modest implications for intracohort differences in aspects of later life cognition.
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Affiliation(s)
| | | | - Sarah Garcia
- University of Minnesota, Minneapolis, Minnesota, USA
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33
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Mosley JD, Benson MD, Smith JG, Melander O, Ngo D, Shaffer CM, Ferguson JF, Herzig MS, McCarty CA, Chute CG, Jarvik GP, Gordon AS, Palmer MR, Crosslin DR, Larson EB, Carrell DS, Kullo IJ, Pacheco JA, Peissig PL, Brilliant MH, Kitchner TE, Linneman JG, Namjou B, Williams MS, Ritchie MD, Borthwick KM, Kiryluk K, Mentch FD, Sleiman PM, Karlson EW, Verma SS, Zhu Y, Vasan RS, Yang Q, Denny JC, Roden DM, Gerszten RE, Wang TJ. Probing the Virtual Proteome to Identify Novel Disease Biomarkers. Circulation 2019; 138:2469-2481. [PMID: 30571344 DOI: 10.1161/circulationaha.118.036063] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. METHODS We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651). RESULTS In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β. CONCLUSIONS We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.
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Affiliation(s)
- Jonathan D Mosley
- Department of Medicine (J.D.M., C.M.S., J.F.F., J.C.D., T.J.W.), Vanderbilt University Medical Center, Nashville, TN
| | - Mark D Benson
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA (M.D.B.).,Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA (M.D.B., M.S.H., R.E.G.)
| | - J Gustav Smith
- Molecular Epidemiology and Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Malmö, Sweden (J.G.S., O.M.)
| | - Olle Melander
- Molecular Epidemiology and Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Malmö, Sweden (J.G.S., O.M.)
| | - Debby Ngo
- Department of Medicine and the Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston (D.N.)
| | - Christian M Shaffer
- Department of Medicine (J.D.M., C.M.S., J.F.F., J.C.D., T.J.W.), Vanderbilt University Medical Center, Nashville, TN
| | - Jane F Ferguson
- Department of Medicine (J.D.M., C.M.S., J.F.F., J.C.D., T.J.W.), Vanderbilt University Medical Center, Nashville, TN
| | - Matthew S Herzig
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA (M.D.B., M.S.H., R.E.G.)
| | | | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.)
| | - Gail P Jarvik
- Departments of Medicine (J.P.J., A.S.G., M.R.P., E.B.L.), University of Washington, Seattle
| | - Adam S Gordon
- Departments of Medicine (J.P.J., A.S.G., M.R.P., E.B.L.), University of Washington, Seattle
| | - Melody R Palmer
- Departments of Medicine (J.P.J., A.S.G., M.R.P., E.B.L.), University of Washington, Seattle
| | - David R Crosslin
- Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle
| | - Eric B Larson
- Departments of Medicine (J.P.J., A.S.G., M.R.P., E.B.L.), University of Washington, Seattle.,Kaiser Permanente Washington Health Research Institute, Seattle, WA (E.B.L., D.S.C.)
| | - David S Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, WA (E.B.L., D.S.C.)
| | - Iftikhar J Kullo
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.)
| | - Jennifer A Pacheco
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.)
| | - Peggy L Peissig
- Biomedical Informatics Research Center (P.L.P., J.G.L.), Marshfield Clinic Research Institute, WI
| | - Murray H Brilliant
- Center for Computational and Biomedical Informatics (M.H.B., T.E.K.), Marshfield Clinic Research Institute, WI
| | - Terrie E Kitchner
- Center for Computational and Biomedical Informatics (M.H.B., T.E.K.), Marshfield Clinic Research Institute, WI
| | - James G Linneman
- Biomedical Informatics Research Center (P.L.P., J.G.L.), Marshfield Clinic Research Institute, WI
| | - Bahram Namjou
- Cincinnati Children's Hospital Medical Center and University of Cincinnati, OH (B.N.)
| | - Marc S Williams
- Genomic Medicine Institute (M.S.W.), Geisinger Health System, Danville, PA
| | - Marylyn D Ritchie
- Departments of Bioinformatics and Genetics (M.D.R.), University of Pennsylvania, Philadelphia
| | - Kenneth M Borthwick
- Biomedical and Translational Informatics (K.M.B.), Geisinger Health System, Danville, PA
| | - Krzysztof Kiryluk
- Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY (K.K.)
| | - Frank D Mentch
- Center for Applied Genomics, Children's Hospital of Philadelphia, PA (F.D.M., P.M.S.)
| | - Patrick M Sleiman
- Center for Applied Genomics, Children's Hospital of Philadelphia, PA (F.D.M., P.M.S.)
| | - Elizabeth W Karlson
- Department of Medicine, Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA (E.W.K.)
| | - Shefali S Verma
- Perelman School of Medicine, Department of Genetics (S.S.V.), University of Pennsylvania, Philadelphia
| | - Yineng Zhu
- Department of Biostatistics, Boston University School of Public Health, MA (Y.Z., Q.Y.)
| | | | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, MA (Y.Z., Q.Y.)
| | - Josh C Denny
- Department of Medicine (J.D.M., C.M.S., J.F.F., J.C.D., T.J.W.), Vanderbilt University Medical Center, Nashville, TN.,Biomedical Informatics (J.C.D., D.M.R.), Vanderbilt University Medical Center, Nashville, TN
| | - Dan M Roden
- Biomedical Informatics (J.C.D., D.M.R.), Vanderbilt University Medical Center, Nashville, TN.,Department of Pharmacology (D.M.R.), Vanderbilt University Medical Center, Nashville, TN
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA (M.D.B., M.S.H., R.E.G.)
