701
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702
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Ksinan AJ, Smith RL, Barr PB, Vazsonyi AT. The Associations of Polygenic Scores for Risky Behaviors and Parenting Behaviors with Adolescent Externalizing Problems. Behav Genet 2021; 52:26-37. [PMID: 34333687 DOI: 10.1007/s10519-021-10079-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 07/24/2021] [Indexed: 11/28/2022]
Abstract
The current study focused on longitudinal effects of genetics and parental behaviors and their interplay on externalizing behaviors in a panel study following individuals from adolescence to young adulthood. The nationally representative sample of Add Health participants of European ancestry included N = 4142 individuals, measured on three occasions. Parenting was operationalized as experiences with child maltreatment and maternal closeness. Externalizing problems were operationalized as alcohol use, cannabis use, and antisocial behaviors. Genetic effects were operationalized as a polygenic score (PGS) of risky behaviors. The results showed significant effects for child maltreatment, maternal closeness, and PGS, above and beyond other factors and previous levels of externalizing behaviors. Furthermore, maternal closeness was found to negatively correlate with PGS. No significant interaction effects of parenting and PGS were found. The results underscore the joint independent effects of parenting and genetics on the change in externalizing behaviors from adolescence to young adulthood.
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Affiliation(s)
- Albert J Ksinan
- Department of Family Sciences, University of Kentucky, 160 Funkhouser Dr, Lexington, KY, 40506-0054, USA. .,Research Center for Toxic Compounds (RECETOX), Masaryk University, Kamenice 5, 62500, Brno, Czech Republic.
| | - Rebecca L Smith
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA, 23284-2018, USA
| | - Peter B Barr
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA, 23284-2018, USA
| | - Alexander T Vazsonyi
- Department of Family Sciences, University of Kentucky, 160 Funkhouser Dr, Lexington, KY, 40506-0054, USA
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703
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Sun Z, Sun Z, Dong W, Shi J, Huang Z. Towards Predictive Analysis on Disease Progression: A Variational Hawkes Process Model. IEEE J Biomed Health Inform 2021; 25:4195-4206. [PMID: 34329176 DOI: 10.1109/jbhi.2021.3101113] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Massively available longitudinal data about long-term disease trajectories of patients provides a golden mine for the understanding of disease progression and efficient health service delivery. It calls for quantitative modeling of disease progression, which is a tricky problem due to the complexity of the disease progression process as well as the irregularity of time documented in trajectories. In this study, we tackle the problem with the goal of predictively analyzing disease progression. Specifically, we propose a novel Variational Hawkes Process (VHP) model to generalize disease progression and predict future patient states based on the clinical observational data of past disease trajectories. First, Hawkes Process captures the intensity of irregular visits in a trajectory documented on medical facilities and controls the aforementioned information flowing into future visits. Thereafter, the captured intensity is incorporated into a Variational Auto-Encoder to generate the representation of the future partial disease trajectory for a target patient in a predictive manner. To further improve the prediction performance, we equip the proposed model with a disease trajectory discriminator to distinguish the generated trajectories from real ones. We evaluate the proposed model on two public datasets from the MIMIC-III database pertaining to heart failure and sepsis patients, respectively, and one real-world dataset from a Chinese hospital pertaining to heart failure patients with multiple admissions. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art baselines, and may derive a set of practical implications that can benefit a wide spectrum of management and applications on disease progression.
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704
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Zhou G, Zhao H. A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics. PLoS Genet 2021; 17:e1009697. [PMID: 34310601 PMCID: PMC8341714 DOI: 10.1371/journal.pgen.1009697] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 08/05/2021] [Accepted: 07/05/2021] [Indexed: 12/27/2022] Open
Abstract
Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available at https://github.com/eldronzhou/SDPR.
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Affiliation(s)
- Geyu Zhou
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Hongyu Zhao
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
- * E-mail:
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705
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Leonenko G, Baker E, Stevenson-Hoare J, Sierksma A, Fiers M, Williams J, de Strooper B, Escott-Price V. Identifying individuals with high risk of Alzheimer's disease using polygenic risk scores. Nat Commun 2021; 12:4506. [PMID: 34301930 PMCID: PMC8302739 DOI: 10.1038/s41467-021-24082-z] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 06/02/2021] [Indexed: 11/09/2022] Open
Abstract
Polygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals' scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals' scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.
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Affiliation(s)
- Ganna Leonenko
- UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Emily Baker
- UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | | | - Annerieke Sierksma
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Laboratory for the Research of Neurodegenerative Diseases, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven (University of Leuven), Leuven, Belgium
| | - Mark Fiers
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Laboratory for the Research of Neurodegenerative Diseases, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven (University of Leuven), Leuven, Belgium
- UK Dementia Research Institute, University College London, London, UK
| | - Julie Williams
- UK Dementia Research Institute, Cardiff University, Cardiff, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Bart de Strooper
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Laboratory for the Research of Neurodegenerative Diseases, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven (University of Leuven), Leuven, Belgium
- UK Dementia Research Institute, University College London, London, UK
| | - Valentina Escott-Price
- UK Dementia Research Institute, Cardiff University, Cardiff, UK.
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
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706
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Privé F. Optimal linkage disequilibrium splitting. Bioinformatics 2021; 38:255-256. [PMID: 34260708 PMCID: PMC8696101 DOI: 10.1093/bioinformatics/btab519] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 05/22/2021] [Accepted: 07/09/2021] [Indexed: 02/05/2023] Open
Abstract
MOTIVATION A few algorithms have been developed for splitting the genome in nearly independent blocks of linkage disequilibrium. Due to the complexity of this problem, these algorithms rely on heuristics, which makes them suboptimal. RESULTS Here, we develop an optimal solution for this problem using dynamic programming. AVAILABILITY This is now implemented as function snp_ldsplit as part of R package bigsnpr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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707
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Shan N, Xie Y, Song S, Jiang W, Wang Z, Hou L. A novel transcriptional risk score for risk prediction of complex human diseases. Genet Epidemiol 2021; 45:811-820. [PMID: 34245595 DOI: 10.1002/gepi.22424] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 06/08/2021] [Accepted: 06/24/2021] [Indexed: 11/06/2022]
Abstract
Recently polygenetic risk score (PRS) has been successfully used in the risk prediction of complex human diseases. Many studies incorporated internal information, such as effect size distribution, or external information, such as linkage disequilibrium, functional annotation, and pleiotropy among multiple diseases, to optimize the performance of PRS. To leverage on multiomics datasets, we developed a novel flexible transcriptional risk score (TRS), in which messenger RNA expression levels were imputed and weighted for risk prediction. In simulation studies, we demonstrated that single-tissue TRS has greater prediction power than LDpred, especially when there is a large effect of gene expression on the phenotype. Multitissue TRS improves prediction accuracy when there are multiple tissues with independent contributions to disease risk. We applied our method to complex traits, including Crohn's disease, type 2 diabetes, and so on. The single-tissue TRS method outperformed LDpred and AnnoPred across the tested traits. The performance of multitissue TRS is trait-dependent. Moreover, our method can easily incorporate information from epigenomic and proteomic data upon the availability of reference datasets.
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Affiliation(s)
- Nayang Shan
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Yuhan Xie
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Shuang Song
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Wei Jiang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Zuoheng Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Lin Hou
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China.,MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China
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708
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Lee J, Kiiskinen T, Mars N, Jukarainen S, Ingelsson E, Neale B, Ripatti S, Natarajan P, Ganna A. Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2021; 14:e003283. [PMID: 34232692 DOI: 10.1161/circgen.120.003283] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Acute coronary syndrome (ACS) is a clinically significant presentation of coronary heart disease. Genetic information has been proposed to improve prediction beyond well-established clinical risk factors. While polygenic scores (PS) can capture an individual's genetic risk for ACS, its prediction performance may vary in the context of diverse correlated clinical conditions. Here, we aimed to test whether clinical conditions impact the association between PS and ACS. METHODS We explored the association between 405 clinical conditions diagnosed before baseline and 9080 incident cases of ACS in 387 832 individuals from the UK Biobank. Results were replicated in 6430 incident cases of ACS in 177 876 individuals from FinnGen. RESULTS We identified 80 conventional (eg, stable angina pectoris and type 2 diabetes) and unconventional (eg, diaphragmatic hernia and inguinal hernia) associations with ACS. The association between PS and ACS was consistent in individuals with and without most clinical conditions. However, a diagnosis of stable angina pectoris yielded a differential association between PS and ACS. PS was associated with a significantly reduced (interaction P=2.87×10-8) risk for ACS in individuals with stable angina pectoris (hazard ratio, 1.163 [95% CI, 1.082-1.251]) compared with individuals without stable angina pectoris (hazard ratio, 1.531 [95% CI, 1.497-1.565]). These findings were replicated in FinnGen (interaction P=1.38×10-6). CONCLUSIONS In summary, while most clinical conditions did not impact utility of PS for prediction of ACS, we found that PS was substantially less predictive of ACS in individuals with prevalent stable coronary heart disease. PS may be more appropriate for prediction of ACS in asymptomatic individuals than symptomatic individuals with clinical suspicion for coronary heart disease.
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Affiliation(s)
- Jiwoo Lee
- Department of Biomedical Data Science, Stanford University, CA (J.L., E.I.).,Broad Institute of MIT and Harvard, Cambridge (J.L., B.N., S.R., P.N., A.G.).,Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston (J.L., B.N., S.R., A.G.).,Finnish Institute for Molecular Medicine, HiLIFE, University of Helsinki, Finland (J.L., T.K., N.M., S.J., S.R., A.G.)
| | - Tuomo Kiiskinen
- Finnish Institute for Molecular Medicine, HiLIFE, University of Helsinki, Finland (J.L., T.K., N.M., S.J., S.R., A.G.)
| | - Nina Mars
- Finnish Institute for Molecular Medicine, HiLIFE, University of Helsinki, Finland (J.L., T.K., N.M., S.J., S.R., A.G.)
| | - Sakari Jukarainen
- Finnish Institute for Molecular Medicine, HiLIFE, University of Helsinki, Finland (J.L., T.K., N.M., S.J., S.R., A.G.)
| | - Erik Ingelsson
- Department of Biomedical Data Science, Stanford University, CA (J.L., E.I.)
| | - Benjamin Neale
- Broad Institute of MIT and Harvard, Cambridge (J.L., B.N., S.R., P.N., A.G.).,Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston (J.L., B.N., S.R., A.G.)
| | - Samuli Ripatti
- Broad Institute of MIT and Harvard, Cambridge (J.L., B.N., S.R., P.N., A.G.).,Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston (J.L., B.N., S.R., A.G.).,Finnish Institute for Molecular Medicine, HiLIFE, University of Helsinki, Finland (J.L., T.K., N.M., S.J., S.R., A.G.)
| | - Pradeep Natarajan
- Broad Institute of MIT and Harvard, Cambridge (J.L., B.N., S.R., P.N., A.G.)
| | - Andrea Ganna
- Broad Institute of MIT and Harvard, Cambridge (J.L., B.N., S.R., P.N., A.G.).,Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston (J.L., B.N., S.R., A.G.).,Finnish Institute for Molecular Medicine, HiLIFE, University of Helsinki, Finland (J.L., T.K., N.M., S.J., S.R., A.G.)
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709
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Kappelmann N, Czamara D, Rost N, Moser S, Schmoll V, Trastulla L, Stochl J, Lucae S, Binder EB, Khandaker GM, Arloth J. Polygenic risk for immuno-metabolic markers and specific depressive symptoms: A multi-sample network analysis study. Brain Behav Immun 2021; 95:256-268. [PMID: 33794315 DOI: 10.1016/j.bbi.2021.03.024] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 03/22/2021] [Accepted: 03/27/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND About every fourth patient with major depressive disorder (MDD) shows evidence of systemic inflammation. Previous studies have shown inflammation-depression associations of multiple serum inflammatory markers and multiple specific depressive symptoms. It remains unclear, however, if these associations extend to genetic/lifetime predisposition to higher inflammatory marker levels and what role metabolic factors such as Body Mass Index (BMI) play. It is also unclear whether inflammation-symptom associations reflect direct or indirect associations, which can be disentangled using network analysis. METHODS This study examined associations of polygenic risk scores (PRSs) for immuno-metabolic markers (C-reactive protein [CRP], interleukin [IL]-6, IL-10, tumour necrosis factor [TNF]-α, BMI) with seven depressive symptoms in one general population sample, the UK Biobank study (n = 110,010), and two patient samples, the Munich Antidepressant Response Signature (MARS, n = 1058) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D, n = 1143) studies. Network analysis was applied jointly for these samples using fused graphical least absolute shrinkage and selection operator (FGL) estimation as primary analysis and, individually, using unregularized model search estimation. Stability of results was assessed using bootstrapping and three consistency criteria were defined to appraise robustness and replicability of results across estimation methods, network bootstrapping, and samples. RESULTS Network analysis results displayed to-be-expected PRS-PRS and symptom-symptom associations (termed edges), respectively, that were mostly positive. Using FGL estimation, results further suggested 28, 29, and six PRS-symptom edges in MARS, STAR*D, and UK Biobank samples, respectively. Unregularized model search estimation suggested three PRS-symptom edges in the UK Biobank sample. Applying our consistency criteria to these associations indicated that only the association of higher CRP PRS with greater changes in appetite fulfilled all three criteria. Four additional associations fulfilled at least two consistency criteria; specifically, higher CRP PRS was associated with greater fatigue and reduced anhedonia, higher TNF-α PRS was associated with greater fatigue, and higher BMI PRS with greater changes in appetite and anhedonia. Associations of the BMI PRS with anhedonia, however, showed an inconsistent valence across estimation methods. CONCLUSIONS Genetic predisposition to higher systemic inflammatory markers are primarily associated with somatic/neurovegetative symptoms of depression such as changes in appetite and fatigue, consistent with previous studies based on circulating levels of inflammatory markers. We extend these findings by providing evidence that associations are direct (using network analysis) and extend to genetic predisposition to immuno-metabolic markers (using PRSs). Our findings can inform selection of patients with inflammation-related symptoms into clinical trials of immune-modulating drugs for MDD.
