Cohort profile: genomic data for 26 622 individuals from the Canadian Longitudinal Study on Aging (CLSA).

Aging / psychology Canada Female Genomics Glaucoma Humans Longitudinal Studies Male
CLSA HLA aging genome-wide genotyping

Journal

BMJ open
ISSN: 2044-6055
Titre abrégé: BMJ Open
Pays: England
ID NLM: 101552874

Informations de publication

Date de publication:
10 03 2022
Historique:
entrez: 11 3 2022
pubmed: 12 3 2022
medline: 28 4 2022
Statut: epublish

Résumé

The Canadian Longitudinal Study on Aging (CLSA) Comprehensive cohort was established to provide unique opportunities to study the genetic and environmental contributions to human disease as well as ageing process. The aim of this report was to describe the genomic data included in CLSA. A total of 26 622 individuals from the CLSA Comprehensive cohort of men and women aged 45-85 recruited between 2010 and 2015 underwent genome-wide genotyping of DNA samples collected from blood. Comprehensive quality control metrics were measured for genetic markers and samples, respectively. The genotypes were imputed to the TOPMed reference panel. Sex chromosome abnormalities were identified by copy number profiling. Classical human leukocyte antigen gene haplotypes were imputed at two-field (four-digit). Of the 26 622 genotyped participants, 24 655 (92.6%) were identified as having European ancestry. These genomic data were linked to physical, lifestyle, medical, economic, environmental and psychosocial factors collected longitudinally in CLSA. The combined analysis, including CLSA genomic data, uncovered over 100 novel loci associated with key parameters to define glaucoma. The CLSA genomic dataset validated the contribution of a polygenic risk score to screen individuals with high fracture risk. It is also a valuable resource to directly identify common genetic variations associated with conditions related to complex traits. Taking advantage of the comprehensive interview and physical information collected in CLSA, this genomic dataset has been linked to psychosocial factors to investigate both the independent and interactive effects on cardiovascular disease. The CLSA overall is ongoing. Follow-up data will continue to be collected from participants in the current genomic subcohort, including the DNA methylation and metabolomic data. Ongoing studies focus on elucidating the role of genetic factors in cognitive decline and cardiovascular diseases. This genomic data resource is available on request through the CLSA data access application process.

Identifiants

DOI: 10.1136/bmjopen-2021-059021 PMID: 35273064 PMC: PMC8915305
pubmed: 35273064
pii: bmjopen-2021-059021
doi: 10.1136/bmjopen-2021-059021
pmc: PMC8915305
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e059021

Informations de copyright

© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

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Auteurs

Vincenzo Forgetta (V)

Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montréal, QC, Canada.

Rui Li (R)

McGill Genome Centre, Department of Human Genetics, McGill University, Montréal, QC, Canada.
Department of Human Genetics, McGill University, Montréal, QC, Canada.
ORCID: 0000-0001-8353-6772

Corinne Darmond-Zwaig (C)

McGill Genome Centre, Department of Human Genetics, McGill University, Montréal, QC, Canada.
Department of Human Genetics, McGill University, Montréal, QC, Canada.

Alexandre Belisle (A)

McGill Genome Centre, Department of Human Genetics, McGill University, Montréal, QC, Canada.
Department of Human Genetics, McGill University, Montréal, QC, Canada.

Cynthia Balion (C)

Hamilton Regional Laboratory Medicine Program, McMaster University, St. Joseph's Hospital St. Luke's Wing, Hamilton, ON, Canada.

Delnaz Roshandel (D)

Genetics & Genomic Biology, The Hospital for Sick Children Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.

Christina Wolfson (C)

Department of Medicine & of Epidemiology and Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.

Guillaume Lettre (G)

Montréal Heart Institute and Université de Montréal, Montréal, QC, Canada.

Guillaume Pare (G)

Hamilton Regional Laboratory Medicine Program, McMaster University, St. Joseph's Hospital St. Luke's Wing, Hamilton, ON, Canada.

Andrew D Paterson (AD)

Genetics & Genomic Biology, The Hospital for Sick Children Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.
Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
ORCID: 0000-0002-9169-118X

Lauren E Griffith (LE)

Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
ORCID: 0000-0002-2794-9692

Chris Verschoor (C)

Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.

Mark Lathrop (M)

McGill Genome Centre, Department of Human Genetics, McGill University, Montréal, QC, Canada.
Department of Human Genetics, McGill University, Montréal, QC, Canada.

Susan Kirkland (S)

Department of Community Health and Epidemiology, Division of Geriatric Medicine, Dalhousie University, Halifax, NS, Canada.

Parminder Raina (P)

Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.

J Brent Richards (JB)

Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montréal, QC, Canada.
Department of Medicine & of Epidemiology and Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.
Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.

Jiannis Ragoussis (J)

McGill Genome Centre, Department of Human Genetics, McGill University, Montréal, QC, Canada ioannis.ragoussis@mcgill.ca.
Department of Human Genetics, McGill University, Montréal, QC, Canada.
Department of Bioengineering, McGill University, Montréal, QC, Canada.

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Classifications MeSH

questionsmedicales.fr › Phénomènes physiologiques › Croissance et développement › Vieillissement › Cohort profile: genomic data for 26 622...
questionsmedicales.fr › Régions géographiques › Amériques › Amérique du Nord › Canada › Cohort profile: genomic data for 26 622...
questionsmedicales.fr › Disciplines des sciences naturelles › Disciplines des sciences biologiques › Biologie › Génétique › Génomique › Cohort profile: genomic data for 26 622...
questionsmedicales.fr › Maladies de l'oeil › Hypertension oculaire › Glaucome › Cohort profile: genomic data for 26 622...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Cohort profile: genomic data for 26 622...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Études longitudinales › Cohort profile: genomic data for 26 622...