501
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Multiphenotype association study of patients randomized to initiate antiretroviral regimens in AIDS Clinical Trials Group protocol A5202. Pharmacogenet Genomics 2017; 27:101-111. [PMID: 28099408 PMCID: PMC5285297 DOI: 10.1097/fpc.0000000000000263] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Supplemental Digital Content is available in the text. Background High-throughput approaches are increasingly being used to identify genetic associations across multiple phenotypes simultaneously. Here, we describe a pilot analysis that considered multiple on-treatment laboratory phenotypes from antiretroviral therapy-naive patients who were randomized to initiate antiretroviral regimens in a prospective clinical trial, AIDS Clinical Trials Group protocol A5202. Participants and methods From among 5 9545 294 polymorphisms imputed genome-wide, we analyzed 2544, including 2124 annotated in the PharmGKB, and 420 previously associated with traits in the GWAS Catalog. We derived 774 phenotypes on the basis of context from six variables: plasma atazanavir (ATV) pharmacokinetics, plasma efavirenz (EFV) pharmacokinetics, change in the CD4+ T-cell count, HIV-1 RNA suppression, fasting low-density lipoprotein-cholesterol, and fasting triglycerides. Permutation testing assessed the likelihood of associations being by chance alone. Pleiotropy was assessed for polymorphisms with the lowest P-values. Results This analysis included 1181 patients. At P less than 1.5×10−4, most associations were not by chance alone. Polymorphisms with the lowest P-values for EFV pharmacokinetics (CYPB26 rs3745274), low-density lipoprotein -cholesterol (APOE rs7412), and triglyceride (APOA5 rs651821) phenotypes had been associated previously with those traits in previous studies. The association between triglycerides and rs651821 was present with ATV-containing regimens, but not with EFV-containing regimens. Polymorphisms with the lowest P-values for ATV pharmacokinetics, CD4 T-cell count, and HIV-1 RNA phenotypes had not been reported previously to be associated with that trait. Conclusion Using data from a prospective HIV clinical trial, we identified expected genetic associations, potentially novel associations, and at least one context-dependent association. This study supports high-throughput strategies that simultaneously explore multiple phenotypes from clinical trials’ datasets for genetic associations.
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502
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Pang SYY, Teo KC, Hsu JS, Chang RSK, Li M, Sham PC, Ho SL. The role of gene variants in the pathogenesis of neurodegenerative disorders as revealed by next generation sequencing studies: a review. Transl Neurodegener 2017; 6:27. [PMID: 29046784 PMCID: PMC5639582 DOI: 10.1186/s40035-017-0098-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 10/02/2017] [Indexed: 12/13/2022] Open
Abstract
The clinical diagnosis of neurodegenerative disorders based on phenotype is difficult in heterogeneous conditions with overlapping symptoms. It does not take into account the disease etiology or the highly variable clinical course even amongst patients diagnosed with the same disorder. The advent of next generation sequencing (NGS) has allowed for a system-wide, unbiased approach to identify all gene variants in the genome simultaneously. With the plethora of new genes being identified, genetic rather than phenotype-based classification of Mendelian diseases such as spinocerebellar ataxia (SCA), hereditary spastic paraplegia (HSP) and Charcot-Marie-Tooth disease (CMT) has become widely accepted. It has also become clear that gene variants play a role in common and predominantly sporadic neurodegenerative diseases such as Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). The observation of pleiotropy has emerged, with mutations in the same gene giving rise to diverse phenotypes, which further increases the complexity of phenotype-genotype correlation. Possible mechanisms of pleiotropy include different downstream effects of different mutations in the same gene, presence of modifier genes, and oligogenic inheritance. Future directions include development of bioinformatics tools and establishment of more extensive public genotype/phenotype databases to better distinguish deleterious gene variants from benign polymorphisms, translation of genetic findings into pathogenic mechanisms through in-vitro and in-vivo studies, and ultimately finding disease-modifying therapies for neurodegenerative disorders.
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Affiliation(s)
- Shirley Yin-Yu Pang
- Division of Neurology, Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China
| | - Kay-Cheong Teo
- Division of Neurology, Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China
| | - Jacob Shujui Hsu
- Centre for Genomic Sciences, University of Hong Kong, Hong Kong, People's Republic of China
| | - Richard Shek-Kwan Chang
- Division of Neurology, Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China
| | - Miaoxin Li
- Centre for Genomic Sciences, University of Hong Kong, Hong Kong, People's Republic of China.,Department of Medical Genetics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China.,Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou, People's Republic of China
| | - Pak-Chung Sham
- Centre for Genomic Sciences, University of Hong Kong, Hong Kong, People's Republic of China
| | - Shu-Leong Ho
- Division of Neurology, Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China
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503
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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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504
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Affiliation(s)
- Stella Aslibekyan
- Department of Epidemiology at the University of Alabama at Birmingham, 1675 University Boulevard, Birmingham, Alabama 35294-3360, USA
| | - W Timothy Garvey
- Department of Nutrition Sciences and at the Birmingham VA Medical Center, University of Alabama at Birmingham, 1675 University Boulevard, Birmingham, Alabama 35294-3360, USA
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505
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Zhou D, Du Q, Chen J, Wang Q, Zhang D. Identification and allelic dissection uncover roles of lncRNAs in secondary growth of Populus tomentosa. DNA Res 2017; 24:473-486. [PMID: 28453813 PMCID: PMC5737087 DOI: 10.1093/dnares/dsx018] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Accepted: 04/04/2017] [Indexed: 12/12/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) function in various biological processes. However, their roles in secondary growth of plants remain poorly understood. Here, 15,691 lncRNAs were identified from vascular cambium, developing xylem, and mature xylem of Populus tomentosa with high and low biomass using RNA-seq, including 1,994 lncRNAs that were differentially expressed (DE) among the six libraries. 3,569 cis-regulated and 3,297 trans-regulated protein-coding genes were predicted as potential target genes (PTGs) of the DE lncRNAs to participate in biological regulation. Then, 476 and 28 lncRNAs were identified as putative targets and endogenous target mimics (eTMs) of Populus known microRNAs (miRNAs), respectively. Genome re-sequencing of 435 individuals from a natural population of P. tomentosa found 34,015 single nucleotide polymorphisms (SNPs) within 178 lncRNA loci and 522 PTGs. Single-SNP associations analysis detected 2,993 associations with 10 growth and wood-property traits under additive and dominance model. Epistasis analysis identified 17,656 epistatic SNP pairs, providing evidence for potential regulatory interactions between lncRNAs and their PTGs. Furthermore, a reconstructed epistatic network, representing interactions of 8 lncRNAs and 15 PTGs, might enrich regulation roles of genes in the phenylpropanoid pathway. These findings may enhance our understanding of non-coding genes in plants.
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MESH Headings
- Cambium/genetics
- Cambium/growth & development
- Cambium/metabolism
- Epistasis, Genetic
- Gene Expression Regulation, Plant
- Genetic Association Studies
- Polymorphism, Single Nucleotide
- Populus/genetics
- Populus/growth & development
- Populus/metabolism
- Quantitative Trait, Heritable
- RNA, Long Noncoding/genetics
- RNA, Long Noncoding/physiology
- RNA, Plant/genetics
- RNA, Plant/physiology
- Sequence Analysis, DNA
- Sequence Analysis, RNA
- Transcriptome
- Xylem/genetics
- Xylem/growth & development
- Xylem/metabolism
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Affiliation(s)
- Daling Zhou
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P. R. China
| | - Qingzhang Du
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P. R. China
| | - Jinhui Chen
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P. R. China
| | - Qingshi Wang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P. R. China
| | - Deqiang Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P. R. China
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506
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Smeland OB, Frei O, Kauppi K, Hill WD, Li W, Wang Y, Krull F, Bettella F, Eriksen JA, Witoelar A, Davies G, Fan CC, Thompson WK, Lam M, Lencz T, Chen CH, Ueland T, Jönsson EG, Djurovic S, Deary IJ, Dale AM, Andreassen OA. Identification of Genetic Loci Jointly Influencing Schizophrenia Risk and the Cognitive Traits of Verbal-Numerical Reasoning, Reaction Time, and General Cognitive Function. JAMA Psychiatry 2017; 74:1065-1075. [PMID: 28746715 PMCID: PMC5710474 DOI: 10.1001/jamapsychiatry.2017.1986] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
IMPORTANCE Schizophrenia is associated with widespread cognitive impairments. Although cognitive deficits are one of the factors most strongly associated with functional outcome in schizophrenia, current treatment strategies largely fail to ameliorate these impairments. To develop more efficient treatment strategies in patients with schizophrenia, a better understanding of the pathogenesis of these cognitive deficits is needed. Accumulating evidence indicates that genetic risk of schizophrenia may contribute to cognitive dysfunction. OBJECTIVE To identify genomic regions jointly influencing schizophrenia and the cognitive domains of reaction time and verbal-numerical reasoning, as well as general cognitive function, a phenotype that captures the shared variation in performance across cognitive domains. DESIGN, SETTING, AND PARTICIPANTS Combining data from genome-wide association studies from multiple phenotypes using conditional false discovery rate analysis provides increased power to discover genetic variants and could elucidate shared molecular genetic mechanisms. Data from the following genome-wide association studies, published from July 24, 2014, to January 17, 2017, were combined: schizophrenia in the Psychiatric Genomics Consortium cohort (n = 79 757 [cases, 34 486; controls, 45 271]); verbal-numerical reasoning (n = 36 035) and reaction time (n = 111 483) in the UK Biobank cohort; and general cognitive function in CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) (n = 53 949) and COGENT (Cognitive Genomics Consortium) (n = 27 888). MAIN OUTCOMES AND MEASURES Genetic loci identified by conditional false discovery rate analysis. Brain messenger RNA expression and brain expression quantitative trait locus functionality were determined. RESULTS Among the participants in the genome-wide association studies, 21 loci jointly influencing schizophrenia and cognitive traits were identified: 2 loci shared between schizophrenia and verbal-numerical reasoning, 6 loci shared between schizophrenia and reaction time, and 14 loci shared between schizophrenia and general cognitive function. One locus was shared between schizophrenia and 2 cognitive traits and represented the strongest shared signal detected (nearest gene TCF20; chromosome 22q13.2), and was shared between schizophrenia (z score, 5.01; P = 5.53 × 10-7), general cognitive function (z score, -4.43; P = 9.42 × 10-6), and verbal-numerical reasoning (z score, -5.43; P = 5.64 × 10-8). For 18 loci, schizophrenia risk alleles were associated with poorer cognitive performance. The implicated genes are expressed in the developmental and adult human brain. Replicable expression quantitative trait locus functionality was identified for 4 loci in the adult human brain. CONCLUSIONS AND RELEVANCE The discovered loci improve the understanding of the common genetic basis underlying schizophrenia and cognitive function, suggesting novel molecular genetic mechanisms.
