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1. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Author

Nancy L. Heard-Costa, Lucas Barwick, Clary B. Clish, Celeste Eng, Joanne M. Murabito, Esteban G. Burchard, Yii-Der Ida Chen, Daniel I. Chasman, Robert C. Kaplan, James B. Meigs, Deborah A. Nickerson, Cashell E. Jaquish, Eric Boerwinkle, Jennifer A. Brody, Charles Kooperberg, Mark T. Gladwin, Sebastian Schoenherr, Keng-Han Lin, John Barnard, Ryan D. Hernandez, Andrew D. Johnson, Edwin K. Silverman, Mollie A. Minear, Michelle Daya, Barbara A. Konkle, Sharon R. Browning, Daniel E. Weeks, Wendy S. Post, Alexander P. Reiner, Kathryn L. Lunetta, Gina M. Peloso, David Van Den Berg, Dan E. Arking, Seung-been Lee, Leslie A. Lange, Cristen J. Willer, Zachary A. Szpiech, Tasha E. Fingerlin, Wayne E. Clarke, Xutong Zhao, Stephen S. Rich, Nora Franceschini, Sudha Seshadri, Chloé Sarnowski, Hyun Min Kang, Sayantan Das, Michael C. Zody, Stephanie M. Fullerton, Dean Bobo, Alanna C. Morrison, Brian Custer, Nona Sotoodehnia, Shannon Kelly, Thomas W. Blackwell, Bruce M. Psaty, Yingze Zhang, Susan R. Heckbert, Robert E. Gerszten, M. Benjamin Shoemaker, Daniel Taliun, Leslie S. Emery, André Corvelo, Michael H. Cho, Braxton D. Mitchell, Xiaoming Liu, Stella Aslibekyan, Paul L. Auer, Brandon Chalazan, Sarah C. Nelson, Seung Hoan Choi, Jeong-Sun Seo, Matthew P. Conomos, Anne-Katrin Emde, Lawrence F. Bielak, Alisa K. Manning, Allison E. Ashley-Koch, Diane Fatkin, Xiaowen Tian, Emelia J. Benjamin, D. C. Rao, Mina K. Chung, Myriam Fornage, Daniel Levy, Michael D. Kessler, Weihong Tang, Daniel J. Gottlieb, Pradeep Natarajan, Jessica Lasky-Su, Amol C. Shetty, Cathy C. Laurie, Dan M. Roden, Timothy D. O’Connor, Jedidiah Carlson, Lewis C. Becker, Achilleas N. Pitsillides, Karine A. Viaud-Martinez, Raul Torres, Adolfo Correa, Christian Fuchsberger, Deborah A. Meyers, Alvaro Alonso, Sanghamitra Mohanty, Jonathon LeFaive, Soren Germer, Julie L. Mikulla, François Aguet, Susan K. Dutcher, Sarah A Gagliano Taliun, Ani Manichaikul, Lori Garman, Xiuqing Guo, Timothy A. Thornton, David D. McManus, Albert V. Smith, Kristin G. Ardlie, Anna Köttgen, Sharon L.R. Kardia, Quenna Wong, Jill M. Johnsen, Andrea Natale, Richard A. Gibbs, Douglas P. Kiel, Ingo Ruczinski, Susan Redline, Lukas Forer, Scott I. Vrieze, May E. Montasser, Rasika A. Mathias, Jerome I. Rotter, Jacob Pleiness, Chunyu Liu, Brian L. Browning, James G. Wilson, Weiniu Gan, Christine M. Albert, Marilyn J. Telen, Courtney G. Montgomery, Steven A. Lubitz, Robert Klemmer, Ramachandran S. Vasan, Nathan Pankratz, Mariza de Andrade, Vivien A. Sheehan, Kenneth Rice, Xihong Lin, Eimear E. Kenny, Stephanie M. Gogarten, John Blangero, Donna K. Arnett, Jiang He, Pankaj Qasba, James F. Casella, Patrick T. Ellinor, Nicholette D. Palmer, R. Graham Barr, Scott T. Weiss, Joanne E. Curran, Bruce S. Weir, Kari E. North, L. Adrienne Cupples, Dawn L. DeMeo, Tanika N. Kelly, Angel C.Y. Mak, Russell P. Tracy, David A. Schwartz, Kent D. Taylor, Rebecca L. Beer, Daniel N. Harris, George J. Papanicolaou, Marguerite R. Irvin, Stephen T. McGarvey, Sebastian Zöllner, Patricia A. Peyser, Brian E. Cade, Ruth J. F. Loos, Douglas Loesch, Nicholas L. Smith, Gonçalo R. Abecasis, Jennifer A. Smith, Michael E. Hall, Lu-Chen Weng, Jeffrey R. O'Connell, Adrienne M. Stilp, Donald W. Bowden, Kathleen C. Barnes, Stacey Gabriel, Michael Boehnke, Wayne Huey-Herng Sheu, and Dawood Darbar

Subjects
Quality Control, Heterozygote, Genomics, Computational biology, Biology, Polymorphism, Single Nucleotide, Genome, Article, DNA sequencing, INDEL Mutation, Loss of Function Mutation, Genetics research, Genetic variation, Humans, Precision Medicine, Genetic association, Population Density, Multidisciplinary, Whole Genome Sequencing, Genome, Human, Genetic Variation, Rare variants, United States, Genetic architecture, Phenotype, Cytochrome P-450 CYP2D6, Haplotypes, Mutagenesis, Sample Size, Next-generation sequencing, National Heart, Lung, and Blood Institute (U.S.), Imputation (genetics), Reference genome
Abstract

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%., The goals, resources and design of the NHLBI Trans-Omics for Precision Medicine (TOPMed) programme are described, and analyses of rare variants detected in the first 53,831 samples provide insights into mutational processes and recent human evolutionary history.