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Affiliation(s)
- Nils Kappelmann
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany; International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany.
| | - Darina Czamara
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Nicolas Rost
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany; International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Sylvain Moser
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany; International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Vanessa Schmoll
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Lucia Trastulla
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Jan Stochl
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Department of Kinanthropology, Charles University, Prague, Czech Republic
| | | | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Golam M Khandaker
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom; MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom; Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Janine Arloth
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany; Institute of Computational Biology, Helmholtz Zentrum Munich, Neuherberg, Germany
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710
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Su J, Kuo SIC, Aliev F, Chan G, Edenberg HJ, Kamarajan C, McCutcheon VV, Meyers JL, Schuckit M, Tischfield J, Dick DM. The associations between polygenic risk, sensation seeking, social support, and alcohol use in adulthood. JOURNAL OF ABNORMAL PSYCHOLOGY 2021; 130:525-536. [PMID: 34472888 PMCID: PMC10882995 DOI: 10.1037/abn0000568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Genetic predispositions play an important role in alcohol use. Understanding the psychosocial mechanisms through which genetic risk unfolds to influence alcohol use outcomes is critical for identifying modifiable targets and developing prevention and intervention efforts. In this study, we examined the role of sensation seeking and social support from family and friends in linking genetic risk to alcohol use. We also examined the role of social support in moderating the associations between genetic risk and sensation seeking and alcohol use. Data were drawn from a sample of 2,836 European American adults from the Collaborative Study on the Genetics of Alcoholism (46% male, mean age = 35.65, standard deviation [SD] = 10.78). Results from path analysis indicated that genome-wide polygenic scores for alcohol consumption (alc-GPS) were associated with higher sensation seeking, which in turn was associated with higher levels of alcohol use. alc-GPS was also associated with higher alcohol use indirectly via lower levels of family support. In addition, high friend support attenuated the association between alc-GPS and sensation seeking and alcohol use. The pattern of associations was similar for males and females, with some differences in the associations between social support and alcohol use observed across age. Our findings highlight the important role of intermediate phenotypes and gene-environment interplay in the pathways of risk from genetic predispositions to complex alcohol use outcomes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Affiliation(s)
- Jinni Su
- Department of Psychology, Arizona State University
| | | | - Fazil Aliev
- Department of Psychology, Virginia Commonwealth University
- Faculty of Business, Karabuk University, Turkey
| | - Grace Chan
- Department of Psychiatry, University of Connecticut
| | | | - Chella Kamarajan
- Department of Psychiatry, State University of New York Downstate Medical Center
| | | | - Jacquelyn L. Meyers
- Department of Psychiatry, State University of New York Downstate Medical Center
| | - Marc Schuckit
- Department of Psychiatry, University of California at San Diego
| | | | - Danielle M. Dick
- Department of Psychology, Virginia Commonwealth University
- Department of Human and Molecular Genetics, Virginia Commonwealth University
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711
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Li R, Chang C, Tanigawa Y, Narasimhan B, Hastie T, Tibshirani R, Rivas MA. Fast Numerical Optimization for Genome Sequencing Data in Population Biobanks. Bioinformatics 2021; 37:4148-4155. [PMID: 34146108 PMCID: PMC9206591 DOI: 10.1093/bioinformatics/btab452] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 06/08/2021] [Accepted: 06/15/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Large-scale and high-dimensional genome sequencing data poses computational challenges. General purpose optimization tools are usually not optimal in terms of computational and memory performance for genetic data. RESULTS We develop two efficient solvers for optimization problems arising from large-scale regularized regressions on millions of genetic variants sequenced from hundreds of thousands of individuals. These genetic variants are encoded by the values in the set {0, 1, 2, NA}. We take advantage of this fact and use two bits to represent each entry in a genetic matrix, which reduces memory requirement by a factor of 32 compared to a double precision floating point representation. Using this representation, we implemented an iteratively reweighted least square algorithm to solve Lasso regressions on genetic matrices, which we name snpnet-2.0. When the dataset contains many rare variants, the predictors can be encoded in a sparse matrix. We utilize the sparsity in the predictor matrix to further reduce memory requirement and computational speed. Our sparse genetic matrix implementation uses both the compact 2-bit representation and a simplified version of compressed sparse block format so that matrix-vector multiplications can be effectively parallelized on multiple CPU cores. To demonstrate the effectiveness of this representation, we implement an accelerated proximal gradient method to solve group Lasso on these sparse genetic matrices. This solver is named sparse-snpnet, and will also be included as part of snpnet R package. Our implementation is able to solve Lasso and group Lasso, linear, logistic and Cox regression problems on sparse genetic matrices that contain 1,000,000 variants and almost 100,000 individuals within 10 minutes and using less than 32GB of memory. AVAILABILITY https://github.com/rivas-lab/snpnet/tree/compact.
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Affiliation(s)
- Ruilin Li
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, 94305, United States
| | | | - Yosuke Tanigawa
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, United States
| | - Balasubramanian Narasimhan
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, United States.,Department of Statistics, Stanford University, Stanford, 94305, United States
| | - Trevor Hastie
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, United States.,Department of Statistics, Stanford University, Stanford, 94305, United States
| | - Robert Tibshirani
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, United States.,Department of Statistics, Stanford University, Stanford, 94305, United States
| | - Manuel A Rivas
- Department of Statistics, Stanford University, Stanford, 94305, United States
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712
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Guillermo R, Elena V, Martin K, Chris W. RápidoPGS: A rapid polygenic score calculator for summary GWAS data without a test dataset. Bioinformatics 2021; 37:4444-4450. [PMID: 34145897 PMCID: PMC8652106 DOI: 10.1093/bioinformatics/btab456] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 04/29/2021] [Accepted: 06/17/2021] [Indexed: 11/14/2022] Open
Abstract
Motivation Polygenic scores (PGS) aim to genetically predict complex traits at an individual level. PGS are typically trained on genome-wide association summary statistics and require an independent test dataset to tune parameters. More recent methods allow parameters to be tuned on the training data, removing the need for independent test data, but approaches are computationally intensive. Based on fine-mapping principles, we present RápidoPGS, a flexible and fast method to compute PGS requiring summary-level Genome-wide association studies (GWAS) datasets only, with little computational requirements and no test data required for parameter tuning. Results We show that RápidoPGS performs slightly less well than two out of three other widely used PGS methods (LDpred2, PRScs and SBayesR) for case–control datasets, with median r2 difference: -0.0092, -0.0042 and 0.0064, respectively, but up to 17 000-fold faster with reduced computational requirements. RápidoPGS is implemented in R and can work with user-supplied summary statistics or download them from the GWAS catalog. Availability and implementation Our method is available with a GPL license as an R package from CRAN and GitHub. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Reales Guillermo
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK.,Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK
| | - Vigorito Elena
- MRC Biostatistics Unit University of Cambridge, School of Clinical Medicine, East Forvie Building, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
| | - Kelemen Martin
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK.,Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Wallace Chris
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK.,Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK.,MRC Biostatistics Unit University of Cambridge, School of Clinical Medicine, East Forvie Building, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
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713
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Abstract
As genome-wide association studies have continued to identify loci associated with complex traits, the implications of and necessity for proper use of these findings, including prediction of disease risk, have become apparent. Many complex diseases have numerous associated loci with detectable effects implicating risk for or protection from disease. A common contemporary approach to using this information for disease prediction is through the application of genetic risk scores. These scores estimate an individual's liability for a specific outcome by aggregating the effects of associated loci into a single measure as described in the previous version of this article. Although genetic risk scores have traditionally included variants that meet criteria for genome-wide significance, an extension known as the polygenic risk score has been developed to include the effects of more variants across the entire genome. Here, we describe common methods and software packages for calculating and interpreting polygenic risk scores. In this revised version of the article, we detail information that is needed to perform a polygenic risk score analysis, considerations for planning the analysis and interpreting results, as well as discussion of the limitations based on the choices made. We also provide simulated sample data and a walkthrough for four different polygenic risk score software. © 2021 Wiley Periodicals LLC.
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Affiliation(s)
- Michael D Osterman
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio
| | - Tyler G Kinzy
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio
| | - Jessica N Cooke Bailey
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio
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714
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Hatoum AS, Johnson EC, Baranger DAA, Paul SE, Agrawal A, Bogdan R. Polygenic risk scores for alcohol involvement relate to brain structure in substance-naïve children: Results from the ABCD study. GENES, BRAIN, AND BEHAVIOR 2021; 20:e12756. [PMID: 34092032 PMCID: PMC8645657 DOI: 10.1111/gbb.12756] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 05/29/2021] [Accepted: 06/02/2021] [Indexed: 05/09/2023]
Abstract
Brain imaging-derived structural correlates of alcohol involvement have largely been speculated to arise as a consequence of alcohol exposure. However, they may also reflect predispositional risk. In substance naïve children of European ancestry who completed the baseline session of the Adolescent Brain Cognitive Development (ABCD) Study (n = 3013), mixed-effects models estimated whether polygenic risk scores (PRS) for problematic alcohol use (PAU-PRS) and drinks per week (DPW-PRS) are associated with magnetic resonance imaging-derived brain structure phenotypes (i.e., total and regional: cortical thickness, surface area and volume; subcortical volume; white matter volume, fractional anisotropy, mean diffusivity). Follow-up analyses evaluated whether any identified regions were also associated with polygenic risk among substance naïve children of African ancestry (n = 898). After adjustment for multiple testing correction, polygenic risk for PAU was associated with lower volume of the left frontal pole and greater cortical thickness of the right supramarginal gyrus (|βs| > 0.009; ps < 0.001; psfdr < 0.046; r2 s < 0.004). PAU PRS and DPW PRS showed nominally significant associations with a host of other regional brain structure phenotypes (e.g., insula surface area and volume). None of these regions showed any, even nominal association among children of African ancestry. Genomic liability to alcohol involvement may manifest as variability in brain structure during middle childhood prior to alcohol use initiation. Broadly, alcohol-related variability in brain morphometry may partially reflect predisposing genomic influence. Larger discovery genome-wide association studies and target samples of diverse ancestries are needed to determine whether observed associations may generalize across ancestral origins.
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Affiliation(s)
- Alexander S Hatoum
- Department of Psychiatry, Washington University St. Louis Medical School, St. Louis, Missouri, USA
| | - Emma C Johnson
- Department of Psychiatry, Washington University St. Louis Medical School, St. Louis, Missouri, USA
| | - David A A Baranger
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Sarah E Paul
- Department of Psychology & Brain Sciences, Washington University St. Louis, St. Louis, Missouri, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University St. Louis Medical School, St. Louis, Missouri, USA
| | - Ryan Bogdan
- Department of Psychology & Brain Sciences, Washington University St. Louis, St. Louis, Missouri, USA
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715
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Albiñana C, Grove J, McGrath JJ, Agerbo E, Wray NR, Bulik CM, Nordentoft M, Hougaard DM, Werge T, Børglum AD, Mortensen PB, Privé F, Vilhjálmsson BJ. Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction. Am J Hum Genet 2021; 108:1001-1011. [PMID: 33964208 PMCID: PMC8206385 DOI: 10.1016/j.ajhg.2021.04.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 04/20/2021] [Indexed: 12/12/2022] Open
Abstract
The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWASs). However, it is now common for researchers to have access to large individual-level data as well, such as the UK Biobank data. To the best of our knowledge, it has not yet been explored how best to combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using 12 real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and meta-PRS. We find that, when large individual-level data are available, the linear combination of PRSs (meta-PRS) is both a simple alternative to meta-GWAS and often more accurate.
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Affiliation(s)
- Clara Albiñana
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; National Centre for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark.
| | - Jakob Grove
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Department of Biomedicine and Center for Integrative Sequencing, iSEQ, Aarhus University, 8000 Aarhus C, Denmark; Center for Genomics and Personalized Medicine, CGPM, Aarhus University, 8000 Aarhus C, Denmark; Bioinformatics Research Centre, Aarhus University, 8000 Aarhus C, Denmark
| | - John J McGrath
- National Centre for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark; Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, QLD 4076, Australia; Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
| | - Esben Agerbo
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; National Centre for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia; Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
| | - Cynthia M Bulik
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden; Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Copenhagen University Hospital, Mental Health Centre Copenhagen Mental Health Services in the Capital Region of Denmark, 2100 Copenhagen Ø, Denmark; Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, 2300 Copenhagen S, Denmark
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Institute of Biological Psychiatry, MHC Sct. Hans, Mental Health Services Copenhagen, 4000 Roskilde, Denmark; Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen N, Denmark; Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, 1350 Copenhagen K, Denmark
| | - Anders D Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Department of Biomedicine and Center for Integrative Sequencing, iSEQ, Aarhus University, 8000 Aarhus C, Denmark; Center for Genomics and Personalized Medicine, CGPM, Aarhus University, 8000 Aarhus C, Denmark
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; National Centre for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Florian Privé
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; National Centre for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Bjarni J Vilhjálmsson
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; National Centre for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark; Bioinformatics Research Centre, Aarhus University, 8000 Aarhus C, Denmark.
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716
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Crone B, Krause AM, Hornsby WE, Willer CJ, Surakka I. Translating genetic association of lipid levels for biological and clinical application. Cardiovasc Drugs Ther 2021; 35:617-626. [PMID: 33604704 PMCID: PMC8272953 DOI: 10.1007/s10557-021-07156-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/09/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE OF REVIEW This review focuses on the foundational evidence from the last two decades of lipid genetics research and describes the current status of data-driven approaches for transethnic GWAS, fine-mapping, transcriptome informed fine-mapping, and disease prediction. RECENT FINDINGS Current lipid genetics research aims to understand the association mechanisms and clinical relevance of lipid loci as well as to capture population specific associations found in global ancestries. Recent genome-wide trans-ethnic association meta-analyses have identified 118 novel lipid loci reaching genome-wide significance. Gene-based burden tests of whole exome sequencing data have identified three genes-PCSK9, LDLR, and APOB-with significant rare variant burden associated with familial dyslipidemia. Transcriptome-wide association studies discovered five previously unreported lipid-associated loci. Additionally, the predictive power of genome-wide genetic risk scores amalgamating the polygenic determinants of lipid levels can potentially be used to increase the accuracy of coronary artery disease prediction. CONCLUSIONS Lipids are one of the most successful group of traits in the era of genome-wide genetic discovery for identification of novel loci and plausible drug targets. However, a substantial fraction of lipid trait heritability remains unexplained. Further analysis of diverse ancestries and state of the art methods for association locus refinement could potentially reveal some of this missing heritability and increase the clinical application of the genomic association results.
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Affiliation(s)
- Bradley Crone
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Amelia M Krause
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Michigan Medicine, Ann Arbor, MI, USA
| | - Whitney E Hornsby
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Michigan Medicine, Ann Arbor, MI, USA
| | - Cristen J Willer
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Michigan Medicine, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Ida Surakka
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Michigan Medicine, Ann Arbor, MI, USA.