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Affiliation(s)
- Olav B. Smeland
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Neuroscience, University of California San Diego, La Jolla
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Karolina Kauppi
- Department of Radiology, University of California San Diego, La Jolla,Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla,Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - W. David Hill
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom,Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Wen Li
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Yunpeng Wang
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Radiology, University of California San Diego, La Jolla,Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla
| | - Florian Krull
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Francesco Bettella
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jon A. Eriksen
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Aree Witoelar
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Gail Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom,Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Chun C. Fan
- Department of Radiology, University of California San Diego, La Jolla,Department of Cognitive Science, University of California, San Diego, La Jolla
| | - Wesley K. Thompson
- Department of Family Medicine and Public Health, University of California, San Diego, La Jolla,Institute of Biological Psychiatry, Roskilde, Denmark
| | - Max Lam
- Institute of Mental Health, Singapore
| | - Todd Lencz
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York,Department of Psychiatry, Hofstra Northwell School of Medicine, Hempstead, New York
| | - Chi-Hua Chen
- Department of Radiology, University of California San Diego, La Jolla,Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla
| | - Torill Ueland
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Psychology, University of Oslo, Olso, Norway
| | - Erik G. Jönsson
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway,NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ian J. Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom,Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Anders M. Dale
- Department of Neuroscience, University of California San Diego, La Jolla,Department of Radiology, University of California San Diego, La Jolla,Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla,Department of Psychiatry, University of California, San Diego, La Jolla
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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507
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Winsvold BS, Bettella F, Witoelar A, Anttila V, Gormley P, Kurth T, Terwindt GM, Freilinger TM, Frei O, Shadrin A, Wang Y, Dale AM, van den Maagdenberg AMJM, Chasman DI, Nyholt DR, Palotie A, Andreassen OA, Zwart JA. Shared genetic risk between migraine and coronary artery disease: A genome-wide analysis of common variants. PLoS One 2017; 12:e0185663. [PMID: 28957430 PMCID: PMC5619824 DOI: 10.1371/journal.pone.0185663] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 09/16/2017] [Indexed: 12/12/2022] Open
Abstract
Migraine is a recurrent pain condition traditionally viewed as a neurovascular disorder, but little is known of its vascular basis. In epidemiological studies migraine is associated with an increased risk of cardiovascular disease, including coronary artery disease (CAD), suggesting shared pathogenic mechanisms. This study aimed to determine the genetic overlap between migraine and CAD, and to identify shared genetic risk loci, utilizing a conditional false discovery rate approach and data from two large-scale genome-wide association studies (GWAS) of CAD (C4D, 15,420 cases, 15,062 controls; CARDIoGRAM, 22,233 cases, 64,762 controls) and one of migraine (22,120 cases, 91,284 controls). We found significant enrichment of genetic variants associated with CAD as a function of their association with migraine, which was replicated across two independent CAD GWAS studies. One shared risk locus in the PHACTR1 gene (conjunctional false discovery rate for index SNP rs9349379 < 3.90 x 10−5), which was also identified in previous studies, explained much of the enrichment. Two further loci (in KCNK5 and AS3MT) showed evidence for shared risk (conjunctional false discovery rate < 0.05). The index SNPs at two of the three loci had opposite effect directions in migraine and CAD. Our results confirm previous reports that migraine and CAD share genetic risk loci in excess of what would be expected by chance, and highlight one shared risk locus in PHACTR1. Understanding the biological mechanisms underpinning this shared risk is likely to improve our understanding of both disorders.
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Affiliation(s)
- Bendik S. Winsvold
- FORMI and Department of Neurology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- * E-mail:
| | - Francesco Bettella
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- NORMENT KG Jebsen Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Aree Witoelar
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- NORMENT KG Jebsen Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Verneri Anttila
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Padhraig Gormley
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Tobias Kurth
- Institute of Public Health, Charité–Universitätsmedizin Berlin, Berlin, Germany
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Gisela M. Terwindt
- Department of Neurology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Tobias M. Freilinger
- Department of Neurology and Epileptology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Oleksander Frei
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- NORMENT KG Jebsen Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Alexey Shadrin
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- NORMENT KG Jebsen Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Yunpeng Wang
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- NORMENT KG Jebsen Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Anders M. Dale
- Center for Multimodal Imaging & Genetics, University of California, San Diego, La Jolla, California, United States of America
| | - Arn M. J. M. van den Maagdenberg
- Department of Neurology, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Dale R. Nyholt
- Statistical and Genomic Epidemiology Laboratory, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Australia
| | - Aarno Palotie
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Ole A. Andreassen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- NORMENT KG Jebsen Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - John-Anker Zwart
- FORMI and Department of Neurology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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508
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The antihypertensive MTHFR gene polymorphism rs17367504-G is a possible novel protective locus for preeclampsia. J Hypertens 2017; 35:132-139. [PMID: 27755385 PMCID: PMC5131692 DOI: 10.1097/hjh.0000000000001131] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
OBJECTIVE Preeclampsia is a complex heterogeneous disease commonly defined by new-onset hypertension and proteinuria in pregnancy. Women experiencing preeclampsia have increased risk for cardiovascular diseases (CVD) later in life. Preeclampsia and CVD share risk factors and pathophysiologic mechanisms, including dysregulated inflammation and raised blood pressure. Despite commonalities, little is known about the contribution of shared genes (pleiotropy) to these diseases. This study aimed to investigate whether genetic risk factors for hypertension or inflammation are pleiotropic by also being associated with preeclampsia. METHODS We genotyped 122 single nucleotide polymorphisms (SNPs) in women with preeclampsia (n = 1006) and nonpreeclamptic controls (n = 816) from the Norwegian HUNT Study. SNPs were chosen on the basis of previously reported associations with either nongestational hypertension or inflammation in genome-wide association studies. The SNPs were tested for association with preeclampsia in a multiple logistic regression model. RESULTS The minor (G) allele of the intronic SNP rs17367504 in the gene methylenetetrahydrofolate reductase (MTHFR) was associated with a protective effect on preeclampsia (odds ratio 0.65, 95% confidence interval 0.53-0.80) in the Norwegian cohort. This association did not replicate in an Australian preeclampsia case-control cohort (P = 0.68, odds ratio 1.05, 95% confidence interval 0.83-1.32, minor allele frequency = 0.15). CONCLUSION MTHFR is important for regulating transmethylation processes and is involved in regulation of folate metabolism. The G allele of rs17367504 has previously been shown to protect against nongestational hypertension. Our study suggests a novel association between this allele and reduced risk for preeclampsia. This is the first study associating the minor (G) allele of a SNP within the MTHFR gene with a protective effect on preeclampsia, and in doing so identifying a possible pleiotropic protective effect on preeclampsia and hypertension.
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509
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Murphy S, Castro V, Mandl K. Grappling with the Future Use of Big Data for Translational Medicine and Clinical Care. Yearb Med Inform 2017; 26:96-102. [PMID: 29063545 DOI: 10.15265/iy-2017-020] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Objectives: Although patients may have a wealth of imaging, genomic, monitoring, and personal device data, it has yet to be fully integrated into clinical care. Methods: We identify three reasons for the lack of integration. The first is that "Big Data" is poorly managed by most Electronic Medical Record Systems (EMRS). The data is mostly available on "cloud-native" platforms that are outside the scope of most EMRs, and even checking if such data is available on a patient often must be done outside the EMRS. The second reason is that extracting features from the Big Data that are relevant to healthcare often requires complex machine learning algorithms, such as determining if a genomic variant is protein-altering. The third reason is that applications that present Big Data need to be modified constantly to reflect the current state of knowledge, such as instructing when to order a new set of genomic tests. In some cases, applications need to be updated nightly. Results: A new architecture for EMRS is evolving which could unite Big Data, machine learning, and clinical care through a microservice-based architecture which can host applications focused on quite specific aspects of clinical care, such as managing cancer immunotherapy. Conclusion: Informatics innovation, medical research, and clinical care go hand in hand as we look to infuse science-based practice into healthcare. Innovative methods will lead to a new ecosystem of applications (Apps) interacting with healthcare providers to fulfill a promise that is still to be determined.
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510
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Calderari S, Ria M, Gérard C, Nogueira TC, Villate O, Collins SC, Neil H, Gervasi N, Hue C, Suarez-Zamorano N, Prado C, Cnop M, Bihoreau MT, Kaisaki PJ, Cazier JB, Julier C, Lathrop M, Werner M, Eizirik DL, Gauguier D. Molecular genetics of the transcription factor GLIS3 identifies its dual function in beta cells and neurons. Genomics 2017; 110:98-111. [PMID: 28911974 DOI: 10.1016/j.ygeno.2017.09.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 08/08/2017] [Accepted: 09/01/2017] [Indexed: 01/06/2023]
Abstract
The GLIS family zinc finger 3 isoform (GLIS3) is a risk gene for Type 1 and Type 2 diabetes, glaucoma and Alzheimer's disease endophenotype. We identified GLIS3 binding sites in insulin secreting cells (INS1) (FDR q<0.05; enrichment range 1.40-9.11 fold) sharing the motif wrGTTCCCArTAGs, which were enriched in genes involved in neuronal function and autophagy and in risk genes for metabolic and neuro-behavioural diseases. We confirmed experimentally Glis3-mediated regulation of the expression of genes involved in autophagy and neuron function in INS1 and neuronal PC12 cells. Naturally-occurring coding polymorphisms in Glis3 in the Goto-Kakizaki rat model of type 2 diabetes were associated with increased insulin production in vitro and in vivo, suggestive alteration of autophagy in PC12 and INS1 and abnormal neurogenesis in hippocampus neurons. Our results support biological pleiotropy of GLIS3 in pathologies affecting β-cells and neurons and underline the existence of trans‑nosology pathways in diabetes and its co-morbidities.
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Affiliation(s)
- Sophie Calderari
- Sorbonne Universities, University Pierre & Marie Curie, University Paris Descartes, Sorbonne Paris Cité, INSERM UMR_S1138, Cordeliers Research Centre, Paris, France
| | - Massimiliano Ria
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Christelle Gérard
- Sorbonne Universities, University Pierre & Marie Curie, University Paris Descartes, Sorbonne Paris Cité, INSERM UMR_S1138, Cordeliers Research Centre, Paris, France
| | - Tatiane C Nogueira
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Olatz Villate
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Stephan C Collins
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Helen Neil
- FRE3377, Institut de Biologie et de Technologies de Saclay (iBiTec-S), Commissariat à l'Energie Atomique et aux Énergies Alternatives (CEA), Gif-sur-Yvette cedex, France
| | | | - Christophe Hue
- Sorbonne Universities, University Pierre & Marie Curie, University Paris Descartes, Sorbonne Paris Cité, INSERM UMR_S1138, Cordeliers Research Centre, Paris, France
| | - Nicolas Suarez-Zamorano
- Sorbonne Universities, University Pierre & Marie Curie, University Paris Descartes, Sorbonne Paris Cité, INSERM UMR_S1138, Cordeliers Research Centre, Paris, France
| | - Cécilia Prado
- Sorbonne Universities, University Pierre & Marie Curie, University Paris Descartes, Sorbonne Paris Cité, INSERM UMR_S1138, Cordeliers Research Centre, Paris, France
| | - Miriam Cnop
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Marie-Thérèse Bihoreau
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Pamela J Kaisaki
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Jean-Baptiste Cazier
- Centre for Computational Biology, Medical School, University of Birmingham, Birmingham, United Kingdom
| | - Cécile Julier
- INSERM UMR-S 958, Faculté de Médecine Paris Diderot, University Paris 7 Denis-Diderot, Paris, Sorbonne Paris Cité, France
| | - Mark Lathrop
- McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, QC H3A 0G1, Canada
| | - Michel Werner
- FRE3377, Institut de Biologie et de Technologies de Saclay (iBiTec-S), Commissariat à l'Energie Atomique et aux Énergies Alternatives (CEA), Gif-sur-Yvette cedex, France
| | - Decio L Eizirik
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Dominique Gauguier
- Sorbonne Universities, University Pierre & Marie Curie, University Paris Descartes, Sorbonne Paris Cité, INSERM UMR_S1138, Cordeliers Research Centre, Paris, France; The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom; McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, QC H3A 0G1, Canada.
