201
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Liu J, Chen J, Perrone-Bizzozero NI, Turner JA, Calhoun VD. Regional enrichment analyses on genetic profiles for schizophrenia and bipolar disorder. Schizophr Res 2018; 192:240-246. [PMID: 28442247 PMCID: PMC5651209 DOI: 10.1016/j.schres.2017.04.033] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 04/14/2017] [Accepted: 04/16/2017] [Indexed: 01/28/2023]
Abstract
Both schizophrenia (SZ) and bipolar disorder (BD) are highly heritable psychiatric disorders. The significant genomic risk loci are of great importance but with no guarantee of known functional impact and they cannot totally explain the genetic inheritance. In this study we present regional enrichment analyses across the genome, aiming to strike a balance between individual risk loci and integrated regional effects. Chromosomes were partitioned into 2 million base-pair regions (indicated by an underscore sign in the cytogenetic bands) on which enrichment tests are performed. In the discovery phase, we leverage the Psychiatric Genomics Consortium SZ and BD initial association test results for European Ancestry (EA) population and dbGAP SNP data for African Ancestry (AA) population. 78 and 48 regions show significantly enriched associations with SZ and BD respectively in the EA population, and nine are in common including MHC, 3p21.1, 7p22.3_2, 2q32.3_2, 8q24.3_4, and 19q13.33_1. The most unique SZ associated region is 1p21.3_3, while the most unique BD associated region is 6q25.2_1. For the AA population fewer regions are discovered with only 10% overlapping with that of EA population. A replication test using Wellcome Trust Case Control Consortium data for EA population verified 9% of the SZ enriched regions and 40% of the BD enriched regions. In summary, we showed that regional enrichment analyses produce reliable genetic association profiles using about one tenth of samples compared to single base-pair genome wide association approach. The identified association regions will be useful for further genetic functional studies.
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Affiliation(s)
- Jingyu Liu
- The Mind Research Network, Albuquerque, NM, USA; Dept. of Electrical Engineering, University of New Mexico, Albuquerque, NM, USA.
| | - Jiayu Chen
- The Mind Research Network, Albuquerque, NM, USA
| | | | - Jessica A Turner
- The Mind Research Network, Albuquerque, NM, USA; Psychology Department and Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, USA; Dept. of Electrical Engineering, University of New Mexico, Albuquerque, NM, USA; Dept. of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
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202
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Abstract
Relatedness within a sample can be of ancient (population stratification) or recent (familial structure) origin, and can either be known (pedigree data) or unknown (cryptic relatedness). All of these forms of familial relatedness have the potential to confound the results of genome-wide association studies. This chapter reviews the major methods available to researchers to adjust for the biases introduced by relatedness and maximize power to detect associations. The advantages and disadvantages of different methods are presented with reference to elements of study design, population characteristics, and computational requirements.
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Affiliation(s)
- Russell Thomson
- Centre for Research in Mathematics, School of Computing, Engineering and Mathematics, Western Sydney University, Parramatta, Australia.
| | - Rebekah McWhirter
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
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203
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Husser D, Büttner P, Stübner D, Ueberham L, Platonov PG, Dinov B, Arya A, Hindricks G, Bollmann A. PR Interval Associated Genes, Atrial Remodeling and Rhythm Outcome of Catheter Ablation of Atrial Fibrillation-A Gene-Based Analysis of GWAS Data. Front Genet 2018; 8:224. [PMID: 29312445 PMCID: PMC5742186 DOI: 10.3389/fgene.2017.00224] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Accepted: 12/11/2017] [Indexed: 01/25/2023] Open
Abstract
Background: PR interval prolongation has recently been shown to associate with advanced left atrial remodeling and atrial fibrillation (AF) recurrence after catheter ablation. While different genome-wide association studies (GWAS) have implicated 13 loci to associate with the PR interval as an AF endophenotype their subsequent associations with AF remodeling and response to catheter ablation are unknown. Here, we perform a gene-based analysis of GWAS data to test the hypothesis that PR interval candidate genes also associate with left atrial remodeling and arrhythmia recurrence following AF catheter ablation. Methods and Results: Samples from 660 patients with paroxysmal (n = 370) or persistent AF (n = 290) undergoing AF catheter ablation were genotyped for ~1,000,000 SNPs. Gene-based association was investigated using VEGAS (versatile gene-based association study). Among the 13 candidate genes, SLC8A1, MEIS1, ITGA9, SCN5A, and SOX5 associated with the PR interval. Of those, ITGA9 and SOX5 were significantly associated with left atrial low voltage areas and left atrial diameter and subsequently with AF recurrence after radiofrequency catheter ablation. Conclusion: This study suggests contributions of ITGA9 and SOX5 to AF remodeling expressed as PR interval prolongation, low voltage areas and left atrial dilatation and subsequently to response to catheter ablation. Future and larger studies are necessary to replicate and apply these findings with the aim of designing AF pathophysiology-based multi-locus risk scores.
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Affiliation(s)
- Daniela Husser
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany
| | - Petra Büttner
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany
| | - Dorian Stübner
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany
| | - Laura Ueberham
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany.,Leipzig Heart Institute, Leipzig, Germany
| | - Pyotr G Platonov
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Borislav Dinov
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany
| | - Arash Arya
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany
| | - Gerhard Hindricks
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany.,Leipzig Heart Institute, Leipzig, Germany
| | - Andreas Bollmann
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany.,Leipzig Heart Institute, Leipzig, Germany
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204
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Gorlova OY, Li Y, Gorlov I, Ying J, Chen WV, Assassi S, Reveille JD, Arnett FC, Zhou X, Bossini-Castillo L, Lopez-Isac E, Acosta-Herrera M, Gregersen PK, Lee AT, Steen VD, Fessler BJ, Khanna D, Schiopu E, Silver RM, Molitor JA, Furst DE, Kafaja S, Simms RW, Lafyatis RA, Carreira P, Simeon CP, Castellvi I, Beltran E, Ortego N, Amos CI, Martin J, Mayes MD. Gene-level association analysis of systemic sclerosis: A comparison of African-Americans and White populations. PLoS One 2018; 13:e0189498. [PMID: 29293537 PMCID: PMC5749683 DOI: 10.1371/journal.pone.0189498] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 11/27/2017] [Indexed: 12/15/2022] Open
Abstract
Gene-level analysis of ImmunoChip or genome-wide association studies (GWAS) data has not been previously reported for systemic sclerosis (SSc, scleroderma). The objective of this study was to analyze genetic susceptibility loci in SSc at the gene level and to determine if the detected associations were shared in African-American and White populations, using data from ImmunoChip and GWAS genotyping studies. The White sample included 1833 cases and 3466 controls (956 cases and 2741 controls from the US and 877 cases and 725 controls from Spain) and the African American sample, 291 cases and 260 controls. In both Whites and African Americans, we performed a gene-level analysis that integrates association statistics in a gene possibly harboring multiple SNPs with weak effect on disease risk, using Versatile Gene-based Association Study (VEGAS) software. The SNP-level analysis was performed using PLINK v.1.07. We identified 4 novel candidate genes (STAT1, FCGR2C, NIPSNAP3B, and SCT) significantly associated and 4 genes (SERBP1, PINX1, TMEM175 and EXOC2) suggestively associated with SSc in the gene level analysis in White patients. As an exploratory analysis we compared the results on Whites with those from African Americans. Of previously established susceptibility genes identified in Whites, only TNFAIP3 was significant at the nominal level (p = 6.13x10-3) in African Americans in the gene-level analysis of the ImmunoChip data. Among the top suggestive novel genes identified in Whites based on the ImmunoChip data, FCGR2C and PINX1 were only nominally significant in African Americans (p = 0.016 and p = 0.028, respectively), while among the top novel genes identified in the gene-level analysis in African Americans, UNC5C (p = 5.57x10-4) and CLEC16A (p = 0.0463) were also nominally significant in Whites. We also present the gene-level analysis of SSc clinical and autoantibody phenotypes among Whites. Our findings need to be validated by independent studies, particularly due to the limited sample size of African Americans.
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Affiliation(s)
- Olga Y. Gorlova
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States of America
| | - Yafang Li
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States of America
| | - Ivan Gorlov
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States of America
| | - Jun Ying
- Department of Internal Medicine, Division of Rheumatology, University of Texas McGovern Medical School, Houston, TX, United States of America
| | - Wei V. Chen
- Department of Biostatistics, UT MD Anderson Cancer Center, Houston, TX, United States of America
| | - Shervin Assassi
- Department of Internal Medicine, Division of Rheumatology, University of Texas McGovern Medical School, Houston, TX, United States of America
| | - John D. Reveille
- Department of Internal Medicine, Division of Rheumatology, University of Texas McGovern Medical School, Houston, TX, United States of America
| | - Frank C. Arnett
- Department of Internal Medicine, Division of Rheumatology, University of Texas McGovern Medical School, Houston, TX, United States of America
| | - Xiaodong Zhou
- Department of Internal Medicine, Division of Rheumatology, University of Texas McGovern Medical School, Houston, TX, United States of America
| | | | - Elena Lopez-Isac
- Institute of Parasitology and Biomedicine López-Neyra, IPBLN-CSIC, Granada, Spain
| | | | - Peter K. Gregersen
- Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, Manhasset, NY, United States of America
| | - Annette T. Lee
- Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, Manhasset, NY, United States of America
| | - Virginia D. Steen
- Division of Rheumatology, Georgetown University Medical Center, Washington, D.C., United States of America
| | - Barri J. Fessler
- Division of Rheumatology, University of Alabama—Birmingham, Birmingham, AL, United States of America
| | - Dinesh Khanna
- Division of Rheumatology, University of Michigan, Ann Arbor, MI, United States of America
| | - Elena Schiopu
- Division of Rheumatology, University of Michigan, Ann Arbor, MI, United States of America
| | - Richard M. Silver
- Division of Rheumatology, Medical University of South Carolina, Charleston, SC, United States of America
| | - Jerry A. Molitor
- Division of Rheumatic and Autoimmune Diseases, University of Minnesota, Minneapolis, MN, United States of America
| | - Daniel E. Furst
- Division of Rheumatology, University of California—Los Angeles, Los Angeles, CA, United States of America
- University of Washington, Seattle, WA, United States of America
- University of Florence, Florence, Italy
| | - Suzanne Kafaja
- Division of Rheumatology, University of California—Los Angeles, Los Angeles, CA, United States of America
| | - Robert W. Simms
- Division of Rheumatology, Boston University, Boston, MA, United States of America
| | | | | | | | | | - Emma Beltran
- Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | | | - Christopher I. Amos
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States of America
| | - Javier Martin
- Institute of Parasitology and Biomedicine López-Neyra, IPBLN-CSIC, Granada, Spain
| | - Maureen D. Mayes
- Department of Internal Medicine, Division of Rheumatology, University of Texas McGovern Medical School, Houston, TX, United States of America
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205
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Reinbold CS, Forstner AJ, Hecker J, Fullerton JM, Hoffmann P, Hou L, Heilbronner U, Degenhardt F, Adli M, Akiyama K, Akula N, Ardau R, Arias B, Backlund L, Benabarre A, Bengesser S, Bhattacharjee AK, Biernacka JM, Birner A, Marie-Claire C, Cervantes P, Chen GB, Chen HC, Chillotti C, Clark SR, Colom F, Cousins DA, Cruceanu C, Czerski PM, Dayer A, Étain B, Falkai P, Frisén L, Gard S, Garnham JS, Goes FS, Grof P, Gruber O, Hashimoto R, Hauser J, Herms S, Jamain S, Jiménez E, Kahn JP, Kassem L, Kittel-Schneider S, Kliwicki S, König B, Kusumi I, Lackner N, Laje G, Landén M, Lavebratt C, Leboyer M, Leckband SG, López Jaramillo CA, MacQueen G, Manchia M, Martinsson L, Mattheisen M, McCarthy MJ, McElroy SL, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Ösby U, Ozaki N, Perlis RH, Pfennig A, Reich-Erkelenz D, Rouleau GA, Schofield PR, Schubert KO, Schweizer BW, Seemüller F, Severino G, Shekhtman T, Shilling PD, Shimoda K, Simhandl C, Slaney CM, Smoller JW, Squassina A, Stamm TJ, Stopkova P, Tighe SK, Tortorella A, Turecki G, Volkert J, Witt SH, Wright AJ, Young LT, Zandi PP, Potash JB, DePaulo JR, Bauer M, Reininghaus E, Novák T, Aubry JM, Maj M, Baune BT, Mitchell PB, Vieta E, Frye MA, Rybakowski JK, Kuo PH, Kato T, Grigoroiu-Serbanescu M, Reif A, Del Zompo M, Bellivier F, Schalling M, Wray NR, Kelsoe JR, Alda M, McMahon FJ, Schulze TG, Rietschel M, Nöthen MM, Cichon S. Analysis of the Influence of microRNAs in Lithium Response in Bipolar Disorder. Front Psychiatry 2018; 9:207. [PMID: 29904359 PMCID: PMC5991073 DOI: 10.3389/fpsyt.2018.00207] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Accepted: 05/03/2018] [Indexed: 12/30/2022] Open
Abstract
Bipolar disorder (BD) is a common, highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. Lithium is the best-established long-term treatment for BD, even though individual response is highly variable. Evidence suggests that some of this variability has a genetic basis. This is supported by the largest genome-wide association study (GWAS) of lithium response to date conducted by the International Consortium on Lithium Genetics (ConLiGen). Recently, we performed the first genome-wide analysis of the involvement of miRNAs in BD and identified nine BD-associated miRNAs. However, it is unknown whether these miRNAs are also associated with lithium response in BD. In the present study, we therefore tested whether common variants at these nine candidate miRNAs contribute to the variance in lithium response in BD. Furthermore, we systematically analyzed whether any other miRNA in the genome is implicated in the response to lithium. For this purpose, we performed gene-based tests for all known miRNA coding genes in the ConLiGen GWAS dataset (n = 2,563 patients) using a set-based testing approach adapted from the versatile gene-based test for GWAS (VEGAS2). In the candidate approach, miR-499a showed a nominally significant association with lithium response, providing some evidence for involvement in both development and treatment of BD. In the genome-wide miRNA analysis, 71 miRNAs showed nominally significant associations with the dichotomous phenotype and 106 with the continuous trait for treatment response. A total of 15 miRNAs revealed nominal significance in both phenotypes with miR-633 showing the strongest association with the continuous trait (p = 9.80E-04) and miR-607 with the dichotomous phenotype (p = 5.79E-04). No association between miRNAs and treatment response to lithium in BD in either of the tested conditions withstood multiple testing correction. Given the limited power of our study, the investigation of miRNAs in larger GWAS samples of BD and lithium response is warranted.