| | - Thomas J Wang
- Department of Medicine (J.D.M., C.M.S., J.F.F., J.C.D., T.J.W.), Vanderbilt University Medical Center, Nashville, TN
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Roberts MR, Asgari MM, Toland AE. Genome-wide association studies and polygenic risk scores for skin cancer: clinically useful yet? Br J Dermatol 2019; 181:1146-1155. [PMID: 30908599 DOI: 10.1111/bjd.17917] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2019] [Indexed: 12/27/2022]
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified thousands of susceptibility variants, although most have been associated with small individual risk estimates that offer little predictive value. However, combining multiple variants into polygenic risk scores (PRS) may be more informative. Multiple studies have developed PRS composed of GWAS-identified variants for cutaneous cancers. This review highlights data from these studies. OBJECTIVES To review published GWAS and PRS studies for melanoma, cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC), and discuss their potential clinical utility. METHODS We searched PubMed and the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue to identify relevant studies. RESULTS Results from 21 GWAS (11 melanoma, 3 cSCC, 7 BCC) and 11 PRS studies are summarized. Six loci in pigmentation genes overlap between these three cancers (ASIP/RALY, IRF4, MC1R, OCA2, SLC45A2 and TYR). Additional loci overlap for cSCC/BCC and BCC/melanoma, but no other loci are shared between cSCC and melanoma. PRS for melanoma show roughly two-to-threefold increases in risk and modest improvements in risk prediction (2-7% increases). PRS are associated with twofold and threefold increases in risk of cSCC and BCC, respectively, with small improvements (2% increase) in predictive ability. CONCLUSIONS Existing data indicate that PRS may offer small, but potentially meaningful, improvements to risk prediction. Additional research is needed to clarify the potential utility of PRS in cutaneous carcinomas. Clinical translation will require well-powered validation studies incorporating known risk factors to evaluate PRS as tools for screening. What's already known about this topic? Over 50 susceptibility loci for melanoma, basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC) have been identified in genome-wide association studies (GWAS). Polygenic risk scores (PRS) using variants identified from GWAS have also been developed for melanoma, BCC and cSCC, and investigated with respect to clinical risk prediction. What does this study add? This review provides an overview of GWAS findings and the potential clinical utility of PRS for melanoma, BCC and cSCC.
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Affiliation(s)
- M R Roberts
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, U.S.A.,Department of Population Medicine, Harvard Pilgrim Healthcare Institute, Boston, MA, U.S.A
| | - M M Asgari
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, U.S.A.,Department of Population Medicine, Harvard Pilgrim Healthcare Institute, Boston, MA, U.S.A
| | - A E Toland
- Department of Cancer Biology and Genetics, Comprehensive Cancer Center, Ohio State University, 998 Biomedical Research Tower, 460 W 12th Ave, Columbus, OH, 43210, U.S.A
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Polygenic and environmental influences on the course of African Americans' alcohol use from early adolescence through young adulthood. Dev Psychopathol 2019; 32:703-718. [PMID: 31256767 DOI: 10.1017/s0954579419000701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The study examined (a) whether alcohol use subgroups could be identified among African Americans assessed from adolescence through early adulthood, and (b) whether subgroup membership was associated with the interaction between internalizing symptoms and antisocial behavior polygenic risk scores (PRSs) and environmental characteristics (i.e., parental monitoring, community disadvantage). Participants (N = 436) were initially recruited for an elementary school-based prevention trial in a Mid-Atlantic city. Youths reported on the frequency of their past year alcohol use from ages 14-26. DNA was obtained from participants at age 21. Internalizing symptoms and antisocial behavior PRSs were created based on a genome-wide association study (GWAS) conducted by Benke et al. (2014) and Tielbeek et al. (2017), respectively. Parental monitoring and community disadvantage were assessed at age 12. Four classes of past year alcohol use were identified: (a) early-onset, increasing; (b) late-onset, moderate use; (c) low steady; and (d) early-onset, decreasing. In high community disadvantaged settings, participants with a higher internalizing symptoms PRS were more likely to be in the early-onset, decreasing class than the low steady class. When exposed to elevated community disadvantage, participants with a higher antisocial behavior PRS were more likely to be in the early-onset, increasing class than the early-onset, decreasing and late-onset, moderate use classes.
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Musci RJ, Fairman B, Masyn KE, Uhl G, Maher B, Sisto DY, Kellam SG, Ialongo NS. Polygenic Score × Intervention Moderation: an Application of Discrete-Time Survival Analysis to Model the Timing of First Marijuana Use Among Urban Youth. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2019; 19:6-14. [PMID: 27817095 DOI: 10.1007/s11121-016-0729-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The present study examines the interaction between a polygenic score and an elementary school-based universal preventive intervention trial and its effects on a discrete-time survival analysis of time to first smoking marijuana. Research has suggested that initiation of substances is both genetically and environmentally driven (Rhee et al., Archives of general psychiatry 60:1256-1264, 2003; Verweij et al., Addiction 105:417-430, 2010). A previous work has found a significant interaction between the polygenic score and the same elementary school-based intervention with tobacco smoking (Musci et al., in press). The polygenic score reflects the contribution of multiple genes and has been shown in prior research to be predictive of smoking cessation, tobacco use, and marijuana use (Uhl et al., Molecular Psychiatry 19:50-54, 2014). Using data from a longitudinal preventive intervention study (N = 678), we examined age of first marijuana use from sixth grade to age 18. Genetic data were collected during emerging adulthood and were genotyped using the Affymetrix 6.0 microarray (N = 545). The polygenic score was computed using these data. Discrete-time survival analysis was employed to test for intervention main and interaction effects with the polygenic score. We found main effect of the polygenic score approaching significance, with the participants with higher polygenic scores reporting their first smoking marijuana at an age significantly later than controls (p = .050). We also found a significant intervention × polygenic score interaction effect at p = .003, with participants at the higher end of the polygenic score benefiting the most from the intervention in terms of delayed age of first use. These results suggest that genetics may play an important role in the age of first use of marijuana and that differences in genetics may account for the differential effectiveness of classroom-based interventions in delaying substance use experimentation.
