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Longchamps RJ, Yang SY, Castellani CA, Shi W, Lane J, Grove ML, Bartz TM, Sarnowski C, Liu C, Burrows K, Guyatt AL, Gaunt TR, Kacprowski T, Yang J, De Jager PL, Yu L, Bergman A, Xia R, Fornage M, Feitosa MF, Wojczynski MK, Kraja AT, Province MA, Amin N, Rivadeneira F, Tiemeier H, Uitterlinden AG, Broer L, Van Meurs JBJ, Van Duijn CM, Raffield LM, Lange L, Rich SS, Lemaitre RN, Goodarzi MO, Sitlani CM, Mak ACY, Bennett DA, Rodriguez S, Murabito JM, Lunetta KL, Sotoodehnia N, Atzmon G, Ye K, Barzilai N, Brody JA, Psaty BM, Taylor KD, Rotter JI, Boerwinkle E, Pankratz N, Arking DE. Genome-wide analysis of mitochondrial DNA copy number reveals loci implicated in nucleotide metabolism, platelet activation, and megakaryocyte proliferation. Hum Genet 2022; 141:127-146. [PMID: 34859289 PMCID: PMC8758627 DOI: 10.1007/s00439-021-02394-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 10/22/2021] [Indexed: 12/18/2022]
Abstract
Mitochondrial DNA copy number (mtDNA-CN) measured from blood specimens is a minimally invasive marker of mitochondrial function that exhibits both inter-individual and intercellular variation. To identify genes involved in regulating mitochondrial function, we performed a genome-wide association study (GWAS) in 465,809 White individuals from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (UKB). We identified 133 SNPs with statistically significant, independent effects associated with mtDNA-CN across 100 loci. A combination of fine-mapping, variant annotation, and co-localization analyses was used to prioritize genes within each of the 133 independent sites. Putative causal genes were enriched for known mitochondrial DNA depletion syndromes (p = 3.09 × 10-15) and the gene ontology (GO) terms for mtDNA metabolism (p = 1.43 × 10-8) and mtDNA replication (p = 1.2 × 10-7). A clustering approach leveraged pleiotropy between mtDNA-CN associated SNPs and 41 mtDNA-CN associated phenotypes to identify functional domains, revealing three distinct groups, including platelet activation, megakaryocyte proliferation, and mtDNA metabolism. Finally, using mitochondrial SNPs, we establish causal relationships between mitochondrial function and a variety of blood cell-related traits, kidney function, liver function and overall (p = 0.044) and non-cancer mortality (p = 6.56 × 10-4).
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Affiliation(s)
- R J Longchamps
- Department of Genetic Medicine, McKusick-Nathans Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - S Y Yang
- Department of Genetic Medicine, McKusick-Nathans Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - C A Castellani
- Department of Genetic Medicine, McKusick-Nathans Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada
| | - W Shi
- Department of Genetic Medicine, McKusick-Nathans Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - J Lane
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - M L Grove
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - T M Bartz
- Cardiovascular Health Research Unit, Departments of Medicine and Biostatistics, University of Washington, Seattle, WA, USA
| | - C Sarnowski
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - C Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - K Burrows
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - A L Guyatt
- Department of Health Sciences, University of Leicester, University Road, Leicester, UK
| | - T R Gaunt
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - T Kacprowski
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
- Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Brunswick, Germany
| | - J Yang
- Rush Alzheimer's Disease Center and Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - P L De Jager
- Center for Translational and Systems Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - L Yu
- Rush Alzheimer's Disease Center and Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - A Bergman
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - R Xia
- Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - M Fornage
- Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, USA
| | - M F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, USA
| | - M K Wojczynski
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, USA
| | - A T Kraja
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, USA
| | - M A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, USA
| | - N Amin
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - F Rivadeneira
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - H Tiemeier
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Social and Behavioral Science, Harvard T.H. School of Public Health, Boston, USA
| | - A G Uitterlinden
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - L Broer
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - J B J Van Meurs
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - C M Van Duijn
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - L M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - L Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - S S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - R N Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - M O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - C M Sitlani
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - A C Y Mak
- Cardiovascular Research Institute and Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - D A Bennett
- Rush Alzheimer's Disease Center and Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - S Rodriguez
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - J M Murabito
- Boston University School of Medicine, Boston University, Boston, MA, USA
| | - K L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - N Sotoodehnia
- Cardiovascular Health Research Unit, Division of Cardiology, University of Washington, Seattle, WA, USA
| | - G Atzmon
- Department of Natural Science, University of Haifa, Haifa, Israel
- Departments of Medicine and Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - K Ye
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - N Barzilai
- Departments of Medicine and Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - J A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - B M Psaty
- Cardiovascular Health Research Unit, Departments of Epidemiology, Medicine and Health Services, University of Washington, Seattle, WA, USA
| | - K D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - J I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - E Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Baylor College of Medicine, Human Genome Sequencing Center, Houston, TX, USA
| | - N Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - D E Arking
- Department of Genetic Medicine, McKusick-Nathans Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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2
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Flannick J, Mercader JM, Fuchsberger C, Udler MS, Mahajan A, Wessel J, Teslovich TM, Caulkins L, Koesterer R, Barajas-Olmos F, Blackwell TW, Boerwinkle E, Brody JA, Centeno-Cruz F, Chen L, Chen S, Contreras-Cubas C, Córdova E, Correa A, Cortes M, DeFronzo RA, Dolan L, Drews KL, Elliott A, Floyd JS, Gabriel S, Garay-Sevilla ME, García-Ortiz H, Gross M, Han S, Heard-Costa NL, Jackson AU, Jørgensen ME, Kang HM, Kelsey M, Kim BJ, Koistinen HA, Kuusisto J, Leader JB, Linneberg A, Liu CT, Liu J, Lyssenko V, Manning AK, Marcketta A, Malacara-Hernandez JM, Martínez-Hernández A, Matsuo K, Mayer-Davis E, Mendoza-Caamal E, Mohlke KL, Morrison AC, Ndungu A, Ng MCY, O'Dushlaine C, Payne AJ, Pihoker C, Post WS, Preuss M, Psaty BM, Vasan RS, Rayner NW, Reiner AP, Revilla-Monsalve C, Robertson NR, Santoro N, Schurmann C, So WY, Soberón X, Stringham HM, Strom TM, Tam CHT, Thameem F, Tomlinson B, Torres JM, Tracy RP, van Dam RM, Vujkovic M, Wang S, Welch RP, Witte DR, Wong TY, Atzmon G, Barzilai N, Blangero J, Bonnycastle LL, Bowden DW, Chambers JC, Chan E, Cheng CY, Cho YS, Collins FS, de Vries PS, Duggirala R, Glaser B, Gonzalez C, Gonzalez ME, Groop L, Kooner JS, Kwak SH, Laakso M, Lehman DM, Nilsson P, Spector TD, Tai ES, Tuomi T, Tuomilehto J, Wilson JG, Aguilar-Salinas CA, Bottinger E, Burke B, Carey DJ, Chan JCN, Dupuis J, Frossard P, Heckbert SR, Hwang MY, Kim YJ, Kirchner HL, Lee JY, Lee J, Loos RJF, Ma RCW, Morris AD, O'Donnell CJ, Palmer CNA, Pankow J, Park KS, Rasheed A, Saleheen D, Sim X, Small KS, Teo YY, Haiman C, Hanis CL, Henderson BE, Orozco L, Tusié-Luna T, Dewey FE, Baras A, Gieger C, Meitinger T, Strauch K, Lange L, Grarup N, Hansen T, Pedersen O, Zeitler P, Dabelea D, Abecasis G, Bell GI, Cox NJ, Seielstad M, Sladek R, Meigs JB, Rich SS, Rotter JI, Altshuler D, Burtt NP, Scott LJ, Morris AP, Florez JC, McCarthy MI, Boehnke M. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature 2019; 570:71-76. [PMID: 31118516 PMCID: PMC6699738 DOI: 10.1038/s41586-019-1231-2] [Citation(s) in RCA: 186] [Impact Index Per Article: 37.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 04/23/2019] [Indexed: 02/08/2023]
Abstract
Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10-3) and candidate genes from knockout mice (P = 5.2 × 10-3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.
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Affiliation(s)
- Jason Flannick
- Program in Metabolism, Broad Institute, Cambridge, MA, USA.
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
- Program in Medical & Population Genetics, Broad Institute, Cambridge, MA, USA.
| | - Josep M Mercader
- Program in Metabolism, Broad Institute, Cambridge, MA, USA
- Program in Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Christian Fuchsberger
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Miriam S Udler
- Program in Metabolism, Broad Institute, Cambridge, MA, USA
- Program in Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Jennifer Wessel
- Department of Epidemiology, Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA
- Department of Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
- Diabetes Translational Research Center, Indiana University, Indianapolis, IN, USA
| | - Tanya M Teslovich
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Lizz Caulkins
- Program in Metabolism, Broad Institute, Cambridge, MA, USA
- Program in Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Ryan Koesterer
- Program in Metabolism, Broad Institute, Cambridge, MA, USA
- Program in Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
| | | | - Thomas W Blackwell
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jennifer A Brody
- Cardiovascular Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | | | - Ling Chen
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Siying Chen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | | | - Emilio Córdova
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Maria Cortes
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ralph A DeFronzo
- Department of Medicine, University of Texas Health Science Center, San Antonio, TX, USA
| | - Lawrence Dolan
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kimberly L Drews
- Biostatistics Center, George Washington University, Rockville, MD, USA
| | - Amanda Elliott
- Program in Metabolism, Broad Institute, Cambridge, MA, USA
- Program in Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - James S Floyd
- Department of Medicine and Epidemiology, University of Washington, Seattle, WA, USA
| | | | - Maria Eugenia Garay-Sevilla
- Department of Medicine, The University of Chicago, Chicago, IL, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | | | - Myron Gross
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Sohee Han
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, South Korea
| | - Nancy L Heard-Costa
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - Anne U Jackson
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Marit E Jørgensen
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark
- Greenland Centre for Health Research, University of Greenland, Nuuk, Greenland
| | - Hyun Min Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Megan Kelsey
- Biostatistics Center, George Washington University, Rockville, MD, USA
| | - Bong-Jo Kim
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, South Korea
| | - Heikki A Koistinen
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
- University of Helsinki and Department of Medicine, Helsinki University Central Hospital, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Medicin, Kuopio University Hospital, Kuopio, Finland
| | | | - Allan Linneberg
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
- Department of Clinical Experimental Research, Rigshospitalet, Copenhagen, Denmark
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Alisa K Manning
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard University, Boston, MA, USA
| | - Anthony Marcketta
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Juan Manuel Malacara-Hernandez
- Department of Medicine, The University of Chicago, Chicago, IL, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | | | - Karen Matsuo
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Karen L Mohlke
- Department of Genetics, University of North Carolina Chapel Hill, Chapel Hill, NC, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Anne Ndungu
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Maggie C Y Ng
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Colm O'Dushlaine
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Anthony J Payne
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Wendy S Post
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Michael Preuss
- Charles R. Bronfman Institute of Personalized Medicine, Mount Sinai School of Medicine, New York, NY, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
| | - Ramachandran S Vasan
- National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
- Preventive Medicine & Epidemiology, Medicine, Boston University School of Medicine, Boston, MA, USA
| | - N William Rayner
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK
| | | | | | - Neil R Robertson
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Nicola Santoro
- Department of Pediatrics, Yale University, New Haven, CT, USA
| | - Claudia Schurmann
- Charles R. Bronfman Institute of Personalized Medicine, Mount Sinai School of Medicine, New York, NY, USA
| | - Wing Yee So
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Xavier Soberón
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Heather M Stringham
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Tim M Strom
- Institute of Human Genetics, Technische Universität München, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Claudia H T Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Farook Thameem
- Health Science Center, Department of Biochemistry, Faculty of Medicine, Kuwait University, Safat, Kuwait
| | - Brian Tomlinson
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Jason M Torres
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Russell P Tracy
- Department of Pathology and Laboratory Medicine, The Robert Larner M.D. College of Medicine, University of Vermont, Burlington, VT, USA
- Department of Biochemistry, The Robert Larner M.D. College of Medicine, University of Vermont, Burlington, VT, USA
| | - Rob M van Dam
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - Marijana Vujkovic
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Shuai Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ryan P Welch
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Daniel R Witte
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Danish Diabetes Academy, Odense, Denmark
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School Singapore, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore
| | - Gil Atzmon