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511
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Shen X, Klarić L, Sharapov S, Mangino M, Ning Z, Wu D, Trbojević-Akmačić I, Pučić-Baković M, Rudan I, Polašek O, Hayward C, Spector TD, Wilson JF, Lauc G, Aulchenko YS. Multivariate discovery and replication of five novel loci associated with Immunoglobulin G N-glycosylation. Nat Commun 2017; 8:447. [PMID: 28878392 PMCID: PMC5587582 DOI: 10.1038/s41467-017-00453-3] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Accepted: 06/29/2017] [Indexed: 01/20/2023] Open
Abstract
Joint modeling of a number of phenotypes using multivariate methods has often been neglected in genome-wide association studies and if used, replication has not been sought. Modern omics technologies allow characterization of functional phenomena using a large number of related phenotype measures, which can benefit from such joint analysis. Here, we report a multivariate genome-wide association studies of 23 immunoglobulin G (IgG) N-glycosylation phenotypes. In the discovery cohort, our multi-phenotype method uncovers ten genome-wide significant loci, of which five are novel (IGH, ELL2, HLA-B-C, AZI1, FUT6-FUT3). We convincingly replicate all novel loci via multivariate tests. We show that IgG N-glycosylation loci are strongly enriched for genes expressed in the immune system, in particular antibody-producing cells and B lymphocytes. We empirically demonstrate the efficacy of multivariate methods to discover novel, reproducible pleiotropic effects.Multivariate analysis methods can uncover the relationship between phenotypic measures characterised by modern omic techniques. Here the authors conduct a multivariate GWAS on IgG N-glycosylation phenotypes and identify 5 novel loci enriched in immune system genes.
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Affiliation(s)
- Xia Shen
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland, UK.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12 A, SE-17 177, Stockholm, Sweden.
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crew Road, Edinburgh, EH4 2XU, Scotland, UK.
| | - Lucija Klarić
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland, UK
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crew Road, Edinburgh, EH4 2XU, Scotland, UK
- Genos Glycoscience Research Laboratory, Hondlova 2/11, Zagreb, 10000, Croatia
| | - Sodbo Sharapov
- Novosibirsk State University, Pirogova 2, Novosibirsk, 630090, Russia
- Institute of Cytology and Genetics SB RAS, Lavrentyeva ave. 10, Novosibirsk, 630090, Russia
| | - Massimo Mangino
- Department for Twin Research, King's College London, London, WC2R 2LS, England, UK
- National Institute for Health Research (NIHR) Biomedical Research Centre at Guy's and St. Thomas' Foundation Trust, London, SE1 9RT, England, UK
| | - Zheng Ning
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12 A, SE-17 177, Stockholm, Sweden
| | - Di Wu
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Tomtebodavägen 23B, Stockholm, SE-171 65, Sweden
| | | | - Maja Pučić-Baković
- Genos Glycoscience Research Laboratory, Hondlova 2/11, Zagreb, 10000, Croatia
| | - Igor Rudan
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland, UK
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crew Road, Edinburgh, EH4 2XU, Scotland, UK
| | - Ozren Polašek
- Faculty of Medicine, University of Split, Šoltanska ul. 2, Split, 21000, Croatia
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crew Road, Edinburgh, EH4 2XU, Scotland, UK
| | - Timothy D Spector
- Department for Twin Research, King's College London, London, WC2R 2LS, England, UK
| | - James F Wilson
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland, UK
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crew Road, Edinburgh, EH4 2XU, Scotland, UK
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, Hondlova 2/11, Zagreb, 10000, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, A. Kovacica 1, Zagreb, 10000, Croatia
| | - Yurii S Aulchenko
- Novosibirsk State University, Pirogova 2, Novosibirsk, 630090, Russia.
- Institute of Cytology and Genetics SB RAS, Lavrentyeva ave. 10, Novosibirsk, 630090, Russia.
- PolyOmica, Het Vlaggeschip 61, 's-Hertogenbosch, 5237PA, The Netherlands.
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512
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Howe LJ, Trela-Larsen L, Taylor M, Heron J, Munafò MR, Taylor AE. Body mass index, body dissatisfaction and adolescent smoking initiation. Drug Alcohol Depend 2017; 178. [PMID: 28647682 PMCID: PMC5558147 DOI: 10.1016/j.drugalcdep.2017.04.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Smoking influences body weight, but there is little evidence as to whether body mass index (BMI) and body dissatisfaction increase smoking initiation in adolescents. METHODS We evaluated the association between measured BMI, body dissatisfaction and latent classes of smoking initiation (never smokers, experimenters, late onset regular smokers, early onset regular smokers) in the Avon Longitudinal Study of Parents and Children. In observational analyses we used BMI (N=3754) and body dissatisfaction at age 10.5 years (N=3349). In Mendelian randomisation (MR) analysis, we used a BMI genetic risk score of 76 single nucleotide polymorphisms (N=4017). RESULTS In females, higher BMI was associated with increased odds of early onset regular smoking (OR: 1.11, 95% CI: 1.04, 1.18) compared to being a never smoker, but not clearly associated with experimenting with smoking (OR: 1.04, 95% CI: 0.99, 1.10) or late onset regular smoking (OR: 1.01, 95% CI: 0.94, 1.09). No clear evidence was found for associations between BMI and smoking initiation classes in males (p-value for sex interaction≤0.001). Body dissatisfaction was associated with increased odds of late-onset regular smoking (OR: 1.71, 95% CI: 1.32, 1.99) in males and females combined (P-value for sex interaction=0.32). There was no clear evidence for an association between the BMI genetic risk score and smoking latent classes in males or females but estimates were imprecise. CONCLUSIONS BMI in females and body dissatisfaction in males and females are associated with increased odds of smoking initiation, highlighting these as potentially important factors for consideration in smoking prevention strategies.
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Affiliation(s)
- Laurence J. Howe
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK,School of Social and Community Medicine, University of Bristol, Bristol, UK,Corresponding author.
| | - Lea Trela-Larsen
- Musculoskeletal Research Unit, University of Bristol, Learning and Research Building (Level 1), Southmead Hospital, Bristol, BS10 5NB, UK
| | - Michelle Taylor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK,School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Jon Heron
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Marcus R. Munafò
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK,UK Centre for Tobacco and Alcohol Studies, School of Experimental Psychology, University of Bristol, Bristol, BS8 1TU, United Kingdom
| | - Amy E. Taylor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK,UK Centre for Tobacco and Alcohol Studies, School of Experimental Psychology, University of Bristol, Bristol, BS8 1TU, United Kingdom
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513
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Kusmec A, Srinivasan S, Nettleton D, Schnable PS. Distinct genetic architectures for phenotype means and plasticities in Zea mays. NATURE PLANTS 2017; 3:715-723. [PMID: 29150689 PMCID: PMC6209453 DOI: 10.1038/s41477-017-0007-7] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Accepted: 07/25/2017] [Indexed: 05/02/2023]
Abstract
Phenotypic plasticity describes the phenotypic variation of a trait when a genotype is exposed to different environments. Understanding the genetic control of phenotypic plasticity in crops such as maize is of paramount importance for maintaining and increasing yields in a world experiencing climate change. Here, we report the results of genome-wide association analyses of multiple phenotypes and two measures of phenotypic plasticity in a maize nested association mapping (US-NAM) population grown in multiple environments and genotyped with ~2.5 million single-nucleotide polymorphisms. We show that across all traits the candidate genes for mean phenotype values and plasticity measures form structurally and functionally distinct groups. Such independent genetic control suggests that breeders will be able to select semi-independently for mean phenotype values and plasticity, thereby generating varieties with both high mean phenotype values and levels of plasticity that are appropriate for the target performance environments.
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Affiliation(s)
- Aaron Kusmec
- Department of Agronomy, Iowa State University, Ames, IA, 50010-3650, USA
| | - Srikant Srinivasan
- Plant Sciences Institute, Iowa State University, Ames, IA, 50010-3650, USA
- School of Computing and Electrical Engineering, IIT Mandi, Mandi, Himachal Pradesh, 175005, India
| | - Dan Nettleton
- Plant Sciences Institute, Iowa State University, Ames, IA, 50010-3650, USA
- Department of Statistics, Iowa State University, Ames, IA, 50010-3650, USA
| | - Patrick S Schnable
- Department of Agronomy, Iowa State University, Ames, IA, 50010-3650, USA.
- Plant Sciences Institute, Iowa State University, Ames, IA, 50010-3650, USA.
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514
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Yang JJ, Williams LK, Buu A. Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function. BMC Bioinformatics 2017; 18:376. [PMID: 28836938 PMCID: PMC5571642 DOI: 10.1186/s12859-017-1791-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2016] [Accepted: 08/15/2017] [Indexed: 11/11/2022] Open
Abstract
Background A multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step models the association between the genotype and marginal phenotype using a linear mixed model. The second step uses the correlation between residuals of the linear mixed model to estimate the null distribution of the Fisher combination test statistic. Results The simulation results show that the proposed method controls the type I error rate and is more powerful than the marginal tests across different population structures (admixed or non-admixed) and relatedness (related or independent). The statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that applying the multivariate association test may facilitate identification of the pleiotropic genes contributing to the risk for alcohol dependence commonly expressed by four correlated phenotypes. Conclusions This study proposes a multivariate method for identifying pleiotropic genes while adjusting for cryptic relatedness and population structure between subjects. The two-step approach is not only powerful but also computationally efficient even when the number of subjects and the number of phenotypes are both very large.
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Affiliation(s)
- James J Yang
- School of Nursing, University of Michigan, Ann Arbor, 48104, Michigan, USA.
| | - L Keoki Williams
- Department of Internal Medicine, Henry Ford Health System, Detroit, 48202, Michigan, USA.,The Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, 48202, Michigan, USA
| | - Anne Buu
- Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, 48104, Michigan, USA
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515
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Xiang R, MacLeod IM, Bolormaa S, Goddard ME. Genome-wide comparative analyses of correlated and uncorrelated phenotypes identify major pleiotropic variants in dairy cattle. Sci Rep 2017; 7:9248. [PMID: 28835686 PMCID: PMC5569018 DOI: 10.1038/s41598-017-09788-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 07/31/2017] [Indexed: 11/10/2022] Open
Abstract
While single nucleotide polymorphisms (SNPs) associated with multiple phenotype have been reported, the knowledge of pleiotropy of uncorrelated phenotype is minimal. Principal components (PCs) and uncorrelated Cholesky transformed traits (CT) were constructed using 25 raw traits (RTs) of 2841 dairy bulls. Multi-trait meta-analyses of single-trait genome-wide association studies for RT, PC and CT in bulls were validated in 6821 cows. Most PCs and CTs had substantial estimates of heritability, suggesting that genes affect phenotype via diverse pathways. Phenotypic orthogonalizations did not eliminate pleiotropy: the meta-analysis achieved an agreement of significant pleiotropic SNPs (p < 1 × 10-5, n = 368) between RTs (416), PCs (466) and CTs (425). From this overlap we identified 21 lead SNPs with 100% validation rate containing two clusters: one consisted of DGAT1 (chr14:1.8 M+), MGST1 (chr5:93 M+), PAEP (chr11:103 M+) and GPAT4 (chr27:36 M+) affecting protein, milk and fat yield and the other included CSN2 (chr6:87 M+), MUC1 (chr3:15.6 M), GHR (chr20:31.2 M+) and SDC2 (chr14:70 M+) affecting protein and milk yield. Combining beef cattle data identified correlated SNPs representing CAPN1 (chr29:44 M+) and CAST (chr 7:96 M+) loci affecting beef tenderness, showing pleiotropic effects in dairy cattle. Our findings show that SNPs with a large effect on one trait are likely to have small effects on other uncorrelated traits.
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Affiliation(s)
- Ruidong Xiang
- Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, Victoria, 3010, Australia.