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Affiliation(s)
- Rashelle J Musci
- Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, 624 North Broadway, Baltimore, MD, 21205, USA.
| | - Brian Fairman
- Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, 624 North Broadway, Baltimore, MD, 21205, USA
| | - Katherine E Masyn
- School of Public Health, Georgia State University, One Park Place, Atlanta, GA, 30303, USA
| | - George Uhl
- New Mexico VA Healthcare System, 1501 San Pedro Drive, DE, Albuquerque, NM, 87108, USA
| | - Brion Maher
- Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, 624 North Broadway, Baltimore, MD, 21205, USA
| | - Danielle Y Sisto
- Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, 624 North Broadway, Baltimore, MD, 21205, USA
| | - Sheppard G Kellam
- Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, 624 North Broadway, Baltimore, MD, 21205, USA
| | - Nicholas S Ialongo
- Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, 624 North Broadway, Baltimore, MD, 21205, USA
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Vrablik M, Hubacek JA, Dlouha D, Satny M, Adamkova V, Ceska R. Strong Association between APOA5 Gene Polymorphisms and Hypertriglyceridaemic Episodes. Folia Biol (Praha) 2019; 65:188-194. [PMID: 31903892 DOI: 10.14712/fb2019065040188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2024]
Abstract
Plasma triglyceride (TG) levels represent a significant risk factor of cardiovascular and total mortality. Concentrations of TG in the plasma depend, to a large extent, on the genetic background, and the apolipoprotein A5 (APOA5) gene seems to be one of the most powerful players in the plasma TG metabolism regulation. In total, we analysed three tagging APOA5 (rs964184 rs662799, rs3135506) SNPs in 209 patients with plasma TG levels over 10 mmol/l (HTG) on at least one occasion and in 379 treatment-naïve controls (NTG) with plasma TG values within the normal range. Minor alleles of all three analysed APOA5 polymorphisms significantly (all P < 0.0001) increased the risk of hypertriglyceridaemia. The most significant association (P < 0.0000001) was observed for the rs964184 polymorphism, where the minor GG homozygotes had the odds ratio (OR, 95% CI) for hypertriglyceridaemia development 21.30 (8.09-56.07, P < 0.000001) in comparison with the major CC allele homozygotes. Carriers of at least one minor allele at rs3135506 had OR (95% CI) 4.19 (2.75-6.40); (P < 0.000005) for HTG development and similarly, carriers of a minor allele at rs662799 had OR (95% CI) 3.07 (2.00-4.72) (P < 0.0001). The cumulative presence of risk alleles (unweighted gene score) significantly differed between patients with episodes of high TG and controls at P < 0.0000001. There were 73 % of subjects without any of the risk alleles among the controls and 46 % in the patients. In contrast, the controls just included 3 % of subjects with score 3 and more in comparison with 18 % in HTG patients. We conclude that common APOA5 variants are very important genetic determinants of episodic hypertriglyceridaemia in the Czech population with a high potential to be applied in personalized medicine.
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Affiliation(s)
- M Vrablik
- 3rd Department of Internal Medicine, Department of Endocrinology and Metabolism, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - J A Hubacek
- Centre for Experimental Medicine, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - D Dlouha
- Centre for Experimental Medicine, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - M Satny
- 3rd Department of Internal Medicine, Department of Endocrinology and Metabolism, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - V Adamkova
- Department of Preventive Cardiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - R Ceska
- 3rd Department of Internal Medicine, Department of Endocrinology and Metabolism, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
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Wang Z, Liu Q, Wilson CL, Easton J, Mulder H, Chang TC, Rusch MC, Edmonson MN, Rice SV, Ehrhardt MJ, Howell RM, Kesserwan CA, Wu G, Nichols KE, Downing JR, Hudson MM, Zhang J, Yasui Y, Robison LL. Polygenic Determinants for Subsequent Breast Cancer Risk in Survivors of Childhood Cancer: The St Jude Lifetime Cohort Study (SJLIFE). Clin Cancer Res 2018; 24:6230-6235. [PMID: 30366939 PMCID: PMC6295266 DOI: 10.1158/1078-0432.ccr-18-1775] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 07/28/2018] [Accepted: 09/05/2018] [Indexed: 01/19/2023]
Abstract
PURPOSE The risk of subsequent breast cancer among female childhood cancer survivors is markedly elevated. We aimed to determine genetic contributions to this risk, focusing on polygenic determinants implicated in breast cancer susceptibility in the general population. EXPERIMENTAL DESIGN Whole-genome sequencing (30×) was performed on survivors in the St Jude Lifetime Cohort, and germline mutations in breast cancer predisposition genes were classified for pathogenicity. A polygenic risk score (PRS) was constructed for each survivor using 170 established common risk variants. Relative rate (RR) and 95% confidence interval (95% CI) of subsequent breast cancer incidence were estimated using multivariable piecewise exponential regression. RESULTS The analysis included 1,133 female survivors of European ancestry (median age at last follow-up = 35.4 years; range, 8.4-67.4), of whom 47 were diagnosed with one or more subsequent breast cancers (median age at subsequent breast cancer = 40.3 years; range, 24.5-53.0). Adjusting for attained age, age at primary diagnosis, chest irradiation, doses of alkylating agents and anthracyclines, and genotype eigenvectors, RRs for survivors with PRS in the highest versus lowest quintiles were 2.7 (95% CI, 1.0-7.3), 3.0 (95% CI, 1.1-8.1), and 2.4 (95% CI, 0.1-81.1) for all survivors and survivors with and without chest irradiation, respectively. Similar associations were observed after excluding carriers of pathogenic/likely pathogenic mutations in breast cancer predisposition genes. Notably, the PRS was associated with the subsequent breast cancer rate under the age of 45 years (RR = 3.2; 95% CI, 1.2-8.3). CONCLUSIONS Genetic profiles comprised of small-effect common variants and large-effect predisposing mutations can inform personalized breast cancer risk and surveillance/intervention in female childhood cancer survivors.