Published
2021

2. Abstracts from the 53rd European Society of Human Genetics (ESHG) Conference: Interactive e-Posters

Author

Matthias Rath, Hannah O'Keefe, Ibrahim H Kaya, Inaki Irastorza, Alireza Pasdar, Jose Ezequiel Martin, Silvana Lobo, Francesca Vespasiano, Victoria Serebrova, Fernando Santos-Simarro, Irene Mademont-Soler, Jana Jedličková, MEHMET ALİ SOYLEMEZ, Georg Goebel, Tuomas Kilpeläinen, Viviana Tritto, Ezgi Gizem Berkay, Mercedes Serrano, Shruti Kane, Christopher Barratt, Malene Revsbech Christiansen, Sarah Martins da Silva, Dilek Colak, Dan Spiegelman, Silvia Paracchini, Sudha Seshadri, VINCENZO RALLO, Eka Kvaratskhelia, Christine Binquet, Torben Hansen, Michael Benros, Avgi Andreou, Sean Brown, and Claudio Catalli

Subjects
Genetics, 0303 health sciences, Abstracts Collection, Biology, Sperm, Male Reproductive Tract, 03 medical and health sciences, 0302 clinical medicine, Beta defensin, 030220 oncology & carcinogenesis, Identification (biology), Gene, Genetics (clinical), 030304 developmental biology
Published
2020

3. Thickness of the cerebral cortex shows positive association with blood levels of triacylglycerols carrying 18-carbon fatty acids

Author

Jean Shin, Sandra E. Black, Sudha Seshadri, Tomáš Paus, Eeva Sliz, Zdenka Pausova, and Catriona Syme

Subjects
Adult, Male, 0301 basic medicine, medicine.medical_specialty, Linoleic acid, Medicine (miscellaneous), Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, chemistry.chemical_compound, Cognition, 0302 clinical medicine, Internal medicine, Lipidomics, medicine, Humans, Metabolomics, Cognitive decline, lcsh:QH301-705.5, Triglycerides, Cerebral Cortex, chemistry.chemical_classification, Fatty Acids, food and beverages, Fatty acid, Organ Size, Metabolism, Middle Aged, Magnetic Resonance Imaging, 030104 developmental biology, medicine.anatomical_structure, Endocrinology, lcsh:Biology (General), chemistry, Cerebral cortex, Docosahexaenoic acid, Metabolome, Female, lipids (amino acids, peptides, and proteins), Arachidonic acid, General Agricultural and Biological Sciences, Biomarkers, 030217 neurology & neurosurgery, Neuroscience
Abstract

Perturbations in fatty acid (FA) metabolism as well as thinning of the cerebral cortex have been associated with cognitive decline in the elderly. Predominant FAs in the brain are docosahexaenoic acid (DHA) and arachidonic acid (ARA). Approximately 2–8% of esterified DHA and 3–5% of esterified ARA in the brain are replaced daily. DHA and ARA are derivatives of 18-carbon essential FAs, α-linolenic acid and linoleic acid, that must be imported into the brain from the circulation. In blood, FAs are primarily transported in triacylglycerols (TAGs) from which they can be released at the blood–brain-barrier and transported inside the brain. We show that circulating levels of TAGs carrying 18-carbon FAs are positively associated with cortical thickness in middle-aged adults. These associations are stronger in cortical regions with higher expression of genes regulating long-chain FA metabolism and cellular membranes, and cortical thickness in the same regions may be related to cognitive performance., Eeva Sliz et al. show an association between triacylglycerols species carrying 18-carbon unsaturated fatty acids and cortical thickness by combining serum lipidomics and brain imaging data. This association is more pronounced in cortical regions with higher expression of genes regulating fatty acid metabolism and cellular membranes.

