1
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Waszczuk MA, Jonas KG, Bornovalova M, Breen G, Bulik CM, Docherty AR, Eley TC, Hettema JM, Kotov R, Krueger RF, Lencz T, Li JJ, Vassos E, Waldman ID. Dimensional and transdiagnostic phenotypes in psychiatric genome-wide association studies. Mol Psychiatry 2023; 28:4943-4953. [PMID: 37402851 PMCID: PMC10764644 DOI: 10.1038/s41380-023-02142-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/17/2023] [Accepted: 06/16/2023] [Indexed: 07/06/2023]
Abstract
Genome-wide association studies (GWAS) provide biological insights into disease onset and progression and have potential to produce clinically useful biomarkers. A growing body of GWAS focuses on quantitative and transdiagnostic phenotypic targets, such as symptom severity or biological markers, to enhance gene discovery and the translational utility of genetic findings. The current review discusses such phenotypic approaches in GWAS across major psychiatric disorders. We identify themes and recommendations that emerge from the literature to date, including issues of sample size, reliability, convergent validity, sources of phenotypic information, phenotypes based on biological and behavioral markers such as neuroimaging and chronotype, and longitudinal phenotypes. We also discuss insights from multi-trait methods such as genomic structural equation modelling. These provide insight into how hierarchical 'splitting' and 'lumping' approaches can be applied to both diagnostic and dimensional phenotypes to model clinical heterogeneity and comorbidity. Overall, dimensional and transdiagnostic phenotypes have enhanced gene discovery in many psychiatric conditions and promises to yield fruitful GWAS targets in the years to come.
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Affiliation(s)
- Monika A Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA.
| | - Katherine G Jonas
- Department of Psychiatry, Stony Brook University School of Medicine, Stony Brook, NY, USA
| | | | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Cynthia M Bulik
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anna R Docherty
- Huntsman Mental Health Institute, Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
| | - Thalia C Eley
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - John M Hettema
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
- Department of Psychiatry, Texas A&M Health Sciences Center, Bryan, TX, USA
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University School of Medicine, Stony Brook, NY, USA
| | - Robert F Krueger
- Psychology Department, University of Minnesota, Minneapolis, MN, USA
| | - Todd Lencz
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
- Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - James J Li
- Department of Psychology, University of Wisconsin, Madison, WI, USA
- Waisman Center, University of Wisconsin, Madison, WI, USA
| | - Evangelos Vassos
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- UK National Institute for Health and Care Research (NIHR) Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Irwin D Waldman
- Department of Psychology, Emory University, Atlanta, GA, USA
- Center for Computational and Quantitative Genetics, Emory University, Atlanta, GA, USA
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2
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Flynn BI, Javan EM, Lin E, Trutner Z, Koenig K, Anighoro KO, Kun E, Gupta A, Singh T, Jayakumar P, Narasimhan VM. Deep learning based phenotyping of medical images improves power for gene discovery of complex disease. NPJ Digit Med 2023; 6:155. [PMID: 37604895 PMCID: PMC10442423 DOI: 10.1038/s41746-023-00903-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 08/10/2023] [Indexed: 08/23/2023] Open
Abstract
Electronic health records are often incomplete, reducing the power of genetic association studies. For some diseases, such as knee osteoarthritis where the routine course of diagnosis involves an X-ray, image-based phenotyping offers an alternate and unbiased way to ascertain disease cases. We investigated this by training a deep-learning model to ascertain knee osteoarthritis cases from knee DXA scans that achieved clinician-level performance. Using our model, we identified 1931 (178%) more cases than currently diagnosed in the health record. Individuals diagnosed as cases by our model had higher rates of self-reported knee pain, for longer durations and with increased severity compared to control individuals. We trained another deep-learning model to measure the knee joint space width, a quantitative phenotype linked to knee osteoarthritis severity. In performing genetic association analysis, we found that use of a quantitative measure improved the number of genome-wide significant loci we discovered by an order of magnitude compared with our binary model of cases and controls despite the two phenotypes being highly genetically correlated. In addition we discovered associations between our quantitative measure of knee osteoarthritis and increased risk of adult fractures- a leading cause of injury-related death in older individuals-, illustrating the capability of image-based phenotyping to reveal epidemiological associations not captured in the electronic health record. For diseases with radiographic diagnosis, our results demonstrate the potential for using deep learning to phenotype at biobank scale, improving power for both genetic and epidemiological association analysis.
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Affiliation(s)
- Brianna I Flynn
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA.
| | - Emily M Javan
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Eugenia Lin
- Department of Surgery and Perioperative Care, Dell Medical School, Austin, TX, USA
| | - Zoe Trutner
- Department of Surgery and Perioperative Care, Dell Medical School, Austin, TX, USA
| | - Karl Koenig
- Department of Surgery and Perioperative Care, Dell Medical School, Austin, TX, USA
| | - Kenoma O Anighoro
- Department of Surgery and Perioperative Care, Dell Medical School, Austin, TX, USA
| | - Eucharist Kun
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Alaukik Gupta
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Tarjinder Singh
- The Department of Psychiatry at Columbia University Irving Medical Center, New York, NY, USA
- The New York Genome Center, New York, NY, USA
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, New York, NY, USA
| | - Prakash Jayakumar
- Department of Surgery and Perioperative Care, Dell Medical School, Austin, TX, USA.
| | - Vagheesh M Narasimhan
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA.
- Department of Statistics and Data Science, The University of Texas at Austin, Austin, TX, USA.
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3
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Wang M, Zhang S, Sha Q. A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS. PLoS One 2022; 17:e0260911. [PMID: 35482827 PMCID: PMC9049312 DOI: 10.1371/journal.pone.0260911] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 04/13/2022] [Indexed: 11/18/2022] Open
Abstract
There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure needs to be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared.
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Affiliation(s)
- Meida Wang
- Mathematical Sciences, Michigan Technological University, Houghton, MI, United States of America
| | - Shuanglin Zhang
- Mathematical Sciences, Michigan Technological University, Houghton, MI, United States of America
| | - Qiuying Sha
- Mathematical Sciences, Michigan Technological University, Houghton, MI, United States of America
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4
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Fu L, Wang Y, Li T, Yang S, Hu YQ. A Novel Hierarchical Clustering Approach for Joint Analysis of Multiple Phenotypes Uncovers Obesity Variants Based on ARIC. Front Genet 2022; 13:791920. [PMID: 35391794 PMCID: PMC8981031 DOI: 10.3389/fgene.2022.791920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/27/2022] [Indexed: 12/02/2022] Open
Abstract
Genome-wide association studies (GWASs) have successfully discovered numerous variants underlying various diseases. Generally, one-phenotype one-variant association study in GWASs is not efficient in identifying variants with weak effects, indicating that more signals have not been identified yet. Nowadays, jointly analyzing multiple phenotypes has been recognized as an important approach to elevate the statistical power for identifying weak genetic variants on complex diseases, shedding new light on potential biological mechanisms. Therefore, hierarchical clustering based on different methods for calculating correlation coefficients (HCDC) is developed to synchronously analyze multiple phenotypes in association studies. There are two steps involved in HCDC. First, a clustering approach based on the similarity matrix between two groups of phenotypes is applied to choose a representative phenotype in each cluster. Then, we use existing methods to estimate the genetic associations with the representative phenotypes rather than the individual phenotypes in every cluster. A variety of simulations are conducted to demonstrate the capacity of HCDC for boosting power. As a consequence, existing methods embedding HCDC are either more powerful or comparable with those of without embedding HCDC in most scenarios. Additionally, the application of obesity-related phenotypes from Atherosclerosis Risk in Communities via existing methods with HCDC uncovered several associated variants. Among these, UQCC1-rs1570004 is reported as a significant obesity signal for the first time, whose differential expression in subcutaneous fat, visceral fat, and muscle tissue is worthy of further functional studies.
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Affiliation(s)
- Liwan Fu
- Center for Non-communicable Disease Management, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China.,State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yuquan Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Tingting Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Siqian Yang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yue-Qing Hu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China.,Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
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Gu X, Huang S, Zhu Z, Ma Y, Yang X, Yao L, Gao X, Zhang M, Liu W, Qiu L, Zhao H, Wang Q, Li Z, Li Z, Meng Q, Yang S, Wang C, Hu X, Ding J. Genome-wide association of single nucleotide polymorphism loci and candidate genes for frogeye leaf spot (Cercospora sojina) resistance in soybean. BMC PLANT BIOLOGY 2021; 21:588. [PMID: 34895144 PMCID: PMC8665500 DOI: 10.1186/s12870-021-03366-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 11/25/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND Frogeye leaf spot (FLS) is a destructive fungal disease that affects soybean production. The most economical and effective strategy to control FLS is the use of resistant cultivars. However, the use of a limited number of resistant loci in FLS management will be countered by the emergence of new high-virulence Cercospora sojina races. Therefore, we identified quantitative trait loci (QTL) that control resistance to FLS and identified novel resistant genes using a genome-wide association study (GWAS) on 234 Chinese soybean cultivars. RESULTS A total of 30,890 single nucleotide polymorphism (SNP) markers were used to estimate linkage disequilibrium (LD) and population structure. The GWAS results showed four loci (p < 0.0001) distributed over chromosomes (Chr.) 5 and 20, that are significantly associated with FLS resistance. No previous studies have reported resistance loci in these regions. Subsequently, 45 genes in the two resistance-related haplotype blocks were annotated. Among them, Glyma20g31630 encoding pyruvate dehydrogenase (PDH), Glyma05g28980, which encodes mitogen-activated protein kinase 7 (MPK7), and Glyma20g31510, Glyma20g31520 encoding calcium-dependent protein kinase 4 (CDPK4) in the haplotype blocks deserves special attention. CONCLUSIONS This study showed that GWAS can be employed as an effective strategy for identifying disease resistance traits in soybean and narrowing SNPs and candidate genes. The prediction of candidate genes in the haplotype blocks identified by disease resistance loci can provide a useful reference to study systemic disease resistance.
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Affiliation(s)
- Xin Gu
- Wuhu Institute of Technology, Wuhu, 241003, China
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Shanshan Huang
- Key Laboratory of Crop Biotechnology Breeding of the Ministry of Agriculture, Beidahuang Kenfeng Seed Co., Ltd., Harbin, 150030, China
| | - Zhiguo Zhu
- Wuhu Institute of Technology, Wuhu, 241003, China
| | - Yansong Ma
- Key Laboratory of Crop Biotechnology Breeding of the Ministry of Agriculture, Beidahuang Kenfeng Seed Co., Ltd., Harbin, 150030, China
| | - Xiaohe Yang
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Liangliang Yao
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Xuedong Gao
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Maoming Zhang
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Wei Liu
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Lei Qiu
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Haihong Zhao
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Qingsheng Wang
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Zengjie Li
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Zhimin Li
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Qingying Meng
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China
| | - Shuai Yang
- Potato Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, 150086, China
| | - Chao Wang
- Key Laboratory of Crop Biotechnology Breeding of the Ministry of Agriculture, Beidahuang Kenfeng Seed Co., Ltd., Harbin, 150030, China
| | - Xiping Hu
- Key Laboratory of Crop Biotechnology Breeding of the Ministry of Agriculture, Beidahuang Kenfeng Seed Co., Ltd., Harbin, 150030, China.
| | - Junjie Ding
- Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences, Ministry of Agriculture Harmful Biology of Crop Scientific Monitoring Station Jiamusi Experiment Station, China Agriculture Research System of MOF and MARA, Jiamusi, 154007, China.
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6
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Liu X, Tian G, Liu Z. Identification of novel genes for triple-negative breast cancer with semiparametric gene-based analysis. J Appl Stat 2021; 50:691-702. [PMID: 36819073 PMCID: PMC9930760 DOI: 10.1080/02664763.2021.1973387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Triple-negative breast cancer (TNBC) is generally considered an aggressive breast cancer subtype associated with poor prognostic outcomes. Up to now, the molecular and cellular mechanisms underlying TNBC pathology have not been fully understood. In this manuscript, we propose a novel semiparametric model with kernel for gene-based analysis with a breast cancer GWAS data. The software of SPMGBA (semiparametric method for gene-based analysis) in MATLAB is available at GitHub (https://github.com/zliu3/SPMGBA). Genetic signatures associated with breast cancer are discovered. We further validate the prognostic power of the identified genes with a large cohort of expression data from the European Genome-Phenome Archive, and discover that SEL1L is associated with the overall survival of TNBC with the p-value of .0002. We conclude that gene SEL1L is down-regulated in TNBC and the expression of SEL1L is positively associated with patient survival.
