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Main LR, Song YE, Lynn A, Laux RA, Miskimen KL, Osterman MD, Cuccaro ML, Ogrocki PK, Lerner AJ, Vance JM, Fuzzell MD, Fuzzell SL, Hochstetler SD, Dorfsman DA, Caywood LJ, Prough MB, Adams LD, Clouse JE, Herington SD, Scott WK, Pericak-Vance MA, Haines JL. Genetic analysis of cognitive preservation in the midwestern Amish reveals a novel locus on chromosome 2. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.13.23299932. [PMID: 38168325 PMCID: PMC10760262 DOI: 10.1101/2023.12.13.23299932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
INTRODUCTION Alzheimer disease (AD) remains a debilitating condition with limited treatments and additional therapeutic targets needed. Identifying AD protective genetic loci may identify new targets and accelerate identification of therapeutic treatments. We examined a founder population to identify loci associated with cognitive preservation into advanced age. METHODS Genome-wide association and linkage analyses were performed on 946 examined and sampled Amish individuals, aged 76-95, who were either cognitively unimpaired (CU) or impaired (CI). RESULTS 12 SNPs demonstrated suggestive association (P≤5×10-4) with cognitive preservation. Genetic linkage analyses identified >100 significant (LOD≥3.3) SNPs, some which overlapped with the association results. Only one locus on chromosome 2 retained significance across multiple analyses. DISCUSSION A novel significant result for cognitive preservation on chromosome 2 includes the genes LRRTM4 and CTNNA2. Additionally, the lead SNP, rs1402906, impacts the POU3F2 transcription factor binding affinity, which regulates LRRTM4 and CTNNA2.
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Affiliation(s)
- Leighanne R Main
- Departments of Genetics and Genome Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, USA, 44106
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
- Cleveland Institute of Computational Biology, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44106
| | - Yeunjoo E Song
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
- Cleveland Institute of Computational Biology, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44106
| | - Audrey Lynn
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
- Cleveland Institute of Computational Biology, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44106
| | - Renee A Laux
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
| | - Kristy L Miskimen
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
| | - Michael D Osterman
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
| | - Michael L Cuccaro
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Paula K Ogrocki
- Department of Neurology, University Hospitals Cleveland Medical Center, 11100 Euclid Ave, Cleveland, OH, USA, 44106
- Department of Neurology, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44106
| | - Alan J Lerner
- Department of Neurology, University Hospitals Cleveland Medical Center, 11100 Euclid Ave, Cleveland, OH, USA, 44106
- Department of Neurology, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44106
| | - Jeffery M Vance
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - M Denise Fuzzell
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
| | - Sarada L Fuzzell
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
| | - Sherri D Hochstetler
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
| | - Daniel A Dorfsman
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Laura J Caywood
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Michael B Prough
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Larry D Adams
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Jason E Clouse
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Sharlene D Herington
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - William K Scott
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Margaret A Pericak-Vance
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Jonathan L Haines
- Departments of Genetics and Genome Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, USA, 44106
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
- Cleveland Institute of Computational Biology, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44106
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Zajac GJM, Gagliano Taliun SA, Sidore C, Graham SE, Åsvold BO, Brumpton B, Nielsen JB, Zhou W, Gabrielsen M, Skogholt AH, Fritsche LG, Schlessinger D, Cucca F, Hveem K, Willer CJ, Abecasis GR. A fast linkage method for population GWAS cohorts with related individuals. Genet Epidemiol 2023; 47:231-248. [PMID: 36739617 PMCID: PMC10027464 DOI: 10.1002/gepi.22516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/27/2022] [Accepted: 01/24/2023] [Indexed: 02/07/2023]
Abstract
Linkage analysis, a class of methods for detecting co-segregation of genomic segments and traits in families, was used to map disease-causing genes for decades before genotyping arrays and dense SNP genotyping enabled genome-wide association studies in population samples. Population samples often contain related individuals, but the segregation of alleles within families is rarely used because traditional linkage methods are computationally inefficient for larger datasets. Here, we describe Population Linkage, a novel application of Haseman-Elston regression as a method of moments estimator of variance components and their standard errors. We achieve additional computational efficiency by using modern methods for detection of IBD segments and variance component estimation, efficient preprocessing of input data, and minimizing redundant numerical calculations. We also refined variance component models to account for the biases in population-scale methods for IBD segment detection. We ran Population Linkage on four blood lipid traits in over 70,000 individuals from the HUNT and SardiNIA studies, successfully detecting 25 known genetic signals. One notable linkage signal that appeared in both was for low-density lipoprotein (LDL) cholesterol levels in the region near the gene APOE (LOD = 29.3, variance explained = 4.1%). This is the region where the missense variants rs7412 and rs429358, which together make up the ε2, ε3, and ε4 alleles each account for 2.4% and 0.8% of variation in circulating LDL cholesterol. Our results show the potential for linkage analysis and other large-scale applications of method of moments variance components estimation.
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Affiliation(s)
- Gregory JM Zajac
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI
| | - Sarah A Gagliano Taliun
- Department of Medicine and Department of Neurosciences, Université de Montréal, Montréal, QC H3T 1J4, Canada
- Montréal Heart Institute, Montréal, QC H1T 1C8, Canada
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica - CNR, Cagliari, Italy
- Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy
| | - Sarah E Graham
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
| | - Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jonas B Nielsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Wei Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Maiken Gabrielsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Anne Heidi Skogholt
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lars G Fritsche
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI
| | | | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica - CNR, Cagliari, Italy
- Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
- Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger 7600, Norway
| | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
- Department of Human Genetics, University of Michigan, Ann Arbor, MI
| | - Gonçalo R Abecasis
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI
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Isaacs A, Barysenka A, Ter Bekke RMA, Helderman-van den Enden ATJM, van den Wijngaard A, Volders PGA, Stoll M. Standing genetic variation affects phenotypic heterogeneity in an SCN5A-mutation founder population with excess sudden cardiac death. Heart Rhythm 2023; 20:720-727. [PMID: 36764349 DOI: 10.1016/j.hrthm.2023.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 01/19/2023] [Accepted: 02/05/2023] [Indexed: 02/11/2023]
Abstract
BACKGROUND The Worm Study, ascertained from a multigeneration pedigree segregating a single amino acid deletion in SCN5A (c.4850_4852delTCT, p.(Phe1617del), rs749697698), is characterized by substantial phenotypic heterogeneity and overlap of sudden cardiac death, long-QT syndrome, cardiac conduction disease, Brugada syndrome, and isorhythmic atrioventricular dissociation. Linkage analysis for a synthetic trait derived from these phenotypes identified a single peak (logarithm of the odds [LOD] = 4.52) at the SCN5A/SCN10A/SCN11A locus on chromosome 3. OBJECTIVE This study explored the role of additional genetic variation in the chromosome 3 locus as a source of phenotypic heterogeneity in the Worm Study population. METHODS Genotypes underlying the linkage peak (n = 70) were characterized using microarrays. Haplotypes were determined using family-aware phasing and a population-specific reference panel. Variants with minor allele frequencies >0.10 were tested for association with cardiac conduction disease and isorhythmic dissociation using LAMP and logistic regression. RESULTS Only 1 haplotype carried the p.Phe1617del/rs749697698 deletion, suggesting relatively recent development (∼18 generations); this haplotype contained 5 other missense variants spanning SCN5A/SCN10A/SCN11A. Noncarrier haplotypes (n = 74) ranged in frequency from 0.5% to 5%. Although no variants were associated with cardiac conduction disease, a homozygous missense variant in SCN10A was associated with isorhythmic dissociation after correction for multiple comparisons (odds ratio 11.23; 95% confidence interval 2.76-23.39; P = 1.2 × 10-4). This variant (rs12632942) was previously associated with PR interval. CONCLUSION Our data suggest that other variants, alongside a pathogenic mutation, are associated with phenotypic heterogeneity. Single-mutation screening may be insufficient to predict electrical heart disease in patients and family members. In the Worm Study population, segregating a pathogenic SCN5A mutation, compound variation in the SCN5A/SCN10A/SCN11A locus determines arrhythmic outcome.
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Affiliation(s)
- Aaron Isaacs
- Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands; Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands; Department of Physiology, Maastricht University, Maastricht, the Netherlands
| | - Andrei Barysenka
- Department of Genetic Epidemiology, Institute of Human Genetics, University of Münster, Münster, Germany
| | - Rachel M A Ter Bekke
- Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands; Department of Cardiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | | | - Arthur van den Wijngaard
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Paul G A Volders
- Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands; Department of Cardiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Monika Stoll
- Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands; Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands; Department of Genetic Epidemiology, Institute of Human Genetics, University of Münster, Münster, Germany; Department of Biochemistry, Maastricht University, Maastricht, the Netherlands.
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4
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Christoffersen B, Mahjani B, Clements M, Kjellström H, Humphreys K. Quasi-Monte Carlo Methods for Binary Event Models with Complex Family Data. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2151454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Benjamin Christoffersen
- Division of Robotics, Perception and Learning, KTH Royal Institute of Technology
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Swedish e-Science Research Center (SeRC)
| | - Behrang Mahjani
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai
| | - Mark Clements
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Swedish e-Science Research Center (SeRC)
| | - Hedvig Kjellström
- Division of Robotics, Perception and Learning, KTH Royal Institute of Technology
- Swedish e-Science Research Center (SeRC)
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Swedish e-Science Research Center (SeRC)
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5
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Ergun U, Say B, Ergun SG, Percin FE, Inan L, Kaygisiz S, Asal PG, Yurteri B, Struchalin M, Shtokalo D, Ergun MA. Genome-wide association and whole exome sequencing studies reveal a novel candidate locus for restless legs syndrome. Eur J Med Genet 2021; 64:104186. [PMID: 33662638 DOI: 10.1016/j.ejmg.2021.104186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 02/05/2021] [Accepted: 02/27/2021] [Indexed: 12/19/2022]
Abstract
INTRODUCTION The restless legs syndrome (RLS) is a common heritable neurologic disorder which is characterized by an irresistible desire to move and unpleasant sensations in the legs. METHODS We aim to identify new variants associated with RLS by performing genome-wide linkage and subsequent association analysis of forty member's family with history of RLS. RESULTS We found evidence of linkage for three loci 7q21.11 (HLOD = 3.02), 7q21.13-7q21.3 (HLOD = 3.02) and 7q22.3 (HLOD = 3.09). Fine-mapping of those regions in association study using exome sequencing identified SEMA3A (p-value = 8.5·10-4), PPP1R9A (p-value = 7.2·10-4), PUS7 (p-value = 8.7·10-4), CDHR3 (p-value = 7.2·10-4), HBP1 (p-value = 1.5·10-4) and COG5 (p-value = 1.5·10-4) genes with p-values below significance threshold. CONCLUSION Linkage analysis with subsequent association study of exome variants identified six new genes associated with RLS mapped on 7q21 and q22.