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Affiliation(s)
- Céline S Reinbold
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland.,Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Andreas J Forstner
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland.,Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.,Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany.,Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Julian Hecker
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Janice M Fullerton
- Neuroscience Research Australia, Sydney, NSW, Australia.,School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Per Hoffmann
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland.,Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.,Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Liping Hou
- Intramural Research Program, US Department of Health & Human Services, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Urs Heilbronner
- Department Psychiatry and Psychotherapy, Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Mazda Adli
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany
| | - Kazufumi Akiyama
- Department of Biological Psychiatry and Neuroscience, Dokkyo Medical University School of Medicine, Mibu, Japan
| | - Nirmala Akula
- Intramural Research Program, US Department of Health & Human Services, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Raffaella Ardau
- Unit of Clinical Pharmacology, University Hospital of Cagliari, Cagliari, Italy
| | - Bárbara Arias
- Zoology and Biological Anthropology Section (Department of Evolutive Biology, Ecology and Environmental Sciences), Facultat de Biologia and Institut de Biomedicina, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Lena Backlund
- Department of Molecular Medicine and Surgery, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Antonio Benabarre
- Bipolar Disorder Program, Institute of Neuroscience, Hospital Clinic, CIBERSAM, IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Susanne Bengesser
- Special Outpatient Center for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | | | - Joanna M Biernacka
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States.,Institut National de la Santé et de la Recherche Médicale, U1144, Paris, France
| | - Armin Birner
- Special Outpatient Center for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Cynthia Marie-Claire
- Université Paris Diderot, Sorbonne Paris Cité, UMR-S 1144, Paris, France.,Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Pablo Cervantes
- The Neuromodulation Unit, McGill University Health Centre, Montreal, QC, Canada
| | - Guo-Bo Chen
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Hsi-Chung Chen
- Department of Psychiatry & Center of Sleep Disorders, National Taiwan University Hospital, Taipei, Taiwan
| | - Caterina Chillotti
- Unit of Clinical Pharmacology, University Hospital of Cagliari, Cagliari, Italy
| | - Scott R Clark
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
| | - Francesc Colom
- Mental Health Research Group, IMIM-Hospital del Mar, Barcelona, Spain
| | - David A Cousins
- Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Cristiana Cruceanu
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Piotr M Czerski
- Psychiatric Genetic Unit, Poznan University of Medical Sciences, Poznan, Poland
| | - Alexandre Dayer
- Mood Disorders Unit, HUG - Geneva University Hospitals, Geneva, Switzerland
| | - Bruno Étain
- Université Paris Diderot, Sorbonne Paris Cité, UMR-S 1144, Paris, France.,Department of Psychiatry, University of California, San Diego, San Diego, CA, United States.,AP-HP, GH Saint-Louis - Lariboisière - F. Widal, Département de Psychiatrie et de Médecine Addictologique, Paris, France
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Louise Frisén
- Department of Molecular Medicine and Surgery, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Sébastien Gard
- Service de Psychiatrie, Hôpital Charles Perrens, Bordeaux, France
| | - Julie S Garnham
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Fernando S Goes
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States
| | - Paul Grof
- Mood Disorders Center of Ottawa, Ottawa, ON, Canada
| | - Oliver Gruber
- Department of Psychiatry and Psychotherapy, University Medical Center, Georg-August University Göttingen, Göttingen, Germany
| | - Ryota Hashimoto
- Molecular Research Center for Children's Mental Development, United Graduate School of Child Development, Osaka University, Osaka, Japan
| | - Joanna Hauser
- Psychiatric Genetic Unit, Poznan University of Medical Sciences, Poznan, Poland
| | - Stefan Herms
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland.,Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.,Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Stéphane Jamain
- Institut National de la Santé et de la Recherche Médicale U955, Psychiatrie Translationnelle, Créteil, France
| | - Esther Jiménez
- Bipolar Disorder Program, Institute of Neuroscience, Hospital Clinic, CIBERSAM, IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Jean-Pierre Kahn
- Service de Psychiatrie et Psychologie Clinique, Centre Psychothérapique de Nancy - Université de Lorraine, Nancy, France
| | - Layla Kassem
- Intramural Research Program, US Department of Health & Human Services, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Sarah Kittel-Schneider
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Sebastian Kliwicki
- Department of Adult Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Barbara König
- Department of Psychiatry and Psychotherapeuthic Medicine, Landesklinikum Neunkirchen, Neunkirchen, Austria
| | - Ichiro Kusumi
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Nina Lackner
- Special Outpatient Center for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Gonzalo Laje
- Intramural Research Program, US Department of Health & Human Services, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Mikael Landén
- Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the Gothenburg University, Gothenburg, Sweden.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Catharina Lavebratt
- Department of Molecular Medicine and Surgery, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Marion Leboyer
- Assistance Publique-Hôpitaux de Paris, Hôpital Albert Chenevier - Henri Mondor, Pôle de Psychiatrie, Créteil, France
| | - Susan G Leckband
- Department of Pharmacy, VA San Diego Healthcare System, San Diego, CA, United States
| | | | - Glenda MacQueen
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.,Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Lina Martinsson
- Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
| | | | - Michael J McCarthy
- Department of Psychiatry, VA San Diego Healthcare System, San Diego, CA, United States
| | - Susan L McElroy
- Department of Psychiatry, Lindner Center of Hope/University of Cincinnati, Mason, OH, United States
| | - Marina Mitjans
- Zoology and Biological Anthropology Section (Department of Evolutive Biology, Ecology and Environmental Sciences), Facultat de Biologia and Institut de Biomedicina, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Francis M Mondimore
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States
| | - Palmiero Monteleone
- Neurosciences Section, Department of Medicine and Surgery, University of Salerno, Salerno, Italy.,Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | | | - Urban Ösby
- Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Norio Ozaki
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Roy H Perlis
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, Medical Faculty, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Daniela Reich-Erkelenz
- Department Psychiatry and Psychotherapy, Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, Germany
| | - Guy A Rouleau
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Peter R Schofield
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia.,Mental Illness, Neuroscience Research Australia, Sydney, NSW, Australia
| | - K Oliver Schubert
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
| | - Barbara W Schweizer
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States
| | - Florian Seemüller
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Giovanni Severino
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | | | - Paul D Shilling
- Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Kazutaka Shimoda
- Department of Psychiatry, Dokkyo Medical University School of Medicine, Mibu, Japan
| | - Christian Simhandl
- Medical school, Sigmund Freud University, Vienna, Austria.,Bipolar Center Wiener Neustadt, Vienna, Austria
| | - Claire M Slaney
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Jordan W Smoller
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Alessio Squassina
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Thomas J Stamm
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School Brandenburg, Neuruppin, Germany
| | - Pavla Stopkova
- Department of Psychiatry, National Institute of Mental Health, Klecany, Czechia
| | - Sarah K Tighe
- Department of Psychiatry, University of Iowa, Iowa, IA, United States
| | - Alfonso Tortorella
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Gustavo Turecki
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Julia Volkert
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Adam J Wright
- School of Psychiatry, University of New South Wales, and Black Dog Institute, Sydney, NSW, Australia
| | - L Trevor Young
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Peter P Zandi
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - James B Potash
- Department of Psychiatry, University of Iowa, Iowa, IA, United States
| | - J Raymond DePaulo
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, Medical Faculty, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Eva Reininghaus
- Special Outpatient Center for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Tomáš Novák
- Department of Psychiatry, National Institute of Mental Health, Klecany, Czechia
| | - Jean-Michel Aubry
- Mood Disorders Unit, HUG - Geneva University Hospitals, Geneva, Switzerland
| | - Mario Maj
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Bernhard T Baune
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
| | - Philip B Mitchell
- School of Psychiatry, University of New South Wales, and Black Dog Institute, Sydney, NSW, Australia
| | - Eduard Vieta
- Bipolar Disorder Program, Institute of Neuroscience, Hospital Clinic, CIBERSAM, IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Mark A Frye
- Institut National de la Santé et de la Recherche Médicale, U1144, Paris, France
| | - Janusz K Rybakowski
- Department of Adult Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Po-Hsiu Kuo
- Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Tadafumi Kato
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Brain Science Institute, Saitama, Japan
| | - Maria Grigoroiu-Serbanescu
- Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Maria Del Zompo
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Frank Bellivier
- Université Paris Diderot, Sorbonne Paris Cité, UMR-S 1144, Paris, France.,Department of Psychiatry, University of California, San Diego, San Diego, CA, United States.,AP-HP, GH Saint-Louis - Lariboisière - F. Widal, Département de Psychiatrie et de Médecine Addictologique, Paris, France
| | - Martin Schalling
- Department of Molecular Medicine and Surgery, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Naomi R Wray
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - John R Kelsoe
- Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.,Department of Psychiatry, National Institute of Mental Health, Klecany, Czechia
| | - Francis J McMahon
- Intramural Research Program, US Department of Health & Human Services, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Thomas G Schulze
- Intramural Research Program, US Department of Health & Human Services, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States.,Department Psychiatry and Psychotherapy, Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, Germany.,Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States.,Department of Psychiatry and Psychotherapy, University Medical Center, Georg-August University Göttingen, Göttingen, Germany.,Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Sven Cichon
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland.,Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.,Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany.,Research Centre Jülich, Institute of Neuroscience and Medicine (INM-1), Jülich, Germany
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206
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Gumpinger AC, Roqueiro D, Grimm DG, Borgwardt KM. Methods and Tools in Genome-wide Association Studies. Methods Mol Biol 2018; 1819:93-136. [PMID: 30421401 DOI: 10.1007/978-1-4939-8618-7_5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Many traits, such as height, the response to a given drug, or the susceptibility to certain diseases are presumably co-determined by genetics. Especially in the field of medicine, it is of major interest to identify genetic aberrations that alter an individual's risk to develop a certain phenotypic trait. Addressing this question requires the availability of comprehensive, high-quality genetic datasets. The technological advancements and the decreasing cost of genotyping in the last decade led to an increase in such datasets. Parallel to and in line with this technological progress, an analysis framework under the name of genome-wide association studies was developed to properly collect and analyze these data. Genome-wide association studies aim at finding statistical dependencies-or associations-between a trait of interest and point-mutations in the DNA. The statistical models used to detect such associations are diverse, spanning the whole range from the frequentist to the Bayesian setting.Since genetic datasets are inherently high-dimensional, the search for associations poses not only a statistical but also a computational challenge. As a result, a variety of toolboxes and software packages have been developed, each implementing different statistical methods while using various optimizations and mathematical techniques to enhance the computations.This chapter is devoted to the discussion of widely used methods and tools in genome-wide association studies. We present the different statistical models and the assumptions on which they are based, explain peculiarities of the data that have to be accounted for and, most importantly, introduce commonly used tools and software packages for the different tasks in a genome-wide association study, complemented with examples for their application.
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Affiliation(s)
- Anja C Gumpinger
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland. .,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Damian Roqueiro
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Dominik G Grimm
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Karsten M Borgwardt
- Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Basel, Switzerland. .,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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207
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Liu H, Wang W, Zhang C, Xu C, Duan H, Tian X, Zhang D. Heritability and Genome-Wide Association Study of Plasma Cholesterol in Chinese Adult Twins. Front Endocrinol (Lausanne) 2018; 9:677. [PMID: 30498476 PMCID: PMC6249314 DOI: 10.3389/fendo.2018.00677] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 10/29/2018] [Indexed: 12/14/2022] Open
Abstract
Dyslipidemia represents a strong and independent risk factor for cardiovascular disease. Plasma cholesterol, such as total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C), is the common indicator of diagnosing dyslipidemia. Here based on 382 Chinese twin pairs, we explored the magnitude of genetic impact on TC, HDL-C, LDL-C variation and further searched for genetic susceptibility loci for them using genome-wide association study (GWAS). The ACE model was the best fit model with additive genetic parameter (A) accounting for 26.6%, common or shared environmental parameter (C) accounting for 47.8%, unique/non-shared environmental parameter (E) accounting for 25.6% for the variance in HDL-C. The AE model was the best fit model for TC (A: 61.4%; E: 38.6%) and LDL-C (A: 65.5%; E: 34.5%). While no SNPs reached the genome-wide significance level (P < 5 × 10-8), 8, 14, 9 SNPs exceeded the suggestive significance level (P < 1 × 10-5) for TC, HDL-C, LDL-C, respectively. The promising genetic regions for TC, HDL-C, LDL-C were on chromosome 11 around rs7107698, chromosome 5 around rs12518218, chromosome 2 around rs10490120, respectively. Gene-based analysis found 1038, 1033 and 1090 genes nominally associated with TC, HDL-C, LDL-C (P < 0.05), especially FAF1, KLKB1 for TC, KLKB1 for HDL-C, and NTRK1, FAF1, SNTB2 for LDL-C, respectively. The number of common related genes among TC, HDL-C and LDL-C was 71, including FAF1, KLKB1, etc. Pathway enrichment analysis discovered known related pathways-zinc transporters, metal ion SLC transporters for TC, cell adhesion molecules CAMs, IL-6 signaling for HDL, FC epsilon RI signaling pathway, NFAT pathway for LDL, respectively. In conclusion, the TC and LDL-C level is moderately heritable and the HDL-C level is lowly heritable in Chinese population. The genomic loci, functional genes and pathways are identified to account for the heritability of plasma cholesterol level. Our findings provide important insights into plasma cholesterol molecular physiology and expect future research to replicate and validate our results.
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Affiliation(s)
- Hui Liu
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
| | - Weijing Wang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
| | - Caixia Zhang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
| | - Chunsheng Xu
- Qingdao Municipal Centre for Disease Control and Prevention, Qingdao, China
| | - Haiping Duan
- Qingdao Municipal Centre for Disease Control and Prevention, Qingdao, China
| | - Xiaocao Tian
- Qingdao Municipal Centre for Disease Control and Prevention, Qingdao, China
| | - Dongfeng Zhang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
- *Correspondence: Dongfeng Zhang
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208
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Wang W, Zhang D, Xu C, Wu Y, Duan H, Li S, Tan Q. Heritability and Genome-Wide Association Analyses of Serum Uric Acid in Middle and Old-Aged Chinese Twins. Front Endocrinol (Lausanne) 2018; 9:75. [PMID: 29559957 PMCID: PMC5845532 DOI: 10.3389/fendo.2018.00075] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Accepted: 02/19/2018] [Indexed: 12/13/2022] Open
Abstract
Serum uric acid (SUA), as the end product of purine metabolism, has proven emerging roles in human disorders. Here based on a sample of 379 middle and old-aged Chinese twin pairs, we aimed to explore the magnitude of genetic impact on SUA variation by performing sex-limitation twin modeling analyses and further detect specific genetic variants related to SUA by conducting a genome-wide association study. Monozygotic (MZ) twin correlation for SUA level (rMZ = 0.56) was larger than for dizygotic (DZ) twin correlation (rDZ = 0.39). The common effects sex-limitation model provided the best fit with additive genetic parameter (A) accounting for 46.3%, common or shared environmental parameter (C) accounting for 26.3% and unique/nonshared environmental parameter (E) accounting for 27.5% for females and 29.9, 33.1, and 37.0% for males, respectively. Although no SUA-related genetic variants reached genome-wide significance level, 25 SNPs were suggestive of association (P < 1 × 10-5). Most of the SNPs were located in an intronic region and detected to have regulatory effects on gene transcription. The cell-type specific enhancer of skeletal muscle was detected which has been reported to implicate SUA. Two promising genetic regions on chromosome 17 around rs2253277 and chromosome 14 around rs11621523 were found. Gene-based analysis found 167 genes nominally associated with SUA level (P < 0.05), including PTGR2, ENTPD5, well-known SLC2A9, etc. Enrichment analysis identified one pathway of transmembrane transport of small molecules and 20 GO gene sets involving in ion transport, transmembrane transporter activity, hydrolase activity acting on acid anhydrides, etc. In conclusion, SUA shows moderate heritability in women and low heritability in men in the Chinese population and genetic variations are significantly involved in functional genes and regulatory domains that mediate SUA level. Our findings provide clues to further elucidate molecular physiology of SUA homeostasis and identify new diagnostic biomarkers and therapeutic targets for hyperuricemia and gout.
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Affiliation(s)
- Weijing Wang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
- Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Dongfeng Zhang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
- *Correspondence: Dongfeng Zhang,
| | - Chunsheng Xu
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China
- Qingdao Institute of Preventive Medicine, Qingdao, China
| | - Yili Wu
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
| | - Haiping Duan
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, China
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China
| | - Shuxia Li
- Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Qihua Tan
- Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
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209
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Genome-Wide MicroRNA Analysis Implicates miR-30b/d in the Etiology of Alopecia Areata. J Invest Dermatol 2017; 138:549-556. [PMID: 29080678 DOI: 10.1016/j.jid.2017.09.046] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Revised: 09/12/2017] [Accepted: 09/13/2017] [Indexed: 12/11/2022]
Abstract
Alopecia areata (AA) is one of the most common forms of human hair loss. Although genetic studies have implicated autoimmune processes in AA etiology, understanding of the etiopathogenesis is incomplete. Recent research has implicated microRNAs, a class of small noncoding RNAs, in diverse autoimmune diseases. To our knowledge, no study has investigated the role of microRNAs in AA. In this study, gene-based analyses were performed for microRNAs using data of the largest genome-wide association meta-analysis of AA to date. Nominally, significant P-values were obtained for 78 of the 617 investigated microRNAs. After correction for multiple testing, three of the 78 microRNAs remained significant. Of these, miR-30b/d was the most significant microRNA for the follow-up analyses, which also showed lower expression in the hair follicle of AA patients. Target gene analyses for the three microRNAs showed 42 significantly associated target genes. These included IL2RA, TNXB, and ERBB3, which had been identified as susceptibility loci in previous genome-wide association studies. Using luciferase assay, site-specific miR-30b regulation of the AA risk genes IL2RA, STX17, and TNXB was validated. This study implicates microRNAs in the pathogenesis of AA. This finding may facilitate the development of future treatment strategies.
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210
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Li C, He J, Chen J, Zhao J, Gu D, Hixson JE, Rao DC, Jaquish CE, Rice TK, Sung YJ, Kelly TN. Genome-Wide Gene-Potassium Interaction Analyses on Blood Pressure: The GenSalt Study (Genetic Epidemiology Network of Salt Sensitivity). CIRCULATION. CARDIOVASCULAR GENETICS 2017; 10:e001811. [PMID: 29212900 PMCID: PMC5728702 DOI: 10.1161/circgenetics.117.001811] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 11/07/2017] [Indexed: 12/24/2022]
Abstract
BACKGROUND Gene-environmental interaction analysis can identify novel genetic factors for blood pressure (BP). We performed genome-wide analyses to identify genomic loci that interact with potassium to influence BP using single-marker (1 and 2 df joint tests) and gene-based tests among Chinese participants of the GenSalt study (Genetic Epidemiology Network of Salt Sensitivity). METHODS AND RESULTS Among 1876 GenSalt participants, the average of 3 urine samples was used to estimate potassium excretion. Nine BP measurements were taken using a random-zero sphygmomanometer. A total of 2.2 million single nucleotide polymorphisms were imputed using Affymetrix 6.0 genotype data and the Chinese Han of Beijing and Japanese of Tokyo HapMap reference panel. Promising findings (P<1.00×10-4) from GenSalt were evaluated for replication among 775 Chinese participants of the MESA (Multi-ethnic Study of Atherosclerosis). Single nucleotide polymorphism and gene-based results were meta-analyzed across the GenSalt and MESA studies to determine genome-wide significance. The 1 df tests identified interactions for ARL15 rs16882447 on systolic BP (P=2.83×10-9) and RANBP3L rs958929 on pulse pressure (P=1.58×10-8). The 2 df tests confirmed the ARL15 rs16882447 signal for systolic BP (P=1.15×10-9). Genome-wide gene-based analysis identified CC2D2A (P=2.59×10-7) at 4p15.32 and BNC2 (P=4.49×10-10) at 9p22.2 for systolic BP, GGNBP1 (P=1.18×10-8), and LINC00336 (P=1.36×10-8) at 6p21 for diastolic BP, DAB1 (P=1.05×10-13) at 1p32.2, and MIR4466 (P=5.34×10-8) at 6q25.3 for pulse pressure. The BNC2 (P=3.57×10-8) gene was also significant for mean arterial pressure. CONCLUSIONS We identified 2 novel BP loci and 6 genes through the examination of single nucleotide polymorphism- and gene-based interactions with potassium.
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Affiliation(s)
- Changwei Li
- From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.).
| | - Jiang He
- From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.)
| | - Jing Chen
- From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.)
| | - Jinying Zhao
- From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.)
| | - Dongfeng Gu
- From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.)
| | - James E Hixson
- From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.)
| | - Dabeeru C Rao
- From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.)
| | - Cashell E Jaquish
- From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.)
| | - Treva K Rice
- From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.)
| | - Yun Ju Sung
- From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.)
| | - Tanika N Kelly
- From the Department of Epidemiology, Shool of Public Health and Tropical Medicine (C.L., J.H., J.C., T.N.K.), and Department of Medicine, School of Medicine (J.H., J.C.), Tulane University, New Orleans, LA; Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA (C.L.); State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (D.G.); Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX (J.E.H.); Division of Biostatistics, Washington University School of Medicine, St. Louis, MO (D.C.R., T.K.R., Y.J.S.); and Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD (C.E.J.)