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Affiliation(s)
- Xiaotong Liu
- Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Guoliang Tian
- Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Zhenqiu Liu
- Department of Public Health Sciences, Pennsylvania State University, Hershey, PA, USA, Zhenqiu Liu Department of Public Health Sciences, Pennsylvania State University, Hershey, PA17033, USA
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7
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Nayor M, Shen L, Hunninghake GM, Kochunov P, Barr RG, Bluemke DA, Broeckel U, Caravan P, Cheng S, de Vries PS, Hoffmann U, Kolossváry M, Li H, Luo J, McNally EM, Thanassoulis G, Arnett DK, Vasan RS. Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop. Circ Cardiovasc Imaging 2021; 14:e012943. [PMID: 34387095 PMCID: PMC8486340 DOI: 10.1161/circimaging.121.012943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Imaging genomics is a rapidly evolving field that combines state-of-the-art bioimaging with genomic information to resolve phenotypic heterogeneity associated with genomic variation, improve risk prediction, discover prevention approaches, and enable precision diagnosis and treatment. Contemporary bioimaging methods provide exceptional resolution generating discrete and quantitative high-dimensional phenotypes for genomics investigation. Despite substantial progress in combining high-dimensional bioimaging and genomic data, methods for imaging genomics are evolving. Recognizing the potential impact of imaging genomics on the study of heart and lung disease, the National Heart, Lung, and Blood Institute convened a workshop to review cutting-edge approaches and methodologies in imaging genomics studies, and to establish research priorities for future investigation. This report summarizes the presentations and discussions at the workshop. In particular, we highlight the need for increased availability of imaging genomics data in diverse populations, dedicated focus on less common conditions, and centralization of efforts around specific disease areas.
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Affiliation(s)
- Matthew Nayor
- Cardiology Division, Department of Medicine, Massachusetts
General Hospital, Harvard Medical School, Boston, MA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics,
Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Gary M. Hunninghake
- Division of Pulmonary and Critical Care Medicine, Harvard
Medical School, Brigham and Women’s Hospital, Boston, MA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of
Psychiatry, University of Maryland School of Medicine, Baltimore, MD
| | - R. Graham Barr
- Department of Medicine and Department of Epidemiology,
Mailman School of Public Health, Columbia University Irving Medical Center, New
York, NY
| | - David A. Bluemke
- Department of Radiology, University of Wisconsin-Madison
School of Medicine and Public Health, Madison, WI
| | - Ulrich Broeckel
- Section of Genomic Pediatrics, Department of Pediatrics,
Medicine and Physiology, Children’s Research Institute and Genomic Sciences
and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI
| | - Peter Caravan
- Institute for Innovation in Imaging, Athinoula A. Martinos
Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical
School, Charlestown, MA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute,
Cedars-Sinai Medical Center, Los Angeles, CA
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human
Genetics, and Environmental Sciences, School of Public Health, The University of
Texas Health Science Center at Houston, Houston, TX
| | - Udo Hoffmann
- Department of Radiology, Harvard Medical School,
Massachusetts General Hospital, Boston, Massachusetts
| | - Márton Kolossváry
- Department of Radiology, Harvard Medical School,
Massachusetts General Hospital, Boston, Massachusetts
| | - Huiqing Li
- Division of Cardiovascular Sciences, National Heart,
Lung, and Blood Institute, Bethesda, MD
| | - James Luo
- Division of Cardiovascular Sciences, National Heart,
Lung, and Blood Institute, Bethesda, MD
| | - Elizabeth M. McNally
- Center for Genetic Medicine, Northwestern University
Feinberg School of Medicine, Chicago, IL
| | - George Thanassoulis
- Preventive and Genomic Cardiology, McGill University
Health Center and Research Institute, Montreal, Quebec, Canada
| | - Donna K. Arnett
- College of Public Health, University of Kentucky,
Lexington KY
| | - Ramachandran S. Vasan
- Sections of Preventive Medicine and Epidemiology, and
Cardiology, Department of Medicine, Department of Epidemiology, Boston University
Schools of Medicine and Public Health, and Center for Computing and Data Sciences,
Boston University, Boston, MA
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8
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Fu L, Wang Y, Li T, Hu YQ. A Novel Approach Integrating Hierarchical Clustering and Weighted Combination for Association Study of Multiple Phenotypes and a Genetic Variant. Front Genet 2021; 12:654804. [PMID: 34220938 PMCID: PMC8249926 DOI: 10.3389/fgene.2021.654804] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 04/20/2021] [Indexed: 11/26/2022] Open
Abstract
As a pivotal research tool, genome-wide association study has successfully identified numerous genetic variants underlying distinct diseases. However, these identified genetic variants only explain a small proportion of the phenotypic variation for certain diseases, suggesting that there are still more genetic signals to be detected. One of the reasons may be that one-phenotype one-variant association study is not so efficient in detecting variants of weak effects. Nowadays, it is increasingly worth noting that joint analysis of multiple phenotypes may boost the statistical power to detect pathogenic variants with weak genetic effects on complex diseases, providing more clues for their underlying biology mechanisms. So a Weighted Combination of multiple phenotypes following Hierarchical Clustering method (WCHC) is proposed for simultaneously analyzing multiple phenotypes in association studies. A series of simulations are conducted, and the results show that WCHC is either the most powerful method or comparable with the most powerful competitor in most of the simulation scenarios. Additionally, we evaluated the performance of WCHC in its application to the obesity-related phenotypes from Atherosclerosis Risk in Communities, and several associated variants are reported.
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Affiliation(s)
- Liwan Fu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China.,Center for Non-communicable Disease Management, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Yuquan Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Tingting Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yue-Qing Hu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China.,Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
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Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS. Nat Genet 2021; 53:445-454. [PMID: 33686288 PMCID: PMC8038973 DOI: 10.1038/s41588-021-00787-1] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 01/14/2021] [Indexed: 01/31/2023]
Abstract
Psychiatric disorders are highly genetically correlated, but little research has been conducted on the genetic differences between disorders. We developed a new method (case-case genome-wide association study; CC-GWAS) to test for differences in allele frequency between cases of two disorders using summary statistics from the respective case-control GWAS, transcending current methods that require individual-level data. Simulations and analytical computations confirm that CC-GWAS is well powered with effective control of type I error. We applied CC-GWAS to publicly available summary statistics for schizophrenia, bipolar disorder, major depressive disorder and five other psychiatric disorders. CC-GWAS identified 196 independent case-case loci, including 72 CC-GWAS-specific loci that were not significant at the genome-wide level in the input case-control summary statistics; two of the CC-GWAS-specific loci implicate the genes KLF6 and KLF16 (from the Krüppel-like family of transcription factors), which have been linked to neurite outgrowth and axon regeneration. CC-GWAS loci replicated convincingly in applications to datasets with independent replication data.
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10
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Associating Multivariate Traits with Genetic Variants Using Collapsing and Kernel Methods with Pedigree- or Population-Based Studies. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:8812282. [PMID: 33628328 PMCID: PMC7889379 DOI: 10.1155/2021/8812282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 01/02/2021] [Accepted: 01/08/2021] [Indexed: 11/18/2022]
Abstract
In genetic association analysis, several relevant phenotypes or multivariate traits with different types of components are usually collected to study complex or multifactorial diseases. Over the past few years, jointly testing for association between multivariate traits and multiple genetic variants has become more popular because it can increase statistical power to identify causal genes in pedigree- or population-based studies. However, most of the existing methods mainly focus on testing genetic variants associated with multiple continuous phenotypes. In this investigation, we develop a framework for identifying the pleiotropic effects of genetic variants on multivariate traits by using collapsing and kernel methods with pedigree- or population-structured data. The proposed framework is applicable to the burden test, the kernel test, and the omnibus test for autosomes and the X chromosome. The proposed multivariate trait association methods can accommodate continuous phenotypes or binary phenotypes and further can adjust for covariates. Simulation studies show that the performance of our methods is satisfactory with respect to the empirical type I error rates and power rates in comparison with the existing methods.
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11
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Yang S, Gill RA, Zaman QU, Ulhassan Z, Zhou W. Insights on SNP types, detection methods and their utilization in Brassica species: Recent progress and future perspectives. J Biotechnol 2020; 324:11-20. [PMID: 32979432 DOI: 10.1016/j.jbiotec.2020.09.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 09/15/2020] [Accepted: 09/20/2020] [Indexed: 01/09/2023]
Abstract
The genus Brassica, family Brassicaceae (Cruciferae), comprises many important species of oil crops, vegetables and medicinal plants including B. rapa, B. oleracea, B. nigra, B. napus, B. juncea, B. carinata. Genomic researches in Brassica species is constrained by polyploidization, mainly due to its complicated genomic structure. However, rapid development of methods for detecting single nucleotide polymorphisms (SNP), such as next generation sequencing and SNP microarray, has accelerated release of reference Brassica species genomes as well as discovery of large numbers and genome-wide SNPs, thus intensifying forward genetics in this genus. In this review, we summarize biological characteristics, classification and various methods for detecting SNPs, focusing on high-throughput techniques. Moreover, we describe the pivotal roles of SNPs in genetic diversity, linkage map construction and QTL mapping, comparative genomics, linkage disequilibrium and genome-wide association studies. These insights are expected to deepen our understanding and guide further advancements in Brassica species research.
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Affiliation(s)
- Su Yang
- College of Life Sciences, China Jiliang University, Hangzhou 310018, China
| | - Rafaqat Ali Gill
- Oil Crops Research Institute, Chinese Academy of Agricultural Science, Wuhan 430062, China.
| | - Qamar U Zaman
- Oil Crops Research Institute, Chinese Academy of Agricultural Science, Wuhan 430062, China
| | - Zaid Ulhassan
- Institute of Crop Science, Ministry of Agriculture and Rural Affairs Laboratory of Spectroscopy Sensing, Zhejiang University, Hangzhou 310058, China
| | - Weijun Zhou
- Institute of Crop Science, Ministry of Agriculture and Rural Affairs Laboratory of Spectroscopy Sensing, Zhejiang University, Hangzhou 310058, China
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Sha Q, Wang Z, Zhang X, Zhang S. A clustering linear combination approach to jointly analyze multiple phenotypes for GWAS. Bioinformatics 2020; 35:1373-1379. [PMID: 30239574 DOI: 10.1093/bioinformatics/bty810] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2017] [Revised: 08/29/2018] [Accepted: 09/18/2018] [Indexed: 12/16/2022] Open
Abstract
SUMMARY There is an increasing interest in joint analysis of multiple phenotypes for genome-wide association studies (GWASs) based on the following reasons. First, cohorts usually collect multiple phenotypes and complex diseases are usually measured by multiple correlated intermediate phenotypes. Second, jointly analyzing multiple phenotypes may increase statistical power for detecting genetic variants associated with complex diseases. Third, there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. In this paper, we develop a clustering linear combination (CLC) method to jointly analyze multiple phenotypes for GWASs. In the CLC method, we first cluster individual statistics into positively correlated clusters and then, combine the individual statistics linearly within each cluster and combine the between-cluster terms in a quadratic form. CLC is not only robust to different signs of the means of individual statistics, but also reduce the degrees of freedom of the test statistic. We also theoretically prove that if we can cluster the individual statistics correctly, CLC is the most powerful test among all tests with certain quadratic forms. Our simulation results show that CLC is either the most powerful test or has similar power to the most powerful test among the tests we compared, and CLC is much more powerful than other tests when effect sizes align with inferred clusters. We also evaluate the performance of CLC through a real case study. AVAILABILITY AND IMPLEMENTATION R code for implementing our method is available at http://www.math.mtu.edu/∼shuzhang/software.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
| | - Zhenchuan Wang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
| | - Xiao Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
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Guo B, Wu B. Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data. Bioinformatics 2020; 35:1366-1372. [PMID: 30239606 DOI: 10.1093/bioinformatics/bty811] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 08/29/2018] [Accepted: 09/18/2018] [Indexed: 01/09/2023] Open
Abstract
MOTIVATION Many GWAS conducted in the past decade have identified tens of thousands of disease related variants, which in total explained only part of the heritability for most traits. There remain many more genetics variants with small effect sizes to be discovered. This has motivated the development of sequencing studies with larger sample sizes and increased resolution of genotyped variants, e.g., the ongoing NHLBI Trans-Omics for Precision Medicine (TOPMed) whole genome sequencing project. An alternative approach is the development of novel and more powerful statistical methods. The current dominating approach in the field of GWAS analysis is the "single trait single variant" association test, despite the fact that most GWAS are conducted in deeply-phenotyped cohorts with many correlated traits measured. In this paper, we aim to develop rigorous methods that integrate multiple correlated traits and multiple variants to improve the power to detect novel variants. In recognition of the difficulty of accessing raw genotype and phenotype data due to privacy and logistic concerns, we develop methods that are applicable to publicly available GWAS summary data. RESULTS We build rigorous statistical models for GWAS summary statistics to motivate novel multi-trait SNP-set association tests, including variance component test, burden test and their adaptive test, and develop efficient numerical algorithms to quickly compute their analytical P-values. We implement the proposed methods in an open source R package. We conduct thorough simulation studies to verify the proposed methods rigorously control type I errors at the genome-wide significance level, and further demonstrate their utility via comprehensive analysis of GWAS summary data for multiple lipids traits and glycemic traits. We identified many novel loci that were not detected by the individual trait based GWAS analysis. AVAILABILITY AND IMPLEMENTATION We have implemented the proposed methods in an R package freely available at http://www.github.com/baolinwu/MSKAT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bin Guo
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Baolin Wu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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Amare AT, Vaez A, Hsu YH, Direk N, Kamali Z, Howard DM, McIntosh AM, Tiemeier H, Bültmann U, Snieder H, Hartman CA. Bivariate genome-wide association analyses of the broad depression phenotype combined with major depressive disorder, bipolar disorder or schizophrenia reveal eight novel genetic loci for depression. Mol Psychiatry 2020; 25:1420-1429. [PMID: 30626913 PMCID: PMC7303007 DOI: 10.1038/s41380-018-0336-6] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 11/15/2018] [Accepted: 12/03/2018] [Indexed: 11/09/2022]
Abstract
Although a genetic basis of depression has been well established in twin studies, identification of genome-wide significant loci has been difficult. We hypothesized that bivariate analyses of findings from a meta-analysis of genome-wide association studies (meta-GWASs) of the broad depression phenotype with those from meta-GWASs of self-reported and recurrent major depressive disorder (MDD), bipolar disorder and schizophrenia would enhance statistical power to identify novel genetic loci for depression. LD score regression analyses were first used to estimate the genetic correlations of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia. Then, we performed four bivariate GWAS analyses. The genetic correlations (rg ± SE) of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia were 0.79 ± 0.07, 0.24 ± 0.08, 0.53 ± 0.09 and 0.57 ± 0.05, respectively. From a total of 20 independent genome-wide significant loci, 13 loci replicated of which 8 were novel for depression. These were MUC21 for the broad depression phenotype with self-reported MDD and ZNF804A, MIR3143, PSORS1C2, STK19, SPATA31D1, RTN1 and TCF4 for the broad depression phenotype with schizophrenia. Post-GWAS functional analyses of these loci revealed their potential biological involvement in psychiatric disorders. Our results emphasize the genetic similarities among different psychiatric disorders and indicate that cross-disorder analyses may be the best way forward to accelerate gene finding for depression, or psychiatric disorders in general.