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
- Faculty of Natural Science, University of Haifa, Haifa, Israel
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - Nir Barzilai
- Department of Medicine, Albert Einstein College of Medicine, New York, NY, USA
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, USA
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley, Edinburg, TX, USA
- South Texas Diabetes and Obesity Institute, Brownsville, TX, USA
| | - Lori L Bonnycastle
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Donald W Bowden
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital NHS Trust, Southall, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Edmund Chan
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore
| | - Ching-Yu Cheng
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Francis S Collins
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ravindranath Duggirala
- Department of Human Genetics, University of Texas Rio Grande Valley, Edinburg, TX, USA
- South Texas Diabetes and Obesity Institute, Brownsville, TX, USA
| | - Benjamin Glaser
- Endocrinology and Metabolism Service, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Clicerio Gonzalez
- Unidad de Diabetes y Riesgo Cardiovascular, Instituto Nacional de Salud Pública, Cuernavaca, Mexico
| | | | - Leif Groop
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
- Institute for Molecular Genetics Finland, University of Helsinki, Helsinki, Finland
| | - Jaspal Singh Kooner
- National Heart and Lung Institute, Cardiovascular Sciences, Imperial College London, London, UK
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Medicin, Kuopio University Hospital, Kuopio, Finland
| | - Donna M Lehman
- Department of Medicine, University of Texas Health Science Center, San Antonio, TX, USA
| | - Peter Nilsson
- Department of Clinical Sciences, Medicine, Lund University, Malmö, Sweden
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - E Shyong Tai
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Duke-NUS Medical School Singapore, Singapore, Singapore
| | - Tiinamaija Tuomi
- Institute for Molecular Genetics Finland, University of Helsinki, Helsinki, Finland
- Folkhälsan Research Centre, Helsinki, Finland
- Department of Endocrinology, Abdominal Centre, Helsinki University Hospital, Helsinki, Finland
- Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
| | - Jaakko Tuomilehto
- Diabetes Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
- Center for Vascular Prevention, Danube University Krems, Krems, Austria
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
- Instituto de Investigacion Sanitaria del Hospital Universario LaPaz (IdiPAZ), University Hospital LaPaz, Autonomous University of Madrid, Madrid, Spain
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | | | - Erwin Bottinger
- Charles R. Bronfman Institute of Personalized Medicine, Mount Sinai School of Medicine, New York, NY, USA
| | - Brian Burke
- Biostatistics Center, George Washington University, Rockville, MD, USA
| | | | - Juliana C N Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Josée Dupuis
- National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | | | - Susan R Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Mi Yeong Hwang
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, South Korea
| | - Young Jin Kim
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, South Korea
| | | | - Jong-Young Lee
- Department of Business Data Convergence, Chungbuk National University, Gyeonggi-do, South Korea
| | - Juyoung Lee
- Division of Genome Research, Center for Genome Science, National Institute of Health, Chungcheongbuk-do, South Korea
| | - Ruth J F Loos
- Charles R. Bronfman Institute of Personalized Medicine, Mount Sinai School of Medicine, New York, NY, USA
- The Mindich Child Health and Development Insititute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Andrew D Morris
- Clinical Research Centre, Centre for Molecular Medicine, Ninewells Hospital and Medical School, Dundee, UK
| | - Christopher J O'Donnell
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Section of Cardiology, Department of Medicine, VA Boston Healthcare, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
- Intramural Administration Management Branch, National Heart Lung and Blood Institute, NIH, Framingham, MA, USA
| | - Colin N A Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, Medical Research Institute, Ninewells Hospital and Medical School, Dundee, UK
| | - James Pankow
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Kyong Soo Park
- National Heart and Lung Institute, Cardiovascular Sciences, Imperial College London, London, UK
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Asif Rasheed
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Danish Saleheen
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Yik Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Life Sciences Institute, National University of Singapore, Singapore, Singapore
- Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Craig L Hanis
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Brian E Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Teresa Tusié-Luna
- Instituto Nacional de Ciencias Medicas y Nutricion, Mexico City, Mexico
- Instituto de Investigaciones Biomédicas, Departamento de Medicina Genómica y Toxicología, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Frederick E Dewey
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Aris Baras
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Thomas Meitinger
- Institute of Human Genetics, Technische Universität München, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Deutsches Forschungszentrum für Herz-Kreislauferkrankungen (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Konstantin Strauch
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Neuherberg, Germany
| | - Leslie Lange
- Department of Medicine, University of Colorado Denver, Aurora, CO, USA
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Philip Zeitler
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Dana Dabelea
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Goncalo Abecasis
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Graeme I Bell
- Department of Medicine, The University of Chicago, Chicago, IL, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Mark Seielstad
- Department of Laboratory Medicine & Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Blood Systems Research Institute, San Francisco, CA, USA
| | - Rob Sladek
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McGill University, Montreal, Quebec, Canada
- McGill University and Génome Québec Innovation Centre, Montreal, Quebec, Canada
| | - James B Meigs
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Steve S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Jerome I Rotter
- Department of Pediatrics, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Medicine, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - David Altshuler
- Program in Metabolism, Broad Institute, Cambridge, MA, USA
- Program in Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Noël P Burtt
- Program in Metabolism, Broad Institute, Cambridge, MA, USA
- Program in Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Laura J Scott
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Andrew P Morris
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Jose C Florez
- Program in Metabolism, Broad Institute, Cambridge, MA, USA
- Program in Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Michael Boehnke
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
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Affiliation(s)
- S S Rich
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana, 47907.,U.S. Department of Agriculture, West Lafayette, Indiana, 47907
| | - A E Bell
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana, 47907.,U.S. Department of Agriculture, West Lafayette, Indiana, 47907
| | - S P Wilson
- Department of Animal Sciences, Purdue University, West Lafayette, Indiana, 47907.,U.S. Department of Agriculture, West Lafayette, Indiana, 47907
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4
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Oelsner EC, Pottinger TD, Burkart KM, Allison M, Buxbaum SG, Hansel NN, Kumar R, Larkin EK, Lange LA, Loehr LR, London SJ, O'Connor GT, Papanicolaou G, Petrini MF, Rabinowitz D, Raghavan S, Redline S, Thyagarajan B, Tracy RP, Wilk JB, White WB, Rich SS, Barr RG. Adhesion molecules, endothelin-1 and lung function in seven population-based cohorts. Biomarkers 2013; 18:196-203. [PMID: 23557128 DOI: 10.3109/1354750x.2012.762805] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
CONTEXT Endothelial function is abnormal in chronic obstructive pulmonary disease (COPD); whether endothelial dysfunction causes COPD is unknown. OBJECTIVE Test associations of endothelial biomarkers with FEV1 using instrumental variables. METHODS Among 26 907 participants with spirometry, ICAM-1, P-selectin, E-selectin and endothelin-1 were measured in subsets. RESULTS ICAM-1 and P-selectin were inversely associated with FEV1 among European-Americans (-29 mL and -34 mL per standard deviation of log-transformed biomarker, p < 0.001), as was endothelin-1 among African-Americans (-22 mL, p = 0.008). Genetically-estimated ICAM-1 and P-selectin were not significantly associated with FEV1. The instrumental variable for endothelin-1 was non-informative. CONCLUSION Although ICAM-1, P-selectin and endothelin-1 were inversely associated with FEV1, associations for ICAM-1 and P-selectin do not appear causal.
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Affiliation(s)
- E C Oelsner
- Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
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Cooper JD, Howson JMM, Smyth D, Walker NM, Stevens H, Yang JHM, She JX, Eisenbarth GS, Rewers M, Todd JA, Akolkar B, Concannon P, Erlich HA, Julier C, Morahan G, Nerup J, Nierras C, Pociot F, Rich SS. Confirmation of novel type 1 diabetes risk loci in families. Diabetologia 2012; 55:996-1000. [PMID: 22278338 PMCID: PMC3296014 DOI: 10.1007/s00125-012-2450-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2011] [Accepted: 12/15/2011] [Indexed: 11/20/2022]
Abstract
AIMS/HYPOTHESIS Over 50 regions of the genome have been associated with type 1 diabetes risk, mainly using large case/control collections. In a recent genome-wide association (GWA) study, 18 novel susceptibility loci were identified and replicated, including replication evidence from 2,319 families. Here, we, the Type 1 Diabetes Genetics Consortium (T1DGC), aimed to exclude the possibility that any of the 18 loci were false-positives due to population stratification by significantly increasing the statistical power of our family study. METHODS We genotyped the most disease-predicting single-nucleotide polymorphisms at the 18 susceptibility loci in 3,108 families and used existing genotype data for 2,319 families from the original study, providing 7,013 parent-child trios for analysis. We tested for association using the transmission disequilibrium test. RESULTS Seventeen of the 18 susceptibility loci reached nominal levels of significance (p < 0.05) in the expanded family collection, with 14q24.1 just falling short (p = 0.055). When we allowed for multiple testing, ten of the 17 nominally significant loci reached the required level of significance (p < 2.8 × 10(-3)). All susceptibility loci had consistent direction of effects with the original study. CONCLUSIONS/INTERPRETATION The results for the novel GWA study-identified loci are genuine and not due to population stratification. The next step, namely correlation of the most disease-associated genotypes with phenotypes, such as RNA and protein expression analyses for the candidate genes within or near each of the susceptibility regions, can now proceed.
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Affiliation(s)
- J D Cooper
- Department of Medical Genetics, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
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6
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Hsu FC, Sides EG, Mychaleckyj JC, Worrall BB, Elias GA, Liu Y, Chen WM, Coull BM, Toole JF, Rich SS, Furie KL, Sale MM. Transcobalamin 2 variant associated with poststroke homocysteine modifies recurrent stroke risk. Neurology 2011; 77:1543-50. [PMID: 21975197 DOI: 10.1212/wnl.0b013e318233b1f9] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVES The Vitamin Intervention for Stroke Prevention trial found an association between baseline poststroke homocysteine (Hcy) and recurrent stroke. We investigated genes for enzymes and cofactors in the Hcy metabolic pathway for association with Hcy and determined whether associated single nucleotide polymorphisms (SNPs) influenced recurrent stroke risk. METHODS Eighty-six SNPs in 9 candidate genes (BHMT1, BHMT2, CBS, CTH, MTHFR, MTR, MTRR, TCN1, and TCN2) were genotyped in 2,206 subjects (83% European American). Associations with Hcy measures were assessed using linear regression models assuming an additive genetic model, adjusting for age, sex, and race and additionally for baseline Hcy when postmethionine load change was assessed. Associations with recurrent stroke were evaluated using survival analyses. RESULTS Five SNPs in the transcobalamin 2 (TCN2) gene were associated with baseline Hcy (false discovery rate [FDR]-adjusted p = 0.049). TCN2 SNP rs731991 was associated with recurrent stroke risk in the low-dose arm of the trial under a recessive model (log-rank test p = 0.009, hazard ratio 0.34). Associations with change in postmethionine load Hcy levels were found with 5 SNPs in the cystathionine β-synthase (CBS) gene (FDR-adjusted p < 0.031). CONCLUSIONS TCN2 variants contribute to poststroke Hcy levels, whereas variants in the CBS gene influence Hcy metabolism. Variation in the TCN2 gene also affects recurrent stroke risk in response to cofactor therapy.
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Affiliation(s)
- F-C Hsu
- Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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Rich SS, Akolkar B, Concannon P, Erlich H, Hilner JE, Julier C, Morahan G, Nerup J, Nierras C, Pociot F, Todd JA. Current status and the future for the genetics of type I diabetes. Genes Immun 2010; 10 Suppl 1:S128-31. [PMID: 19956094 DOI: 10.1038/gene.2009.100] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The Type I Diabetes Genetics Consortium (T1DGC) is an international collaboration whose primary goal is to identify genes whose variants modify an individual's risk of type I diabetes (T1D). An integral part of the T1DGC's mission is the establishment of clinical and data resources that can be used by, and that are fully accessible to, the T1D research community (http://www.t1dgc.org). The T1DGC has organized the collection and analyses of study samples and conducted several major research projects focused on T1D gene discovery: a genome-wide linkage scan, an intensive evaluation of the human major histocompatibility complex, a detailed examination of published candidate genes, and a genome-wide association scan. These studies have provided important information to the scientific community regarding the function of specific genes or chromosomal regions on T1D risk. The results are continually being updated and displayed (http://www.t1dbase.org). The T1DGC welcomes all investigators interested in using these data for scientific endeavors on T1D. The T1DGC resources provide a framework for future research projects, including examination of structural variation, re-sequencing of candidate regions in a search for T1D-associated genes and causal variants, correlation of T1D risk genotypes with biomarkers obtained from T1DGC serum and plasma samples, and in-depth bioinformatics analyses.