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Affiliation(s)
- Ufuk Ergun
- Kırıkkale University Faculty of Medicine, Department of Neurology, Kırıkkale, Turkey
| | - Bahar Say
- Kırıkkale University Faculty of Medicine, Department of Neurology, Kırıkkale, Turkey
| | - Sezen Guntekin Ergun
- Hacettepe University Faculty of Medicine, Department of Medical Biology, Anakara, Turkey
| | - Ferda Emriye Percin
- Gazi University Faculty of Medicine, Department of Medical Genetics, Ankara, Turkey
| | - Levent Inan
- Ministry of Health Ankara Research and Training Hospital Neurology and Algology Department, Ankara, Turkey
| | - Sukran Kaygisiz
- Ministry of Health Ordu University Traning and Research Hospital, Ordu, Turkey
| | - Pınar Gelener Asal
- Dr. Suat Gunsel University of Kyrenia Hospital, Kyrenia, Turkish Republic of Northern Cyprus
| | - Buket Yurteri
- Hacettepe University Faculty of Medicine, Department of Pediatric Basic Sciences, Ankara, Turkey
| | | | - Dmitry Shtokalo
- AcademGene Ltd, Russia; A.P.Ershov Institute of Informatics Systems SB RAS, Russia
| | - Mehmet Ali Ergun
- Gazi University Faculty of Medicine, Department of Medical Genetics, Ankara, Turkey.
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Jupp B, Pitzoi S, Petretto E, Mar AC, Oliver YP, Jordan ER, Taylor S, Atanur SS, Srivastava PK, Saar K, Hubner N, Sommer WH, Staehlin O, Spanagel R, Robinson ES, Schumann G, Moreno M, Everitt BJ, Robbins TW, Aitman TJ, Dalley JW. Impulsivity is a heritable trait in rodents and associated with a novel quantitative trait locus on chromosome 1. Sci Rep 2020; 10:6684. [PMID: 32317713 PMCID: PMC7174407 DOI: 10.1038/s41598-020-63646-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 04/01/2020] [Indexed: 12/30/2022] Open
Abstract
Impulsivity describes the tendency to act prematurely without appropriate foresight and is symptomatic of a number of neuropsychiatric disorders. Although a number of genes for impulsivity have been identified, no study to date has carried out an unbiased, genome-wide approach to identify genetic markers associated with impulsivity in experimental animals. Herein we report a linkage study of a six-generational pedigree of adult rats phenotyped for one dimension of impulsivity, namely premature responding on the five-choice serial reaction time task, combined with genome wide sequencing and transcriptome analysis to identify candidate genes associated with the expression of the impulsivity trait. Premature responding was found to be heritable (h2 = 13-16%), with significant linkage (LOD 5.2) identified on chromosome 1. Fine mapping of this locus identified a number of polymorphic candidate genes, however only one, beta haemoglobin, was differentially expressed in both the founder strain and F6 generation. These findings provide novel insights into the genetic substrates and putative neurobiological mechanisms of impulsivity with broader translational relevance for impulsivity-related disorders in humans.
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Affiliation(s)
- Bianca Jupp
- 0000000121885934grid.5335.0Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Silvia Pitzoi
- 0000 0001 2113 8111grid.7445.2MRC Clinical Sciences Centre, Faculty of Medicine, Imperial College, London, UK
| | - Enrico Petretto
- 0000 0001 2113 8111grid.7445.2MRC Clinical Sciences Centre, Faculty of Medicine, Imperial College, London, UK ,0000 0004 0385 0924grid.428397.3Duke-NUS Medical School, Singapore, Singapore
| | - Adam C. Mar
- 0000000121885934grid.5335.0Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK ,0000 0004 1936 8753grid.137628.9NYU School of Medicine, New York, USA
| | - Yolanda Pena Oliver
- 0000 0004 1936 7590grid.12082.39School of Psychology, University of Sussex, Brighton, UK
| | - Emily R. Jordan
- 0000000121885934grid.5335.0Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Stephanie Taylor
- 0000000121885934grid.5335.0Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Santosh S. Atanur
- 0000 0001 2113 8111grid.7445.2MRC Clinical Sciences Centre, Faculty of Medicine, Imperial College, London, UK
| | - Prashant K. Srivastava
- 0000 0001 2113 8111grid.7445.2MRC Clinical Sciences Centre, Faculty of Medicine, Imperial College, London, UK
| | - Kathrin Saar
- 0000 0001 1014 0849grid.419491.0Max Delbruck Centre for Molecular Medicine, Berlin, Germany
| | - Norbert Hubner
- 0000 0001 1014 0849grid.419491.0Max Delbruck Centre for Molecular Medicine, Berlin, Germany
| | - Wolfgang H. Sommer
- 0000 0004 0477 2235grid.413757.3Institute of Psychopharmacology, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Oliver Staehlin
- 0000 0004 0477 2235grid.413757.3Institute of Psychopharmacology, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Rainer Spanagel
- 0000 0004 0477 2235grid.413757.3Institute of Psychopharmacology, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Emma S. Robinson
- 0000 0004 1936 7603grid.5337.2School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK
| | - Gunter Schumann
- 0000 0001 2322 6764grid.13097.3cCentre for Population Neuroscience and Stratified Medicine, Institute of Psychiatry, Psychology and Neuroscience, King¹s College, London, UK
| | - Margarita Moreno
- 0000000101969356grid.28020.38Department of Psychology & Health Research Centre (CEINSA), University of Almería, Almería, Spain
| | - Barry J. Everitt
- 0000000121885934grid.5335.0Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Trevor W. Robbins
- 0000000121885934grid.5335.0Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Timothy J. Aitman
- 0000 0004 1936 7988grid.4305.2Centre for Genomics and Experimental Medicine, MRC Institute of Genetics & Molecular Medicine, The University of Edinburgh, Edinburgh, UK
| | - Jeffrey W. Dalley
- 0000000121885934grid.5335.0Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK ,0000000121885934grid.5335.0Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
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Lindholm Carlström E, Halvardson J, Etemadikhah M, Wetterberg L, Gustavson KH, Feuk L. Linkage and exome analysis implicate multiple genes in non-syndromic intellectual disability in a large Swedish family. BMC Med Genomics 2019; 12:156. [PMID: 31694657 PMCID: PMC6833288 DOI: 10.1186/s12920-019-0606-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 10/18/2019] [Indexed: 01/20/2023] Open
Abstract
Background Non-syndromic intellectual disability is genetically heterogeneous with dominant, recessive and complex forms of inheritance. We have performed detailed genetic studies in a large multi-generational Swedish family, including several members diagnosed with non-syndromic intellectual disability. Linkage analysis was performed on 22 family members, nine affected with mild to moderate intellectual disability and 13 unaffected family members. Methods Family members were analyzed with Affymetrix Genome-Wide Human SNP Array 6.0 and the genetic data was used to detect copy number variation and to perform genome wide linkage analysis with the SNP High Throughput Linkage analysis system and the Merlin software. For the exome sequencing, the samples were prepared using the Sure Select Human All Exon Kit (Agilent Technologies, Santa Clara, CA, USA) and sequenced using the Ion Proton™ System. Validation of identified variants was performed with Sanger sequencing. Results The linkage analysis results indicate that intellectual disability in this family is genetically heterogeneous, with suggestive linkage found on chromosomes 1q31-q41, 4q32-q35, 6p25 and 14q24-q31 (LOD scores of 2.4, simulated p-value of 0.000003 and a simulated genome-wide p-value of 0.06). Exome sequencing was then performed in 14 family members and 7 unrelated individuals from the same region. The analysis of coding variation revealed a pathogenic and candidate variants in different branches of the family. In three patients we find a known homozygous pathogenic mutation in the Homo sapiens solute carrier family 17 member 5 (SLC17A5), causing Salla disease. We also identify a deletion overlapping KDM3B and a duplication overlapping MAP3K4 and AGPAT4, both overlapping variants previously reported in developmental disorders. Conclusions DNA samples from the large family analyzed in this study were initially collected based on a hypothesis that affected members shared a major genetic risk factor. Our results show that a complex phenotype such as mild intellectual disability in large families from genetically isolated populations may show considerable genetic heterogeneity.
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Affiliation(s)
- Eva Lindholm Carlström
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory Uppsala, Uppsala University, Box 815, SE-751 08, Uppsala, Sweden.
| | - Jonatan Halvardson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory Uppsala, Uppsala University, Box 815, SE-751 08, Uppsala, Sweden
| | - Mitra Etemadikhah
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory Uppsala, Uppsala University, Box 815, SE-751 08, Uppsala, Sweden
| | - Lennart Wetterberg
- Department of Clinical Neuroscience (CNS), K8, Karolinska Institutet, Stockholm, Sweden
| | - Karl-Henrik Gustavson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory Uppsala, Uppsala University, Box 815, SE-751 08, Uppsala, Sweden
| | - Lars Feuk
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory Uppsala, Uppsala University, Box 815, SE-751 08, Uppsala, Sweden
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8
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Rare variants and loci for age-related macular degeneration in the Ohio and Indiana Amish. Hum Genet 2019; 138:1171-1182. [PMID: 31367973 PMCID: PMC6745026 DOI: 10.1007/s00439-019-02050-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Accepted: 07/21/2019] [Indexed: 01/10/2023]
Abstract
Age-related macular degeneration (AMD) is a leading cause of blindness in the world. While dozens of independent genomic variants are associated with AMD, about one-third of AMD heritability is still unexplained. To identify novel variants and loci for AMD, we analyzed Illumina HumanExome chip data from 87 Amish individuals with early or late AMD, 79 unaffected Amish individuals, and 15 related Amish individuals with unknown AMD affection status. We retained 37,428 polymorphic autosomal variants across 175 samples for association and linkage analyses. After correcting for multiple testing (n = 37,428), we identified four variants significantly associated with AMD: rs200437673 (LCN9, p = 1.50 × 10−11), rs151214675 (RTEL1, p = 3.18 × 10−8), rs140250387 (DLGAP1, p = 4.49 × 10−7), and rs115333865 (CGRRF1, p = 1.05 × 10−6). These variants have not been previously associated with AMD and are not in linkage disequilibrium with the 52 known AMD-associated variants reported by the International AMD Genomics Consortium based on physical distance. Genome-wide significant linkage peaks were observed on chromosomes 8q21.11–q21.13 (maximum recessive HLOD = 4.03) and 18q21.2–21.32 (maximum dominant HLOD = 3.87; maximum recessive HLOD = 4.27). These loci do not overlap with loci previously linked to AMD. Through gene ontology enrichment analysis with ClueGO in Cytoscape, we determined that several genes in the 1-HLOD support interval of the chromosome 8 locus are involved in fatty acid binding and triglyceride catabolic processes, and the 1-HLOD support interval of the linkage region on chromosome 18 is enriched in genes that participate in serine-type endopeptidase inhibitor activity and the positive regulation of epithelial to mesenchymal transition. These results nominate novel variants and loci for AMD that require further investigation.