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Affiliation(s)
- Azmeraw T. Amare
- 0000 0000 9558 4598grid.4494.dDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands ,0000 0004 0565 2606grid.430453.5South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA Australia ,0000 0004 1936 7304grid.1010.0School of Medicine, University of Adelaide, Adelaide, SA Australia ,0000 0000 8994 5086grid.1026.5Division of Health Sciences, University of South Australia, Adelaide, SA Australia
| | - Ahmad Vaez
- 0000 0000 9558 4598grid.4494.dDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands ,0000 0001 1498 685Xgrid.411036.1Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Yi-Hsiang Hsu
- 000000041936754Xgrid.38142.3cHSL Institute for Aging Research, Harvard Medical School, Boston, MA USA ,000000041936754Xgrid.38142.3cProgram for Quantitative Genomics, Harvard School of Public Health, Boston, MA USA ,grid.66859.34Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Nese Direk
- 000000040459992Xgrid.5645.2Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands ,0000 0001 2183 9022grid.21200.31Department of Psychiatry, School of Medicine, Dokuz Eylul University, Izmir, Turkey
| | - Zoha Kamali
- 0000 0001 1498 685Xgrid.411036.1Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - David M. Howard
- 0000 0000 9845 9303grid.416119.aDivision of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Andrew M. McIntosh
- 0000 0000 9845 9303grid.416119.aDivision of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK ,0000 0004 1936 7988grid.4305.2Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Henning Tiemeier
- 000000040459992Xgrid.5645.2Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands ,000000040459992Xgrid.5645.2Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Ute Bültmann
- 0000 0000 9558 4598grid.4494.dDepartment of Health Sciences, Community and Occupational Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| | - Catharina A. Hartman
- 0000 0000 9558 4598grid.4494.dInterdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Shen L, Thompson PM. Brain Imaging Genomics: Integrated Analysis and Machine Learning. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:125-162. [PMID: 31902950 PMCID: PMC6941751 DOI: 10.1109/jproc.2019.2947272] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.
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Affiliation(s)
- Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90232, USA
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16
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Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide. Mol Psychiatry 2020; 25:2422-2430. [PMID: 30610202 PMCID: PMC6609505 DOI: 10.1038/s41380-018-0326-8] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 09/15/2018] [Accepted: 11/12/2018] [Indexed: 11/25/2022]
Abstract
Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12 × 10-4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51 × 10-2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10-5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34-0.81) as well as several psychiatric disorders (rg = 0.26-0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.
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Abstract
The genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease.
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18
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Effect of non-normality and low count variants on cross-phenotype association tests in GWAS. Eur J Hum Genet 2019; 28:300-312. [PMID: 31582815 DOI: 10.1038/s41431-019-0514-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 09/01/2019] [Accepted: 09/05/2019] [Indexed: 01/21/2023] Open
Abstract
Many complex human diseases, such as type 2 diabetes, are characterized by multiple underlying traits/phenotypes that have substantially shared genetic architecture. Multivariate analysis of correlated traits has the potential to increase the power of detecting underlying common genetic loci. Several cross-phenotype association methods have been proposed-some require individual-level data on traits and genotypes, while the others require only summary-level data. In this article, we explore whether non-normality of multivariate trait distribution affects the inference from some of the existing multi-trait methods and how that effect is dependent on the allele count of the genetic variant being tested. We find that most of these tests are susceptible to biases that lead to spurious association signals. Even after controlling for confounders that may contribute to non-normality and then applying inverse normal transformation on the residuals of each trait, these tests may have inflated type I errors for variants with low minor allele counts (MACs). A likelihood ratio test of association based on the ordinal regression of individual-level genotype conditional on the traits seems to be the least biased and can maintain type I error when the MAC is reasonably large (e.g., MAC > 30). Application of these methods to publicly available summary statistics of eight amino acid traits on European samples seem to exhibit systematic inflation (especially for variants with low MAC), which is consistent with our findings from simulation experiments.
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Carlson MO, Montilla-Bascon G, Hoekenga OA, Tinker NA, Poland J, Baseggio M, Sorrells ME, Jannink JL, Gore MA, Yeats TH. Multivariate Genome-Wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat ( Avena sativa L.). G3 (BETHESDA, MD.) 2019; 9:2963-2975. [PMID: 31296616 PMCID: PMC6723141 DOI: 10.1534/g3.119.400228] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 07/08/2019] [Indexed: 01/06/2023]
Abstract
Oat (Avena sativa L.) has a high concentration of oils, comprised primarily of healthful unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to identify loci associated with variation in fatty acid composition, defined as the types and quantities of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. Measurements of individual fatty acids were highly correlated across samples, consistent with fatty acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for population structure and relatedness among cultivars. In the second, we performed a univariate association test for each principal component (PC) derived from a singular value decomposition of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we also conducted univariate association tests for each trait. The multivariate mixed model approach yielded 148 genome-wide significant single-nucleotide polymorphisms (SNPs) at a 10% false-discovery rate, compared to 129 and 73 significant SNPs in the PC and univariate analyses, respectively. Thus, explicit modeling of the correlation structure between fatty acids in a multivariate framework enabled identification of loci associated with variation in seed fatty acid concentration that were not detected in the univariate analyses. Ultimately, a detailed characterization of the loci underlying fatty acid variation can be used to enhance the nutritional profile of oats through breeding.
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Affiliation(s)
- Maryn O Carlson
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | | | | | - Nicholas A Tinker
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Jesse Poland
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, and
| | - Matheus Baseggio
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Mark E Sorrells
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Jean-Luc Jannink
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
- R.W. Holley Center for Agriculture and Health, US Department of Agriculture, Agricultural Research Service, Ithaca, NY 14853
| | - Michael A Gore
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Trevor H Yeats
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
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Zhang J, Sha Q, Liu G, Wang X. A gene based approach to test genetic association based on an optimally weighted combination of multiple traits. PLoS One 2019; 14:e0220914. [PMID: 31398229 PMCID: PMC6688794 DOI: 10.1371/journal.pone.0220914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 07/25/2019] [Indexed: 01/11/2023] Open
Abstract
There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases for which multiple correlated traits are often measured. Joint analysis of multiple traits could increase statistical power by aggregating multiple weak effects. Existing methods for multiple trait association tests usually study each of the multiple traits separately and then combine the univariate test statistics or combine p-values of the univariate tests for identifying disease associated genetic variants. However, ignoring correlation between phenotypes may cause power loss. Additionally, the genetic variants in one gene (including common and rare variants) are often viewed as a whole that affects the underlying disease since the basic functional unit of inheritance is a gene rather than a genetic variant. Thus, results from gene level association tests can be more readily integrated with downstream functional and pathogenic investigation, whereas many existing methods for multiple trait association tests only focus on testing a single common variant rather than a gene. In this article, we propose a statistical method by Testing an Optimally Weighted Combination of Multiple traits (TOW-CM) to test the association between multiple traits and multiple variants in a genomic region (a gene or pathway). We investigate the performance of the proposed method through extensive simulation studies. Our simulation studies show that the proposed method has correct type I error rates and is either the most powerful test or comparable with the most powerful tests. Additionally, we illustrate the usefulness of TOW-CM based on a COPDGene study.
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Affiliation(s)
- Jianjun Zhang
- Department of Mathematics, University of North Texas, Denton, TX, United States of America
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, United States of America
| | - Guanfu Liu
- School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai, China
| | - Xuexia Wang
- Department of Mathematics, University of North Texas, Denton, TX, United States of America
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21
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Masotti M, Guo B, Wu B. Pleiotropy informed adaptive association test of multiple traits using genome-wide association study summary data. Biometrics 2019; 75:1076-1085. [PMID: 31021400 DOI: 10.1111/biom.13076] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 04/16/2019] [Indexed: 12/17/2022]
Abstract
Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large-scale genome-wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They have successfully identified tens of thousands of disease-related variants. However, in total these identified variants explain only part of the variation for most complex traits. There remain many genetic variants with small effect sizes to be discovered, which calls for the development of (a) GWAS with more samples and more comprehensively genotyped variants, for example, the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program is planning to conduct whole genome sequencing on over 100 000 individuals; and (b) novel and more powerful statistical analysis methods. The current dominating GWAS analysis approach is the "single trait" association test, despite the fact that many GWAS are conducted in deeply phenotyped cohorts including many correlated and well-characterized outcomes, which can help improve the power to detect novel variants if properly analyzed, as suggested by increasing evidence that pleiotropy, where a genetic variant affects multiple traits, is the norm in genome-phenome associations. We aim to develop pleiotropy informed powerful association test methods across multiple traits for GWAS. Since it is generally very hard to access individual-level GWAS phenotype and genotype data for those existing GWAS, due to privacy concerns and various logistical considerations, we develop rigorous statistical methods for pleiotropy informed adaptive multitrait association test methods that need only summary association statistics publicly available from most GWAS. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical P-value for the proposed adaptive test without the need of resampling or permutation. We illustrate the performance of proposed methods through application to joint association test of GWAS meta-analysis summary data for several glycemic traits. Our proposed adaptive test identified several novel loci missed by individual trait based GWAS meta-analysis. All the proposed methods are implemented in a publicly available R package.
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Affiliation(s)
- Maria Masotti
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Bin Guo
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Baolin Wu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
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Dimou NL, Pantavou KG, Braliou GG, Bagos PG. Multivariate Methods for Meta-Analysis of Genetic Association Studies. Methods Mol Biol 2019; 1793:157-182. [PMID: 29876897 DOI: 10.1007/978-1-4939-7868-7_11] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.
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Affiliation(s)
- Niki L Dimou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.,Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Katerina G Pantavou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Georgia G Braliou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.