- AgriBio, Department Economic Development, Jobs, Transport & Resources, Bundoora, Victoria, 3083, Australia.
| | - Iona M MacLeod
- AgriBio, Department Economic Development, Jobs, Transport & Resources, Bundoora, Victoria, 3083, Australia
| | - Sunduimijid Bolormaa
- AgriBio, Department Economic Development, Jobs, Transport & Resources, Bundoora, Victoria, 3083, Australia
- Cooperative Research Centre for Sheep Industry Innovation, Armidale, NSW 2351, Australia
| | - Michael E Goddard
- Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, Victoria, 3010, Australia
- AgriBio, Department Economic Development, Jobs, Transport & Resources, Bundoora, Victoria, 3083, Australia
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516
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Genome-wide dissection of heterosis for yield traits in two-line hybrid rice populations. Sci Rep 2017; 7:7635. [PMID: 28794433 PMCID: PMC5550440 DOI: 10.1038/s41598-017-06742-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 06/16/2017] [Indexed: 11/24/2022] Open
Abstract
Heterosis has been widely utilized in agriculture and is important for world food safety. Many genetic models have been proposed as mechanisms underlying heterosis during the past century, yet more evidence is needed to support such models. To investigate heterosis in two-line hybrid rice, we generated a partial diallel crossing scheme, which consisted of approximately 500 F1 hybrids derived from 14 male sterile lines and 39 restorer lines. In this population, increased panicle number played the most important role in yield heterosis of hybrid rice. Genome-wide association studies identified many QTLs related to the yield traits of F1 hybrids, better paternal heterosis and special combining ability. Relevant genes, including Hd3a, qGL3, OsmiR156h, and LAX2, were identified as candidates within these QTLs. Nearly forty percent of the QTLs had only two genotypes in the F1 hybrids, mainly because the maternal lines were under intense selective pressure. Further analysis found male sterile lines and restorer lines made different superior allele contributions to F1 hybrids, and their contributions varied among different traits. These results extend our understanding of the molecular basis of heterosis in two-line hybrid rice.
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517
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Fourteen sequence variants that associate with multiple sclerosis discovered by meta-analysis informed by genetic correlations. NPJ Genom Med 2017; 2:24. [PMID: 29263835 PMCID: PMC5677966 DOI: 10.1038/s41525-017-0027-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2016] [Revised: 05/18/2017] [Accepted: 06/23/2017] [Indexed: 12/21/2022] Open
Abstract
A meta-analysis of publicly available summary statistics on multiple sclerosis combined with three Nordic multiple sclerosis cohorts (21,079 cases, 371,198 controls) revealed seven sequence variants associating with multiple sclerosis, not reported previously. Using polygenic risk scores based on public summary statistics of variants outside the major histocompatibility complex region we quantified genetic overlap between common autoimmune diseases in Icelanders and identified disease clusters characterized by autoantibody presence/absence. As multiple sclerosis-polygenic risk scores captures the risk of primary biliary cirrhosis and vice versa (P = 1.6 × 10−7, 4.3 × 10−9) we used primary biliary cirrhosis as a proxy-phenotype for multiple sclerosis, the idea being that variants conferring risk of primary biliary cirrhosis have a prior probability of conferring risk of multiple sclerosis. We tested 255 variants forming the primary biliary cirrhosis-polygenic risk score and found seven multiple sclerosis-associating variants not correlated with any previously established multiple sclerosis variants. Most of the variants discovered are close to or within immune-related genes. One is a low-frequency missense variant in TYK2, another is a missense variant in MTHFR that reduces the function of the encoded enzyme affecting methionine metabolism, reported to be dysregulated in multiple sclerosis brain. Combining studies and comparing across diseases turned up 14 novel gene variants linked to multiple sclerosis (MS). A team led by Kári Stefánsson and Ingileif Jónsdóttir from deCODE genetics in Reykjavík, Iceland, amalgamated data from a large international study of MS with three smaller ones from Sweden, Norway and Iceland. They conducted a meta-analysis on the combined data set — which encompassed around 21,000 MS patients and 372,000 population controls — and uncovered seven new genetic risk variants linked to MS. The researchers then compared the genetic overlap between various autoimmune diseases in the Icelandic cohort, and documented a close relationship between MS and primary biliary cirrhosis (PBC). They looked more closely at variants linked to PBC, and found that seven also increased the risk for MS, bringing the tally of novel gene variants up to fourteen.
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518
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Coram MA, Fang H, Candille SI, Assimes TL, Tang H. Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations. Am J Hum Genet 2017; 101:218-226. [PMID: 28757202 DOI: 10.1016/j.ajhg.2017.06.015] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 06/29/2017] [Indexed: 11/20/2022] Open
Abstract
An essential component of precision medicine is the ability to predict an individual's risk of disease based on genetic and non-genetic factors. For complex traits and diseases, assessing the risk due to genetic factors is challenging because it requires knowledge of both the identity of variants that influence the trait and their corresponding allelic effects. Although the set of risk variants and their allelic effects may vary between populations, a large proportion of these variants were identified based on studies in populations of European descent. Heterogeneity in genetic architecture underlying complex traits and diseases, while broadly acknowledged, remains poorly characterized. Ignoring such heterogeneity likely reduces predictive accuracy for minority individuals. In this study, we propose an approach, called XP-BLUP, which ameliorates this ethnic disparity by combining trans-ethnic and ethnic-specific information. We build a polygenic model for complex traits that distinguishes candidate trait-relevant variants from the rest of the genome. The set of candidate variants are selected based on studies in any human population, yet the allelic effects are evaluated in a population-specific fashion. Simulation studies and real data analyses demonstrate that XP-BLUP adaptively utilizes trans-ethnic information and can substantially improve predictive accuracy in minority populations. At the same time, our study highlights the importance of the continued expansion of minority cohorts.
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Affiliation(s)
- Marc A Coram
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Huaying Fang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sophie I Candille
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Themistocles L Assimes
- Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
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519
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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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520
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Ugalde-Morales E, Li J, Humphreys K, Ludvigsson JF, Yang H, Hall P, Czene K. Common shared genetic variation behind decreased risk of breast cancer in celiac disease. Sci Rep 2017; 7:5942. [PMID: 28725034 PMCID: PMC5517429 DOI: 10.1038/s41598-017-06287-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 06/09/2017] [Indexed: 02/06/2023] Open
Abstract
There is epidemiologic evidence showing that women with celiac disease have reduced risk of later developing breast cancer, however, the etiology of this association is unclear. Here, we assess the extent of genetic overlap between the two diseases. Through analyses of summary statistics on densely genotyped immunogenic regions, we show a significant genetic correlation (r = −0.17, s.e. 0.05, P < 0.001) and overlap (Ppermuted < 0.001) between celiac disease and breast cancer. Using individual-level genotype data from a Swedish cohort, we find higher genetic susceptibility to celiac disease summarized by polygenic risk scores to be associated with lower breast cancer risk (ORper-SD, 0.94, 95% CI 0.91 to 0.98). Common single nucleotide polymorphisms between the two diseases, with low P-values (PCD < 1.00E-05, PBC ≤ 0.05), mapped onto genes enriched for immunoregulatory and apoptotic processes. Our results suggest that the link between breast cancer and celiac disease is due to a shared polygenic variation of immune related regions, uncovering pathways which might be important for their development.
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Affiliation(s)
- Emilio Ugalde-Morales
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Jingmei Li
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Human Genetics, Genome Institute of Singapore, Singapore, 138672, Singapore
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jonas F Ludvigsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Pediatrics, Örebro University Hospital, Örebro, Sweden
| | - Haomin Yang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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521
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Julià A, Blanco F, Fernández-Gutierrez B, González A, Cañete JD, Maymó J, Alperi-López M, Olivè A, Corominas H, Martínez-Taboada V, González-Álvaro I, Fernandez-Nebro A, Erra A, Sánchez-Fernández S, Alonso A, López-Lasanta M, Tortosa R, Codó L, Lluis Gelpi J, García-Montero AC, Bertranpetit J, Absher D, Myers RM, Tornero J, Marsal S. Identification of IRX1 as a Risk Locus for Rheumatoid Factor Positivity in Rheumatoid Arthritis in a Genome-Wide Association Study. Arthritis Rheumatol 2017; 68:1384-91. [PMID: 26815016 DOI: 10.1002/art.39591] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 01/07/2016] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Rheumatoid factor (RF) is a well-established diagnostic and prognostic biomarker in rheumatoid arthritis (RA). However, ∼20% of RA patients are negative for this anti-IgG antibody. To date, only variation at the HLA-DRB1 gene has been associated with the presence of RF. This study was undertaken to identify additional genetic variants associated with RF positivity. METHODS A genome-wide association study (GWAS) for RF positivity was performed using an Illumina Quad610 genotyping platform. A total of 937 RF-positive and 323 RF-negative RA patients were genotyped for >550,000 single-nucleotide polymorphisms (SNPs). Association testing was performed using an allelic chi-square test implemented in Plink software. An independent cohort of 472 RF-positive and 190 RF-negative RA patients was used to validate the most significant findings. RESULTS In the discovery stage, a SNP in the IRX1 locus on chromosome 5p15.3 (SNP rs1502644) showed a genome-wide significant association with RF positivity (P = 4.13 × 10(-8) , odds ratio [OR] 0.37 [95% confidence interval (95% CI) 0.26-0.53]). In the validation stage, the association of IRX1 with RF was replicated in an independent group of RA patients (P = 0.034, OR 0.58 [95% CI 0.35-0.97] and combined P = 1.14 × 10(-8) , OR 0.43 [95% CI 0.32-0.58]). CONCLUSION To our knowledge, this is the first GWAS of RF positivity in RA. Variation at the IRX1 locus on chromosome 5p15.3 is associated with the presence of RF. Our findings indicate that IRX1 and HLA-DRB1 are the strongest genetic factors for RF production in RA.
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Affiliation(s)
- Antonio Julià
- Vall d'Hebron Hospital Research Institute, Barcelona, Spain
| | - Francisco Blanco
- Instituto de Investigación Biomédica de A Coruña-Hospital Universitario A Coruña, A Coruña, Spain
| | | | - Antonio González
- Instituto de Investigación Sanitaria and Hospital Clinico Universitario de Santiago, Santiago de Compostela, Spain
| | | | - Joan Maymó
- Hospital del Mar, Barcelona, Barcelona, Spain
| | | | - Alex Olivè
- Hospital Universitari Germans Trias i Pujol, Barcelona, Spain
| | | | | | | | - Antonio Fernandez-Nebro
- Instituto de Investigación Biomédica de Málaga, Hospital Regional Universitario de Málaga, and Universidad de Málaga, Málaga, Spain
| | - Alba Erra
- Hospital Sant Rafael, Barcelona, Spain
| | | | - Arnald Alonso
- Vall d'Hebron Hospital Research Institute, Barcelona, Spain
| | | | - Raül Tortosa
- Vall d'Hebron Hospital Research Institute, Barcelona, Spain
| | - Laia Codó
- Barcelona Supercomputing Center, Barcelona, Spain
| | | | | | - Jaume Bertranpetit
- National Genotyping Center and Pompeu Fabra University, Barcelona, Spain
| | - Devin Absher
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama
| | - Jesús Tornero
- Hospital Universitario de Guadalajara, Guadalajara, Spain
| | - Sara Marsal
- Vall d'Hebron Hospital Research Institute, Barcelona, Spain
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522
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Dey R, Schmidt EM, Abecasis GR, Lee S. A Fast and Accurate Algorithm to Test for Binary Phenotypes and Its Application to PheWAS. Am J Hum Genet 2017; 101:37-49. [PMID: 28602423 DOI: 10.1016/j.ajhg.2017.05.014] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 05/17/2017] [Indexed: 12/19/2022] Open
Abstract
The availability of electronic health record (EHR)-based phenotypes allows for genome-wide association analyses in thousands of traits and has great potential to enable identification of genetic variants associated with clinical phenotypes. We can interpret the phenome-wide association study (PheWAS) result for a single genetic variant by observing its association across a landscape of phenotypes. Because a PheWAS can test thousands of binary phenotypes, and most of them have unbalanced or often extremely unbalanced case-control ratios (1:10 or 1:600, respectively), existing methods cannot provide an accurate and scalable way to test for associations. Here, we propose a computationally fast score-test-based method that estimates the distribution of the test statistic by using the saddlepoint approximation. Our method is much (∼100 times) faster than the state-of-the-art Firth's test. It can also adjust for covariates and control type I error rates even when the case-control ratio is extremely unbalanced. Through application to PheWAS data from the Michigan Genomics Initiative, we show that the proposed method can control type I error rates while replicating previously known association signals even for traits with a very small number of cases and a large number of controls.