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9
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Ullah E, Mall R, Abbas MM, Kunji K, Nato AQ, Bensmail H, Wijsman EM, Saad M. Comparison and assessment of family- and population-based genotype imputation methods in large pedigrees. Genome Res 2018; 29:125-134. [PMID: 30514702 PMCID: PMC6314157 DOI: 10.1101/gr.236315.118] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 11/30/2018] [Indexed: 01/19/2023]
Abstract
Genotype imputation is widely used in genome-wide association studies to boost variant density, allowing increased power in association testing. Many studies currently include pedigree data due to increasing interest in rare variants coupled with the availability of appropriate analysis tools. The performance of population-based (subjects are unrelated) imputation methods is well established. However, the performance of family- and population-based imputation methods on family data has been subject to much less scrutiny. Here, we extensively compare several family- and population-based imputation methods on family data of large pedigrees with both European and African ancestry. Our comparison includes many widely used family- and population-based tools and another method, Ped_Pop, which combines family- and population-based imputation results. We also compare four subject selection strategies for full sequencing to serve as the reference panel for imputation: GIGI-Pick, ExomePicks, PRIMUS, and random selection. Moreover, we compare two imputation accuracy metrics: the Imputation Quality Score and Pearson's correlation R 2 for predicting power of association analysis using imputation results. Our results show that (1) GIGI outperforms Merlin; (2) family-based imputation outperforms population-based imputation for rare variants but not for common ones; (3) combining family- and population-based imputation outperforms all imputation approaches for all minor allele frequencies; (4) GIGI-Pick gives the best selection strategy based on the R 2 criterion; and (5) R 2 is the best measure of imputation accuracy. Our study is the first to extensively evaluate the imputation performance of many available family- and population-based tools on the same family data and provides guidelines for future studies.
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Affiliation(s)
- Ehsan Ullah
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Mostafa M Abbas
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Khalid Kunji
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Alejandro Q Nato
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington 98195-9460, USA.,Department of Biomedical Sciences, Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia 25755, USA
| | - Halima Bensmail
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
| | - Ellen M Wijsman
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington 98195-9460, USA.,Department of Biostatistics, University of Washington, Seattle, Washington 98195-9460, USA
| | - Mohamad Saad
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
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10
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Vojinovic D, Kavousi M, Ghanbari M, Brouwer RWW, van Rooij JGJ, van den Hout MCGN, Kraaij R, van Ijcken WFJ, Uitterlinden AG, van Duijn CM, Amin N. Whole-Genome Linkage Scan Combined With Exome Sequencing Identifies Novel Candidate Genes for Carotid Intima-Media Thickness. Front Genet 2018; 9:420. [PMID: 30356672 PMCID: PMC6189289 DOI: 10.3389/fgene.2018.00420] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 09/10/2018] [Indexed: 01/06/2023] Open
Abstract
Carotid intima-media thickness (cIMT) is an established heritable marker for subclinical atherosclerosis. In this study, we aim to identify rare variants with large effects driving differences in cIMT by performing genome-wide linkage analysis of individuals in the extremes of cIMT trait distribution (>90th percentile) in a large family-based study from a genetically isolated population in the Netherlands. Linked regions were subsequently explored by fine-mapping using exome sequencing. We observed significant evidence of linkage on chromosomes 2p16.3 [rs1017418, heterogeneity LOD (HLOD) = 3.35], 19q13.43 (rs3499, HLOD = 9.09), 20p13 (rs1434789, HLOD = 4.10), and 21q22.12 (rs2834949, HLOD = 3.59). Fine-mapping using exome sequencing data identified a non-coding variant (rs62165235) in PNPT1 gene under the linkage peak at chromosome 2 that is likely to have a regulatory function. The variant was associated with quantitative cIMT in the family-based study population (effect = 0.27, p-value = 0.013). Furthermore, we identified several genes under the linkage peak at chromosome 21 highly expressed in tissues relevant for atherosclerosis. To conclude, our linkage analysis identified four genomic regions significantly linked to cIMT. Further analyses are needed to demonstrate involvement of identified candidate genes in development of atherosclerosis.
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Affiliation(s)
- Dina Vojinovic
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands.,Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Rutger W W Brouwer
- Department of Cell Biology, Center for Biomics, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Jeroen G J van Rooij
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Mirjam C G N van den Hout
- Department of Cell Biology, Center for Biomics, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Robert Kraaij
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Wilfred F J van Ijcken
- Department of Cell Biology, Center for Biomics, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Andre G Uitterlinden
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands.,Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands.,Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
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11
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A rare missense variant in RCL1 segregates with depression in extended families. Mol Psychiatry 2018; 23:1120-1126. [PMID: 28322274 PMCID: PMC5984098 DOI: 10.1038/mp.2017.49] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 01/27/2017] [Accepted: 01/30/2017] [Indexed: 02/07/2023]
Abstract
Depression is the most prevalent psychiatric disorder with a complex and elusive etiology that is moderately heritable. Identification of genes would greatly facilitate the elucidation of the biological mechanisms underlying depression, however, its complex etiology has proved to be a major bottleneck in the identification of its genetic risk factors, especially in genome-wide association-like studies. In this study, we exploit the properties of a genetic isolate and its family-based structure to explore whether relatively rare exonic variants influence the burden of depressive symptoms in families. Using a multistep approach involving linkage and haplotype analyses followed by exome sequencing in the Erasmus Rucphen Family (ERF) study, we identified a rare (minor allele frequency (MAF)=1%) missense c.1114C>T mutation (rs115482041) in the RCL1 gene segregating with depression across multiple generations. Rs115482041 showed significant association with depressive symptoms (N=2393, βT-allele=2.33, P-value=1 × 10-4) and explained 2.9% of the estimated genetic variance of depressive symptoms (22%) in ERF. Despite being twice as rare (MAF<0.5%), c.1114C>T showed similar effect and significant association with depressive symptoms in samples from the independent population-based Rotterdam study (N=1604, βT-allele=3.60, P-value=3 × 10-2). A comparison of RCL1 expression in human and mouse brain revealed a striking co-localization of RCL1 with the layer 1 interlaminar subclass of astrocytes found exclusively in higher-order primates. Our findings identify RCL1 as a novel candidate gene for depression and offer insights into mechanisms through which RCL1 may be relevant for depression.
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12
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Nedeljkovic I, Terzikhan N, Vonk JM, van der Plaat DA, Lahousse L, van Diemen CC, Hobbs BD, Qiao D, Cho MH, Brusselle GG, Postma DS, Boezen HM, van Duijn CM, Amin N. A Genome-Wide Linkage Study for Chronic Obstructive Pulmonary Disease in a Dutch Genetic Isolate Identifies Novel Rare Candidate Variants. Front Genet 2018; 9:133. [PMID: 29725345 PMCID: PMC5916965 DOI: 10.3389/fgene.2018.00133] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 04/03/2018] [Indexed: 01/06/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a complex and heritable disease, associated with multiple genetic variants. Specific familial types of COPD may be explained by rare variants, which have not been widely studied. We aimed to discover rare genetic variants underlying COPD through a genome-wide linkage scan. Affected-only analysis was performed using the 6K Illumina Linkage IV Panel in 142 cases clustered in 27 families from a genetic isolate, the Erasmus Rucphen Family (ERF) study. Potential causal variants were identified by searching for shared rare variants in the exome-sequence data of the affected members of the families contributing most to the linkage peak. The identified rare variants were then tested for association with COPD in a large meta-analysis of several cohorts. Significant evidence for linkage was observed on chromosomes 15q14-15q25 [logarithm of the odds (LOD) score = 5.52], 11p15.4-11q14.1 (LOD = 3.71) and 5q14.3-5q33.2 (LOD = 3.49). In the chromosome 15 peak, that harbors the known COPD locus for nicotinic receptors, and in the chromosome 5 peak we could not identify shared variants. In the chromosome 11 locus, we identified four rare (minor allele frequency (MAF) <0.02), predicted pathogenic, missense variants. These were shared among the affected family members. The identified variants localize to genes including neuroblast differentiation-associated protein (AHNAK), previously associated with blood biomarkers in COPD, phospholipase C Beta 3 (PLCB3), shown to increase airway hyper-responsiveness, solute carrier family 22-A11 (SLC22A11), involved in amino acid metabolism and ion transport, and metallothionein-like protein 5 (MTL5), involved in nicotinate and nicotinamide metabolism. Association of SLC22A11 and MTL5 variants were confirmed in the meta-analysis of 9,888 cases and 27,060 controls. In conclusion, we have identified novel rare variants in plausible genes related to COPD. Further studies utilizing large sample whole-genome sequencing should further confirm the associations at chromosome 11 and investigate the chromosome 15 and 5 linked regions.
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Affiliation(s)
- Ivana Nedeljkovic
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Natalie Terzikhan
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Judith M. Vonk
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
- Groningen Research Institute for Asthma and COPD, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Diana A. van der Plaat
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
- Groningen Research Institute for Asthma and COPD, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Lies Lahousse
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
- Pharmaceutical Care Unit, Department of Bioanalysis, Ghent University, Ghent, Belgium
| | - Cleo C. van Diemen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Brian D. Hobbs
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Dandi Qiao
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Guy G. Brusselle
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
- Department of Respiratory Medicine, Erasmus Medical Center, Rotterdam, Netherlands
| | - Dirkje S. Postma
- Groningen Research Institute for Asthma and COPD, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
- Department of Pulmonary Medicine and Tuberculosis, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - H. M. Boezen
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
- Groningen Research Institute for Asthma and COPD, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | | | - Najaf Amin
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
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13
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Abstract
Supplemental Digital Content is available in the text Objectives: To examine factors influencing retinal vasculature in two environmentally contrasted, cross-sectional studies of adult participants of European descent and to estimate the extent and specificity of genetic contributions to each retinal vasculature feature. Methods: Retinal images from 1088 participants in the Orkney Complex Disease Study and 387 in the CROATIA-Korčula study, taken using the same nonmydriatic camera system and graded by the same person, were evaluated. Using general linear models, we estimated the influence of an extensive range of systemic risk factors, calculated retinal traits heritabilities and genetic correlations. Main results: Systemic covariates explained little (<4%) of the variation in vessel tortuosity, substantially more (>10%, up to 31.7%) of the variation in vessel width and monofractal dimension. Suggestive not well trodden associations of biological interest included that of urate, tissue plasminogen activator and cardiac PR interval with arteriolar narrowing, that of carotid intima–media thickness with less-tortuous arterioles and of cardiac QT interval with more tortuous venules. The genetic underpinning of tortuosity is largely distinct from that of the other retinal vascular features, whereas that of fractal dimension and vessel width greatly overlaps. The previously recognized influence of ocular axial length on vessel widths was high and can be expected to lead to artefactual genetic associations [genetic correlation with central retinal arteriolar equivalent: −0.53 (standard error 0.11)]. The significant genetic correlation between SBP and central retinal arteriolar equivalent, −0.53 (standard error 0.22) (after adjusting for age, sex and axial length of the eye), augurs more favourably for the discovery of genetic variants relevant to vascular physiology.