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Joint Analysis of Multiple Phenotypes in Association Studies based on Cross-Validation Prediction Error. Sci Rep 2019; 9:1073. [PMID: 30705317 PMCID: PMC6355816 DOI: 10.1038/s41598-018-37538-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 11/19/2018] [Indexed: 01/28/2023] Open
Abstract
In genome-wide association studies (GWAS), joint analysis of multiple phenotypes could have increased statistical power over analyzing each phenotype individually to identify genetic variants that are associated with complex diseases. With this motivation, several statistical methods that jointly analyze multiple phenotypes have been developed, such as O’Brien’s method, Trait-based Association Test that uses Extended Simes procedure (TATES), multivariate analysis of variance (MANOVA), and joint model of multiple phenotypes (MultiPhen). However, the performance of these methods under a wide range of scenarios is not consistent: one test may be powerful in some situations, but not in the others. Thus, one challenge in joint analysis of multiple phenotypes is to construct a test that could maintain good performance across different scenarios. In this article, we develop a novel statistical method to test associations between a genetic variant and Multiple Phenotypes based on cross-validation Prediction Error (MultP-PE). Extensive simulations are conducted to evaluate the type I error rates and to compare the power performance of MultP-PE with various existing methods. The simulation studies show that MultP-PE controls type I error rates very well and has consistently higher power than the tests we compared in all simulation scenarios. We conclude with the recommendation for the use of MultP-PE for its good performance in association studies with multiple phenotypes.
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Comparison of methods for multivariate gene-based association tests for complex diseases using common variants. Eur J Hum Genet 2019; 27:811-823. [PMID: 30683923 PMCID: PMC6461986 DOI: 10.1038/s41431-018-0327-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 10/30/2018] [Accepted: 12/04/2018] [Indexed: 12/29/2022] Open
Abstract
Complex diseases are usually associated with multiple correlated phenotypes, and the analysis of composite scores or disease status may not fully capture the complexity (or multidimensionality). Joint analysis of multiple disease-related phenotypes in genetic tests could potentially increase power to detect association of a disease with common SNPs (or genes). Gene-based tests are designed to identify genes containing multiple risk variants that individually are weakly associated with a univariate trait. We combined three multivariate association tests (O'Brien method, TATES, and MultiPhen) with two gene-based association tests (GATES and VEGAS) and compared performance (type I error and power) of six multivariate gene-based methods using simulated data. Data (n = 2000) for genetic sequence and correlated phenotypes were simulated by varying causal variant proportions and phenotype correlations for various scenarios. These simulations showed that two multivariate association tests (TATES and MultiPhen, but not O'Brien) paired with VEGAS have inflated type I error in all scenarios, while the three multivariate association tests paired with GATES have correct type I error. MultiPhen paired with GATES has higher power than competing methods if the correlations among phenotypes are low (r < 0.57). We applied these gene-based association methods to a GWAS dataset from the Alzheimer's Disease Genetics Consortium containing three neuropathological traits related to Alzheimer disease (neuritic plaque, neurofibrillary tangles, and cerebral amyloid angiopathy) measured in 3500 autopsied brains. Gene-level significant evidence (P < 2.7 × 10-6) was identified in a region containing three contiguous genes (TRAPPC12, TRAPPC12-AS1, ADI1) using O'Brien and VEGAS. Gene-wide significant associations were not observed in univariate gene-based tests.
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Amare AT, Schubert KO, Tekola-Ayele F, Hsu YH, Sangkuhl K, Jenkins G, Whaley RM, Barman P, Batzler A, Altman RB, Arolt V, Brockmöller J, Chen CH, Domschke K, Hall-Flavin DK, Hong CJ, Illi A, Ji Y, Kampman O, Kinoshita T, Leinonen E, Liou YJ, Mushiroda T, Nonen S, Skime MK, Wang L, Kato M, Liu YL, Praphanphoj V, Stingl JC, Bobo WV, Tsai SJ, Kubo M, Klein TE, Weinshilboum RM, Biernacka JM, Baune BT. The association of obesity and coronary artery disease genes with response to SSRIs treatment in major depression. J Neural Transm (Vienna) 2019; 126:35-45. [PMID: 30610379 DOI: 10.1007/s00702-018-01966-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 12/18/2018] [Indexed: 01/22/2023]
Abstract
Selective serotonin reuptake inhibitors (SSRIs) are first-line antidepressants for the treatment of major depressive disorder (MDD). However, treatment response during an initial therapeutic trial is often poor and is difficult to predict. Heterogeneity of response to SSRIs in depressed patients is partly driven by co-occurring somatic disorders such as coronary artery disease (CAD) and obesity. CAD and obesity may also be associated with metabolic side effects of SSRIs. In this study, we assessed the association of CAD and obesity with treatment response to SSRIs in patients with MDD using a polygenic score (PGS) approach. Additionally, we performed cross-trait meta-analyses to pinpoint genetic variants underpinnings the relationship of CAD and obesity with SSRIs treatment response. First, PGSs were calculated at different p value thresholds (PT) for obesity and CAD. Next, binary logistic regression was applied to evaluate the association of the PGSs to SSRIs treatment response in a discovery sample (ISPC, N = 865), and in a replication cohort (STAR*D, N = 1,878). Finally, a cross-trait GWAS meta-analysis was performed by combining summary statistics. We show that the PGSs for CAD and obesity were inversely associated with SSRIs treatment response. At the most significant thresholds, the PGS for CAD and body mass index accounted 1.3%, and 0.8% of the observed variability in treatment response to SSRIs, respectively. In the cross-trait meta-analyses, we identified (1) 14 genetic loci (including NEGR1, CADM2, PMAIP1, PARK2) that are associated with both obesity and SSRIs treatment response; (2) five genetic loci (LINC01412, PHACTR1, CDKN2B, ATXN2, KCNE2) with effects on CAD and SSRIs treatment response. Our findings implicate that the genetic variants of CAD and obesity are linked to SSRIs treatment response in MDD. A better SSRIs treatment response might be achieved through a stratified allocation of treatment for MDD patients with a genetic risk for obesity or CAD.
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Affiliation(s)
- Azmeraw T Amare
- Discipline of Psychiatry, School of Medicine, University of Adelaide, North Terrace, Adelaide, SA, 5005, Australia
- South Australian Academic Health Science and Translation Centre, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
| | - Klaus Oliver Schubert
- Discipline of Psychiatry, School of Medicine, University of Adelaide, North Terrace, Adelaide, SA, 5005, Australia
- Northern Adelaide Local Health Network, Mental Health Services, Adelaide, SA, Australia
| | - Fasil Tekola-Ayele
- Epidemiology Branch, Division of Intramural Population Health Research, National Institute of Child Health and Human Development, Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yi-Hsiang Hsu
- HSL Institute for Aging Research, Harvard Medical School, Boston, MA, USA
- Program for Quantitative Genomics, Harvard School of Public Health, Boston, MA, USA
| | - Katrin Sangkuhl
- Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Gregory Jenkins
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Ryan M Whaley
- Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Poulami Barman
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Anthony Batzler
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Volker Arolt
- Department of Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany
| | - Jürgen Brockmöller
- Department of Clinical Pharmacology, University Göttingen, Göttingen, Germany
| | - Chia-Hui Chen
- Department of Psychiatry, Taipei Medical University-Shuangho Hospital, New Taipei City, Taiwan
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | - Chen-Jee Hong
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Ari Illi
- Department of Psychiatry, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
| | - Yuan Ji
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Olli Kampman
- Department of Psychiatry, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
- Department of Psychiatry, Seinäjoki Hospital District, Seinäjoki, Finland
| | | | - Esa Leinonen
- Department of Psychiatry, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
- Department of Psychiatry, Tampere University Hospital, Tampere, Finland
| | - Ying-Jay Liou
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Taisei Mushiroda
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Shinpei Nonen
- Department of Pharmacy, Hyogo University of Health Sciences, Kobe, Hyogo, Japan
| | - Michelle K Skime
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Masaki Kato
- Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
| | - Yu-Li Liu
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Verayuth Praphanphoj
- Center for Medical Genetics Research, Department of Mental Health, Ministry of Public Health Bangkok, Rajanukul Institute, Bangkok, Thailand
| | - Julia C Stingl
- Research Division Federal Institute for Drugs and Medical Devices, Bonn, Germany
| | - William V Bobo
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Teri E Klein
- Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Richard M Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Joanna M Biernacka
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Bernhard T Baune
- Discipline of Psychiatry, School of Medicine, University of Adelaide, North Terrace, Adelaide, SA, 5005, Australia.
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Zhang H, Liu D, Zhao J, Bi X. Modeling Hybrid Traits for Comorbidity and Genetic Studies of Alcohol and Nicotine Co-Dependence. Ann Appl Stat 2018; 12:2359-2378. [PMID: 30666272 PMCID: PMC6338437 DOI: 10.1214/18-aoas1156] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
We propose a novel multivariate model for analyzing hybrid traits and identifying genetic factors for comorbid conditions. Comorbidity is a common phenomenon in mental health in which an individual suffers from multiple disorders simultaneously. For example, in the Study of Addiction: Genetics and Environment (SAGE), alcohol and nicotine addiction were recorded through multiple assessments that we refer to as hybrid traits. Statistical inference for studying the genetic basis of hybrid traits has not been well-developed. Recent rank-based methods have been utilized for conducting association analyses of hybrid traits but do not inform the strength or direction of effects. To overcome this limitation, a parametric modeling framework is imperative. Although such parametric frameworks have been proposed in theory, they are neither well-developed nor extensively used in practice due to their reliance on complicated likelihood functions that have high computational complexity. Many existing parametric frameworks tend to instead use pseudo-likelihoods to reduce computational burdens. Here, we develop a model fitting algorithm for the full likelihood. Our extensive simulation studies demonstrate that inference based on the full likelihood can control the type-I error rate, and gains power and improves the effect size estimation when compared with several existing methods for hybrid models. These advantages remain even if the distribution of the latent variables is misspecified. After analyzing the SAGE data, we identify three genetic variants (rs7672861, rs958331, rs879330) that are significantly associated with the comorbidity of alcohol and nicotine addiction at the chromosome-wide level. Moreover, our approach has greater power in this analysis than several existing methods for hybrid traits.Although the analysis of the SAGE data motivated us to develop the model, it can be broadly applied to analyze any hybrid responses.
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Affiliation(s)
- Heping Zhang
- Heping Zhang is Susan Dwight Bliss Professor , Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520; Dungang Liu is Assistant Professor , Department of Operations, Business Analytics and Information Systems, University of Cincinnati Lindner College of Business, Cincinnati, OH 45221; Jiwei Zhao is Assistant Professor , Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY 14214; and Xuan Bi is Postdoctoral Associate, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520
| | - Dungang Liu
- Heping Zhang is Susan Dwight Bliss Professor , Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520; Dungang Liu is Assistant Professor , Department of Operations, Business Analytics and Information Systems, University of Cincinnati Lindner College of Business, Cincinnati, OH 45221; Jiwei Zhao is Assistant Professor , Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY 14214; and Xuan Bi is Postdoctoral Associate, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520
| | - Jiwei Zhao
- Heping Zhang is Susan Dwight Bliss Professor , Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520; Dungang Liu is Assistant Professor , Department of Operations, Business Analytics and Information Systems, University of Cincinnati Lindner College of Business, Cincinnati, OH 45221; Jiwei Zhao is Assistant Professor , Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY 14214; and Xuan Bi is Postdoctoral Associate, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520
| | - Xuan Bi
- Heping Zhang is Susan Dwight Bliss Professor , Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520; Dungang Liu is Assistant Professor , Department of Operations, Business Analytics and Information Systems, University of Cincinnati Lindner College of Business, Cincinnati, OH 45221; Jiwei Zhao is Assistant Professor , Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY 14214; and Xuan Bi is Postdoctoral Associate, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520
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Phenome-wide association studies across large population cohorts support drug target validation. Nat Commun 2018; 9:4285. [PMID: 30327483 PMCID: PMC6191429 DOI: 10.1038/s41467-018-06540-3] [Citation(s) in RCA: 107] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 09/05/2018] [Indexed: 12/12/2022] Open
Abstract
Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery. Testing the association between genetic variants and a range of phenotypes can assist drug development. Here, in a phenome-wide association study in up to 697,815 individuals, Diogo et al. identify genotype–phenotype associations predicting efficacy, alternative indications or adverse drug effects.