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Affiliation(s)
- Zhaoming Wang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee.
| | - Qi Liu
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | - Carmen L Wilson
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - John Easton
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Heather Mulder
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Ti-Cheng Chang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Michael C Rusch
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Michael N Edmonson
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Stephen V Rice
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Matthew J Ehrhardt
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Rebecca M Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Chimene A Kesserwan
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Gang Wu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Kim E Nichols
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - James R Downing
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Melissa M Hudson
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Jinghui Zhang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Yutaka Yasui
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Leslie L Robison
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee
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A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers. Nat Commun 2018; 9:3522. [PMID: 30166544 PMCID: PMC6117367 DOI: 10.1038/s41467-018-05624-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Accepted: 07/13/2018] [Indexed: 01/05/2023] Open
Abstract
Defining the full spectrum of human disease associated with a biomarker is necessary to advance the biomarker into clinical practice. We hypothesize that associating biomarker measurements with electronic health record (EHR) populations based on shared genetic architectures would establish the clinical epidemiology of the biomarker. We use Bayesian sparse linear mixed modeling to calculate SNP weightings for 53 biomarkers from the Atherosclerosis Risk in Communities study. We use the SNP weightings to computed predicted biomarker values in an EHR population and test associations with 1139 diagnoses. Here we report 116 associations meeting a Bonferroni level of significance. A false discovery rate (FDR)-based significance threshold reveals more known and undescribed associations across a broad range of biomarkers, including biometric measures, plasma proteins and metabolites, functional assays, and behaviors. We confirm an inverse association between LDL-cholesterol level and septicemia risk in an independent epidemiological cohort. This approach efficiently discovers biomarker-disease associations. Biomarker identification requires prohibitively large cohorts with gene expression and phenotype data. The approach introduced here learns polygenic predictors of expression from genetic and expression data, used to infer biomarker levels in patients with genetic and disease information.
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Abstract
An important aspect of public health is disease prediction and health promotion through better targeting of preventive strategies. Well-targeted preventive strategies will eventually decrease burden of diseases and thus precise prediction plays a crucial role in public health. Many investigators put efforts into finding models that improve prediction using known risk factors of diseases. Recently with the overwhelming load of genetic loci discovered for complex diseases through genome-wide association studies (GWAS), much of attention has been focused on the role of these genetic loci to improve prediction models. Genetic loci in solo explain little variance of diseases. It is thus necessary to create new genetic parameters that combine the effect of as many genetic loci as possible. Such new parameters aim to better distinguish individuals who will develop a disease from those who will not. In this chapter, various polygenic methods that use multiple genetic loci to directly or indirectly improve precision of genetic prediction are discussed.
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Avramopoulos D. Recent Advances in the Genetics of Schizophrenia. MOLECULAR NEUROPSYCHIATRY 2018; 4:35-51. [PMID: 29998117 PMCID: PMC6032037 DOI: 10.1159/000488679] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 03/21/2018] [Indexed: 12/27/2022]
Abstract
The last decade brought tremendous progress in the field of schizophrenia genetics. As a result of extensive collaborations and multiple technological advances, we now recognize many types of genetic variants that increase the risk. These include large copy number variants, rare coding inherited and de novο variants, and over 100 loci harboring common risk variants. While the type and contribution to the risk vary among genetic variants, there is concordance in the functions of genes they implicate, such as those whose RNA binds the fragile X-related protein FMRP and members of the activity-regulated cytoskeletal complex involved in learning and memory. Gene expression studies add important information on the biology of the disease and recapitulate the same functional gene groups. Studies of alternative phenotypes help us widen our understanding of the genetic architecture of mental function and dysfunction, how diseases overlap not only with each other but also with non-disease phenotypes. The challenge is to apply this new knowledge to prevention and treatment and help patients. The data generated so far and emerging technologies, including new methods in cell engineering, offer significant promise that in the next decade we will unlock the translational potential of these significant discoveries.