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717
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Chen J, Spracklen CN, Marenne G, Varshney A, Corbin LJ, Luan J, Willems SM, Wu Y, Zhang X, Horikoshi M, Boutin TS, Mägi R, Waage J, Li-Gao R, Chan KHK, Yao J, Anasanti MD, Chu AY, Claringbould A, Heikkinen J, Hong J, Hottenga JJ, Huo S, Kaakinen MA, Louie T, März W, Moreno-Macias H, Ndungu A, Nelson SC, Nolte IM, North KE, Raulerson CK, Ray D, Rohde R, Rybin D, Schurmann C, Sim X, Southam L, Stewart ID, Wang CA, Wang Y, Wu P, Zhang W, Ahluwalia TS, Appel EVR, Bielak LF, Brody JA, Burtt NP, Cabrera CP, Cade BE, Chai JF, Chai X, Chang LC, Chen CH, Chen BH, Chitrala KN, Chiu YF, de Haan HG, Delgado GE, Demirkan A, Duan Q, Engmann J, Fatumo SA, Gayán J, Giulianini F, Gong JH, Gustafsson S, Hai Y, Hartwig FP, He J, Heianza Y, Huang T, Huerta-Chagoya A, Hwang MY, Jensen RA, Kawaguchi T, Kentistou KA, Kim YJ, Kleber ME, Kooner IK, Lai S, Lange LA, Langefeld CD, Lauzon M, Li M, Ligthart S, Liu J, Loh M, Long J, Lyssenko V, Mangino M, Marzi C, Montasser ME, Nag A, Nakatochi M, Noce D, Noordam R, Pistis G, Preuss M, Raffield L, Rasmussen-Torvik LJ, Rich SS, Robertson NR, Rueedi R, Ryan K, Sanna S, Saxena R, Schraut KE, Sennblad B, Setoh K, Smith AV, Sparsø T, Strawbridge RJ, Takeuchi F, Tan J, Trompet S, van den Akker E, van der Most PJ, Verweij N, Vogel M, Wang H, Wang C, Wang N, Warren HR, Wen W, Wilsgaard T, Wong A, Wood AR, Xie T, Zafarmand MH, Zhao JH, Zhao W, Amin N, Arzumanyan Z, Astrup A, Bakker SJL, Baldassarre D, Beekman M, Bergman RN, Bertoni A, Blüher M, Bonnycastle LL, Bornstein SR, Bowden DW, Cai Q, Campbell A, Campbell H, Chang YC, de Geus EJC, Dehghan A, Du S, Eiriksdottir G, Farmaki AE, Frånberg M, Fuchsberger C, Gao Y, Gjesing AP, Goel A, Han S, Hartman CA, Herder C, Hicks AA, Hsieh CH, Hsueh WA, Ichihara S, Igase M, Ikram MA, Johnson WC, Jørgensen ME, Joshi PK, Kalyani RR, Kandeel FR, Katsuya T, Khor CC, Kiess W, Kolcic I, Kuulasmaa T, Kuusisto J, Läll K, Lam K, Lawlor DA, Lee NR, Lemaitre RN, Li H, Lin SY, Lindström J, Linneberg A, Liu J, Lorenzo C, Matsubara T, Matsuda F, Mingrone G, Mooijaart S, Moon S, Nabika T, Nadkarni GN, Nadler JL, Nelis M, Neville MJ, Norris JM, Ohyagi Y, Peters A, Peyser PA, Polasek O, Qi Q, Raven D, Reilly DF, Reiner A, Rivideneira F, Roll K, Rudan I, Sabanayagam C, Sandow K, Sattar N, Schürmann A, Shi J, Stringham HM, Taylor KD, Teslovich TM, Thuesen B, Timmers PRHJ, Tremoli E, Tsai MY, Uitterlinden A, van Dam RM, van Heemst D, van Hylckama Vlieg A, van Vliet-Ostaptchouk JV, Vangipurapu J, Vestergaard H, Wang T, Willems van Dijk K, Zemunik T, Abecasis GR, Adair LS, Aguilar-Salinas CA, Alarcón-Riquelme ME, An P, Aviles-Santa L, Becker DM, Beilin LJ, Bergmann S, Bisgaard H, Black C, Boehnke M, Boerwinkle E, Böhm BO, Bønnelykke K, Boomsma DI, Bottinger EP, Buchanan TA, Canouil M, Caulfield MJ, Chambers JC, Chasman DI, Chen YDI, Cheng CY, Collins FS, Correa A, Cucca F, de Silva HJ, Dedoussis G, Elmståhl S, Evans MK, Ferrannini E, Ferrucci L, Florez JC, Franks PW, Frayling TM, Froguel P, Gigante B, Goodarzi MO, Gordon-Larsen P, Grallert H, Grarup N, Grimsgaard S, Groop L, Gudnason V, Guo X, Hamsten A, Hansen T, Hayward C, Heckbert SR, Horta BL, Huang W, Ingelsson E, James PS, Jarvelin MR, Jonas JB, Jukema JW, Kaleebu P, Kaplan R, Kardia SLR, Kato N, Keinanen-Kiukaanniemi SM, Kim BJ, Kivimaki M, Koistinen HA, Kooner JS, Körner A, Kovacs P, Kuh D, Kumari M, Kutalik Z, Laakso M, Lakka TA, Launer LJ, Leander K, Li H, Lin X, Lind L, Lindgren C, Liu S, Loos RJF, Magnusson PKE, Mahajan A, Metspalu A, Mook-Kanamori DO, Mori TA, Munroe PB, Njølstad I, O'Connell JR, Oldehinkel AJ, Ong KK, Padmanabhan S, Palmer CNA, Palmer ND, Pedersen O, Pennell CE, Porteous DJ, Pramstaller PP, Province MA, Psaty BM, Qi L, Raffel LJ, Rauramaa R, Redline S, Ridker PM, Rosendaal FR, Saaristo TE, Sandhu M, Saramies J, Schneiderman N, Schwarz P, Scott LJ, Selvin E, Sever P, Shu XO, Slagboom PE, Small KS, Smith BH, Snieder H, Sofer T, Sørensen TIA, Spector TD, Stanton A, Steves CJ, Stumvoll M, Sun L, Tabara Y, Tai ES, Timpson NJ, Tönjes A, Tuomilehto J, Tusie T, Uusitupa M, van der Harst P, van Duijn C, Vitart V, Vollenweider P, Vrijkotte TGM, Wagenknecht LE, Walker M, Wang YX, Wareham NJ, Watanabe RM, Watkins H, Wei WB, Wickremasinghe AR, Willemsen G, Wilson JF, Wong TY, Wu JY, Xiang AH, Yanek LR, Yengo L, Yokota M, Zeggini E, Zheng W, Zonderman AB, Rotter JI, Gloyn AL, McCarthy MI, Dupuis J, Meigs JB, Scott RA, Prokopenko I, Leong A, Liu CT, Parker SCJ, Mohlke KL, Langenberg C, Wheeler E, Morris AP, Barroso I. The trans-ancestral genomic architecture of glycemic traits. Nat Genet 2021; 53:840-860. [PMID: 34059833 PMCID: PMC7610958 DOI: 10.1038/s41588-021-00852-9] [Citation(s) in RCA: 314] [Impact Index Per Article: 104.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 03/22/2021] [Indexed: 02/02/2023]
Abstract
Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.
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Affiliation(s)
- Ji Chen
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - Cassandra N Spracklen
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, USA
| | - Gaëlle Marenne
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
- Inserm, Univ Brest, EFS, UMR 1078, GGB, Brest, France
| | - Arushi Varshney
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Laura J Corbin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Sara M Willems
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Ying Wu
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Xiaoshuai Zhang
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Biostatistics, School of Public Health, Shandong University, Jinan, China
| | - Momoko Horikoshi
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Centre for Integrative Medical Sciences, Yokohama, Japan
| | - Thibaud S Boutin
- Medical Research Council Human Genetics Unit, Institute for Genetics and Molecular Medicine, Edinburgh, UK
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Johannes Waage
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Kei Hang Katie Chan
- Department of Epidemiology, Brown University School of Public Health, Brown University, Providence, RI, USA
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Mila D Anasanti
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Audrey Y Chu
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Annique Claringbould
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jani Heikkinen
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Jaeyoung Hong
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Faculty of Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Shaofeng Huo
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Marika A Kaakinen
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Section of Statistical Multi-omics, Department of Clinical and Experimental Research, University of Surrey, Guildford, UK
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Winfried März
- SYNLAB Academy, SYNLAB Holding Deutschland GmbH, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University Graz, Graz, Austria
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, Heidelberg University, Mannheim, Baden-Württemberg, Germany
| | | | - Anne Ndungu
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Sarah C Nelson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Kari E North
- CVD Genetic Epidemiology Computational Laboratory, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | | | - Debashree Ray
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Rebecca Rohde
- CVD Genetic Epidemiology Computational Laboratory, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Denis Rybin
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Claudia Schurmann
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- HPI Digital Health Center, Digital Health and Personalized Medicine, Hasso Plattner Institute, Potsdam, Germany
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National Univeristy of Singapore and National University Health System, Singapore, Singapore
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Lorraine Southam
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
- Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Isobel D Stewart
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Carol A Wang
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, The University of Newcastle, Newcastle, New South Wales, Australia
| | - Yujie Wang
- CVD Genetic Epidemiology Computational Laboratory, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
| | - Tarunveer S Ahluwalia
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Emil V R Appel
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer A Brody
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Noël P Burtt
- Metabolism Program, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Claudia P Cabrera
- Department of Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, UK
| | - Brian E Cade
- Department of Medicine, Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Jin Fang Chai
- Saw Swee Hock School of Public Health, National Univeristy of Singapore and National University Health System, Singapore, Singapore
| | - Xiaoran Chai
- Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, National University of Singapore and National University Health System, Singapore, Singapore
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Brian H Chen
- Department of Epidemiology, The Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Kumaraswamy Naidu Chitrala
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yen-Feng Chiu
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Hugoline G de Haan
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Graciela E Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, Heidelberg University, Mannheim, Baden-Württemberg, Germany
| | - Ayse Demirkan
- Section of Statistical Multi-omics, Department of Clinical and Experimental Research, University of Surrey, Guildford, UK
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Qing Duan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jorgen Engmann
- Institute of Cardiovascular Science, University College London, London, UK
| | - Segun A Fatumo
- Uganda Medical Informatics Centre (UMIC), MRC/UVRI and London School of Hygiene & Tropical Medicine (Uganda Research Unit), Entebbe, Uganda
- London School of Hygiene & Tropical Medicine, London, UK
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | | | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jung Ho Gong
- Department of Epidemiology, Brown University School of Public Health, Brown University, Providence, RI, USA
| | - Stefan Gustafsson
- Molecular Epidemiology and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Yang Hai
- Department of Statistics, The University of Auckland, Science Center, Auckland, New Zealand
| | - Fernando P Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Jing He
- Department of Medicine, Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yoriko Heianza
- Department of Epidemiology, Tulane University Obesity Research Center, Tulane University, New Orleans, LA, USA
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Alicia Huerta-Chagoya
- Molecular Biology and Genomic Medicine Unit, National Council for Science and Technology, Mexico City, Mexico
- Molecular Biology and Genomic Medicine Unit, National Institute of Medical Sciences and Nutrition, Mexico City, Mexico
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju, South Korea
| | - Richard A Jensen
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Katherine A Kentistou
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Centre for Cardiovascular Sciences, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju, South Korea
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, Heidelberg University, Mannheim, Baden-Württemberg, Germany
| | - Ishminder K Kooner
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
| | - Shuiqing Lai
- Department of Epidemiology, Brown University School of Public Health, Brown University, Providence, RI, USA
| | - Leslie A Lange
- Department of Medicine, Divison of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Denver, CO, USA
| | - Carl D Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Marie Lauzon
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Man Li
- Department of Medicine, Division of Nephrology and Hypertension, University of Utah, Salt Lake City, UT, USA
| | - Symen Ligthart
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Jun Liu
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Marie Loh
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Valeriya Lyssenko
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmo, Sweden
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
- NIHR Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Carola Marzi
- Institute of Epidemiology, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - May E Montasser
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Abhishek Nag
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Damia Noce
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Giorgio Pistis
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Monserrato, Italy
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laura Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Neil R Robertson
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Kathleen Ryan
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Serena Sanna
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Monserrato, Italy
| | - Richa Saxena
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Katharina E Schraut
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Centre for Cardiovascular Sciences, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Bengt Sennblad
- Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Kazuya Setoh
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Albert V Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Icelandic Heart Association, Kopavogur, Iceland
| | - Thomas Sparsø
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Rona J Strawbridge
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Department of Medicine Solna, Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Jingyi Tan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stella Trompet
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Erik van den Akker
- Department of Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, the Netherlands
- Department of Biomedical Data Sciences, Leiden Computational Biology Center, Leiden University Medical Center, Leiden, the Netherlands
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Genomics PLC, Oxford, UK
| | - Mandy Vogel
- Center of Pediatric Research, University Children's Hospital Leipzig, University of Leipzig Medical Center, Leipzig, Germany
| | - Heming Wang
- Department of Medicine, Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Nan Wang
- Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
- University of Southern California Diabetes and Obesity Research Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Helen R Warren
- Department of Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, UK
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Tom Wilsgaard
- Department of Community Medicine, Faculty of Health Sciences, UIT the Arctic University of Norway, Tromsø, Norway
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at University College London, London, UK
| | - Andrew R Wood
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Tian Xie
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Mohammad Hadi Zafarmand
- Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
- Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Jing-Hua Zhao
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Najaf Amin
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Zorayr Arzumanyan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Arne Astrup
- Department of Nutrition, Exercise, and Sports, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Stephan J L Bakker
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Damiano Baldassarre
- Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy
- Centro Cardiologico Monzino, IRCCS, Milan, Italy
| | - Marian Beekman
- Department of Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Richard N Bergman
- Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Alain Bertoni
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Matthias Blüher
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Lori L Bonnycastle
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institues of Health, Bethesda, MD, USA
| | - Stefan R Bornstein
- Department for Prevention and Care of Diabetes, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Qiuyin Cai
- Department of Medicine, Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Yi Cheng Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan
| | - Eco J C de Geus
- Department of Biological Psychology, Faculty of Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Shufa Du
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | | | - Aliki Eleni Farmaki
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London, UK
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Athens, Greece
| | - Mattias Frånberg
- Department of Medicine Solna, Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Yutang Gao
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China
| | - Anette P Gjesing
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anuj Goel
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Sohee Han
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju, South Korea
| | - Catharina A Hartman
- Department of Psychiatry, Interdisciplinary Center Psychopathy and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Düsseldorf, Germany
| | - Andrew A Hicks
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Chang-Hsun Hsieh
- Internal Medicine, Endocrine and Metabolism, Tri-Service General Hospital, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Willa A Hsueh
- Internal Medicine, Endocrinology, Diabetes and Metabolism, Diabetes and Metabolism Research Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Sahoko Ichihara
- Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, Shimotsuke, Japan
| | - Michiya Igase
- Department of Anti-aging Medicine, Ehime University Graduate School of Medicine, Toon, Japan
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Marit E Jørgensen
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- National Institute of Public Health, University of Southern Denmark, Odense, Denmark
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Rita R Kalyani
- Department of Medicine, Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fouad R Kandeel
- Clinical Diabetes, Endocrinology and Metabolism, Translational Research and Cellular Therapeutics, Beckman Research Institute of the City of Hope, Duarte, CA, USA
| | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Geriatric and General Medicine, Osaka University Graduate School of Medicine, Suita, Japan
| | - Chiea Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Wieland Kiess
- Center of Pediatric Research, University Children's Hospital Leipzig, University of Leipzig Medical Center, Leipzig, Germany
| | - Ivana Kolcic
- Department of Public Health, University of Split School of Medicine, Split, Croatia
| | - Teemu Kuulasmaa
- Institute of Biomedicine, Bioinformatics Center, Univeristy of Eastern Finland, Kuopio, Finland
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kristi Läll
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kelvin Lam
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nanette R Lee
- USC-Office of Population Studies Foundation, University of San Carlos, Cebu City, the Philippines
- Department of Anthropology, Sociology and History, University of San Carlos, Cebu City, the Philippines
| | - Rozenn N Lemaitre
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Honglan Li
- State Key Laboratory of Oncogene and Related Genes and Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Shih-Yi Lin
- Center for Geriatrics and Gerontology, Taichung Veterans General Hospital, Taichung, Taiwan
- National Defense Medical Center, National Yang-Ming University, Taipei, Taiwan
| | - Jaana Lindström
- Diabetes Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Carlos Lorenzo
- Department of Medicine, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Tatsuaki Matsubara
- Department of Internal Medicine, Aichi Gakuin University School of Dentistry, Nagoya, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Geltrude Mingrone
- Department of Diabetes, Diabetes, and Nutritional Sciences, James Black Centre, King's College London, London, UK
| | - Simon Mooijaart
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Sanghoon Moon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju, South Korea
| | - Toru Nabika
- Department of Functional Pathology, Shimane University School of Medicine, Izumo, Japan
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jerry L Nadler
- Department of Medicine and Pharmacology, New York Medical College School of Medicine, Valhalla, NY, USA
| | - Mari Nelis
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Matt J Neville
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Jill M Norris
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Yasumasa Ohyagi
- Department of Geriatric Medicine and Neurology, Ehime University Graduate School of Medicine, Toon, Japan
| | - Annette Peters
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians University Munich, Munich, Germany
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Ozren Polasek
- Department of Public Health, University of Split School of Medicine, Split, Croatia
- Gen-Info, Zagreb, Croatia
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA
| | - Dennis Raven
- Department of Psychiatry, Interdisciplinary Center Psychopathy and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Dermot F Reilly
- Genetics and Pharmacogenomics, Merck Sharp & Dohme, Kenilworth, NJ, USA
| | - Alex Reiner
- Department of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Fernando Rivideneira
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Kathryn Roll
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Igor Rudan
- Centre for Global Health, The Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Charumathi Sabanayagam
- Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Kevin Sandow
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Naveed Sattar
- BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Annette Schürmann
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Jinxiu Shi
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Academy of Science & Technology (SAST), Shanghai, China
| | - Heather M Stringham
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Betina Thuesen
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Paul R H J Timmers
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Medical Research Council Human Genetics Unit, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | | | - Michael Y Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Andre Uitterlinden
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National Univeristy of Singapore and National University Health System, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Jana V van Vliet-Ostaptchouk