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Affiliation(s)
- S S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA.
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Erlich HA, Lohman K, Mack SJ, Valdes AM, Julier C, Mirel D, Noble JA, Morahan GE, Rich SS. Association analysis of SNPs in the IL4R locus with type I diabetes. Genes Immun 2010; 10 Suppl 1:S33-41. [PMID: 19956098 DOI: 10.1038/gene.2009.89] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The Type I Diabetes Genetics Consortium (T1DGC) has collected thousands of multiplex and simplex families with type I diabetes (T1D) with the goal of identifying genes involved in T1D susceptibility. These families have all been genotyped for the HLA class I and class II loci and a subset of samples has been typed for an major histocompatibility complex (MHC) single-nucleotide polymorphism (SNP) panel. In addition, the T1DGC has genotyped SNPs in candidate genes to evaluate earlier reported T1D associations. Individual SNPs and SNP haplotypes in IL4R, which encodes the alpha-chain of the IL4 and IL13 receptors, have been associated with T1D in some reports, but not in others. In this study, 38 SNPs in IL4R were genotyped using the Sequenom iPLEX Gold MassARRAY technology in 2042 multiplex families from nine cohorts. Association analyses (transmission-disequilibrium test and parental-disequilibrium test) were performed on individual SNPs and on three-SNP haplotypes. Analyses were also stratified on the high-risk HLA DR3/DR4-DQB1*0302 genotype. A modest T1D association in HBDI families (n=282) was confirmed in this larger collection of HBDI families (n=424). The variant alleles at the non-synonymous SNPs (rs1805011 (E400A), rs1805012 (C431R), and rs1801275 (Q576R)), which are in strong linkage disequilibrium, were negatively associated with T1D risk. These SNPs were more associated with T1D among non-DR3/DR4-DQB1*0302 genotypes than DR3/DR4-DQB1*0302 genotypes. This association was stronger, both in terms of odds ratio and P-values, than the initial report of the smaller collection of HBDI families. However, the IL4R SNPs and the three-SNP haplotype containing the variant alleles were not associated with T1D in the total data. Thus, in the overall families, these results do not show evidence for an association of SNPs in IL4R with T1D.
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Affiliation(s)
- H A Erlich
- Department of Human Genetics, Discovery Research, Roche Molecular Systems Inc., 4300 Hacienda Drive, Pleasanton, CA 94588, USA.
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Palmer ND, Langefeld CD, Ziegler JT, Hsu F, Haffner SM, Fingerlin T, Norris JM, Chen YI, Rich SS, Haritunians T, Taylor KD, Bergman RN, Rotter JI, Bowden DW. Candidate loci for insulin sensitivity and disposition index from a genome-wide association analysis of Hispanic participants in the Insulin Resistance Atherosclerosis (IRAS) Family Study. Diabetologia 2010; 53:281-9. [PMID: 19902172 PMCID: PMC2809812 DOI: 10.1007/s00125-009-1586-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2009] [Accepted: 10/05/2009] [Indexed: 01/11/2023]
Abstract
AIMS/HYPOTHESIS The majority of type 2 diabetes genome-wide association studies (GWAS) to date have been performed in European-derived populations and have identified few variants that mediate their effect through insulin resistance. The aim of this study was to evaluate two quantitative, directly assessed measures of insulin resistance, namely insulin sensitivity index (S(I)) and insulin disposition index (DI), in Hispanic-American participants using an agnostic, high-density single nucleotide polymorphism (SNP) scan, and to validate these findings in additional samples. METHODS A two-stage GWAS was performed in Hispanic-American samples from the Insulin Resistance Atherosclerosis Family Study. In Stage 1, 317,000 SNPs were assessed using 229 DNA samples. SNPs with evidence of association with glucose homeostasis and adiposity traits were then genotyped on the entire set of Hispanic-American samples (n = 1,190). This report focuses on the glucose homeostasis traits: S(I) and DI. RESULTS Although evidence of association did not reach genome-wide significance (p = 5 x 10(-7)), in the combined analysis SNPs had admixture-adjusted p values of p (ADD) = 0.00010-0.0020 with 8 to 41% differences in genotypic means for S(I) and DI. CONCLUSIONS/INTERPRETATION Several candidate loci were identified that are nominally associated with S(I) and/or DI in Hispanic-American participants. Replication of these findings in independent cohorts and additional focused analysis of these loci is warranted.
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Affiliation(s)
- N D Palmer
- Department of Biochemistry, Centers for Human Genomics & Diabetes Research, Wake Forest University School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC 27157, USA
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Rich SS, Goodarzi MO, Palmer ND, Langefeld CD, Ziegler J, Haffner SM, Bryer-Ash M, Norris JM, Taylor KD, Haritunians T, Rotter JI, Chen YDI, Wagenknecht LE, Bowden DW, Bergman RN. A genome-wide association scan for acute insulin response to glucose in Hispanic-Americans: the Insulin Resistance Atherosclerosis Family Study (IRAS FS). Diabetologia 2009; 52:1326-33. [PMID: 19430760 PMCID: PMC2793118 DOI: 10.1007/s00125-009-1373-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2008] [Accepted: 04/07/2009] [Indexed: 10/20/2022]
Abstract
AIMS/HYPOTHESIS This study sought to identify genes and regions in the human genome that are associated with the acute insulin response to glucose (AIRg), an important predictor of type 2 diabetes, in Hispanic-American participants from the Insulin Resistance Atherosclerosis Family Study (IRAS FS). METHODS A two-stage genome-wide association scan (GWAS) was performed in IRAS FS Hispanic-American samples. In the first stage, 317K single nucleotide polymorphisms (SNPs) were assessed in 229 Hispanic-American DNA samples from 34 families from San Antonio, TX, USA. SNPs with the most significant associations with AIRg were genotyped in the entire set of IRAS FS Hispanic-American samples (n = 1,190). In chromosomal regions with evidence of association, additional SNPs were genotyped to capture variation in genes. RESULTS No individual SNP achieved genome-wide levels of significance (p < 5 x 10(-7)); however, two regions (chromosomes 6p21 and 20p11) had multiple highly ranked SNPs that were associated with AIRg. Additional genotyping in these regions supported the initial evidence of variants contributing to variation in AIRg. One region resides in a gene desert between PXT1 and KCTD20 on 6p21, while the region on 20p11 has several viable candidate genes (ENTPD6, PYGB, GINS1 and RP4-691N24.1). CONCLUSIONS/INTERPRETATION A GWAS in Hispanic-American samples identified several candidate genes and loci that may be associated with AIRg. These associations explain a small component of variation in AIRg. The genes identified are involved in phosphorylation and ion transport, and provide preliminary evidence that these processes are important in beta cell response.
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Affiliation(s)
- S S Rich
- Center for Public Health Genomics, University of Virginia, 6111 West Complex, Charlottesville, VA 22908, USA.
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Rich SS, Akolkar B, Concannon P, Erlich H, Hilner J, Julier C, Morahan G, Nerup J, Nierras C, Pociot F, Todd JA. Results of the MHC fine mapping workshop. Diabetes Obes Metab 2009; 11 Suppl 1:108-9. [PMID: 19143823 PMCID: PMC2745921 DOI: 10.1111/j.1463-1326.2008.01011.x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- S S Rich
- University of Virginia, Department of Public Health Sciences, Charlottesville, VA, USA.
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Affiliation(s)
- S S Rich
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
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Leak TS, Perlegas PS, Smith SG, Keene KL, Hicks PJ, Langefeld CD, Mychaleckyj JC, Rich SS, Kirk JK, Freedman BI, Bowden DW, Sale MM. Variants in intron 13 of the ELMO1 gene are associated with diabetic nephropathy in African Americans. Ann Hum Genet 2009; 73:152-9. [PMID: 19183347 DOI: 10.1111/j.1469-1809.2008.00498.x] [Citation(s) in RCA: 70] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Variants in the engulfment and cell motility 1 (ELMO1) gene are associated with nephropathy due to type 2 diabetes mellitus (T2DM) in a Japanese cohort. We comprehensively evaluated this gene in African American (AA) T2DM patients with end-stage renal disease (ESRD). Three hundred and nine HapMap tagging SNPs and 9 reportedly associated SNPs were genotyped in 577 AA T2DM-ESRD patients and 596 AA non-diabetic controls, plus 43 non-diabetic European American controls and 45 Yoruba Nigerian samples for admixture adjustment. Replication analyses were conducted in 558 AA with T2DM-ESRD and 564 controls without diabetes. Extension analyses included 328 AA with T2DM lacking nephropathy and 326 with non-diabetic ESRD. The original and replication analyses confirmed association with four SNPs in intron 13 (permutation p-values for combined analyses = 0.001-0.003), one in intron 1 (P = 0.004) and one in intron 5 (P = 0.002) with T2DM-associated ESRD. In a subsequent combined analysis of all 1,135 T2DM-ESRD cases and 1,160 controls, an additional 7 intron 13 SNPs produced evidence of association (P = 3.5 x 10(-5)- P = 0.05). No associations were seen with these SNPs in those with T2DM lacking nephropathy or with ESRD due to non-diabetic causes. Variants in intron 13 of the ELMO1 gene appear to confer risk for diabetic nephropathy in AA.
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Affiliation(s)
- T S Leak
- Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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Bento JL, Palmer ND, Zhong M, Roh B, Lewis JP, Wing MR, Pandya H, Freedman BI, Langefeld CD, Rich SS, Bowden DW, Mychaleckyj JC. Heterogeneity in gene loci associated with type 2 diabetes on human chromosome 20q13.1. Genomics 2008; 92:226-34. [PMID: 18602983 DOI: 10.1016/j.ygeno.2008.06.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2008] [Revised: 04/11/2008] [Accepted: 06/04/2008] [Indexed: 11/26/2022]
Abstract
Human chromosome 20q12-q13.1 has been linked to type 2 diabetes mellitus (T2DM) in multiple studies. We screened a 5.795-Mb region for diabetes-related susceptibility genes in a Caucasian cohort of 310 controls and 300 cases with T2DM and end-stage renal disease (ESRD), testing 390 SNPs for association with T2DM-ESRD. The most significant SNPs were found in the perigenic regions: HNF4A (hepatocyte nuclear factor 4alpha), SLC12A5 (potassium-chloride cotransporter member 5), CDH22 (cadherin-like 22), ELMO2 (engulfment and cell motility 2), SLC13A3 (sodium-dependent dicarboxylate transporter member 3), and PREX1 (phosphatidylinositol 3,4,5-triphosphate-dependent RAC exchanger 1). Haplotype analysis found six haplotype blocks globally associated with disease (p<0.05). We replicated the PREX1 SNP association in an independent case-control T2DM population and inferred replication of CDH22, ELMO2, SLC13A3, SLC12A5, and PREX1 using in silico perigenic analysis of two T2DM Genome-Wide Association Study data sets. We found substantial heterogeneity between study results.
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Affiliation(s)
- J L Bento
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
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Bowden DW, Lehtinen AB, Ziegler JT, Rudock ME, Xu J, Wagenknecht LE, Herrington DM, Rich SS, Freedman BI, Carr JJ, Langefeld CD. Genetic epidemiology of subclinical cardiovascular disease in the diabetes heart study. Ann Hum Genet 2008; 72:598-610. [PMID: 18460048 DOI: 10.1111/j.1469-1809.2008.00446.x] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
A genome-wide linkage scan of 357 European American (EA) and 72 African American (AA) pedigrees multiplex for type 2 diabetes mellitus (T2DM) was performed with multipoint nonparametric QTL linkage analysis. Four subclinical measures of cardiovascular disease (CVD): coronary artery (CCP), carotid artery (CarCP), and abdominal aortic calcified plaque (AACP) and carotid artery intima-media thickness (IMT) were mapped. Analyses were adjusted for age, gender, body mass index, and (if appropriate) ethnicity and diabetes status. Evidence for linkage was observed in EA T2DM subjects to CarCP near 16p13 (LOD=4.39 at 8.4 cM; P = 0.00001). When all EA subjects were included, the LOD score was 2.52, suggesting an amplification of the linkage by diabetes. Linkage analysis of a principal components measure of vascular calcium (LOD = 3.85 at 9.3 cM on 16p in EA T2DM subjects) and bivariate analysis of CarCP X IMT (LOD = 3.77 at 9.3 cM on 16p in EA T2DM subjects) were consistent with this linkage. In addition, evidence for linkage was observed with CCP near D15S1515 (LOD = 2.34) in EAs. Additional loci on chromosomes 1, 2, 7, 10, 13, and 21 had LODs > 2.0. The identification of trait-determining polymorphisms underlying these linkages will help delineate risk factors for CVD in T2DM and the general population.