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211
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Xia Y. Testing and support recovery of multiple high-dimensional covariance matrices with false discovery rate control. TEST-SPAIN 2017. [DOI: 10.1007/s11749-017-0533-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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212
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Glessner JT, Li J, Wang D, March M, Lima L, Desai A, Hadley D, Kao C, Gur RE, Cohen N, Sleiman PMA, Li Q, Hakonarson H. Copy number variation meta-analysis reveals a novel duplication at 9p24 associated with multiple neurodevelopmental disorders. Genome Med 2017; 9:106. [PMID: 29191242 PMCID: PMC5709845 DOI: 10.1186/s13073-017-0494-1] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 11/14/2017] [Indexed: 12/24/2022] Open
Abstract
Background Neurodevelopmental and neuropsychiatric disorders represent a wide spectrum of heterogeneous yet inter-related disease conditions. The overlapping clinical presentations of these diseases suggest a shared genetic etiology. We aim to identify shared structural variants spanning the spectrum of five neuropsychiatric disorders. Methods We investigated copy number variations (CNVs) in five cohorts, including schizophrenia (SCZ), bipolar disease (BD), autism spectrum disorders (ASD), attention deficit hyperactivity disorder (ADHD), and depression, from 7849 cases and 10,799 controls. CNVs were called based on intensity data from genome-wide SNP arrays and CNV frequency was compared between cases and controls in each disease cohort separately. Meta-analysis was performed via a gene-based approach. Quantitative PCR (qPCR) was employed to validate novel significant loci. Results In our meta-analysis, two genes containing CNVs with exonic overlap reached genome-wide significance threshold of meta P value < 9.4 × 10−6 for deletions and 7.5 × 10−6 for duplications. We observed significant overlap between risk CNV loci across cohorts. In addition, we identified novel significant associations of DOCK8/KANK1 duplications (meta P value = 7.5 × 10−7) across all cohorts, and further validated the CNV region with qPCR. Conclusions In the first large scale meta-analysis of CNVs across multiple neurodevelopmental/psychiatric diseases, we uncovered novel significant associations of structural variants in the locus of DOCK8/KANK1 shared by five diseases, suggesting common etiology of these clinically distinct neurodevelopmental conditions. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0494-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Joseph T Glessner
- The Center for Applied Genomics, Abramson Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Jin Li
- The Center for Applied Genomics, Abramson Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.,Department of Cell Biology, Tianjin Medical University, Tianjin, China
| | - Dai Wang
- Janssen Research & Development, LLC, Raritan, NJ, 08869, USA
| | - Michael March
- The Center for Applied Genomics, Abramson Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Leandro Lima
- The Center for Applied Genomics, Abramson Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Akshatha Desai
- The Center for Applied Genomics, Abramson Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Dexter Hadley
- The Center for Applied Genomics, Abramson Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Charlly Kao
- The Center for Applied Genomics, Abramson Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nadine Cohen
- Janssen Research & Development, LLC, Raritan, NJ, 08869, USA
| | - Patrick M A Sleiman
- The Center for Applied Genomics, Abramson Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.,Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Qingqin Li
- Janssen Research & Development, LLC, Titusville, NJ, 08560, USA
| | - Hakon Hakonarson
- The Center for Applied Genomics, Abramson Research Center, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. .,Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. .,Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
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Functional mapping and annotation of genetic associations with FUMA. Nat Commun 2017; 8:1826. [PMID: 29184056 PMCID: PMC5705698 DOI: 10.1038/s41467-017-01261-5] [Citation(s) in RCA: 1938] [Impact Index Per Article: 276.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 08/30/2017] [Indexed: 02/06/2023] Open
Abstract
A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations. Prioritizing genetic variants is a major challenge in genome-wide association studies. Here, the authors develop FUMA, a web-based bioinformatics tool that uses a combination of positional, eQTL and chromatin interaction mapping to prioritize likely causal variants and genes.
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Abstract
Acne vulgaris is a skin disease with a multifactorial and complex pathology. While several twin studies have estimated that acne has a heritability of up to 80%, the genomic elements responsible for the origin and pathology of acne are still undiscovered. Here we performed a twin-based structural equation model, using available data on acne severity for an Australian sample of 4,491 twins and their siblings aged from 10 to 24. This study extends by a factor of 3 an earlier analysis of the genetic factors of acne. Acne severity was rated by nurses on a 4-point scale (1 = absent to 4 = severe) on up to three body sites (face, back, chest) and on up to three occasions (age 12, 14, and 16). The phenotype that we analyzed was the most severe rating at any site or age. The polychoric correlation for monozygotic twins was higher (r MZ = 0.86, 95% CI [0.81, 0.90]) than for dizygotic twins (r DZ = 0.42, 95% CI [0.35, 0.47]). A model that includes additive genetic effects and unique environmental effects was the most parsimonious model to explain the genetic variance of acne severity, and the estimated heritability was 0.85 (95% CI [0.82, 0.87]). We then conducted a genome-wide analysis including an additional 271 siblings - for a total of 4,762 individuals. A genome-wide association study (GWAS) scan did not detect loci associated with the severity of acne at the threshold of 5E-08 but suggestive association was found for three SNPs: rs10515088 locus 5q13.1 (p = 3.9E-07), rs12738078 locus 1p35.5 (p = 6.7E-07), and rs117943429 locus 18q21.2 (p = 9.1E-07). The 5q13.1 locus is close to PIK3R1, a gene that has a potential regulatory effect on sebocyte differentiation.
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Calderon D, Bhaskar A, Knowles DA, Golan D, Raj T, Fu AQ, Pritchard JK. Inferring Relevant Cell Types for Complex Traits by Using Single-Cell Gene Expression. Am J Hum Genet 2017; 101:686-699. [PMID: 29106824 PMCID: PMC5673624 DOI: 10.1016/j.ajhg.2017.09.009] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 09/13/2017] [Indexed: 01/09/2023] Open
Abstract
Previous studies have prioritized trait-relevant cell types by looking for an enrichment of genome-wide association study (GWAS) signal within functional regions. However, these studies are limited in cell resolution by the lack of functional annotations from difficult-to-characterize or rare cell populations. Measurement of single-cell gene expression has become a popular method for characterizing novel cell types, and yet limited work has linked single-cell RNA sequencing (RNA-seq) to phenotypes of interest. To address this deficiency, we present RolyPoly, a regression-based polygenic model that can prioritize trait-relevant cell types and genes from GWAS summary statistics and gene expression data. RolyPoly is designed to use expression data from either bulk tissue or single-cell RNA-seq. In this study, we demonstrated RolyPoly's accuracy through simulation and validated previously known tissue-trait associations. We discovered a significant association between microglia and late-onset Alzheimer disease and an association between schizophrenia and oligodendrocytes and replicating fetal cortical cells. Additionally, RolyPoly computes a trait-relevance score for each gene to reflect the importance of expression specific to a cell type. We found that differentially expressed genes in the prefrontal cortex of individuals with Alzheimer disease were significantly enriched with genes ranked highly by RolyPoly gene scores. Overall, our method represents a powerful framework for understanding the effect of common variants on cell types contributing to complex traits.
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Affiliation(s)
- Diego Calderon
- Program in Biomedical Informatics, Stanford University, Stanford, CA 94305, USA.
| | - Anand Bhaskar
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - David A Knowles
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Radiology, Stanford University, Stanford, CA 94305, USA
| | - David Golan
- Faculty of Industrial Engineering & Management, Technion, Haifa 3200003, Israel
| | - Towfique Raj
- Department of Neuroscience, Mount Sinai School of Medicine, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY 10029, USA
| | - Audrey Q Fu
- Department of Statistical Science, University of Idaho, Moscow, ID 83844, USA
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA; Department of Biology, Stanford University, Stanford, CA 94305, USA
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218
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Jiang T, Du F, Qin N, Lu Q, Dai J, Shen H, Hu Z. Systematical analyses of variants in DNase I hypersensitive sites to identify hepatocellular carcinoma susceptibility loci in a Chinese population. J Gastroenterol Hepatol 2017; 32:1887-1894. [PMID: 28321907 DOI: 10.1111/jgh.13790] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2016] [Revised: 02/20/2017] [Accepted: 03/16/2017] [Indexed: 12/21/2022]
Abstract
BACKGROUND AND AIM Although several variants located at coding and non-coding regions were evaluated by previous studies, the evidence for associations between variants located in DNase I-hypersensitive sites (DHSs) and hepatocellular carcinoma (HCC) risk was still limited. Recent advances using ENCODE data indicated that genetic variants in DHSs played an important role in carcinogenesis. Therefore, systematically investigating the associations between regulatory variants in DHSs and HCC risk should be put on the agenda. METHODS We conducted a case-control design (1538 HCC cases vs 1465 normal controls) to evaluate the effects on HCC for the variants located at the uniform DNase I hypersensitive sites sequencing peaks in a Chinese population. RESULTS We found two novel single nucleotide polymorphisms rs12309362 (odds ratio = 0.64, P = 5.61 × 10-6 ) and rs9970827 (odds ratio = 0.73, P = 7.23 × 10-6 ) significantly associated with decreased risk of HCC. Conditional analysis proved that both of them independently contributed to the susceptibility of HCC. Expression quantitative trait loci analysis found that A allele of rs12309362 was significantly associated with an elevated expression of phosphatase phosphoglycerate mutase 5 in liver tissues. In addition, gene-based analysis indicated that CEBPB (P = 1 × 10-4 ) was associated with the risk of HCC, and the expression of CEBPB was significantly lower in 50 The Cancer Genome Atlas HCC tumor tissues compared with matched normal tissues. CONCLUSIONS Our results indicated that rs12309362 (G > A), rs9970827 (A > G) in DHSs, and elevated expression of CEBPB were associated with a decreased risk of HCC. These results may contribute us to understand the function of regulatory DNA sequences in HCC development.
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Affiliation(s)
- Tao Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Fangzhi Du
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Na Qin
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Qun Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Juncheng Dai
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, China
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Bearden CE, Glahn DC. Cognitive genomics: Searching for the genetic roots of neuropsychological functioning. Neuropsychology 2017; 31:1003-1019. [PMID: 29376674 PMCID: PMC5791763 DOI: 10.1037/neu0000412] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVE Human cognition has long been known to be under substantial genetic control. With the complete mapping of the human genome, genome-wide association studies for many complex traits have proliferated; however, the highly polygenic nature of intelligence has made the identification of the precise genes that influence both global and specific cognitive abilities more difficult than anticipated. METHOD Here, we review the latest developments in the genomics of cognition, including a discussion of methodological advances in the genetic analysis of complex traits, and shared genetic contributions to cognitive abilities and neuropsychiatric disorders. RESULTS A wealth of twin and family studies have provided compelling evidence for a strong heritable component of both global and specific cognitive abilities, and for the existence of "generalist genes" responsible for a large portion of the variance in diverse cognitive abilities. Increasingly sophisticated analytic tools and ever-larger sample sizes are now facilitating the identification of specific genetic and molecular underpinnings of cognitive abilities, leading to optimism regarding possibilities for novel treatments for illnesses related to cognitive function. CONCLUSIONS We conclude with a set of future directions for the field, which will further accelerate discoveries regarding the biological pathways relevant to cognitive abilities. These, in turn, may be further interrogated in order to link biological mechanisms to behavior. (PsycINFO Database Record
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Affiliation(s)
- Carrie E Bearden
- Department of Psychiatry, University of California at Los Angeles
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Wang M, Huang J, Liu Y, Ma L, Potash JB, Han S. COMBAT: A Combined Association Test for Genes Using Summary Statistics. Genetics 2017; 207:883-891. [PMID: 28878002 PMCID: PMC5676236 DOI: 10.1534/genetics.117.300257] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 08/30/2017] [Indexed: 11/18/2022] Open
Abstract
Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Traditional analysis of GWAS typically examines one marker at a time, usually single nucleotide polymorphisms (SNPs), to identify individual variants associated with a disease. However, due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. As a complementary approach to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal. Here we propose a combined association test (COMBAT) for genes, which incorporates strengths from existing gene-based tests and shows higher overall performance than any individual test. Our method does not require raw genotype or phenotype data, but needs only SNP-level P-values and correlations between SNPs from ancestry-matched samples. Extensive simulations showed that COMBAT has an appropriate type I error rate, maintains higher power across a wide range of genetic models, and is more robust than any individual gene-based test. We further demonstrated the superior performance of COMBAT over several other gene-based tests through reanalysis of the meta-analytic results of GWAS for bipolar disorder. Our method allows for the more powerful application of gene-based analysis to complex diseases, which will have broad use given that GWAS summary results are increasingly publicly available.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Jianfei Huang
- Department of Psychiatry, University of Iowa, Iowa City, Iowa 52242
- College of Mathematical Sciences, Yangzhou University, 225002, China
| | - Yiyuan Liu
- Joan & Sanford I. Weill Department of Medicine, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York 10021
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742
| | - James B Potash
- Department of Psychiatry, University of Iowa, Iowa City, Iowa 52242
- Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, Iowa 52242
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland 21287
| | - Shizhong Han
- Department of Psychiatry, University of Iowa, Iowa City, Iowa 52242
- Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, Iowa 52242
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland 21287
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Pinto R, Assis J, Nogueira A, Pereira C, Pereira D, Medeiros R. Rethinking ovarian cancer genomics: where genome-wide association studies stand? Pharmacogenomics 2017; 18:1611-1625. [DOI: 10.2217/pgs-2017-0108] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Genome-wide association studies (GWAS) allow the finding of genetic variants associated with several traits. Regarding ovarian cancer (OC), 15 GWAS have been conducted since 2009, with the discovery of 49 SNPs associated with disease susceptibility and 46 with impact in the clinical outcome of patients (p < 5.00 × 10-2). Among them, 14 variants reached the genome-wide significance (p < 5.00 × 10−8). Despite the results obtained, they should be validated in independent sets. So far, five validation studies have been conducted which could confirm the association of 12 OC susceptibility SNPs. Consequently, post-GWAS studies are crucial unravel the biological plausibility of GWAS’ findings and the allelic spectrum of OC.
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Affiliation(s)
- Ricardo Pinto
- Molecular Oncology & Viral Pathology Group-Research Center, Portuguese Institute of Oncology, Edifício Laboratórios. 4° piso, Rua Dr. António Bernardino de Almeida, 4200–4072, Porto, Portugal
- ICBAS, Abel Salazar Institute for the Biomedical Sciences, Rua de Jorge Viterbo Ferreira, 228, 4050-313, Porto, Portugal
| | - Joana Assis
- Molecular Oncology & Viral Pathology Group-Research Center, Portuguese Institute of Oncology, Edifício Laboratórios. 4° piso, Rua Dr. António Bernardino de Almeida, 4200–4072, Porto, Portugal
- FMUP, Faculty of Medicine, Porto University, Alameda Prof. Hernâni Monteiro, 4200-319, Porto, Portugal
| | - Augusto Nogueira
- Molecular Oncology & Viral Pathology Group-Research Center, Portuguese Institute of Oncology, Edifício Laboratórios. 4° piso, Rua Dr. António Bernardino de Almeida, 4200–4072, Porto, Portugal
- FMUP, Faculty of Medicine, Porto University, Alameda Prof. Hernâni Monteiro, 4200-319, Porto, Portugal
| | - Carina Pereira
- Molecular Oncology & Viral Pathology Group-Research Center, Portuguese Institute of Oncology, Edifício Laboratórios. 4° piso, Rua Dr. António Bernardino de Almeida, 4200–4072, Porto, Portugal
- CINTESIS, Center for Health technology and Services Research, Faculty of Medicine, Porto University, Rua Dr. Plácido da Costa, 4200-450, Porto, Portugal
| | - Deolinda Pereira
- Oncology Department, Portuguese Institute of Oncology, Rua Dr. António Bernardino de Almeida, 4200-072, Porto, Portugal
| | - Rui Medeiros
- Molecular Oncology & Viral Pathology Group-Research Center, Portuguese Institute of Oncology, Edifício Laboratórios. 4° piso, Rua Dr. António Bernardino de Almeida, 4200–4072, Porto, Portugal
- Research Department, Portuguese League AgainstCancer (NRNorte), Estrada Interior da Circunvalação, 6657, 4200-172, Porto, Portugal
- CEBIMED, Faculty of Health Sciences, FernandoPessoa University, Praça 9 de Abril, 349, 4249-004, Porto, Portugal
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Corre T, Arjona FJ, Hayward C, Youhanna S, de Baaij JHF, Belge H, Nägele N, Debaix H, Blanchard MG, Traglia M, Harris SE, Ulivi S, Rueedi R, Lamparter D, Macé A, Sala C, Lenarduzzi S, Ponte B, Pruijm M, Ackermann D, Ehret G, Baptista D, Polasek O, Rudan I, Hurd TW, Hastie ND, Vitart V, Waeber G, Kutalik Z, Bergmann S, Vargas-Poussou R, Konrad M, Gasparini P, Deary IJ, Starr JM, Toniolo D, Vollenweider P, Hoenderop JGJ, Bindels RJM, Bochud M, Devuyst O. Genome-Wide Meta-Analysis Unravels Interactions between Magnesium Homeostasis and Metabolic Phenotypes. J Am Soc Nephrol 2017; 29:335-348. [PMID: 29093028 DOI: 10.1681/asn.2017030267] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2017] [Accepted: 07/19/2017] [Indexed: 12/15/2022] Open
Abstract
Magnesium (Mg2+) homeostasis is critical for metabolism. However, the genetic determinants of the renal handling of Mg2+, which is crucial for Mg2+ homeostasis, and the potential influence on metabolic traits in the general population are unknown. We obtained plasma and urine parameters from 9099 individuals from seven cohorts, and conducted a genome-wide meta-analysis of Mg2+ homeostasis. We identified two loci associated with urinary magnesium (uMg), rs3824347 (P=4.4×10-13) near TRPM6, which encodes an epithelial Mg2+ channel, and rs35929 (P=2.1×10-11), a variant of ARL15, which encodes a GTP-binding protein. Together, these loci account for 2.3% of the variation in 24-hour uMg excretion. In human kidney cells, ARL15 regulated TRPM6-mediated currents. In zebrafish, dietary Mg2+ regulated the expression of the highly conserved ARL15 ortholog arl15b, and arl15b knockdown resulted in renal Mg2+ wasting and metabolic disturbances. Finally, ARL15 rs35929 modified the association of uMg with fasting insulin and fat mass in a general population. In conclusion, this combined observational and experimental approach uncovered a gene-environment interaction linking Mg2+ deficiency to insulin resistance and obesity.