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Qi G, Chatterjee N. Heritability informed power optimization (HIPO) leads to enhanced detection of genetic associations across multiple traits. PLoS Genet 2018; 14:e1007549. [PMID: 30289880 PMCID: PMC6192650 DOI: 10.1371/journal.pgen.1007549] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Revised: 10/17/2018] [Accepted: 07/09/2018] [Indexed: 12/31/2022] Open
Abstract
Genome-wide association studies have shown that pleiotropy is a common phenomenon that can potentially be exploited for enhanced detection of susceptibility loci. We propose heritability informed power optimization (HIPO) for conducting powerful pleiotropic analysis using summary-level association statistics. We find optimal linear combinations of association coefficients across traits that are expected to maximize non-centrality parameter for the underlying test statistics, taking into account estimates of heritability, sample size variations and overlaps across the traits. Simulation studies show that the proposed method has correct type I error, robust to population stratification and leads to desired genome-wide enrichment of association signals. Application of the proposed method to publicly available data for three groups of genetically related traits, lipids (N = 188,577), psychiatric diseases (Ncase = 33,332, Ncontrol = 27,888) and social science traits (N ranging between 161,460 to 298,420 across individual traits) increased the number of genome-wide significant loci by 12%, 200% and 50%, respectively, compared to those found by analysis of individual traits. Evidence of replication is present for many of these loci in subsequent larger studies for individual traits. HIPO can potentially be extended to high-dimensional phenotypes as a way of dimension reduction to maximize power for subsequent genetic association testing.
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Affiliation(s)
- Guanghao Qi
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Department of Oncology, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
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29
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Wang Z, Sha Q, Fang S, Zhang K, Zhang S. Testing an optimally weighted combination of common and/or rare variants with multiple traits. PLoS One 2018; 13:e0201186. [PMID: 30048520 PMCID: PMC6062080 DOI: 10.1371/journal.pone.0201186] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Accepted: 07/10/2018] [Indexed: 12/25/2022] Open
Abstract
Recently, joint analysis of multiple traits has become popular because it can increase statistical power to identify genetic variants associated with complex diseases. In addition, there is increasing evidence indicating that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods test the association between multiple traits and a single genetic variant. However, these methods by analyzing one variant at a time may not be ideal for rare variant association studies because of the allelic heterogeneity as well as the extreme rarity of rare variants. In this article, we developed a statistical method by testing an optimally weighted combination of variants with multiple traits (TOWmuT) to test the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is robust to the directions of effects of causal variants and is applicable to different types of traits. Using extensive simulation studies, we compared the performance of TOWmuT with the following five existing methods: gene association with multiple traits (GAMuT), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), single-TOW, and MANOVA. Our results showed that, in all of the simulation scenarios, TOWmuT has correct type I error rates and is consistently more powerful than the other five tests. We also illustrated the usefulness of TOWmuT by analyzing a whole-genome genotyping data from a lung function study.
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Affiliation(s)
- Zhenchuan Wang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Shurong Fang
- Department of Mathematics and Computer Science, John Carroll University, University Heights, Ohio, United States of America
| | - Kui Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
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Hackinger S, Zeggini E. Statistical methods to detect pleiotropy in human complex traits. Open Biol 2018; 7:rsob.170125. [PMID: 29093210 PMCID: PMC5717338 DOI: 10.1098/rsob.170125] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 09/29/2017] [Indexed: 12/13/2022] Open
Abstract
In recent years pleiotropy, the phenomenon of one genetic locus influencing several traits, has become a widely researched field in human genetics. With the increasing availability of genome-wide association study summary statistics, as well as the establishment of deeply phenotyped sample collections, it is now possible to systematically assess the genetic overlap between multiple traits and diseases. In addition to increasing power to detect associated variants, multi-trait methods can also aid our understanding of how different disorders are aetiologically linked by highlighting relevant biological pathways. A plethora of available tools to perform such analyses exists, each with their own advantages and limitations. In this review, we outline some of the currently available methods to conduct multi-trait analyses. First, we briefly introduce the concept of pleiotropy and outline the current landscape of pleiotropy research in human genetics; second, we describe analytical considerations and analysis methods; finally, we discuss future directions for the field.
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Fast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:2564531. [PMID: 29743933 PMCID: PMC5878919 DOI: 10.1155/2018/2564531] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 01/24/2018] [Indexed: 02/03/2023]
Abstract
Multiple correlated traits are often collected in genetic studies. By jointly analyzing multiple traits, we can increase power by aggregating multiple weak effects and reveal additional insights into the genetic architecture of complex human diseases. In this article, we propose a multivariate linear regression-based method to test the joint association of multiple quantitative traits. It is flexible to accommodate any covariates, has very accurate control of type I errors, and offers very competitive performance. We also discuss fast and accurate significance p value computation especially for genome-wide association studies with small-to-medium sample sizes. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to genome-wide association analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) study. We found some very interesting associations with diabetes traits which have not been reported before. We implemented the proposed methods in a publicly available R package.
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Significance of risk polymorphisms for depression depends on stress exposure. Sci Rep 2018; 8:3946. [PMID: 29500446 PMCID: PMC5834495 DOI: 10.1038/s41598-018-22221-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 02/12/2018] [Indexed: 12/15/2022] Open
Abstract
Depression is a polygenic and multifactorial disorder where environmental effects exert a significant impact, yet most genetic studies do not consider the effect of stressors which may be one reason for the lack of replicable results in candidate gene studies, GWAS and between human studies and animal models. Relevance of functional polymorphisms in seven candidate genes previously implicated in animal and human studies on a depression-related phenotype given various recent stress exposure levels was assessed with Bayesian relevance analysis in 1682 subjects. This Bayesian analysis indicated a gene-environment interaction whose significance was also tested with a traditional multivariate analysis using general linear models. The investigated genetic factors were only relevant in the moderate and/or high stress exposure groups. Rank order of genes was GALR2 > BDNF > P2RX7 > HTR1A > SLC6A4 > CB1 > HTR2A, with strong relevance for the first four. Robust gene-gene-environment interaction was found between BDNF and HTR1A. Gene-environment interaction effect was confirmed, namely no main effect of genes, but a significant modulatory effect on environment-induced development of depression were found. Our data support the strong causative role of the environment modified by genetic factors, similar to animal models. Gene-environment interactions point to epigenetic factors associated with risk SNPs. Galanin-2 receptor, BDNF and X-type purin-7 receptor could be drug targets for new antidepressants.
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He Q, Liu Y, Peters U, Hsu L. Multivariate association analysis with somatic mutation data. Biometrics 2018; 74:176-184. [PMID: 28722765 PMCID: PMC5967890 DOI: 10.1111/biom.12745] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Revised: 04/01/2017] [Accepted: 05/01/2017] [Indexed: 12/21/2022]
Abstract
Somatic mutations are the driving forces for tumor development, and recent advances in cancer genome sequencing have made it feasible to evaluate the association between somatic mutations and cancer-related traits in large sample sizes. However, despite increasingly large sample sizes, it remains challenging to conduct statistical analysis for somatic mutations, because the vast majority of somatic mutations occur at very low frequencies. Furthermore, cancer is a complex disease and it is often accompanied by multiple traits that reflect various aspects of cancer; how to combine the information of these traits to identify important somatic mutations poses additional challenges. In this article, we introduce a statistical approach, named as SOMAT, for detecting somatic mutations associated with multiple cancer-related traits. Our approach provides a flexible framework for analyzing continuous, binary, or a mixture of both types of traits, and is statistically powerful and computationally efficient. In addition, we propose a data-adaptive procedure, which is grid-search free, for effectively combining test statistics to enhance statistical power. We conduct an extensive study and show that the proposed approach maintains correct type I error and is more powerful than existing approaches under the scenarios considered. We also apply our approach to an exome-sequencing study of liver tumor for illustration.
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Affiliation(s)
- Qianchuan He
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, U.S.A
| | - Yang Liu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, U.S.A
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, U.S.A
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, U.S.A
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34
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Chen L, Wang Y, Zhou Y. Association analysis of multiple traits by an approach of combining P values. J Genet 2018; 97:79-85. [PMID: 29666327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants.
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Affiliation(s)
- Lili Chen
- Department of Mathematics, School of Sciences, Harbin Institute of Technology, Harbin 150001, People's Republic of China.
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35
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Chung J, Zhang X, Allen M, Wang X, Ma Y, Beecham G, Montine TJ, Younkin SG, Dickson DW, Golde TE, Price ND, Ertekin-Taner N, Lunetta KL, Mez J, Mayeux R, Haines JL, Pericak-Vance MA, Schellenberg G, Jun GR, Farrer LA. Genome-wide pleiotropy analysis of neuropathological traits related to Alzheimer's disease. ALZHEIMERS RESEARCH & THERAPY 2018; 10:22. [PMID: 29458411 PMCID: PMC5819208 DOI: 10.1186/s13195-018-0349-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2017] [Accepted: 01/22/2018] [Indexed: 12/12/2022]
Abstract
BACKGROUND Simultaneous consideration of two neuropathological traits related to Alzheimer's disease (AD) has not been attempted in a genome-wide association study. METHODS We conducted genome-wide pleiotropy analyses using association summary statistics from the Beecham et al. study (PLoS Genet 10:e1004606, 2014) for AD-related neuropathological traits, including neuritic plaque (NP), neurofibrillary tangle (NFT), and cerebral amyloid angiopathy (CAA). Significant findings were further examined by expression quantitative trait locus and differentially expressed gene analyses in AD vs. control brains using gene expression data. RESULTS Genome-wide significant pleiotropic associations were observed for the joint model of NP and NFT (NP + NFT) with the single-nucleotide polymorphism (SNP) rs34487851 upstream of C2orf40 (alias ECRG4, P = 2.4 × 10-8) and for the joint model of NFT and CAA (NFT + CAA) with the HDAC9 SNP rs79524815 (P = 1.1 × 10-8). Gene-based testing revealed study-wide significant associations (P ≤ 2.0 × 10-6) for the NFT + CAA outcome with adjacent genes TRAPPC12, TRAPPC12-AS1, and ADI1. Risk alleles of proxy SNPs for rs79524815 were associated with significantly lower expression of HDAC9 in the brain (P = 3.0 × 10-3), and HDAC9 was significantly downregulated in subjects with AD compared with control subjects in the prefrontal (P = 7.9 × 10-3) and visual (P = 5.6 × 10-4) cortices. CONCLUSIONS Our findings suggest that pleiotropy analysis is a useful approach to identifying novel genetic associations with complex diseases and their endophenotypes. Functional studies are needed to determine whether ECRG4 or HDAC9 is plausible as a therapeutic target.
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Affiliation(s)
- Jaeyoon Chung
- Bioinformatics Graduate Program, Boston University, Boston, MA, USA.,Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Xiaoling Zhang
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Xue Wang
- Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA
| | - Yiyi Ma
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Gary Beecham
- Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Thomas J Montine
- Department of Pathology, University of Washington, Seattle, WA, USA
| | | | | | - Todd E Golde
- Center for Translational Research in Neurodegenerative Disease, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Nathan D Price
- Institute for Systems Biology, University of Washington, Seattle, WA, USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA.,Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jesse Mez
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | | | - Richard Mayeux
- Department of Neurology and Sergievsky Center, Columbia University, New York, NY, USA
| | - Jonathan L Haines
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA
| | - Margaret A Pericak-Vance
- Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Gerard Schellenberg
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyungah R Jun
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA.,Neurogenetics and Integrated Genomics, Andover Innovative Medicines Institute, Eisai Inc., Andover, MA, USA
| | - Lindsay A Farrer
- Bioinformatics Graduate Program, Boston University, Boston, MA, USA. .,Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA. .,Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA. .,Department of Neurology, Boston University School of Medicine, Boston, MA, USA. .,Department of Ophthalmology, Boston University School of Medicine, Boston, MA, USA. .,Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
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36
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Chen L, Wang Y, Zhou Y. Association analysis of multiple traits by an approach of combining
$$P$$
P
values. J Genet 2018. [DOI: 10.1007/s12041-018-0885-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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37
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Association analysis of rare and common variants with multiple traits based on variable reduction method. Genet Res (Camb) 2018; 100:e2. [PMID: 29386084 DOI: 10.1017/s0016672317000052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Pleiotropy, the effect of one variant on multiple traits, is widespread in complex diseases. Joint analysis of multiple traits can improve statistical power to detect genetic variants and uncover the underlying genetic mechanism. Currently, a large number of existing methods target one common variant or only rare variants. Increasing evidence shows that complex diseases are caused by common and rare variants. Here we propose a region-based method to test both rare and common variant associated multiple traits based on variable reduction method (abbreviated as MULVR). However, in the presence of noise traits, the MULVR method may lose power, so we propose the MULVR-O method, which jointly analyses the optimal number of traits associated with genetic variants by the MULVR method, to guard against the effect of noise traits. Extensive simulation studies show that our proposed method (MULVR-O) is applied to not only multiple quantitative traits but also qualitative traits, and is more powerful than several other comparison methods in most scenarios. An application to the two genes (SHBG and CHRM3) and two phenotypes (systolic blood pressure and diastolic blood pressure) from the GAW19 dataset illustrates that our proposed methods (MULVR and MULVR-O) are feasible and efficient as a region-based method.