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Affiliation(s)
- Rounak Dey
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ellen M Schmidt
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Goncalo R Abecasis
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Seunggeun Lee
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
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523
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Shooshtari P, Huang H, Cotsapas C. Integrative Genetic and Epigenetic Analysis Uncovers Regulatory Mechanisms of Autoimmune Disease. Am J Hum Genet 2017; 101:75-86. [PMID: 28686857 DOI: 10.1016/j.ajhg.2017.06.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Accepted: 05/31/2017] [Indexed: 12/18/2022] Open
Abstract
Genome-wide association studies in autoimmune and inflammatory diseases (AID) have uncovered hundreds of loci mediating risk. These associations are preferentially located in non-coding DNA regions and in particular in tissue-specific DNase I hypersensitivity sites (DHSs). While these analyses clearly demonstrate the overall enrichment of disease risk alleles on gene regulatory regions, they are not designed to identify individual regulatory regions mediating risk or the genes under their control, and thus uncover the specific molecular events driving disease risk. To do so we have departed from standard practice by identifying regulatory regions which replicate across samples and connect them to the genes they control through robust re-analysis of public data. We find significant evidence of regulatory potential in 78/301 (26%) risk loci across nine autoimmune and inflammatory diseases, and we find that individual genes are targeted by these effects in 53/78 (68%) of these. Thus, we are able to generate testable mechanistic hypotheses of the molecular changes that drive disease risk.
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524
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Gui H, Kwan JS, Sham PC, Cherny SS, Li M. Sharing of Genes and Pathways Across Complex Phenotypes: A Multilevel Genome-Wide Analysis. Genetics 2017; 206:1601-1609. [PMID: 28495956 PMCID: PMC5500153 DOI: 10.1534/genetics.116.198150] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 04/20/2017] [Indexed: 12/15/2022] Open
Abstract
Evidence from genome-wide association studies (GWAS) suggest that pleiotropic effects on human complex phenotypes are very common. Recently, an atlas of genetic correlations among complex phenotypes has broadened our understanding of human diseases and traits. Here, we examine genetic overlap, from a gene-centric perspective, among the same 24 phenotypes previously investigated for genetic correlations. After adopting the multilevel pipeline (freely available at http://grass.cgs.hku.hk/limx/kgg/), which includes intragenic single nucleotide polymorphisms (SNPs), genes, and gene-sets, to estimate genetic similarities across phenotypes, a large amount of sharing of several biologically related phenotypes was confirmed. In addition, significant genetic overlaps were also found among phenotype pairs that were previously unidentified by SNP-level approaches. All these pairs with new genetic links are supported by earlier epidemiological evidence, although only a few of them have pleiotropic genes in the GWAS Catalog. Hence, our gene and gene-set analyses are able to provide new insights into cross-phenotype connections. The investigation on genetic sharing at three different levels presents a complementary picture of how common DNA sequence variations contribute to disease comorbidities and trait manifestations.
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Affiliation(s)
- Hongsheng Gui
- Center for Genomic Sciences, University of Hong Kong, Hong Kong SAR, China
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan 48202
| | - Johnny S Kwan
- Department of Psychiatry, University of Hong Kong, Hong Kong SAR, China
| | - Pak C Sham
- Center for Genomic Sciences, University of Hong Kong, Hong Kong SAR, China
- Department of Psychiatry, University of Hong Kong, Hong Kong SAR, China
- The State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Hong Kong SAR, China
| | - Stacey S Cherny
- Center for Genomic Sciences, University of Hong Kong, Hong Kong SAR, China
- Department of Psychiatry, University of Hong Kong, Hong Kong SAR, China
- The State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Hong Kong SAR, China
| | - Miaoxin Li
- Center for Genomic Sciences, University of Hong Kong, Hong Kong SAR, China
- Department of Psychiatry, University of Hong Kong, Hong Kong SAR, China
- Department of Medical Genetics, Center for Genome Research, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510275 China
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525
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Chawes BL, Stokholm J, Schoos AMM, Fink NR, Brix S, Bisgaard H. Allergic sensitization at school age is a systemic low-grade inflammatory disorder. Allergy 2017; 72:1073-1080. [PMID: 27992959 DOI: 10.1111/all.13108] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/13/2016] [Indexed: 12/13/2022]
Abstract
BACKGROUND Systemic low-grade inflammation has been demonstrated in a range of the frequent noncommunicable diseases (NCDs) proposing a shared mechanism, but is largely unexplored in relation to allergic sensitization. We therefore aimed to investigate the possible association with childhood allergic sensitization. METHODS High-sensitivity C-reactive protein (hs-CRP), interleukin-1β (IL-1β), IL-6, tumor necrosis factor-α (TNF-α), and chemokine (C-X-C motif) ligand 8 (CXCL8) were measured in plasma at age 6 months (N = 214) and 7 years (N = 277) in children from the Copenhagen Prospective Studies on Asthma in Childhood2000 (COPSAC2000 ) birth cohort. Allergic sensitization against common inhalant and food allergens was determined longitudinally at ages ½, 1½, 4 and 6 years by specific IgE assessments and skin prick tests. Associations between inflammatory biomarkers and sensitization phenotypes were tested with logistic regression and principal component analyses (PCAs). RESULTS Adjusted for gender, recent infections, and a CRP genetic risk score, hs-CRP at 7 years was associated with concurrent elevated specific IgE against any allergen [adjusted OR (aOR) = 1.40; 95% CI, 1.14-1.72; P = 0.001], aeroallergens (aOR, 1.43; 1.15-1.77; P = 0.001), food allergens (aOR, 1.31; 95% CI, 1.02-1.67; P = 0.04), sensitization without any clinical allergy symptoms (aOR = 1.40; 1.06-1.85; P = 0.02), and with similar findings for skin prick tests. The other inflammatory markers were not univariately associated with sensitization, but multiparametric PCA suggested a specific inflammatory response among sensitized children. Inflammatory markers at age 6 months were not associated with subsequent development of sensitization phenotypes. CONCLUSIONS Elevated hs-CRP is associated with allergic sensitization in school-aged children suggesting systemic low-grade inflammation as a phenotypic characteristic of this early-onset NCD.
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Affiliation(s)
- B. L. Chawes
- COPSAC; Copenhagen Prospective Studies on Asthma in Childhood; Herlev and Gentofte Hospital; University of Copenhagen; Copenhagen Denmark
| | - J. Stokholm
- COPSAC; Copenhagen Prospective Studies on Asthma in Childhood; Herlev and Gentofte Hospital; University of Copenhagen; Copenhagen Denmark
- Department of Pediatrics; Naestved Hospital; Naestved Denmark
| | - A.-M. M. Schoos
- COPSAC; Copenhagen Prospective Studies on Asthma in Childhood; Herlev and Gentofte Hospital; University of Copenhagen; Copenhagen Denmark
| | - N. R. Fink
- COPSAC; Copenhagen Prospective Studies on Asthma in Childhood; Herlev and Gentofte Hospital; University of Copenhagen; Copenhagen Denmark
| | - S. Brix
- Department of Systems Biology; Center for Biological Sequence Analysis; Technical University of Denmark; Lyngby Denmark
| | - H. Bisgaard
- COPSAC; Copenhagen Prospective Studies on Asthma in Childhood; Herlev and Gentofte Hospital; University of Copenhagen; Copenhagen Denmark
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526
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Abstract
Human genetic studies have been the driving force in bringing to light the underlying biology of psychiatric conditions. As these studies fill in the gaps in our knowledge of the mechanisms at play, we will be better equipped to design therapies in rational and targeted ways, or repurpose existing therapies in previously unanticipated ways. This review is intended for those unfamiliar with psychiatric genetics as a field and provides a primer on different modes of genetic variation, the technologies currently used to probe them, and concepts that provide context for interpreting the gene-phenotype relationship. Like other subfields in human genetics, psychiatric genetics is moving from microarray technology to sequencing-based approaches as barriers of cost and expertise are removed, and the ramifications of this transition are discussed here. A summary is then given of recent genetic discoveries in a number of neuropsychiatric conditions, with particular emphasis on neurodevelopmental conditions. The general impact of genetics on drug development has been to underscore the extensive etiological heterogeneity in seemingly cohesive diagnostic categories. Consequently, the path forward is not in therapies hoping to reach large swaths of patients sharing a clinically defined diagnosis, but rather in targeting patients belonging to specific "biotypes" defined through a combination of objective, quantifiable data, including genotype.
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Affiliation(s)
- Jacob J Michaelson
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA.
- Department of Biomedical Engineering, University of Iowa College of Engineering, Iowa City, IA, USA.
- Department of Communication Sciences and Disorders, University of Iowa College of Liberal Arts and Sciences, Iowa City, IA, USA.
- Iowa Institute of Human Genetics, University of Iowa, Iowa City, IA, USA.
- Genetics Cluster Initiative, University of Iowa, Iowa City, IA, USA.
- The DeLTA Center, University of Iowa, Iowa City, IA, USA.
- University of Iowa Informatics Initiative, University of Iowa, Iowa City, IA, USA.
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527
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Genetics: Implications for Prevention and Management of Coronary Artery Disease. J Am Coll Cardiol 2017; 68:2797-2818. [PMID: 28007143 DOI: 10.1016/j.jacc.2016.10.039] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 10/12/2016] [Accepted: 10/24/2016] [Indexed: 12/21/2022]
Abstract
An exciting new era has dawned for the prevention and management of coronary artery disease (CAD) utilizing genetic risk variants. The recent identification of over 60 susceptibility loci for CAD confirms not only the importance of established risk factors, but also the existence of many novel causal pathways that are expected to improve our understanding of the genetic basis of CAD and facilitate the development of new therapeutic agents over time. Concurrently, Mendelian randomization studies have provided intriguing insights on the causal relationship between CAD-related traits, and highlight the potential benefits of long-term modifications of risk factors. Last, genetic risk scores of CAD may serve not only as prognostic, but also as predictive markers, and carry the potential to considerably improve the delivery of established prevention strategies. This review will summarize the evolution and discovery of genetic risk variants for CAD and their current and future clinical applications.
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528
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Liu Z, Lin X. Multiple phenotype association tests using summary statistics in genome-wide association studies. Biometrics 2017; 74:165-175. [PMID: 28653391 DOI: 10.1111/biom.12735] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2016] [Revised: 05/01/2017] [Accepted: 05/01/2017] [Indexed: 12/13/2022]
Abstract
We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis.