Published
2020

4. Trends in the incidence of dementia: design and methods in the Alzheimer Cohorts Consortium

Author

Lori B. Chibnik, Osorio Meirelles, Joshua C. Bis, Christophe Tzourio, Bruce M. Psaty, Vilmundur Gudnason, Albert Hofman, Reem Waziry, Claudine Berr, Eric Boerwinkle, Catherine Helmer, Blossom C. M. Stephan, Carol Brayne, Frank J. Wolters, Alison Ower, Silke Kern, Lewis H. Kuller, Jean-François Dartigues, Ingmar Skoog, Mei Mei Wong, Leslie Grasset, M. Arfan Ikram, Anna Zettergren, M. Kamran Ikram, Fiona E. Matthews, Daniel Bos, Kristoffer Bäckman, Oscar L. Lopez, Thomas H. Mosley, Kendra Davis-Plourde, Lenore J. Launer, Stéphanie Debette, Sirwan K.L. Darweesh, Claudia L. Satizabal, Christoforos Hadjichrysanthou, Carole Dufouil, Myriam Fornage, Alexa S. Beiser, Sudha Seshadri, Epidemiology, Neurology, Radiology & Nuclear Medicine, Department of Epidemiology [Boston, MA, USA], Harvard School of Public Health, Brigham & Women’s Hospital [Boston] (BWH), Harvard Medical School [Boston] (HMS), Erasmus University Medical Center [Rotterdam] (Erasmus MC), University of Gothenburg (GU), School of Public Health [Boston], Boston University [Boston] (BU), Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Department of Medicine, University of Washington [Seattle], The University of Texas School of Public Health [Houston, TX, USA], The University of Texas Health Science Center at Houston (UTHealth), Department of Public Health and Primary Care, University of Cambridge [UK] (CAM), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Neuroépidémiologie, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Icelandic Heart Association, Heart Preventive Clinic and Research Institute, Imperial College London, University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), National Institute on Aging [Bethesda, USA] (NIA), National Institutes of Health [Bethesda] (NIH), Nextel S.A. [Bilbao], University of Mississippi Medical Center (UMMC), Boston University School of Medicine (BUSM), Institute of Neuroscience and Physiology [Göteborg], Newcastle University [Newcastle], Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, The Alzheimer Cohorts Consortium is supported by an unrestricted grant from the Janssen Prevention Center to the Harvard T.H. Chan School of Public Health. Age, Gene/Environment Susceptibility (AGES) This study is supported by National Institute of Aging contracts (N01-AG-12100 and HHSN271201200022C) with contributions from the National Eye Institute, National Institute on Deafness and Other Communication Disorders, and the National Heart, Lung and Blood Institute, the National Institute of Aging Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament), Atherosclerosis Risk in Communities (ARIC) This study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). Neurocognitive data is collected by (U01 HL096812, HL096814, HL096899, HL096902, HL096917) with funding also provided by the National Institute of Neurologic Disorders and Stroke, Cardiovascular Health Study (CHS) This research was supported by contracts (HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086), and grants U01HL080295, U01HL130114 and HL105756 from the National Heart, Lung, and Blood Institute (NHLBI), with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by the National Institute on Aging (R01AG023629) and, in part, by grants (AG20098, AG15928, and AG05133). The funding sources did not have any role in the study design, collection, analysis, or interpretation of data, preparation of the manuscript, or decision to submit it for publication, Cognitive Function and Ageing Studies (CFAS) Medical Research Council (MRC) CFAS I was funded by the MRC (Research Grant: G9901400) and the National Health Service (NHS). CFAS II has been supported by the UK Medical Research Council (Research Grant:G06010220) and received additional support from the National Institute for Health Research (NIHR), comprehensive clinical research networks in West Anglia, Nottingham City and Nottinghamshire County NHS Primary Care trusts and the dementias and neurodegenerative disease research Network (DeNDRoN) in Newcastle, Framingham Heart Study (FHS) This work was supported by the National Heart, Lung, and Blood Institute’s Framingham Heart Study (contracts N01-HC-25195 and HHSN268201500001I). This study was also supported by grants from the National Institute on Aging: (AG054076, U01-AG049505, and AG008122 (S. Seshadri)). S. Seshadri and A. Beiser were also supported by additional grants from the National Institute on Aging (R01AG049607, AG033193, AG033040) and the National Institute of Neurological Disorders and Stroke (R01-NS017950), The Gothenburg study This study was supported by grants from The Swedish Research Council 2012-5041, 2015-02830, 2013-8717, Swedish Research Council for Health, Working Life and Wellfare (no 2001-2646, 2003-0234, 2004-0150, 2006-0020, 2008-1229, 2012-1138, 2004-0145, 2006-0596, 2008-1111, 2010-0870, 2013-1202, 2001-2849, 2005-0762,2008-1210, 2013-2300, 013-2496, Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse, Hja¨rnfonden, Sahlgrenska University Hospital (ALF), The Alzheimer’s Association Zenith Award (ZEN-01-3151), The Alzheimer’s Association Stephanie B. Overstreet Scholars (IIRG-00-2159), Alzheimer’s Association (IIRG-03-6168), The Alzheimer’s Association (IIRG-09-131338), Eivind och ElsaK:son Sylvans stiftelse, Stiftelsen So¨derstro¨m-Ko¨nigska Sjukhemmet, Stiftelsen fo¨ r Gamla Tja¨narinnor, Handlanden Hjalmar Svenssons Forskningsfond, Stiftelsen Professor Bror Gadelius’ Minnesfond, Swedish Alzheimer foundation, PAQUID The PAQUID cohort was supported by IPSEN France, NOVARTIS Pharma France, and the CNSA (Caisse Nationale de Solidarité et d’Autonomie). This study is supported by the Erasmus Medical Centre and Erasmus University Rotterdam, The Netherlands Organization for Scientific Research (NWO), The Netherlands Organization for Health Research and Development (ZonMW), the Research Institute for Diseases in the Elderly (RIDE), The Netherlands Genomics Initiative, the Ministry of Education, Culture and Science, the Ministry of Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. Further support was obtained from the Netherlands Consortium for Healthy Ageing and the Dutch Heart Foundation (2012T008). This research was further supported by funding from the European Union Seventh Framework Program (FP7/2007e2013) under grant agreement no. 601055, VPHDare@ IT (FP7-ICT-2011-9e601055), and funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 667375 (Co-STREAM) and under grant agreement no. 678543 (European Research Council (ERC) funded project: ORACLE). None of the funding organizations or sponsors were involved in study design, in collection, analysis, and interpretation of data, in writing of the report, or in the decision to submit the article for publication, The 3-Cites Study This study is conducted under a partnership agreement among the Institut National de la Santé et de la Recherche Médicale (INSERM), the Victor Segalen–Bordeaux II University, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study is also supported by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, Mutuelle Générale de l’Education Nationale (MGEN), Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, and Ministry of Research–INSERM Programme ‘‘Cohortes et collections de donne´es biologiques’’, Infrastructure for the CHARGE Consortium is supported in part by National Heart, Lung and Blood Institute (HL105756) and for the neurology working group by National Institutes of Aging (AG033193 and AG049505)., Berr, Claudine, Brayne, Carol [0000-0001-5307-663X], Matthews, Fiona [0000-0002-1728-2388], and Apollo - University of Cambridge Repository

Subjects
Male, Gerontology, medicine.medical_specialty, Epidemiology, [SDV]Life Sciences [q-bio], Population, Cohort Studies, New Consortium, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, medicine, Humans, Dementia, Prospective Studies, 030212 general & internal medicine, Poisson regression, education, Aged, Proportional Hazards Models, Aged, 80 and over, education.field_of_study, Proportional hazards model, business.industry, Incidence, Incidence (epidemiology), medicine.disease, 3. Good health, [SDV] Life Sciences [q-bio], 1117 Public Health And Health Services, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Population Surveillance, Cohort, symbols, Female, Gene-Environment Interaction, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Alzheimer disease, Cohort analysis, business, Consortium, 030217 neurology & neurosurgery, Cohort study
Abstract

International audience; Several studies have reported a decline in incidence of dementia which may have large implications for the projected burden of disease, and provide important guidance to preventive efforts. However, reports are conflicting or inconclusive with regard to the impact of gender and education with underlying causes of a presumed declining trend remaining largely unidentified. The Alzheimer Cohorts Consortium aggregates data from nine international population-based cohorts to determine changes in the incidence of dementia since 1990. We will employ Poisson regression models to calculate incidence rates in each cohort and Cox proportional hazard regression to compare 5-year cumulative hazards across study-specific epochs. Finally, we will meta-analyse changes per decade across cohorts, and repeat all analysis stratified by sex, education and APOE genotype. In all cohorts combined, there are data on almost 69,000 people at risk of dementia with the range of follow-up years between 2 and 27. The average age at baseline is similar across cohorts ranging between 72 and 77. Uniting a wide range of disease-specific and methodological expertise in research teams, the first analyses within the Alzheimer Cohorts Consortium are underway to tackle outstanding challenges in the assessment of time-trends in dementia occurrence.