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Affiliation(s)
- Tanguy Corre
- Institute of Social and Preventive Medicine.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Francisco J Arjona
- Department of Physiology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine
| | - Sonia Youhanna
- Institute of Physiology, University of Zürich, Zurich, Switzerland
| | - Jeroen H F de Baaij
- Department of Physiology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Hendrica Belge
- Institute of Physiology, University of Zürich, Zurich, Switzerland
| | - Nadine Nägele
- Institute of Physiology, University of Zürich, Zurich, Switzerland
| | - Huguette Debaix
- Institute of Physiology, University of Zürich, Zurich, Switzerland
| | - Maxime G Blanchard
- Department of Physiology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Michela Traglia
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology.,Medical Genetics Section, University of Edinburgh Centre for Genomic and Experimental Medicine and Medical Research Council Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, Scotland, UK
| | - Sheila Ulivi
- Department of Medical Genetics, Institute for Maternal and Child Health, Istituto di Ricovero e Cura a Carattere Scientifico "Burlo Garofolo," Trieste, Italy
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - David Lamparter
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Aurélien Macé
- Institute of Social and Preventive Medicine.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Cinzia Sala
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
| | - Stefania Lenarduzzi
- Department of Medical Genetics, Institute for Maternal and Child Health, Istituto di Ricovero e Cura a Carattere Scientifico "Burlo Garofolo," Trieste, Italy
| | | | - Menno Pruijm
- Service of Nephrology, University Hospital of Lausanne, Lausanne, Switzerland
| | - Daniel Ackermann
- University Clinic for Nephrology and Hypertension, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Georg Ehret
- Division of Cardiology, Department of Internal Medicine Specialties, University Hospital of Geneva, Geneva, Switzerland
| | - Daniela Baptista
- Division of Cardiology, Department of Internal Medicine Specialties, University Hospital of Geneva, Geneva, Switzerland
| | - Ozren Polasek
- Faculty of Medicine, University of Split, Split, Croatia
| | - Igor Rudan
- Usher Institute of Population Health Sciences and Informatics
| | - Toby W Hurd
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine
| | - Nicholas D Hastie
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine
| | | | - Zoltán Kutalik
- Institute of Social and Preventive Medicine.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Rosa Vargas-Poussou
- Department of Genetics, Hôpital Européen Georges Pompidou, Assistance Publique Hôpitaux de Paris, Paris, France.,Centre de Référence des Maladies Rénales Héréditaires de l'Enfant et de l'Adulte, Paris, France
| | - Martin Konrad
- Department of General Pediatrics, University Hospital Münster, Munster, Germany
| | - Paolo Gasparini
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy; and.,Department of Experimental Genetics, Sidra, Doha, Qatar
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology.,Department of Psychology, and
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology.,Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, Scotland, UK
| | - Daniela Toniolo
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
| | | | - Joost G J Hoenderop
- Department of Physiology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - René J M Bindels
- Department of Physiology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Olivier Devuyst
- Institute of Physiology, University of Zürich, Zurich, Switzerland;
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Nongmaithem SS, Joglekar CV, Krishnaveni GV, Sahariah SA, Ahmad M, Ramachandran S, Gandhi M, Chopra H, Pandit A, Potdar RD, H D Fall C, Yajnik CS, Chandak GR. GWAS identifies population-specific new regulatory variants in FUT6 associated with plasma B12 concentrations in Indians. Hum Mol Genet 2017; 26:2551-2564. [PMID: 28334792 PMCID: PMC5886186 DOI: 10.1093/hmg/ddx071] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Accepted: 02/20/2017] [Indexed: 01/26/2023] Open
Abstract
Vitamin B12 is an important cofactor in one-carbon metabolism whose dysregulation is associated with various clinical conditions. Indians have a high prevalence of B12 deficiency but little is known about the genetic determinants of circulating B12 concentrations in Indians. We performed a genome-wide association study in 1001 healthy participants in the Pune Maternal Nutrition Study (PMNS), replication studies in 3418 individuals from other Indian cohorts and by meta-analysis identified new variants, rs3760775 (P = 1.2 × 10−23) and rs78060698 (P = 8.3 × 10−17) in FUT6 to be associated with circulating B12 concentrations. Although in-silico analysis replicated both variants in Europeans, differences in the effect allele frequency, effect size and the linkage disequilibrium structure of credible set variants with the reported variants suggest population-specific characteristics in this region. We replicated previously reported variants rs602662, rs601338 in FUT2, rs3760776, rs708686 in FUT6, rs34324219 in TCN1 (all P < 5 × 10−8), rs1131603 in TCN2 (P = 3.4 × 10−5), rs12780845 in CUBN (P = 3.0 × 10−3) and rs2270655 in MMAA (P = 2.0 × 10−3). Circulating B12 concentrations in the PMNS and Parthenon study showed a significant decline with increasing age (P < 0.001), however, the genetic contribution to B12 concentrations remained constant. Luciferase reporter and electrophoretic-mobility shift assay for the FUT6 variant rs78060698 using HepG2 cell line demonstrated strong allele-specific promoter and enhancer activity and differential binding of HNF4α, a key regulator of expression of various fucosyltransferases. Hence, the rs78060698 variant, through regulation of fucosylation may control intestinal host-microbial interaction which could influence B12 concentrations. Our results suggest that in addition to established genetic variants, population-specific variants are important in determining plasma B12 concentrations.
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Affiliation(s)
- Suraj S Nongmaithem
- Genomic Research on Complex Diseases (GRC Group), CSIR-Centre for Cellular and Molecular Biology, Hyderabad, Telangana 500 007, India
| | - Charudatta V Joglekar
- Diabetes Unit, King Edward Memorial Hospital and Research Centre, Rasta Peth, Pune, Maharashtra 411 011, India
| | - Ghattu V Krishnaveni
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, Karnataka 570 021, India
| | - Sirazul A Sahariah
- Research Department, Centre for the Study of Social Change, Mumbai, Maharashtra 400 051, India
| | - Meraj Ahmad
- Genomic Research on Complex Diseases (GRC Group), CSIR-Centre for Cellular and Molecular Biology, Hyderabad, Telangana 500 007, India
| | - Swetha Ramachandran
- Genomic Research on Complex Diseases (GRC Group), CSIR-Centre for Cellular and Molecular Biology, Hyderabad, Telangana 500 007, India
| | - Meera Gandhi
- Research Department, Centre for the Study of Social Change, Mumbai, Maharashtra 400 051, India
| | - Harsha Chopra
- Research Department, Centre for the Study of Social Change, Mumbai, Maharashtra 400 051, India
| | - Anand Pandit
- Department of Pediatrics, King Edward Memorial Hospital and Research Centre, Rasta Peth, Pune, Maharashtra 411 011, India
| | - Ramesh D Potdar
- Research Department, Centre for the Study of Social Change, Mumbai, Maharashtra 400 051, India
| | - Caroline H D Fall
- Research Department, Centre for the Study of Social Change, Mumbai, Maharashtra 400 051, India.,MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UK
| | - Chittaranjan S Yajnik
- Diabetes Unit, King Edward Memorial Hospital and Research Centre, Rasta Peth, Pune, Maharashtra 411 011, India
| | - Giriraj R Chandak
- Genomic Research on Complex Diseases (GRC Group), CSIR-Centre for Cellular and Molecular Biology, Hyderabad, Telangana 500 007, India.,Human Genetics Unit, Genome Institute of Singapore, Biopolis, 138 672, Singapore
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224
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Mishra A, Ferrari R, Heutink P, Hardy J, Pijnenburg Y, Posthuma D. Gene-based association studies report genetic links for clinical subtypes of frontotemporal dementia. Brain 2017; 140:1437-1446. [PMID: 28387812 DOI: 10.1093/brain/awx066] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 01/23/2017] [Indexed: 01/02/2023] Open
Abstract
Genome-wide association studies in frontotemporal dementia showed limited success in identifying associated loci. This is possibly due to small sample size, allelic heterogeneity, small effect sizes of single genetic variants, and the necessity to statistically correct for testing millions of genetic variants. To overcome these issues, we performed gene-based association studies on 3348 clinically identified frontotemporal dementia cases and 9390 controls (discovery, replication and joint-cohort analyses). We report association of APOE and TOMM40 with behavioural variant frontotemporal dementia, and ARHGAP35 and SERPINA1 with progressive non-fluent aphasia. Further, we found the ɛ2 and ɛ4 alleles of APOE harbouring protective and risk increasing effects, respectively, in clinical subtypes of frontotemporal dementia against neurologically normal controls. The APOE-locus association with behavioural variant frontotemporal dementia indicates its potential risk-increasing role across different neurodegenerative diseases, whereas the novel genetic associations of ARHGAP35 and SERPINA1 with progressive non-fluent aphasia point towards a potential role of the stress-signalling pathway in its pathophysiology.
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Affiliation(s)
- Aniket Mishra
- Department of Complex Trait Genetics, VU University, Center for Neurogenomics and Cognitive Research, Amsterdam, 1081 HV, The Netherlands
| | - Raffaele Ferrari
- Department of Molecular Neuroscience, UCL, Russell Square House, 9-12 Russell Square House London, WC1B 5EH, UK
| | - Peter Heutink
- Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, 72076, Tübingen, Germany.,German Center for Neurodegenerative Diseases (DZNE)-Tübingen, 72076, Tübingen, Germany
| | - John Hardy
- Department of Molecular Neuroscience, UCL, Russell Square House, 9-12 Russell Square House London, WC1B 5EH, UK
| | - Yolande Pijnenburg
- Alzheimer Center and Department of Neurology, VU University Medical Center (VUMC), Neuroscience Campus Amsterdam, Amsterdam, 1081 HV, The Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, VU University, Center for Neurogenomics and Cognitive Research, Amsterdam, 1081 HV, The Netherlands.,Department of Clinical Genetics, VU University Medical Center (VUMC), Neuroscience Campus Amsterdam, Amsterdam, 1081 HV, The Netherlands
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225
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A Novel Approach for Pathway Analysis of GWAS Data Highlights Role of BMP Signaling and Muscle Cell Differentiation in Colorectal Cancer Susceptibility. Twin Res Hum Genet 2017; 20:1-9. [PMID: 28105966 DOI: 10.1017/thg.2016.100] [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] [Indexed: 01/02/2023]
Abstract
Genome-wide association studies (GWAS) have revolutionized the field of gene mapping. As the GWAS field matures, it is becoming clear that for many complex traits, a proportion of the missing heritability is attributable to common variants of individually small effect. Detecting these small effects individually can be difficult, and statistical power would be increased if relevant variants could be grouped together for testing. Here, we propose a VEGAS2Pathway approach that aggregates association strength of individual markers into pre-specified biological pathways. It accounts for gene size and linkage disequilibrium between markers using simulations from the multivariate normal distribution. Pathway size is taken into account via a resampling approach. Importantly, since the approach only requires summary data, the method can easily be applied in all GWASs, including meta-analysis, singleton-based, family-based, and DNA-pooling-based designs. This approach is implemented in a user-friendly web page https://vegas2.qimrberghofer.edu.au and a command line tool. The web implementation uses gene-sets from the gene ontology (GO), curated gene-sets from MSigDB (containing canonical pathways and gene-sets from BIOCARTA, REACTOME, KEGG databases), PANTHER, and pathway commons databases, enabling analysis of a wide range of complex traits. We applied this method on a colorectal cancer GWAS meta-analysis data set (10,934 cases, 12,328 controls) from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). We report statistically significant enrichment of association signal for the 'BMP signaling' and 'muscle cell differentiation' pathways, suggesting a possible role for these pathways onto the risk of colorectal cancer.
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226
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Zhu X, Stephens M. BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES. Ann Appl Stat 2017; 11:1561-1592. [PMID: 29399241 PMCID: PMC5796536 DOI: 10.1214/17-aoas1046] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a "Regression with Summary Statistics" (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss.
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227
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Liu Y, Zhao J, Jiang T, Yu M, Jiang G, Hu Y. A pathway analysis of genome-wide association study highlights novel type 2 diabetes risk pathways. Sci Rep 2017; 7:12546. [PMID: 28970525 PMCID: PMC5624908 DOI: 10.1038/s41598-017-12873-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Accepted: 09/11/2017] [Indexed: 02/04/2023] Open
Abstract
Genome-wide association studies (GWAS) have been widely used to identify common type 2 diabetes (T2D) variants. However, the known variants just explain less than 20% of the overall estimated genetic contribution to T2D. Pathway-based methods have been applied into T2D GWAS datasets to investigate the biological mechanisms and reported some novel T2D risk pathways. However, few pathways were shared in these studies. Here, we performed a pathway analysis using the summary results from a large-scale meta-analysis of T2D GWAS to investigate more genetic signals in T2D. Here, we selected PLNK and VEGAS to perform the gene-based test and WebGestalt to perform the pathway-based test. We identified 8 shared KEGG pathways after correction for multiple tests in both methods. We confirm previous findings, and highlight some new T2D risk pathways. We believe that our results may be helpful to study the genetic mechanisms of T2D.
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Affiliation(s)
- Yang Liu
- College of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Jing Zhao
- The Department of Obstetrics and Gynaecology, Heilongjiang Provincial Forestry General Hospital, Harbin, Heilongjiang, China
| | - Tao Jiang
- The 224th Hospital of Chinese People's Liberation Army, Harbin, Heilongjiang, China
| | - Mei Yu
- Research institute of Chinese Medicine in Heilongjiang province, Harbin, Heilongjiang, China
| | - Guohua Jiang
- College of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Yang Hu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.
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228
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Ortega-Alonso A, Ekelund J, Sarin AP, Miettunen J, Veijola J, Järvelin MR, Hennah W. Genome-Wide Association Study of Psychosis Proneness in the Finnish Population. Schizophr Bull 2017; 43:1304-1314. [PMID: 28525603 PMCID: PMC5737890 DOI: 10.1093/schbul/sbx006] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The current study examined quantitative measures of psychosis proneness in a nonpsychotic population, in order to elucidate their underlying genetic architecture and to observe if there is any commonality to that already detected in the studies of individuals with overt psychotic conditions, such as schizophrenia and bipolar disorder. Heritability, univariate and multivariate genome-wide association (GWAs) tests, including a series of comprehensive gene-based association analyses, were developed in 4269 nonpsychotic persons participating in the Northern Finland Birth Cohort 1966 study with information on the following psychometric measures: Hypomanic Personality, Perceptual Aberration, Physical and Social Anhedonia (also known as Chapman's Schizotypia scales), and Schizoidia scale. Genome-wide genetic data was available for ~9.84 million SNPs. Heritability estimates ranged from 16% to 27%. Phenotypic, genetic and environmental correlations ranged from 0.04-0.43, 0.25-0.73, and 0.12-0.43, respectively. Univariate GWAs tests revealed an intronic SNP (rs12449097) at the TMC7 gene (16p12.3) that significantly associated (P = 3.485 × 10-8) with the hypomanic scale. Bivariate GWAs tests including the hypomanic and physical anhedonia scales suggested a further borderline significant SNP (rs188320715; P-value = 5.261 × 10-8, ~572 kb downstream the ARID1B gene at 6q25.3). Gene-based tests highlighted 20 additional genes of which 5 had previously been associated to schizophrenia and/or bipolar disorder: CSMD1, CCDC141, SLC1A2, CACNA1C, and SNAP25. Altogether the findings explained from 3.7% to 14.1% of the corresponding trait heritability. In conclusion, this study provides preliminary genomic evidence suggesting that qualitatively similar biological factors may underlie different psychosis proneness measures, some of which could further predispose to schizophrenia and bipolar disorder.
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Affiliation(s)
- Alfredo Ortega-Alonso
- Institute for Molecular Medicine Finland-FIMM, Helsinki, Finland,Department of Health, National Institute for Health and Welfare, Helsinki, Finland,To whom correspondence should be addressed; Institute for Molecular Medicine Finland-FIMM, PO Box 20, FI-00014 University of Helsinki, Finland; e-mail:
| | - Jesper Ekelund
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland,Department of Psychiatry, University of Helsinki, Helsinki, Finland,Department of Psychiatry, Vaasa Hospital District, Vaasa, Finland
| | - Antti-Pekka Sarin
- Institute for Molecular Medicine Finland-FIMM, Helsinki, Finland,Department of Health, National Institute for Health and Welfare, Helsinki, Finland,Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Jouko Miettunen
- Center for Life Course Health Research, University of Oulu, Oulu, Finland,Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Juha Veijola
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland,DDepartment of Psychiatry, Research Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland,Department of Psychiatry, Oulu University Hospital, Oulu, Finland
| | - Marjo-Riitta Järvelin
- Center for Life Course Health Research, University of Oulu, Oulu, Finland,Department of Epidemiology and Biostatistics, MRC–PHE Centre for Environment & Health, School of Public Health, Imperial College London, London, UK,Biocenter Oulu, University of Oulu, Oulu, Finland,Unit of Primary Care, Oulu University Hospital, Oulu, Finland
| | - William Hennah
- Institute for Molecular Medicine Finland-FIMM, Helsinki, Finland,Department of Health, National Institute for Health and Welfare, Helsinki, Finland
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229
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Beecham A, Dong C, Wright CB, Dueker N, Brickman AM, Wang L, DeCarli C, Blanton SH, Rundek T, Mayeux R, Sacco RL. Genome-wide scan in Hispanics highlights candidate loci for brain white matter hyperintensities. NEUROLOGY-GENETICS 2017; 3:e185. [PMID: 28975155 PMCID: PMC5619914 DOI: 10.1212/nxg.0000000000000185] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 08/01/2017] [Indexed: 12/22/2022]
Abstract
Objective: To investigate genetic variants influencing white matter hyperintensities (WMHs) in the understudied Hispanic population. Methods: Using 6.8 million single nucleotide polymorphisms (SNPs), we conducted a genome-wide association study (GWAS) to identify SNPs associated with WMH volume (WMHV) in 922 Hispanics who underwent brain MRI as a cross-section of 2 community-based cohorts in the Northern Manhattan Study and the Washington Heights–Inwood Columbia Aging Project. Multiple linear modeling with PLINK was performed to examine the additive genetic effects on ln(WMHV) after controlling for age, sex, total intracranial volume, and principal components of ancestry. Gene-based tests of association were performed using VEGAS. Replication was performed in independent samples of Europeans, African Americans, and Asians. Results: From the SNP analysis, a total of 17 independent SNPs in 7 genes had suggestive evidence of association with WMHV in Hispanics (p < 1 × 10−5) and 5 genes from the gene-based analysis with p < 1 × 10−3. One SNP (rs9957475 in GATA6) and 1 gene (UBE2C) demonstrated evidence of association (p < 0.05) in the African American sample. Four SNPs with p < 1 × 10−5 were shown to affect binding of SPI1 using RegulomeDB. Conclusions: This GWAS of 2 community-based Hispanic cohorts revealed several novel WMH-associated genetic variants. Further replication is needed in independent Hispanic samples to validate these suggestive associations, and fine mapping is needed to pinpoint causal variants.