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Affiliation(s)
- D W Bowden
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, North Carolina, 27157, USA.
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Freedman BI, Bowden DW, Rich SS, Xu J, Wagenknecht LE, Ziegler J, Hicks PJ, Langefeld CD. Genome-wide linkage scans for renal function and albuminuria in Type 2 diabetes mellitus: the Diabetes Heart Study. Diabet Med 2008; 25:268-76. [PMID: 18307454 DOI: 10.1111/j.1464-5491.2007.02361.x] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
AIMS/HYPOTHESIS Glomerular filtration rate (GFR), end-stage renal disease and albuminuria are highly heritable. We performed a genome-wide linkage scan in 416 Diabetes Heart Study (DHS) families to detect loci that contributed to renal function and albuminuria. MATERIALS AND METHODS A total of 1067 individuals (900 with Type 2 diabetes mellitus) from 348 European American and 68 African American DHS families had measures of urine albumin : creatinine ratio (ACR), serum creatinine concentration and Modification of Diet in Renal Disease estimated GFR (eGFR). Variance components quantitative trait linkage analysis (using SOLAR) was computed. RESULTS Participants had mean +/- sd age 61.4 +/- 9.4 years; diabetes duration 10.5 +/- 7.4 years; eGFR 1.15 +/- 0.32 ml/sec; and urine ACR 15.8 +/- 67.2 mmol/l (median 1.4). In all families, significant evidence for linkage of GFR was observed on chromosome 2p16 (log of the odds; LOD = 4.31 at 72.0 cM, ATA47C04P/D2S1352) and 1p36 (LOD = 3.81 at 45.0 cM, D1S3669/D1S3720), with suggestive evidence on 7q21 (LOD = 2.42 at 99.0 cM, D7S820/D7S821) and 13q13 (LOD = 2.28 at 28.0 cM, D13S1493/D13S894). The evidence for linkage to ACR was far weaker, on 13q21-q22 (LOD = 1.84 at 50 cM, D13S1807/D13S800), 3p24-p23 (LOD = 1.81 at 58 cM, D3S3038/D3S2432) and 10p11 (LOD = 1.78 at 71.0 cM, D10S1208/D10S1221). CONCLUSIONS/INTERPRETATIONS The eGFR linkage peaks on 2p16, 7q21 and 13q13 closely overlap with nephropathy peaks identified in family studies enriched for severe kidney disease. These diabetes-enriched families provide an opportunity to map genes regulating renal function, potentially leading to the identification of genes producing nephropathy susceptibility in subjects with Type 2 diabetes.
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Affiliation(s)
- B I Freedman
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157-1053, USA.
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Acton RT, Snively BM, Barton JC, McLaren CE, Adams PC, Rich SS, Eckfeldt JH, Press RD, Sholinsky P, Leiendecker-Foster C, McLaren GD, Speechley MR, Harris EL, Dawkins FW, Gordeuk VR. A genome-wide linkage scan for iron phenotype quantitative trait loci: the HEIRS Family Study. Clin Genet 2007; 71:518-29. [PMID: 17539901 DOI: 10.1111/j.1399-0004.2007.00804.x] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Iron overload phenotypes in persons with and without hemochromatosis are variable. To investigate this further, probands with hemochromatosis or evidence of elevated iron stores and their family members were recruited for a genome-wide linkage scan to identify potential quantitative trait loci (QTL) that contribute to variation in transferrin saturation (TS), unsaturated iron-binding capacity (UIBC), and serum ferritin (SF). Genotyping utilized 402 microsatellite markers with average spacing of 9 cM. A total of 943 individuals, 64% Caucasian, were evaluated from 174 families. After adjusting for age, gender, and race/ethnicity, there was evidence for linkage of UIBC to chromosome 4q logarithm of the odds (LOD) = 2.08, p = 0.001) and of UIBC (LOD = 9.52), TS (LOD = 4.78), and SF (LOD = 2.75) to the chromosome 6p region containing HFE (each p < 0.0001). After adjustments for HFE genotype and other covariates, there was evidence of linkage of SF to chromosome 16p (LOD = 2.63, p = 0.0007) and of UIBC to chromosome 5q (LOD = 2.12, p = 0.002) and to chromosome 17q (LOD = 2.19, p = 0.002). We conclude that these regions should be considered for fine mapping studies to identify QTL that contribute to variation in SF and UIBC.
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Affiliation(s)
- R T Acton
- Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL 35209-0005, USA.
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Wiklund PG, Brown WM, Brott TG, Stegmayr B, Brown RD, Nilsson-Ardnor S, Hardy JA, Kissela BM, Singleton A, Holmberg D, Rich SS, Meschia JF. Lack of aggregation of ischemic stroke subtypes within affected sibling pairs. Neurology 2007; 68:427-31. [PMID: 17283317 DOI: 10.1212/01.wnl.0000252955.17126.6a] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To establish whether subtypes of ischemic stroke aggregate within ischemic stroke-affected sibling pairs more than expected by chance alone. METHODS This retrospective family study was based on a pooled analysis of two cohorts of male and female adult sibling pairs with symptomatic ischemic stroke. One hospital-based cohort of 404 individuals (first proband seen August 30, 1999) was recruited from the United States and Canada, and another population-based cohort of 198 individuals (first proband seen April 17, 1997) was recruited from Umeå, Sweden. Subtype diagnoses were based on Trial of Org 10172 in Acute Stroke Treatment (TOAST) criteria. RESULTS Agreement for subtype diagnoses within families was poor (mean +/- asymptotic SE kappa = 0.17 +/- 0.04). Occurrence of one ischemic stroke subtype in a proband was not associated with a greater likelihood of that subtype being the qualifying stroke subtype in the sibling. Comparable levels of agreement were seen when restricting the analysis to same-sex sibling pairs (kappa = 0.22 +/- 0.05) to sibling pairs in which the proband's stroke occurred before the age of 65 years (kappa = 0.16 +/- 0.05) or to pairs in which the proband's stroke occurred at or after the age of 65 years (kappa = 0.19 +/- 0.05). CONCLUSIONS The subtype of ischemic stroke in a proband was a poor determinant of the subtype of ischemic stroke in the respective sibling. This suggests that many genetic risk factors for ischemic stroke may not be specific for one subtype.
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Affiliation(s)
- P G Wiklund
- Department of Medicine, Umeå University, Umeå, Sweden
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Abstract
BACKGROUND A family history of stroke is an independent risk factor for stroke. OBJECTIVE To assess whether severity of neurologic deficit after stroke is associated with a family history of stroke. METHODS The Ischemic Stroke Genetics Study, a five-center study of first-ever symptomatic ischemic stroke, assessed case subjects prospectively for a family history of stroke-affected first-degree relatives. Certified adjudicators used the NIH Stroke Scale (NIHSS) to determine the severity of neurologic deficit. RESULTS A total of 505 case subjects were enrolled (median age, 65 years; 55% male), with 81% enrolled within 1 week of onset of symptoms. A sibling history of stroke was associated with more severe stroke. The odds of an NIHSS score of 5 or higher were 2.0 times greater for cases with a sibling history of stroke compared with cases with no sibling history (95% CI, 1.0 to 3.9). An association of family history of stroke in parents or children with stroke severity was not detected. CONCLUSIONS A sibling history of stroke increased the likelihood of a more severe stroke in the case subjects, independent of age, sex, and other potential confounding factors. Other family history characteristics were not associated with stroke severity.
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Affiliation(s)
- J F Meschia
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA.
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Krolewski AS, Poznik GD, Placha G, Canani L, Dunn J, Walker W, Smiles A, Krolewski B, Fogarty DG, Moczulski D, Araki S, Makita Y, Ng DPK, Rogus J, Duggirala R, Rich SS, Warram JH. A genome-wide linkage scan for genes controlling variation in urinary albumin excretion in type II diabetes. Kidney Int 2006; 69:129-36. [PMID: 16374433 DOI: 10.1038/sj.ki.5000023] [Citation(s) in RCA: 91] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The main hallmark of diabetic nephropathy is elevation in urinary albumin excretion. We performed a genome-wide linkage scan in 63 extended families with multiple members with type II diabetes. Urinary albumin excretion, measured as the albumin-to-creatinine ratio (ACR), was determined in 426 diabetic and 431 nondiabetic relatives who were genotyped for 383 markers. The data were analyzed using variance components linkage analysis. Heritability (h2) of ACR was significant in diabetic (h2=0.23, P=0.0007), and nondiabetic (h2=0.39, P=0.0001) relatives. There was no significant difference in genetic variance of ACR between diabetic and nondiabetic relatives (P=0.16), and the genetic correlation (rG=0.64) for ACR between these two groups was not different from 1 (P=0.12). These results suggested that similar genes contribute to variation in ACR in diabetic and nondiabetic relatives. This hypothesis was supported further by the linkage results. Support for linkage to ACR was suggestive in diabetic relatives and became significant in all relatives for chromosome 22q (logarithm of odds, LOD=3.7) and chromosome 7q (LOD=3.1). When analyses were restricted to 59 Caucasian families, support for linkage in all relatives increased and became significant for 5q (LOD=3.4). In conclusion, genes on chromosomes 22q, 5q and 7q may contribute to variation in urinary albumin excretion in diabetic and nondiabetic individuals.
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Affiliation(s)
- A S Krolewski
- Research Division, Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts 02215, USA.
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Sale MM, Freedman BI, Hicks PJ, Williams AH, Langefeld CD, Gallagher CJ, Bowden DW, Rich SS. Loci contributing to adult height and body mass index in African American families ascertained for type 2 diabetes. Ann Hum Genet 2005; 69:517-27. [PMID: 16138910 DOI: 10.1046/j.1529-8817.2005.00176.x] [Citation(s) in RCA: 17] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Height and body mass index (BMI) have high heritability in most studies. High BMI and reduced height are well-recognized as important risk factors for a number of cardiovascular diseases. We investigated these phenotypes in African American families originally ascertained for studies of linkage with type 2 diabetes using self-reported height and weight. We conducted a genome wide scan in 221 families containing 580 individuals and 672 relative pairs of African American descent. Estimates of heritability and support for linkage were assessed by genetic variance component analyses using SOLAR software. The estimated heritabilities for height and BMI were 0.43 and 0.64, respectively. We have identified major loci contributing to variation in height on chromosomes 15 (LOD = 2.61 at 35 cM, p = 0.0004), 3 (LOD = 1.82 at 84 cM, p = 0.0029), 8 (LOD = 1.92 at 135 cM, p = 0.0024) and 17 (LOD = 1.70 at 110 cM, p = 0.0044). A broad region on chromosome 4 supported evidence of linkage to variation in BMI, with the highest LOD = 2.66 at 168 cM (p = 0.0005). Two height loci and two BMI loci appear to confirm the existence of quantitative trait loci previously identified by other studies, providing important replicative data to allow further resolution of linkage regions suitable for positional cloning of these cardiovascular disease risk loci.
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Affiliation(s)
- M M Sale
- Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem NC 27157, USA.
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Freedman BI, Hsu FC, Langefeld CD, Rich SS, Herrington DM, Carr JJ, Xu J, Bowden DW, Wagenknecht LE. The impact of ethnicity and sex on subclinical cardiovascular disease: the Diabetes Heart Study. Diabetologia 2005; 48:2511-8. [PMID: 16261310 DOI: 10.1007/s00125-005-0017-2] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2005] [Accepted: 08/04/2005] [Indexed: 10/25/2022]
Abstract
AIMS/HYPOTHESIS African-Americans with type 2 diabetes and access to adequate healthcare are at lower risk of clinical coronary artery disease than are white diabetic patients. We evaluated whether ethnic differences in subclinical cardiovascular disease, coronary and carotid artery calcified plaque and carotid artery intima-medial thickness (IMT) were present in members of The Diabetes Heart Study families. SUBJECTS AND METHODS In a bi-racial cohort of 1,180 individuals from families enriched for members with type 2 diabetes, we calculated coronary and carotid artery calcified plaque using fast-gated helical computed tomography, and measured carotid artery IMT and clinical risk factor profiles. Generalised estimating equations were used to test for an association between measures of subclinical cardiovascular disease and ethnicity and sex. RESULTS After adjustment for age, ethnicity and kidney function, African-Americans had significantly lower amounts of coronary artery calcified plaque (mean+/-SE) (866+/-158 vs 1,915+/-135, respectively; p=0.0466) and carotid artery calcified plaque (179+/-51 vs 355+/-27, respectively; p=0.0240) relative to whites, despite having increased carotid IMT (0.71+/-0.01 vs 0.67+/-0.004 cm, respectively; p=0.0007), and higher blood pressure, albuminuria and HbA1c. Sex-specific analyses revealed that African-American men had significantly lower coronary and carotid artery calcified atheroma than white men. In women, ethnic differences in calcified carotid artery plaque, but not coronary artery plaque, were observed. CONCLUSIONS/INTERPRETATION In families enriched for members with type 2 diabetes, African-American men had markedly lower levels of coronary and carotid artery calcified plaque than white men, despite increased carotid artery IMT and conventional risk factors. These findings suggest that susceptibility to subclinical cardiovascular disease differs markedly according to ethnicity and sex.