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Affiliation(s)
- Zhonghua Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston 02115, U.S.A
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston 02115, U.S.A
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529
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Waldron JS, Malone SM, McGue M, Iacono WG. Genetic and environmental sources of covariation between early drinking and adult functioning. PSYCHOLOGY OF ADDICTIVE BEHAVIORS 2017; 31:589-600. [PMID: 28594187 DOI: 10.1037/adb0000283] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The vast majority of individuals initiate alcohol consumption for the first time in adolescence. Given the widespread nature of its use and evidence that adolescents may be especially vulnerable to its effects, there is concern about the long-term detrimental impact of adolescent drinking on adult functioning. While some researchers have suggested that genetic processes may confound the relationship, the mechanisms linking drinking and later adjustment remain unclear. The current study utilized a genetically informed sample and biometric modeling to examine the nature of the familial influences on this association and identify the potential for genetic confounding. The sample was drawn from the Minnesota Twin Family Study (MTFS), a longitudinal study consisting of 2,764 twins assessed in 2 cohorts at regular follow-ups from age 17 to age 29 (older cohort) or age 11 to age 29 (younger cohort). A broad range of adult measures was included assessing substance use, antisocial behavior, personality, socioeconomic status, and social functioning. A bivariate Cholesky decomposition was used to examine the common genetic and environmental influences on adolescent drinking and each of the measures of adult adjustment. The results revealed that genetic factors and nonshared environmental influences were generally most important in explaining the relationship between adolescent drinking and later functioning. While the presence of nonshared environmental influences on the association are not inconsistent with a causal impact of adolescent drinking, the findings suggest that many of the adjustment issues associated with adolescent alcohol consumption are best understood as genetically influenced vulnerabilities. (PsycINFO Database Record
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Affiliation(s)
| | | | - Matt McGue
- Department of Psychology, University of Minnesota
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530
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Alharbi O, El-Sohemy A. Lactose Intolerance ( LCT-13910C>T) Genotype Is Associated with Plasma 25-Hydroxyvitamin D Concentrations in Caucasians: A Mendelian Randomization Study. J Nutr 2017; 147:1063-1069. [PMID: 28446633 DOI: 10.3945/jn.116.246108] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 01/11/2017] [Accepted: 03/24/2017] [Indexed: 11/14/2022] Open
Abstract
Background: The LCT-13910C>T gene variant is associated with lactose intolerance (LI) in different ethnic groups. Individuals with LI often limit or avoid dairy consumption, a major dietary source of vitamin D in North America, which may lead to inadequate vitamin D intake.Objective: The objective was to determine the prevalence of genotypes predictive of LI in different ethnic groups living in Canada and to determine whether the LCT genotype is associated with plasma 25(OH)D concentrations.Methods: Blood samples were drawn from a total of 1495 men and women aged 20-29 y from the Toronto Nutrigenomics and Health Study for genotyping and plasma 25(OH)D analysis. Intakes of dairy were assessed by using a 196-item food frequency questionnaire. The prevalence of LCT-13910C>T genotypes was compared by using χ2 analysis. Using a Mendelian randomization approach, we examined the association between LCT genotypes and 25(OH)D concentrations.Results: Approximately 32% of Caucasians, 99% of East Asians, 74% of South Asians, and 59% of those with other or mixed ethnicities had the CC genotype associated with LI. Compared with those with the TT genotype, those with the CC genotype had a lower mean ± SE total dairy intake (2.15 ± 0.09 compared with 2.67 ± 0.12 servings/d, P = 0.003), a lower skim-milk intake (0.20 ± 0.03 compared with 0.46 ± 0.06 servings/d, P = 0.0004), and a lower plasma 25(OH)D concentration (63 ± 1.9 compared with 75.8 ± 2.4 nmol/L, P < 0.0001). The CT and CC genotypes were associated with a 50% and a 2-fold increased risk, respectively, of a suboptimal plasma 25(OH)D concentration (<75 nmol/L).Conclusions: In Caucasians, the CC genotype that predicts LI is associated with a lower plasma 25(OH)D concentration, which is attributable at least in part to a lower intake of dairy, particularly skim milk. Increased risk of suboptimal concentrations of vitamin D was also observed among those with the CT genotype, suggesting an intermediate effect of the heterozygous genotype.
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Affiliation(s)
- Ohood Alharbi
- Department of Nutritional Sciences, University of Toronto, Toronto, Canada
| | - Ahmed El-Sohemy
- Department of Nutritional Sciences, University of Toronto, Toronto, Canada
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531
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Arnar DO, Palsson R. Genetics of common complex diseases: a view from Iceland. Eur J Intern Med 2017; 41:3-9. [PMID: 28433481 DOI: 10.1016/j.ejim.2017.03.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 03/22/2017] [Accepted: 03/24/2017] [Indexed: 12/21/2022]
Abstract
In the past decade, large scale genotyping has led to discoveries of numerous sequence variants that confer increased risk of many common complex diseases. Interestingly, a substantial proportion of pioneering genetic work has originated from the small nation of Iceland and has been facilitated by an extensive genealogy database. We provide examples of relevant observations made so far in several major disease categories central to internal medicine practice. Some of these findings offer new mechanistic clues into the pathophysiology of common disorders and may suggest novel approaches in diagnosis and drug therapy. However, a number of unresolved issues remain that will be subject of future research, driven by recent advances in high-throughput sequencing of the genome. At the same time, we are ready to begin transforming the abundant existing genetic data into practical clinical knowledge with the aim of improving the delivery of medical care. The era of precision medicine has arrived.
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Affiliation(s)
- David O Arnar
- Division of Cardiology, Internal Medicine Services, Landspitali - The National University Hospital of Iceland, Reykjavik, Iceland; Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
| | - Runolfur Palsson
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland; Division of Nephrology, Internal Medicine Services, Landspitali - The National University Hospital of Iceland, Reykjavik, Iceland
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532
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Genetic evidence for role of integration of fast and slow neurotransmission in schizophrenia. Mol Psychiatry 2017; 22:792-801. [PMID: 28348379 PMCID: PMC5495879 DOI: 10.1038/mp.2017.33] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 01/05/2017] [Accepted: 01/17/2017] [Indexed: 12/12/2022]
Abstract
The most recent genome-wide association studies (GWAS) of schizophrenia (SCZ) identified hundreds of risk variants potentially implicated in the disease. Further, novel statistical methodology designed for polygenic architecture revealed more potential risk variants. This can provide a link between individual genetic factors and the mechanistic underpinnings of SCZ. Intriguingly, a large number of genes coding for ionotropic and metabotropic receptors for various neurotransmitters-glutamate, γ-aminobutyric acid (GABA), dopamine, serotonin, acetylcholine and opioids-and numerous ion channels were associated with SCZ. Here, we review these findings from the standpoint of classical neurobiological knowledge of neuronal synaptic transmission and regulation of electrical excitability. We show that a substantial proportion of the identified genes are involved in intracellular cascades known to integrate 'slow' (G-protein-coupled receptors) and 'fast' (ionotropic receptors) neurotransmission converging on the protein DARPP-32. Inspection of the Human Brain Transcriptome Project database confirms that that these genes are indeed expressed in the brain, with the expression profile following specific developmental trajectories, underscoring their relevance to brain organization and function. These findings extend the existing pathophysiology hypothesis by suggesting a unifying role of dysregulation in neuronal excitability and synaptic integration in SCZ. This emergent model supports the concept of SCZ as an 'associative' disorder-a breakdown in the communication across different slow and fast neurotransmitter systems through intracellular signaling pathways-and may unify a number of currently competing hypotheses of SCZ pathophysiology.
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533
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Roda F, Walter GM, Nipper R, Ortiz‐Barrientos D. Genomic clustering of adaptive loci during parallel evolution of an Australian wildflower. Mol Ecol 2017; 26:3687-3699. [DOI: 10.1111/mec.14150] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Revised: 03/07/2017] [Accepted: 04/03/2017] [Indexed: 12/11/2022]
Affiliation(s)
- Federico Roda
- School of Biological Sciences The University of Queensland St. Lucia QLD Australia
- Harvard University Boston MA USA
| | - Greg M. Walter
- School of Biological Sciences The University of Queensland St. Lucia QLD Australia
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534
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Smeland OB, Wang Y, Lo MT, Li W, Frei O, Witoelar A, Tesli M, Hinds DA, Tung JY, Djurovic S, Chen CH, Dale AM, Andreassen OA. Identification of genetic loci shared between schizophrenia and the Big Five personality traits. Sci Rep 2017; 7:2222. [PMID: 28533504 PMCID: PMC5440373 DOI: 10.1038/s41598-017-02346-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Accepted: 04/10/2017] [Indexed: 11/25/2022] Open
Abstract
Schizophrenia is associated with differences in personality traits, and recent studies suggest that personality traits and schizophrenia share a genetic basis. Here we aimed to identify specific genetic loci shared between schizophrenia and the Big Five personality traits using a Bayesian statistical framework. Using summary statistics from genome-wide association studies (GWAS) on personality traits in the 23andMe cohort (n = 59,225) and schizophrenia in the Psychiatric Genomics Consortium cohort (n = 82,315), we evaluated overlap in common genetic variants. The Big Five personality traits neuroticism, extraversion, openness, agreeableness and conscientiousness were measured using a web implementation of the Big Five Inventory. Applying the conditional false discovery rate approach, we increased discovery of genetic loci and identified two loci shared between neuroticism and schizophrenia and six loci shared between openness and schizophrenia. The study provides new insights into the relationship between personality traits and schizophrenia by highlighting genetic loci involved in their common genetic etiology.
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Affiliation(s)
- Olav B Smeland
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway.
| | - Yunpeng Wang
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
- Department of Neurosciences, University of California San Diego, La Jolla, CA, 92093, United States of America
| | - Min-Tzu Lo
- Department of Radiology, University of California, San Diego, La Jolla, CA, 92093, United States of America
| | - Wen Li
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Aree Witoelar
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Martin Tesli
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
- Lovisenberg Diakonale Hospital, 0456, Oslo, Norway
| | - David A Hinds
- 23andMe, Inc., Mountain View, CA, 94041, United States of America
| | - Joyce Y Tung
- 23andMe, Inc., Mountain View, CA, 94041, United States of America
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Chi-Hua Chen
- Department of Radiology, University of California, San Diego, La Jolla, CA, 92093, United States of America
| | - Anders M Dale
- Department of Neurosciences, University of California San Diego, La Jolla, CA, 92093, United States of America
- Department of Radiology, University of California, San Diego, La Jolla, CA, 92093, United States of America
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway.
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535
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Karnes JH, Bastarache L, Shaffer CM, Gaudieri S, Xu Y, Glazer AM, Mosley JD, Zhao S, Raychaudhuri S, Mallal S, Ye Z, Mayer JG, Brilliant MH, Hebbring SJ, Roden DM, Phillips EJ, Denny JC. Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants. Sci Transl Med 2017; 9:eaai8708. [PMID: 28490672 PMCID: PMC5563969 DOI: 10.1126/scitranslmed.aai8708] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 03/27/2017] [Indexed: 12/22/2022]
Abstract
Although many phenotypes have been associated with variants in human leukocyte antigen (HLA) genes, the full phenotypic impact of HLA variants across all diseases is unknown. We imputed HLA genomic variation from two populations of 28,839 and 8431 European ancestry individuals and tested association of HLA variation with 1368 phenotypes. A total of 104 four-digit and 92 two-digit HLA allele phenotype associations were significant in both discovery and replication cohorts, the strongest being HLA-DQB1*03:02 and type 1 diabetes. Four previously unidentified associations were identified across the spectrum of disease with two- and four-digit HLA alleles and 10 with nonsynonymous variants. Some conditions associated with multiple HLA variants and stronger associations with more severe disease manifestations were identified. A comprehensive, publicly available catalog of clinical phenotypes associated with HLA variation is provided. Examining HLA variant disease associations in this large data set allows comprehensive definition of disease associations to drive further mechanistic insights.