Published
2017

5. A genome-wide association study identifies genetic loci associated with specific lobar brain volumes

Author

Diana van Heemst, Lisa R. Yanek, Tamara B. Harris, Fabrice Crivello, Hieab H.H. Adams, Paul A. Nyquist, Bernard Mazoyer, Najaf Amin, Pauline Maillard, Diane M. Becker, Marian Beekman, Shuo Li, Ganesh Chauhan, Charles DeCarli, Wanting Zhao, Neda Jahanshad, Rasika A. Mathias, Cornelia M. van Duijn, Manon Bernard, Derrek P. Hibar, Alexa S. Beiser, Meike W. Vernooij, Vilmundur Gudnason, Joshua C. Bis, Tomáš Paus, Edith Hofer, Lei Yu, Stéphanie Debette, Helena Schmidt, M. Kamran Ikram, P. Eline Slagboom, Erik B. van den Akker, Lenore J. Launer, Stefan Boehringer, Kent D. Taylor, Maria J. Knol, Wiro J. Niessen, Kenneth Rice, Zdenka Pausova, Gennady V. Roshchupkin, Sven J. van der Lee, Philip L. De Jager, Ching-Yu Cheng, Sudha Seshadri, Qiong Yang, Konstantinos Arfanakis, Yasaman Saba, Jeroen van der Grond, W. T. Longstreth, Albert V. Smith, Claudia L. Satizabal, Oscar L. Lopez, Bruce M. Psaty, Christopher Chen, Philippe Amouyel, Josh W. Cheung, M. Arfan Ikram, Tien Yin Wong, Reinhold Schmidt, David A. Bennett, Saima Hilal, Paul M. Thompson, Erasmus University Medical Center [Rotterdam] (Erasmus MC), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Department of Medicine, University of Washington [Seattle], Keck School of Medicine [Los Angeles], University of Southern California (USC), National University of Singapore (NUS), Rush University Medical Center [Chicago], Groupe d'imagerie neurofonctionnelle (GIN), Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 (RID-AGE), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Los Angeles Biomedical Research Institute (LA BioMed), School of Public Health [Boston], Boston University [Boston] (BU), Columbia University Medical Center (CUMC), Columbia University [New York], University of Glasgow, Leiden University Medical Center (LUMC), University of Texas Health Science Center, The University of Texas Health Science Center at Houston (UTHealth), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Epidemiology, Radiology & Nuclear Medicine, Medical Informatics, Neurology, Clinical Genetics, Læknadeild (HÍ), Faculty of Medicine (UI), Heilbrigðisvísindasvið (HÍ), School of Health Sciences (UI), Háskóli Íslands, University of Iceland, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Universiteit Leiden

Subjects
Pathology, Heredity, Neurology, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Medicine (miscellaneous), Genome-wide association study, Genome-wide association studies, Taugasjúkdómar, 0302 clinical medicine, Parietal Lobe, 2.1 Biological and endogenous factors, Developmental, Aetiology, lcsh:QH301-705.5, 0303 health sciences, Parietal lobe, Gene Expression Regulation, Developmental, Organ Size, Magnetic Resonance Imaging, Temporal Lobe, Frontal Lobe, 3. Good health, Phenotype, Mental Health, Neurological, symbols, Occipital Lobe, Erfðarannsóknir, General Agricultural and Biological Sciences, medicine.medical_specialty, Genotype, 1.1 Normal biological development and functioning, Biology, Article, General Biochemistry, Genetics and Molecular Biology, Temporal lobe, 03 medical and health sciences, symbols.namesake, Underpinning research, Genetics, medicine, Humans, Gene, 030304 developmental biology, Genetic association, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], Human Genome, Heilinn, Neurosciences, Genetic Variation, United Kingdom, Brain Disorders, Gene Expression Regulation, lcsh:Biology (General), Genetic Loci, Mendelian inheritance, Occipital lobe, Biomarkers, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Publisher's version (útgefin grein)., Brain lobar volumes are heritable but genetic studies are limited. We performed genome-wide association studies of frontal, occipital, parietal and temporal lobe volumes in 16,016 individuals, and replicated our findings in 8,789 individuals. We identified six genetic loci associated with specific lobar volumes independent of intracranial volume. Two loci, associated with occipital (6q22.32) and temporal lobe volume (12q14.3), were previously reported to associate with intracranial and hippocampal volume, respectively. We identified four loci previously unknown to affect brain volumes: 3q24 for parietal lobe volume, and 1q22, 4p16.3 and 14q23.1 for occipital lobe volume. The associated variants were located in regions enriched for histone modifications (DAAM1 and THBS3), or close to genes causing Mendelian brain-related diseases (ZIC4 and FGFRL1). No genetic overlap between lobar volumes and neurological or psychiatric diseases was observed. Our findings reveal part of the complex genetics underlying brain development and suggest a role for regulatory regions in determining brain volumes., 23Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD 20892, USA. 24Research Unit-Genetic Epidemiology, Gottfried Schatz Research Centre for Cell Signaling, Metabolism and Aging, Molecular Biology and Biochemistry, Medical University of Graz, 8010 Graz, Austria. 25Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15260, USA. 26Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA. 27Department of Radiology, Leiden University Medical Center, Leiden 2333ZA, the Netherlands. 28Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto M4G 1R8, Canada. 29Departments of Psychology and Psychiatry, University of Toronto, Toronto M5S 1A1, Canada.

Published
2019

6. Evaluation of power of the Illumina HumanOmni5M-4v1 BeadChip to detect risk variants for human complex diseases

Author

Philip A. Wolf, Yi-Hsiang Hsu, Sudha Seshadri, Anita L. DeStefano, Nancy L. Heard-Costa, Chuanhua Xing, Douglas P. Kiel, Josée Dupuis, Jie Huang, and L. Adrienne Cupples

Subjects
0301 basic medicine, Genotyping Techniques, Quantitative Trait Loci, Single-nucleotide polymorphism, Genome-wide association study, Computational biology, Quantitative trait locus, Biology, Hippocampus, Sensitivity and Specificity, Article, 03 medical and health sciences, Bone Density, Genetics, Humans, Genetic Testing, International HapMap Project, 1000 Genomes Project, Genotyping, Genetics (clinical), Oligonucleotide Array Sequence Analysis, Organ Size, Minor allele frequency, 030104 developmental biology, Mutation, Imputation (genetics), Genome-Wide Association Study
Abstract

Although emerging sequencing technologies can characterize all genetic variants, the cost is still high. Illumina released the HumanOmni5M-4v1 (Omni5) genotype array with ~4.3M assayed SNPs, a much denser array compared with other available arrays. The Omni5 balances both cost and array density. In this article, we illustrate the power of Omni5 to detect genetic associations. The Omni5 includes variants with a wide range of minor allele frequencies down to