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Affiliation(s)
- B I Freedman
- Wake Forest University School of Medicine, Department of Internal Medicine/Section on Nephrology, Medical Center Boulevard, 27157-1053, Winston-Salem, NC 27157-1053, USA.
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Chen DT, Worrall BB, Brown RD, Brott TG, Kissela BM, Olson TS, Rich SS, Meschia JF. The impact of privacy protections on recruitment in a multicenter stroke genetics study. Neurology 2005; 64:721-4. [PMID: 15728301 PMCID: PMC1713191 DOI: 10.1212/01.wnl.0000152042.07414.cc] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The authors reviewed the recruitment of stroke-affected sibling pairs using a letter-based, proband-initiated contact strategy. The authors randomly sampled 99 proband enrollment forms (Phase 1) and randomly sampled 50 sibling reply cards (Phase 2). The sibling response rate was 30.6%, for a pedigree response rate of 58%. Of the siblings who replied, 96% authorized further contact. Median time from proband enrollment to pedigree DNA banking, which required 3+ probands, was 134 days.
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Affiliation(s)
- D T Chen
- Department of Health Evaluation Sciences, University of Virginia, Charlottesville, USA
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Lewis CE, North KE, Arnett D, Borecki IB, Coon H, Ellison RC, Hunt SC, Oberman A, Rich SS, Province MA, Miller MB. Sex-specific findings from a genome-wide linkage analysis of human fatness in non-Hispanic whites and African Americans: the HyperGEN study. Int J Obes (Lond) 2005; 29:639-49. [PMID: 15809668 DOI: 10.1038/sj.ijo.0802916] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
OBJECTIVE To conduct a full genome search for genes potentially influencing two related phenotypes: body mass index (BMI, kg/m2) and percent body fat (PBF) from bioelectric impedance in men and women. DESIGN A total of 3383 participants, 1348 men and 2035 women; recruitment was initiated with hypertensive sibpairs and expanded to first-degree relatives in a multicenter study of hypertension genetics. MEASUREMENTS Genotypes for 387 highly polymorphic markers spaced to provide a 10 cM map (CHLC-8) were generated by the NHLBI Mammalian Genotyping Service (Marshfield, WI, USA). Quantitative trait loci for obesity phenotypes, BMI and PBF, were examined with a variance components method using SOLAR, adjusting for hypertensive status, ethnicity, center, age, age2, sex, and age2 x sex. As we detected a significant genotype-by-sex interaction in initial models and because of the importance of sex effects in the expression of these phenotypes, models thereafter were stratified by sex. No genotype-by-ethnicity interactions were found. RESULTS A QTL influencing PBF in women was detected on chromosome12q (12q24.3-12q24.32, maximum empirical LOD score=3.8); a QTL influencing this phenotype in men was found on chromosome 15q (15q25.3, maximum empirical LOD score=3.0). These QTLs were detected in African-American and white women (12q) and men (15q). QTLs influencing both BMI and PBF were found over a broad region on chromosome 3 in men. QTLs on chromosomes 3 and 12 were found in the combined sample of men and women, but with weaker significance. CONCLUSION The locations with highest LOD scores have been previously reported for obesity phenotypes, indicating that at least two genomic regions influence obesity-related traits. Furthermore, our results indicate the importance of considering context-dependent effects in the search for obesity QTLs.
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Affiliation(s)
- C E Lewis
- Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35205, USA.
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Schanen JG, Iribarren C, Shahar E, Punjabi NM, Rich SS, Sorlie PD, Folsom AR. Asthma and incident cardiovascular disease: the Atherosclerosis Risk in Communities Study. Thorax 2005; 60:633-8. [PMID: 16061703 PMCID: PMC1747501 DOI: 10.1136/thx.2004.026484] [Citation(s) in RCA: 113] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
BACKGROUND A possible association between asthma and cardiovascular disease has been described in several exploratory studies. METHODS The association of self-reported, doctor diagnosed asthma and incident cardiovascular disease was examined in a biracial cohort of 45-64 year old adults (N = 13501) followed over 14 years. RESULTS Compared with never having asthma, the multivariate adjusted hazard ratio (HR) of stroke (n = 438) was 1.50 (95% CI 1.04 to 2.15) for a baseline report of ever having asthma (prevalence 5.2%) and 1.55 (95% CI 0.95 to 2.52) for current asthma (prevalence 2.7%). The relative risk of stroke was 1.43 (95% CI 1.03 to 1.98) using a time dependent analysis incorporating follow up reports of asthma. Participants reporting wheeze attacks with shortness of breath also had greater risk for stroke (HR = 1.56, 95% CI 1.18 to 2.06) than participants without these symptoms. The multivariate adjusted relative risk of coronary heart disease (n = 1349) was 0.87 (95% CI 0.66 to 1.14) for ever having asthma, 0.69 (95% CI 0.46 to 1.05) for current asthma at baseline, and 0.88 (95% CI 0.69 to 1.11) using the time dependent analysis. CONCLUSIONS Asthma may be an independent risk factor for incident stroke but not coronary heart disease in middle aged adults. This finding warrants replication and may motivate a search for possible mechanisms that link asthma and stroke.
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Affiliation(s)
- J G Schanen
- University of Minnesota, School of Public Health, Division of Epidemiology and Community Health, 1300 South 2nd Street, Suite 300, Minneapolis, MN 55454, USA
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Freedman BI, Rich SS, Sale MM, Heiss G, Djoussé L, Pankow JS, Province MA, Rao DC, Lewis CE, Chen YDI, Beck SR. Genome-wide scans for heritability of fasting serum insulin and glucose concentrations in hypertensive families. Diabetologia 2005; 48:661-8. [PMID: 15747111 DOI: 10.1007/s00125-005-1679-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2004] [Accepted: 11/07/2004] [Indexed: 10/25/2022]
Abstract
AIMS/HYPOTHESIS The heritability of fasting serum insulin and glucose concentrations in non-diabetic members of multiplex hypertensive families is unknown. METHODS We calculated the familial aggregation of fasting serum glucose and insulin concentrations and performed a genome-wide scan to assess whether quantitative trait loci contribute to these phenotypes in 2,412 non-diabetic individuals from 1,030 families enrolled in the Hypertension Genetic Epidemiology Network (HyperGEN) in the Family Blood Pressure Program. RESULTS The heritability (+/-SE) of fasting serum insulin was 0.47+/-0.085 in European Americans and 0.28+/-0.08 in African Americans (p<0.0001 for both), after adjusting for age, sex, and BMI. A genome-wide scan for fasting serum insulin yielded a maximum log of the odds (LOD) score of 2.36 on chromosome 5 at 20 cM (p=0.0004) in European Americans, and an LOD score of 2.28 on chromosome 19 at 11 cM (p=0.0004) in African Americans. The heritability of fasting serum glucose was 0.5109+/-0.08 in the former and 0.29+/-0.09 in the latter (p<0.0003 for both) after adjusting for age, sex and BMI. A genome-wide scan for fasting serum glucose revealed a maximum LOD score of 2.07 on chromosome 5 at 26 cM (p=0.0009) in European Americans. CONCLUSIONS/INTERPRETATION These analyses demonstrate the marked heritability of fasting serum insulin and glucose concentrations in families enriched for the presence of members with hypertension. They suggest that genes associated with fasting serum insulin concentration are present on chromosomes 19 and 5, and that genes associated with fasting serum glucose concentration are on chromosome 5, in families enriched for hypertension.
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Affiliation(s)
- B I Freedman
- Department of Internal Medicine, Section on Nephrology, The Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157-1053, USA.
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Norris JM, Langefeld CD, Scherzinger AL, Rich SS, Bookman E, Beck SR, Saad MF, Haffner SM, Bergman RN, Bowden DW, Wagenknecht LE. Quantitative trait loci for abdominal fat and BMI in Hispanic-Americans and African-Americans: the IRAS Family study. Int J Obes (Lond) 2005; 29:67-77. [PMID: 15534617 DOI: 10.1038/sj.ijo.0802793] [Citation(s) in RCA: 36] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE To conduct linkage analysis for body mass index (BMI, kg/m2), waist-to-hip ratio (WHR), visceral adipose tissue mass (VAT, cm2) and subcutaneous adipose tissue mass (SAT, cm2) using a whole genome scan. DESIGN Cross-sectional family study. STUDY SUBJECTS African-American families from Los Angeles (AA, n=21 extended pedigrees) and Hispanic-American families (HA) from San Antonio, TX (HA-SA, n=33 extended pedigrees) and San Luis Valley, CO (HA-SLV, n=12 extended pedigrees), totaling 1049 individuals in the Insulin Resistance and Atherosclerosis (IRAS) Family Study. MEASUREMENTS VAT and SAT were measured using a computed tomography scan obtained at the fourth and fifth lumbar vertebrae. All phenotypes were adjusted for age, gender, and study center. VAT, SAT, and WHR were analyzed both unadjusted and adjusted for BMI. RESULTS Significant linkage to BMI was found at D3S2387 (LOD=3.67) in African-Americans, and at D17S1290 in Hispanic-Americans (LOD=2.76). BMI-adjusted WHR was linked to 12q13-21 (D12S297 (LOD=2.67) and D12S1052 (LOD=2.60)) in Hispanic-Americans. The peak LOD score for BMI-adjusted VAT was found at D11S2006 (2.36) in Hispanic families from San Antonio. BMI-adjusted SAT was linked to D5S820 in Hispanic families (LOD=2.64). Evidence supporting linkage of WHR at D11S2006, VAT at D17S1290, and SAT at D1S1609, D3S2387, and D6S1056 was dependent on BMI, such that the LOD scores became nonsignificant after adjustment of these phenotypes for BMI. CONCLUSIONS Our findings both replicate previous linkage regions and suggest novel regions in the genome that may harbor quantitative trait locis contributing to variation in measures of adiposity.
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Affiliation(s)
- J M Norris
- Department of Preventive Medicine and Biometrics, University of Colorado Health Sciences Center, Denver, CO 80262, USA.
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Abstract
The authors found a correlation between the age at which probands experience an incident stroke and the age at which their siblings experience an incident stroke (r = 0.68; p < 0.0001). Proband-sibling incident stroke latency correlations were observed in analyses restricted to siblings concordant for smoking (r = 0.68; p < 0.0001), diabetes (r = 0.73; p < 0.0001), and hypertension (r = 0.63; p < 0.0001). In the authors' cohort of affected sibling pairs, inherited factors were important determinants of incident ischemic stroke latency.
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Affiliation(s)
- J F Meschia
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA.
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Blumenthal MN, Ober C, Beaty TH, Bleecker ER, Langefeld CD, King RA, Lester L, Cox N, Barnes K, Togias A, Mathias R, Meyers DA, Oetting W, Rich SS. Genome scan for loci linked to mite sensitivity: the Collaborative Study on the Genetics of Asthma (CSGA). Genes Immun 2004; 5:226-31. [PMID: 15029235 DOI: 10.1038/sj.gene.6364063] [Citation(s) in RCA: 32] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Mite sensitivity has been reported to be a major risk factor for asthma. As part of the Collaborative Study on the Genetics of Asthma (CSGA), a genome scan using mite reactivity (Dermatophagoides Pteronyssinus (Der p) and Dermatophagoides farinae (Der f)) as the phenotype was conducted. In 287 CSGA families, 122 were informative for linkage. Evidence supporting linkage was observed for regions on chromosome 19 (D19S591, lod=2.43, P=0.0008; D19S1037, lod=1.57, P=0.007) and chromosome 20 (D20S473/D20S604, lod=1.41, P=0.01). All three ethnic groups appeared to contribute to the evidence for linkage on chromosome 20. African-American families gave strongest support for linkage on chromosomes 3 (D3S2409, lod=1.33, P=0.01), 12 (D12S373, lod=1.51, P=0.008) and 18 (ATA82B02, lod=1.32, P=0.01). Caucasian families showed strong evidence for linkage on chromosome 19 (D19S591, lod=3.51, P=0.00006). Hispanic families supported linkage on chromosomes 11 (D11S1984, lod=1.56, P=0.007), 13 (D13S787, lod=1.30, P=0.01) and 20 (D20S470, lod=1.71, P=0.005). These results suggest that multiple genes may be involved in controlling skin reactivity to Dermatophoigoies.