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Affiliation(s)
- Dimitrios Avramopoulos
- Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Psychiatry, Johns Hopkins University, Baltimore, Maryland, USA
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Musci RJ, Bettencourt AF, Sisto D, Maher B, Uhl G, Ialongo N, Bradshaw CP. Evaluating the genetic susceptibility to peer reported bullying behaviors. Psychiatry Res 2018; 263:193-198. [PMID: 29573659 PMCID: PMC6085882 DOI: 10.1016/j.psychres.2018.03.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 03/04/2018] [Accepted: 03/05/2018] [Indexed: 10/17/2022]
Abstract
Bullying is a significant public health concern with lasting impacts on youth. Although environmental risk factors for bullying have been well-characterized, genetic influences on bullying are not well understood. This study explored the role of genetics on early childhood bullying behavior. Participants were 561 children who participated in a longitudinal randomized control trial of a preventive intervention beginning in first grade who were present for the first grade peer nominations used to measure early childhood bullying and who provided genetic data during the age 19-21 year follow-up in the form of blood or saliva. Measures included a polygenic risk score (PRS) derived from a conduct disorder genome wide association study. Latent profile analysis identified three profiles of bullying behaviors during early childhood. Results suggest that the PRS was significantly associated with class membership, with individuals in the moderate bully-victim profile having the highest levels of the PRS and those in the high bully-victim profile having the lowest levels. This line of research has important implications for understanding genetic vulnerability to bullying in early childhood.
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Affiliation(s)
- Rashelle J Musci
- Department of Mental Health, Johns Hopkins University, Bloomberg School of Public Health, 624 North Broadway, Baltimore, MD 21205, USA.
| | - Amie F Bettencourt
- Department of Mental Health, Johns Hopkins University, Bloomberg School of Public Health, 624 North Broadway, Baltimore, MD 21205, USA; Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Johns Hopkins School of Medicine, 550 North Broadway, Baltimore, MD 21205, USA
| | - Danielle Sisto
- Department of Mental Health, Johns Hopkins University, Bloomberg School of Public Health, 624 North Broadway, Baltimore, MD 21205, USA
| | - Brion Maher
- Department of Mental Health, Johns Hopkins University, Bloomberg School of Public Health, 624 North Broadway, Baltimore, MD 21205, USA
| | - George Uhl
- Research Service, New Mexico VA Healthcare System, Departments of Neurology, Neuroscience and Molecular Genetics and Microbiology, University of New Mexico, Departments of Neurology, Neuroscience and Mental Health, Johns Hopkins Medical Institutions
| | - Nicholas Ialongo
- Department of Mental Health, Johns Hopkins University, Bloomberg School of Public Health, 624 North Broadway, Baltimore, MD 21205, USA
| | - Catherine P Bradshaw
- Department of Mental Health, Johns Hopkins University, Bloomberg School of Public Health, 624 North Broadway, Baltimore, MD 21205, USA; Curry School of Education, University of Virginia, PO Box 400270, Charlottesille, VA 22904, USA
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Salvatore JE, Savage JE, Barr P, Wolen AR, Aliev F, Vuoksimaa E, Latvala A, Pulkkinen L, Rose RJ, Kaprio J, Dick DM. Incorporating Functional Genomic Information to Enhance Polygenic Signal and Identify Variants Involved in Gene-by-Environment Interaction for Young Adult Alcohol Problems. Alcohol Clin Exp Res 2018; 42:413-423. [PMID: 29121402 PMCID: PMC5785466 DOI: 10.1111/acer.13551] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 11/02/2017] [Indexed: 12/18/2022]
Abstract
BACKGROUND Characterizing aggregate genetic risk for alcohol misuse and identifying variants involved in gene-by-environment (G × E) interaction effects has so far been a major challenge. We hypothesized that functional genomic information could be used to enhance detection of polygenic signal underlying alcohol misuse and to prioritize identification of single nucleotide polymorphisms (SNPs) most likely to exhibit G × E effects. METHODS We examined these questions in the young adult FinnTwin12 sample (n = 1,170). We used genomewide association estimates from an independent sample to derive 2 types of polygenic scores for alcohol problems in FinnTwin12. Genomewide polygenic scores included all SNPs surpassing a designated p-value threshold. DNase polygenic scores were a subset of the genomewide polygenic scores including only variants in DNase I hypersensitive sites (DHSs), which are open chromatin marks likely to index regions with a regulatory function. We conducted parallel analyses using height as a nonpsychiatric model phenotype to evaluate the consistency of effects. For the G × E analyses, we examined whether SNPs in DHSs were overrepresented among SNPs demonstrating significant G × E effects in an interaction between romantic relationship status and intoxication frequency. RESULTS Contrary to our expectations, we found that DNase polygenic scores were not more strongly predictive of alcohol problems than conventional polygenic scores. However, variants in DNase polygenic scores had per-SNP effects that were up to 1.4 times larger than variants in conventional polygenic scores. This same pattern of effects was also observed in supplementary analyses with height. In G × E models, SNPs in DHSs were modestly overrepresented among SNPs with significant interaction effects for intoxication frequency. CONCLUSIONS These findings highlight the potential utility of integrating functional genomic annotation information to increase the signal-to-noise ratio in polygenic scores and identify genetic variants that may be most susceptible to environmental modification.