Published
2015

7. Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume

Author

Charles DeCarli, Vilmundur Gudnason, Debra A. Fleischman, Henning Tiemeier, Rebecca F. Gottesman, William S. Kremen, Xueqiu Jian, Henry Brodaty, Maria del C. Valdés Hernández, Markus Loeffler, Reinhold Schmidt, Shahzad Ahmad, Paul A. Nyquist, Oscar L. Lopez, Anbupalam Thalamuthu, Helena Schmidt, Peter R. Schofield, Tonya White, Edith Hofer, Philip L. De Jager, David Ames, Qiong Yang, Ole A. Andreassen, Cora E. Lewis, Matthias Nauck, David S. Knopman, Anders M. Dale, Sven J. van der Lee, Jeroen van der Grond, Konstantinos Arfanakis, Margaret J. Wright, Jingyun Yang, John B.J. Kwok, Hieab H.H. Adams, Matthew S. Panizzon, Meike W. Vernooij, Yasaman Saba, David A. Bennett, Cornelia M. van Duijn, Tamara B. Harris, Pauline Maillard, Frauke Beyer, M. Arfan Ikram, Jerome I. Rotter, Ryan L. Muetzel, Tomáš Paus, Pamela J. Schreiner, Zdenka Pausova, Katharina Wittfeld, Diane M. Becker, Bruce M. Psaty, Alexander Teumer, Wei Wen, A. Veronica Witte, Karen A. Mather, Hanan El Marroun, William T. Longstreth, Perminder S. Sachdev, Mark E. Bastin, Nathan A. Gillespie, Kenneth Rice, Stella Trompet, Lisa R. Yanek, David J. Stott, Alexa S. Beiser, R. Nick Bryan, Alexander Neumann, Natalie A. Royle, Myriam Fornage, Manon Bernard, Dina Vojinovic, Najaf Amin, Ian J. Deary, Arno Villringer, Georg Homuth, Nicola J. Armstrong, Stephen Sidney, Sudha Seshadri, Joanna M. Wardlaw, Thomas H. Mosley, Hans J. Grabe, Michelle Luciano, Lenore J. Launer, J. Wouter Jukema, Robin Bülow, Markus Scholz, André G. Uitterlinden, Albert V. Smith, Shuo Li, Claudia L. Satizabal, Joshua C. Bis, Epidemiology, Radiology & Nuclear Medicine, Child and Adolescent Psychiatry / Psychology, Clinical Psychology, Internal Medicine, Psychiatry, Neurology, Faculty of Medicine (UI), Læknadeild (HÍ), Heilbrigðisvísindasvið (HÍ), School of Health Sciences (UI), Háskóli Íslands (HÍ), and University of Iceland (UI)

Subjects
0301 basic medicine, Genetics of the nervous system, Cytoskeleton organization, General Physics and Astronomy, Genome-wide association study, Mental disorders, Genome-wide association studies, Genome, Lateral ventricles, 0302 clinical medicine, Lateral Ventricles, Ventricle (lateral), lcsh:Science, Genetics, anatomy & histology [Lateral Ventricles], education.field_of_study, Enlargement, Multidisciplinary, Brain, Organ Size, Middle Aged, Schizophrenia, ddc:500, Science, Population, Elderly people, Biology, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, medicine, Erfðafræði, Humans, Genomes, education, Aged, Genetic association, genetics [Organ Size], Genome, Human, Heilinn, Brain morphometry, Litningarannsóknir, Rannsóknir, General Chemistry, medicine.disease, 030104 developmental biology, Heritable quantitative trait, lcsh:Q, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Publisher's version (útgefin grein) Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations., The volume of the lateral ventricles (LV) increases with age and their abnormal enlargement is a key feature of several neurological and psychiatric diseases. Although lateral ventricular volume is heritable, a comprehensive investigation of its genetic determinants is lacking. In this meta-analysis of genome-wide association studies of 23,533 healthy middle-aged to elderly individuals from 26 population-based cohorts, we identify 7 genetic loci associated with LV volume. These loci map to chromosomes 3q28, 7p22.3, 10p12.31, 11q23.1, 12q23.3, 16q24.2, and 22q13.1 and implicate pathways related to tau pathology, S1P signaling, and cytoskeleton organization. We also report a significant genetic overlap between the thalamus and LV volumes (ρgenetic = −0.59, p-value = 3.14 × 10−6), suggesting that these brain structures may share a common biology. These genetic associations of LV volume provide insights into brain morphology., See Supplementary Note 2 for information on funding sources.

Published
2018

8. Diabetes and stroke

Author

Rohit R. Das and Sudha Seshadri

Subjects
Pharmacology, medicine.medical_specialty, business.industry, United Kingdom Prospective Diabetes Study, Type 2 diabetes, medicine.disease, Internal medicine, Diabetes mellitus, Physical therapy, Medicine, Pharmacology (medical), cardiovascular diseases, Metabolic syndrome, Risk factor, business, Pioglitazone, Stroke, medicine.drug, Glycemic
Abstract

Diabetes is a recognized risk factor for stroke. This review summarizes recent studies detailing this risk. Recent reports have found that the risk appears significantly higher with type 1 than type 2 diabetes. Risk was assessed in various ethnic subgroups, and diabetes is at least as important a risk factor for stroke in blacks, South Asians, and Native Americans as it is in whites. Concomitant vascular risk factors and markers of diabetes severity (microalbuminuria, glycemic control) modify the association between diabetes and stroke risk. Several large studies have reinforced the association of prediabetic states, including the metabolic syndrome, with stroke risk in nondiabetics. Moreover, components of the metabolic syndrome increase stroke risk in diabetics. Diabetes adversely affects survival and cognitive and disability outcomes following stroke. Statins, oral hypoglycemics, and peroxisome proliferatoractivated receptor-γ inhibitors may be beneficial in preventing stroke or attenuating its adverse impact in this vulnerable population.

