Shared genetic architecture between metabolic traits and Alzheimer's disease: a large-scale genome-wide cross-trait analysis.

Alzheimer Disease / diagnosis Blood Glucose Energy Metabolism / genetics Fasting Genetic Association Studies Genetic Predisposition to Disease Genome-Wide Association Study Humans Insulin / blood Linkage Disequilibrium Metabolic Networks and Pathways Phenotype Quantitative Trait Loci Quantitative Trait, Heritable

Journal

Human genetics
ISSN: 1432-1203
Titre abrégé: Hum Genet
Pays: Germany
ID NLM: 7613873

Informations de publication

Date de publication:
Mar 2019
Historique:
received: 02 08 2018
accepted: 20 02 2019
pubmed: 26 2 2019
medline: 26 3 2019
entrez: 27 2 2019
Statut: ppublish

Résumé

A growing number of studies clearly demonstrate a substantial link between metabolic dysfunction and the risk of Alzheimer's disease (AD), especially glucose-related dysfunction; one hypothesis for this comorbidity is the presence of a common genetic etiology. We conducted a large-scale cross-trait GWAS to investigate the genetic overlap between AD and ten metabolic traits. Among all the metabolic traits, fasting glucose, fasting insulin and HDL were found to be genetically associated with AD. Local genetic covariance analysis found that 19q13 region had strong local genetic correlation between AD and T2D (P = 6.78 × 10

Identifiants

DOI: 10.1007/s00439-019-01988-9 PMID: 30805717 PMC: PMC7193309
pubmed: 30805717
doi: 10.1007/s00439-019-01988-9
pii: 10.1007/s00439-019-01988-9
pmc: PMC7193309
mid: NIHMS1579247
doi:

Substances chimiques

Blood Glucose 0
Insulin 0

Types de publication

Journal Article Meta-Analysis

Langues

eng

Sous-ensembles de citation

IM

Pagination

271-285

Subventions

Organisme : CSRD VA
ID : I01 CX000934
Pays : United States
Organisme : NIEHS NIH HHS
ID : P30 ES000002
Pays : United States
Organisme : NIEHS NIH HHS
ID : P30ES000002
Pays : United States
Organisme : VA Merit Review Award Clinical Science R&D
ID : I01CX000934-01A1

Commentaires et corrections

Type : ErratumIn

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Auteurs

Zhaozhong Zhu (Z)

Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Yifei Lin (Y)

Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Xihao Li (X)

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Jane A Driver (JA)

Geriatric Research Education and Clinical Center and Massachusetts Veterans Epidemiology Research and Information Center, VA Medical Center, Boston, MA, USA.
Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Liming Liang (L)

Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. lliang@hsph.harvard.edu.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. lliang@hsph.harvard.edu.

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