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38
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Zhu H, Zhang S, Sha Q. A novel method to test associations between a weighted combination of phenotypes and genetic variants. PLoS One 2018; 13:e0190788. [PMID: 29329304 PMCID: PMC5766098 DOI: 10.1371/journal.pone.0190788] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2017] [Accepted: 12/20/2017] [Indexed: 11/18/2022] Open
Abstract
Many complex diseases like diabetes, hypertension, metabolic syndrome, et cetera, are measured by multiple correlated phenotypes. However, most genome-wide association studies (GWAS) focus on one phenotype of interest or study multiple phenotypes separately for identifying genetic variants associated with complex diseases. Analyzing one phenotype or the related phenotypes separately may lose power due to ignoring the information obtained by combining phenotypes, such as the correlation between phenotypes. In order to increase statistical power to detect genetic variants associated with complex diseases, we develop a novel method to test a weighted combination of multiple phenotypes (WCmulP). We perform extensive simulation studies as well as real data (COPDGene) analysis to evaluate the performance of the proposed method. Our simulation results show that WCmulP has correct type I error rates and is either the most powerful test or comparable to the most powerful test among the methods we compared. WCmulP also has an outstanding performance for identifying single-nucleotide polymorphisms (SNPs) associated with COPD-related phenotypes.
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Affiliation(s)
- Huanhuan Zhu
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
- * E-mail:
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39
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Amare AT, Schubert KO, Tekola-Ayele F, Hsu YH, Sangkuhl K, Jenkins G, Whaley RM, Barman P, Batzler A, Altman RB, Arolt V, Brockmöller J, Chen CH, Domschke K, Hall-Flavin DK, Hong CJ, Illi A, Ji Y, Kampman O, Kinoshita T, Leinonen E, Liou YJ, Mushiroda T, Nonen S, Skime MK, Wang L, Kato M, Liu YL, Praphanphoj V, Stingl JC, Bobo WV, Tsai SJ, Kubo M, Klein TE, Weinshilboum RM, Biernacka JM, Baune BT. Association of the Polygenic Scores for Personality Traits and Response to Selective Serotonin Reuptake Inhibitors in Patients with Major Depressive Disorder. Front Psychiatry 2018; 9:65. [PMID: 29559929 PMCID: PMC5845551 DOI: 10.3389/fpsyt.2018.00065] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 02/19/2018] [Indexed: 12/31/2022] Open
Abstract
Studies reported a strong genetic correlation between the Big Five personality traits and major depressive disorder (MDD). Moreover, personality traits are thought to be associated with response to antidepressants treatment that might partly be mediated by genetic factors. In this study, we examined whether polygenic scores (PGSs) derived from the Big Five personality traits predict treatment response and remission in patients with MDD who were prescribed selective serotonin reuptake inhibitors (SSRIs). In addition, we performed meta-analyses of genome-wide association studies (GWASs) on these traits to identify genetic variants underpinning the cross-trait polygenic association. The PGS analysis was performed using data from two cohorts: the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS, n = 529) and the International SSRI Pharmacogenomics Consortium (ISPC, n = 865). The cross-trait GWAS meta-analyses were conducted by combining GWAS summary statistics on SSRIs treatment outcome and on the personality traits. The results showed that the PGS for openness and neuroticism were associated with SSRIs treatment outcomes at p < 0.05 across PT thresholds in both cohorts. A significant association was also found between the PGS for conscientiousness and SSRIs treatment response in the PGRN-AMPS sample. In the cross-trait GWAS meta-analyses, we identified eight loci associated with (a) SSRIs response and conscientiousness near YEATS4 gene and (b) SSRI remission and neuroticism eight loci near PRAG1, MSRA, XKR6, ELAVL2, PLXNC1, PLEKHM1, and BRUNOL4 genes. An assessment of a polygenic load for personality traits may assist in conjunction with clinical data to predict whether MDD patients might respond favorably to SSRIs.
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Affiliation(s)
- Azmeraw T Amare
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia
| | - Klaus Oliver Schubert
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia.,Northern Adelaide Local Health Network, Mental Health Services, Adelaide, SA, Australia
| | - Fasil Tekola-Ayele
- Epidemiology Branch, Division of Intramural Population Health Research, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
| | - Yi-Hsiang Hsu
- HSL Institute for Aging Research, Harvard Medical School, Boston, MA, United States.,Program for Quantitative Genomics, Harvard School of Public Health, Boston, MA, United States.,Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Katrin Sangkuhl
- Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Gregory Jenkins
- Department of Health Sciences Research, Mayo Clinic, Rochester, NY, United States
| | - Ryan M Whaley
- Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Poulami Barman
- Department of Health Sciences Research, Mayo Clinic, Rochester, NY, United States
| | - Anthony Batzler
- Department of Health Sciences Research, Mayo Clinic, Rochester, NY, United States
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Volker Arolt
- Department of Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany
| | - Jürgen Brockmöller
- Department of Clinical Pharmacology, University Göttingen, Göttingen, Germany
| | - Chia-Hui Chen
- Department of Psychiatry, Taipei Medical University-Shuangho Hospital, New Taipei City, Taiwan
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Daniel K Hall-Flavin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, NY, United States
| | - Chen-Jee Hong
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Ari Illi
- Department of Psychiatry, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
| | - Yuan Ji
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic Rochester, Rochester, MN, United States
| | - Olli Kampman
- Department of Psychiatry, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.,Department of Psychiatry, Seinäjoki Hospital District, Seinäjoki, Finland
| | | | - Esa Leinonen
- Department of Psychiatry, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.,Department of Psychiatry, Tampere University Hospital, Tampere, Finland
| | - Ying-Jay Liou
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | | | - Shinpei Nonen
- Department of Pharmacy, Hyogo University of Health Sciences, Hyogo, Japan
| | - Michelle K Skime
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, NY, United States
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic Rochester, Rochester, MN, United States
| | - Masaki Kato
- Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
| | - Yu-Li Liu
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Verayuth Praphanphoj
- Center for Medical Genetics Research, Rajanukul Institute, Department of Mental Health, Ministry of Public Health Bangkok, Bangkok, Thailand
| | - Julia C Stingl
- Research Division Federal Institute for Drugs and Medical Devices, Bonn, Germany
| | - William V Bobo
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, NY, United States
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Teri E Klein
- Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Richard M Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic Rochester, Rochester, MN, United States
| | - Joanna M Biernacka
- Department of Health Sciences Research, Mayo Clinic, Rochester, NY, United States.,Department of Psychiatry and Psychology, Mayo Clinic, Rochester, NY, United States
| | - Bernhard T Baune
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia
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40
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Amare AT, Schubert KO, Hou L, Clark SR, Papiol S, Heilbronner U, Degenhardt F, Tekola-Ayele F, Hsu YH, Shekhtman T, Adli M, Akula N, Akiyama K, Ardau R, Arias B, Aubry JM, Backlund L, Bhattacharjee AK, Bellivier F, Benabarre A, Bengesser S, Biernacka JM, Birner A, Brichant-Petitjean C, Cervantes P, Chen HC, Chillotti C, Cichon S, Cruceanu C, Czerski PM, Dalkner N, Dayer A, Del Zompo M, DePaulo JR, Étain B, Falkai P, Forstner AJ, Frisen L, Frye MA, Fullerton JM, Gard S, Garnham JS, Goes FS, Grigoroiu-Serbanescu M, Grof P, Hashimoto R, Hauser J, Herms S, Hoffmann P, Hofmann A, Jamain S, Jiménez E, Kahn JP, Kassem L, Kuo PH, Kato T, Kelsoe J, Kittel-Schneider S, Kliwicki S, König B, Kusumi I, Laje G, Landén M, Lavebratt C, Leboyer M, Leckband SG, Tortorella A, Manchia M, Martinsson L, McCarthy MJ, McElroy S, Colom F, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Nöthen MM, Novák T, O’Donovan C, Ozaki N, Ösby U, Pfennig A, Potash JB, Reif A, Reininghaus E, Rouleau GA, Rybakowski JK, Schalling M, Schofield PR, Schweizer BW, Severino G, Shilling PD, Shimoda K, Simhandl C, Slaney CM, Squassina A, Stamm T, Stopkova P, Maj M, Turecki G, Vieta E, Volkert J, Witt S, Wright A, Zandi PP, Mitchell PB, Bauer M, Alda M, Rietschel M, McMahon FJ, Schulze TG, Baune BT. Association of Polygenic Score for Schizophrenia and HLA Antigen and Inflammation Genes With Response to Lithium in Bipolar Affective Disorder: A Genome-Wide Association Study. JAMA Psychiatry 2018; 75:65-74. [PMID: 29121268 PMCID: PMC5833535 DOI: 10.1001/jamapsychiatry.2017.3433] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
IMPORTANCE Lithium is a first-line mood stabilizer for the treatment of bipolar affective disorder (BPAD). However, the efficacy of lithium varies widely, with a nonresponse rate of up to 30%. Biological response markers are lacking. Genetic factors are thought to mediate treatment response to lithium, and there is a previously reported genetic overlap between BPAD and schizophrenia (SCZ). OBJECTIVES To test whether a polygenic score for SCZ is associated with treatment response to lithium in BPAD and to explore the potential molecular underpinnings of this association. DESIGN, SETTING, AND PARTICIPANTS A total of 2586 patients with BPAD who had undergone lithium treatment were genotyped and assessed for long-term response to treatment between 2008 and 2013. Weighted SCZ polygenic scores were computed at different P value thresholds using summary statistics from an international multicenter genome-wide association study (GWAS) of 36 989 individuals with SCZ and genotype data from patients with BPAD from the Consortium on Lithium Genetics. For functional exploration, a cross-trait meta-GWAS and pathway analysis was performed, combining GWAS summary statistics on SCZ and response to treatment with lithium. Data analysis was performed from September 2016 to February 2017. MAIN OUTCOMES AND MEASURES Treatment response to lithium was defined on both the categorical and continuous scales using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score. The effect measures include odds ratios and the proportion of variance explained. RESULTS Of the 2586 patients in the study (mean [SD] age, 47.2 [13.9] years), 1478 were women and 1108 were men. The polygenic score for SCZ was inversely associated with lithium treatment response in the categorical outcome, at a threshold P < 5 × 10-2. Patients with BPAD who had a low polygenic load for SCZ responded better to lithium, with odds ratios for lithium response ranging from 3.46 (95% CI, 1.42-8.41) at the first decile to 2.03 (95% CI, 0.86-4.81) at the ninth decile, compared with the patients in the 10th decile of SCZ risk. In the cross-trait meta-GWAS, 15 genetic loci that may have overlapping effects on lithium treatment response and susceptibility to SCZ were identified. Functional pathway and network analysis of these loci point to the HLA antigen complex and inflammatory cytokines. CONCLUSIONS AND RELEVANCE This study provides evidence for a negative association between high genetic loading for SCZ and poor response to lithium in patients with BPAD. These results suggest the potential for translational research aimed at personalized prescribing of lithium.
