Structural Brain MRI Trait Polygenic Score Prediction of Cognitive Abilities

Luciano, Michelle, Marioni, Riccardo E., Valdes Hernandez, Maria, Maniega, Susana, Hamilton, Iona F., Royle, Natalie A., Scotland, Generation, Chauhan, Ganesh, Bis, Joshua C., Debette, Stephanie, Decarli, Charles, Fornage, Myriam, Schmidt, Reinhold, Arfan Ikram, M., Launer, Lenore J., Seshadri, Sudha, Bastin, Mark E., Porteous, David J., Wardlaw, Joanna and Deary, Ian J. (2015) Structural Brain MRI Trait Polygenic Score Prediction of Cognitive Abilities. Twin Research and Human Genetics, 18 6: 738-745. doi:10.1017/thg.2015.71


Author Luciano, Michelle
Marioni, Riccardo E.
Valdes Hernandez, Maria
Maniega, Susana
Hamilton, Iona F.
Royle, Natalie A.
Scotland, Generation
Chauhan, Ganesh
Bis, Joshua C.
Debette, Stephanie
Decarli, Charles
Fornage, Myriam
Schmidt, Reinhold
Arfan Ikram, M.
Launer, Lenore J.
Seshadri, Sudha
Bastin, Mark E.
Porteous, David J.
Wardlaw, Joanna
Deary, Ian J.
Title Structural Brain MRI Trait Polygenic Score Prediction of Cognitive Abilities
Journal name Twin Research and Human Genetics   Check publisher's open access policy
ISSN 1839-2628
1832-4274
Publication date 2015-12-01
Year available 2015
Sub-type Article (original research)
DOI 10.1017/thg.2015.71
Open Access Status Not yet assessed
Volume 18
Issue 6
Start page 738
End page 745
Total pages 8
Place of publication Cambridge, United Kingdom
Publisher Cambridge University Press
Collection year 2016
Language eng
Formatted abstract
Structural brain magnetic resonance imaging (MRI) traits share part of their genetic variance with cognitive traits. Here, we use genetic association results from large meta-analytic studies of genome-wide association (GWA) for brain infarcts (BI), white matter hyperintensities, intracranial, hippocampal, and total brain volumes to estimate polygenic scores for these traits in three Scottish samples: Generation Scotland: Scottish Family Health Study (GS:SFHS), and the Lothian Birth Cohorts of 1936 (LBC1936) and 1921 (LBC1921). These five brain MRI trait polygenic scores were then used to: (1) predict corresponding MRI traits in the LBC1936 (numbers ranged 573 to 630 across traits), and (2) predict cognitive traits in all three cohorts (in 8,115–8,250 persons). In the LBC1936, all MRI phenotypic traits were correlated with at least one cognitive measure, and polygenic prediction of MRI traits was observed for intracranial volume. Meta-analysis of the correlations between MRI polygenic scores and cognitive traits revealed a significant negative correlation (maximal r = 0.08) between the HV polygenic score and measures of global cognitive ability collected in childhood and in old age in the Lothian Birth Cohorts. The lack of association to a related general cognitive measure when including the GS:SFHS points to either type 1 error or the importance of using prediction samples that closely match the demographics of the GWA samples from which prediction is based. Ideally, these analyses should be repeated in larger samples with data on both MRI and cognition, and using MRI GWA results from even larger meta-analysis studies.
Keyword Polygenic prediction
White matter hyperintensities
Brain infarct
Intracranial volume
Hippocampal volume
Total brain volume
General cognitive ability
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2016 Collection
 
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