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14
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Zhuang WV, Murabito JM, Lunetta KL. Phenotypically Enriched Genotypic Imputation in Genetic Association Tests. Hum Hered 2016; 81:35-45. [PMID: 27576319 DOI: 10.1159/000446986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2014] [Accepted: 05/20/2016] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND In longitudinal epidemiological studies there may be individuals with rich phenotype data who die or are lost to follow-up before providing DNA for genetic studies. Often, the genotypic and phenotypic data of the relatives are available. Two strategies for analyzing the incomplete data are to exclude ungenotyped subjects from analysis (the complete-case method, CC) and to include phenotyped but ungenotyped individuals in analysis by using relatives' genotypes for genotype imputation (GI). In both strategies, the information in the phenotypic data was not used to handle the missing-genotype problem. METHODS We propose a phenotypically enriched genotypic imputation (PEGI) method that uses the EM (expectation-maximization)-based maximum likelihood method to incorporate observed phenotypes into genotype imputation. RESULTS Our simulations with genotypes missing completely at random show that, for a single-nucleotide polymorphism (SNP) with moderate to strong effect on a phenotype, PEGI improves power more than GI without excess type I errors. Using the Framingham Heart Study data set, we compare the ability of the PEGI, GI, and CC to detect the associations between 5 SNPs and age at natural menopause. CONCLUSION The PEGI method may improve power to detect an association over both CC and GI under many circumstances.
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Affiliation(s)
- Wei Vivian Zhuang
- Department of Biostatistics, Boston University School of Public Health, Boston, Mass., USA
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15
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Chiu YF, Lee CY, Hsu FC. Multipoint association mapping for longitudinal family data: an application to hypertension phenotypes. BMC Proc 2016; 10:315-320. [PMID: 27980655 PMCID: PMC5133529 DOI: 10.1186/s12919-016-0049-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
It is essential to develop adequate statistical methods to fully utilize information from longitudinal family studies. We extend our previous multipoint linkage disequilibrium approach—simultaneously accounting for correlations between markers and repeat measurements within subjects, and the correlations between subjects in families—to detect loci relevant to disease through gene-based analysis. Estimates of disease loci and their genetic effects along with their 95 % confidence intervals (or significance levels) are reported. Four different phenotypes—ever having hypertension at 4 visits, incidence of hypertension, hypertension status at baseline only, and hypertension status at 4 visits—are studied using the proposed approach. The efficiency of estimates of disease locus positions (inverse of standard error) improves when using the phenotypes from 4 visits rather than using baseline only.
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Affiliation(s)
- Yen-Feng Chiu
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, 35053 Taiwan Republic of China
| | - Chun-Yi Lee
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, 35053 Taiwan Republic of China
| | - Fang-Chi Hsu
- Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, 27157 USA
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16
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Amin N, Allebrandt KV, van der Spek A, Müller-Myhsok B, Hek K, Teder-Laving M, Hayward C, Esko T, van Mill JG, Mbarek H, Watson NF, Melville SA, Del Greco FM, Byrne EM, Oole E, Kolcic I, Chen TH, Evans DS, Coresh J, Vogelzangs N, Karjalainen J, Willemsen G, Gharib SA, Zgaga L, Mihailov E, Stone KL, Campbell H, Brouwer RWW, Demirkan A, Isaacs A, Dogas Z, Marciante KD, Campbell S, Borovecki F, Luik AI, Li M, Hottenga JJ, Huffman JE, van den Hout MCGN, Cummings SR, Aulchenko YS, Gehrman PR, Uitterlinden AG, Wichmann HE, Müller-Nurasyid M, Fehrmann RSN, Montgomery GW, Hofman A, Kao WHL, Oostra BA, Wright AF, Vink JM, Wilson JF, Pramstaller PP, Hicks AA, Polasek O, Punjabi NM, Redline S, Psaty BM, Heath AC, Merrow M, Tranah GJ, Gottlieb DJ, Boomsma DI, Martin NG, Rudan I, Tiemeier H, van IJcken WFJ, Penninx BW, Metspalu A, Meitinger T, Franke L, Roenneberg T, van Duijn CM. Genetic variants in RBFOX3 are associated with sleep latency. Eur J Hum Genet 2016; 24:1488-95. [PMID: 27142678 PMCID: PMC5027680 DOI: 10.1038/ejhg.2016.31] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Revised: 01/13/2016] [Accepted: 02/01/2016] [Indexed: 01/30/2023] Open
Abstract
Time to fall asleep (sleep latency) is a major determinant of sleep quality. Chronic, long sleep latency is a major characteristic of sleep-onset insomnia and/or delayed sleep phase syndrome. In this study we aimed to discover common polymorphisms that contribute to the genetics of sleep latency. We performed a meta-analysis of genome-wide association studies (GWAS) including 2 572 737 single nucleotide polymorphisms (SNPs) established in seven European cohorts including 4242 individuals. We found a cluster of three highly correlated variants (rs9900428, rs9907432 and rs7211029) in the RNA-binding protein fox-1 homolog 3 gene (RBFOX3) associated with sleep latency (P-values=5.77 × 10(-08), 6.59 × 10(-)(08) and 9.17 × 10(-)(08)). These SNPs were replicated in up to 12 independent populations including 30 377 individuals (P-values=1.5 × 10(-)(02), 7.0 × 10(-)(03) and 2.5 × 10(-)(03); combined meta-analysis P-values=5.5 × 10(-07), 5.4 × 10(-07) and 1.0 × 10(-07)). A functional prediction of RBFOX3 based on co-expression with other genes shows that this gene is predominantly expressed in brain (P-value=1.4 × 10(-316)) and the central nervous system (P-value=7.5 × 10(-)(321)). The predicted function of RBFOX3 based on co-expression analysis with other genes shows that this gene is significantly involved in the release cycle of neurotransmitters including gamma-aminobutyric acid and various monoamines (P-values<2.9 × 10(-11)) that are crucial in triggering the onset of sleep. To conclude, in this first large-scale GWAS of sleep latency we report a novel association of variants in RBFOX3 gene. Further, a functional prediction of RBFOX3 supports the involvement of RBFOX3 with sleep latency.
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Affiliation(s)
- Najaf Amin
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Karla V Allebrandt
- Institute of Medical Psychology, Ludwig-Maximilians-University, Munich, Germany
| | - Ashley van der Spek
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | | | - Karin Hek
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Psychiatry, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Maris Teder-Laving
- Estonian Genome Center, University of Tartu and Estonian Biocenter, Tartu, Estonia
| | - Caroline Hayward
- Medical Research Council, Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu and Estonian Biocenter, Tartu, Estonia
| | - Josine G van Mill
- Department of Psychiatry, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Hamdi Mbarek
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Nathaniel F Watson
- Department of Neurology, University of Washington, Seattle, WA, USA
- University of Washington Medicine Sleep Center, Seattle, WA, USA
| | - Scott A Melville
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Fabiola M Del Greco
- Center for Biomedicine, European Academy of Bolzano, Bolzano, Italy - Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Enda M Byrne
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Queensland Institute of Medical Research, Brisbane, QLD, Australia
| | - Edwin Oole
- Center for Biomics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Ivana Kolcic
- School of Medicine, University of Split, Split, Croatia
| | - Ting-hsu Chen
- VA Boston Healthcare System, Boston University, Boston, MA, USA
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Josef Coresh
- Departments of Epidemiology, Biostatistics, and Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Nicole Vogelzangs
- Department of Psychiatry, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Juha Karjalainen
- Department of Genetics, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Sina A Gharib
- University of Washington Medicine Sleep Center, Seattle, WA, USA
- Department of Medicine, Division of Pulmonary & Critical Care Medicine, University of Washington, Seattle, WA, USA
| | - Lina Zgaga
- Medical Research Council, Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland
| | - Evelin Mihailov
- Estonian Genome Center, University of Tartu and Estonian Biocenter, Tartu, Estonia
| | - Katie L Stone
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland
| | - Rutger WW Brouwer
- Center for Biomics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Ayse Demirkan
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Aaron Isaacs
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Zoran Dogas
- Department of Neuroscience and Sleep Medicine Centre, University of Split School of Medicine, Split, Croatia
| | - Kristin D Marciante
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Susan Campbell
- Medical Research Council, Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland
| | - Fran Borovecki
- Centre for Functional Genomics and Department of Neurology, Faculty of Medicine, University of Zagreb, Zagreb, Croatia
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Man Li
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - Jouke Jan Hottenga
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Jennifer E Huffman
- Medical Research Council, Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland
| | | | - Steven R Cummings
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Yurii S Aulchenko
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Philip R Gehrman
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
- Netherlands Consortium for Healthy Ageing and National Genomics Initiative, Leiden, The Netherlands
| | - Heinz-Erich Wichmann
- Institute of Epidemiology I, Helmholtz Zentrum Munich-German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-University and Klinikum Grosshadern, Munich, Germany
- Institute of Medical Statistics and Epidemiology, Technical University Munich, Munich, Germany
| | - Martina Müller-Nurasyid
- Institute of Epidemiology I, Helmholtz Zentrum Munich-German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Rudolf SN Fehrmann
- Department of Genetics, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands
| | | | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Wen Hong Linda Kao
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - Ben A Oostra
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Alan F Wright
- Medical Research Council, Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland
| | - Jacqueline M Vink
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - James F Wilson
- Medical Research Council, Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland
| | - Peter P Pramstaller
- Center for Biomedicine, European Academy of Bolzano, Bolzano, Italy - Affiliated Institute of the University of Lübeck, Lübeck, Germany
- Department of Neurology, General Central Hospital, Bolzano, Italy
- Department of Neurology, University of Lübeck, Lübeck, Germany
| | - Andrew A Hicks
- Center for Biomedicine, European Academy of Bolzano, Bolzano, Italy - Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Ozren Polasek
- School of Medicine, University of Split, Split, Croatia
- Centre for Global Health, University of Split School of Medicine, Split, Croatia
| | - Naresh M Punjabi
- Department of Pulmonary Medicine and Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital and Beth Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
- Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA
| | - Andrew C Heath
- Department of Psychiatry, Washington University, St Louis, MO, USA
| | - Martha Merrow
- Institute of Medical Psychology, Ludwig-Maximilians-University, Munich, Germany
| | - Gregory J Tranah
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Daniel J Gottlieb
- Department of Medicine, Brigham and Women's Hospital and Beth Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | | | - Igor Rudan
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland
| | - Henning Tiemeier
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Psychiatry, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Child and Adolescent Psychiatry, Erasmus MC, Rotterdam, The Netherlands
| | | | - Brenda W Penninx
- Department of Psychiatry, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu and Estonian Biocenter, Tartu, Estonia
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Human Genetics, Techinsche Universität München, München, Germany
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands
| | - Till Roenneberg
- Institute of Medical Psychology, Ludwig-Maximilians-University, Munich, Germany
| | - Cornelia M van Duijn
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
- Netherlands Consortium for Healthy Ageing and National Genomics Initiative, Leiden, The Netherlands
- Centre for Medical Systems Biology, Leiden, The Netherlands
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17
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Chung RH, Tsai WY, Kang CY, Yao PJ, Tsai HJ, Chen CH. FamPipe: An Automatic Analysis Pipeline for Analyzing Sequencing Data in Families for Disease Studies. PLoS Comput Biol 2016; 12:e1004980. [PMID: 27272119 PMCID: PMC4894624 DOI: 10.1371/journal.pcbi.1004980] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 05/12/2016] [Indexed: 11/18/2022] Open
Abstract
In disease studies, family-based designs have become an attractive approach to analyzing next-generation sequencing (NGS) data for the identification of rare mutations enriched in families. Substantial research effort has been devoted to developing pipelines for automating sequence alignment, variant calling, and annotation. However, fewer pipelines have been designed specifically for disease studies. Most of the current analysis pipelines for family-based disease studies using NGS data focus on a specific function, such as identifying variants with Mendelian inheritance or identifying shared chromosomal regions among affected family members. Consequently, some other useful family-based analysis tools, such as imputation, linkage, and association tools, have yet to be integrated and automated. We developed FamPipe, a comprehensive analysis pipeline, which includes several family-specific analysis modules, including the identification of shared chromosomal regions among affected family members, prioritizing variants assuming a disease model, imputation of untyped variants, and linkage and association tests. We used simulation studies to compare properties of some modules implemented in FamPipe, and based on the results, we provided suggestions for the selection of modules to achieve an optimal analysis strategy. The pipeline is under the GNU GPL License and can be downloaded for free at http://fampipe.sourceforge.net.
