Assessing the genetic overlap between BMI and cognitive function

Marioni, R. E., Yang, J., Dykiert, D., Mottus, R., Campbell, A., Davies, G., Hayward, C., Porteous, D. J., Visscher, P. M. and Deary, I. J. (2016) Assessing the genetic overlap between BMI and cognitive function. Molecular Psychiatry, 21 10: 1477-1482. doi:10.1038/mp.2015.205


Author Marioni, R. E.
Yang, J.
Dykiert, D.
Mottus, R.
Campbell, A.
Davies, G.
Hayward, C.
Porteous, D. J.
Visscher, P. M.
Deary, I. J.
Title Assessing the genetic overlap between BMI and cognitive function
Journal name Molecular Psychiatry   Check publisher's open access policy
ISSN 1476-5578
1359-4184
Publication date 2016-10-01
Sub-type Article (original research)
DOI 10.1038/mp.2015.205
Open Access Status DOI
Volume 21
Issue 10
Start page 1477
End page 1482
Total pages 6
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Language eng
Formatted 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<5 × 10-5, which is significantly more than expected by chance (P=0.007). All these results suggest there are shared genetic contributions to BMI and cognitive function.
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Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Brain Institute Publications
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