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jagadish Vangipurapu
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Henrik Vestergaard
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Medicine, Bornholms Hospital, Rønne, Denmark
| | - Tao Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA
| | - Ko Willems van Dijk
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
- Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Tatijana Zemunik
- Department of Human Biology, University of Split School of Medicine, Split, Croatia
| | - Gonçalo R Abecasis
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Linda S Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
| | - Carlos Alberto Aguilar-Salinas
- Department of Endocrinology and Metabolism, Instituto Nacional de Ciencias Medicas y Nutricion, Mexico City, Mexico
- Unidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición and Tec Salud, Mexico City, Mexico
- Instituto Tecnológico y de Estudios Superiores de Monterrey Tec Salud, Monterrey, Mexico
| | - Marta E Alarcón-Riquelme
- Department of Medical Genomics, Pfizer/University of Granada/Andalusian Government Center for Genomics and Oncological Research (GENYO), Granada, Spain
- Institute for Environmental Medicine, Chronic Inflammatory Diseases, Karolinska Institutet, Solna, Sweden
| | - Ping An
- Department of Genetics, Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Larissa Aviles-Santa
- Clinical and Health Services Research, National Institute on Minority Health and Health Disparities, Bethesda, MD, USA
| | - Diane M Becker
- Department of Medicine, General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lawrence J Beilin
- Medical School, Royal Perth Hospital Unit, University of Western Australia, Perth, Western Australia, Australia
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Hans Bisgaard
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Corri Black
- Aberdeen Centre for Health Data Science, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Michael Boehnke
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Bernhard O Böhm
- Division of Endocrinology and Diabetes, Graduate School of Molecular Endocrinology and Diabetes, University of Ulm, Ulm, Germany
- LKC School of Medicine, Nanyang Technological University, Singapore and Imperial College London, UK, Singapore, Singapore
| | - Klaus Bønnelykke
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - D I Boomsma
- Department of Biological Psychology, Faculty of Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Erwin P Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Digital Health Center, Hasso Plattner Institut, University Potsdam, Potsdam, Germany
| | - Thomas A Buchanan
- University of Southern California Diabetes and Obesity Research Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
- Department of Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
- Department of Physiology and Neuroscience, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Mickaël Canouil
- INSERM UMR 1283/CNRS UMR 8199, European Institute for Diabetes (EGID), Université de Lille, Lille, France
- INSERM UMR 1283/CNRS UMR 8199, European Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, France
| | - Mark J Caulfield
- Department of Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ching-Yu Cheng
- Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Francis S Collins
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institues of Health, Bethesda, MD, USA
| | - Adolfo Correa
- Department of Medicine, Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS, USA
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Monserrato, Italy
| | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Kallithea, Greece
| | - Sölve Elmståhl
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | | | - Luigi Ferrucci
- Intramural Research Program, National Institute of Aging, Baltimore, MD, USA
| | - Jose C Florez
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Paul W Franks
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmo, Sweden
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Timothy M Frayling
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Philippe Froguel
- INSERM UMR 1283/CNRS UMR 8199, European Institute for Diabetes (EGID), Université de Lille, Lille, France
- INSERM UMR 1283/CNRS UMR 8199, European Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille, France
- Department of Genomics of Common Disease, Imperial College London, London, UK
| | - Bruna Gigante
- Department of Medicine, Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Mark O Goodarzi
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
| | - Harald Grallert
- Institute of Epidemiology, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sameline Grimsgaard
- Department of Community Medicine, Faculty of Health Sciences, UIT the Arctic University of Norway, Tromsø, Norway
| | - Leif Groop
- Diabetes Centre, Lund University, Lund, Sweden
- Finnish Institute of Molecular Medicine, Helsinki University, Helsinki, Finland
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Anders Hamsten
- Department of Medicine Solna, Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Susan R Heckbert
- Department of Epidemiology, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Bernardo L Horta
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Wei Huang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Academy of Science & Technology (SAST), Shanghai, China
| | - Erik Ingelsson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Pankow S James
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Marjo-Ritta Jarvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu Univerisity Hospital, OYS, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Jost B Jonas
- Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Institute of Molecular and Clinical Ophthalmology Basel IOB, Basel, Switzerland
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | | | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA
- Department of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Norihiro Kato
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Sirkka M Keinanen-Kiukaanniemi
- Faculty of Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland
- Unit of General Practice, Oulu University Hospital, Oulu, Finland
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju, South Korea
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Heikki A Koistinen
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Antje Körner
- Center of Pediatric Research, University Children's Hospital Leipzig, University of Leipzig Medical Center, Leipzig, Germany
| | - Peter Kovacs
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
- IFB Adiposity Diseases, University of Leipzig Medical Center, Leipzig, Germany
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at University College London, London, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | - Zoltan Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Institute of Primary Care and Public Health, Division of Biostatistics, University of Lausanne, Lausanne, Switzerland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Timo A Lakka
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Karin Leander
- Institute of Environmental Medicine, Cardiovascular and Nutritional Epidemiology, Karolinska Institutet, Stockholm, Sweden
| | - Huaixing Li
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xu Lin
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Lars Lind
- Department of Medical Sciences, University of Uppsala, Uppsala, Sweden
| | - Cecilia Lindgren
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - Simin Liu
- Department of Epidemiology, Brown University School of Public Health, Brown University, Providence, RI, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics and the Swedish Twin Registry, Karolinska Institutet, Stockholm, Sweden
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Trevor A Mori
- Medical School, Royal Perth Hospital Unit, University of Western Australia, Perth, Western Australia, Australia
| | - Patricia B Munroe
- Department of Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, UK
| | - Inger Njølstad
- Department of Community Medicine, Faculty of Health Sciences, UIT the Arctic University of Norway, Tromsø, Norway
| | - Jeffrey R O'Connell
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Albertine J Oldehinkel
- Department of Psychiatry, Interdisciplinary Center Psychopathy and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Colin N A Palmer
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Craig E Pennell
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, The University of Newcastle, Newcastle, New South Wales, Australia
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | | | - Michael A Province
- Department of Genetics, Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Bruce M Psaty
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Epidemiology, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Health Services, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Lu Qi
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Leslie J Raffel
- Department of Pediatrics, Genetic and Genomic Medicine, University of California, Irvine, Irvine, CA, USA
| | - Rainer Rauramaa
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Susan Redline
- Department of Medicine, Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Havard Medical School, Boston, MA, USA
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Timo E Saaristo
- Tampere, Finnish Diabetes Association, Tampere, Finland
- Pirkanmaa Hospital District, Tampere, Finland
| | | | | | | | - Peter Schwarz
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Department for Prevention and Care of Diabetes, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden of the Helmholtz Center Munich, University Hospital and Faculty of Medicine, Dresden, Germany
| | - Laura J Scott
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Peter Sever
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - P Eline Slagboom
- Department of Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Blair H Smith
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Tamar Sofer
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Thorkild I A Sørensen
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Alice Stanton
- Department of Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
- Department of Ageing and Health, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Michael Stumvoll
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Liang Sun
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National Univeristy of Singapore and National University Health System, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Cardiovascular and Metabolic Disease Signature Research Program, Duke-NUS Medical School, Singapore, Singapore
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Anke Tönjes
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Jaakko Tuomilehto
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Teresa Tusie
- Molecular Biology and Genomic Medicine Unit, National Institute of Medical Sciences and Nutrition, Mexico City, Mexico
- Department of Genomic Medicine and Environmental Toxicology, Instituto de Investigaciones Biomedicas, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
| | - Matti Uusitupa
- Department of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Pim van der Harst
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Cornelia van Duijn
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Tanja G M Vrijkotte
- Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Lynne E Wagenknecht
- Department of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Mark Walker
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Ya X Wang
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Nick J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Richard M Watanabe
- Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
- University of Southern California Diabetes and Obesity Research Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
- Department of Physiology and Neuroscience, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Hugh Watkins
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Wen B Wei
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | | | - Gonneke Willemsen
- Department of Biological Psychology, Faculty of Behaviour and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Medical Research Council Human Genetics Unit, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Tien-Yin Wong
- Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Anny H Xiang
- Department of Research and Evaluation, Kaiser Permanente of Southern California, Pasadena, CA, USA
| | - Lisa R Yanek
- Department of Medicine, General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Loïc Yengo
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, Australia
| | | | - Eleftheria Zeggini
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
- Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- TUM School of Medicine, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Anna L Gloyn
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA, USA
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - James B Meigs
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Robert A Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Inga Prokopenko
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Section of Statistical Multi-omics, Department of Clinical and Experimental Research, University of Surrey, Guildford, UK
| | - Aaron Leong
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Diabetes Unit and Endocrine Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Stephen C J Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Eleanor Wheeler
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Andrew P Morris
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
- Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK.
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK.
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
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Christiansen MK, Winther S, Nissen L, Vilhjálmsson BJ, Frost L, Johansen JK, Møller PL, Schmidt SE, Westra J, Holm NR, Jensen HK, Christiansen EH, Guðbjartsson DF, Hólm H, Stefánsson K, Bøtker HE, Bøttcher M, Nyegaard M. Polygenic Risk Score-Enhanced Risk Stratification of Coronary Artery Disease in Patients With Stable Chest Pain. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2021; 14:e003298. [PMID: 34032468 DOI: 10.1161/circgen.120.003298] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Polygenic risk scores (PRSs) are associated with coronary artery disease (CAD), but the clinical potential of using PRSs at the single-patient level for risk stratification has yet to be established. We investigated whether adding a PRS to clinical risk factors (CRFs) improves risk stratification in patients referred to coronary computed tomography angiography on a suspicion of obstructive CAD. METHODS In this prespecified diagnostic substudy of the Dan-NICAD trial (Danish study of Non-Invasive testing in Coronary Artery Disease), we included 1617 consecutive patients with stable chest symptoms and no history of CAD referred for coronary computed tomography angiography. CRFs used for risk stratification were age, sex, symptoms, prior or active smoking, antihypertensive treatment, lipid-lowering treatment, and diabetes. In addition, patients were genotyped, and their PRSs were calculated. All patients underwent coronary computed tomography angiography. Patients with a suspected ≥50% stenosis also underwent invasive coronary angiography with fractional flow reserve. A combined end point of obstructive CAD was defined as a visual invasive coronary angiography stenosis >90%, fractional flow reserve <0.80, or a quantitative coronary analysis stenosis >50% if fractional flow reserve measurements were not feasible. RESULTS The PRS was associated with obstructive CAD independent of CRFs (adjusted odds ratio, 1.8 [95% CI, 1.5-2.2] per SD). The PRS had an area under the curve of 0.63 (0.59-0.68), which was similar to that for age and sex. Combining the PRS with CRFs led to a CRF+PRS model with area under the curve of 0.75 (0.71-0.79), which was 0.04 more than the CRF model (P=0.0029). By using pretest probability (pretest probability) cutoffs at 5% and 15%, a net reclassification improvement of 15.8% (P=3.1×10-4) was obtained, with a down-classification of risk in 24% of patients (211 of 862) in whom the pretest probability was 5% to 15% based on CRFs alone. CONCLUSIONS Adding a PRS improved risk stratification of obstructive CAD beyond CRFs, suggesting a modest clinical potential of using PRSs to guide diagnostic testing in the contemporary clinical setting. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02264717.
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Affiliation(s)
- Morten Krogh Christiansen
- Department of Cardiology (M.K.C., J.W., N.R.H., H.K.J., E.H.C., H.E.B.), Aarhus University Hospital, Denmark.,Department of Internal Medicine, Horsens Regional Hospital, Denmark (M.K.C.)
| | - Simon Winther
- Department of Cardiology (S.W., M.B.), Hospital Unit West, Herning, Denmark
| | - Louise Nissen
- Department of Radiology (L.N.), Hospital Unit West, Herning, Denmark
| | | | - Lars Frost
- Department of Cardiology, Silkeborg Regional Hospital, Denmark (L.F., J.K.J.)
| | - Jane Kirk Johansen
- Department of Cardiology, Silkeborg Regional Hospital, Denmark (L.F., J.K.J.)
| | - Peter Loof Møller
- Department of Biomedicine (P.L.M., M.N.), Aarhus University, Denmark
| | - Samuel Emil Schmidt
- Department of Health Science and Technology, Aalborg University, Denmark (S.E.S., M.N.)
| | - Jelmer Westra
- Department of Cardiology (M.K.C., J.W., N.R.H., H.K.J., E.H.C., H.E.B.), Aarhus University Hospital, Denmark
| | - Niels Ramsing Holm
- Department of Cardiology (M.K.C., J.W., N.R.H., H.K.J., E.H.C., H.E.B.), Aarhus University Hospital, Denmark
| | - Henrik Kjærulf Jensen
- Department of Cardiology (M.K.C., J.W., N.R.H., H.K.J., E.H.C., H.E.B.), Aarhus University Hospital, Denmark.,Department of Clinical Medicine, Faculty of Health (H.K.J., H.E.B.), Aarhus University, Denmark
| | - Evald Høj Christiansen
- Department of Cardiology (M.K.C., J.W., N.R.H., H.K.J., E.H.C., H.E.B.), Aarhus University Hospital, Denmark
| | | | - Hilma Hólm
- deCODE Genetics/Amgen, Inc, Reykjavik, Iceland (D.F.G., H.H., K.S.)
| | - Kári Stefánsson
- deCODE Genetics/Amgen, Inc, Reykjavik, Iceland (D.F.G., H.H., K.S.)
| | - Hans Erik Bøtker
- Department of Cardiology (M.K.C., J.W., N.R.H., H.K.J., E.H.C., H.E.B.), Aarhus University Hospital, Denmark.,Department of Clinical Medicine, Faculty of Health (H.K.J., H.E.B.), Aarhus University, Denmark
| | - Morten Bøttcher
- Department of Cardiology (S.W., M.B.), Hospital Unit West, Herning, Denmark
| | - Mette Nyegaard
- Department of Clinical Genetics (M.N.), Aarhus University Hospital, Denmark.,Department of Biomedicine (P.L.M., M.N.), Aarhus University, Denmark.,Department of Health Science and Technology, Aalborg University, Denmark (S.E.S., M.N.)
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719
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Pain O, Glanville KP, Hagenaars S, Selzam S, Fürtjes A, Coleman JRI, Rimfeld K, Breen G, Folkersen L, Lewis CM. Imputed gene expression risk scores: a functionally informed component of polygenic risk. Hum Mol Genet 2021; 30:727-738. [PMID: 33611520 PMCID: PMC8127405 DOI: 10.1093/hmg/ddab053] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/08/2021] [Accepted: 02/15/2021] [Indexed: 11/12/2022] Open
Abstract
Integration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results. The predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study. GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression-genotype panels, termed single nucleotide polymorphism (SNP)-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression. GeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for height (P = 0.023) to 4% for rheumatoid arthritis (P = 5.9 × 10-8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalized genes. GeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.
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Affiliation(s)
- Oliver Pain
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Kylie P Glanville
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Saskia Hagenaars
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Saskia Selzam
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Anna Fürtjes
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Jonathan R I Coleman
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Kaili Rimfeld
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
| | - Lasse Folkersen
- Institute of Biological Psychiatry, Sankt Hans Hospital, Copenhagen 4000 Roskilde, Denmark
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London SE5 8AF, UK
- Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King’s College London, London WC2R 2LS, UK
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720
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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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721
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Martucci VL, Richmond B, Davis LK, Blackwell TS, Cox NJ, Samuels D, Velez Edwards D, Aldrich MC. Fate or coincidence: do COPD and major depression share genetic risk factors? Hum Mol Genet 2021; 30:619-628. [PMID: 33704461 PMCID: PMC8120137 DOI: 10.1093/hmg/ddab068] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 02/24/2021] [Accepted: 02/27/2021] [Indexed: 01/12/2023] Open
Abstract
Major depressive disorder (MDD) is a common comorbidity in chronic obstructive pulmonary disease (COPD), affecting up to 57% of patients with COPD. Although the comorbidity of COPD and MDD is well established, the causal relationship between these two diseases is unclear. A large-scale electronic health record clinical biobank and genome-wide association study summary statistics for MDD and lung function traits were used to investigate potential shared underlying genetic susceptibility between COPD and MDD. Linkage disequilibrium score regression was used to estimate genetic correlation between phenotypes. Polygenic risk scores (PRS) for MDD and lung function traits were developed and used to perform a phenome-wide association study (PheWAS). Multi-trait-based conditional and joint analysis identified single-nucleotide polymorphisms (SNPs) influencing both lung function and MDD. We found genetic correlations between MDD and all lung function traits were small and not statistically significant. A PRS-MDD was significantly associated with an increased risk of COPD in a PheWAS [odds ratio (OR) = 1.12, 95% confidence interval (CI): 1.09-1.16] when adjusting for age, sex and genetic ancestry, but this relationship became attenuated when controlling for smoking history (OR = 1.08, 95% CI: 1.04-1.13). No significant associations were found between the lung function PRS and MDD. Multi-trait-based conditional and joint analysis identified three SNPs that may contribute to both traits, two of which were previously associated with mood disorders and COPD. Our findings suggest that the observed relationship between COPD and MDD may not be driven by a strong shared genetic architecture.