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Affiliation(s)
| | - Azmeraw T. Amare
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia
| | - Klaus Oliver Schubert
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia2Northern Adelaide Local Health Network, Mental Health Services, Adelaide, South Australia, Australia
| | - Liping Hou
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland
| | - Scott R. Clark
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia
| | - Sergi Papiol
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig-Maximilian University of Munich, Munich, Germany5Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University of Munich, Munich, Germany
| | - Urs Heilbronner
- Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig-Maximilian University of Munich, Munich, Germany6Department of Psychiatry and Psychotherapy, University Medical Center, Georg-August University Göttingen, Göttingen, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics and Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Fasil Tekola-Ayele
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland
| | - Yi-Hsiang Hsu
- Hebrew SeniorLife Institute for Aging Research, Harvard Medical School, Boston, Massachusetts10Program for Quantitative Genomics, Harvard School of Public Health, Boston, Massachusetts11Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
| | | | - Mazda Adli
- Department of Psychiatry and Psychotherapy, Charité–Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
| | - Nirmala Akula
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland
| | - Kazufumi Akiyama
- Department of Biological Psychiatry and Neuroscience, Dokkyo Medical University School of Medicine, Mibu, Tochigi, Japan
| | - Raffaella Ardau
- Unit of Clinical Pharmacology, Hospital University Agency of Cagliari, Cagliari, Italy
| | - Bárbara Arias
- Unitat de Zoologia i Antropologia Biològica (Dpt Biologia Evolutiva, Ecologia i Ciències Ambientals), Facultat de Biologia and Institut de Biomedicina, University of Barcelona, Centro de Investigación Biomédica en Red de Salud Mental, Barcelona, Spain
| | - Jean-Michel Aubry
- Department of Psychiatry, Mood Disorders Unit, Geneva University Hospitals, Geneva, Switzerland
| | - Lena Backlund
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden, and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | | | - Frank Bellivier
- Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche Scientifique 1144, Université Paris Diderot, Département de Psychiatrie et de Médecine Addictologique, Assistance Publique–Hôpitaux de Paris, Groupe Hospitalier Saint-Louis-Lariboisière-F. Widal, Paris, France
| | - Antonio Benabarre
- Bipolar Disorder Program, Institute of Neuroscience, Hospital Clinic, University of Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red Salud Mental, Barcelona, Catalonia, Spain
| | - Susanne Bengesser
- Department of Psychiatry and Psychotherapeutic Medicine, Research Unit for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Joanna M. Biernacka
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota23Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota
| | - Armin Birner
- Department of Psychiatry and Psychotherapeutic Medicine, Research Unit for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Clara Brichant-Petitjean
- Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche Scientifique 1144, Université Paris Diderot, Département de Psychiatrie et de Médecine Addictologique, Assistance Publique–Hôpitaux de Paris, Groupe Hospitalier Saint-Louis-Lariboisière-F. Widal, Paris, France
| | - Pablo Cervantes
- The Neuromodulation Unit, McGill University Health Centre, Montreal, Canada
| | - Hsi-Chung Chen
- Department of Psychiatry and Center of Sleep Disorders, National Taiwan University Hospital, Taipei, Taiwan
| | - Caterina Chillotti
- Unit of Clinical Pharmacology, Hospital University Agency of Cagliari, Cagliari, Italy
| | - Sven Cichon
- Institute of Human Genetics and Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany26Human Genomics Research Group, Department of Biomedicine, University Hospital Basel, Basel, Switzerland
| | - Cristiana Cruceanu
- Douglas Mental Health University Institute, McGill University, Montreal, Canada
| | - Piotr M. Czerski
- Psychiatric Genetic Unit, Poznan University of Medical Sciences, Poznan, Poland
| | - Nina Dalkner
- Department of Psychiatry and Psychotherapeutic Medicine, Research Unit for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Alexandre Dayer
- Department of Psychiatry, Mood Disorders Unit, Geneva University Hospitals, Geneva, Switzerland
| | - Maria Del Zompo
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - J. Raymond DePaulo
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Bruno Étain
- Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche Scientifique 1144, Université Paris Diderot, Département de Psychiatrie et de Médecine Addictologique, Assistance Publique–Hôpitaux de Paris, Groupe Hospitalier Saint-Louis-Lariboisière-F. Widal, Paris, France
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University of Munich, Munich, Germany
| | - Andreas J. Forstner
- Institute of Human Genetics and Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany26Human Genomics Research Group, Department of Biomedicine, University Hospital Basel, Basel, Switzerland31Department of Psychiatry, University of Basel, Basel, Switzerland
| | - Louise Frisen
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden, and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Mark A. Frye
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota
| | - Janice M. Fullerton
- Neuroscience Research Australia, Sydney, New South Wales, Australia33School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Sébastien Gard
- Service de Psychiatrie, Hôpital Charles Perrens, Bordeaux, France
| | - Julie S. Garnham
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Fernando S. Goes
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Maria Grigoroiu-Serbanescu
- Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania
| | - Paul Grof
- Mood Disorders Center of Ottawa, Ontario, Canada
| | - Ryota Hashimoto
- Molecular Research Center for Children’s Mental Development, United Graduate School of Child Development, Osaka University, Osaka, Japan39Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Joanna Hauser
- Psychiatric Genetic Unit, Poznan University of Medical Sciences, Poznan, Poland
| | - Stefan Herms
- Institute of Human Genetics and Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany26Human Genomics Research Group, Department of Biomedicine, University Hospital Basel, Basel, Switzerland
| | - Per Hoffmann
- Institute of Human Genetics and Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany26Human Genomics Research Group, Department of Biomedicine, University Hospital Basel, Basel, Switzerland
| | - Andrea Hofmann
- Institute of Human Genetics and Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Stephane Jamain
- Institut National de la Santé et de la Recherche Médicale Unité 955, Psychiatrie Translationnelle, Créteil, France
| | - Esther Jiménez
- Bipolar Disorder Program, Institute of Neuroscience, Hospital Clinic, University of Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red Salud Mental, Barcelona, Catalonia, Spain
| | - Jean-Pierre Kahn
- Service de Psychiatrie et Psychologie Clinique, Centre Psychothérapique de Nancy, Université de Lorraine, Nancy, France
| | - Layla Kassem
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland
| | - Po-Hsiu Kuo
- Department of Public Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Tadafumi Kato
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Brain Science Institute, Saitama, Japan
| | - John Kelsoe
- Department of Psychiatry, University of California San Diego
| | - Sarah Kittel-Schneider
- Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Sebastian Kliwicki
- Department of Adult Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Barbara König
- Department of Psychiatry and Psychotherapeutic Medicine, Landesklinikum Neunkirchen, Neunkirchen, Austria
| | - Ichiro Kusumi
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Gonzalo Laje
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland
| | - Mikael Landén
- Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the Gothenburg University, Gothenburg, Sweden49Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Catharina Lavebratt
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden, and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Marion Leboyer
- 50Inserm U955, Translational Psychiatry Laboratory, Université Paris-Est-Créteil, Department of Psychiatry and Addictology of Mondor University Hospital, Assistance Publique–Hôpitaux de Paris, Hôpital Albert Chenevier–Henri Mondor, Pôle de Psychiatrie, Créteil, France
| | - Susan G. Leckband
- Department of Pharmacy, Veterans Affairs San Diego Healthcare System, San Diego, California
| | | | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy54Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Lina Martinsson
- Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
| | - Michael J. McCarthy
- Department of Psychiatry, University of California San Diego56Department of Psychiatry, Veterans Affairs San Diego Healthcare System, San Diego, California
| | - Susan McElroy
- Department of Psychiatry, Lindner Center of Hope and University of Cincinnati, Mason, Ohio
| | - Francesc Colom
- Bipolar Disorder Program, Institute of Neuroscience, Hospital Clinic, University of Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red Salud Mental, Barcelona, Catalonia, Spain58Mental Health Research Group, IMIM–Hospital del Mar, Barcelona, Catalonia, Spain
| | - Marina Mitjans
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain60Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany
| | - Francis M. Mondimore
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Palmiero Monteleone
- Neurosciences Section, Department of Medicine and Surgery, University of Salerno, Salerno, Italy62Department of Psychiatry, Second University of Naples, Naples, Italy
| | | | - Markus M. Nöthen
- Institute of Human Genetics and Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Tomas Novák
- National Institute of Mental Health, Klecany, Czech Republic
| | - Claire O’Donovan
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Norio Ozaki
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Urban Ösby
- Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Medical Faculty, Technische Universität Dresden, Germany
| | - James B. Potash
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Andreas Reif
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Brain Science Institute, Saitama, Japan
| | - Eva Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Research Unit for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Guy A. Rouleau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Janusz K. Rybakowski
- Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Martin Schalling
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden, and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Peter R. Schofield
- Neuroscience Research Australia, Sydney, New South Wales, Australia33School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Barbara W. Schweizer
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Giovanni Severino
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Paul D. Shilling
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden, and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Katzutaka Shimoda
- Department of Psychiatry, Dokkyo University School of Medicine, Mibu, Tochigi, Japan
| | - Christian Simhandl
- Bipolar Center Wiener Neustadt, Sigmund Freud University, Medical Faculty, Vienna, Austria
| | - Claire M. Slaney
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Alessio Squassina
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Thomas Stamm
- Department of Psychiatry and Psychotherapy, Charité–Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
| | - Pavla Stopkova
- National Institute of Mental Health, Klecany, Czech Republic
| | - Mario Maj
- Department of Psychiatry, Second University of Naples, Naples, Italy
| | - Gustavo Turecki
- Douglas Mental Health University Institute, McGill University, Montreal, Canada
| | - Eduard Vieta
- Bipolar Disorder Program, Institute of Neuroscience, Hospital Clinic, University of Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red Salud Mental, Barcelona, Catalonia, Spain
| | - Julia Volkert
- Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Stephanie Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Adam Wright
- School of Psychiatry, University of New South Wales, and Black Dog Institute, Sydney, New South Wales, Australia
| | - Peter P. Zandi
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Philip B. Mitchell
- School of Psychiatry, University of New South Wales, and Black Dog Institute, Sydney, New South Wales, Australia
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Medical Faculty, Technische Universität Dresden, Germany
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Francis J. McMahon
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland
| | - Thomas G. Schulze
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland4Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig-Maximilian University of Munich, Munich, Germany6Department of Psychiatry and Psychotherapy, University Medical Center
- Georg-August University Göttingen, Göttingen, Germany30Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland70Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Bernhard T. Baune
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia
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Wang W, Zhang D, Xu C, Wu Y, Duan H, Li S, Tan Q. Heritability and Genome-Wide Association Analyses of Serum Uric Acid in Middle and Old-Aged Chinese Twins. Front Endocrinol (Lausanne) 2018; 9:75. [PMID: 29559957 PMCID: PMC5845532 DOI: 10.3389/fendo.2018.00075] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Accepted: 02/19/2018] [Indexed: 12/13/2022] Open
Abstract
Serum uric acid (SUA), as the end product of purine metabolism, has proven emerging roles in human disorders. Here based on a sample of 379 middle and old-aged Chinese twin pairs, we aimed to explore the magnitude of genetic impact on SUA variation by performing sex-limitation twin modeling analyses and further detect specific genetic variants related to SUA by conducting a genome-wide association study. Monozygotic (MZ) twin correlation for SUA level (rMZ = 0.56) was larger than for dizygotic (DZ) twin correlation (rDZ = 0.39). The common effects sex-limitation model provided the best fit with additive genetic parameter (A) accounting for 46.3%, common or shared environmental parameter (C) accounting for 26.3% and unique/nonshared environmental parameter (E) accounting for 27.5% for females and 29.9, 33.1, and 37.0% for males, respectively. Although no SUA-related genetic variants reached genome-wide significance level, 25 SNPs were suggestive of association (P < 1 × 10-5). Most of the SNPs were located in an intronic region and detected to have regulatory effects on gene transcription. The cell-type specific enhancer of skeletal muscle was detected which has been reported to implicate SUA. Two promising genetic regions on chromosome 17 around rs2253277 and chromosome 14 around rs11621523 were found. Gene-based analysis found 167 genes nominally associated with SUA level (P < 0.05), including PTGR2, ENTPD5, well-known SLC2A9, etc. Enrichment analysis identified one pathway of transmembrane transport of small molecules and 20 GO gene sets involving in ion transport, transmembrane transporter activity, hydrolase activity acting on acid anhydrides, etc. In conclusion, SUA shows moderate heritability in women and low heritability in men in the Chinese population and genetic variations are significantly involved in functional genes and regulatory domains that mediate SUA level. Our findings provide clues to further elucidate molecular physiology of SUA homeostasis and identify new diagnostic biomarkers and therapeutic targets for hyperuricemia and gout.
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Affiliation(s)
- Weijing Wang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
- Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Dongfeng Zhang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
- *Correspondence: Dongfeng Zhang,
| | - Chunsheng Xu
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China
- Qingdao Institute of Preventive Medicine, Qingdao, China
| | - Yili Wu
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
| | - Haiping Duan
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China
| | - Shuxia Li
- Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Qihua Tan
- Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
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Vicente CT, Revez JA, Ferreira MAR. Lessons from ten years of genome-wide association studies of asthma. Clin Transl Immunology 2017; 6:e165. [PMID: 29333270 PMCID: PMC5750453 DOI: 10.1038/cti.2017.54] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 10/10/2017] [Accepted: 10/31/2017] [Indexed: 12/13/2022] Open
Abstract
Twenty-five genome-wide association studies (GWAS) of asthma were published between 2007 and 2016, the largest with a sample size of 157242 individuals. Across these studies, 39 genetic variants in low linkage disequilibrium (LD) with each other were reported to associate with disease risk at a significance threshold of P<5 × 10−8, including 31 in populations of European ancestry. Results from analyses of the UK Biobank data (n=380 503) indicate that at least 28 of the 31 associations reported in Europeans represent true-positive findings, collectively explaining 2.5% of the variation in disease liability (median of 0.06% per variant). We identified 49 transcripts as likely target genes of the published asthma risk variants, mostly based on LD with expression quantitative trait loci (eQTL). Of these genes, 16 were previously implicated in disease pathophysiology by functional studies, including TSLP, TNFSF4, ADORA1, CHIT1 and USF1. In contrast, at present, there is limited or no functional evidence directly implicating the remaining 33 likely target genes in asthma pathophysiology. Some of these genes have a known function that is relevant to allergic disease, including F11R, CD247, PGAP3, AAGAB, CAMK4 and PEX14, and so could be prioritized for functional follow-up. We conclude by highlighting three areas of research that are essential to help translate GWAS findings into clinical research or practice, namely validation of target gene predictions, understanding target gene function and their role in disease pathophysiology and genomics-guided prioritization of targets for drug development.