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Affiliation(s)
- Jessica E. Salvatore
- Department of Psychology, Virginia Commonwealth University, PO Box 842018, Richmond, VA 23284-2018, United States
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980126, Richmond, VA 23298, United States
| | - Jeanne E. Savage
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980126, Richmond, VA 23298, United States
| | - Peter Barr
- Department of Psychology, Virginia Commonwealth University, PO Box 842018, Richmond, VA 23284-2018, United States
| | - Aaron R. Wolen
- Center for Clinical and Translational Research, Virginia Commonwealth University, P.O. Box 980261, Richmond, VA 23298-0261, United States
| | - Fazil Aliev
- Department of Psychology, Virginia Commonwealth University, PO Box 842018, Richmond, VA 23284-2018, United States
- Faculty of Business, Karabuk University, 78050 Karabuk, Turkey
| | - Eero Vuoksimaa
- Institute for Molecular Medicine FIMM, University of Helsinki, PO Box 20 (Tukholmankatu 8), FI-00014 Helsinki, Finland
| | - Antti Latvala
- Institute for Molecular Medicine FIMM, University of Helsinki, PO Box 20 (Tukholmankatu 8), FI-00014 Helsinki, Finland
| | - Lea Pulkkinen
- Department of Psychology, University of Jyväskylä, PO Box 35, 40014 University of Jyväskylä, Jyväskylä, Finland
| | - Richard J. Rose
- Department of Psychological & Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington, IN 47405, United States
| | - Jaakko Kaprio
- Institute for Molecular Medicine FIMM, University of Helsinki, PO Box 20 (Tukholmankatu 8), FI-00014 Helsinki, Finland
| | - Danielle M. Dick
- Department of Psychology, Virginia Commonwealth University, PO Box 842018, Richmond, VA 23284-2018, United States
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Latendresse SJ, Musci R, Maher BS. Critical Issues in the Inclusion of Genetic and Epigenetic Information in Prevention and Intervention Trials. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2018; 19:58-67. [PMID: 28409280 PMCID: PMC5640466 DOI: 10.1007/s11121-017-0785-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Human genetic research in the past decade has generated a wealth of data from the genome-wide association scan era, much of which is catalogued and freely available. These data will typically test the relationship between a single nucleotide variant or polymorphism (SNP) and some outcome, disease, or trait. Ongoing investigations will yield a similar wealth of data regarding epigenetic phenomena. These data will typically test the relationship between DNA methylation at a single genomic location/region and some outcome. Most of these findings will be the result of cross-sectional investigations typically using ascertained cases and controls. Consequently, most methodological consideration focuses on methods appropriate for simple case-control comparisons. It is expected that a growing number of investigators with longitudinal experimental prevention or intervention cohorts will also measure genetic and epigenetic indicators as part of their investigations, harvesting the wealth of information generated by the genome-wide association study (GWAS) era to allow for targeted hypothesis testing in the next generation of prevention and intervention trials. Herein, we discuss appropriate quality control and statistical modelling of genetic, polygenic, and epigenetic measures in longitudinal models. We specifically discuss quality control, population stratification, genotype imputation, pathway approaches, and proper modelling of an interaction between a specific genetic variant and an environment variable (GxE interaction).
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Affiliation(s)
- Shawn J Latendresse
- Department of Psychology and Neuroscience, Baylor University, One Bear Place #97334, Waco, TX, 76798, USA.
| | - Rashelle Musci
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway Ave, Baltimore, MD, 21205, USA
| | - Brion S Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway Ave, Baltimore, MD, 21205, USA.
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Maher BS, Latendresse S, Vanyukov MM. Informing Prevention and Intervention Policy Using Genetic Studies of Resistance. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2018; 19:49-57. [PMID: 27943075 PMCID: PMC5466512 DOI: 10.1007/s11121-016-0730-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The common paradigm for conceptualizing the influence of genetic and environmental factors on a particular disease relies on the concept of risk. Consequently, the bulk of etiologic, including genetic, work focuses on "risk" factors. These factors are aggregated at the high end of the distribution of liability to disease, the latent variable underlying the distribution of probability and severity of a disorder. However, liability has a symmetric but distinct aspect to risk, resistance to disorder. Resistance factors, aggregated at the low end of the liability distribution and supporting health and recovery, appear to be more promising for effective prevention and intervention. Herein, we discuss existing work on resistance factors, highlighting those with known genetic influences. We examine the utility of incorporating resistance genetics in prevention and intervention trials and compare the statistical power of a series of ascertainment schemes to develop a general framework for examining resistance outcomes in genetically informative designs. We find that an approach that samples individuals discordant on measured liability, a low-risk design, is the most feasible design and yields power equivalent to or higher than commonly used designs for detecting resistance genetic and environmental effects.
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Affiliation(s)
- Brion S Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway Ave., Baltimore, MD, 21205, USA.
| | - Shawn Latendresse
- Department of Psychology and Neuroscience, Baylor University, Waco, TX, USA
| | - Michael M Vanyukov
- Departments of Pharmaceutical Sciences, Psychiatry, and Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
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Wang SH, Hsiao PC, Yeh LL, Liu CM, Liu CC, Hwang TJ, Hsieh MH, Chien YL, Lin YT, Chandler SD, Faraone SV, Laird N, Neale B, McCarroll SA, Glatt SJ, Tsuang MT, Hwu HG, Chen WJ. Polygenic risk for schizophrenia and neurocognitive performance in patients with schizophrenia. GENES BRAIN AND BEHAVIOR 2017; 17:49-55. [PMID: 28719030 DOI: 10.1111/gbb.12401] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Revised: 06/15/2017] [Accepted: 07/13/2017] [Indexed: 12/21/2022]
Abstract
Both neurocognitive deficits and schizophrenia are highly heritable. Genetic overlap between neurocognitive deficits and schizophrenia has been observed in both the general population and in the clinical samples. This study aimed to examine if the polygenic architecture of susceptibility to schizophrenia modified neurocognitive performance in schizophrenia patients. Schizophrenia polygenic risk scores (PRSs) were first derived from the Psychiatric Genomics Consortium (PGC) on schizophrenia, and then the scores were calculated in our independent sample of 1130 schizophrenia trios, who had PsychChip data and were part of the Schizophrenia Families from Taiwan project. Pseudocontrols generated from the nontransmitted parental alleles of the parents in these trios were compared with alleles in schizophrenia patients in assessing the replicability of PGC-derived susceptibility variants. Schizophrenia PRS at the P-value threshold (PT) of 0.1 explained 0.2% in the variance of disease status in this Han-Taiwanese samples, and the score itself had a P-value 0.05 for the association test with the disorder. Each patient underwent neurocognitive evaluation on sustained attention using the continuous performance test and executive function using the Wisconsin Card Sorting Test. We applied a structural equation model to construct the neurocognitive latent variable estimated from multiple measured indices in these 2 tests, and then tested the association between the PRS and the neurocognitive latent variable. Higher schizophrenia PRS generated at the PT of 0.1 was significantly associated with poorer neurocognitive performance with explained variance 0.5%. Our findings indicated that schizophrenia susceptibility variants modify the neurocognitive performance in schizophrenia patients.