Published
2008

9. Association between genetic variant on chromosome 12p13 and stroke survival and recurrence: a one year prospective study in Taiwan

Author

Chyi Huey Bai, Chin I. Chen, Hung Yi Chiou, Li Ming Lien, Yi-Hsiang Hsu, Huey Juan Lin, Jiann-Shing Jeng, Fang I. Hsieh, Sung-Chun Tang, Chaur Jong Hu, Wen Ting Chung, Hung Pin Tseng, Chia Chen Yu, Sudha Seshadri, and Yi Chen Hsieh

Subjects
Male, Oncology, Endocrinology, Diabetes and Metabolism, Clinical Biochemistry, lcsh:Medicine, Brain Ischemia, Brain ischemia, single nucleotide polymorphisms, 0302 clinical medicine, Recurrence, Risk Factors, Odds Ratio, Pharmacology (medical), Prospective Studies, Prospective cohort study, Stroke, Genetics, 0303 health sciences, Hazard ratio, General Medicine, Middle Aged, stroke, 3. Good health, Female, medicine.medical_specialty, Genotype, Taiwan, Single-nucleotide polymorphism, Polymorphism, Single Nucleotide, survival, 03 medical and health sciences, Internal medicine, medicine, Humans, cardiovascular diseases, Molecular Biology, Aged, Proportional Hazards Models, 030304 developmental biology, Biochemistry, medical, Chromosomes, Human, Pair 12, business.industry, Proportional hazards model, Research, lcsh:R, Biochemistry (medical), Case-control study, Cell Biology, Odds ratio, medicine.disease, Case-Control Studies, business, 030217 neurology & neurosurgery
Abstract

Background The association between ischemic stroke and 2 single nucleotide polymorphisms (SNPs) on chromosome 12p13, rs12425791 and rs11833579 appears inconsistent across different samples. These SNPs are close to the ninjurin2 gene which may alter the risk of stroke by affecting brain response to ischemic injury. The purpose of this study was to investigate the association between these two SNPs and ischemic stroke risk, as well as prognostic outcomes in a Taiwanese sample. Methods We examined the relations of these two SNPs to the odds of new-onset ischemic stroke, ischemic stroke subtypes, and to the one year risk of stroke-related death or recurrent stroke following initial stroke in a case-control study. A total of 765 consecutive patients who had first-ever ischemic stroke were compared to 977 stroke-free, age-matched controls. SNPs were genotyped by Taqman fluorescent allelic discrimination assay. The association between ischemic stroke and SNPs were analyzed by multivariate logistic regression. Cox proportional hazard model was used to assess the effect of individual SNPs on stroke-related mortality or recurrent stroke. Results There was no significant association between SNP rs12425791 and rs11833579 and ischemic stroke after multiple testing corrections. However, the marginal significant association was observed between SNP rs12425791 and large artery atherosclerosis under recessive model (OR, 2.30; 95%CI, 1.22-4.34; q-value = 0.062). Among the 765 ischemic stroke patients, 59 died or developed a recurrent stroke. After adjustment for age, sex, vascular risk factors and baseline stroke severity, Cox proportional hazard analysis indicated that the hazard ratios were 2.76 (95%CI, 1.34-5.68; q-value, 0.02) and 2.15 (95%CI, 1.15-4.02; q-value, 0.03) for individuals with homozygous variant allele of rs12425791 and rs11833579, respectively. Conclusions This is a precedent study that found genetic variants of rs12425791 and rs11833579 on chromosome 12p13 are independent predictors of stroke-related mortality or stroke recurrence in patients with incident ischemic stroke in Taiwan. Further study is needed to explore the details of the physiological function and the molecular mechanisms underlying the association of this genetic locus with ischemic stroke.

Published
2012

10. Genetic correlates of longevity and selected age-related phenotypes: a genome-wide association study in the Framingham Study

Author

Joseph M. Massaro, Chao-Yu Guo, Raju Govindaraju, Sudha Seshadri, Emelia J. Benjamin, Ralph B. D'Agostino, Michael J. Pencina, David Karasik, Joanne M. Murabito, Douglas P. Kiel, Kathryn L. Lunetta, and Margaret Kelly-Hayes

Subjects
Adult, Genetic Markers, Male, Candidate gene, Genotype, Genetic Linkage, media_common.quotation_subject, Longevity, Genome-wide association study, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Framingham Heart Study, Genetics, Humans, SNP, Genetics(clinical), Genetics (clinical), 030304 developmental biology, media_common, 0303 health sciences, Genome, Human, Proportional hazards model, Research, Middle Aged, Minor allele frequency, Phenotype, Cardiovascular Diseases, Evolutionary biology, Female, 030217 neurology & neurosurgery
Abstract

Background Family studies and heritability estimates provide evidence for a genetic contribution to variation in the human life span. Methods We conducted a genome wide association study (Affymetrix 100K SNP GeneChip) for longevity-related traits in a community-based sample. We report on 5 longevity and aging traits in up to 1345 Framingham Study participants from 330 families. Multivariable-adjusted residuals were computed using appropriate models (Cox proportional hazards, logistic, or linear regression) and the residuals from these models were used to test for association with qualifying SNPs (70, 987 autosomal SNPs with genotypic call rate ≥80%, minor allele frequency ≥10%, Hardy-Weinberg test p ≥ 0.001). Results In family-based association test (FBAT) models, 8 SNPs in two regions approximately 500 kb apart on chromosome 1 (physical positions 73,091,610 and 73, 527,652) were associated with age at death (p-value < 10-5). The two sets of SNPs were in high linkage disequilibrium (minimum r2 = 0.58). The top 30 SNPs for generalized estimating equation (GEE) tests of association with age at death included rs10507486 (p = 0.0001) and rs4943794 (p = 0.0002), SNPs intronic to FOXO1A, a gene implicated in lifespan extension in animal models. FBAT models identified 7 SNPs and GEE models identified 9 SNPs associated with both age at death and morbidity-free survival at age 65 including rs2374983 near PON1. In the analysis of selected candidate genes, SNP associations (FBAT or GEE p-value < 0.01) were identified for age at death in or near the following genes: FOXO1A, GAPDH, KL, LEPR, PON1, PSEN1, SOD2, and WRN. Top ranked SNP associations in the GEE model for age at natural menopause included rs6910534 (p = 0.00003) near FOXO3a and rs3751591 (p = 0.00006) in CYP19A1. Results of all longevity phenotype-genotype associations for all autosomal SNPs are web posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. Conclusion Longevity and aging traits are associated with SNPs on the Affymetrix 100K GeneChip. None of the associations achieved genome-wide significance. These data generate hypotheses and serve as a resource for replication as more genes and biologic pathways are proposed as contributing to longevity and healthy aging.