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Affiliation(s)
- Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
- * E-mail:
| | - Wei-Yun Tsai
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
| | - Chen-Yu Kang
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
| | - Po-Ju Yao
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
| | - Hui-Ju Tsai
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
- Department of Public Health, China Medical University, Taichung, Taiwan
- Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Chia-Hsiang Chen
- Department of Psychiatry, Chang Gung Memorial Hospital-Linkou, Gueishan, Taoyuan, Taiwan
- Department and Graduate Institute of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan
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18
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Wetzel-Smith MK, Hunkapiller J, Bhangale TR, Srinivasan K, Maloney JA, Atwal JK, Sa SM, Yaylaoglu MB, Foreman O, Ortmann W, Rathore N, Hansen DV, Tessier-Lavigne M, Mayeux R, Pericak-Vance M, Haines J, Farrer LA, Schellenberg GD, Goate A, Behrens TW, Cruchaga C, Watts RJ, Graham RR. A rare mutation in UNC5C predisposes to late-onset Alzheimer's disease and increases neuronal cell death. Nat Med 2014; 20:1452-7. [PMID: 25419706 DOI: 10.1038/nm.3736] [Citation(s) in RCA: 102] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2013] [Accepted: 09/23/2014] [Indexed: 12/23/2022]
Abstract
We have identified a rare coding mutation, T835M (rs137875858), in the UNC5C netrin receptor gene that segregated with disease in an autosomal dominant pattern in two families enriched for late-onset Alzheimer's disease and that was associated with disease across four large case-control cohorts (odds ratio = 2.15, Pmeta = 0.0095). T835M alters a conserved residue in the hinge region of UNC5C, and in vitro studies demonstrate that this mutation leads to increased cell death in human HEK293T cells and in rodent neurons. Furthermore, neurons expressing T835M UNC5C are more susceptible to cell death from multiple neurotoxic stimuli, including β-amyloid (Aβ), glutamate and staurosporine. On the basis of these data and the enriched hippocampal expression of UNC5C in the adult nervous system, we propose that one possible mechanism in which T835M UNC5C contributes to the risk of Alzheimer's disease is by increasing susceptibility to neuronal cell death, particularly in vulnerable regions of the Alzheimer's disease brain.
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Affiliation(s)
| | - Julie Hunkapiller
- Department of Human Genetics, Genentech, South San Francisco, California, USA
| | - Tushar R Bhangale
- Department of Bioinformatics and Computational Biology, Genentech, South San Francisco, California, USA
| | | | - Janice A Maloney
- Department of Neuroscience, Genentech, South San Francisco, California, USA
| | - Jasvinder K Atwal
- Department of Neuroscience, Genentech, South San Francisco, California, USA
| | - Susan M Sa
- Department of Pathology, Genentech, South San Francisco, California, USA
| | - Murat B Yaylaoglu
- Department of Pathology, Genentech, South San Francisco, California, USA
| | - Oded Foreman
- Department of Pathology, Genentech, South San Francisco, California, USA
| | - Ward Ortmann
- Department of Human Genetics, Genentech, South San Francisco, California, USA
| | - Nisha Rathore
- Department of Human Genetics, Genentech, South San Francisco, California, USA
| | - David V Hansen
- Department of Neuroscience, Genentech, South San Francisco, California, USA
| | - Marc Tessier-Lavigne
- Laboratory of Brain Development and Repair, Rockefeller University, New York, New York, USA
| | | | - Richard Mayeux
- 1] Department of Neurology, Taub Institute on Alzheimer's Disease and the Aging Brain, Columbia University, New York, New York, USA. [2] Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA
| | - Margaret Pericak-Vance
- 1] The John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida, USA. [2] Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, Florida, USA
| | - Jonathan Haines
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA
| | - Lindsay A Farrer
- 1] Department of Medicine (Biomedical Genetics), Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA. [2] Department of Neurology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA. [3] Department of Ophthalmology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA. [4] Department of Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA. [5] Department of Biostatistics, Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA
| | - Gerard D Schellenberg
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Alison Goate
- 1] Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA. [2] Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA. [3] Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Timothy W Behrens
- Department of Human Genetics, Genentech, South San Francisco, California, USA
| | - Carlos Cruchaga
- 1] Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA. [2] Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Ryan J Watts
- Department of Neuroscience, Genentech, South San Francisco, California, USA
| | - Robert R Graham
- Department of Human Genetics, Genentech, South San Francisco, California, USA
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19
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Chiu YF, Chung RH, Lee CY, Kao HY, Hou L, Hsu FC. Identification of rare variants for hypertension with incorporation of linkage information. BMC Proc 2014; 8:S109. [PMID: 25519312 PMCID: PMC4144469 DOI: 10.1186/1753-6561-8-s1-s109] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
We conducted linkage analysis using the genome-wide association study data on chromosome 3, and then assessed association between hypertension and rare variants of genes located in the regions showing evidence of linkage. The rare variants were collapsed if their minor allele frequencies were less than or equal to the thresholds: 0.01, 0.03, or 0.05. In the collapsing process, they were either unweighted or weighted by the nonparametric linkage log of odds scores in 2 different schemes: exponential weighting and cumulative weighting. Logistic regression models using the generalized estimating equations approach were used to assess association between the collapsed rare variants and hypertension adjusting for age and gender. Evidence of association from the weighted and unweighted collapsing schemes with minor allele frequencies ≤0.01, after accounting for multiple testing, was found for genes DOCK3 (p = 0.0090), ARMC8 (p = 1.29E-5), KCNAB1 (p = 5.8E-4), and MYRIP (p = 5.79E-6). DOCK3 and MYRIP are newly discovered. Incorporating linkage scores as weights was found to help identify rare causal variants with a large effect size.
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Affiliation(s)
- Yen-Feng Chiu
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Rd., Zhunan, Miaoli 35053, Taiwan, ROC
| | - Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Rd., Zhunan, Miaoli 35053, Taiwan, ROC
| | - Chun-Yi Lee
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Rd., Zhunan, Miaoli 35053, Taiwan, ROC
| | - Hui-Yi Kao
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Rd., Zhunan, Miaoli 35053, Taiwan, ROC
| | - Lin Hou
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, Connecticut 06520, USA
| | - Fang-Chi Hsu
- Department of Biostatistical Sciences, Division of Public Health Sciences, 1834 Wake Forest Rd., Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
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20
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Hoffman JD, Cooke Bailey JN, D'Aoust L, Cade W, Ayala-Haedo J, Fuzzell D, Laux R, Adams LD, Reinhart-Mercer L, Caywood L, Whitehead-Gay P, Agarwal A, Wang G, Scott WK, Pericak-Vance MA, Haines JL. Rare complement factor H variant associated with age-related macular degeneration in the Amish. Invest Ophthalmol Vis Sci 2014; 55:4455-60. [PMID: 24906858 DOI: 10.1167/iovs.13-13684] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
PURPOSE Age-related macular degeneration is the leading cause of blindness among the adult population in the developed world. To further the understanding of this disease, we have studied the genetically isolated Amish population of Ohio and Indiana. METHODS Cumulative genetic risk scores were calculated using the 19 known allelic associations. Exome sequencing was performed in three members of a small Amish family with AMD who lacked the common risk alleles in complement factor H (CFH) and ARMS2/HTRA1. Follow-up genotyping and association analysis was performed in a cohort of 973 Amish individuals, including 95 with self-reported AMD. RESULTS The cumulative genetic risk score analysis generated a mean genetic risk score of 1.12 (95% confidence interval [CI]: 1.10, 1.13) in the Amish controls and 1.18 (95% CI: 1.13, 1.22) in the Amish cases. This mean difference in genetic risk scores is statistically significant (P = 0.0042). Exome sequencing identified a rare variant (P503A) in CFH. Association analysis in the remainder of the Amish sample revealed that the P503A variant is significantly associated with AMD (P = 9.27 × 10(-13)). Variant P503A was absent when evaluated in a cohort of 791 elderly non-Amish controls, and 1456 non-Amish cases. CONCLUSIONS Data from the cumulative genetic risk score analysis suggests that the variants reported by the AMDGene consortium account for a smaller genetic burden of disease in the Amish compared with the non-Amish Caucasian population. Using exome sequencing data, we identified a novel missense mutation that is shared among a densely affected nuclear Amish family and located in a gene that has been previously implicated in AMD risk.
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Affiliation(s)
- Joshua D Hoffman
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Jessica N Cooke Bailey
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States
| | - Laura D'Aoust
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - William Cade
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - Juan Ayala-Haedo
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - Denise Fuzzell
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States
| | - Renee Laux
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States
| | - Larry D Adams
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - Lori Reinhart-Mercer
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - Laura Caywood
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - Patrice Whitehead-Gay
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - Anita Agarwal
- Department of Ophthalmology and Visual Sciences, Vanderbilt University, Nashville, Tennessee, United States
| | - Gaofeng Wang
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - William K Scott
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - Margaret A Pericak-Vance
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - Jonathan L Haines
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States
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21
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Davis MF, Cummings AC, D'Aoust LN, Jiang L, Velez Edwards DR, Laux R, Reinhart-Mercer L, Fuzzell D, Scott WK, Pericak-Vance MA, Lee SL, Haines JL. Parkinson disease loci in the mid-western Amish. Hum Genet 2013; 132:1213-21. [PMID: 23793441 PMCID: PMC3797866 DOI: 10.1007/s00439-013-1316-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2012] [Accepted: 05/16/2013] [Indexed: 11/26/2022]
Abstract
Previous evidence has shown that Parkinson disease (PD) has a heritable component, but only a small proportion of the total genetic contribution to PD has been identified. Genetic heterogeneity complicates the verification of proposed PD genes and the identification of new PD susceptibility genes. Our approach to overcome the problem of heterogeneity is to study a population isolate, the mid-western Amish communities of Indiana and Ohio. We performed genome-wide association and linkage analyses on 798 individuals (31 with PD), who are part of a 4,998 member pedigree. Through these analyses, we identified a region on chromosome 5q31.3 that shows evidence of association (p value < 1 × 10(-4)) and linkage (multipoint HLOD = 3.77). We also found further evidence of linkage on chromosomes 6 and 10 (multipoint HLOD 4.02 and 4.35 respectively). These data suggest that locus heterogeneity, even within the Amish, may be more extensive than previously appreciated.