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Affiliation(s)
- Victoria L Martucci
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Bradley Richmond
- Department of Veterans Affairs Medical Center, Nashville, TN 37212, USA
- Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Lea K Davis
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Timothy S Blackwell
- Department of Veterans Affairs Medical Center, Nashville, TN 37212, USA
- Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - David Samuels
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Digna Velez Edwards
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Melinda C Aldrich
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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722
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Kunøe A, Sevelsted A, Chawes BL, Stokholm J, Eliasen A, Krakauer M, Bønnelykke K, Bisgaard H. Associations between Inhaled Corticosteroid in the First 6 Years of Life and Obesity Related Traits. Am J Respir Crit Care Med 2021; 204:642-650. [PMID: 33975528 DOI: 10.1164/rccm.202009-3537oc] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Rationale Infants and young children might be particularly susceptible to the potential clinical side effects of inhaled corticosteroids (ICS) on body mass index (BMI), adiposity rebound and body composition, but this has rarely been studied in long-term studies in this age-group. Objective To determine the association between ICS exposure in the first 6 years of life on BMI, adiposity rebound, body composition and blood lipid levels. Methods Children from the two Copenhagen Prospective Studies on Asthma in Childhood mother-child cohorts were included. ICS use was registered prospectively to age 6 and the cumulative dose was calculated. Multiple linear regression models were used for analysis. Measurements and Main Results A total of 932 (84%) of the 1111 children from the COPSAC cohorts had data on BMI, 786 (71%) had DXA scan data at age 6 years, and 815 (73%) had adiposity rebound age calculated. 291 children (31%) received a cumulative ICS dose > 10 weeks of standard treatment before age 6. ICS treatment during 0-6 years of age was associated with an increased BMI z-score; 0.05 SD [95% CI: 0.005 to 0.09] per one-year standard treatment, p=0.03, an earlier age at adiposity rebound; -0.18 year [95% CI: -0.28 to -0.08], p=0.0006, and a 2 % increased geometric mean android fat percentage, p=0.05. ICS exposure and DXA scan data were not associated. Conclusions ICS use in early childhood was associated with increased BMI z-score at age six, an earlier adiposity rebound and a trend of association with increased android body fat percentage.
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Affiliation(s)
- Asja Kunøe
- University of Copenhagen, 4321, COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, Kobenhavn, Denmark
| | - Astrid Sevelsted
- University of Copenhagen, 4321, COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, Kobenhavn, Denmark
| | - Bo L Chawes
- Brigham and Women's Hospital, 1861, Channing Division of Network Medicine, Boston, Massachusetts, United States.,University of Copenhagen, 4321, COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Kobenhavn, Denmark
| | - Jakob Stokholm
- University of Copenhagen, 4321, COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, Kobenhavn, Denmark
| | - Anders Eliasen
- University of Copenhagen, 4321, COPSAC Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, Kobenhavn, Denmark
| | - Martin Krakauer
- Gentofte University Hospital, 53147, Department of Clinical Physiology and Nuclear Medicine, Hellerup, Denmark
| | - Klaus Bønnelykke
- University of Copenhagen, 4321, Copenhagen Prospective Studies on Asthma in Childhood; The Danish Pediatric Asthma Center, Kobenhavn, Denmark
| | - Hans Bisgaard
- University of Copenhagen, 4321, COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, Kobenhavn, Denmark;
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723
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Strausz S, Ruotsalainen S, Ollila HM, Karjalainen J, Kiiskinen T, Reeve M, Kurki M, Mars N, Havulinna AS, Luonsi E, Mansour Aly D, Ahlqvist E, Teder-Laving M, Palta P, Groop L, Mägi R, Mäkitie A, Salomaa V, Bachour A, Tuomi T, Palotie A, Palotie T, Ripatti S. Genetic analysis of obstructive sleep apnoea discovers a strong association with cardiometabolic health. Eur Respir J 2021; 57:13993003.03091-2020. [PMID: 33243845 DOI: 10.1183/13993003.03091-2020] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 11/04/2020] [Indexed: 12/20/2022]
Abstract
There is currently limited understanding of the genetic aetiology of obstructive sleep apnoea (OSA). We aimed to identify genetic loci associated with OSA risk, and to test if OSA and its comorbidities share a common genetic background.We conducted the first large-scale genome-wide association study of OSA using the FinnGen study (217 955 individuals) with 16 761 OSA patients identified using nationwide health registries.We estimated 0.08 (95% CI 0.06-0.11) heritability and identified five loci associated with OSA (p<5.0×10-8): rs4837016 near GAPVD1 (GTPase activating protein and VPS9 domains 1), rs10928560 near CXCR4 (C-X-C motif chemokine receptor type 4), rs185932673 near CAMK1D (calcium/calmodulin-dependent protein kinase ID) and rs9937053 near FTO (fat mass and obesity-associated protein; a variant previously associated with body mass index (BMI)). In a BMI-adjusted analysis, an association was observed for rs10507084 near RMST/NEDD1 (rhabdomyosarcoma 2 associated transcript/NEDD1 γ-tubulin ring complex targeting factor). We found high genetic correlations between OSA and BMI (rg=0.72 (95% CI 0.62-0.83)), and with comorbidities including hypertension, type 2 diabetes, coronary heart disease, stroke, depression, hypothyroidism, asthma and inflammatory rheumatic disease (rg>0.30). The polygenic risk score for BMI showed 1.98-fold increased OSA risk between the highest and the lowest quintile, and Mendelian randomisation supported a causal relationship between BMI and OSA.Our findings support the causal link between obesity and OSA, and the joint genetic basis between OSA and comorbidities.
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Affiliation(s)
- Satu Strausz
- Dept of Oral and Maxillofacial Diseases, Helsinki University Hospital, Helsinki, Finland.,Orthodontics, Dept of Oral and Maxillofacial Diseases, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Sanni Ruotsalainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Hanna M Ollila
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.,Dept of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Juha Karjalainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Analytic and Translational Genetics Unit, Depts of Medicine, Neurology and Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Tuomo Kiiskinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.,Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Mary Reeve
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Mitja Kurki
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Analytic and Translational Genetics Unit, Depts of Medicine, Neurology and Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Nina Mars
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Aki S Havulinna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.,Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Elina Luonsi
- Orthodontics, Dept of Oral and Maxillofacial Diseases, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Dina Mansour Aly
- Lund University Diabetes Centre, Dept of Clinical Sciences, Lund University, Malmö, Sweden.,Skåne University Hospital, Lund University, Malmö, Sweden
| | - Emma Ahlqvist
- Lund University Diabetes Centre, Dept of Clinical Sciences, Lund University, Malmö, Sweden.,Skåne University Hospital, Lund University, Malmö, Sweden
| | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Priit Palta
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.,Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Leif Groop
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.,Lund University Diabetes Centre, Dept of Clinical Sciences, Lund University, Malmö, Sweden.,Skåne University Hospital, Lund University, Malmö, Sweden
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Antti Mäkitie
- Dept of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Adel Bachour
- Sleep Unit, Heart and Lung Center, Helsinki University Hospital, Helsinki, Finland
| | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.,Lund University Diabetes Centre, Dept of Clinical Sciences, Lund University, Malmö, Sweden.,Skåne University Hospital, Lund University, Malmö, Sweden.,Endocrinology, Abdominal Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Research Program for Clinical and Molecular Medicine, University of Helsinki and Folkhälsan Research Center, Helsinki, Finland
| | | | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Analytic and Translational Genetics Unit, Depts of Medicine, Neurology and Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Tuula Palotie
- Dept of Oral and Maxillofacial Diseases, Helsinki University Hospital, Helsinki, Finland.,Orthodontics, Dept of Oral and Maxillofacial Diseases, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Dept of Public Health, University of Helsinki, Helsinki, Finland
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724
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Pain O, Glanville KP, Hagenaars SP, Selzam S, Fürtjes AE, Gaspar HA, Coleman JRI, Rimfeld K, Breen G, Plomin R, Folkersen L, Lewis CM. Evaluation of polygenic prediction methodology within a reference-standardized framework. PLoS Genet 2021; 17:e1009021. [PMID: 33945532 PMCID: PMC8121285 DOI: 10.1371/journal.pgen.1009021] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 05/14/2021] [Accepted: 03/28/2021] [Indexed: 12/16/2022] Open
Abstract
The predictive utility of polygenic scores is increasing, and many polygenic scoring methods are available, but it is unclear which method performs best. This study evaluates the predictive utility of polygenic scoring methods within a reference-standardized framework, which uses a common set of variants and reference-based estimates of linkage disequilibrium and allele frequencies to construct scores. Eight polygenic score methods were tested: p-value thresholding and clumping (pT+clump), SBLUP, lassosum, LDpred1, LDpred2, PRScs, DBSLMM and SBayesR, evaluating their performance to predict outcomes in UK Biobank and the Twins Early Development Study (TEDS). Strategies to identify optimal p-value thresholds and shrinkage parameters were compared, including 10-fold cross validation, pseudovalidation and infinitesimal models (with no validation sample), and multi-polygenic score elastic net models. LDpred2, lassosum and PRScs performed strongly using 10-fold cross-validation to identify the most predictive p-value threshold or shrinkage parameter, giving a relative improvement of 16-18% over pT+clump in the correlation between observed and predicted outcome values. Using pseudovalidation, the best methods were PRScs, DBSLMM and SBayesR. PRScs pseudovalidation was only 3% worse than the best polygenic score identified by 10-fold cross validation. Elastic net models containing polygenic scores based on a range of parameters consistently improved prediction over any single polygenic score. Within a reference-standardized framework, the best polygenic prediction was achieved using LDpred2, lassosum and PRScs, modeling multiple polygenic scores derived using multiple parameters. This study will help researchers performing polygenic score studies to select the most powerful and predictive analysis methods.
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Affiliation(s)
- Oliver Pain
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Kylie P. Glanville
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Saskia P. Hagenaars
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Saskia Selzam
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Anna E. Fürtjes
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Héléna A. Gaspar
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Jonathan R. I. Coleman
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Kaili Rimfeld
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Robert Plomin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Lasse Folkersen
- Institute of Biological Psychiatry, Sankt Hans Hospital, Copenhagen, Denmark
| | - Cathryn M. Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
- Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
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725
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Muto V, Koshmanova E, Ghaemmaghami P, Jaspar M, Meyer C, Elansary M, Van Egroo M, Chylinski D, Berthomier C, Brandewinder M, Mouraux C, Schmidt C, Hammad G, Coppieters W, Ahariz N, Degueldre C, Luxen A, Salmon E, Phillips C, Archer SN, Yengo L, Byrne E, Collette F, Georges M, Dijk DJ, Maquet P, Visscher PM, Vandewalle G. Alzheimer's disease genetic risk and sleep phenotypes in healthy young men: association with more slow waves and daytime sleepiness. Sleep 2021; 44:5872145. [PMID: 32671396 DOI: 10.1093/sleep/zsaa137] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/11/2020] [Indexed: 11/13/2022] Open
Abstract
STUDY OBJECTIVES Sleep disturbances and genetic variants have been identified as risk factors for Alzheimer's disease (AD). Our goal was to assess whether genome-wide polygenic risk scores (PRS) for AD associate with sleep phenotypes in young adults, decades before typical AD symptom onset. METHODS We computed whole-genome PRS for AD and extensively phenotyped sleep under different sleep conditions, including baseline sleep, recovery sleep following sleep deprivation, and extended sleep opportunity, in a carefully selected homogenous sample of 363 healthy young men (22.1 years ± 2.7) devoid of sleep and cognitive disorders. RESULTS AD PRS was associated with more slow-wave energy, that is, the cumulated power in the 0.5-4 Hz EEG band, a marker of sleep need, during habitual sleep and following sleep loss, and potentially with larger slow-wave sleep rebound following sleep deprivation. Furthermore, higher AD PRS was correlated with higher habitual daytime sleepiness. CONCLUSIONS These results imply that sleep features may be associated with AD liability in young adults, when current AD biomarkers are typically negative, and support the notion that quantifying sleep alterations may be useful in assessing the risk for developing AD.
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Affiliation(s)
- Vincenzo Muto
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Walloon Excellence in Life sciences and Biotechnology (WELBIO), Wallonia, Belgium
| | - Ekaterina Koshmanova
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Pouya Ghaemmaghami
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Mathieu Jaspar
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Walloon Excellence in Life sciences and Biotechnology (WELBIO), Wallonia, Belgium.,Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Christelle Meyer
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Walloon Excellence in Life sciences and Biotechnology (WELBIO), Wallonia, Belgium
| | | | - Maxime Van Egroo
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Daphne Chylinski
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | | | | | - Charlotte Mouraux
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Christina Schmidt
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Grégory Hammad
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | | | - Naima Ahariz
- GIGA-Medical Genomics, University of Liège, Liège, Belgium
| | - Christian Degueldre
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - André Luxen
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Eric Salmon
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium.,Department of Neurology, University Hospital of Liège, Liège, Belgium
| | - Christophe Phillips
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,GIGA-In Silico Medicine, University of Liège, Liège, Belgium
| | - Simon N Archer
- Sleep Research Centre, University of Surrey, Guildford, UK
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Enda Byrne
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Fabienne Collette
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Michel Georges
- GIGA-Medical Genomics, University of Liège, Liège, Belgium
| | - Derk-Jan Dijk
- Sleep Research Centre, University of Surrey, Guildford, UK.,UK Dementia Research Institute, University of Surrey, Guildford, UK
| | - Pierre Maquet
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Walloon Excellence in Life sciences and Biotechnology (WELBIO), Wallonia, Belgium.,Department of Neurology, University Hospital of Liège, Liège, Belgium
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Gilles Vandewalle
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
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726
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Ringwald KG, Meller T, Schmitt S, Andlauer TFM, Stein F, Brosch K, Pfarr JK, Steinsträter O, Meinert S, Lemke H, Waltemate L, Thiel K, Grotegerd D, Enneking V, Klug M, Jansen A, Forstner AJ, Streit F, Witt SH, Rietschel M, Müller-Myhsok B, Nöthen MM, Dannlowski U, Krug A, Nenadić I, Kircher T. Interaction of developmental factors and ordinary stressful life events on brain structure in adults. Neuroimage Clin 2021; 30:102683. [PMID: 34215153 PMCID: PMC8102615 DOI: 10.1016/j.nicl.2021.102683] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 04/15/2021] [Accepted: 04/17/2021] [Indexed: 11/28/2022]
Abstract
An interplay of early environmental and genetic risk factors with recent stressful life events (SLEs) in adulthood increases the risk for adverse mental health outcomes. The interaction of early risk and current SLEs on brain structure has hardly been investigated. Whole brain voxel-based morphometry analysis was performed in N = 786 (64.6% female, mean age = 33.39) healthy subjects to identify correlations of brain clusters with commonplace recent SLEs. Genetic and early environmental risk factors, operationalized as those for severe psychopathology (i.e., polygenic scores for neuroticism, childhood maltreatment, urban upbringing and paternal age) were assessed as modulators of the impact of SLEs on the brain. SLEs were negatively correlated with grey matter volume in the left medial orbitofrontal cortex (mOFC, FWE p = 0.003). This association was present for both, positive and negative, life events. Cognitive-emotional variables, i.e., neuroticism, perceived stress, trait anxiety, intelligence, and current depressive symptoms did not account for the SLE-mOFC association. Further, genetic and environmental risk factors were not correlated with grey matter volume in the left mOFC cluster and did not affect the association between SLEs and left mOFC grey matter volume. The orbitofrontal cortex has been implicated in stress-related psychopathology, particularly major depression in previous studies. We find that SLEs are associated with this area. Important early life risk factors do not interact with current SLEs on brain morphology in healthy subjects.
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Affiliation(s)
- Kai G Ringwald
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany.
