Predicting white matter integrity from multiple common genetic variants

Kohannim, Omid, Jahanshad, Neda, Braskie, Meredith N., Stein, Jason L., Chiang, Ming-Chang, Reese. April H., Hibar, Derrek P., Toga, Arthur W., McMahon, Katie L., de Zubicaray, Greig I., Medland, Sarah E., Montgomery, Grant W., Martin, Nicholas G., Wright. Margaret J. and Thompson, Paul M. (2012) Predicting white matter integrity from multiple common genetic variants. Neuropsychopharmacology, 37 9: 2012-2019. doi:10.1038/npp.2012.49


Author Kohannim, Omid
Jahanshad, Neda
Braskie, Meredith N.
Stein, Jason L.
Chiang, Ming-Chang
Reese. April H.
Hibar, Derrek P.
Toga, Arthur W.
McMahon, Katie L.
de Zubicaray, Greig I.
Medland, Sarah E.
Montgomery, Grant W.
Martin, Nicholas G.
Wright. Margaret J.
Thompson, Paul M.
Total Author Count Override 15
Title Predicting white matter integrity from multiple common genetic variants
Journal name Neuropsychopharmacology   Check publisher's open access policy
ISSN 0893-133X
1740-634X
Publication date 2012
Sub-type Article (original research)
DOI 10.1038/npp.2012.49
Open Access Status
Volume 37
Issue 9
Start page 2012
End page 2019
Total pages 8
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Collection year 2013
Language eng
Abstract Several common genetic variants have recently been discovered that appear to influence white matter microstructure, as measured by diffusion tensor imaging (DTI). Each genetic variant explains only a small proportion of the variance in brain microstructure, so we set out to explore their combined effect on the white matter integrity of the corpus callosum. We measured six common candidate single-nucleotide polymorphisms (SNPs) in the COMT, NTRK1, BDNF, ErbB4, CLU, and HFE genes, and investigated their individual and aggregate effects on white matter structure in 395 healthy adult twins and siblings (age: 20-30 years). All subjects were scanned with 4-tesla 94-direction high angular resolution diffusion imaging. When combined using mixed-effects linear regression, a joint model based on five of the candidate SNPs (COMT, NTRK1, ErbB4, CLU, and HFE) explained ∼6% of the variance in the average fractional anisotropy (FA) of the corpus callosum. This predictive model had detectable effects on FA at 82% of the corpus callosum voxels, including the genu, body, and splenium. Predicting the brain's fiber microstructure from genotypes may ultimately help in early risk assessment, and eventually, in personalized treatment for neuropsychiatric disorders in which brain integrity and connectivity are affected.
Keyword Neuroimaging
Brain structure
DTI
Genetics
Genetic profiles
Prediction
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Advance online publication: 18 April 2012.

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 27 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 29 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 15 May 2012, 15:32:27 EST by Sandrine Ducrot on behalf of Centre for Advanced Imaging