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Affiliation(s)
- M F Davis
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN, USA
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22
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Edwards DRV, Gilbert JR, Hicks JE, Myers JL, Jiang L, Cummings AC, Guo S, Gallins PJ, Konidari I, Caywood L, Reinhart-Mercer L, Fuzzell D, Knebusch C, Laux R, Jackson CE, Pericak-Vance MA, Haines JL, Scott WK. Linkage and association of successful aging to the 6q25 region in large Amish kindreds. AGE (DORDRECHT, NETHERLANDS) 2013; 35:1467-1477. [PMID: 22773346 PMCID: PMC3705095 DOI: 10.1007/s11357-012-9447-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Accepted: 06/11/2012] [Indexed: 06/01/2023]
Abstract
Successful aging (SA) is a multidimensional phenotype involving living to older age with high physical function, preserved cognition, and continued social engagement. Several domains underlying SA are heritable, and identifying health-promoting polymorphisms and their interactions with the environment could provide important information regarding the health of older adults. In the present study, we examined 263 cognitively intact Amish individuals age 80 and older (74 SA and 189 "normally aged") all of whom are part of a single 13-generation pedigree. A genome-wide association study of 630,309 autosomal single nucleotide polymorphisms (SNPs) was performed and analyzed for linkage using multipoint analyses and for association using the modified quasi-likelihood score test. There was evidence for linkage on 6q25-27 near the fragile site FRA6E region with a dominant model maximum multipoint heterogeneity LOD score = 3.2. The 1-LOD-down support interval for this linkage contained one SNP for which there was regionally significant evidence of association (rs205990, p = 2.36 × 10(-5)). This marker survived interval-wide Bonferroni correction for multiple testing and was located between the genes QKI and PDE10A. Other areas of chromosome 6q25-q27 (including the FRA6E region) contained several SNPs associated with SA (minimum p = 2.89 × 10(-6)). These findings suggest potentially novel genes in the 6q25-q27 region linked and associated with SA in the Amish; however, these findings should be verified in an independent replication cohort.
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Affiliation(s)
- Digna R. Velez Edwards
- />Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute of Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Avenue, Room 414, Miami, FL 33136 USA
- />Center for Human Genetics Research, Vanderbilt University Epidemiology Center, Institute of Medicine and Public Health, Department of Obstetrics and Gynecology, Vanderbilt University, Nashville, TN USA
| | - John R. Gilbert
- />Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute of Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Avenue, Room 414, Miami, FL 33136 USA
| | - James E. Hicks
- />Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute of Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Avenue, Room 414, Miami, FL 33136 USA
| | - Jamie L. Myers
- />Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute of Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Avenue, Room 414, Miami, FL 33136 USA
| | - Lan Jiang
- />Center for Human Genetics Research, Vanderbilt University, Nashville, TN USA
| | - Anna C. Cummings
- />Center for Human Genetics Research, Vanderbilt University, Nashville, TN USA
| | - Shengru Guo
- />Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute of Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Avenue, Room 414, Miami, FL 33136 USA
| | - Paul J. Gallins
- />Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute of Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Avenue, Room 414, Miami, FL 33136 USA
| | - Ioanna Konidari
- />Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute of Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Avenue, Room 414, Miami, FL 33136 USA
| | - Laura Caywood
- />Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute of Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Avenue, Room 414, Miami, FL 33136 USA
| | - Lori Reinhart-Mercer
- />Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute of Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Avenue, Room 414, Miami, FL 33136 USA
| | - Denise Fuzzell
- />Center for Human Genetics Research, Vanderbilt University, Nashville, TN USA
| | - Claire Knebusch
- />Center for Human Genetics Research, Vanderbilt University, Nashville, TN USA
| | - Renee Laux
- />Center for Human Genetics Research, Vanderbilt University, Nashville, TN USA
| | | | - Margaret A. Pericak-Vance
- />Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute of Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Avenue, Room 414, Miami, FL 33136 USA
| | - Jonathan L. Haines
- />Center for Human Genetics Research, Vanderbilt University, Nashville, TN USA
| | - William K. Scott
- />Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute of Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Avenue, Room 414, Miami, FL 33136 USA
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Cummings AC, Torstenson E, Davis MF, D'Aoust LN, Scott WK, Pericak-Vance MA, Bush WS, Haines JL. Evaluating power and type 1 error in large pedigree analyses of binary traits. PLoS One 2013; 8:e62615. [PMID: 23658753 PMCID: PMC3643945 DOI: 10.1371/journal.pone.0062615] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2012] [Accepted: 03/22/2013] [Indexed: 01/11/2023] Open
Abstract
Studying population isolates with large, complex pedigrees has many advantages for discovering genetic susceptibility loci; however, statistical analyses can be computationally challenging. Allelic association tests need to be corrected for relatedness among study participants, and linkage analyses require subdividing and simplifying the pedigree structures. We have extended GenomeSIMLA to simulate SNP data in complex pedigree structures based on an Amish pedigree to generate the same structure and distribution of sampled individuals. We evaluated type 1 error rates when no disease SNP was simulated and power when disease SNPs with recessive, additive, and dominant modes of inheritance and odds ratios of 1.1, 1.5, 2.0, and 5.0 were simulated. We generated subpedigrees with a maximum bit-size of 24 using PedCut and performed two-point and multipoint linkage using Merlin. We also ran MQLS on the subpedigrees and unified pedigree. We saw no inflation of type 1 error when running MQLS on either the whole pedigrees or the sub-pedigrees, and we saw low type 1 error for two-point and multipoint linkage. Power was reduced when running MQLS on the subpedigrees versus the whole pedigree, and power was low for two-point and multipoint linkage analyses of the subpedigrees. These data suggest that MQLS has appropriate type 1 error rates in our Amish pedigree structure, and while type 1 error does not seem to be affected when dividing the pedigree prior to linkage analysis, power to detect linkage is diminished when the pedigree is divided.
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Affiliation(s)
- Anna C Cummings
- Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States of America
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Shah KP, Douglas JA. A method to prioritize quantitative traits and individuals for sequencing in family-based studies. PLoS One 2013; 8:e62545. [PMID: 23626830 PMCID: PMC3633859 DOI: 10.1371/journal.pone.0062545] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2012] [Accepted: 03/21/2013] [Indexed: 11/18/2022] Open
Abstract
Owing to recent advances in DNA sequencing, it is now technically feasible to evaluate the contribution of rare variation to complex traits and diseases. However, it is still cost prohibitive to sequence the whole genome (or exome) of all individuals in each study. For quantitative traits, one strategy to reduce cost is to sequence individuals in the tails of the trait distribution. However, the next challenge becomes how to prioritize traits and individuals for sequencing since individuals are often characterized for dozens of medically relevant traits. In this article, we describe a new method, the Rare Variant Kinship Test (RVKT), which leverages relationship information in family-based studies to identify quantitative traits that are likely influenced by rare variants. Conditional on nuclear families and extended pedigrees, we evaluate the power of the RVKT via simulation. Not unexpectedly, the power of our method depends strongly on effect size, and to a lesser extent, on the frequency of the rare variant and the number and type of relationships in the sample. As an illustration, we also apply our method to data from two genetic studies in the Old Order Amish, a founder population with extensive genealogical records. Remarkably, we implicate the presence of a rare variant that lowers fasting triglyceride levels in the Heredity and Phenotype Intervention (HAPI) Heart study (p = 0.044), consistent with the presence of a previously identified null mutation in the APOC3 gene that lowers fasting triglyceride levels in HAPI Heart study participants.
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Affiliation(s)
- Kaanan P. Shah
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Julie A. Douglas
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
- * E-mail:
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Plasma phosphatidylcholine and sphingomyelin concentrations are associated with depression and anxiety symptoms in a Dutch family-based lipidomics study. J Psychiatr Res 2013. [PMID: 23207112 DOI: 10.1016/j.jpsychires.2012.11.001] [Citation(s) in RCA: 99] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The central nervous system has the second highest concentration of lipids after adipose tissue. Alterations in neural membrane phospho- and sphingolipid composition can influence crucial intra- and intercellular signalling and alter the membrane's properties. Recently, the polyunsaturated fatty acids (PUFA) hypothesis for depression suggests that phospho- and sphingolipid metabolism includes potential pathways for the disease. In 742 people from a Dutch family-based study, we assessed the relationships between 148 different plasma phospho- and sphingolipid species and depression/anxiety symptoms as measured by the Hospital Anxiety and Depression Scales (HADS-A and HADS-D) and the Centre for Epidemiological Studies Depression Scale (CES-D). We observed significant differences in plasma sphingomyelins (SPM), particularly the SPM 23:1/SPM 16:0 ratio, which was inversely correlated with depressive symptom scores. We observed a similar trend for plasma phosphatidylcholines (PC), particularly the molar proportion of PC O 36:4 and its ratio to ceramide CER 20:0. Absolute levels of PC O 36:4 were also associated with depression symptoms in an independent replication. To our knowledge this is the first study on depressive symptoms that focuses on specific phospho- and sphingolipid molecules in plasma rather than total PUFA concentrations. The findings of this lipidomic study suggests that plasma sphingomyelins and ether phospholipids should be further studied for their potential as biomarkers and for a better understanding of the underlying mechanisms of this systemic disease.
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Cummings AC, Jiang L, Velez Edwards DR, McCauley JL, Laux R, McFarland LL, Fuzzell D, Knebusch C, Caywood L, Reinhart-Mercer L, Nations L, Gilbert JR, Konidari I, Tramontana M, Cuccaro ML, Scott WK, Pericak-Vance MA, Haines JL. Genome-wide association and linkage study in the Amish detects a novel candidate late-onset Alzheimer disease gene. Ann Hum Genet 2012; 76:342-51. [PMID: 22881374 DOI: 10.1111/j.1469-1809.2012.00721.x] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
To identify novel late-onset Alzheimer disease (LOAD) risk genes, we have analysed Amish populations of Ohio and Indiana. We performed genome-wide SNP linkage and association studies on 798 individuals (109 with LOAD). We tested association using the Modified Quasi-Likelihood Score test and also performed two-point and multipoint linkage analyses. We found that LOAD was significantly associated with APOE (P= 9.0 × 10-6) in all our ascertainment regions except for the Adams County, Indiana, community (P= 0.55). Genome-wide, the most strongly associated SNP was rs12361953 (P= 7.92 × 10-7). A very strong, genome-wide significant multipoint peak [recessive heterogeneity multipoint LOD (HLOD) = 6.14, dominant HLOD = 6.05] was detected on 2p12. Three additional loci with multipoint HLOD scores >3 were detected on 3q26, 9q31 and 18p11. Converging linkage and association results, the most significantly associated SNP under the 2p12 peak was at rs2974151 (P= 1.29 × 10-4). This SNP is located in CTNNA2, which encodes catenin alpha 2, a neuronal-specific catenin known to have function in the developing brain. These results identify CTNNA2 as a novel candidate LOAD gene, and implicate three other regions of the genome as novel LOAD loci. These results underscore the utility of using family-based linkage and association analyses in isolated populations to identify novel loci for traits with complex genetic architecture.