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Affiliation(s)
- M N Blumenthal
- Department of Medicine, University of Minnesota, Minneapolis, MN, USA.
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Huang C, Kim Y, Caramori ML, Fish AJ, Rich SS, Miller ME, Russell GB, Mauer M. Cellular basis of diabetic nephropathy: III. In vitro GLUT1 mRNA expression and risk of diabetic nephropathy in type 1 diabetic patients. Diabetologia 2004; 47:1789-94. [PMID: 15502921 DOI: 10.1007/s00125-004-1533-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2004] [Accepted: 07/12/2004] [Indexed: 12/14/2022]
Abstract
AIMS/HYPOTHESIS Altered glucose transporter expression has been implicated in the pathogenesis of diabetic nephropathy. There is increasing evidence that genetic factors convey risk of, or protection from, diabetic nephropathy and that the behaviour of cultured skin fibroblasts from type 1 diabetic patients may reflect these genetic influences. This study aimed to compare GLUT1 mRNA expression levels in skin fibroblasts from type 1 diabetic patients with either rapid ("fast-track", n=25) or slow ("slow-track", n=25) development of diabetic nephropathy and from non-diabetic normal control subjects (controls, n=25). METHODS Skin fibroblasts were cultured in Dulbecco's Modified Eagle's Medium with 25 mmol/l glucose for 36 h. Total RNA was isolated, and GLUT1 mRNA levels were estimated by microarray analysis and RT-PCR. RESULTS Levels of GLUT1 mRNA expression in skin fibroblasts from "slow-track" patients were greater than those from "fast-track" patients (p=0.02), as initially detected by microarray. GLUT1 mRNA expression levels were confirmed by RT-PCR to be higher in skin fibroblasts from "slow-track" patients (4.59+/-2.04) than in those from "fast-track" patients (3.34+/-1.2, p=0.02), and were also higher than in skin fibroblasts from control subjects (3.52+/-1.66, p=0.03). There was no statistically significant difference between levels of expression in the "fast-track" patients and the control subjects. CONCLUSIONS/INTERPRETATION This finding is consistent with the presence of cellular protection factors against diabetic nephropathy in the "slow-track" patients. These factors could be associated with the regulation of the GLUT1 pathway and may be genetically determined.
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Affiliation(s)
- C Huang
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, Minnesota, USA
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Rich SS. Introduction: linkage analysis of full-genome screens. Genet Epidemiol 2002; 21 Suppl 1:S115-6. [PMID: 11793652 DOI: 10.1002/gepi.2001.21.s1.s115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- S S Rich
- Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina, 27157-1063, USA
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Davis CC, Brown WM, Lange EM, Rich SS, Langefeld CD. Nonparametric linkage regression. II: Identification of influential pedigrees in tests for linkage. Genet Epidemiol 2002; 21 Suppl 1:S123-9. [PMID: 11793654 DOI: 10.1002/gepi.2001.21.s1.s123] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
We applied case-deletion-based diagnostics to the combined Caucasian genome scan data for asthma and IgE from the Collaborative Study on the Genetics of Asthma (CSGA) and German family studies in order to identify influential pedigrees in tests for linkage. These methods identified 12 pedigrees whose data appear not to fit the asthma linkage model and for whom alternative genetic and nongenetic explanations can be explored. The methods also identified four pedigrees for chromosome 1 and two pedigrees for chromosome 2 that provide strong evidence for linkage at their respective loci. Similarly, these methods helped identify four pedigrees that strongly influenced the linkage tests for IgE. From these data, we can construct an enriched subset of pedigrees to be used in further analysis for mapping region-specific putative trait predisposing loci.
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Affiliation(s)
- C C Davis
- Section on Biostatistics, Department of Public Health Sciences, Wake Forest Univ. School of Medicine, Med. Ctr. Blvd., Winston-Salem, NC 27157-1063, USA
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Abstract
AIMS The goal was to estimate the sibling recurrence-risk ratio for Type 2 diabetes in families with diabetes occurring in middle age. Because diabetes aetiology involves environmental exposures and genetic susceptibility, we sought to identify determinants of the recurrence risk. METHODS We surveyed patients diagnosed at ages 35-59 years (n = 563) to obtain information on the occurrence of diabetes in their relatives, particularly siblings (n = 1675). Age-specific prevalences of diabetes in the US population were used for comparison. RESULTS The overall sibling recurrence-risk ratio for diabetes was low, about 1.8 in the Joslin families and even lower in three other studies that were reanalysed for comparison. In all studies, the diabetes risk in siblings of index cases without a history of diabetes in a parent was similar to that in the general population, suggesting that genetic factors contributed to the occurrence of diabetes in only a minority of these siblings. The fact that recurrence-risk ratios were elevated only in families with one or two diabetic parents indicates that susceptibility to Type 2 diabetes is transmitted primarily through an affected parent. In addition, the sibling recurrence-risk ratios were elevated even further in families with diabetes in both a parent and grandparent of the index case, and in siblings of non-obese index cases (percent ideal body weight < 120%). CONCLUSIONS The selection of families with non-obese index cases and vertical transmission of diabetes through three generations may improve the success of efforts to map susceptibility genes for Type 2 diabetes.
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Affiliation(s)
- C F Weijnen
- Research Division, Joslin Diabetes Center, Wake Forest University School of Medicine, Winston Salem, NC, USA
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Peacock JM, Arnett DK, Atwood LD, Myers RH, Coon H, Rich SS, Province MA, Heiss G. Genome scan for quantitative trait loci linked to high-density lipoprotein cholesterol: The NHLBI Family Heart Study. Arterioscler Thromb Vasc Biol 2001; 21:1823-8. [PMID: 11701472 DOI: 10.1161/hq1101.097804] [Citation(s) in RCA: 44] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We conducted a genome-wide linkage scan for quantitative trait loci influencing total HDL-cholesterol (HDL-C) concentration in a sample of 1027 whites from 101 families participating in the NHLBI Family Heart Study. To maximize the relative contribution of genetic components of variance to the total variance of HDL-C, the HDL-C phenotype was adjusted for age, age(2), body mass index, and Family Heart Study field center, and standardized HDL-C residuals were created separately for men and women. All analyses were completed by the variance components method, as implemented in the program GENEHUNTER using 383 anonymous markers typed at the NHLBI Mammalian Genotyping Service in Marshfield, Wis. Evidence for linkage of residual HDL-C was detected near marker D5S1470 at location 39.9 cM from the p-terminal of chromosome 5 (LOD=3.64). Suggestive linkage was detected near marker D13S1493 at location 27.5 cM on chromosome 13 (LOD=2.36). We conclude that at least 1 genomic region is likely to harbor a gene that influences interindividual variation in HDL cholesterol.
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Affiliation(s)
- J M Peacock
- Division of Epidemiology, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
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35
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Lester LA, Rich SS, Blumenthal MN, Togias A, Murphy S, Malveaux F, Miller ME, Dunston GM, Solway J, Wolf RL, Samet JM, Marsh DG, Meyers DA, Ober C, Bleecker ER. Ethnic differences in asthma and associated phenotypes: collaborative study on the genetics of asthma. J Allergy Clin Immunol 2001; 108:357-62. [PMID: 11544453 DOI: 10.1067/mai.2001.117796] [Citation(s) in RCA: 88] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
BACKGROUND In the Collaborative Study on the Genetics of Asthma, 314 families with 2584 subjects were characterized for asthma and allergy. OBJECTIVE The purpose of this investigation was to examine clinical heterogeneity observed in asthma and allergic characteristics among 3 ethnic groups (African American, white, and Hispanic family members). METHODS Pulmonary function parameters and asthma associated phenotypes were compared among the ethnic groups. RESULTS In comparison with the other groups, African American sibling pairs had a significantly lower baseline FEV(1) percent of predicted (P =.0001) and a higher rate of skin test reactivity to cockroach allergen (P =.0001); Hispanic sibling pairs had significantly more skin reactivity overall (P =.001); and white sibling pairs had significantly lower total serum IgE (P <.05). In addition, there were significantly more relatives with asthma among the African American families than among the white and the Hispanic families (P =.001). CONCLUSION Although different environmental backgrounds should be considered, these clinical differences could be due to differences in genetic susceptibility among the ethnic groups, such as those suggested by our previous genome screen.
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Fossey SC, Mychaleckyj JC, Pendleton JK, Snyder JR, Bensen JT, Hirakawa S, Rich SS, Freedman BI, Bowden DW. A high-resolution 6.0-megabase transcript map of the type 2 diabetes susceptibility region on human chromosome 20. Genomics 2001; 76:45-57. [PMID: 11549316 DOI: 10.1006/geno.2001.6584] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Recent linkage studies and association analyses indicate the presence of at least one type 2 diabetes susceptibility gene in human chromosome region 20q12-q13.1. We have constructed a high-resolution 6.0-megabase (Mb) transcript map of this interval using two parallel, complementary strategies to construct the map. We assembled a series of bacterial artificial chromosome (BAC) contigs from 56 overlapping BAC clones, using STS/marker screening of 42 genes, 43 ESTs, 38 STSs, 22 polymorphic, and 3 BAC end sequence markers. We performed map assembly with GraphMap, a software program that uses a greedy path searching algorithm, supplemented with local heuristics. We anchored the resulting BAC contigs and oriented them within a yeast artificial chromosome (YAC) scaffold by observing the retention patterns of shared markers in a panel of 21 YAC clones. Concurrently, we assembled a sequence-based map from genomic sequence data released by the Human Genome Project, using a seed-and-walk approach. The map currently provides near-continuous coverage between SGC32867 and WI-17676 ( approximately 6.0 Mb). EST database searches and genomic sequence alignments of ESTs, mRNAs, and UniGene clusters enabled the annotation of the sequence interval with experimentally confirmed and putative transcripts. We have begun to systematically evaluate candidate genes and novel ESTs within the transcript map framework. So far, however, we have found no statistically significant evidence of functional allelic variants associated with type 2 diabetes. The combination of the BAC transcript map, YAC-to-BAC scaffold, and reference Human Genome Project sequence provides a powerful integrated resource for future genomic analysis of this region.
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Affiliation(s)
- S C Fossey
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, North Carolina, 27157, USA
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Abstract
Type 2 diabetes is widely recognized as a major risk factor for atherosclerotic cardiovascular disease, including subclinical atherosclerosis as measured by noninvasive procedures. However, the role of genetic factors that contribute to various measures of subclinical atherosclerosis is largely unknown. We hypothesize that subclinical atherosclerosis, measured as coronary artery calcification (CAC), will be extensive in individuals with type 2 diabetes and that its presence depends on both genetic and environmental factors. The genetic factors should result in the familial aggregation of CAC. To determine the extent of familial aggregation of CAC in the presence of type 2 diabetes, we studied 122 individuals with type 2 diabetes (mean age 60 years) and 13 individuals without diabetes in 56 families. CAC was measured by fast-gated helical computed tomography. Other measured factors included blood pressure, body size, lipids, HbA1c, and self-reported medical history. To test for an association between CAC and these factors while accounting for the potential familial correlation of CAC, generalized estimating equations were used. CAC was detectable in 80% of individuals with diabetes (median score 84, range 0-5,776). Extent of CAC, adjusted for age, was positively associated with male sex (P = 0.0003), reduced HDL (P = 0.02), albumin-to-creatinine ratio (P = 0.008), and cigarette pack-years (P = 0.03). CAC was also positively associated with a history of angina, myocardial infarction, stroke, and vascular procedures (all P < 0.01). HbA1c and fasting glucose were positively, but nonsignificantly, associated with the extent of CAC (P = 0.14 and 0.08, respectively). CAC, adjusted for age, sex, race, and diabetes status, was heritable (h2 = 0.50; P = 0.009). In multivariate analysis with additional adjustment for HDL, BMI, hypertension, and smoking, h2 = 0.40 (P = 0.038). These results suggest that strong (independent) genetic factors as well as environmental factors contribute to the variance of CAC in individuals with type 2 diabetes. In these data, CAC seems heritable and may serve as an important feature in designing studies to map genes contributing to both atherosclerosis and type 2 diabetes.