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Simon Schmitt
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany; Marburg University Hospital - UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Till F M Andlauer
- Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany; Marburg University Hospital - UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Olaf Steinsträter
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Susanne Meinert
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Hannah Lemke
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Lena Waltemate
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Katharina Thiel
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Dominik Grotegerd
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Verena Enneking
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Melissa Klug
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany; Core-Facility BrainImaging, Faculty of Medicine, Rudolf-Bultmann-Str. 8, 35039 Philipps-Universität Marburg, Germany
| | - Andreas J Forstner
- Centre for Human Genetics, Philipps-Universität Marburg, Baldingerstr., 35033 Marburg, Germany; Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159 Mannheim, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159 Mannheim, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159 Mannheim, Germany
| | - Bertram Müller-Myhsok
- Max-Planck-Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Feodor-Lynen-Str. 17, 81377 Munich, Germany; Institute of Translational Medicine, University of Liverpool, Crown Street, Liverpool L69 3BX, UK
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Udo Dannlowski
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany; Marburg University Hospital - UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; Department of Psychiatry and Psychotherapy, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany; Marburg University Hospital - UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany; Marburg University Hospital - UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
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727
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Shi S, Li X, Fang L, Liu A, Su G, Zhang Y, Luobu B, Ding X, Zhang S. Genomic Prediction Using Bayesian Regression Models With Global-Local Prior. Front Genet 2021; 12:628205. [PMID: 33936162 PMCID: PMC8083873 DOI: 10.3389/fgene.2021.628205] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 03/02/2021] [Indexed: 12/02/2022] Open
Abstract
Bayesian regression models are widely used in genomic prediction for various species. By introducing the global parameter τ, which can shrink marker effects to zero, and the local parameter λk, which can allow markers with large effects to escape from the shrinkage, we developed two novel Bayesian models, named BayesHP and BayesHE. The BayesHP model uses Horseshoe+ prior, whereas the BayesHE model assumes local parameter λk, after a half-t distribution with an unknown degree of freedom. The performances of BayesHP and BayesHE models were compared with three classical prediction models, including GBLUP, BayesA, and BayesB, and BayesU, which also applied global–local prior (Horseshoe prior). To assess model performances for traits with various genetic architectures, simulated data and real data in cattle (milk production, health, and type traits) and mice (type and growth traits) were analyzed. The results of simulation data analysis indicated that models based on global–local priors, including BayesU, BayesHP, and BayesHE, performed better in traits with higher heritability and fewer quantitative trait locus. The results of real data analysis showed that BayesHE was optimal or suboptimal for all traits, whereas BayesHP was not superior to other classical models. For BayesHE, its flexibility to estimate hyperparameter automatically allows the model to be more adaptable to a wider range of traits. The BayesHP model, however, tended to be suitable for traits having major/large quantitative trait locus, given its nature of the “U” type-like shrinkage pattern. Our results suggested that auto-estimate the degree of freedom (e.g., BayesHE) would be a better choice other than increasing the local parameter layers (e.g., BayesHP). In this study, we introduced the global–local prior with unknown hyperparameter to Bayesian regression models for genomic prediction, which can trigger further investigations on model development.
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Affiliation(s)
- Shaolei Shi
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xiujin Li
- Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Lingzhao Fang
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, United Kingdom
| | - Aoxing Liu
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Yi Zhang
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Basang Luobu
- Shannan Animal Husbandry and Veterinary Master Station, Shannan, China
| | - Xiangdong Ding
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Shengli Zhang
- National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
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728
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Sanchez-Roige S, Cox NJ, Johnson EO, Hancock DB, Davis LK. Alcohol and cigarette smoking consumption as genetic proxies for alcohol misuse and nicotine dependence. Drug Alcohol Depend 2021; 221:108612. [PMID: 33631543 PMCID: PMC8026738 DOI: 10.1016/j.drugalcdep.2021.108612] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 12/24/2020] [Accepted: 01/21/2021] [Indexed: 11/28/2022]
Abstract
PURPOSE To investigate the role of consumption phenotypes as genetic proxies for alcohol misuse and nicotine dependence. METHODS We leveraged GWAS data from well-powered studies of consumption, alcohol misuse, and nicotine dependence phenotypes measured in individuals of European ancestry from the UK Biobank (UKB) and other population-based cohorts (largest total N = 263,954), and performed genetic correlations within a medical-center cohort, BioVU (N = 66,915). For alcohol, we used quantitative measures of consumption and misuse via AUDIT from UKB. For smoking, we used cigarettes per day from UKB and non-UKB cohorts comprising the GSCAN consortium, and nicotine dependence via ICD codes from UKB and Fagerström Test for Nicotine Dependence from non-UKB cohorts. RESULTS In a large phenome-wide association study, we show that smoking consumption and dependence phenotypes show similar strongly negatively associations with a plethora of diseases, whereas alcohol consumption shows patterns of genetic association that diverge from those of alcohol misuse. CONCLUSIONS Our study suggests that cigarette smoking consumption, which can be easily measured in the general population, may be good a genetic proxy for nicotine dependence, whereas alcohol consumption is not a direct genetic proxy of alcohol misuse.
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Affiliation(s)
- Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
| | - Nancy J Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Eric O Johnson
- GenOmics, Bioinformatics, and Translational Research Center, RTI International, Research Triangle Park, NC 27709, USA; Fellow Program, RTI International, Research Triangle Park, NC 27709, USA
| | - Dana B Hancock
- GenOmics, Bioinformatics, and Translational Research Center, RTI International, Research Triangle Park, NC 27709, USA
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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729
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Meyers JL, Salvatore JE. Genetic and Social-Environmental Influences on Substance Use and Disorders. Psychiatr Ann 2021. [DOI: 10.3928/00485713-20210311-02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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730
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Polygenic risk score, healthy lifestyles, and risk of incident depression. Transl Psychiatry 2021; 11:189. [PMID: 33782378 PMCID: PMC8007584 DOI: 10.1038/s41398-021-01306-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 02/16/2021] [Accepted: 02/24/2021] [Indexed: 02/01/2023] Open
Abstract
Genetic factors increase the risk of depression, but the extent to which this can be offset by modifiable lifestyle factors is unknown. We investigated whether a combination of healthy lifestyles is associated with lower risk of depression regardless of genetic risk. Data were obtained from the UK Biobank and consisted of 339,767 participants (37-73 years old) without depression between 2006 and 2010. Genetic risk was categorized as low, intermediate, or high according to polygenic risk score for depression. A combination of healthy lifestyles factors-including no current smoking, regular physical activity, a healthy diet, moderate alcohol intake and a body mass index <30 kg/m2-was categorized into favorable, intermediate, and unfavorable lifestyles. The risk of depression was 22% higher among those at high genetic risk compared with those at low genetic risk (HR = 1.22, 95% CI: 1.14-1.30). Participants with high genetic risk and unfavorable lifestyle had a more than two-fold risk of incident depression compared with low genetic risk and favorable lifestyle (HR = 2.18, 95% CI: 1.84-2.58). There was no significant interaction between genetic risk and lifestyle factors (P for interaction = 0.69). Among participants at high genetic risk, a favorable lifestyle was associated with nearly 50% lower relative risk of depression than an unfavorable lifestyle (HR = 0.51, 95% CI: 0.43-0.60). We concluded that genetic and lifestyle factors were independently associated with risk of incident depression. Adherence to healthy lifestyles may lower the risk of depression regardless of genetic risk.
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731
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Nayar K, Sealock JM, Maltman N, Bush L, Cook EH, Davis LK, Losh M. Elevated Polygenic Burden for Autism Spectrum Disorder Is Associated With the Broad Autism Phenotype in Mothers of Individuals With Autism Spectrum Disorder. Biol Psychiatry 2021; 89:476-485. [PMID: 33229037 PMCID: PMC7901138 DOI: 10.1016/j.biopsych.2020.08.029] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 08/15/2020] [Accepted: 08/31/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder that encompasses a complex and heterogeneous set of traits. Subclinical traits that mirror the core features of ASD, referred to as the broad autism phenotype (BAP), have been documented repeatedly in unaffected relatives and are believed to reflect underlying genetic liability to ASD. The BAP may help inform the etiology of ASD by allowing the stratification of families into more phenotypically and etiologically homogeneous subgroups. This study explores polygenic scores related to the BAP. METHODS Phenotypic and genotypic information were obtained from 2614 trios from the Simons Simplex Collection. Polygenic scores of ASD (ASD-PGSs) were generated across the sample to determine the shared genetic overlap between the BAP and ASD. Maternal and paternal ASD-PGSs were explored in relation to BAP traits and their child's ASD symptomatology. RESULTS Maternal pragmatic language was related to child's social communicative atypicalities. In fathers, rigid personality was related to increased repetitive behaviors in children. Maternal (but not paternal) ASD-PGSs were related to the pragmatic language and rigid BAP domains. CONCLUSIONS Associations emerged between parent and child phenotypes, with more associations emerging in mothers than in fathers. ASD-PGS associations emerged with BAP in mothers only, highlighting the potential for a female protective factor, and implicating the polygenic etiology of ASD-related phenotypes in the BAP.
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Affiliation(s)
| | | | | | | | | | | | - Molly Losh
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois.
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732
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Vaura F, Kauko A, Suvila K, Havulinna AS, Mars N, Salomaa V, FinnGen, Cheng S, Niiranen T. Polygenic Risk Scores Predict Hypertension Onset and Cardiovascular Risk. Hypertension 2021; 77:1119-1127. [PMID: 33611940 PMCID: PMC8025831 DOI: 10.1161/hypertensionaha.120.16471] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Felix Vaura
- From the Department of Internal Medicine, University of Turku, Finland (F.V., A.K., K.S., T.N.)
| | - Anni Kauko
- From the Department of Internal Medicine, University of Turku, Finland (F.V., A.K., K.S., T.N.)
| | - Karri Suvila
- From the Department of Internal Medicine, University of Turku, Finland (F.V., A.K., K.S., T.N.).,Division of Medicine, Turku University Hospital, Finland (K.S., T.N.)
| | - Aki S Havulinna
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland (A.S.H., V.S., T.N.).,Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki (A.S.H., N.M.)
| | - Nina Mars
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki (A.S.H., N.M.)
| | - Veikko Salomaa
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland (A.S.H., V.S., T.N.)
| | | | - Susan Cheng
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA (S.C.).,Division of Cardiology, Brigham and Women's Hospital, Boston, MA (S.C.)
| | - Teemu Niiranen
- From the Department of Internal Medicine, University of Turku, Finland (F.V., A.K., K.S., T.N.).,Division of Medicine, Turku University Hospital, Finland (K.S., T.N.).,Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland (A.S.H., V.S., T.N.)
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733
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Shi H, Gazal S, Kanai M, Koch EM, Schoech AP, Siewert KM, Kim SS, Luo Y, Amariuta T, Huang H, Okada Y, Raychaudhuri S, Sunyaev SR, Price AL. Population-specific causal disease effect sizes in functionally important regions impacted by selection. Nat Commun 2021; 12:1098. [PMID: 33597505 PMCID: PMC7889654 DOI: 10.1038/s41467-021-21286-1] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 01/15/2021] [Indexed: 01/31/2023] Open
Abstract
Many diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.
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Affiliation(s)
- Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Evan M Koch
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Armin P Schoech
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Katherine M Siewert
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuel S Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yang Luo
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tiffany Amariuta
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Soumya Raychaudhuri
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Shamil R Sunyaev
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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734
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Escott-Price V, Schmidt KM. Challenges of Adjusting Single-Nucleotide Polymorphism Effect Sizes for Linkage Disequilibrium. Hum Hered 2021; 85:1-11. [PMID: 33582669 DOI: 10.1159/000513303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 11/19/2020] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) were successful in identifying SNPs showing association with disease, but their individual effect sizes are small and require large sample sizes to achieve statistical significance. Methods of post-GWAS analysis, including gene-based, gene-set and polygenic risk scores, combine the SNP effect sizes in an attempt to boost the power of the analyses. To avoid giving undue weight to SNPs in linkage disequilibrium (LD), the LD needs to be taken into account in these analyses. OBJECTIVES We review methods that attempt to adjust the effect sizes (β-coefficients) of summary statistics, instead of simple LD pruning. METHODS We subject LD adjustment approaches to a mathematical analysis, recognising Tikhonov regularisation as a framework for comparison. RESULTS Observing the similarity of the processes involved with the more straightforward Tikhonov-regularised ordinary least squares estimate for multivariate regression coefficients, we note that current methods based on a Bayesian model for the effect sizes effectively provide an implicit choice of the regularisation parameter, which is convenient, but at the price of reduced transparency and, especially in smaller LD blocks, a risk of incomplete LD correction. CONCLUSIONS There is no simple answer to the question which method is best, but where interpretability of the LD adjustment is essential, as in research aiming at identifying the genomic aetiology of disorders, our study suggests that a more direct choice of mild regularisation in the correction of effect sizes may be preferable.
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Affiliation(s)
- Valentina Escott-Price
- Division of Psychiatry and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom,
- Dementia Research Institute, Cardiff University, Cardiff, United Kingdom,
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735
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Choquet H, Yin J, Jorgenson E. Cigarette smoking behaviors and the importance of ethnicity and genetic ancestry. Transl Psychiatry 2021; 11:120. [PMID: 33633108 PMCID: PMC7907280 DOI: 10.1038/s41398-021-01244-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 01/12/2021] [Accepted: 01/15/2021] [Indexed: 11/09/2022] Open
Abstract
Cigarette smoking contributes to numerous diseases and is one of the leading causes of death in the United States. Smoking behaviors vary widely across race/ethnicity, but it is not clear why. Here, we examine the contribution of genetic ancestry to variation in two smoking-related traits in 43,485 individuals from four race/ethnicity groups (non-Hispanic white, Hispanic/Latino, East Asian, and African American) from a single U.S. healthcare plan. Smoking prevalence was the lowest among East Asians (22.7%) and the highest among non-Hispanic whites (38.5%). We observed significant associations between genetic ancestry and smoking-related traits. Within East Asians, we observed higher smoking prevalence with greater European (versus Asian) ancestry (P = 9.95 × 10-12). Within Hispanic/Latinos, higher cigarettes per day (CPD) was associated with greater European ancestry (P = 3.34 × 10-25). Within non-Hispanic whites, the lowest number of CPD was observed for individuals of southeastern European ancestry (P = 9.06 × 10-5). These associations remained after considering known smoking-associated loci, education, socioeconomic factors, and marital status. Our findings support the role of genetic ancestry and socioeconomic factors in cigarette smoking behaviors in non-Hispanic whites, Hispanic/Latinos, and East Asians.
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Affiliation(s)
- Hélène Choquet
- Division of Research, Kaiser Permanente Northern California (KPNC), Oakland, CA, 94612, USA.
| | - Jie Yin
- grid.280062.e0000 0000 9957 7758Division of Research, Kaiser Permanente Northern California (KPNC), Oakland, CA 94612 USA
| | - Eric Jorgenson
- grid.280062.e0000 0000 9957 7758Division of Research, Kaiser Permanente Northern California (KPNC), Oakland, CA 94612 USA
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736
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Aguirre M, Tanigawa Y, Venkataraman GR, Tibshirani R, Hastie T, Rivas MA. Polygenic risk modeling with latent trait-related genetic components. Eur J Hum Genet 2021; 29:1071-1081. [PMID: 33558700 DOI: 10.1038/s41431-021-00813-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 12/26/2020] [Accepted: 01/14/2021] [Indexed: 02/06/2023] Open
Abstract
Polygenic risk models have led to significant advances in understanding complex diseases and their clinical presentation. While polygenic risk scores (PRS) can effectively predict outcomes, they do not generally account for disease subtypes or pathways which underlie within-trait diversity. Here, we introduce a latent factor model of genetic risk based on components from Decomposition of Genetic Associations (DeGAs), which we call the DeGAs polygenic risk score (dPRS). We compute DeGAs using genetic associations for 977 traits and find that dPRS performs comparably to standard PRS while offering greater interpretability. We show how to decompose an individual's genetic risk for a trait across DeGAs components, with examples for body mass index (BMI) and myocardial infarction (heart attack) in 337,151 white British individuals in the UK Biobank, with replication in a further set of 25,486 non-British white individuals. We find that BMI polygenic risk factorizes into components related to fat-free mass, fat mass, and overall health indicators like physical activity. Most individuals with high dPRS for BMI have strong contributions from both a fat-mass component and a fat-free mass component, whereas a few "outlier" individuals have strong contributions from only one of the two components. Overall, our method enables fine-scale interpretation of the drivers of genetic risk for complex traits.