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Affiliation(s)
- Anna C Cummings
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN, USA
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Silberstein M, Weissbrod O, Otten L, Tzemach A, Anisenia A, Shtark O, Tuberg D, Galfrin E, Gannon I, Shalata A, Borochowitz ZU, Dechter R, Thompson E, Geiger D. A system for exact and approximate genetic linkage analysis of SNP data in large pedigrees. Bioinformatics 2012; 29:197-205. [PMID: 23162081 DOI: 10.1093/bioinformatics/bts658] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The use of dense single nucleotide polymorphism (SNP) data in genetic linkage analysis of large pedigrees is impeded by significant technical, methodological and computational challenges. Here we describe Superlink-Online SNP, a new powerful online system that streamlines the linkage analysis of SNP data. It features a fully integrated flexible processing workflow comprising both well-known and novel data analysis tools, including SNP clustering, erroneous data filtering, exact and approximate LOD calculations and maximum-likelihood haplotyping. The system draws its power from thousands of CPUs, performing data analysis tasks orders of magnitude faster than a single computer. By providing an intuitive interface to sophisticated state-of-the-art analysis tools coupled with high computing capacity, Superlink-Online SNP helps geneticists unleash the potential of SNP data for detecting disease genes. RESULTS Computations performed by Superlink-Online SNP are automatically parallelized using novel paradigms, and executed on unlimited number of private or public CPUs. One novel service is large-scale approximate Markov Chain-Monte Carlo (MCMC) analysis. The accuracy of the results is reliably estimated by running the same computation on multiple CPUs and evaluating the Gelman-Rubin Score to set aside unreliable results. Another service within the workflow is a novel parallelized exact algorithm for inferring maximum-likelihood haplotyping. The reported system enables genetic analyses that were previously infeasible. We demonstrate the system capabilities through a study of a large complex pedigree affected with metabolic syndrome. AVAILABILITY Superlink-Online SNP is freely available for researchers at http://cbl-hap.cs.technion.ac.il/superlink-snp. The system source code can also be downloaded from the system website. CONTACT omerw@cs.technion.ac.il SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mark Silberstein
- Department of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel
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29
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Amin N, Schuur M, Gusareva ES, Isaacs A, Aulchenko YS, Kirichenko AV, Zorkoltseva IV, Axenovich TI, Oostra BA, Janssens ACJW, van Duijn CM. A genome-wide linkage study of individuals with high scores on NEO personality traits. Mol Psychiatry 2012; 17:1031-41. [PMID: 21826060 DOI: 10.1038/mp.2011.97] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The NEO-Five-Factor Inventory divides human personality traits into five dimensions: neuroticism, extraversion, openness, conscientiousness and agreeableness. In this study, we sought to identify regions harboring genes with large effects on the five NEO personality traits by performing genome-wide linkage analysis of individuals scoring in the extremes of these traits (>90th percentile). Affected-only linkage analysis was performed using an Illumina 6K linkage array in a family-based study, the Erasmus Rucphen Family study. We subsequently determined whether distinct, segregating haplotypes found with linkage analysis were associated with the trait of interest in the population. Finally, a dense single-nucleotide polymorphism genotyping array (Illumina 318K) was used to search for copy number variations (CNVs) in the associated regions. In the families with extreme phenotype scores, we found significant evidence of linkage for conscientiousness to 20p13 (rs1434789, log of odds (LOD)=5.86) and suggestive evidence of linkage (LOD >2.8) for neuroticism to 19q, 21q and 22q, extraversion to 1p, 1q, 9p and12q, openness to 12q and 19q, and agreeableness to 2p, 6q, 17q and 21q. Further analysis determined haplotypes in 21q22 for neuroticism (P-values = 0.009, 0.007), in 17q24 for agreeableness (marginal P-value = 0.018) and in 20p13 for conscientiousness (marginal P-values = 0.058, 0.038) segregating in families with large contributions to the LOD scores. No evidence for CNVs in any of the associated regions was found. Our findings imply that there may be genes with relatively large effects involved in personality traits, which may be identified with next-generation sequencing techniques.
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Affiliation(s)
- N Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
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Edwards DRV, Gilbert JR, Jiang L, Gallins PJ, Caywood L, Creason M, Fuzzell D, Knebusch C, Jackson CE, Pericak-Vance MA, Haines JL, Scott WK. Successful aging shows linkage to chromosomes 6, 7, and 14 in the Amish. Ann Hum Genet 2011; 75:516-28. [PMID: 21668908 DOI: 10.1111/j.1469-1809.2011.00658.x] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Successful aging (SA) is a multidimensional phenotype involving preservation of cognitive ability, physical function, and social engagement throughout life. Multiple components of SA are heritable, supporting a genetic component. The Amish are genetically and socially isolated with homogeneous lifestyles, making them a suitable population for studying the genetics of SA. DNA and measures of SA were collected on 214 cognitively intact Amish individuals over age 80. Individuals were grouped into a 13-generation pedigree using the Anabaptist Genealogy Database. A linkage screen of 5944 single nucleotide polymorphisms (SNPs) was performed using 12 informative subpedigrees with an affected-only 2-point and multipoint linkage analysis. Eleven SNPs produced 2-point LOD scores >2, suggestive of linkage. Multipoint linkage analyses, allowing for heterogeneity, detected significant LOD scores on chromosomes 6 (HLOD = 4.50), 7 (LOD*= 3.11), and 14 (HLOD = 4.17), suggesting multiple new loci underlying SA.
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Affiliation(s)
- Digna R Velez Edwards
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
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31
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Han L, Abney M. Identity by descent estimation with dense genome-wide genotype data. Genet Epidemiol 2011; 35:557-67. [PMID: 21769932 DOI: 10.1002/gepi.20606] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2011] [Revised: 05/06/2011] [Accepted: 05/31/2011] [Indexed: 11/11/2022]
Abstract
We present a novel method, IBDLD, for estimating the probability of identity by descent (IBD) for a pair of related individuals at a locus, given dense genotype data and a pedigree of arbitrary size and complexity. IBDLD overcomes the challenges of exact multipoint estimation of IBD in pedigrees of potentially large size and eliminates the difficulty of accommodating the background linkage disequilibrium (LD) that is present in high-density genotype data. We show that IBDLD is much more accurate at estimating the true IBD sharing than methods that remove LD by pruning SNPs and is highly robust to pedigree errors or other forms of misspecified relationships. The method is fast and can be used to estimate the probability for each possible IBD sharing state at every SNP from a high-density genotyping array for hundreds of thousands of pairs of individuals. We use it to estimate point-wise and genomewide IBD sharing between 185,745 pairs of subjects all of whom are related through a single, large and complex 13-generation pedigree and genotyped with the Affymetrix 500 k chip. We find that we are able to identify the true pedigree relationship for individuals who were misidentified in the collected data and estimate empirical kinship coefficients that can be used in follow-up QTL mapping studies. IBDLD is implemented as an open source software package and is freely available.
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Affiliation(s)
- Lide Han
- Department of Human Genetics, University of Chicago, Illinois, USA
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Abstract
The first generation of genome-wide association studies (GWA studies) for psychiatric disorders has led to new insights regarding the genetic architecture of these disorders. We now start to realize that a larger number of genes, each with a small contribution, are likely to explain the heritability of psychiatric diseases. The contribution of a large number of genes to complex traits can be analyzed with genome-wide profiling. In a discovery sample, a genetic risk profile for depression was defined based on a GWA study of 1738 adult cases and 1802 controls. The genetic risk scores were tested in two population-based samples of elderly participants. The genetic risk profiles were evaluated for depression and anxiety in the Rotterdam Study cohort and the Erasmus Rucphen Family (ERF) study. The genetic risk scores were significantly associated with different measures of depression and explained up to ∼0.7% of the variance in depression in Rotterdam Study and up to ∼1% in ERF study. The genetic score for depression was also significantly associated with anxiety explaining up to 2.1% in Rotterdam study. These findings suggest the presence of many genetic loci of small effect that influence both depression and anxiety. Remarkably, the predictive value of these profiles was as large in the sample of elderly participants as in the middle-aged samples.
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Cummings AC, Lee SL, McCauley JL, Jiang L, Crunk A, McFarland LL, Gallins PJ, Fuzzell D, Knebusch C, Jackson CE, Scott WK, Pericak-Vance MA, Haines JL. A genome-wide linkage screen in the Amish with Parkinson disease points to chromosome 6. Ann Hum Genet 2011; 75:351-8. [PMID: 21488853 DOI: 10.1111/j.1469-1809.2011.00643.x] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Parkinson disease (PD) is a common complex neurodegenerative disorder with an underlying genetic etiology that has been difficult to dissect. Although some PD risk genes have been discovered, most of the underlying genetic etiology remains unknown. To further elucidate the genetic component, we have undertaken a genome-wide linkage screen in an isolated founder population of Amish living in the Midwestern United States. We performed tests for linkage and for association using a marker set of nearly 6000 single-nucleotide polymorphisms. Parametric multipoint linkage analysis generated a logarithm of the odds of linkage (LOD) score of 2.44 on chromosome 6 in the SYNE1 gene, approximately 8 Mbp from the PARK2 gene. In a different region on chromosome 6 (∼67 Mbp from PARK2) an association was found for rs4302647 (p < 4.0 × 10(-6) ), which is not within 300 kb of any gene. While the association exceeds Bonferroni correction, it may yet represent a false positive due to the small sample size and the low minor allele frequency. The minor allele frequency in affecteds is 0.07 compared to 0.01 in unaffecteds. Taken together, these results support involvement of loci on chromosome 6 in the genetic etiology of PD.