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Affiliation(s)
- Ashley Beecham
- John T. McDonald Department of Human Genetics (A.B., L.W., S.H.B., R.L.S.), John P Hussman Institute for Human Genomics (A.B., N.D., L.W., S.H.B.), Evelyn F. McKnight Brain Institute (C.D., C.B.W., T.R., R.L.S.), Department of Neurology (C.D., C.B.W., T.R., R.L.S.), and Department of Epidemiology and Public Health (C.B.W., T.R., R.L.S.), Miller School of Medicine, University of Miami, FL; Gertrude H. Sergievsky Center (A.M.B., R.M.), Taub Institute for Research on Alzheimer's Disease and the Aging Brain (A.M.B., R.M.), and Department of Neurology (A.M.B., R.M.), College of Physicians and Surgeons, Columbia University, New York; and Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis, Sacramento
| | - Chuanhui Dong
- John T. McDonald Department of Human Genetics (A.B., L.W., S.H.B., R.L.S.), John P Hussman Institute for Human Genomics (A.B., N.D., L.W., S.H.B.), Evelyn F. McKnight Brain Institute (C.D., C.B.W., T.R., R.L.S.), Department of Neurology (C.D., C.B.W., T.R., R.L.S.), and Department of Epidemiology and Public Health (C.B.W., T.R., R.L.S.), Miller School of Medicine, University of Miami, FL; Gertrude H. Sergievsky Center (A.M.B., R.M.), Taub Institute for Research on Alzheimer's Disease and the Aging Brain (A.M.B., R.M.), and Department of Neurology (A.M.B., R.M.), College of Physicians and Surgeons, Columbia University, New York; and Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis, Sacramento
| | - Clinton B Wright
- John T. McDonald Department of Human Genetics (A.B., L.W., S.H.B., R.L.S.), John P Hussman Institute for Human Genomics (A.B., N.D., L.W., S.H.B.), Evelyn F. McKnight Brain Institute (C.D., C.B.W., T.R., R.L.S.), Department of Neurology (C.D., C.B.W., T.R., R.L.S.), and Department of Epidemiology and Public Health (C.B.W., T.R., R.L.S.), Miller School of Medicine, University of Miami, FL; Gertrude H. Sergievsky Center (A.M.B., R.M.), Taub Institute for Research on Alzheimer's Disease and the Aging Brain (A.M.B., R.M.), and Department of Neurology (A.M.B., R.M.), College of Physicians and Surgeons, Columbia University, New York; and Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis, Sacramento
| | - Nicole Dueker
- John T. McDonald Department of Human Genetics (A.B., L.W., S.H.B., R.L.S.), John P Hussman Institute for Human Genomics (A.B., N.D., L.W., S.H.B.), Evelyn F. McKnight Brain Institute (C.D., C.B.W., T.R., R.L.S.), Department of Neurology (C.D., C.B.W., T.R., R.L.S.), and Department of Epidemiology and Public Health (C.B.W., T.R., R.L.S.), Miller School of Medicine, University of Miami, FL; Gertrude H. Sergievsky Center (A.M.B., R.M.), Taub Institute for Research on Alzheimer's Disease and the Aging Brain (A.M.B., R.M.), and Department of Neurology (A.M.B., R.M.), College of Physicians and Surgeons, Columbia University, New York; and Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis, Sacramento
| | - Adam M Brickman
- John T. McDonald Department of Human Genetics (A.B., L.W., S.H.B., R.L.S.), John P Hussman Institute for Human Genomics (A.B., N.D., L.W., S.H.B.), Evelyn F. McKnight Brain Institute (C.D., C.B.W., T.R., R.L.S.), Department of Neurology (C.D., C.B.W., T.R., R.L.S.), and Department of Epidemiology and Public Health (C.B.W., T.R., R.L.S.), Miller School of Medicine, University of Miami, FL; Gertrude H. Sergievsky Center (A.M.B., R.M.), Taub Institute for Research on Alzheimer's Disease and the Aging Brain (A.M.B., R.M.), and Department of Neurology (A.M.B., R.M.), College of Physicians and Surgeons, Columbia University, New York; and Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis, Sacramento
| | - Liyong Wang
- John T. McDonald Department of Human Genetics (A.B., L.W., S.H.B., R.L.S.), John P Hussman Institute for Human Genomics (A.B., N.D., L.W., S.H.B.), Evelyn F. McKnight Brain Institute (C.D., C.B.W., T.R., R.L.S.), Department of Neurology (C.D., C.B.W., T.R., R.L.S.), and Department of Epidemiology and Public Health (C.B.W., T.R., R.L.S.), Miller School of Medicine, University of Miami, FL; Gertrude H. Sergievsky Center (A.M.B., R.M.), Taub Institute for Research on Alzheimer's Disease and the Aging Brain (A.M.B., R.M.), and Department of Neurology (A.M.B., R.M.), College of Physicians and Surgeons, Columbia University, New York; and Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis, Sacramento
| | - Charles DeCarli
- John T. McDonald Department of Human Genetics (A.B., L.W., S.H.B., R.L.S.), John P Hussman Institute for Human Genomics (A.B., N.D., L.W., S.H.B.), Evelyn F. McKnight Brain Institute (C.D., C.B.W., T.R., R.L.S.), Department of Neurology (C.D., C.B.W., T.R., R.L.S.), and Department of Epidemiology and Public Health (C.B.W., T.R., R.L.S.), Miller School of Medicine, University of Miami, FL; Gertrude H. Sergievsky Center (A.M.B., R.M.), Taub Institute for Research on Alzheimer's Disease and the Aging Brain (A.M.B., R.M.), and Department of Neurology (A.M.B., R.M.), College of Physicians and Surgeons, Columbia University, New York; and Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis, Sacramento
| | - Susan H Blanton
- John T. McDonald Department of Human Genetics (A.B., L.W., S.H.B., R.L.S.), John P Hussman Institute for Human Genomics (A.B., N.D., L.W., S.H.B.), Evelyn F. McKnight Brain Institute (C.D., C.B.W., T.R., R.L.S.), Department of Neurology (C.D., C.B.W., T.R., R.L.S.), and Department of Epidemiology and Public Health (C.B.W., T.R., R.L.S.), Miller School of Medicine, University of Miami, FL; Gertrude H. Sergievsky Center (A.M.B., R.M.), Taub Institute for Research on Alzheimer's Disease and the Aging Brain (A.M.B., R.M.), and Department of Neurology (A.M.B., R.M.), College of Physicians and Surgeons, Columbia University, New York; and Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis, Sacramento
| | - Tatjana Rundek
- John T. McDonald Department of Human Genetics (A.B., L.W., S.H.B., R.L.S.), John P Hussman Institute for Human Genomics (A.B., N.D., L.W., S.H.B.), Evelyn F. McKnight Brain Institute (C.D., C.B.W., T.R., R.L.S.), Department of Neurology (C.D., C.B.W., T.R., R.L.S.), and Department of Epidemiology and Public Health (C.B.W., T.R., R.L.S.), Miller School of Medicine, University of Miami, FL; Gertrude H. Sergievsky Center (A.M.B., R.M.), Taub Institute for Research on Alzheimer's Disease and the Aging Brain (A.M.B., R.M.), and Department of Neurology (A.M.B., R.M.), College of Physicians and Surgeons, Columbia University, New York; and Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis, Sacramento
| | - Richard Mayeux
- John T. McDonald Department of Human Genetics (A.B., L.W., S.H.B., R.L.S.), John P Hussman Institute for Human Genomics (A.B., N.D., L.W., S.H.B.), Evelyn F. McKnight Brain Institute (C.D., C.B.W., T.R., R.L.S.), Department of Neurology (C.D., C.B.W., T.R., R.L.S.), and Department of Epidemiology and Public Health (C.B.W., T.R., R.L.S.), Miller School of Medicine, University of Miami, FL; Gertrude H. Sergievsky Center (A.M.B., R.M.), Taub Institute for Research on Alzheimer's Disease and the Aging Brain (A.M.B., R.M.), and Department of Neurology (A.M.B., R.M.), College of Physicians and Surgeons, Columbia University, New York; and Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis, Sacramento
| | - Ralph L Sacco
- John T. McDonald Department of Human Genetics (A.B., L.W., S.H.B., R.L.S.), John P Hussman Institute for Human Genomics (A.B., N.D., L.W., S.H.B.), Evelyn F. McKnight Brain Institute (C.D., C.B.W., T.R., R.L.S.), Department of Neurology (C.D., C.B.W., T.R., R.L.S.), and Department of Epidemiology and Public Health (C.B.W., T.R., R.L.S.), Miller School of Medicine, University of Miami, FL; Gertrude H. Sergievsky Center (A.M.B., R.M.), Taub Institute for Research on Alzheimer's Disease and the Aging Brain (A.M.B., R.M.), and Department of Neurology (A.M.B., R.M.), College of Physicians and Surgeons, Columbia University, New York; and Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis, Sacramento
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Burren OS, Rubio García A, Javierre BM, Rainbow DB, Cairns J, Cooper NJ, Lambourne JJ, Schofield E, Castro Dopico X, Ferreira RC, Coulson R, Burden F, Rowlston SP, Downes K, Wingett SW, Frontini M, Ouwehand WH, Fraser P, Spivakov M, Todd JA, Wicker LS, Cutler AJ, Wallace C. Chromosome contacts in activated T cells identify autoimmune disease candidate genes. Genome Biol 2017; 18:165. [PMID: 28870212 PMCID: PMC5584004 DOI: 10.1186/s13059-017-1285-0] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 07/21/2017] [Indexed: 12/19/2022] Open
Abstract
Background Autoimmune disease-associated variants are preferentially found in regulatory regions in immune cells, particularly CD4+ T cells. Linking such regulatory regions to gene promoters in disease-relevant cell contexts facilitates identification of candidate disease genes. Results Within 4 h, activation of CD4+ T cells invokes changes in histone modifications and enhancer RNA transcription that correspond to altered expression of the interacting genes identified by promoter capture Hi-C. By integrating promoter capture Hi-C data with genetic associations for five autoimmune diseases, we prioritised 245 candidate genes with a median distance from peak signal to prioritised gene of 153 kb. Just under half (108/245) prioritised genes related to activation-sensitive interactions. This included IL2RA, where allele-specific expression analyses were consistent with its interaction-mediated regulation, illustrating the utility of the approach. Conclusions Our systematic experimental framework offers an alternative approach to candidate causal gene identification for variants with cell state-specific functional effects, with achievable sample sizes. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1285-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Oliver S Burren
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, CB2 0SP, UK.,JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK
| | - Arcadio Rubio García
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.,Present address: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Biola-Maria Javierre
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK
| | - Daniel B Rainbow
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.,Present address: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Jonathan Cairns
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK
| | - Nicholas J Cooper
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK
| | - John J Lambourne
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK
| | - Ellen Schofield
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK
| | - Xaquin Castro Dopico
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK
| | - Ricardo C Ferreira
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.,Present address: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Richard Coulson
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK
| | - Frances Burden
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK
| | - Sophia P Rowlston
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK
| | - Kate Downes
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK
| | - Steven W Wingett
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK
| | - Mattia Frontini
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Willem H Ouwehand
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK.,Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1HH, UK
| | - Peter Fraser
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK
| | - Mikhail Spivakov
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK
| | - John A Todd
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.,Present address: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Linda S Wicker
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.,Present address: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Antony J Cutler
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.,Present address: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Chris Wallace
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, CB2 0SP, UK. .,JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK. .,MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
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231
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Shared genetic variants for polypoidal choroidal vasculopathy and typical neovascular age-related macular degeneration in East Asians. J Hum Genet 2017; 62:1049-1055. [PMID: 28835638 DOI: 10.1038/jhg.2017.83] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Revised: 06/13/2017] [Accepted: 07/20/2017] [Indexed: 01/09/2023]
Abstract
Polypoidal choroidal vasculopathy (PCV), a subtype of age-related macular degeneration (AMD) more frequently seen in East Asians, has both common and distinct clinical manifestations with typical neovascular AMD (tAMD). We aim to examine the extent to which common genetic variants are shared between these two subtypes. We performed the meta-analysis of association in a total of 1062 PCV patients, 1157 tAMD patients and 5275 controls of East Asian descent from the Genetics of AMD in Asians Consortium at the 34 known AMD loci. A total of eight loci were significantly associated with PCV, including age-related maculopathy susceptibility 2 (ARMS2)-HtrA serine peptidase 1 (HTRA1), complement factor H (CFH), C2-CFB-SKIV2L, CETP, VEGFA, ADAMTS9-AS2 and TGFBR1 (P<5 × 10-4) from the single-nucleotide polymorphism-based test and COL4A3 from the gene-based tests (Pgene=2.02 × 10-4). PCV and tAMD are genetically highly correlated (rg=0.69, P=4.68 × 10-3), with AMD known loci accounting for up to 36% variation. Weaker association for PCV was observed at ARMS2-HTRA1 (Pdif=4.39 × 10-4) and KMT2E-SRPK2(Pdif=4.43 × 10-3), compared with tAMD. Variants at CFH, CETP and VEGFA exhibited different association signals in East Asians, in contrast to those in European individuals. Our data suggest a substantially shared genetic susceptibility for PCV and tAMD, while also highlight the unique associations for PCV, which is useful in understanding the pathogenesis of PCV.
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232
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Xu C, Zhang D, Wu Y, Tian X, Pang Z, Li S, Tan Q. A genome-wide association study of cognitive function in Chinese adult twins. Biogerontology 2017; 18:811-819. [PMID: 28808816 DOI: 10.1007/s10522-017-9725-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 08/09/2017] [Indexed: 01/16/2023]
Abstract
Multiple loci or genes have been identified using genome-wide association studies mainly in western countries but with inconsistent results. No similar studies have been conducted in the world's largest and rapidly aging Chinese population. The paper aimed to identify the specific genetic variants associated with cognitive function in middle and old-aged Chinese dizygotic twins (DZ). Cognitive function was measured on 139 pairs of DZ by Montreal Cognitive Assessment. The subjects were genotyped at 1048575 SNP positions. Regression-based mixed-effect kinship model of GWAS was conducted to test the SNPs. Gene-based analysis was performed on VEGAS2. The statistically significant genes were then subject to gene set enrichment analysis to further identify the specific biological pathways associated with cognitive function. No SNPs reached genome-wide significance although there were 13 SNPs of suggestive significance (P < 10-5). Gene-based analysis found 823 significant genes topped by TNRC18P1 (P = 1.00 × 10-6), FGFR1OP2 (P = 6.00 × 10-6), and AKR1D1 (P = 2.30 × 10-5). Enrichment analysis identified 46 biological pathways, mainly involving in signaling transmission, metabolic process and Alzheimer's disease. Analysis of SNPs involved in the regulatory motif detected cell-type specific enhancers involving aorta and colon smooth muscle both have been reported to implicate in cognition. We conclude that genetic variations are significantly involved in functional genes, biological pathways and the regulatory domain that mediate cognitive performances.
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Affiliation(s)
- Chunsheng Xu
- Department of Epidemiology and Health Statistics, School of Public Health, Qingdao University, Qingdao, Shandong, China.,Qingdao Municipal Center for Disease Control and Prevention, Qingdao, Shandong, China.,Qingdao Institute of Preventive Medicine, Qingdao, Shandong, China
| | - Dongfeng Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Qingdao University, Qingdao, Shandong, China.
| | - Yili Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Qingdao University, Qingdao, Shandong, China
| | - Xiaocao Tian
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, Shandong, China.,Qingdao Institute of Preventive Medicine, Qingdao, Shandong, China
| | - Zengchang Pang
- Department of Epidemiology and Health Statistics, School of Public Health, Qingdao University, Qingdao, Shandong, China.,Qingdao Municipal Center for Disease Control and Prevention, Qingdao, Shandong, China.,Qingdao Institute of Preventive Medicine, Qingdao, Shandong, China
| | - Shuxia Li
- Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Qihua Tan
- Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark.,Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark
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233
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Liu L, Zhang L, Li HM, Wang ZR, Xie XF, Mei JP, Jin JL, Shi J, Sun L, Li SC, Tan YL, Yang L, Wang J, Yang HM, Qian QJ, Wang YF. The SNP-set based association study identifies ITGA1 as a susceptibility gene of attention-deficit/hyperactivity disorder in Han Chinese. Transl Psychiatry 2017; 7:e1201. [PMID: 28809852 PMCID: PMC5611725 DOI: 10.1038/tp.2017.156] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 05/20/2017] [Accepted: 06/07/2017] [Indexed: 01/02/2023] Open
Abstract
Genome-wide association studies, which detect the association between single-nucleotide polymorphisms (SNPs) and disease susceptibility, have been extensively applied to study attention-deficit/hyperactivity disorder (ADHD), but genome-wide significant associations have not been found yet. Genetic heterogeneity and insufficient genomic coverage may account for the missing heritability. We performed a two-stage association study for ADHD in the Han Chinese population. In the discovery stage, 1033 ADHD patients and 950 healthy controls were genotyped using both the Affymetrix Genome-Wide Human SNP Array 6.0 and the Illumina Infinium HumanExome BeadChip. The genotyped SNPs were combined to generate a powerful SNP set with better genomic coverage especially for the nonsynonymous variants. In addition to the association of single SNPs, we collected adjacent SNPs as SNP sets, which were determined by either genes or successive sliding windows, to evaluate their synergetic effect. The candidate susceptibility SNPs were further replicated in an independent cohort of 1441 ADHD patients and 1447 healthy controls. No genome-wide significant SNPs or gene-based SNP sets were found to be associated with ADHD. However, two continuous sliding windows located in ITGA1 (P-value=8.33E-7 and P-value=8.43E-7) were genome-wide significant. The quantitative trait analyses also demonstrated their association with ADHD core symptoms and executive functions. The association was further validated by follow-up replications for four selected SNPs: rs1979398 (P-value=2.64E-6), rs16880453 (P-value=3.58E-4), rs1531545 (P-value=7.62E-4) and rs4074793 (P-value=2.03E-4). Our results suggest that genetic variants in ITGA1 may be involved in the etiology of ADHD and the SNP-set based analysis is a promising strategy for the detection of underlying genetic risk factors.