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Affiliation(s)
- Jason H Karnes
- Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tucson, AZ 85721, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Christian M Shaffer
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Silvana Gaudieri
- School of Anatomy, Physiology and Human Biology, University of Western Australia, Nedlands, Western Australia, Australia
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
| | - Yaomin Xu
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Andrew M Glazer
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jonathan D Mosley
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Shilin Zhao
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Soumya Raychaudhuri
- Division of Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA 02115, USA
- Institute of Inflammation and Repair, University of Manchester, Manchester, UK
- Department of Medicine, Karolinska Institutet and Karolinska University Hospital Solna, Stockholm, Sweden
| | - Simon Mallal
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Zhan Ye
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - John G Mayer
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Murray H Brilliant
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Scott J Hebbring
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
| | - Dan M Roden
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Elizabeth J Phillips
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
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536
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Ombrello MJ, Arthur VL, Remmers EF, Hinks A, Tachmazidou I, Grom AA, Foell D, Martini A, Gattorno M, Özen S, Prahalad S, Zeft AS, Bohnsack JF, Ilowite NT, Mellins ED, Russo R, Len C, Hilario MOE, Oliveira S, Yeung RSM, Rosenberg AM, Wedderburn LR, Anton J, Haas JP, Rosen-Wolff A, Minden K, Tenbrock K, Demirkaya E, Cobb J, Baskin E, Signa S, Shuldiner E, Duerr RH, Achkar JP, Kamboh MI, Kaufman KM, Kottyan LC, Pinto D, Scherer SW, Alarcón-Riquelme ME, Docampo E, Estivill X, Gül A, Langefeld CD, Thompson S, Zeggini E, Kastner DL, Woo P, Thomson W. Genetic architecture distinguishes systemic juvenile idiopathic arthritis from other forms of juvenile idiopathic arthritis: clinical and therapeutic implications. Ann Rheum Dis 2017; 76:906-913. [PMID: 27927641 PMCID: PMC5530341 DOI: 10.1136/annrheumdis-2016-210324] [Citation(s) in RCA: 101] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 10/27/2016] [Accepted: 11/12/2016] [Indexed: 01/14/2023]
Abstract
OBJECTIVES Juvenile idiopathic arthritis (JIA) is a heterogeneous group of conditions unified by the presence of chronic childhood arthritis without an identifiable cause. Systemic JIA (sJIA) is a rare form of JIA characterised by systemic inflammation. sJIA is distinguished from other forms of JIA by unique clinical features and treatment responses that are similar to autoinflammatory diseases. However, approximately half of children with sJIA develop destructive, long-standing arthritis that appears similar to other forms of JIA. Using genomic approaches, we sought to gain novel insights into the pathophysiology of sJIA and its relationship with other forms of JIA. METHODS We performed a genome-wide association study of 770 children with sJIA collected in nine countries by the International Childhood Arthritis Genetics Consortium. Single nucleotide polymorphisms were tested for association with sJIA. Weighted genetic risk scores were used to compare the genetic architecture of sJIA with other JIA subtypes. RESULTS The major histocompatibility complex locus and a locus on chromosome 1 each showed association with sJIA exceeding the threshold for genome-wide significance, while 23 other novel loci were suggestive of association with sJIA. Using a combination of genetic and statistical approaches, we found no evidence of shared genetic architecture between sJIA and other common JIA subtypes. CONCLUSIONS The lack of shared genetic risk factors between sJIA and other JIA subtypes supports the hypothesis that sJIA is a unique disease process and argues for a different classification framework. Research to improve sJIA therapy should target its unique genetics and specific pathophysiological pathways.
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Affiliation(s)
- Michael J Ombrello
- Translational Genetics and Genomics Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health,US Department of Health and Human Services, Bethesda, Maryland, USA
| | - Victoria L Arthur
- Translational Genetics and Genomics Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health,US Department of Health and Human Services, Bethesda, Maryland, USA
| | - Elaine F Remmers
- Inflammatory Disease Section, National Human Genome Research Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, USA
| | - Anne Hinks
- Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, Manchester, UK
| | | | - Alexei A Grom
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Dirk Foell
- Department of Pediatric Rheumatology and Immunology, University Hospital Münster, Münster, Germany
| | - Alberto Martini
- Department of Pediatrics, University of Genova, Genoa, Italy
- Pediatrics II Unit, Giannina Gaslini Institute, Genoa, Italy
| | - Marco Gattorno
- Pediatrics II Unit, Giannina Gaslini Institute, Genoa, Italy
| | - Seza Özen
- Department of Pediatric Rheumatology, Hacettepe University, Ankara, Turkey
| | - Sampath Prahalad
- Departments of Pediatrics and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
- Children's Healthcare of Atlanta, Atlanta, Georgia, USA
| | - Andrew S Zeft
- Department of Pediatrics, Cleveland Clinic, Cleveland, Ohio, USA
| | - John F Bohnsack
- Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
| | - Norman T Ilowite
- Department of Pediatrics, Albert Einstein College of Medicine and Children's Hospital at Montefiore, Bronx, New York, USA
| | | | - Ricardo Russo
- Service of Immunology and Rheumatology, Hospital de Pediatria Garrahan, Buenos Aires, Argentina
| | - Claudio Len
- Department of Pediatrics, Universidade Federal de São Paulo, São Paulo, Brazil
| | | | - Sheila Oliveira
- Universidade Federal de Rio de Janeiro, Rio de Janeiro, Brazil
| | - Rae S M Yeung
- Department of Pediatrics, University of Toronto, Toronto, Canada
- Department of Immunology, University of Toronto, Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Alan M Rosenberg
- Department of Pediatrics, University of Saskatchewan, Saskatoon, Canada
| | - Lucy R Wedderburn
- Institute of Child Health, University College London, London, UK
- Center of Paediatric and Adolescent Rheumatology, University College London, London, UK
| | - Jordi Anton
- Pediatric Rheumatology Unit, Hospital Sant Joan de Déu, Universitat de Barcelona, Barcelona, Spain
| | - Johannes-Peter Haas
- German Center for Pediatric and Adolescent Rheumatology, Garmisch-Partenkirchen, Germany
| | | | - Kirsten Minden
- Department of Rheumatology and Clinical Immunology, Charité -University Medicine, Berlin, Germany
- Epidemiology Unit, German Rheumatism Research Centre, Berlin, Germany
| | - Klaus Tenbrock
- Department of Pediatrics, RWTH Aachen University, Aachen, Germany
| | - Erkan Demirkaya
- Department of Pediatric Rheumatology, Hacettepe University, Ankara, Turkey
| | - Joanna Cobb
- Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, Manchester, UK
- National Institute for Health Research Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Elizabeth Baskin
- Translational Genetics and Genomics Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health,US Department of Health and Human Services, Bethesda, Maryland, USA
| | - Sara Signa
- Department of Pediatrics, University of Genova, Genoa, Italy
| | - Emily Shuldiner
- Translational Genetics and Genomics Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health,US Department of Health and Human Services, Bethesda, Maryland, USA
| | - Richard H Duerr
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Jean-Paul Achkar
- Department of Gastroenterology and Hepatology, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Pathobiology, Cleveland Clinic, Cleveland, Ohio, USA
| | - M Ilyas Kamboh
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Kenneth M Kaufman
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Leah C Kottyan
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Dalila Pinto
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Stephen W Scherer
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Marta E Alarcón-Riquelme
- Center for Genomics and Oncological Research, Pfizer-University of Granada-Andalusian Government, Granada, Spain
- Unit of Chronic Inflammatory Diseases, Institute for Environmental Medicine, Karolinska Institutet, Solna, Sweden
| | - Elisa Docampo
- Interdisciplinary Cluster for Applied Genoproteomics-Université de Liège, Liège, Belgium
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, and Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Xavier Estivill
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, and Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Sidra Medical and Research Centre, Doha, Qatar
| | - Ahmet Gül
- Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | | | - Carl D Langefeld
- Department of Biostatistical Sciences, Wake Forest University Health Sciences, Winston-Salem, North Carolina, USA
| | - Susan Thompson
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | | | - Daniel L Kastner
- Inflammatory Disease Section, National Human Genome Research Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, USA
| | - Patricia Woo
- Center of Paediatric and Adolescent Rheumatology, University College London, London, UK
| | - Wendy Thomson
- Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, Manchester, UK
- National Institute for Health Research Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
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537
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Gordon D, Londono D, Patel P, Kim W, Finch SJ, Heiman GA. An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance. Hum Hered 2017; 81:194-209. [PMID: 28315880 DOI: 10.1159/000457135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Accepted: 01/20/2017] [Indexed: 01/14/2023] Open
Abstract
Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes.
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Affiliation(s)
- Derek Gordon
- Department of Genetics, The State University of New Jersey, Piscataway, NJ, USA
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538
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Ruffieux H, Davison AC, Hager J, Irincheeva I. Efficient inference for genetic association studies with multiple outcomes. Biostatistics 2017; 18:618-636. [DOI: 10.1093/biostatistics/kxx007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 02/06/2017] [Indexed: 02/04/2023] Open
Abstract
SUMMARY
Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson and others (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes.
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Affiliation(s)
- Helene Ruffieux
- Nestlé Institute of Health Sciences SA, EPFL Innovation Park, 1015 Lausanne, Switzerland Ecole Polytechnique Fédérale de Lausanne, EPFL SB MATH STAT, Station 8, 1015 Lausanne, Switzerland
| | - Anthony C. Davison
- Ecole Polytechnique Fédérale de Lausanne, EPFL SB MATH STAT, Station 8, 1015 Lausanne, Switzerland
| | - Jorg Hager
- Nestlé Institute of Health Sciences SA, EPFL Innovation Park, 1015 Lausanne, Switzerland
| | - Irina Irincheeva
- Nestlé Institute of Health Sciences SA, EPFL Innovation Park, 1015 Lausanne, Switzerland
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539
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Porter HF, O’Reilly PF. Multivariate simulation framework reveals performance of multi-trait GWAS methods. Sci Rep 2017; 7:38837. [PMID: 28287610 PMCID: PMC5347376 DOI: 10.1038/srep38837] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 10/19/2016] [Indexed: 01/22/2023] Open
Abstract
Burgeoning availability of genome-wide association study (GWAS) results and national biobank data has led to growing interest in performing multi-trait genetic analyses. Numerous multi-trait GWAS methods that exploit either summary statistics or individual-level data have been developed, but their relative performance is unclear. Here we develop a simulation framework to model the complex networks underlying multivariate genetic epidemiology, enabling the vast model space of genetic effects on multiple correlated traits to be explored systematically. We perform a comprehensive comparison of the leading multi-trait GWAS methods, finding: (1) method performance is highly sensitive to the specific combination of genetic effects and phenotypic correlations, (2) most of the current multivariate methods have remarkably similar statistical power, and (3) multivariate methods may offer a substantial increase in the discovery of genetic variants over the standard univariate approach. We believe our findings offer the clearest picture to date of the relative performance of multi-trait GWAS methods and act as a guide for method selection. We provide a web application and open-source software program implementing our simulation framework, for: (i) further benchmarking of multivariate GWAS methods, (ii) power calculations for multivariate genetic studies, and (iii) generating data for testing any multivariate method in genetic epidemiology.