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Affiliation(s)
| | - Joana A Revez
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
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Lin N, Zhu Y, Fan R, Xiong M. A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data. PLoS Comput Biol 2017; 13:e1005788. [PMID: 29040274 PMCID: PMC5659802 DOI: 10.1371/journal.pcbi.1005788] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 10/27/2017] [Accepted: 09/21/2017] [Indexed: 01/12/2023] Open
Abstract
Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore correlation information of genetic variants, effectively reduce data dimensions, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new statistic method referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the ten competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and ten other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the ten other statistics.
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Affiliation(s)
- Nan Lin
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Yun Zhu
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States of America
| | - Ruzong Fan
- Biostatistics and Bioinformatics Branch (BBB), Division of Intramural Population Health Research (DIPHR), Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, MD, United States of America
| | - Momiao Xiong
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States of America
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Zhang W, Yang L, Tang LL, Liu A, Mills JL, Sun Y, Li Q. GATE: an efficient procedure in study of pleiotropic genetic associations. BMC Genomics 2017; 18:552. [PMID: 28732532 PMCID: PMC5521155 DOI: 10.1186/s12864-017-3928-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Accepted: 07/06/2017] [Indexed: 11/10/2022] Open
Abstract
Background The association studies on human complex traits are admittedly propitious to identify deleterious genetic markers. Compared to single-trait analyses, multiple-trait analyses can arguably make better use of the information on both traits and markers, and thus improve statistical power of association tests prominently. Principal component analysis (PCA) is a well-known useful tool in multivariate analysis and can be applied to this task. Generally, PCA is first performed on all traits and then a certain number of top principal components (PCs) that explain most of the trait variations are selected to construct the test statistics. However, under some situations, only utilizing these top PCs would lead to a loss of important evidences from discarded PCs and thus makes the capability compromised. Methods To overcome this drawback while keeping the advantages of using the top PCs, we propose a group accumulated test evidence (GATE) procedure. By dividing the PCs which is sorted in the descending order according to the corresponding eigenvalues into a few groups, GATE integrates the information of traits at the group level. Results Simulation studies demonstrate the superiority of the proposed approach over several existing methods in terms of statistical power. Sometimes, the increase of power can reach 25%. These methods are further illustrated using the Heterogeneous Stock Mice data which is collected from a quantitative genome-wide association study. Conclusions Overall, GATE provides a powerful test for pleiotropic genetic associations. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3928-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wei Zhang
- Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.,Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA
| | - Liu Yang
- College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, China
| | - Larry L Tang
- Department of Statistics, George Mason University, Fairfax, VA, USA.,Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Aiyi Liu
- Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - James L Mills
- Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Yuanchang Sun
- Department of Mathematics and Statistics, Florida International University, Miami, FL, USA
| | - Qizhai Li
- Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
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Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits. Genetics 2017. [PMID: 28642271 DOI: 10.1534/genetics.116.199646] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Many genetic association studies collect a wide range of complex traits. As these traits may be correlated and share a common genetic mechanism, joint analysis can be statistically more powerful and biologically more meaningful. However, most existing tests for multiple traits cannot be used for high-dimensional and possibly structured traits, such as network-structured transcriptomic pathway expressions. To overcome potential limitations, in this article we propose the dual kernel-based association test (DKAT) for testing the association between multiple traits and multiple genetic variants, both common and rare. In DKAT, two individual kernels are used to describe the phenotypic and genotypic similarity, respectively, between pairwise subjects. Using kernels allows for capturing structure while accommodating dimensionality. Then, the association between traits and genetic variants is summarized by a coefficient which measures the association between two kernel matrices. Finally, DKAT evaluates the hypothesis of nonassociation with an analytical P-value calculation without any computationally expensive resampling procedures. By collapsing information in both traits and genetic variants using kernels, the proposed DKAT is shown to have a correct type-I error rate and higher power than other existing methods in both simulation studies and application to a study of genetic regulation of pathway gene expressions.
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46
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Lutz SM, Fingerlin TE, Hokanson JE, Lange C. A general approach to testing for pleiotropy with rare and common variants. Genet Epidemiol 2017; 41:163-170. [PMID: 27900789 PMCID: PMC5472207 DOI: 10.1002/gepi.22011] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Revised: 08/01/2016] [Accepted: 09/19/2016] [Indexed: 12/22/2022]
Abstract
Through genome-wide association studies, numerous genes have been shown to be associated with multiple phenotypes. To determine the overlap of genetic susceptibility of correlated phenotypes, one can apply multivariate regression or dimension reduction techniques, such as principal components analysis, and test for the association with the principal components of the phenotypes rather than the individual phenotypes. However, as these approaches test whether there is a genetic effect for at least one of the phenotypes, a significant test result does not necessarily imply pleiotropy. Recently, a method called Pleiotropy Estimation and Test Bootstrap (PET-B) has been proposed to specifically test for pleiotropy (i.e., that two normally distributed phenotypes are both associated with the single nucleotide polymorphism of interest). Although the method examines the genetic overlap between the two quantitative phenotypes, the extension to binary phenotypes, three or more phenotypes, and rare variants is not straightforward. We provide two approaches to formally test this pleiotropic relationship in multiple scenarios. These approaches depend on permuting the phenotypes of interest and comparing the set of observed P-values to the set of permuted P-values in relation to the origin (e.g., a vector of zeros) either using the Hausdorff metric or a cutoff-based approach. These approaches are appropriate for categorical and quantitative phenotypes, more than two phenotypes, common variants and rare variants. We evaluate these approaches under various simulation scenarios and apply them to the COPDGene study, a case-control study of chronic obstructive pulmonary disease in current and former smokers.
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Affiliation(s)
- Sharon M Lutz
- Department of Biostatistics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Tasha E Fingerlin
- Department of Biostatistics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
- Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, USA
| | - John E Hokanson
- Department of Epidemiology, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Christoph Lange
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Wu B, Pankow JS. Genome-wide association test of multiple continuous traits using imputed SNPs. STATISTICS AND ITS INTERFACE 2017; 10:379-386. [PMID: 28217245 PMCID: PMC5310616 DOI: 10.4310/sii.2017.v10.n3.a2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
More and more large cohort studies have conducted or are conducting genome-wide association studies (GWAS) to reveal the genetic components of many complex human diseases. These large cohort studies often collected a broad array of correlated phenotypes that reflect common physiological processes. By jointly analyzing these correlated traits, we can gain more power by aggregating multiple weak effects and shed light on the mechanisms underlying complex human diseases. The majority of existing multi-trait association test methods are based on jointly modeling the multivariate traits conditional on the genotype as covariate, and can readily accommodate the imputed SNPs by using their imputed dosage as a covariate. An alternative class of multi-trait association tests is based on the inverted regression, which models the distribution of genotypes conditional on the covariate and multivariate traits, and has been shown to have competitive performance. To our knowledge, all existing inverted regression approaches have implicitly used the "best-guess" genotypes, which is not efficient and known to lead to dramatic power loss, and there have not been any proposed methods of incorporating imputation uncertainty into inverted regressions. In this work, we propose a general and efficient framework that can account for the imputation uncertainty to further improve the association test power of inverted regression models for imputed SNPs. We demonstrate through extensive numerical studies that the proposed method has competitive performance. We further illustrate its usefulness by application to association test of diabetes-related glycemic traits in the Atherosclerosis Risk in Communities (ARIC) Study.
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Affiliation(s)
- Baolin Wu
- Division of Biostatistics, University of Minnesota
| | - James S. Pankow
- Division of Epidemiology and Community Health School of Public Health, University of Minnesota
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48
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Kwak IY, Pan W. Gene- and pathway-based association tests for multiple traits with GWAS summary statistics. Bioinformatics 2017; 33:64-71. [PMID: 27592708 PMCID: PMC5198520 DOI: 10.1093/bioinformatics/btw577] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 08/08/2016] [Accepted: 08/29/2016] [Indexed: 11/15/2022] Open
Abstract
To identify novel genetic variants associated with complex traits and to shed new insights on underlying biology, in addition to the most popular single SNP-single trait association analysis, it would be useful to explore multiple correlated (intermediate) traits at the gene- or pathway-level by mining existing single GWAS or meta-analyzed GWAS data. For this purpose, we present an adaptive gene-based test and a pathway-based test for association analysis of multiple traits with GWAS summary statistics. The proposed tests are adaptive at both the SNP- and trait-levels; that is, they account for possibly varying association patterns (e.g. signal sparsity levels) across SNPs and traits, thus maintaining high power across a wide range of situations. Furthermore, the proposed methods are general: they can be applied to mixed types of traits, and to Z-statistics or P-values as summary statistics obtained from either a single GWAS or a meta-analysis of multiple GWAS. Our numerical studies with simulated and real data demonstrated the promising performance of the proposed methods. AVAILABILITY AND IMPLEMENTATION The methods are implemented in R package aSPU, freely and publicly available at: https://cran.r-project.org/web/packages/aSPU/ CONTACT: weip@biostat.umn.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Il-Youp Kwak
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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Abstract
For over a decade, genome-wide association studies (GWAS) have been a major tool for detecting genetic variants underlying complex traits. Recent studies have demonstrated that the same variant or gene can be associated with multiple traits, and such associations are termed cross-phenotype (CP) associations. CP association analysis can improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this chapter, we discuss existing statistical methods for analyzing association between a single marker and multivariate phenotypes, we introduce a general approach, CPASSOC, to detect the CP associations, and explain how to conduct the analysis in practice.
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Affiliation(s)
- Xiaoyin Li
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA.
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
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50
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Brunel H, Massanet R, Martinez-Perez A, Ziyatdinov A, Martin-Fernandez L, Souto JC, Perera A, Soria JM. The Central Role of KNG1 Gene as a Genetic Determinant of Coagulation Pathway-Related Traits: Exploring Metaphenotypes. PLoS One 2016; 11:e0167187. [PMID: 28005926 PMCID: PMC5178993 DOI: 10.1371/journal.pone.0167187] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Accepted: 11/09/2016] [Indexed: 01/12/2023] Open
Abstract
Traditional genetic studies of single traits may be unable to detect the pleiotropic effects involved in complex diseases. To detect the correlation that exists between several phenotypes involved in the same biological process, we introduce an original methodology to analyze sets of correlated phenotypes involved in the coagulation cascade in genome-wide association studies. The methodology consists of a two-stage process. First, we define new phenotypic meta-variables (linear combinations of the original phenotypes), named metaphenotypes, by applying Independent Component Analysis for the multivariate analysis of correlated phenotypes (i.e. the levels of coagulation pathway–related proteins). The resulting metaphenotypes integrate the information regarding the underlying biological process (i.e. thrombus/clot formation). Secondly, we take advantage of a family based Genome Wide Association Study to identify genetic elements influencing these metaphenotypes and consequently thrombosis risk. Our study utilized data from the GAIT Project (Genetic Analysis of Idiopathic Thrombophilia). We obtained 15 metaphenotypes, which showed significant heritabilities, ranging from 0.2 to 0.7. These results indicate the importance of genetic factors in the variability of these traits. We found 4 metaphenotypes that showed significant associations with SNPs. The most relevant were those mapped in a region near the HRG, FETUB and KNG1 genes. Our results are provocative since they show that the KNG1 locus plays a central role as a genetic determinant of the entire coagulation pathway and thrombus/clot formation. Integrating data from multiple correlated measurements through metaphenotypes is a promising approach to elucidate the hidden genetic mechanisms underlying complex diseases.
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Affiliation(s)
- Helena Brunel
- Unit of Genomics of Complex Diseases, Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain
| | - Raimon Massanet
- B2SLab, Departament d’Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Angel Martinez-Perez
- Unit of Genomics of Complex Diseases, Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain
| | - Andrey Ziyatdinov
- Unit of Genomics of Complex Diseases, Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain
| | - Laura Martin-Fernandez
- Unit of Genomics of Complex Diseases, Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain
| | - Juan Carlos Souto
- Thrombosis and Haemostasis Unit, Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain
| | - Alexandre Perera
- Thrombosis and Haemostasis Unit, Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain
| | - José Manuel Soria
- Unit of Genomics of Complex Diseases, Sant Pau Institute of Biomedical Research (IIB-Sant Pau), Barcelona, Spain
- * E-mail:
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