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Affiliation(s)
- S-H Wang
- Graduate Institute of Biostatistics, China Medical University, Taichung, Taiwan
| | - P-C Hsiao
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - L-L Yeh
- Department of Healthcare Administration, College of Medical and Health Science, Asia University, Taichung, Taiwan
| | - C-M Liu
- Department of Psychiatry, College of Medicine and National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
| | - C-C Liu
- Department of Psychiatry, College of Medicine and National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
| | - T-J Hwang
- Department of Psychiatry, College of Medicine and National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
| | - M H Hsieh
- Department of Psychiatry, College of Medicine and National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
| | - Y-L Chien
- Department of Psychiatry, College of Medicine and National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
| | - Y-T Lin
- Department of Psychiatry, College of Medicine and National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
| | - S D Chandler
- Center for Behavioral Genomics, Department of Psychiatry; & Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - S V Faraone
- Departments of Psychiatry and Behavioral Sciences and Neuroscience and Physiology, Medical Genetics Research Center, SUNY Upstate Medical University, Syracuse, NY, USA
| | - N Laird
- Department of Biostatistics, Harvard University, Boston, MA, USA
| | - B Neale
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - S A McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - S J Glatt
- Departments of Psychiatry and Behavioral Sciences and Neuroscience and Physiology, Medical Genetics Research Center, SUNY Upstate Medical University, Syracuse, NY, USA
| | - M T Tsuang
- Center for Behavioral Genomics, Department of Psychiatry; & Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - H-G Hwu
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.,Department of Psychiatry, College of Medicine and National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan.,Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - W J Chen
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.,Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan.,Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.,Genetic Epidemiology Core Laboratory, Division of Genomic Medicine, Research Center for Medical Excellence, National Taiwan University, Taipei, Taiwan
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Improving polygenic risk prediction from summary statistics by an empirical Bayes approach. Sci Rep 2017; 7:41262. [PMID: 28145530 PMCID: PMC5286518 DOI: 10.1038/srep41262] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 12/20/2016] [Indexed: 11/24/2022] Open
Abstract
Polygenic risk scores (PRS) from genome-wide association studies (GWAS) are increasingly used to predict disease risks. However some included variants could be false positives and the raw estimates of effect sizes from them may be subject to selection bias. In addition, the standard PRS approach requires testing over a range of p-value thresholds, which are often chosen arbitrarily. The prediction error estimated from the optimized threshold may also be subject to an optimistic bias. To improve genomic risk prediction, we proposed new empirical Bayes approaches to recover the underlying effect sizes and used them as weights to construct PRS. We applied the new PRS to twelve cardio-metabolic traits in the Northern Finland Birth Cohort and demonstrated improvements in predictive power (in R2) when compared to standard PRS at the best p-value threshold. Importantly, for eleven out of the twelve traits studied, the predictive performance from the entire set of genome-wide markers outperformed the best R2 from standard PRS at optimal p-value thresholds. Our proposed methodology essentially enables an automatic PRS weighting scheme without the need of choosing tuning parameters. The new method also performed satisfactorily in simulations. It is computationally simple and does not require assumptions on the effect size distributions.
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Pergola G, Di Carlo P, D'Ambrosio E, Gelao B, Fazio L, Papalino M, Monda A, Scozia G, Pietrangelo B, Attrotto M, Apud JA, Chen Q, Mattay VS, Rampino A, Caforio G, Weinberger DR, Blasi G, Bertolino A. DRD2 co-expression network and a related polygenic index predict imaging, behavioral and clinical phenotypes linked to schizophrenia. Transl Psychiatry 2017; 7:e1006. [PMID: 28094815 PMCID: PMC5545721 DOI: 10.1038/tp.2016.253] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 09/28/2016] [Accepted: 10/13/2016] [Indexed: 12/14/2022] Open
Abstract
Genetic risk for schizophrenia (SCZ) is determined by many genetic loci whose compound biological effects are difficult to determine. We hypothesized that co-expression pathways of SCZ risk genes are associated with system-level brain function and clinical phenotypes of SCZ. We examined genetic variants related to the dopamine D2 receptor gene DRD2 co-expression pathway and associated them with working memory (WM) behavior, the related brain activity and treatment response. Using two independent post-mortem prefrontal messenger RNA (mRNA) data sets (total N=249), we identified a DRD2 co-expression pathway enriched for SCZ risk genes. Next, we identified non-coding single-nucleotide polymorphisms (SNPs) associated with co-expression of this pathway. These SNPs were associated with regulatory genetic loci in the dorsolateral prefrontal cortex (P<0.05). We summarized their compound effect on co-expression into a Polygenic Co-expression Index (PCI), which predicted DRD2 pathway co-expression in both mRNA data sets (all P<0.05). We associated the PCI with brain activity during WM performance in two independent samples of healthy individuals (total N=368) and 29 patients with SCZ who performed the n-back task. Greater predicted DRD2 pathway prefrontal co-expression was associated with greater prefrontal activity and longer WM reaction times (all corrected P<0.05), thus indicating inefficient WM processing. Blind prediction of treatment response to antipsychotics in two independent samples of patients with SCZ suggested better clinical course of patientswith greater PCI (total N=87; P<0.05). The findings on this DRD2 co-expression pathway are a proof of concept that gene co-expression can parse SCZ risk genes into biological pathways associated with intermediate phenotypes as well as with clinically meaningful information.
