Identifying candidate gene effects by restricting search space in a multivariate genetic genetic analysis of white matter microstructure

Warstadt, Nicholus M., Jahanshad, Neda, Dennis, Emily L., Kohannim, Omid, McMahon, Katie L., de Zubicaray, Greig I., Montgomery, Grant W., Henders, Anjali K., Martin, Nicholas G., Whitfield, John B., Wright, Margaret J. and Thompson, Paul M. (2014). Identifying candidate gene effects by restricting search space in a multivariate genetic genetic analysis of white matter microstructure. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). Biomedical Imaging ISBI, Beijing, China, (353-356). 29 April - 2 May 2014. doi:10.1109/ISBI.2014.6867881


Author Warstadt, Nicholus M.
Jahanshad, Neda
Dennis, Emily L.
Kohannim, Omid
McMahon, Katie L.
de Zubicaray, Greig I.
Montgomery, Grant W.
Henders, Anjali K.
Martin, Nicholas G.
Whitfield, John B.
Wright, Margaret J.
Thompson, Paul M.
Title of paper Identifying candidate gene effects by restricting search space in a multivariate genetic genetic analysis of white matter microstructure
Conference name Biomedical Imaging ISBI
Conference location Beijing, China
Conference dates 29 April - 2 May 2014
Convener IEEE
Proceedings title 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2014
Sub-type Fully published paper
DOI 10.1109/ISBI.2014.6867881
ISSN 1945-7928
Start page 353
End page 356
Total pages 4
Language eng
Abstract/Summary Several genetic variants are thought to influence white matter (WM) integrity, measured with diffusion tensor imaging (DTI). Voxel based methods can test genetic associations, but heavy multiple comparisons corrections are required to adjust for searching the whole brain and for all genetic variants analyzed. Thus, genetic associations are hard to detect even in large studies. Using a recently developed multi-SNP analysis, we examined the joint predictive power of a group of 18 cholesterol-related single nucleotide polymorphisms (SNPs) on WM integrity, measured by fractional anisotropy. To boost power, we limited the analysis to brain voxels that showed significant associations with total serum cholesterol levels. From this space, we identified two genes with effects that replicated in individual voxel-wise analyses of the whole brain. Multivariate analyses of genetic variants on a reduced anatomical search space may help to identify SNPs with strongest effects on the brain from a broad panel of genes.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Tue, 12 Jan 2016, 09:51:44 EST by Margaret Wright on behalf of School of Psychology