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Affiliation(s)
- Heather F. Porter
- MRC SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Paul F. O’Reilly
- MRC SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, UK
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540
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Tong P, Monahan J, Prendergast JGD. Shared regulatory sites are abundant in the human genome and shed light on genome evolution and disease pleiotropy. PLoS Genet 2017; 13:e1006673. [PMID: 28282383 PMCID: PMC5365138 DOI: 10.1371/journal.pgen.1006673] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 03/24/2017] [Accepted: 03/07/2017] [Indexed: 12/16/2022] Open
Abstract
Large-scale gene expression datasets are providing an increasing understanding of the location of cis-eQTLs in the human genome and their role in disease. However, little is currently known regarding the extent of regulatory site-sharing between genes. This is despite it having potentially wide-ranging implications, from the determination of the way in which genetic variants may shape multiple phenotypes to the understanding of the evolution of human gene order. By first identifying the location of non-redundant cis-eQTLs, we show that regulatory site-sharing is a relatively common phenomenon in the human genome, with over 10% of non-redundant regulatory variants linked to the expression of multiple nearby genes. We show that these shared, local regulatory sites are linked to high levels of chromatin looping between the regulatory sites and their associated genes. In addition, these co-regulated gene modules are found to be strongly conserved across mammalian species, suggesting that shared regulatory sites have played an important role in shaping human gene order. The association of these shared cis-eQTLs with multiple genes means they also appear to be unusually important in understanding the genetics of human phenotypes and pleiotropy, with shared regulatory sites more often linked to multiple human phenotypes than other regulatory variants. This study shows that regulatory site-sharing is likely an underappreciated aspect of gene regulation and has important implications for the understanding of various biological phenomena, including how the two and three dimensional structures of the genome have been shaped and the potential causes of disease pleiotropy outside coding regions.
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Affiliation(s)
- Pin Tong
- Wellcome Trust Centre for Cell Biology and Institute of Cell Biology, School of Biological Sciences, The University of Edinburgh, Max Born Crescent, Edinburgh, Scotland, United Kingdom
| | - Jack Monahan
- The European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - James G. D. Prendergast
- The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, Scotland, United Kingdom
- * E-mail:
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541
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Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types. Nat Genet 2017; 49:600-605. [PMID: 28218759 PMCID: PMC5374036 DOI: 10.1038/ng.3795] [Citation(s) in RCA: 155] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 01/26/2017] [Indexed: 12/13/2022]
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542
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Wangler MF, Hu Y, Shulman JM. Drosophila and genome-wide association studies: a review and resource for the functional dissection of human complex traits. Dis Model Mech 2017; 10:77-88. [PMID: 28151408 PMCID: PMC5312009 DOI: 10.1242/dmm.027680] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Human genome-wide association studies (GWAS) have successfully identified thousands of susceptibility loci for common diseases with complex genetic etiologies. Although the susceptibility variants identified by GWAS usually have only modest effects on individual disease risk, they contribute to a substantial burden of trait variation in the overall population. GWAS also offer valuable clues to disease mechanisms that have long proven to be elusive. These insights could lead the way to breakthrough treatments; however, several challenges hinder progress, making innovative approaches to accelerate the follow-up of results from GWAS an urgent priority. Here, we discuss the largely untapped potential of the fruit fly, Drosophila melanogaster, for functional investigation of findings from human GWAS. We highlight selected examples where strong genomic conservation with humans along with the rapid and powerful genetic tools available for flies have already facilitated fine mapping of association signals, elucidated gene mechanisms, and revealed novel disease-relevant biology. We emphasize current research opportunities in this rapidly advancing field, and present bioinformatic analyses that systematically explore the applicability of Drosophila for interrogation of susceptibility signals implicated in more than 1000 human traits, based on all GWAS completed to date. Thus, our discussion is targeted at both human geneticists seeking innovative strategies for experimental validation of findings from GWAS, as well as the Drosophila research community, by whom ongoing investigations of the implicated genes will powerfully inform our understanding of human disease.
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Affiliation(s)
- Michael F Wangler
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
- Program in Developmental Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yanhui Hu
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Joshua M Shulman
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
- Program in Developmental Biology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
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543
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Rodríguez JA, Marigorta UM, Hughes DA, Spataro N, Bosch E, Navarro A. Antagonistic pleiotropy and mutation accumulation influence human senescence and disease. Nat Ecol Evol 2017; 1:55. [DOI: 10.1038/s41559-016-0055] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 12/14/2016] [Indexed: 11/10/2022]
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544
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A systematic SNP selection approach to identify mechanisms underlying disease aetiology: linking height to post-menopausal breast and colorectal cancer risk. Sci Rep 2017; 7:41034. [PMID: 28117334 PMCID: PMC5259777 DOI: 10.1038/srep41034] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 12/15/2016] [Indexed: 01/28/2023] Open
Abstract
Data from GWAS suggest that SNPs associated with complex diseases or traits tend to co-segregate in regions of low recombination, harbouring functionally linked gene clusters. This phenomenon allows for selecting a limited number of SNPs from GWAS repositories for large-scale studies investigating shared mechanisms between diseases. For example, we were interested in shared mechanisms between adult-attained height and post-menopausal breast cancer (BC) and colorectal cancer (CRC) risk, because height is a risk factor for these cancers, though likely not a causal factor. Using SNPs from public GWAS repositories at p-values < 1 × 10−5 and a genomic sliding window of 1 mega base pair, we identified SNP clusters including at least one SNP associated with height and one SNP associated with either post-menopausal BC or CRC risk (or both). SNPs were annotated to genes using HapMap and GRAIL and analysed for significantly overrepresented pathways using ConsensuspathDB. Twelve clusters including 56 SNPs annotated to 26 genes were prioritised because these included at least one height- and one BC risk- or CRC risk-associated SNP annotated to the same gene. Annotated genes were involved in Indian hedgehog signalling (p-value = 7.78 × 10−7) and several cancer site-specific pathways. This systematic approach identified a limited number of clustered SNPs, which pinpoint potential shared mechanisms linking together the complex phenotypes height, post-menopausal BC and CRC.
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545
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Identifying Pleiotropic Genes in Genome-Wide Association Studies for Multivariate Phenotypes with Mixed Measurement Scales. PLoS One 2017; 12:e0169893. [PMID: 28081206 PMCID: PMC5231271 DOI: 10.1371/journal.pone.0169893] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Accepted: 12/22/2016] [Indexed: 11/30/2022] Open
Abstract
We propose a multivariate genome-wide association test for mixed continuous, binary, and ordinal phenotypes. A latent response model is used to estimate the correlation between phenotypes with different measurement scales so that the empirical distribution of the Fisher’s combination statistic under the null hypothesis is estimated efficiently. The simulation study shows that our proposed correlation estimation methods have high levels of accuracy. More importantly, our approach conservatively estimates the variance of the test statistic so that the type I error rate is controlled. The simulation also shows that the proposed test maintains the power at the level very close to that of the ideal analysis based on known latent phenotypes while controlling the type I error. In contrast, conventional approaches–dichotomizing all observed phenotypes or treating them as continuous variables–could either reduce the power or employ a linear regression model unfit for the data. Furthermore, the statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that conducting a multivariate test on multiple phenotypes can increase the power of identifying markers that may not be, otherwise, chosen using marginal tests. The proposed method also offers a new approach to analyzing the Fagerström Test for Nicotine Dependence as multivariate phenotypes in genome-wide association studies.
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546
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Ittisoponpisan S, Alhuzimi E, Sternberg MJE, David A. Landscape of Pleiotropic Proteins Causing Human Disease: Structural and System Biology Insights. Hum Mutat 2017; 38:289-296. [PMID: 27957775 DOI: 10.1002/humu.23155] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Accepted: 12/03/2016] [Indexed: 12/13/2022]
Abstract
Pleiotropy is the phenomenon by which the same gene can result in multiple phenotypes. Pleiotropic proteins are emerging as important contributors to rare and common disorders. Nevertheless, little is known on the mechanisms underlying pleiotropy and the characteristic of pleiotropic proteins. We analyzed disease-causing proteins reported in UniProt and observed that 12% are pleiotropic (variants in the same protein cause more than one disease). Pleiotropic proteins were enriched in deleterious and rare variants, but not in common variants. Pleiotropic proteins were more likely to be involved in the pathogenesis of neoplasms, neurological, and circulatory diseases and congenital malformations, whereas non-pleiotropic proteins in endocrine and metabolic disorders. Pleiotropic proteins were more essential and had a higher number of interacting partners compared with non-pleiotropic proteins. Significantly more pleiotropic than non-pleiotropic proteins contained at least one intrinsically long disordered region (P < 0.001). Deleterious variants occurring in structurally disordered regions were more commonly found in pleiotropic, rather than non-pleiotropic proteins. In conclusion, pleiotropic proteins are an important contributor to human disease. They represent a biologically different class of proteins compared with non-pleiotropic proteins and a better understanding of their characteristics and genetic variants can greatly aid in the interpretation of genetic studies and drug design.
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Affiliation(s)
- Sirawit Ittisoponpisan
- Structural Bioinformatics Group, Department of Life Sciences, Imperial College London, London, UK
| | - Eman Alhuzimi
- Structural Bioinformatics Group, Department of Life Sciences, Imperial College London, London, UK
| | - Michael J E Sternberg
- Structural Bioinformatics Group, Department of Life Sciences, Imperial College London, London, UK
| | - Alessia David
- Structural Bioinformatics Group, Department of Life Sciences, Imperial College London, London, UK
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547
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Mägi R, Suleimanov YV, Clarke GM, Kaakinen M, Fischer K, Prokopenko I, Morris AP. SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes. BMC Bioinformatics 2017; 18:25. [PMID: 28077070 PMCID: PMC5225593 DOI: 10.1186/s12859-016-1437-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2016] [Accepted: 12/17/2016] [Indexed: 11/10/2022] Open
Abstract
Background Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits. Results We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements “reverse regression” methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10−8), which has not been reported in previous large-scale GWAS of lipid traits. Conclusions The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach.
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Affiliation(s)
- Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Yury V Suleimanov
- Computation-based Science and Technology Research Center, Cyprus Institute, Nicosia, Cyprus.,Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Geraldine M Clarke
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - Krista Fischer
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | | | - Andrew P Morris
- Estonian Genome Center, University of Tartu, Tartu, Estonia. .,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. .,Department of Biostatistics, University of Liverpool, Liverpool, UK.
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548
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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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549
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Gong C, Du Q, Xie J, Quan M, Chen B, Zhang D. Dissection of Insertion-Deletion Variants within Differentially Expressed Genes Involved in Wood Formation in Populus. FRONTIERS IN PLANT SCIENCE 2017; 8:2199. [PMID: 29403506 PMCID: PMC5778123 DOI: 10.3389/fpls.2017.02199] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Accepted: 12/14/2017] [Indexed: 05/02/2023]
Abstract
Short insertions and deletions (InDels) are one of the major genetic variants and are distributed widely across the genome; however, few investigations of InDels have been conducted in long-lived perennial plants. Here, we employed a combination of RNA-seq and population resequencing to identify InDels within differentially expressed (DE) genes underlying wood formation in a natural population of Populus tomentosa (435 individuals) and utilized InDel-based association mapping to detect the causal variants under additive, dominance, and epistasis underlying growth and wood properties. In the present paper, 5,482 InDels detected from 629 DE genes showed uneven distributions throughout all 19 chromosomes, and 95.9% of these loci were diallelic InDels. Seventy-four InDels (positive false discovery rate q ≤ 0.10) from 68 genes exhibited significant additive/dominant effects on 10 growth and wood-properties, with an average of 14.7% phenotypic variance explained. Potential pleiotropy was observed in one-third of the InDels (representing 24 genes). Seven genes exhibited significantly differential expression among the genotypic classes of associated InDels, indicating possible important roles for these InDels. Epistasis analysis showed that overlapping interacting genes formed unique interconnected networks for each trait, supporting the putative biochemical links that control quantitative traits. Therefore, the identification and utilization of InDels in trees will be recognized as an effective marker system for molecular marker-assisted breeding applications, and further facilitate our understanding of quantitative genomics.
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Affiliation(s)
- Chenrui Gong
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- College of Forestry, Henan Agricultural University, Zhengzhou, China
| | - Qingzhang Du
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China
| | - Jianbo Xie
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Mingyang Quan
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Beibei Chen
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Deqiang Zhang
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- *Correspondence: Deqiang Zhang,
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550
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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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