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Affiliation(s)
- G Pergola
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - P Di Carlo
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - E D'Ambrosio
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - B Gelao
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - L Fazio
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - M Papalino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - A Monda
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - G Scozia
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - B Pietrangelo
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - M Attrotto
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - J A Apud
- National Institutes of Health, National Institute of Mental Health, Clinical and Translational Neuroscience Branch, NIMH, Bethesda, MD, USA
| | - Q Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - V S Mattay
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Departments of Neurology and Radiology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - A Rampino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- Institute of Psychiatry, Department of Neuroscience, Sense Organs and Locomotive System, Bari University Hospital, Bari, Italy
| | - G Caforio
- Institute of Psychiatry, Department of Neuroscience, Sense Organs and Locomotive System, Bari University Hospital, Bari, Italy
| | - D R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Departments of Psychiatry, Neurology, Neuroscience and The Mckusick-Nathans Institute of Genomic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - G Blasi
- Institute of Psychiatry, Department of Neuroscience, Sense Organs and Locomotive System, Bari University Hospital, Bari, Italy
| | - A Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- Institute of Psychiatry, Department of Neuroscience, Sense Organs and Locomotive System, Bari University Hospital, Bari, Italy
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Mosley JD, van Driest SL, Wells QS, Shaffer CM, Edwards TL, Bastarache L, McCarty CA, Thompson W, Chute CG, Jarvik GP, Crosslin DR, Larson EB, Kullo IJ, Pacheco JA, Peissig PL, Brilliant MH, Linneman JG, Denny JC, Roden DM. Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data. ACTA ACUST UNITED AC 2016; 9:521-530. [PMID: 27780847 DOI: 10.1161/circgenetics.116.001530] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Accepted: 09/28/2016] [Indexed: 01/01/2023]
Abstract
BACKGROUND Continued reductions in morbidity and mortality attributable to ischemic heart disease (IHD) require an understanding of the changing epidemiology of this disease. We hypothesized that we could use genetic correlations, which quantify the shared genetic architectures of phenotype pairs and extant risk factors from a historical prospective study to define the risk profile of a contemporary IHD phenotype. METHODS AND RESULTS We used 37 phenotypes measured in the ARIC study (Atherosclerosis Risk in Communities; n=7716, European ancestry subjects) and clinical diagnoses from an electronic health record (EHR) data set (n=19 093). All subjects had genome-wide single-nucleotide polymorphism genotyping. We measured pairwise genetic correlations (rG) between the ARIC and EHR phenotypes using linear mixed models. The genetic correlation estimates between the ARIC risk factors and the EHR IHD were modestly linearly correlated with hazards ratio estimates for incident IHD in ARIC (Pearson correlation [r]=0.62), indicating that the 2 IHD phenotypes had differing risk profiles. For comparison, this correlation was 0.80 when comparing EHR and ARIC type 2 diabetes mellitus phenotypes. The EHR IHD phenotype was most strongly correlated with ARIC metabolic phenotypes, including total:high-density lipoprotein cholesterol ratio (rG=-0.44, P=0.005), high-density lipoprotein (rG=-0.48, P=0.005), systolic blood pressure (rG=0.44, P=0.02), and triglycerides (rG=0.38, P=0.02). EHR phenotypes related to type 2 diabetes mellitus, atherosclerotic, and hypertensive diseases were also genetically correlated with these ARIC risk factors. CONCLUSIONS The EHR IHD risk profile differed from ARIC and indicates that treatment and prevention efforts in this population should target hypertensive and metabolic disease.
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Affiliation(s)
- Jonathan D Mosley
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI.
| | - Sara L van Driest
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Quinn S Wells
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Christian M Shaffer
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Todd L Edwards
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Lisa Bastarache
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Catherine A McCarty
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Will Thompson
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Christopher G Chute
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Gail P Jarvik
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - David R Crosslin
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Eric B Larson
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Iftikhar J Kullo
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Jennifer A Pacheco
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Peggy L Peissig
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Murray H Brilliant
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - James G Linneman
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Josh C Denny
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
| | - Dan M Roden
- From the Department of Medicine (J.D.M., S.L.v.D., Q.S.W., C.M.S., J.C.D., D.M.R.), Department of Pediatrics (S.L.v.D.), Vanderbilt Epidemiology Center (T.L.E.), Biomedical Informatics (L.B., J.C.D., D.M.R.), and Department of Pharmacology (D.M.R.), Vanderbilt University, Nashville, TN; Essentia Institute of Rural Health, Duluth, MN (C.A.M.); Center for Biomedical Research Informatics, North Shore University Health System, Evanston, IL (W.T.); Department of Health Policy and Management Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD (C.G.C.); Departments of Medicine (Medical Genetics) and Genome Sciences (G.P.J.) and Departments of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle; Group Health Research Institute, Seattle, WA (E.B.L.); Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.); Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.); and Biomedical Informatics Research Center (P.L.P., J.G.L.) and Center for Human Genetics (M.H.B.), Marshfield Clinic Research Foundation, WI
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