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Affiliation(s)
- L Liu
- Department of Child Psychiatry, Peking University Sixth Hospital/Institute of Mental Health, Beijing, China,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - L Zhang
- BGI Genomics, BGI-Shenzhen, Shenzhen, China,Department of Computer Science, City University of Hong Kong, Hong Kong, China,Department of Computer Science, Stanford University, Stanford, CA, USA
| | - H M Li
- Department of Child Psychiatry, Peking University Sixth Hospital/Institute of Mental Health, Beijing, China,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Z R Wang
- Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University, Beijing, China
| | - X F Xie
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
| | - J P Mei
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
| | - J L Jin
- Department of Child Psychiatry, Peking University Sixth Hospital/Institute of Mental Health, Beijing, China,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - J Shi
- Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University, Beijing, China
| | - L Sun
- Department of Child Psychiatry, Peking University Sixth Hospital/Institute of Mental Health, Beijing, China,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - S C Li
- Department of Computer Science, City University of Hong Kong, Hong Kong, China
| | - Y L Tan
- Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University, Beijing, China
| | - L Yang
- Department of Child Psychiatry, Peking University Sixth Hospital/Institute of Mental Health, Beijing, China,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - J Wang
- BGI Genomics, BGI-Shenzhen, Shenzhen, China,James D. Watson Institute of Genome Sciences, Hangzhou, China
| | - H M Yang
- BGI Genomics, BGI-Shenzhen, Shenzhen, China,James D. Watson Institute of Genome Sciences, Hangzhou, China
| | - Q J Qian
- Department of Child Psychiatry, Peking University Sixth Hospital/Institute of Mental Health, Beijing, China,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China,Peking University Sixth Hospital/Institute of Mental Health, No. 51, Hua Yuan Bei Lu, Haidian Disrtrict, Beijing 100191, China. E-mail: or
| | - Y F Wang
- Department of Child Psychiatry, Peking University Sixth Hospital/Institute of Mental Health, Beijing, China,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China,Peking University Sixth Hospital/Institute of Mental Health, No. 51, Hua Yuan Bei Lu, Haidian Disrtrict, Beijing 100191, China. E-mail: or
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234
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Bogdan R, Salmeron BJ, Carey CE, Agrawal A, Calhoun VD, Garavan H, Hariri AR, Heinz A, Hill MN, Holmes A, Kalin NH, Goldman D. Imaging Genetics and Genomics in Psychiatry: A Critical Review of Progress and Potential. Biol Psychiatry 2017; 82:165-175. [PMID: 28283186 PMCID: PMC5505787 DOI: 10.1016/j.biopsych.2016.12.030] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Revised: 12/21/2016] [Accepted: 12/28/2016] [Indexed: 12/17/2022]
Abstract
Imaging genetics and genomics research has begun to provide insight into the molecular and genetic architecture of neural phenotypes and the neural mechanisms through which genetic risk for psychopathology may emerge. As it approaches its third decade, imaging genetics is confronted by many challenges, including the proliferation of studies using small sample sizes and diverse designs, limited replication, problems with harmonization of neural phenotypes for meta-analysis, unclear mechanisms, and evidence that effect sizes may be more modest than originally posited, with increasing evidence of polygenicity. These concerns have encouraged the field to grow in many new directions, including the development of consortia and large-scale data collection projects and the use of novel methods (e.g., polygenic approaches, machine learning) that enhance the quality of imaging genetic studies but also introduce new challenges. We critically review progress in imaging genetics and offer suggestions and highlight potential pitfalls of novel approaches. Ultimately, the strength of imaging genetics and genomics lies in their translational and integrative potential with other research approaches (e.g., nonhuman animal models, psychiatric genetics, pharmacologic challenge) to elucidate brain-based pathways that give rise to the vast individual differences in behavior as well as risk for psychopathology.
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Affiliation(s)
- Ryan Bogdan
- BRAIN Lab, Department of Psychological and Brain Sciences, St. Louis, Missouri.
| | - Betty Jo Salmeron
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, Maryland
| | - Caitlin E Carey
- BRAIN Lab, Department of Psychological and Brain Sciences, St. Louis, Missouri
| | - Arpana Agrawal
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
| | - Vince D Calhoun
- Mind Research Network and Lovelace Biomedical and Environmental Research Institute, University of New Mexico, Albuquerque, New Mexico; Departments of Psychiatry and Neuroscience, University of New Mexico, Albuquerque, New Mexico; Electronic and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington, Vermont
| | - Ahmad R Hariri
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, North Carolina
| | - Andreas Heinz
- Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Matthew N Hill
- Hotchkiss Brain Institute, Departments of Cell Biology and Anatomy and Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Andrew Holmes
- Laboratory of Behavioral and Genomic Neuroscience, National Institute on Alcohol Abuse and Alcoholism, Bethesda, Maryland
| | - Ned H Kalin
- Department of Psychiatry, University of Wisconsin, Madison, Wisconsin; Neuroscience Training Program (NHK, RK, PHR, DPMT, MEE), University of Wisconsin, Madison, Wisconsin; Wisconsin National Primate Research Center (NHK, MEE), Madison, Wisconsin
| | - David Goldman
- Laboratory of Neurogenetics, Intramural Research Program, National Institute on Alcohol Abuse and Alcoholism, Bethesda, Maryland
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235
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Cukier HN, Kunkle BK, Hamilton KL, Rolati S, Kohli MA, Whitehead PL, Jaworski J, Vance JM, Cuccaro ML, Carney RM, Gilbert JR, Farrer LA, Martin ER, Beecham GW, Haines JL, Pericak-Vance MA. Exome Sequencing of Extended Families with Alzheimer's Disease Identifies Novel Genes Implicated in Cell Immunity and Neuronal Function. ACTA ACUST UNITED AC 2017; 7. [PMID: 29177109 PMCID: PMC5698805 DOI: 10.4172/2161-0460.1000355] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Objective Alzheimer’s disease (AD) is a neurodegenerative disorder for which more than 20 genetic loci have been implicated to date. However, studies demonstrate not all genetic factors have been identified. Therefore, in this study we seek to identify additional rare variants and novel genes potentially contributing to AD. Methods Whole exome sequencing was performed on 23 multi-generational families with an average of eight affected subjects. Exome sequencing was filtered for rare, nonsynonymous and loss-of-function variants. Alterations predicted to have a functional consequence and located within either a previously reported AD gene, a linkage peak (LOD>2), or clustering in the same gene across multiple families, were prioritized. Results Rare variants were found in known AD risk genes including AKAP9, CD33, CR1, EPHA1, INPP5D, NME8, PSEN1, SORL1, TREM2 and UNC5C. Three families had five variants of interest in linkage regions with LOD>2. Genes with segregating alterations in these peaks include CD163L1 and CLECL1, two genes that have both been implicated in immunity, CTNNA1, which encodes a catenin in the cerebral cortex and MIEF1, a gene that may induce mitochondrial dysfunction and has the potential to damage neurons. Four genes were identified with alterations in more than one family include PLEKHG5, a gene that causes Charcot-Marie-Tooth disease and THBS2, which promotes synaptogenesis. Conclusion Utilizing large families with a heavy burden of disease allowed for the identification of rare variants co-segregating with disease. Variants were identified in both known AD risk genes and in novel genes.
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Affiliation(s)
- H N Cukier
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.,Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - B K Kunkle
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - K L Hamilton
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - S Rolati
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - M A Kohli
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - P L Whitehead
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - J Jaworski
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - J M Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.,John T. Macdonald Foundation, Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - M L Cuccaro
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.,John T. Macdonald Foundation, Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - R M Carney
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.,Mental Health and Behavioral Sciences Service, Miami Veterans Affairs, Miami, FL, USA
| | - J R Gilbert
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.,John T. Macdonald Foundation, Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - L A Farrer
- Departments of Medicine, Neurology, Ophthalmology, Genetics and Genomics, Epidemiology and Biostatistics, Boston University, Boston, MA, USA
| | - E R Martin
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.,John T. Macdonald Foundation, Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - G W Beecham
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.,John T. Macdonald Foundation, Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - J L Haines
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - M A Pericak-Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.,Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA.,John T. Macdonald Foundation, Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
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236
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Glucocorticoid therapy regulates podocyte motility by inhibition of Rac1. Sci Rep 2017; 7:6725. [PMID: 28751734 PMCID: PMC5532274 DOI: 10.1038/s41598-017-06810-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 06/19/2017] [Indexed: 02/03/2023] Open
Abstract
Nephrotic syndrome (NS) occurs when the glomerular filtration barrier becomes excessively permeable leading to massive proteinuria. In childhood NS, immune system dysregulation has been implicated and increasing evidence points to the central role of podocytes in the pathogenesis. Children with NS are typically treated with an empiric course of glucocorticoid (Gc) therapy; a class of steroids that are activating ligands for the glucocorticoid receptor (GR) transcription factor. Although Gc-therapy has been the cornerstone of NS management for decades, the mechanism of action, and target cell, remain poorly understood. We tested the hypothesis that Gc acts directly on the podocyte to produce clinically useful effects without involvement of the immune system. In human podocytes, we demonstrated that the basic GR-signalling mechanism is intact and that Gc induced an increase in podocyte barrier function. Defining the GR-cistrome identified Gc regulation of motility genes. These findings were functionally validated with live-cell imaging. We demonstrated that treatment with Gc reduced the activity of the pro-migratory small GTPase regulator Rac1. Furthermore, Rac1 inhibition had a direct, protective effect on podocyte barrier function. Our studies reveal a new mechanism for Gc action directly on the podocyte, with translational relevance to designing new selective synthetic Gc molecules.
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Boueiz A, Lutz SM, Cho MH, Hersh CP, Bowler RP, Washko GR, Halper-Stromberg E, Bakke P, Gulsvik A, Laird NM, Beaty TH, Coxson HO, Crapo JD, Silverman EK, Castaldi PJ, DeMeo DL. Genome-Wide Association Study of the Genetic Determinants of Emphysema Distribution. Am J Respir Crit Care Med 2017; 195:757-771. [PMID: 27669027 DOI: 10.1164/rccm.201605-0997oc] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
RATIONALE Emphysema has considerable variability in the severity and distribution of parenchymal destruction throughout the lungs. Upper lobe-predominant emphysema has emerged as an important predictor of response to lung volume reduction surgery. Yet, aside from alpha-1 antitrypsin deficiency, the genetic determinants of emphysema distribution remain largely unknown. OBJECTIVES To identify the genetic influences of emphysema distribution in non-alpha-1 antitrypsin-deficient smokers. METHODS A total of 11,532 subjects with complete genotype and computed tomography densitometry data in the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease [COPD]; non-Hispanic white and African American), ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints), and GenKOLS (Genetics of Chronic Obstructive Lung Disease) studies were analyzed. Two computed tomography scan emphysema distribution measures (difference between upper-third and lower-third emphysema; ratio of upper-third to lower-third emphysema) were tested for genetic associations in all study subjects. Separate analyses in each study population were followed by a fixed effect metaanalysis. Single-nucleotide polymorphism-, gene-, and pathway-based approaches were used. In silico functional evaluation was also performed. MEASUREMENTS AND MAIN RESULTS We identified five loci associated with emphysema distribution at genome-wide significance. These loci included two previously reported associations with COPD susceptibility (4q31 near HHIP and 15q25 near CHRNA5) and three new associations near SOWAHB, TRAPPC9, and KIAA1462. Gene set analysis and in silico functional evaluation revealed pathways and cell types that may potentially contribute to the pathogenesis of emphysema distribution. CONCLUSIONS This multicohort genome-wide association study identified new genomic loci associated with differential emphysematous destruction throughout the lungs. These findings may point to new biologic pathways on which to expand diagnostic and therapeutic approaches in chronic obstructive pulmonary disease. Clinical trial registered with www.clinicaltrials.gov (NCT 00608764).
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Affiliation(s)
- Adel Boueiz
- 1 Channing Division of Network Medicine.,2 Pulmonary and Critical Care Division, Department of Medicine, and
| | - Sharon M Lutz
- 3 Department of Biostatistics, Colorado School of Public Health, University of Colorado, Aurora, Colorado
| | - Michael H Cho
- 1 Channing Division of Network Medicine.,2 Pulmonary and Critical Care Division, Department of Medicine, and
| | - Craig P Hersh
- 1 Channing Division of Network Medicine.,2 Pulmonary and Critical Care Division, Department of Medicine, and
| | - Russell P Bowler
- 4 Division of Pulmonary Medicine, Department of Medicine, National Jewish Health, Denver, Colorado
| | - George R Washko
- 2 Pulmonary and Critical Care Division, Department of Medicine, and
| | - Eitan Halper-Stromberg
- 4 Division of Pulmonary Medicine, Department of Medicine, National Jewish Health, Denver, Colorado
| | - Per Bakke
- 5 Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Amund Gulsvik
- 5 Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Nan M Laird
- 6 Harvard School of Public Health, Boston, Massachusetts
| | - Terri H Beaty
- 7 Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland; and
| | - Harvey O Coxson
- 8 Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - James D Crapo
- 4 Division of Pulmonary Medicine, Department of Medicine, National Jewish Health, Denver, Colorado
| | - Edwin K Silverman
- 1 Channing Division of Network Medicine.,2 Pulmonary and Critical Care Division, Department of Medicine, and
| | - Peter J Castaldi
- 1 Channing Division of Network Medicine.,9 Division of General Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Dawn L DeMeo
- 1 Channing Division of Network Medicine.,2 Pulmonary and Critical Care Division, Department of Medicine, and
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Gene-wide Association Study Reveals RNF122 Ubiquitin Ligase as a Novel Susceptibility Gene for Attention Deficit Hyperactivity Disorder. Sci Rep 2017; 7:5407. [PMID: 28710364 PMCID: PMC5511183 DOI: 10.1038/s41598-017-05514-7] [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: 01/31/2017] [Accepted: 05/31/2017] [Indexed: 01/07/2023] Open
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a common childhood-onset neurodevelopmental condition characterized by pervasive impairment of attention, hyperactivity, and/or impulsivity that can persist into adulthood. The aetiology of ADHD is complex and multifactorial and, despite the wealth of evidence for its high heritability, genetic studies have provided modest evidence for the involvement of specific genes and have failed to identify consistent and replicable results. Due to the lack of robust findings, we performed gene-wide and pathway enrichment analyses using pre-existing GWAS data from 607 persistent ADHD subjects and 584 controls, produced by our group. Subsequently, expression profiles of genes surpassing a follow-up threshold of P-value < 1e-03 in the gene-wide analyses were tested in peripheral blood mononucleated cells (PBMCs) of 45 medication-naive adults with ADHD and 39 healthy unrelated controls. We found preliminary evidence for genetic association between RNF122 and ADHD and for its overexpression in adults with ADHD. RNF122 encodes for an E3 ubiquitin ligase involved in the proteasome-mediated processing, trafficking, and degradation of proteins that acts as an essential mediator of the substrate specificity of ubiquitin ligation. Thus, our findings support previous data that place the ubiquitin-proteasome system as a promising candidate for its involvement in the aetiology of ADHD.
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Holmqvist Jämsen S, Johansson A, Westberg L, Santtila P, von der Pahlen B, Simberg S. Associations Between Vocal Symptoms and Genetic Variants in the Oxytocin Receptor and Arginine Vasopressin 1A Receptor Gene. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2017; 60:1843-1854. [PMID: 28687839 DOI: 10.1044/2016_jslhr-s-16-0059] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 12/27/2016] [Indexed: 06/07/2023]
Abstract
PURPOSE Oxytocin and arginine vasopressin are associated with different aspects of the stress response. As stress is regarded as a risk factor for vocal symptoms, we wanted to explore the association between the oxytocin receptor gene (OXTR) and arginine vasopressin 1A receptor gene (AVPR1A) single-nucleotide polymorphisms (SNPs) and vocal symptoms. We also wanted to explore whether such effects might be mediated by cortisol because oxytocin and vasopressin are associated with cortisol levels. METHOD A population-based sample (N = 657) of Finnish twins (born 1961-1989) completed a web questionnaire on the occurrence of vocal symptoms. A total of 170 participants submitted saliva samples for hormone analysis. A total of 20 OXTR and AVPR1A SNPs were analyzed. RESULTS Three OXTR polymorphisms (rs2270465, rs2268493, rs7632287) and 2 AVPR1A polymorphisms (rs1587097, rs1042615) showed nominal effects (p < .05) on vocal symptoms, of which 1 (rs1587097) remained significant after correcting for multiple testing (p = .003). We found potential mediation of the effect of the OXTR rs2268493 polymorphism on vocal symptoms through levels of cortisol. CONCLUSIONS The associations between variants of OXTR and AVPR1A and vocal symptoms indicate that oxytocin and vasopressin might influence vocal symptoms. The effect of oxytocin seems to be partly mediated through cortisol actions.
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Affiliation(s)
| | - Ada Johansson
- Faculty of Arts, Psychology and Theology, Åbo Akademi University, Turku, FinlandDepartment of Psychology and Speech-Language Pathology, Faculty of Social Sciences, University of Turku, FinlandDepartment of Pharmacology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Lars Westberg
- Department of Pharmacology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Pekka Santtila
- Faculty of Arts, Psychology and Theology, Åbo Akademi University, Turku, Finland
| | | | - Susanna Simberg
- Faculty of Arts, Psychology and Theology, Åbo Akademi University, Turku, FinlandDepartment of Special Needs Education, Faculty of Educational Sciences, University of Oslo, Norway
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Willems SM, Wright DJ, Day FR, Trajanoska K, Joshi PK, Morris JA, Matteini AM, Garton FC, Grarup N, Oskolkov N, Thalamuthu A, Mangino M, Liu J, Demirkan A, Lek M, Xu L, Wang G, Oldmeadow C, Gaulton KJ, Lotta LA, Miyamoto-Mikami E, Rivas MA, White T, Loh PR, Aadahl M, Amin N, Attia JR, Austin K, Benyamin B, Brage S, Cheng YC, Cięszczyk P, Derave W, Eriksson KF, Eynon N, Linneberg A, Lucia A, Massidda M, Mitchell BD, Miyachi M, Murakami H, Padmanabhan S, Pandey A, Papadimitriou I, Rajpal DK, Sale C, Schnurr TM, Sessa F, Shrine N, Tobin MD, Varley I, Wain LV, Wray NR, Lindgren CM, MacArthur DG, Waterworth DM, McCarthy MI, Pedersen O, Khaw KT, Kiel DP, Pitsiladis Y, Fuku N, Franks PW, North KN, van Duijn CM, Mather KA, Hansen T, Hansson O, Spector T, Murabito JM, Richards JB, Rivadeneira F, Langenberg C, Perry JRB, Wareham NJ, Scott RA. Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness. Nat Commun 2017; 8:16015. [PMID: 29313844 PMCID: PMC5510175 DOI: 10.1038/ncomms16015] [Citation(s) in RCA: 120] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 05/22/2017] [Indexed: 02/02/2023] Open
Abstract
Hand grip strength is a widely used proxy of muscular fitness, a marker of frailty, and predictor of a range of morbidities and all-cause mortality. To investigate the genetic determinants of variation in grip strength, we perform a large-scale genetic discovery analysis in a combined sample of 195,180 individuals and identify 16 loci associated with grip strength (P<5 × 10-8) in combined analyses. A number of these loci contain genes implicated in structure and function of skeletal muscle fibres (ACTG1), neuronal maintenance and signal transduction (PEX14, TGFA, SYT1), or monogenic syndromes with involvement of psychomotor impairment (PEX14, LRPPRC and KANSL1). Mendelian randomization analyses are consistent with a causal effect of higher genetically predicted grip strength on lower fracture risk. In conclusion, our findings provide new biological insight into the mechanistic underpinnings of grip strength and the causal role of muscular strength in age-related morbidities and mortality.
