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Assessing the genetic overlap between BMI and cognitive function.

TitleAssessing the genetic overlap between BMI and cognitive function.
Publication TypeJournal Article
Year of Publication2016
AuthorsMarioni RE, Yang J, Dykiert D, Mõttus R, Campbell A, Davies G, Hayward C, Porteous DJ, Visscher PM
Secondary AuthorsDeary IJ
Corporate AuthorsCHARGE Cognitive Working Group
JournalMol Psychiatry
Volume21
Issue10
Pagination1477-82
Date Published2016 10
ISSN1476-5578
KeywordsAged, Body Mass Index, Cognition, Female, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Male, Middle Aged, Multifactorial Inheritance, Obesity, Phenotype, Polymorphism, Single Nucleotide, Scotland
Abstract

Obesity and low cognitive function are associated with multiple adverse health outcomes across the life course. They have a small phenotypic correlation (r=-0.11; high body mass index (BMI)-low cognitive function), but whether they have a shared genetic aetiology is unknown. We investigated the phenotypic and genetic correlations between the traits using data from 6815 unrelated, genotyped members of Generation Scotland, an ethnically homogeneous cohort from five sites across Scotland. Genetic correlations were estimated using the following: same-sample bivariate genome-wide complex trait analysis (GCTA)-GREML; independent samples bivariate GCTA-GREML using Generation Scotland for cognitive data and four other samples (n=20 806) for BMI; and bivariate LDSC analysis using the largest genome-wide association study (GWAS) summary data on cognitive function (n=48 462) and BMI (n=339 224) to date. The GWAS summary data were also used to create polygenic scores for the two traits, with within- and cross-trait prediction taking place in the independent Generation Scotland cohort. A large genetic correlation of -0.51 (s.e. 0.15) was observed using the same-sample GCTA-GREML approach compared with -0.10 (s.e. 0.08) from the independent-samples GCTA-GREML approach and -0.22 (s.e. 0.03) from the bivariate LDSC analysis. A genetic profile score using cognition-specific genetic variants accounts for 0.08% (P=0.020) of the variance in BMI and a genetic profile score using BMI-specific variants accounts for 0.42% (P=1.9 × 10(-7)) of the variance in cognitive function. Seven common genetic variants are significantly associated with both traits at P

DOI10.1038/mp.2015.205
Alternate JournalMol Psychiatry
PubMed ID26857597
PubMed Central IDPMC4863955
Grant List / BB_ / Biotechnology and Biological Sciences Research Council / United Kingdom
R01 AG017917 / AG / NIA NIH HHS / United States
MR/K026992/1 / MRC_ / Medical Research Council / United Kingdom
R01 HL105756 / HL / NHLBI NIH HHS / United States
R01 AG008122 / AG / NIA NIH HHS / United States
CZD/16/6/4 / CSO_ / Chief Scientist Office / United Kingdom
P30 AG010129 / AG / NIA NIH HHS / United States
U01 AG049505 / AG / NIA NIH HHS / United States
R01 NS017950 / NS / NINDS NIH HHS / United States
CZD/16/6 / CSO_ / Chief Scientist Office / United Kingdom
RF1 AG015819 / AG / NIA NIH HHS / United States
R01 AG054076 / AG / NIA NIH HHS / United States
P30 AG010161 / AG / NIA NIH HHS / United States
R01 AG033193 / AG / NIA NIH HHS / United States