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Affiliation(s)
- L E Wagenknecht
- Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157-1063, USA.
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Mathias RA, Freidhoff LR, Blumenthal MN, Meyers DA, Lester L, King R, Xu JF, Solway J, Barnes KC, Pierce J, Stine OC, Togias A, Oetting W, Marshik PL, Hetmanski JB, Huang SK, Ehrlich E, Dunston GM, Malveaux F, Banks-Schlegel S, Cox NJ, Bleecker E, Ober C, Beaty TH, Rich SS. Genome-wide linkage analyses of total serum IgE using variance components analysis in asthmatic families. Genet Epidemiol 2001; 20:340-55. [PMID: 11255243 DOI: 10.1002/gepi.5] [Citation(s) in RCA: 70] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Variance components models were used to analyze total IgE levels in families ascertained though the Collaborative Study of the Genetics of Asthma (CSGA) using a genome-wide array of polymorphic markers. While IgE levels are known to be associated with clinical asthma and recognized to be under strong genetic control (here the heritability was estimated at 44-60% in the three racial groups), specific genes influencing this trait are still largely unknown. Multipoint analysis of 323 markers yielded little indication of specific regions containing a trait locus controlling total serum IgE levels (adjusted for age and gender). Although a number of regions showed LOD statistics above 1.5 in Caucasian families (chromosome 4) and in African-American families (chromosomes 2 and 4), none yielded consistent evidence in all three racial groups. Analysis of total IgE adjusted for gender, age and Allergy Index (a quantitative score of skin test sensitivity to 14 common aeroallergens) was conducted on these data. In this analysis, a much stronger signal for a trait locus controlling adjusted log[total IgE] was seen on the telomeric end of chromosome 18, but only in Caucasian families. This region accounted for most of the genetic variation in log[total IgE], and may represent a quantitative trait locus for IgE levels independent of atopic response. Oligogenic analysis accounting simultaneously for the contribution of this locus on chromosome 18 and other chromosomal regions showing some evidence of linkage in these Caucasian families (on chromosomes 2, 4 and 20) failed to yield significant evidence for interaction.
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Affiliation(s)
- R A Mathias
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland 21205, USA
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Gray-McGuire C, Moser KL, Gaffney PM, Kelly J, Yu H, Olson JM, Jedrey CM, Jacobs KB, Kimberly RP, Neas BR, Rich SS, Behrens TW, Harley JB. Genome scan of human systemic lupus erythematosus by regression modeling: evidence of linkage and epistasis at 4p16-15.2. Am J Hum Genet 2000; 67:1460-9. [PMID: 11078476 PMCID: PMC1287923 DOI: 10.1086/316891] [Citation(s) in RCA: 116] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2000] [Accepted: 10/19/2000] [Indexed: 11/03/2022] Open
Abstract
Systemic lupus erythematosus (SLE) is a complex autoimmune disorder involving at least hormonal, environmental, and genetic factors. Familial aggregation, a 2%-3% sibling recurrence rate, monozygotic twin concordance >20%, association with several candidate genes, as well as the results of five genome scans support a genetic component. We present here the results of a genome scan of 126 pedigrees multiplex for SLE, including 469 sibling pairs (affected and unaffected) and 175 affected relative pairs. Using the revised multipoint Haseman-Elston regression technique for concordant and discordant sibling pairs and a conditional logistic regression technique for affected relative pairs, we identify a novel linkage to chromosome 4p16-15.2 (P=.0003 and LOD=3.84) and present evidence of an epistatic interaction between chromosome 4p16-15.2 and chromosome 5p15 in our European American families. We confirm the evidence of linkage to chromosome 4p16-15.2 in European American families using data from an independent pedigree collection. In addition, our data support the published results of three independent studies for nine purportedly linked regions and agree with the previously published results from a subset of these data for three regions. In summary, results from two new analytical techniques establish and confirm linkage with SLE at 4p16-15.2, indicate epistasis between 4p16-15.2 and 5p15, and confirm other linkage effects with SLE that have been reported elsewhere.
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Affiliation(s)
- C Gray-McGuire
- Arthritis and Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, 73104, USA
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Klupa T, Malecki MT, Pezzolesi M, Ji L, Curtis S, Langefeld CD, Rich SS, Warram JH, Krolewski AS. Further evidence for a susceptibility locus for type 2 diabetes on chromosome 20q13.1-q13.2. Diabetes 2000; 49:2212-6. [PMID: 11118028 DOI: 10.2337/diabetes.49.12.2212] [Citation(s) in RCA: 84] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
We previously reported suggestive linkage between type 2 diabetes and markers in a region on chromosome 20q using data from a collection of 29 Caucasian families in which type 2 diabetes with middle-age-onset was segregated as an autosomal-dominant disorder. To map more precisely the susceptibility locus (or loci) within this broad region, we increased the family collection and genotyped all families for additional markers, both within the critical region and spaced over the rest of chromosome 20. Altogether 526 individuals (including 241 with diabetes) from the total collection of 43 families were included in the study. All individuals were genotyped for 23 highly polymorphic markers. Positive evidence for linkage was found for a 10-cM region on the long arm of chromosome 20q13.1-q13.2 between markers D20S119 and D20S428. The strongest evidence in two-point as well as multipoint linkage analysis (P = 1.8 x 10(-5)) occurred at the position corresponding to marker D20S196. The individuals with diabetes in the seven most strongly linked families had high serum insulin levels during fasting and 2-h post-glucose load periods. We did not find any evidence for linkage between type 2 diabetes and any other region on chromosome 20. In conclusion, our larger and more comprehensive study showed very strong evidence for a susceptibility gene for insulin-resistant type 2 diabetes located on the long arm of chromosome 20 around marker D20S196.
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Affiliation(s)
- T Klupa
- Joslin Diabetes Center, Department of Medicine, Harvard Medical School, Boston, Massachusetts 02215-5397, USA
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Freedman BI, Yu H, Anderson PJ, Roh BH, Rich SS, Bowden DW. Genetic analysis of nitric oxide and endothelin in end-stage renal disease. Nephrol Dial Transplant 2000; 15:1794-800. [PMID: 11071967 DOI: 10.1093/ndt/15.11.1794] [Citation(s) in RCA: 51] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Genetic factors have been implicated in the development of the common aetiologies of end-stage renal disease (ESRD), including renal failure attributed to hypertension, diabetes mellitus, systemic lupus erythematosus and human immunodeficiency virus infection. Nitric oxide (NO) and endothelin are powerful vasoactive mediators involved in inflammation and regulation of vascular tone and blood pressure. We evaluated the role of the neuronal constitutive (NOS1) and endothelial constitutive (NOS3) nitric oxide synthase genes and the endothelin-1 (EDN-1) gene in predisposition to chronic renal failure in African-Americans. METHODS The study population for the linkage and association analyses in ESRD consisted of 361 individuals from 168 multiplex African-American families. These individuals comprised 207 unweighted sibling pairs concordant for all-cause ESRD. Microsatellite markers NOS1B (NOS1), D7S636 (NOS3) and CPHD1-1/2 (EDN-1) were genotyped in the sample. In addition, a mutation, Glu298Asp, in exon 7 of NOS3 and a 27 bp variable number tandem repeat (VNTR) marker in intron 4 of NOS3 were evaluated in the sibling pairs and in an additional 92 unrelated African-Americans with type 2 diabetes mellitus-associated ESRD (singletons). Association analyses utilized the relative predispositional effect method. Model independent linkage analyses were performed using GeneHunter-plus and MapMaker/SIBS (exclusion analysis) software. RESULTS Significant evidence for association with ESRD was detected for alleles 7 and 9 of the NOS1 gene (11.9 and 34.2%, respectively, in unrelated probands of ESRD families versus 6.5 and 27.5%, respectively, in race-matched controls, both P:<0.01). These associations were maintained when the unrelated first sibling from each family was used in a case-control comparison and was most pronounced in the non-diabetic ESRD cases. The NOS3 and EDN-1 markers failed to provide consistent evidence for association in the sibling pairs and the diabetic ESRD singletons, although we identified two novel endothelial constitutive NOS4 (ecNOS4) VNTR alleles in African-Americans. Significant evidence for linkage was not detected between the NOS genes or the EDN-1 gene in either all-cause ESRD or when the ESRD sibling pairs were stratified by aetiology (type 2 diabetic ESRD or non-diabetic aetiologies). CONCLUSION Based upon the consistent allelic associations, we believe that further evaluation of the NOS1 gene in ESRD susceptibility in African-Americans is warranted.
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Affiliation(s)
- B I Freedman
- Department of Internal Medicine/Nephrology, The Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157-1053, USA
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Yu H, Anderson PJ, Freedman BI, Rich SS, Bowden DW. Genomic structure of the human plasma prekallikrein gene, identification of allelic variants, and analysis in end-stage renal disease. Genomics 2000; 69:225-34. [PMID: 11031105 DOI: 10.1006/geno.2000.6330] [Citation(s) in RCA: 43] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Kallikreins are serine proteases that catalyze the release of kinins and other vasoactive peptides. Previously, we have studied one tissue-specific (H. Yu et al., 1996, J. Am. Soc. Nephrol. 7: 2559-2564) and one plasma-specific (H. Yu et al., 1998, Hypertension 31: 906-911) human kallikrein gene in end-stage renal disease (ESRD). Short sequence repeat polymorphisms for the human plasma kallikrein gene (KLKB1; previously known as KLK3) on chromosome 4 were associated with ESRD in an African American study population. This study of KLKB1 in ESRD has been extended by determining the genomic structure of KLKB1 and searching for allelic variants that may be associated with ESRD. Exon-spanning PCR primer sets were identified by serial testing of primer pairs designed from KLKB1 cDNA sequence and DNA sequencing of PCR products. Like the rat plasma kallikrein gene and the closely related human factor XI gene, the human KLKB1 gene contains 15 exons and 14 introns. The longest intron, F, is almost 12 kb long. The total length of the gene is approximately 30 kb. Sequence of the 5'-proximal promoter region of KLKB1 was obtained by shotgun cloning of genomic fragments from a bacterial artificial clone containing the KLKB1 gene, followed by screening of the clones using exon 1-specific probes. Primers flanking the exons and 5'-proximal promoter region were used to screen for allelic variants in the genomic DNA from ESRD patients and controls using the single-strand conformation polymorphism technique. We identified 12 allelic variants in the 5'-proximal promoter and 7 exons. Of note were a common polymorphism (30% of the population) at position 521 of KLKB1 cDNA, which leads to the replacement of asparagine with a serine at position 124 in the heavy chain of the A2 domain of the protein. In addition, an A716C polymorphism in exon 7 resulting in the amino acid change H189P in the A3 domain of the heavy chain was observed in 5 patients belonging to 3 ESRD families. A third polymorphism in the coding sequence was a C699A shift that caused an amino acid change, H183Q. This allele was observed in 8 cases from 6 ESRD families but was not found in any control DNAs. Individually or combined, the allelic variants observed are not statistically associated with ESRD, though in several cases (e.g., H183Q) the small number of people in the population carrying these alleles limits our ability to statistically test for significant association with ESRD. Two new CA/GT repeat polymorphic markers, designated KLK3f and KLK3g, that have heterozygosities of 0.65 and 0.84, respectively, were identified within introns M and N. Analysis using the relative predispositional effect technique indicated that the frequencies of alleles 4 and 8 of KLK3f and allele 8 of KLK3g were significantly different between controls and ESRD cases. They accounted for 0.226, 0.096, and 0.313, respectively, in the probands of 166 ESRD families compared to 0.172, 0.066, and 0.244 in 139 healthy race-matched controls (allele P and total P < 0.05 for all three alleles). Therefore, although polymorphisms in the coding and 5'-proximal promoter of KLKB1 show no statistically significant association with ESRD in African Americans, there is still evidence for association of this part of chromosome 4 with ESRD. This observation suggests that other sequences within or near KLKB1, or another gene nearby, may contribute to ESRD susceptibility.