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Affiliation(s)
- Sara M. Willems
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Daniel J. Wright
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Felix R. Day
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Katerina Trajanoska
- Department of Internal Medicine, Erasmus Medical Center, 3015 CE Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, 3015 CE Rotterdam, The Netherlands
| | - Peter K. Joshi
- Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
| | - John A. Morris
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, Quebec, Canada QC H3T 1E2
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada H3G 0B1
| | - Amy M. Matteini
- Division of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
| | - Fleur C. Garton
- Queensland Brain Institute, University of Queensland, St Lucia, Queensland 4072, Australia
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Nikolay Oskolkov
- Lund University Diabetes Center, Department of Clinical Sciences, Diabetes and Endocrinology, Skånes University Hospital, 222 41 Lund, Sweden
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, New South Wales 2031, Australia
| | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, Kings College London, London SE1 7EH, UK
- NIHR Biomedical Research Centre at Guy’s and St. Thomas’ NHS Foundation Trust, London SE1 9RT, UK
| | - Jun Liu
- Department of Epidemiology, Erasmus Medical Center, 3015 CE Rotterdam, The Netherlands
| | - Ayse Demirkan
- Department of Epidemiology, Erasmus Medical Center, 3015 CE Rotterdam, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Monkol Lek
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Maryland 02114, USA
- Harvard Medical School, Boston, Maryland 02115, USA
| | - Liwen Xu
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Maryland 02114, USA
- Harvard Medical School, Boston, Maryland 02115, USA
| | - Guan Wang
- Centre for Sport and Exercise Science and Medicine (SESAME), University of Brighton, Eastbourne BN20 7SN, UK
| | | | - Kyle J. Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla, California 92093, USA
| | - Luca A. Lotta
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Eri Miyamoto-Mikami
- Japan Society for the Promotion of Science, Tokyo 102-0083, Japan
- Department of Sports and Life Science, National Institute of Fitness and Sports, Kanoya, Kagoshima 891-2393, Japan
| | - Manuel A. Rivas
- Department of Biomedical Data Sciences, Stanford University, Stanford, California 94305, USA
- BROAD Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts 02142, USA
| | - Tom White
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Po-Ru Loh
- BROAD Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts 02142, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | - Mette Aadahl
- Research Centre for Prevention and Health, Capital Region of Denmark, Glostrup University Hospital, DK-2600 Glostrup, Denmark
| | - Najaf Amin
- Department of Epidemiology, Erasmus Medical Center, 3015 CE Rotterdam, The Netherlands
| | - John R. Attia
- Hunter Medical Research Institute, Newcastle, New South Wales 2305, Australia
- Faculty of Health and Medicine, University of Newcastle, Newcastle, New South Wales 2308, Australia
- John Hunter Hospital, New Lambton, New South Wales 2305, Australia
| | - Krista Austin
- Centre for Sport and Exercise Science and Medicine (SESAME), University of Brighton, Eastbourne BN20 7SN, UK
| | - Beben Benyamin
- Queensland Brain Institute, University of Queensland, St Lucia, Queensland 4072, Australia
- Institute for Molecular Bioscience, University of Queensland, St Lucia, Queensland 4072, Australia
| | - Søren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Yu-Ching Cheng
- Division of Endocrinology Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
| | - Paweł Cięszczyk
- Faculty of Physical Education, Gdańsk University of Physical Education and Sport, 80-336 Gdańsk, Poland
| | - Wim Derave
- Department of Movement and Sports Sciences, Ghent University, 9000 Ghent, Belgium
| | - Karl-Fredrik Eriksson
- Lund University Diabetes Center, Department of Clinical Sciences, Diabetes and Endocrinology, Skånes University Hospital, 222 41 Lund, Sweden
| | - Nir Eynon
- Institute of Sport, Exercise & Active Living (ISEAL), Victoria University, Melbourne, Victoria 8001, Australia
- Murdoch Children’s Research Institute, Royal Children’s Hospital, Melbourne, Victoria 3052, Australia
| | - Allan Linneberg
- Research Centre for Prevention and Health, Capital Region of Denmark, Glostrup University Hospital, DK-2600 Glostrup, Denmark
- Department of Clinical Experimental Research, Rigshospitalet, 2600 Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Alejandro Lucia
- Universidad Europea de Madrid, 28670 Villaviciosa de Odón, Madrid, Spain
- Research Institute ‘i+12’, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain
| | - Myosotis Massidda
- Department of Life and Environmental Sciences, University of Cagliari, 09124 Cagliari, Italy
| | - Braxton D. Mitchell
- Division of Endocrinology Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, Maryland 21201, USA
| | - Motohiko Miyachi
- National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo 162-8636, Japan
| | - Haruka Murakami
- National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo 162-8636, Japan
| | - Sandosh Padmanabhan
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK
| | - Ashutosh Pandey
- Target Sciences, GlaxoSmithKline, King of Prussia, Pennsylvania 19406, USA
| | - Ioannis Papadimitriou
- Institute of Sport, Exercise & Active Living (ISEAL), Victoria University, Melbourne, Victoria 8001, Australia
| | - Deepak K. Rajpal
- Target Sciences, GlaxoSmithKline, King of Prussia, Pennsylvania 19406, USA
| | - Craig Sale
- Musculoskeletal Physiology Research Group, Sport, Health and Performance Enhancement (SHAPE) Research Centre, Nottingham Trent University, Nottingham NG1 4FQ, UK
| | - Theresia M. Schnurr
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Francesco Sessa
- Department of Clinical and Experimental Medicine, Medical Genetics, University of Foggia, 71122 Foggia FG, Italy
| | - Nick Shrine
- Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK
- National Institute for Health Research, Leicester Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK
| | - Martin D. Tobin
- Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK
- National Institute for Health Research, Leicester Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK
| | - Ian Varley
- Musculoskeletal Physiology Research Group, Sport, Health and Performance Enhancement (SHAPE) Research Centre, Nottingham Trent University, Nottingham NG1 4FQ, UK
| | - Louise V. Wain
- Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK
- National Institute for Health Research, Leicester Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK
| | - Naomi R. Wray
- Queensland Brain Institute, University of Queensland, St Lucia, Queensland 4072, Australia
- Institute for Molecular Bioscience, University of Queensland, St Lucia, Queensland 4072, Australia
| | - Cecilia M. Lindgren
- BROAD Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts 02142, USA
- The Big Data Institute, University of Oxford, Oxford OX3 7BN, UK
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Daniel G. MacArthur
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Maryland 02114, USA
- BROAD Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts 02142, USA
| | - Dawn M. Waterworth
- Target Sciences, GlaxoSmithKline, King of Prussia, Pennsylvania 19406, USA
| | - Mark I. McCarthy
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LE, UK
- NIHR Oxford Biomedical Research Centre, Oxford OX3 7LE, UK
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK
| | - Douglas P. Kiel
- Harvard Medical School, Boston, Maryland 02115, USA
- Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts 02131, USA
- Department of Medicine, Beth Israel Deaconess Medical Centre, Boston, Massachusetts 02215, USA
| | - Yannis Pitsiladis
- Centre for Sport and Exercise Science and Medicine (SESAME), University of Brighton, Eastbourne BN20 7SN, UK
| | - Noriyuki Fuku
- Graduate School of Health and Sports Science, Juntendo University, Chiba 270-1695, Japan
| | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Skånes University Hospital, 222 41 Lund, Sweden
- Public Health and Clinical Medicine, Section for Medicine, Umeå University, 901 87 Umeå, Sweden
- Biobank Research, Umeå University, 901 87 Umeå, Sweden
| | - Kathryn N. North
- Murdoch Children’s Research Institute, Royal Children’s Hospital, Melbourne, Victoria 3052, Australia
| | - Cornelia M. van Duijn
- Department of Epidemiology, Erasmus Medical Center, 3015 CE Rotterdam, The Netherlands
| | - Karen A. Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, New South Wales 2031, Australia
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
- Faculty of Health Sciences, University of Southern Denmark, 5230 Odense M, Denmark
| | - Ola Hansson
- Lund University Diabetes Center, Department of Clinical Sciences, Diabetes and Endocrinology, Skånes University Hospital, 222 41 Lund, Sweden
| | - Tim Spector
- Department of Twin Research & Genetic Epidemiology, Kings College London, London SE1 7EH, UK
| | - Joanne M. Murabito
- Boston University School of Medicine, Department of Medicine, Section of General Internal Medicine, Boston, Massachusetts 02118, USA
- National Heart Lung and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Massachusetts 01702, USA
| | - J. Brent Richards
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, Quebec, Canada QC H3T 1E2
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada H3G 0B1
- Department of Twin Research & Genetic Epidemiology, Kings College London, London SE1 7EH, UK
- Department of Medicine, McGill University, Montreal, Quebec, Canada H3G 1A4
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus Medical Center, 3015 CE Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, 3015 CE Rotterdam, The Netherlands
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - John R. B. Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Nick J. Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Robert A. Scott
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
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Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet 2017; 101:5-22. [PMID: 28686856 DOI: 10.1016/j.ajhg.2017.06.005] [Citation(s) in RCA: 1924] [Impact Index Per Article: 274.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.
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242
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Smith AK, Jovanovic T, Kilaru V, Lori A, Gensler L, Lee SS, Norrholm SD, Massa N, Cuthbert B, Bradley B, Ressler KJ, Duncan E. A Gene-Based Analysis of Acoustic Startle Latency. Front Psychiatry 2017; 8:117. [PMID: 28729842 PMCID: PMC5498475 DOI: 10.3389/fpsyt.2017.00117] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 06/19/2017] [Indexed: 12/16/2022] Open
Abstract
Latency of the acoustic startle response is the time required from the presentation of startling auditory stimulus until the startle response is elicited and provides an index of neural processing speed. Latency is prolonged in subjects with schizophrenia compared to controls in some but not all studies and is 68-90% heritable in baseline startle trials. In order to determine the genetic association with latency as a potential inroad into genetically based vulnerability to psychosis, we conducted a gene-based study of latency followed by an independent replication study of significant gene findings with a single-nucleotide polymorphism (SNP)-based analysis of schizophrenia and control subjects. 313 subjects from an urban population of low socioeconomic status with mixed psychiatric diagnoses were included in the gene-based study. Startle testing was conducted using a Biopac M150 system according to our published methods. Genotyping was performed with the Omni-Quad 1M or the Omni Express BeadChip. The replication study was conducted on 154 schizophrenia subjects and 123 psychiatric controls. Genetic analyses were conducted with Illumina Human Omni1-Quad and OmniExpress BeadChips. Twenty-nine SNPs were selected from four genes that were significant in the gene-based analysis and also associated with startle and/or schizophrenia in the literature. Linear regressions on latency were conducted, controlling for age, race, and diagnosis as a dichotomous variable. In the gene-based study, 2,870 genes demonstrated the evidence of association after correction for multiple comparisons (false discovery rate < 0.05). Pathway analysis of these genes revealed enrichment for relevant biological processes including neural transmission (p = 0.0029), synaptic transmission (p = 0.0032), and neuronal development (p = 0.024). The subsequent SNP-based replication analysis revealed a strong association of onset latency with the SNP rs901561 on the neuregulin gene (NRG1) in an additive model (beta = 0.21, p = 0.001), indicating that subjects with the AA and AG genotypes had slower mean latency than subjects with GG genotype. In conclusion, startle latency, a highly heritable measure that is slowed in schizophrenia, may be a useful biological probe for genetic contributions to psychotic disorders. Our analyses in two independent populations point to a significant prediction of startle latency by genetic variation in NRG1.
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Affiliation(s)
- Alicia K. Smith
- Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA, United States
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Varun Kilaru
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Adriana Lori
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Lauren Gensler
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Samuel S. Lee
- Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Seth Davin Norrholm
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
- Mental Health Service Line, Atlanta Veterans Affairs Medical Center, Decatur, GA, United States
| | - Nicholas Massa
- Mental Health Service Line, Atlanta Veterans Affairs Medical Center, Decatur, GA, United States
| | - Bruce Cuthbert
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
- Mental Health Service Line, Atlanta Veterans Affairs Medical Center, Decatur, GA, United States
| | - Bekh Bradley
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
- Mental Health Service Line, Atlanta Veterans Affairs Medical Center, Decatur, GA, United States
| | - Kerry J. Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Erica Duncan
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
- Mental Health Service Line, Atlanta Veterans Affairs Medical Center, Decatur, GA, United States
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Shim JE, Bang C, Yang S, Lee T, Hwang S, Kim CY, Singh-Blom UM, Marcotte EM, Lee I. GWAB: a web server for the network-based boosting of human genome-wide association data. Nucleic Acids Res 2017; 45:W154-W161. [PMID: 28449091 PMCID: PMC5793838 DOI: 10.1093/nar/gkx284] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2017] [Revised: 04/01/2017] [Accepted: 04/17/2017] [Indexed: 12/29/2022] Open
Abstract
During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10-8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.
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Affiliation(s)
- Jung Eun Shim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 120-749, Korea
| | - Changbae Bang
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 120-749, Korea
| | - Sunmo Yang
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 120-749, Korea
| | - Tak Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 120-749, Korea
| | - Sohyun Hwang
- Department of Biomedical Science, College of Life Science, CHA University, Seongnam-si 13496, Korea
| | - Chan Yeong Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 120-749, Korea
| | - U Martin Singh-Blom
- Cognition Group, Schibsted Products & Technologies, Västra Järnvägsgatan 21, 111 64 Stockholm, Sweden
| | - Edward M Marcotte
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas, Austin, TX 78712, USA
- Department of Molecular Biosciences, University of Texas at Austin, TX 78712, USA
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 120-749, Korea
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244
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Hayden LP, Cho MH, McDonald MLN, Crapo JD, Beaty TH, Silverman EK, Hersh CP. Susceptibility to Childhood Pneumonia: A Genome-Wide Analysis. Am J Respir Cell Mol Biol 2017; 56:20-28. [PMID: 27508494 DOI: 10.1165/rcmb.2016-0101oc] [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] [Indexed: 12/15/2022] Open
Abstract
Previous studies have indicated that in adult smokers, a history of childhood pneumonia is associated with reduced lung function and chronic obstructive pulmonary disease. There have been few previous investigations using genome-wide association studies to investigate genetic predisposition to pneumonia. This study aims to identify the genetic variants associated with the development of pneumonia during childhood and over the course of the lifetime. Study subjects included current and former smokers with and without chronic obstructive pulmonary disease participating in the COPDGene Study. Pneumonia was defined by subject self-report, with childhood pneumonia categorized as having the first episode at <16 years. Genome-wide association studies for childhood pneumonia (843 cases, 9,091 control subjects) and lifetime pneumonia (3,766 cases, 5,659 control subjects) were performed separately in non-Hispanic whites and African Americans. Non-Hispanic white and African American populations were combined in the meta-analysis. Top genetic variants from childhood pneumonia were assessed in network analysis. No single-nucleotide polymorphisms reached genome-wide significance, although we identified potential regions of interest. In the childhood pneumonia analysis, this included variants in NGR1 (P = 6.3 × 10-8), PAK6 (P = 3.3 × 10-7), and near MATN1 (P = 2.8 × 10-7). In the lifetime pneumonia analysis, this included variants in LOC339862 (P = 8.7 × 10-7), RAPGEF2 (P = 8.4 × 10-7), PHACTR1 (P = 6.1 × 10-7), near PRR27 (P = 4.3 × 10-7), and near MCPH1 (P = 2.7 × 10-7). Network analysis of the genes associated with childhood pneumonia included top networks related to development, blood vessel morphogenesis, muscle contraction, WNT signaling, DNA damage, apoptosis, inflammation, and immune response (P ≤ 0.05). We have identified genes potentially associated with the risk of pneumonia. Further research will be required to confirm these associations and to determine biological mechanisms. CLINICAL TRIAL REGISTRATION NCT00608764.