Published
2007

11. Framingham Heart Study 100K project: genome-wide associations for cardiovascular disease outcomes

Author

Nancy L. Heard-Costa, Christopher Newton-Cheh, Caroline S. Fox, Ramachandran S. Vasan, Christopher J. O'Donnell, Joanne M. Murabito, Martin G. Larson, Chao-Yu Guo, Daniel Levy, Larry D. Atwood, Diddahally R. Govindaraju, Emelia J. Benjamin, Ralph B. D'Agostino, Shih-Jen Hwang, Philip A. Wolf, Sudha Seshadri, Thomas J. Wang, and L. Adrienne Cupples

Subjects
Adult, Genetic Markers, Male, Gerontology, Genotype, Genetic Linkage, Disease outcome, MEDLINE, 030204 cardiovascular system & hematology, Polymorphism, Single Nucleotide, Genome, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Framingham Heart Study, Genetics, Humans, Medicine, Genetics(clinical), cardiovascular diseases, Genetics (clinical), Proportional Hazards Models, 030304 developmental biology, National health, 0303 health sciences, Framingham Risk Score, Models, Genetic, Genome, Human, Extramural, business.industry, Research, Middle Aged, 3. Good health, Phenotype, Treatment Outcome, Cardiovascular Diseases, Female, Disease Susceptibility, Chromosomes, Human, Pair 9, business, Cohort study
Abstract

Cardiovascular disease (CVD) and its most common manifestations--including coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF)--are major causes of morbidity and mortality. In many industrialized countries, cardiovascular disease (CVD) claims more lives each year than any other disease. Heart disease and stroke are the first and third leading causes of death in the United States. Prior investigations have reported several single gene variants associated with CHD, stroke, HF, and AF. We report a community-based genome-wide association study of major CVD outcomes.In 1345 Framingham Heart Study participants from the largest 310 pedigrees (54% women, mean age 33 years at entry), we analyzed associations of 70,987 qualifying SNPs (Affymetrix 100K GeneChip) to four major CVD outcomes: major atherosclerotic CVD (n = 142; myocardial infarction, stroke, CHD death), major CHD (n = 118; myocardial infarction, CHD death), AF (n = 151), and HF (n = 73). Participants free of the condition at entry were included in proportional hazards models. We analyzed model-based deviance residuals using generalized estimating equations to test associations between SNP genotypes and traits in additive genetic models restricted to autosomal SNPs with minor allele frequencyor =0.10, genotype call rateor =0.80, and Hardy-Weinberg equilibrium p-valueor = 0.001.Six associations yielded p10(-5). The lowest p-values for each CVD trait were as follows: major CVD, rs499818, p = 6.6 x 10(-6); major CHD, rs2549513, p = 9.7 x 10(-6); AF, rs958546, p = 4.8 x 10(-6); HF: rs740363, p = 8.8 x 10(-6). Of note, we found associations of a 13 Kb region on chromosome 9p21 with major CVD (p 1.7-1.9 x 10(-5)) and major CHD (p 2.5-3.5 x 10(-4)) that confirm associations with CHD in two recently reported genome-wide association studies. Also, rs10501920 in CNTN5 was associated with AF (p = 9.4 x 10(-6)) and HF (p = 1.2 x 10(-4)). Complete results for these phenotypes can be found at the dbgap website http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 webcite.No association attained genome-wide significance, but several intriguing findings emerged. Notably, we replicated associations of chromosome 9p21 with major CVD. Additional studies are needed to validate these results. Finding genetic variants associated with CVD may point to novel disease pathways and identify potential targeted preventive therapies.

Published
2007

12. The Framingham Heart Study 100K SNP genome-wide association study resource: overview of 17 phenotype working group reports

Author

Zhen Y Wang, Douglas P. Kiel, Nancy L. Heard-Costa, Chao-Yu Guo, James B. Meigs, Josée Dupuis, Daniel J. Gottlieb, Joanne M. Murabito, Martin G. Larson, Mona Pandey, Kathryn L. Lunetta, Caroline S. Fox, Chunyu Liu, Anita L. DeStefano, Christopher Newton-Cheh, Matthew D. Mailman, Larry D. Atwood, Alisa K. Manning, Kathleen Falls, Sudha Seshadri, Shih-Jen Hwang, Diddahally R. Govindaraju, Philip A. Wolf, George T. O'Connor, Qiong Yang, Jason M. Laramie, Emelia J. Benjamin, Sekar Kathiresan, Serkalem Demissie, Ramachandran S. Vasan, Ralph B. D'Agostino, Daniel Levy, L. Adrienne Cupples, Heather T Arruda, Christopher J. O'Donnell, and Jemma B. Wilk

Subjects
Adult, Genetic Markers, Male, Single-nucleotide polymorphism, Genome-wide association study, Disease, Biology, Polymorphism, Single Nucleotide, Cohort Studies, 03 medical and health sciences, Framingham Heart Study, Genetics, Humans, SNP, Genetics(clinical), Genetics (clinical), 030304 developmental biology, Introduction, 0303 health sciences, Genome, Human, 030305 genetics & heredity, Middle Aged, Human genetics, 3. Good health, Phenotype, Cardiovascular Diseases, Cohort, Female, Disease Susceptibility, Cohort study
Abstract

The Framingham Heart Study (FHS), founded in 1948 to examine the epidemiology of cardiovascular disease, is among the most comprehensively characterized multi-generational studies in the world. Many collected phenotypes have substantial genetic contributors; yet most genetic determinants remain to be identified. Using single nucleotide polymorphisms (SNPs) from a 100K genome-wide scan, we examine the associations of common polymorphisms with phenotypic variation in this community-based cohort and provide a full-disclosure, web-based resource of results for future replication studies.Adult participants (n = 1345) of the largest 310 pedigrees in the FHS, many biologically related, were genotyped with the 100K Affymetrix GeneChip. These genotypes were used to assess their contribution to 987 phenotypes collected in FHS over 56 years of follow up, including: cardiovascular risk factors and biomarkers; subclinical and clinical cardiovascular disease; cancer and longevity traits; and traits in pulmonary, sleep, neurology, renal, and bone domains. We conducted genome-wide variance components linkage and population-based and family-based association tests.The participants were white of European descent and from the FHS Original and Offspring Cohorts (examination 1 Offspring mean age 32 +/- 9 years, 54% women). This overview summarizes the methods, selected findings and limitations of the results presented in the accompanying series of 17 manuscripts. The presented association results are based on 70,897 autosomal SNPs meeting the following criteria: minor allele frequencyor + 10%, genotype call rateor = 80%, Hardy-Weinberg equilibrium p-valueor = 0.001, and satisfying Mendelian consistency. Linkage analyses are based on 11,200 SNPs and short-tandem repeats. Results of phenotype-genotype linkages and associations for all autosomal SNPs are posted on the NCBI dbGaP website at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 webcite.We have created a full-disclosure resource of results, posted on the dbGaP website, from a genome-wide association study in the FHS. Because we used three analytical approaches to examine the association and linkage of 987 phenotypes with thousands of SNPs, our results must be considered hypothesis-generating and need to be replicated. Results from the FHS 100K project with NCBI web posting provides a resource for investigators to identify high priority findings for replication.