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Affiliation(s)
- Matthew Aguirre
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.,Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA, USA
| | - Yosuke Tanigawa
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA
| | - Guhan Ram Venkataraman
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA
| | - Rob Tibshirani
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.,Department of Statistics, Stanford University, Stanford, CA, USA
| | - Trevor Hastie
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.,Department of Statistics, Stanford University, Stanford, CA, USA
| | - Manuel A Rivas
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
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737
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Brown MA, Aletaha D. Genetic risk scores in inflammatory arthritis: a new era? Nat Rev Rheumatol 2021; 16:545-546. [PMID: 32690928 DOI: 10.1038/s41584-020-0473-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Matthew A Brown
- Guy's & St Thomas' NHS Foundation Trust and King's College London NIHR Biomedical Research Centre, London, England.
| | - Daniel Aletaha
- Division of Rheumatology, Department of Medicine 3, Medical University of Vienna, Vienna, Austria
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738
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Dennis JK, Sealock JM, Straub P, Lee YH, Hucks D, Actkins K, Faucon A, Feng YCA, Ge T, Goleva SB, Niarchou M, Singh K, Morley T, Smoller JW, Ruderfer DM, Mosley JD, Chen G, Davis LK. Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease. Genome Med 2021; 13:6. [PMID: 33441150 PMCID: PMC7807864 DOI: 10.1186/s13073-020-00820-8] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 12/08/2020] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are stored in electronic health records (EHRs), and a growing number of EHRs are linked to patient DNA, offering unprecedented opportunities to query relationships between genetic risk for complex disease and quantitative physiological measurements collected on large populations. METHODS A total of 3075 quantitative lab tests were extracted from Vanderbilt University Medical Center's (VUMC) EHR system and cleaned for population-level analysis according to our QualityLab protocol. Lab values extracted from BioVU were compared with previous population studies using heritability and genetic correlation analyses. We then tested the hypothesis that polygenic risk scores for biomarkers and complex disease are associated with biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of the lipids and CAD polygenic risk scores across 315 heritable lab tests then replicated the pipeline and analyses in the Massachusetts General Brigham Biobank. RESULTS Heritability estimates of lipid values (after cleaning with QualityLab) were comparable to previous reports and polygenic scores for lipids were strongly associated with their referent lipid in a LabWAS. LabWAS of the polygenic score for CAD recapitulated canonical heart disease biomarker profiles including decreased HDL, increased pre-medication LDL, triglycerides, blood glucose, and glycated hemoglobin (HgbA1C) in European and African descent populations. Notably, many of these associations remained even after adjusting for the presence of cardiovascular disease and were replicated in the MGBB. CONCLUSIONS Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses, providing new avenues for discovery of novel biomarkers and deeper understanding of disease trajectories in pre-symptomatic individuals. We present two methods and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan.
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Affiliation(s)
- Jessica K Dennis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Julia M Sealock
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Peter Straub
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Younga H Lee
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Donald Hucks
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Ky'Era Actkins
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Department of Microbiology, Immunology, and Physiology, Meharry Medical College, Nashville, TN, 37232, USA
| | - Annika Faucon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Yen-Chen Anne Feng
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Tian Ge
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Slavina B Goleva
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Maria Niarchou
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Kritika Singh
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Theodore Morley
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Jordan W Smoller
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Douglas M Ruderfer
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Jonathan D Mosley
- Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Guanhua Chen
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Lea K Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, 511-A Light Hall, 2215 Garland Ave, Nashville, TN, 37232, USA.
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739
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Ye Y, Chen X, Han J, Jiang W, Natarajan P, Zhao H. Interactions Between Enhanced Polygenic Risk Scores and Lifestyle for Cardiovascular Disease, Diabetes, and Lipid Levels. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2021; 14:e003128. [PMID: 33433237 DOI: 10.1161/circgen.120.003128] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Both lifestyle and genetic factors confer risk for cardiovascular diseases, type 2 diabetes, and dyslipidemia. However, the interactions between these 2 groups of risk factors were not comprehensively understood due to previous poor estimation of genetic risk. Here we set out to develop enhanced polygenic risk scores (PRS) and systematically investigate multiplicative and additive interactions between PRS and lifestyle for coronary artery disease, atrial fibrillation, type 2 diabetes, total cholesterol, triglyceride, and LDL-cholesterol. METHODS Our study included 276 096 unrelated White British participants from the UK Biobank. We investigated several PRS methods (P+T, LDpred, PRS continuous shrinkage, and AnnoPred) and showed that AnnoPred achieved consistently improved prediction accuracy for all 6 diseases/traits. With enhanced PRS and combined lifestyle status categorized by smoking, body mass index, physical activity, and diet, we investigated both multiplicative and additive interactions between PRS and lifestyle using regression models. RESULTS We observed that healthy lifestyle reduced disease incidence by similar multiplicative magnitude across different PRS groups. The absolute risk reduction from lifestyle adherence was, however, significantly greater in individuals with higher PRS. Specifically, for type 2 diabetes, the absolute risk reduction from lifestyle adherence was 12.4% (95% CI, 10.0%-14.9%) in the top 1% PRS versus 2.8% (95% CI, 2.3%-3.3%) in the bottom PRS decile, leading to a ratio of >4.4. We also observed a significant interaction effect between PRS and lifestyle on triglyceride level. CONCLUSIONS By leveraging functional annotations, AnnoPred outperforms state-of-the-art methods on quantifying genetic risk through PRS. Our analyses based on enhanced PRS suggest that individuals with high genetic risk may derive similar relative but greater absolute benefit from lifestyle adherence.
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Affiliation(s)
- Yixuan Ye
- Program of Computational Biology and Bioinformatics (Y.Y., H.Z.), Yale University
| | - Xi Chen
- Department of Statistics and Data Science (X.C., J.H.), Yale University.,Department of Molecular Biophysics and Biochemistry (X.C., J.H.), Yale University
| | - James Han
- Department of Statistics and Data Science (X.C., J.H.), Yale University.,Department of Molecular Biophysics and Biochemistry (X.C., J.H.), Yale University
| | - Wei Jiang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT (W.J., H.Z.)
| | - Pradeep Natarajan
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston (P.N.).,Program in Medical and Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA (P.N.)
| | - Hongyu Zhao
- Program of Computational Biology and Bioinformatics (Y.Y., H.Z.), Yale University.,Department of Biostatistics, Yale School of Public Health, New Haven, CT (W.J., H.Z.)
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740
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Clinical and genetic differences between bipolar disorder type 1 and 2 in multiplex families. Transl Psychiatry 2021; 11:31. [PMID: 33431802 PMCID: PMC7801527 DOI: 10.1038/s41398-020-01146-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/30/2020] [Accepted: 12/04/2020] [Indexed: 01/29/2023] Open
Abstract
The two major subtypes of bipolar disorder (BD), BD-I and BD-II, are distinguished based on the presence of manic or hypomanic episodes. Historically, BD-II was perceived as a less severe form of BD-I. Recent research has challenged this concept of a severity continuum. Studies in large samples of unrelated patients have described clinical and genetic differences between the subtypes. Besides an increased schizophrenia polygenic risk load in BD-I, these studies also observed an increased depression risk load in BD-II patients. The present study assessed whether such clinical and genetic differences are also found in BD patients from multiplex families, which exhibit reduced genetic and environmental heterogeneity. Comparing 252 BD-I and 75 BD-II patients from the Andalusian Bipolar Family (ABiF) study, the clinical course, symptoms during depressive and manic episodes, and psychiatric comorbidities were analyzed. Furthermore, polygenic risk scores (PRS) for BD, schizophrenia, and depression were assessed. BD-I patients not only suffered from more severe symptoms during manic episodes but also more frequently showed incapacity during depressive episodes. A higher BD PRS was significantly associated with suicidal ideation. Moreover, BD-I cases exhibited lower depression PRS. In line with a severity continuum from BD-II to BD-I, our results link BD-I to a more pronounced clinical presentation in both mania and depression and indicate that the polygenic risk load of BD predisposes to more severe disorder characteristics. Nevertheless, our results suggest that the genetic risk burden for depression also shapes disorder presentation and increases the likelihood of BD-II subtype development.
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741
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Genome-wide association study of problematic opioid prescription use in 132,113 23andMe research participants of European ancestry. Mol Psychiatry 2021; 26:6209-6217. [PMID: 34728798 PMCID: PMC8562028 DOI: 10.1038/s41380-021-01335-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 09/21/2021] [Accepted: 09/29/2021] [Indexed: 12/31/2022]
Abstract
The growing prevalence of opioid use disorder (OUD) constitutes an urgent health crisis. Ample evidence indicates that risk for OUD is heritable. As a surrogate (or proxy) for OUD, we explored the genetic basis of using prescription opioids 'not as prescribed'. We hypothesized that misuse of opiates might be a heritable risk factor for OUD. To test this hypothesis, we performed a genome-wide association study (GWAS) of problematic opioid use (POU) in 23andMe research participants of European ancestry (N = 132,113; 21% cases). We identified two genome-wide significant loci (rs3791033, an intronic variant of KDM4A; rs640561, an intergenic variant near LRRIQ3). POU showed positive genetic correlations with the two largest available GWAS of OUD and opioid dependence (rg = 0.64, 0.80, respectively). We also identified numerous additional genetic correlations with POU, including alcohol dependence (rg = 0.74), smoking initiation (rg = 0.63), pain relief medication intake (rg = 0.49), major depressive disorder (rg = 0.44), chronic pain (rg = 0.42), insomnia (rg = 0.39), and loneliness (rg = 0.28). Although POU was positively genetically correlated with risk-taking (rg = 0.38), conditioning POU on risk-taking did not substantially alter the magnitude or direction of these genetic correlations, suggesting that POU does not simply reflect a genetic tendency towards risky behavior. Lastly, we performed phenome- and lab-wide association analyses, which uncovered additional phenotypes that were associated with POU, including respiratory failure, insomnia, ischemic heart disease, and metabolic and blood-related biomarkers. We conclude that opioid misuse can be measured in population-based cohorts and provides a cost-effective complementary strategy for understanding the genetic basis of OUD.
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742
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Actkins KV, Singh K, Hucks D, Velez Edwards DR, Aldrich M, Cha J, Wellons M, Davis LK. Characterizing the Clinical and Genetic Spectrum of Polycystic Ovary Syndrome in Electronic Health Records. J Clin Endocrinol Metab 2021; 106:153-167. [PMID: 32961557 PMCID: PMC7765638 DOI: 10.1210/clinem/dgaa675] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 09/19/2020] [Indexed: 02/08/2023]
Abstract
CONTEXT Polycystic ovary syndrome (PCOS) is one of the leading causes of infertility, yet current diagnostic criteria are ineffective at identifying patients whose symptoms reside outside strict diagnostic criteria. As a result, PCOS is underdiagnosed and its etiology is poorly understood. OBJECTIVE We aim to characterize the phenotypic spectrum of PCOS clinical features within and across racial and ethnic groups. METHODS We developed a strictly defined PCOS algorithm (PCOSkeyword-strict) using the International Classification of Diseases, ninth and tenth revisions and keywords mined from clinical notes in electronic health records (EHRs) data. We then systematically relaxed the inclusion criteria to evaluate the change in epidemiological and genetic associations resulting in 3 subsequent algorithms (PCOScoded-broad, PCOScoded-strict, and PCOSkeyword-broad). We evaluated the performance of each phenotyping approach and characterized prominent clinical features observed in racially and ethnically diverse PCOS patients. RESULTS The best performance came from the PCOScoded-strict algorithm, with a positive predictive value of 98%. Individuals classified as cases by this algorithm had significantly higher body mass index (BMI), insulin levels, free testosterone values, and genetic risk scores for PCOS, compared to controls. Median BMI was higher in African American females with PCOS compared to White and Hispanic females with PCOS. CONCLUSIONS PCOS symptoms are observed across a severity spectrum that parallels the continuous genetic liability to PCOS in the general population. Racial and ethnic group differences exist in PCOS symptomology and metabolic health across different phenotyping strategies.
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Affiliation(s)
- Ky’Era V Actkins
- Department of Microbiology, Immunology, and Physiology, Meharry Medical College, Nashville, Tennessee
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Kritika Singh
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Donald Hucks
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Digna R Velez Edwards
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
- Vanderbilt Epidemiology Center, Institute of Medicine and Public Health, Vanderbilt University Medical Center, Nashville, Tennessee
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Melinda Aldrich
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jeeyeon Cha
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Melissa Wellons
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lea K Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
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743
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Privé F, Arbel J, Vilhjálmsson BJ. LDpred2: better, faster, stronger. Bioinformatics 2020; 36:5424-5431. [PMID: 33326037 PMCID: PMC8016455 DOI: 10.1093/bioinformatics/btaa1029] [Citation(s) in RCA: 274] [Impact Index Per Article: 68.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 11/24/2020] [Accepted: 12/01/2020] [Indexed: 12/20/2022] Open
Abstract
Motivation Polygenic scores have become a central tool in human genetics research. LDpred is a popular method for deriving polygenic scores based on summary statistics and a matrix of correlation between genetic variants. However, LDpred has limitations that may reduce its predictive performance. Results Here, we present LDpred2, a new version of LDpred that addresses these issues. We also provide two new options in LDpred2: a ‘sparse’ option that can learn effects that are exactly 0, and an ‘auto’ option that directly learns the two LDpred parameters from data. We benchmark predictive performance of LDpred2 against the previous version on simulated and real data, demonstrating substantial improvements in robustness and predictive accuracy compared to LDpred1. We then show that LDpred2 also outperforms other polygenic score methods recently developed, with a mean AUC over the 8 real traits analyzed here of 65.1%, compared to 63.8% for lassosum, 62.9% for PRS-CS and 61.5% for SBayesR. Note that LDpred2 provides more accurate polygenic scores when run genome-wide, instead of per chromosome. Availability and implementation LDpred2 is implemented in R package bigsnpr. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Florian Privé
- National Centre for Register-Based Research, Aarhus University, Aarhus, 8210, Denmark
| | - Julyan Arbel
- Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, 38000, France
| | - Bjarni J Vilhjálmsson
- National Centre for Register-Based Research, Aarhus University, Aarhus, 8210, Denmark.,Bioinformatics Research Centre, Aarhus University, Aarhus, 8000, Denmark
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744
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Mars N, Widén E, Kerminen S, Meretoja T, Pirinen M, Della Briotta Parolo P, Palta P, Palotie A, Kaprio J, Joensuu H, Daly M, Ripatti S. The role of polygenic risk and susceptibility genes in breast cancer over the course of life. Nat Commun 2020; 11:6383. [PMID: 33318493 PMCID: PMC7736877 DOI: 10.1038/s41467-020-19966-5] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 11/06/2020] [Indexed: 12/24/2022] Open
Abstract
Polygenic risk scores (PRS) for breast cancer have potential to improve risk prediction, but there is limited information on their utility in various clinical situations. Here we show that among 122,978 women in the FinnGen study with 8401 breast cancer cases, the PRS modifies the breast cancer risk of two high-impact frameshift risk variants. Similarly, we show that after the breast cancer diagnosis, individuals with elevated PRS have an elevated risk of developing contralateral breast cancer, and that the PRS can considerably improve risk assessment among their female first-degree relatives. In more detail, women with the c.1592delT variant in PALB2 (242-fold enrichment in Finland, 336 carriers) and an average PRS (10–90th percentile) have a lifetime risk of breast cancer at 55% (95% CI 49–61%), which increases to 84% (71–97%) with a high PRS ( > 90th percentile), and decreases to 49% (30–68%) with a low PRS ( < 10th percentile). Similarly, for c.1100delC in CHEK2 (3.7–fold enrichment; 1648 carriers), the respective lifetime risks are 29% (27–32%), 59% (52–66%), and 9% (5–14%). The PRS also refines the risk assessment of women with first-degree relatives diagnosed with breast cancer, particularly among women with positive family history of early-onset breast cancer. Here we demonstrate the opportunities for a comprehensive way of assessing genetic risk in the general population, in breast cancer patients, and in unaffected family members. Identifying women at high risk of breast cancer has important implications for screening. Here, the authors demonstrate that polygenic risk scores improve breast cancer risk prediction in the population, in women with mutations in high-risk genes and in women with close relatives with the disease.