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Affiliation(s)
- Anna C Cummings
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN, USA
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Schuur M, Henneman P, van Swieten JC, Zillikens MC, de Koning I, Janssens ACJW, Witteman JCM, Aulchenko YS, Frants RR, Oostra BA, van Dijk KW, van Duijn CM. Insulin-resistance and metabolic syndrome are related to executive function in women in a large family-based study. Eur J Epidemiol 2010; 25:561-8. [PMID: 20585974 PMCID: PMC2921069 DOI: 10.1007/s10654-010-9476-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2010] [Accepted: 05/31/2010] [Indexed: 12/25/2022]
Abstract
While type 2 diabetes is well-known to be associated with poorer cognitive performance, few studies have reported on the association of metabolic syndrome (MetS) and contributing factors, such as insulin-resistance (HOMA-IR), low adiponectin-, and high C-reactive protein (CRP)- levels. We studied whether these factors are related to cognitive function and which of the MetS components are independently associated. The study was embedded in an ongoing family-based cohort study in a Dutch population. All participants underwent physical examinations, biomedical measurements, and neuropsychological testing. Linear regression models were used to determine the association between MetS, HOMA-IR, adiponectin levels, CRP, and cognitive test scores. Cross-sectional analyses were performed in 1,898 subjects (mean age 48 years, 43% men). People with MetS had significantly higher HOMA-IR scores, lower adiponectin levels, and higher CRP levels. MetS and high HOMA-IR were associated with poorer executive function in women (P = 0.03 and P = 0.009). MetS and HOMA-IR are associated with poorer executive function in women.
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Affiliation(s)
- M Schuur
- Genetic Epidemiology Unit Ee2173, Department of Epidemiology, Erasmus MC University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
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35
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Axenovich TI, Aulchenko YS. MQScore_SNP software for multipoint parametric linkage analysis of quantitative traits in large pedigrees. Ann Hum Genet 2010; 74:286-9. [PMID: 20529018 DOI: 10.1111/j.1469-1809.2010.00576.x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
We describe software for multipoint parametric linkage analysis of quantitative traits using information about SNP genotypes. A mixed model of major gene and polygene inheritance is implemented in this software. Implementation of several algorithms to avoid computational underflow and decrease running time permits application of our software to the analysis of very large pedigrees collected in human genetically isolated populations. We tested our software by performing linkage analysis of adult height in a large pedigree from a Dutch isolated population. Three significant and four suggestive loci were identified with the help of our programs, whereas variance-component-based linkage analysis, which requires the pedigree fragmentation, demonstrated only three suggestive peaks. The software package MQScore_SNP is available at http://mga.bionet.nsc.ru/soft/index.html.
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Affiliation(s)
- Tatiana I Axenovich
- Institute of Cytology & Genetics, Siberian Division, Russian Academy of Sciences, Novosibirsk, 630090, Russia.
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36
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Sieh W, Choi Y, Chapman NH, Craig UK, Steinbart EJ, Rothstein JH, Oyanagi K, Garruto RM, Bird TD, Galasko DR, Schellenberg GD, Wijsman EM. Identification of novel susceptibility loci for Guam neurodegenerative disease: challenges of genome scans in genetic isolates. Hum Mol Genet 2009; 18:3725-38. [PMID: 19567404 PMCID: PMC2742398 DOI: 10.1093/hmg/ddp300] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2009] [Revised: 06/17/2009] [Accepted: 06/25/2009] [Indexed: 12/17/2022] Open
Abstract
Amyotrophic lateral sclerosis/parkinsonism-dementia complex (ALS/PDC) is a fatal neurodegenerative disease found in the Chamorro people of Guam and other Pacific Island populations. The etiology is unknown, although both genetic and environmental factors appear important. To identify loci for ALS/PDC, we conducted both genome-wide linkage and association analyses, using approximately 400 microsatellite markers, in the largest sample assembled to date, comprising a nearly complete sample of all living and previously sampled deceased cases. A single, large, complex pedigree was ascertained from a village on Guam, with smaller families and a case-control sample ascertained from the rest of Guam by population-based neurological screening and archival review. We found significant evidence for two regions with novel ALS/PDC loci on chromosome 12 and supportive evidence for the involvement of the MAPT region on chromosome 17. D12S1617 on 12p gave the strongest evidence of linkage (maximum LOD score, Z(max) = 4.03) in our initial scan, with additional support in the complete case-control sample in the form of evidence of allelic association at this marker and another nearby marker. D12S79 on 12q also provided significant evidence of linkage (Z(max) = 3.14) with support from flanking markers. Our results suggest that ALS/PDC may be influenced by as many as three loci, while illustrating challenges that are intrinsic in genetic analyses of isolated populations, as well as analytical strategies that are useful in this context. Elucidation of the genetic basis of ALS/PDC should improve our understanding of related neurodegenerative disorders including Alzheimer disease, Parkinson disease, frontotemporal dementia and ALS.
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Affiliation(s)
- Weiva Sieh
- Division of Medical Genetics, Department of Medicine
- Division of Epidemiology, Department of Health Research and Policy, Stanford University, Stanford, CA 94305, USA
| | | | | | - Ulla-Katrina Craig
- Micronesian Health and Aging Study, University of Guam, Mangilao, Guam 96923, USA
| | - Ellen J. Steinbart
- Department of Neurology
- Geriatric Research Education and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, WA 98108, USA
| | | | - Kiyomitsu Oyanagi
- Department of Neuropathology, Tokyo Metropolitan Institute for Neuroscience, Tokyo, Japan
| | - Ralph M. Garruto
- Laboratory of Biomedical Anthropology and Neurosciences, Department of Anthropology, Binghamton University, Binghamton, NY 13902, USA
| | - Thomas D. Bird
- Division of Medical Genetics, Department of Medicine
- Department of Neurology
- Geriatric Research Education and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, WA 98108, USA
| | - Douglas R. Galasko
- Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, USA and
| | - Gerard D. Schellenberg
- Department of Neurology
- Division of Gerontology and Geriatric Medicine, Department of Medicine
- Department of Pharmacology and
- Geriatric Research Education and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, WA 98108, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ellen M. Wijsman
- Division of Medical Genetics, Department of Medicine
- Department of Biostatistics
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
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Kirichenko AV, Belonogova NM, Aulchenko YS, Axenovich TI. PedStr software for cutting large pedigrees for haplotyping, IBD computation and multipoint linkage analysis. Ann Hum Genet 2009; 73:527-31. [PMID: 19604226 DOI: 10.1111/j.1469-1809.2009.00531.x] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We propose an automatic heuristic algorithm for splitting large pedigrees into fragments of no more than a user-specified bit size. The algorithm specifically aims to split large pedigrees where many close relatives are genotyped and to produce a set of sub-pedigrees for haplotype reconstruction, IBD computation or multipoint linkage analysis with the help of the Lander-Green-Kruglyak algorithm. We demonstrate that a set of overlapping pedigree fragments constructed with the help of our algorithm allows fast and effective haplotype reconstruction and detection of an allele's parental origin. Moreover, we compared pedigree fragments constructed with the help of our algorithm and existing programs PedCut and Jenti for multipoint linkage analysis. Our algorithm demonstrated significantly higher linkage power than the algorithm of Jenti and significantly shorter running time than the algorithm of PedCut. The software package PedStr implementing our algorithms is available at http://mga.bionet.nsc.ru/soft/index.html.
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Affiliation(s)
- Anatoly V Kirichenko
- Institute of Cytology & Genetics, Siberian Division, Russian Academy of Sciences, Novosibirsk, 630090 Russia
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Amin N, Aulchenko YS, Dekker MC, Ferdinand RF, van Spreeken A, Temmink AH, Verhulst FC, Oostra BA, van Duijn CM. Suggestive linkage of ADHD to chromosome 18q22 in a young genetically isolated Dutch population. Eur J Hum Genet 2009; 17:958-66. [PMID: 19156173 PMCID: PMC2986494 DOI: 10.1038/ejhg.2008.260] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2008] [Revised: 11/27/2008] [Accepted: 12/04/2008] [Indexed: 01/13/2023] Open
Abstract
Attention deficit/hyperactivity disorder (ADHD) is a common, highly heritable, neuropsychiatric disorder among children. Linkage studies in isolated populations have proved powerful to detect variants for complex diseases, such as ADHD. We performed a genome-wide linkage scan for ADHD in nine patients from a genetically isolated population in the Netherlands, who were linked to each other within 10 generations through multiple lines of descent. The genome-wide scan was performed with a set of 400 microsatellite markers with an average spacing of +/-10-12 cM. We performed multipoint parametric linkage analyses using both recessive and dominant models. Our genome scan pointed to several chromosomal regions that may harbour ADHD susceptibility genes. None exceeded the empirical genome-wide significance threshold, but the Log of odds (LOD) scores were >1.5 for regions 6p22 (Heterogenetic log of odds (HLOD)=1.67) and 18q21-22 (HLOD=2.13) under a recessive model. We followed up these two regions in a larger sample of ADHD patients (n=21, 9 initial and 12 extra patients). The LOD scores did not increase after increasing the sample size (6p22 (HLOD=1.51), 18q21-22 (HLOD=1.83)). However, the LOD score on 6p22 increased to 2 when a separate analysis was performed for the inattentive type ADHD children. The linkage region on chromosome 18q overlaps with the findings of association of rs2311120 (P=10(-5)) and rs4149601 (P=10(-4)) in the genome-wide association analysis for ADHD performed by the Genetic Association Information Network consortium. Furthermore, there was an excess of regions harbouring serotonin receptors (HTR1B, HTR1E, HTR4, HTR1D, and HTR6) that showed a LOD score >1 in our genome-wide scan.
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Affiliation(s)
- Najaf Amin
- Genetic Epidemiology Unit, Department of Epidemiology & Biostatistics and Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands
| | - Yuri S Aulchenko
- Genetic Epidemiology Unit, Department of Epidemiology & Biostatistics and Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands
| | - Marieke C Dekker
- Genetic Epidemiology Unit, Department of Epidemiology & Biostatistics and Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands
- Department of Neurology, University Medical Centre, Nijmegen, The Netherlands
| | | | - Alwin van Spreeken
- Department of Neurology, Sint Franciscus Hospital, Roosendaal, The Netherlands
| | - Alfons H Temmink
- Department of Neurology, Amphia Hospital, Breda, The Netherlands
| | - Frank C Verhulst
- Department of Child and Adolescent Psychiatry, Erasmus MC Sophia, Rotterdam, The Netherlands
| | - Ben A Oostra
- Genetic Epidemiology Unit, Department of Epidemiology & Biostatistics and Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands
| | - Cornelia M van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology & Biostatistics and Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands
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Identity-by-descent estimation and mapping of qualitative traits in large, complex pedigrees. Genetics 2008; 179:1577-90. [PMID: 18622032 DOI: 10.1534/genetics.108.089912] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Computing identity-by-descent sharing between individuals connected through a large, complex pedigree is a computationally demanding task that often cannot be done using exact methods. What I present here is a rapid computational method for estimating, in large complex pedigrees, the probability that pairs of alleles are IBD given the single-point genotype data at that marker for all individuals. The method can be used on pedigrees of essentially arbitrary size and complexity without the need to divide the individuals into separate subpedigrees. I apply the method to do qualitative trait linkage mapping using the nonparametric sharing statistic S(pairs). The validity of the method is demonstrated via simulation studies on a 13-generation 3028-person pedigree with 700 genotyped individuals. An analysis of an asthma data set of individuals in this pedigree finds four loci with P-values <10(-3) that were not detected in prior analyses. The mapping method is fast and can complete analyses of approximately 150 affected individuals within this pedigree for thousands of markers in a matter of hours.
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