Published
2007

13. Circulating biomarkers that predict incident dementia

Author

Galit Weinstein and Sudha Seshadri

Subjects
medicine.medical_specialty, Neurology, medicine.diagnostic_test, Amyloid, Lumbar puncture, business.industry, Cognitive Neuroscience, Neural degeneration, Review, Disease, Brain damage, medicine.disease, Bioinformatics, 3. Good health, mental disorders, medicine, Dementia, Neurology (clinical), medicine.symptom, business, Geriatric psychiatry
Abstract

Dementia is currently diagnosed based on clinical symptoms and signs, but significant brain damage has already occurred by the time a clinical diagnosis of dementia is made, and it is increasingly recognized that this may be too late for any effective intervention. It would therefore be of great public health and preventive value to define a variety of biomarkers that could permit early detection of persons at a higher risk for developing dementia, and specifically dementia due to Alzheimer’s disease. Nevertheless, for the purpose of large-scale screening, circulating biomarkers are more appropriate because they are less invasive than lumbar puncture, less costly than brain amyloid imaging and can be easily assessed repeatedly in a primary care clinic setting. In this brief review we will review a number of candidate molecules implicated as possible predictors of dementia risk. These candidates include markers of vascular injury, metabolic and inflammatory states, amyloid and tau pathway markers, measures of neural degeneration and repair efforts, and other molecules that might contribute to anatomical and functional changes characteristic of dementia and Alzheimer’s disease.

Published
2014

15. Erratum: Common variants at 12q15 and 12q24 are associated with infant head circumference

Author

Sudha Seshadri, Gerard H. Koppelman, H.-Erich Wichmann, Meike W. Vernooij, Paul F. O'Reilly, Cecilia E. Kim, Norman Klopp, Inga Prokopenko, Nicholas J. Timpson, Craig E. Pennell, Joachim Heinrich, Rachel M. Freathy, Aarno Palotie, Mark I. McCarthy, Rosetta M. Chiavacci, Elisabeth Widen, Frank Geller, Beate St Pourcain, Jordi Sunyer, Lawrence J. Beilin, Myriam Fornage, Pimphen Charoen, Vilmundur Gudnason, André G. Uitterlinden, George McMahon, Mads Melbye, Anne Mølgaard, John P. Kemp, Marika Kaakinen, Carla M. T. Tiesler, Susan M. Ring, Carmen Iñiguez, Fernando Rivadeneira, Lyle J. Palmer, Hanieh Yaghootkar, Dennis O. Mook-Kanamori, M. Arfan Ikram, Aad van der Lugt, Cornelia M. van Duijn, Bjarke Feenstra, Hakon Hakonarson, Johan G. Eriksson, Stéphanie Debette, Bo L. Chawes, Vincent W. V. Jaddoe, A Pouta, Jonathan P. Bradfield, John P. Newnham, Martina Müller-Nurasyid, Mark Lathrop, Albert Hofman, H. Rob Taal, Thomas H. Mosley, Alexandra I. F. Blakemore, Klaus Bønnelykke, Lenore J. Launer, Marjo-Riitta Järvelin, Hans Bisgaard, Diana L. Cousminer, Monique M.B. Breteler, Frank D. Mentch, Wendy L. McArdle, Mònica Guxens, Shikta Das, David M. Evans, Eskil Kreiner-Møller, Jari Lahti, Struan F.A. Grant, Charles DeCarli, Marjan Kerkhof, Nicole M. Warrington, Nadja Hawwa Vissing, Eric A.P. Steegers, Jessica L. Buxton, Albert V. Smith, Elisabeth Thiering, Marie Standl, Anna-Liisa Hartikainen, Stephen J. Lye, Xavier Estivill, George Davey Smith, Andrew T. Hattersley, Ulla Sovio, Julio Fernandez-Banet, and Albert J. van der Heijden

Subjects
Head circumference, Genetics, Anatomy, Biology
Published
2013

16. Erratum: Common variants at 6q22 and 17q21 are associated with intracranial volume

Author

Craig E. Pennell, Henri A. Vrooman, Beate St Pourcain, Albert V. Smith, Clifford R. Jack, Monique M.B. Breteler, Alex P. Zijdenbos, André G. Uitterlinden, Charles DeCarli, Nicholas J. Timpson, David S. Knopman, Sudha Seshadri, Wiro J. Niessen, Mark A. van Buchem, Rhoda Au, Vilmundur Gudnason, Ulla Sovio, Aad van der Lugt, Laura H. Coker, Cornelia M. van Duijn, Hakon Hakonarson, Marjo-Riitta Järvelin, Maksim Struchalin, W. T. Longstreth, Stefan Ropele, Albert Hofman, H. Rob Taal, George Davey Smith, Tamara B. Harris, Elisabeth Thiering, Mark I. McCarthy, Stéphanie Debette, Myriam Fornage, Alexa S. Beiser, Joachim Heinrich, Meike W. Vernooij, Anna-Liisa Hartikainen, Lyle J. Palmer, B. Gwen Windham, Mike A. Nalls, Dennis O. Mook-Kanamori, Thomas H. Mosley, Vincent W. V. Jaddoe, Lenore J. Launer, Philip A. Wolf, Sigurdur Sigurdsson, Helena Schmidt, Fernando Rivadeneira, M. Arfan Ikram, Anita L. DeStefano, Haukur Gudnason, William M. Meeks, Reinhold E. Schmidt, Eric A.P. Steegers, Struan F.A. Grant, and Diane J. Catellier

Subjects
Intracranial volume, Genetics, Biology, Bioinformatics
Published
2012

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