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Affiliation(s)
- Nina Mars
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Elisabeth Widén
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Sini Kerminen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Tuomo Meretoja
- Breast Surgery Unit, Comprehensive Cancer Center, Helsinki University Hospital, Helsinki, Finland.,University of Helsinki, Helsinki, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.,Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.,Department of Public Health, University of Helsinki, Helsinki, Finland
| | | | - Priit Palta
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.,Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.,Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Analytic and Translational Genetics Unit, Department of Medicine, and the Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.,Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Heikki Joensuu
- University of Helsinki, Helsinki, Finland.,Comprehensive Cancer Center, Helsinki University Hospital, Helsinki, Finland
| | - Mark Daly
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland. .,Department of Public Health, University of Helsinki, Helsinki, Finland. .,Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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745
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Johnson EC, Demontis D, Thorgeirsson TE, Walters RK, Polimanti R, Hatoum AS, Sanchez-Roige S, Paul SE, Wendt FR, Clarke TK, Lai D, Reginsson GW, Zhou H, He J, Baranger DAA, Gudbjartsson DF, Wedow R, Adkins DE, Adkins AE, Alexander J, Bacanu SA, Bigdeli TB, Boden J, Brown SA, Bucholz KK, Bybjerg-Grauholm J, Corley RP, Degenhardt L, Dick DM, Domingue BW, Fox L, Goate AM, Gordon SD, Hack LM, Hancock DB, Hartz SM, Hickie IB, Hougaard DM, Krauter K, Lind PA, McClintick JN, McQueen MB, Meyers JL, Montgomery GW, Mors O, Mortensen PB, Nordentoft M, Pearson JF, Peterson RE, Reynolds MD, Rice JP, Runarsdottir V, Saccone NL, Sherva R, Silberg JL, Tarter RE, Tyrfingsson T, Wall TL, Webb BT, Werge T, Wetherill L, Wright MJ, Zellers S, Adams MJ, Bierut LJ, Boardman JD, Copeland WE, Farrer LA, Foroud TM, Gillespie NA, Grucza RA, Harris KM, Heath AC, Hesselbrock V, Hewitt JK, Hopfer CJ, Horwood J, Iacono WG, Johnson EO, Kendler KS, Kennedy MA, Kranzler HR, Madden PAF, Maes HH, Maher BS, Martin NG, McGue M, McIntosh AM, Medland SE, Nelson EC, Porjesz B, Riley BP, Stallings MC, Vanyukov MM, Vrieze S, Davis LK, Bogdan R, Gelernter J, Edenberg HJ, Stefansson K, Børglum AD, Agrawal A. A large-scale genome-wide association study meta-analysis of cannabis use disorder. Lancet Psychiatry 2020; 7:1032-1045. [PMID: 33096046 PMCID: PMC7674631 DOI: 10.1016/s2215-0366(20)30339-4] [Citation(s) in RCA: 175] [Impact Index Per Article: 43.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50-70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder. METHODS To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations. FINDINGS We identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07-1·15, p=1·84 × 10-9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86-0·93, p=6·46 × 10-9). Cannabis use disorder and cannabis use were genetically correlated (rg 0·50, p=1·50 × 10-21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia. INTERPRETATION These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder. FUNDING National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.
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Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA.
| | - Ditte Demontis
- Department of Biomedicine-Human Genetics and Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | | | - Raymond K Walters
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Renato Polimanti
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA; Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sarah E Paul
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, St. Louis, MO, USA
| | - Frank R Wendt
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA; Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Toni-Kim Clarke
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Hang Zhou
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA; Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - June He
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - David A A Baranger
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Daniel F Gudbjartsson
- Statistics Department, Reykjavik, Iceland; School of Engineering and Natural Sciences, Iceland University, Reykjavik, Iceland
| | - Robbee Wedow
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel E Adkins
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA; Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA; College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, USA
| | - Amy E Adkins
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA; Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA; College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, USA
| | - Jeffry Alexander
- Virginia Commonwealth University Alcohol Research Center, Virginia Commonwealth University, Richmond, VA, USA
| | - Silviu-Alin Bacanu
- Virginia Commonwealth University Alcohol Research Center, Virginia Commonwealth University, Richmond, VA, USA
| | - Tim B Bigdeli
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Joseph Boden
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - Sandra A Brown
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Psychology and Office of Research Affairs, University of California San Diego, La Jolla, CA, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Jonas Bybjerg-Grauholm
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Department for Congenital Disorders, Center for Neonatal Screening, Statens Serum Institut, Copenhagen, Denmark
| | - Robin P Corley
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Louisa Degenhardt
- National Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA; Department of Human & Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Benjamin W Domingue
- Stanford University Graduate School of Education, Stanford University, Stanford, CA, USA
| | - Louis Fox
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Alison M Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Laura M Hack
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Dana B Hancock
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, Durham, NC, USA
| | - Sarah M Hartz
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Department for Congenital Disorders, Center for Neonatal Screening, Statens Serum Institut, Copenhagen, Denmark
| | - Kenneth Krauter
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA; University of Colorado Boulder, Boulder, CO, USA
| | - Penelope A Lind
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jeanette N McClintick
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Matthew B McQueen
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
| | - Jacquelyn L Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA; Henri Begleiter Neurodynamics Laboratory, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Grant W Montgomery
- Institute for Molecular Bioscience, University of Queensland, QLD, Australia
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Psychosis Research Unit, Aarhus University Hospital, Aarhus, Denmark
| | - Preben B Mortensen
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark; Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, Denmark
| | - John F Pearson
- Biostatistics and Computational Biology Unit, University of Otago, Christchurch, New Zealand; Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Roseann E Peterson
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | | | - John P Rice
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | | | - Nancy L Saccone
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA; Division of Biostatistics, Washington University School of Medicine, St Louis, MO, USA
| | - Richard Sherva
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Judy L Silberg
- Department of Human & Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA; Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Ralph E Tarter
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Tamara L Wall
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Bradley T Webb
- Virginia Commonwealth University Alcohol Research Center, Virginia Commonwealth University, Richmond, VA, USA
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen, Denmark; Department of Clinical Medicine, and Center for GeoGenetics, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Leah Wetherill
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, QLD, Australia
| | - Stephanie Zellers
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Jason D Boardman
- Institute of Behavioral Science and Department of Sociology, University of Colorado Boulder, Boulder, CO, USA
| | - William E Copeland
- Department of Psychiatry, University of Vermont Medical Center, Burlington, VT, USA
| | - Lindsay A Farrer
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Tatiana M Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Nathan A Gillespie
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Richard A Grucza
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Kathleen Mullan Harris
- Department of Sociology, and The Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Andrew C Heath
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - John K Hewitt
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Christian J Hopfer
- Department of Psychiatry, University of Colorado Denver, Aurora, CO, USA
| | - John Horwood
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - William G Iacono
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Eric O Johnson
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, Durham, NC, USA
| | - Kenneth S Kendler
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Martin A Kennedy
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Henry R Kranzler
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Pamela A F Madden
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Hermine H Maes
- Department of Human & Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA; Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Brion S Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Matthew McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | | | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Elliot C Nelson
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA; Henri Begleiter Neurodynamics Laboratory, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Brien P Riley
- Virginia Commonwealth University Alcohol Research Center, Virginia Commonwealth University, Richmond, VA, USA
| | - Michael C Stallings
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | | | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Psychiatry and Behavioral Sciences, and Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ryan Bogdan
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, St. Louis, MO, USA
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA; Department of Genetics, and Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA; Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kari Stefansson
- deCODE Genetics/Amgen, Reykjavik, Iceland; Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Anders D Børglum
- Department of Biomedicine-Human Genetics and Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
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746
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Fritsche LG, Patil S, Beesley LJ, VandeHaar P, Salvatore M, Ma Y, Peng RB, Taliun D, Zhou X, Mukherjee B. Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks. Am J Hum Genet 2020; 107:815-836. [PMID: 32991828 PMCID: PMC7675001 DOI: 10.1016/j.ajhg.2020.08.025] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Accepted: 08/28/2020] [Indexed: 02/06/2023] Open
Abstract
To facilitate scientific collaboration on polygenic risk scores (PRSs) research, we created an extensive PRS online repository for 35 common cancer traits integrating freely available genome-wide association studies (GWASs) summary statistics from three sources: published GWASs, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWASs. Our framework condenses these summary statistics into PRSs using various approaches such as linkage disequilibrium pruning/p value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRSs in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRSs. We expect this integrated platform to accelerate PRS-related cancer research.
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Affiliation(s)
- Lars G Fritsche
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Snehal Patil
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Lauren J Beesley
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Peter VandeHaar
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Maxwell Salvatore
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Ying Ma
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Robert B Peng
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Department of Statistics, Northwestern University, Evanston, IL 60208, USA
| | - Daniel Taliun
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA; Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA.
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747
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Biere S, Kranz TM, Matura S, Petrova K, Streit F, Chiocchetti AG, Grimm O, Brum M, Brunkhorst-Kanaan N, Oertel V, Malyshau A, Pfennig A, Bauer M, Schulze TG, Kittel-Schneider S, Reif A. Risk Stratification for Bipolar Disorder Using Polygenic Risk Scores Among Young High-Risk Adults. Front Psychiatry 2020; 11:552532. [PMID: 33192665 PMCID: PMC7653940 DOI: 10.3389/fpsyt.2020.552532] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 09/10/2020] [Indexed: 11/30/2022] Open
Abstract
Objective: Identifying high-risk groups with an increased genetic liability for bipolar disorder (BD) will provide insights into the etiology of BD and contribute to early detection of BD. We used the BD polygenic risk score (PRS) derived from BD genome-wide association studies (GWAS) to explore how such genetic risk manifests in young, high-risk adults. We postulated that BD-PRS would be associated with risk factors for BD. Methods: A final sample of 185 young, high-risk German adults (aged 18-35 years) were grouped into three risk groups and compared to a healthy control group (n = 1,100). The risk groups comprised 117 cases with attention deficit hyperactivity disorder (ADHD), 45 with major depressive disorder (MDD), and 23 help-seeking adults with early recognition symptoms [ER: positive family history for BD, (sub)threshold affective symptomatology and/or mood swings, sleeping disorder]. BD-PRS was computed for each participant. Logistic regression models (controlling for sex, age, and the first five ancestry principal components) were used to assess associations of BD-PRS and the high-risk phenotypes. Results: We observed an association between BD-PRS and combined risk group status (OR = 1.48, p < 0.001), ADHD diagnosis (OR = 1.32, p = 0.009), MDD diagnosis (OR = 1.96, p < 0.001), and ER group status (OR = 1.7, p = 0.025; not significant after correction for multiple testing) compared to healthy controls. Conclusions: In the present study, increased genetic risk for BD was a significant predictor for MDD and ADHD status, but not for ER. These findings support an underlying shared risk for both MDD and BD as well as ADHD and BD. Improving our understanding of the underlying genetic architecture of these phenotypes may aid in early identification and risk stratification.
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Affiliation(s)
- Silvia Biere
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Thorsten M. Kranz
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Silke Matura
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Kristiyana Petrova
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/University of Heidelberg, Mannheim, Germany
| | - Andreas G. Chiocchetti
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence Frankfurt, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Oliver Grimm
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Murielle Brum
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Natalie Brunkhorst-Kanaan
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Viola Oertel
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Aliaksandr Malyshau
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Dresden University of Technology, Dresden, Germany
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Dresden University of Technology, Dresden, Germany
| | - Thomas G. Schulze
- Institute of Psychiatric Phenomics and Genomics, University Hospital Munich, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Sarah Kittel-Schneider
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital of Würzburg, University of Würzburg, Würzburg, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
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748
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Qian J, Tanigawa Y, Du W, Aguirre M, Chang C, Tibshirani R, Rivas MA, Hastie T. A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank. PLoS Genet 2020; 16:e1009141. [PMID: 33095761 PMCID: PMC7641476 DOI: 10.1371/journal.pgen.1009141] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 11/04/2020] [Accepted: 09/04/2020] [Indexed: 11/18/2022] Open
Abstract
The UK Biobank is a very large, prospective population-based cohort study across the United Kingdom. It provides unprecedented opportunities for researchers to investigate the relationship between genotypic information and phenotypes of interest. Multiple regression methods, compared with genome-wide association studies (GWAS), have already been showed to greatly improve the prediction performance for a variety of phenotypes. In the high-dimensional settings, the lasso, since its first proposal in statistics, has been proved to be an effective method for simultaneous variable selection and estimation. However, the large-scale and ultrahigh dimension seen in the UK Biobank pose new challenges for applying the lasso method, as many existing algorithms and their implementations are not scalable to large applications. In this paper, we propose a computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size. We introduce snpnet, an R package that implements the proposed algorithm on top of glmnet and optimizes for single nucleotide polymorphism (SNP) datasets. It currently supports ℓ1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with ℓ1/ℓ2 penalty. We demonstrate results on the UK Biobank dataset, where we achieve competitive predictive performance for all four phenotypes considered (height, body mass index, asthma, high cholesterol) using only a small fraction of the variants compared with other established polygenic risk score methods.
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Affiliation(s)
- Junyang Qian
- Department of Statistics, Stanford University, Stanford, CA, United States of America
| | - Yosuke Tanigawa
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States of America
| | - Wenfei Du
- Department of Statistics, Stanford University, Stanford, CA, United States of America
| | - Matthew Aguirre
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States of America
| | - Chris Chang
- Grail, Inc., Menlo Park, CA, United States of America
| | - Robert Tibshirani
- Department of Statistics, Stanford University, Stanford, CA, United States of America
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States of America
| | - Manuel A. Rivas
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States of America
| | - Trevor Hastie
- Department of Statistics, Stanford University, Stanford, CA, United States of America
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States of America
- * E-mail:
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749
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Deep neural network improves the estimation of polygenic risk scores for breast cancer. J Hum Genet 2020; 66:359-369. [PMID: 33009504 DOI: 10.1038/s10038-020-00832-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 08/10/2020] [Indexed: 11/08/2022]
Abstract
Polygenic risk scores (PRS) estimate the genetic risk of an individual for a complex disease based on many genetic variants across the whole genome. In this study, we compared a series of computational models for estimation of breast cancer PRS. A deep neural network (DNN) was found to outperform alternative machine learning techniques and established statistical algorithms, including BLUP, BayesA, and LDpred. In the test cohort with 50% prevalence, the Area Under the receiver operating characteristic Curve (AUC) were 67.4% for DNN, 64.2% for BLUP, 64.5% for BayesA, and 62.4% for LDpred. BLUP, BayesA, and LPpred all generated PRS that followed a normal distribution in the case population. However, the PRS generated by DNN in the case population followed a bimodal distribution composed of two normal distributions with distinctly different means. This suggests that DNN was able to separate the case population into a high-genetic-risk case subpopulation with an average PRS significantly higher than the control population and a normal-genetic-risk case subpopulation with an average PRS similar to the control population. This allowed DNN to achieve 18.8% recall at 90% precision in the test cohort with 50% prevalence, which can be extrapolated to 65.4% recall at 20% precision in a general population with 12% prevalence. Interpretation of the DNN model identified salient variants that were assigned insignificant p values by association studies, but were important for DNN prediction. These variants may be associated with the phenotype through nonlinear relationships.
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750
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Holmans PA. Using Genetics to Increase Specificity of Outcome Prediction in Psychiatric Disorders: Prospects for Progression. Am J Psychiatry 2020; 177:884-887. [PMID: 32998554 DOI: 10.1176/appi.ajp.2020.20081181] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Peter A Holmans
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, United Kingdom
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