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Affiliation(s)
- Lystra P Hayden
- 1 Division of Respiratory Diseases, Boston Children's Hospital, Boston, Massachusetts.,2 Channing Division of Network Medicine and
| | - Michael H Cho
- 2 Channing Division of Network Medicine and.,3 Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | | | | | - Terri H Beaty
- 5 Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland
| | - Edwin K Silverman
- 2 Channing Division of Network Medicine and.,3 Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Craig P Hersh
- 2 Channing Division of Network Medicine and.,3 Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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245
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Lin YY, Yu MW, Lin SM, Lee SD, Chen CL, Chen DS, Chen PJ. Genome-wide association analysis identifies a GLUL haplotype for familial hepatitis B virus-related hepatocellular carcinoma. Cancer 2017; 123:3966-3976. [PMID: 28662289 DOI: 10.1002/cncr.30851] [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: 03/22/2017] [Revised: 05/02/2017] [Accepted: 05/08/2017] [Indexed: 12/24/2022]
Abstract
BACKGROUND A family history of liver cancer increases the risk of developing hepatocellular carcinoma (HCC) by 2-fold to 10-fold among patients with chronic hepatitis B virus (HBV). Previous genome-wide association studies have identified many possible susceptible loci associated with sporadic HBV-related HCC. However, despite family history being a well-known risk factor for HBV-related HCC, to the authors' knowledge its genetic mechanisms and associating loci remain largely unknown or unexplored, most likely due to the relative rarity of familial HCC and the difficulty of sample collection. METHODS The authors conducted a genome-wide association study with 139 male cases with familial HBV-related HCC and 139 non-HCC male controls with chronic HBV. The results were corroborated further with an independent cohort of 101 patients with familial HBV-related HCC and comparison with both the 1000 Genomes Project and the Taiwan Biobank. RESULTS A total of 51 risk single-nucleotide polymorphisms (P≤1E-04) were identified in the association analyses, which included 2 clusters of associated single-nucleotide polymorphisms and haplotypes at 1q25.3 (glutamate-ammonia ligase [GLUL]/transmembrane epididymal protein 1 [TEDDM1]/long intergenic non-protein-coding RNA 272 [LINC00272]/regulator of G-protein signaling-like 1 [RGSL1]) and 17q11.2 (solute carrier family 13 member 2 [SLC13A2]/forkhead box N1 [FOXN1]). Both the GLUL and SLC13A2/FOXN1 haplotypes have large effect sizes and were found to be different from those found from genome-wide association studies of sporadic HCCs. CONCLUSIONS To the authors' knowledge, the current study is the first genome-wide association study to identify genetic factors for familial HBV-related HCC. The results identified 2 large effect susceptible haplotypes located at GLUL and SLC13A2/FOXN1. The current study findings also suggest different genetic susceptibility between familial and sporadic HBV-related HCC. Cancer 2017;123:3966-76. © 2017 American Cancer Society.
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Affiliation(s)
- You-Yu Lin
- Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, Taiwan
| | - Ming-Whei Yu
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Shi-Ming Lin
- Liver Research Unit, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taipei, Taiwan
| | - Shou-Dong Lee
- Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan.,Division of Gastroenterology, Department of Medicine, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Chih-Ling Chen
- Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, Taiwan
| | - Ding-Shinn Chen
- Hepatitis Research Center, National Taiwan University, Taipei, Taiwan
| | - Pei-Jer Chen
- Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, Taiwan
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246
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Mandage R, Telford M, Rodríguez JA, Farré X, Layouni H, Marigorta UM, Cundiff C, Heredia-Genestar JM, Navarro A, Santpere G. Genetic factors affecting EBV copy number in lymphoblastoid cell lines derived from the 1000 Genome Project samples. PLoS One 2017; 12:e0179446. [PMID: 28654678 PMCID: PMC5487016 DOI: 10.1371/journal.pone.0179446] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 05/29/2017] [Indexed: 12/22/2022] Open
Abstract
Epstein-Barr virus (EBV), human herpes virus 4, has been classically associated with infectious mononucleosis, multiple sclerosis and several types of cancers. Many of these diseases show marked geographical differences in prevalence, which points to underlying genetic and/or environmental factors. Those factors may include a different susceptibility to EBV infection and viral copy number among human populations. Since EBV is commonly used to transform B-cells into lymphoblastoid cell lines (LCLs) we hypothesize that differences in EBV copy number among individual LCLs may reflect differential susceptibility to EBV infection. To test this hypothesis, we retrieved whole-genome sequenced EBV-mapping reads from 1,753 LCL samples derived from 19 populations worldwide that were sequenced within the context of the 1000 Genomes Project. An in silico methodology was developed to estimate the number of EBV copy number in LCLs and validated these estimations by real-time PCR. After experimentally confirming that EBV relative copy number remains stable over cell passages, we performed a genome wide association analysis (GWAS) to try detecting genetic variants of the host that may be associated with EBV copy number. Our GWAS has yielded several genomic regions suggestively associated with the number of EBV genomes per cell in LCLs, unraveling promising candidate genes such as CAND1, a known inhibitor of EBV replication. While this GWAS does not unequivocally establish the degree to which genetic makeup of individuals determine viral levels within their derived LCLs, for which a larger sample size will be needed, it potentially highlighted human genes affecting EBV-related processes, which constitute interesting candidates to follow up in the context of EBV related pathologies.
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Affiliation(s)
- Rajendra Mandage
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i la Salut, Universitat Pompeu Fabra, PRBB, Barcelona, Catalonia, Spain
| | - Marco Telford
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i la Salut, Universitat Pompeu Fabra, PRBB, Barcelona, Catalonia, Spain
| | - Juan Antonio Rodríguez
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i la Salut, Universitat Pompeu Fabra, PRBB, Barcelona, Catalonia, Spain
| | - Xavier Farré
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i la Salut, Universitat Pompeu Fabra, PRBB, Barcelona, Catalonia, Spain
| | - Hafid Layouni
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i la Salut, Universitat Pompeu Fabra, PRBB, Barcelona, Catalonia, Spain
- Bioinformatics Studies, ESCI-UPF, Pg. Pujades 1, Barcelona, Spain
| | - Urko M. Marigorta
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i la Salut, Universitat Pompeu Fabra, PRBB, Barcelona, Catalonia, Spain
- Georgia Institute of Technology, Department of Biology, Atlanta, Georgia, United States of America
| | - Caitlin Cundiff
- Georgia Institute of Technology, Department of Biology, Atlanta, Georgia, United States of America
| | - Jose Maria Heredia-Genestar
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i la Salut, Universitat Pompeu Fabra, PRBB, Barcelona, Catalonia, Spain
| | - Arcadi Navarro
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i la Salut, Universitat Pompeu Fabra, PRBB, Barcelona, Catalonia, Spain
- National Institute for Bioinformatics (INB), PRBB, Barcelona, Catalonia, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), PRBB, Barcelona, Catalonia, Spain
- Center for Genomic Regulation (CRG), PRBB, Barcelona, Catalonia, Spain
- * E-mail: (AN); (GS)
| | - Gabriel Santpere
- Institute of Evolutionary Biology (UPF-CSIC), Departament de Ciències Experimentals i la Salut, Universitat Pompeu Fabra, PRBB, Barcelona, Catalonia, Spain
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States of America
- * E-mail: (AN); (GS)
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247
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Rivera NV, Ronninger M, Shchetynsky K, Franke A, Nöthen MM, Müller-Quernheim J, Schreiber S, Adrianto I, Karakaya B, van Moorsel CHM, Navratilova Z, Kolek V, Rybicki BA, Iannuzzi MC, Petrek M, Grutters JC, Montgomery C, Fischer A, Eklund A, Padyukov L, Grunewald J. High-Density Genetic Mapping Identifies New Susceptibility Variants in Sarcoidosis Phenotypes and Shows Genomic-driven Phenotypic Differences. Am J Respir Crit Care Med 2017; 193:1008-22. [PMID: 26651848 DOI: 10.1164/rccm.201507-1372oc] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
RATIONALE Sarcoidosis is a multisystem disease of unknown cause. Löfgren's syndrome (LS) is a characteristic subgroup of sarcoidosis that is associated with a good prognosis in sarcoidosis. However, little is known about its genetic architecture or its broader phenotype, non-LS sarcoidosis. OBJECTIVES To address the genetic architecture of sarcoidosis phenotypes, LS and non-LS. METHODS An association study in a white Swedish cohort of 384 LS, 664 non-LS, and 2,086 control subjects, totaling 3,134 subjects using a fine-mapping genotyping platform was conducted. Replication was performed in four independent cohorts, three of white European descent (Germany, n = 4,975; the Netherlands, n = 613; and Czech Republic, n = 521), and one of black African descent (United States, n = 1,657), totaling 7,766 subjects. MEASUREMENTS AND MAIN RESULTS A total of 727 LS-associated variants expanding throughout the extended major histocompatibility complex (MHC) region and 68 non-LS-associated variants located in the MHC class II region were identified and confirmed. A shared overlap between LS and non-LS defined by 17 variants located in the MHC class II region was found. Outside the MHC region, two LS-associated loci, in ADCY3 and between CSMD1 and MCPH1, were observed and replicated. CONCLUSIONS Comprehensive and integrative analyses of genetics, transcription, and pathway modeling on LS and non-LS indicates that these sarcoidosis phenotypes have different genetic susceptibility, genomic distributions, and cellular activities, suggesting distinct molecular mechanisms in pathways related to immune response with a common region.
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Affiliation(s)
- Natalia V Rivera
- 1 Respiratory Unit, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.,2 Center for Molecular Medicine, and
| | - Marcus Ronninger
- 1 Respiratory Unit, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.,2 Center for Molecular Medicine, and
| | - Klementy Shchetynsky
- 2 Center for Molecular Medicine, and.,3 Rheumatology Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Andre Franke
- 4 Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein, Campus Kiel and Kiel University, Kiel, Germany
| | - Markus M Nöthen
- 5 Institute of Human Genetics, and.,6 Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | | | - Stefan Schreiber
- 4 Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein, Campus Kiel and Kiel University, Kiel, Germany.,8 Popgen Biobank and.,9 Clinic of Internal Medicine I, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Indra Adrianto
- 10 Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma
| | - Bekir Karakaya
- 11 Center of Interstitial Lung Diseases, St. Antonius Hospital, Nieuwegein, the Netherlands
| | - Coline H M van Moorsel
- 11 Center of Interstitial Lung Diseases, St. Antonius Hospital, Nieuwegein, the Netherlands.,12 Division of Heart & Lungs, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Vitezslav Kolek
- 14 Department of Respiratory Medicine, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
| | - Benjamin A Rybicki
- 15 Department of Public Health Sciences, Henry Ford Health Systems, Detroit, Michigan; and
| | - Michael C Iannuzzi
- 16 State University of New York Upstate Medical University Syracuse, Syracuse, New York
| | | | - Jan C Grutters
- 11 Center of Interstitial Lung Diseases, St. Antonius Hospital, Nieuwegein, the Netherlands.,12 Division of Heart & Lungs, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Courtney Montgomery
- 10 Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma
| | - Annegret Fischer
- 4 Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein, Campus Kiel and Kiel University, Kiel, Germany
| | - Anders Eklund
- 1 Respiratory Unit, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Leonid Padyukov
- 2 Center for Molecular Medicine, and.,3 Rheumatology Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Johan Grunewald
- 2 Center for Molecular Medicine, and.,3 Rheumatology Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
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248
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Improving the detection of pathways in genome-wide association studies by combined effects of SNPs from Linkage Disequilibrium blocks. Sci Rep 2017; 7:3512. [PMID: 28615668 PMCID: PMC5471232 DOI: 10.1038/s41598-017-03826-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 05/05/2017] [Indexed: 01/31/2023] Open
Abstract
Genome-wide association studies (GWAS) have successfully identified single variants associated with diseases. To increase the power of GWAS, gene-based and pathway-based tests are commonly employed to detect more risk factors. However, the gene- and pathway-based association tests may be biased towards genes or pathways containing a large number of single-nucleotide polymorphisms (SNPs) with small P-values caused by high linkage disequilibrium (LD) correlations. To address such bias, numerous pathway-based methods have been developed. Here we propose a novel method, DGAT-path, to divide all SNPs assigned to genes in each pathway into LD blocks, and to sum the chi-square statistics of LD blocks for assessing the significance of the pathway by permutation tests. The method was proven robust with the type I error rate >1.6 times lower than other methods. Meanwhile, the method displays a higher power and is not biased by the pathway size. The applications to the GWAS summary statistics for schizophrenia and breast cancer indicate that the detected top pathways contain more genes close to associated SNPs than other methods. As a result, the method identified 17 and 12 significant pathways containing 20 and 21 novel associated genes, respectively for two diseases. The method is available online by http://sparks-lab.org/server/DGAT-path.
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249
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Hebbar P, Alkayal F, Nizam R, Melhem M, Elkum N, John SE, Abufarha M, Alsmadi O, Thanaraj TA. The TCN2 variant of rs9606756 [Ile23Val] acts as risk loci for obesity-related traits and mediates by interacting with Apo-A1. Obesity (Silver Spring) 2017; 25:1098-1108. [PMID: 28417558 DOI: 10.1002/oby.21826] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Revised: 02/02/2017] [Accepted: 02/22/2017] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Despite alarming obesity levels in the Arabian Peninsula, its population lacks convincingly identified genetic determinants of obesity. A genome-wide association study was performed for obesity-related anthropometric traits in Arabs and to decipher mechanisms by which the variants mediate traits. METHODS The Illumina HumanOmniExpress BeadChip was used to genotype 1,353 Arab individuals (largely with Class I obesity) from Kuwait. Genome-wide association tests for obesity-related anthropometric traits were performed. Top associations were tested for replication in an independent cohort (1,176 unrelated Arabs). Resultant variants were investigated for interactions with obesity-related plasma biomarkers. Pathway analysis was performed on genes harboring markers in linkage disequilibrium (LD) with identified variants. RESULTS The rs9606756[c.67A>G,p.Ile23Val] variant from TCN2 was associated with waist circumference (WC) at nearly genome-wide significance (P = 8.92E-08). WC was inversely related with Apo-A1 or high-density lipoprotein levels; individuals with the AG genotype exhibited stronger relationship than those with the reference AA genotype. Interaction involving the AG genotype (effect allele = G) significantly contributed to an increase in anthropometric traits (particularly WC). Genes harboring single-nucleotide polymorphisms in LD with rs9606756 mapped onto an interaction network (with TP53 as central element) of established obesity/diabetes-related protein components. CONCLUSIONS The TCN2 variant acts as a risk factor for WC in the Arab population. The variant mediates obesity-related anthropometric traits via interactions with Apo-A1/high-density lipoprotein or TP53.
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Affiliation(s)
| | | | | | | | - Naser Elkum
- Sidra Medical and Research Center, Research Department Doha, Qatar
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250
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Cong W, Meng X, Li J, Zhang Q, Chen F, Liu W, Wang Y, Cheng S, Yao X, Yan J, Kim S, Saykin AJ, Liang H, Shen L. Genome-wide network-based pathway analysis of CSF t-tau/Aβ1-42 ratio in the ADNI cohort. BMC Genomics 2017; 18:421. [PMID: 28558704 PMCID: PMC5450240 DOI: 10.1186/s12864-017-3798-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2016] [Accepted: 05/16/2017] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The cerebrospinal fluid (CSF) levels of total tau (t-tau) and Aβ1-42 are potential early diagnostic markers for probable Alzheimer's disease (AD). The influence of genetic variation on these CSF biomarkers has been investigated in candidate or genome-wide association studies (GWAS). However, the investigation of statistically modest associations in GWAS in the context of biological networks is still an under-explored topic in AD studies. The main objective of this study is to gain further biological insights via the integration of statistical gene associations in AD with physical protein interaction networks. RESULTS The CSF and genotyping data of 843 study subjects (199 CN, 85 SMC, 239 EMCI, 207 LMCI, 113 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed. PLINK was used to perform GWAS on the t-tau/Aβ1-42 ratio using quality controlled genotype data, including 563,980 single nucleotide polymorphisms (SNPs), with age, sex and diagnosis as covariates. Gene-level p-values were obtained by VEGAS2. Genes with p-value ≤ 0.05 were mapped on to a protein-protein interaction (PPI) network (9,617 nodes, 39,240 edges, from the HPRD Database). We integrated a consensus model strategy into the iPINBPA network analysis framework, and named it as CM-iPINBPA. Four consensus modules (CMs) were discovered by CM-iPINBPA, and were functionally annotated using the pathway analysis tool Enrichr. The intersection of four CMs forms a common subnetwork of 29 genes, including those related to tau phosphorylation (GSK3B, SUMO1, AKAP5, CALM1 and DLG4), amyloid beta production (CASP8, PIK3R1, PPA1, PARP1, CSNK2A1, NGFR, and RHOA), and AD (BCL3, CFLAR, SMAD1, and HIF1A). CONCLUSIONS This study coupled a consensus module (CM) strategy with the iPINBPA network analysis framework, and applied it to the GWAS of CSF t-tau/Aβ1-42 ratio in an AD study. The genome-wide network analysis yielded 4 enriched CMs that share not only genes related to tau phosphorylation or amyloid beta production but also multiple genes enriching several KEGG pathways such as Alzheimer's disease, colorectal cancer, gliomas, renal cell carcinoma, Huntington's disease, and others. This study demonstrated that integration of gene-level associations with CMs could yield statistically significant findings to offer valuable biological insights (e.g., functional interaction among the protein products of these genes) and suggest high confidence candidates for subsequent analyses.
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Affiliation(s)
- Wang Cong
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Xianglian Meng
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
- Harbin Huade University, No.288 Xue Yuan Rd. Limin Development Zone, Harbin, 150025 China
| | - Jin Li
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Qiushi Zhang
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
- College of Information Engineering, Northeast Dianli University, 169 Changchun Street, Jilin City, Jilin 132012 China
| | - Feng Chen
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Wenjie Liu
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Ying Wang
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Sipu Cheng
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Xiaohui Yao
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St, Suite 4100, Indianapolis, IN 46202 USA
- School of Informatics and Computing, Indiana University, 719 Indiana Avenue, Indianapolis, IN 46202 USA
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St, Suite 4100, Indianapolis, IN 46202 USA
- School of Informatics and Computing, Indiana University, 719 Indiana Avenue, Indianapolis, IN 46202 USA
- Indiana University Network Science Institute, Bloomington, IN 47405 USA
| | - Sungeun Kim
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St, Suite 4100, Indianapolis, IN 46202 USA
- Indiana University Network Science Institute, Bloomington, IN 47405 USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St, Suite 4100, Indianapolis, IN 46202 USA
- Indiana University Network Science Institute, Bloomington, IN 47405 USA
| | - Hong Liang
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St, Suite 4100, Indianapolis, IN 46202 USA
- School of Informatics and Computing, Indiana University, 719 Indiana Avenue, Indianapolis, IN 46202 USA
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- College of Automation, Harbin Engineering University, 145 Nantong Street, BLDG 61-5029, Harbin, 150001 China
- Harbin Huade University, No.288 Xue Yuan Rd. Limin Development Zone, Harbin, 150025 China
- College of Information Engineering, Northeast Dianli University, 169 Changchun Street, Jilin City, Jilin 132012 China
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St, Suite 4100, Indianapolis, IN 46202 USA
- School of Informatics and Computing, Indiana University, 719 Indiana Avenue, Indianapolis, IN 46202 USA
- Indiana University Network Science Institute, Bloomington, IN 47405 USA
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