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Affiliation(s)
- H Yu
- Department of Biochemistry, Department of Internal Medicine, Department of Public Health Sciences, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157, USA
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Akamizu T, Sale MM, Rich SS, Hiratani H, Noh JY, Kanamoto N, Saijo M, Miyamoto Y, Saito Y, Nakao K, Bowden DW. Association of autoimmune thyroid disease with microsatellite markers for the thyrotropin receptor gene and CTLA-4 in Japanese patients. Thyroid 2000; 10:851-8. [PMID: 11081251 DOI: 10.1089/thy.2000.10.851] [Citation(s) in RCA: 47] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In a previous study we identified a microsatellite marker near the thyrotropin receptor (TSHR) gene. Studies with this marker, TSHR-CA, revealed a significant association between autoimmune thyroid disease (AITD) in Japanese patients and one specific allele (allele 1; 180 base pair [bp]) of the microsatellite sequence. In addition, weak evidence for association of AITD with two alleles of the CTLA-4 gene was observed. In the present study, TSHR-CA has been mapped to approximately 600 kb of the TSHR gene using radiation hybrid mapping. TSHR-CA and another TSHR microsatellite marker, TSHR-AT, which is located in intron 2 of TSHR gene, were genotyped in a set of 349 unrelated Japanese AITD patients and 218 Japanese controls. The TSHR-AT marker showed association in this Japanese AITD population with a significant increase in allele 5 (294 bp; p < 0.05) and a significant decrease in allele 7 (298 bp; p < 0.05). The association of allele 5 of TSHR-AT was also significant in hypothyroid patients (thyrotropin-binding inhibitory immunoglobulin-positive [TBII+], P < 0.01; thyrotropin-binding inhibitory immunoglobulin-negative [TBII-], p < 0.05). The association of allele 7 of TSHR-AT were also significant for the hypothyroid TBII+ patients (p < 0.05). The CTLA-4 gene was also genotyped in this expanded set of Japanese AITD patients and controls. Association between AITD susceptibility and allele 2 (102 bp; p < 0.01) and allele 4 (106 bp; p < 0.01) were observed. These associations were also observed with GD patients (allele 2, p < 0.01; allele 4, p < 0.01). Associations with TSHR-CA were observed for Hashimoto's thyroiditis (HT) patients with respect to alleles 3 (179 bp; p < 0.05) and 5 (175 bp; p < 0.05) and with hypothyroid TBII- patients for allele 4 (177 bp; p < 0.05). The presence of specific alleles of TSHR-CA, TSHR-AT, and CTLA-4 contribute significant increase in risk of development of AITD. These results confirm and expand on our previous study suggesting that alleles of the TSHR and CTLA-4 genes, or genes near them contribute to AITD susceptibility and set the stage for future studies of interactions between these genes and AITD.
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Affiliation(s)
- T Akamizu
- Department of Medicine and Clinical Science, Kyoto University Graduate School of Medicine, Japan.
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Abstract
Readers of narratives keep track of narrative events and the information associated with these events. Does some of this associated information help structure the processing of and memory for the narrative? In three experiments, we examined the role of basic event building blocks (character, time, and location) in event indexing during text comprehension. These three experiments dealt with perceived coherence, perceived cohesion, and on-line processing, respectively. The results indicated that characters are more likely to serve as event indexes. Although the findings with respect to indexing were similar in all three experiments, interesting differences emerged as a function of the level of text comprehension examined (coherence, cohesion, or on-line processing).
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Affiliation(s)
- S S Rich
- Texas Woman's University, Denton, Texas, USA
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Wilk JB, Djousse L, Arnett DK, Rich SS, Province MA, Hunt SC, Crapo RO, Higgins M, Myers RH. Evidence for major genes influencing pulmonary function in the NHLBI family heart study. Genet Epidemiol 2000; 19:81-94. [PMID: 10861898 DOI: 10.1002/1098-2272(200007)19:1<81::aid-gepi6>3.0.co;2-8] [Citation(s) in RCA: 76] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Segregation analysis was performed on the pulmonary measures forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and the ratio of FEV1/FVC in 455 randomly ascertained families from the NHLBI Family Heart Study (FHS). Gender specific standardized residuals were used as the phenotypic variable in both familial correlation and segregation analyses. These residuals represented adjustments for the effects of age, age(2), age(3), Body Mass Index (BMI, kg/m(2)), height, the ratio of waist to hip measurements (WHR), the presence of coronary heart disease, smoking history, and pack years for current smokers. Sibling correlations were not different from parent-offspring correlations for all three traits, and heritability estimates for FEV1, FVC, and the FEV1/FVC ratio were 0. 515, 0.540, and 0.449, respectively. Segregation analysis of FEV1, a trait that measures airflow, indicated that a dominant major gene best fits the data, although a residual familial correlation supports the presence of an additional polygenic or common environmental component. For FVC, a trait that measures lung volume, alternative models could not be statistically differentiated, but the transmission probabilities do not support a Mendelian major gene. The best model for FEV1/FVC ratio is a non-Mendelian codominant model, perhaps due to the mixing of the individual underlying distributions influencing airflow and lung volume. These results support the hypothesis that complex relationships exist for lung function traits and that multiple genes and environmental factors influence lung function.
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Affiliation(s)
- J B Wilk
- Section of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, Massachusetts 02118, USA.
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Abstract
PURPOSE The impact of laws restricting health insurers' use of genetic information has been assessed from two main vantage points: (1) whether they reduce the extent of genetic discrimination and (2) whether they reduce the fear of discrimination and the resulting deterrence to undergo genetic testing. A previous report from this study concluded that there are almost no well-documented cases of health insurers either asking for or using presymptomatic genetic test results in their underwriting decisions, either before or after these laws, or in states with or without these laws. This report evaluates the perceptions and the resulting behavior by patients and clinicians. METHODS A comparative case study analysis was performed in seven states with different laws respecting health insurers' use of genetic information (no law, new prohibition, mature prohibition). Semistructured interviews were conducted in person with five patient advocates and with 30 experienced genetic counselors or medical geneticists, most of whom deal with adult-onset disorders. Also, multiple informed consent forms and patient information brochures were collected and analyzed using qualitative methods. RESULTS Patients' and clinicians' fear of genetic discrimination greatly exceeds reality, at least for health insurance. It is uncertain how much this fear actually deters genetic testing. The greatest deterrence is to those who do not want to submit the costs of testing for reimbursement and who cannot afford to pay for testing. There appears to be little deterrence for tests that are more easily affordable or when the need for the information is much greater. Fear of discrimination plays virtually no role in testing decisions in pediatric or prenatal situations, but is significant for adult-onset genetic conditions. CONCLUSION Existing laws have not greatly reduced the fear of discrimination. This may be due, in part, to clinicians' lack of confidence that these laws can prevent discrimination until there are test cases of actual enforcement. Ironically, there may be so little actual discrimination that it may not be possible to initiate good test cases.
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Affiliation(s)
- M A Hall
- Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157-1063, USA
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Sellers TA, Weaver TW, Phillips B, Altmann M, Rich SS. Environmental factors can confound identification of a major gene effect: results from a segregation analysis of a simulated population of lung cancer families. Genet Epidemiol 2000; 15:251-62. [PMID: 9593112 DOI: 10.1002/(sici)1098-2272(1998)15:3<251::aid-gepi4>3.0.co;2-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Proper control of environmental factors can be crucial to the identification of genes that influence susceptibility to a complex trait, especially for a trait such as lung cancer, for which the environmental factor (smoking) accounts for a significant etiologic fraction of the disease. An earlier segregation analysis of 337 Louisiana families, which incorporated direct measure of tobacco consumption, provided evidence for autosomal codominant inheritance of a major gene that influenced age at onset of lung cancer. Subsequent analyses were performed in which the families were stratified into two subsets based on birth cohort of the proband; results suggested the presence of heterogeneity that were postulated to reflect the influence of cohort trends in tobacco consumption. To evaluate this hypothesis further, we simulated a population of three-generation pedigrees in which an autosomal dominant mode of susceptibility to lung cancer was transmitted, but tobacco use varied across generations corresponding to published trends in smoking. A total of 200,000 individuals in families of various sizes, ages, and cigarette smoking habits were simulated from 1900 to 1980. From this population, 324 families (2,405 individuals) with 380 cases of lung cancer were ascertained through 328 lung cancer probands. Complex segregation analysis was performed using the REGTL program of S.A.G.E. in which pack-years of tobacco exposure were incorporated directly into the likelihood calculations. Although the no major gene, environmental, and Mendelian recessive hypotheses were rejected, both dominant and codominant transmission provided a good fit to the data. Thus in a population of simulated families with autosomal dominant susceptibility to lung cancer, intergenerational differences in tobacco consumption led to the detection of autosomal codominant transmission as an acceptable hypothesis. These results underscore the potential danger of segregation analysis of complex traits in which exposure to known environmental influences may differ across generations.
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Affiliation(s)
- T A Sellers
- Division of Epidemiology, School of Public Health, Institute of Human Genetics, University of Minnesota, Minneapolis 55454-1015, USA
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Abstract
Proband-reported family histories are widely used in research and counseling, yet little is known about the validity of family history reporting. The Family Heart Study (FHS), a population-based study of familial cardiovascular disease, gathered family history information from 3,020 middle-aged probands in four U.S. communities. Probands reported on the history of coronary heart disease (CHD), diabetes, hypertension, and asthma among a total of 10,316 living relatives (9,186 siblings, 1,130 parents) and 2,685 spouses. Questionnaires were returned by 6,672 siblings, 901 parents, and 2,347 spouses, yielding response rates of 73, 79, and 87%, respectively. Utilizing the relatives' self-report as the standard, sensitivity of the proband report on their spouse, parent, and sibling was 87, 85, and 81% for CHD, 83, 87, and 72% for diabetes, 77, 76, and 56% for hypertension, and 66, 53, and 39% for asthma, respectively. Most specificity values were above 90%. Analyses using generalized estimating equations (GEE) were performed to evaluate differences in proband accuracy based on the proband's age, gender, disease state, center, and ethnicity. In multivariate models, age, gender, and disease status were significantly associated with the accuracy of proband's report of sibling disease history, but had little effect on the accuracy of their report on spouses or parents. In general, older probands were significantly less accurate reporters of disease than younger probands. These results demonstrate that CHD family history can be captured effectively based on proband reports, but suggest that additional family contacts may be helpful when working with older probands or with chronic diseases that have few recognized medical events or procedures.
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Affiliation(s)
- J T Bensen
- Department of Public Health Sciences, Bowman Gray School of Medicine, Wake Forest University, Winston-Salem, North Carolina 27157, USA.
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Criswell LA, Moser KL, Gaffney PM, Inda S, Ortmann WA, Lin D, Chen JJ, Li H, Gray-McGuire C, Neas BR, Rich SS, Harley JB, Behrens TW, Seldin MF. PARP alleles and SLE: failure to confirm association with disease susceptibility. J Clin Invest 2000; 105:1501-2. [PMID: 10841503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023] Open
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Abstract
Elevated urinary albumin excretion (UAE) is a predictor of the development of nephropathy and cardiovascular mortality. To study whether genetic factors may determine UAE, we examined familial aggregation of UAE in 96 large multigenerational pedigrees ascertained for type 2 diabetes. A total of 1,269 subjects had UAE measured as the urinary albumin-to-creatinine ratio (ACR). This included 630 subjects with type 2 diabetes and 639 subjects without diabetes. A significant correlation (Spearman's correlation 0.34, P < 0.001) was found between the median ACR values determined separately in nondiabetic and diabetic members of the same family. To determine whether this familial aggregation of ACR could be explained by the transmission of 1 or more major genes and thus be suitable for gene mapping studies, segregation analyses were performed. In these analyses, ACR was modeled as a continuous trait with the inclusion of age, sex, and duration of diabetes as covariates. Likelihood ratio tests were performed to test competing hypotheses, and Akaike's information criterion was used to determine the most parsimonious models. The Mendelian model with multifactorial inheritance was supported more strongly than Mendelian inheritance alone. These analyses suggested that the best model for ACR levels was multifactorial with evidence for a common major gene. When the analyses were repeated for diabetic subjects only, the evidence for Mendelian inheritance was improved, although a single major locus with additional multifactorial effects was more strongly supported. The results from the current study suggest that levels of UAE are determined by a mixture of genes with large and small effects as well as other measured covariates, such as diabetes.
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Affiliation(s)
- D G Fogarty
- Research Division, Joslin Diabetes Center, Boston, Massachusetts 02215, USA
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