Bivariate genome-wide association study of genetically correlated neuroimaging phenotypes from DTI and MRI through a seemingly unrelated regression model

Jahanshad, N., Bhatt, P., Hibar, D. P., Villalon, J. E., Nir, T. M., Toga, A. W., Jack Jr, C. R., Bernstein, M. A., Weiner, M. W., McMahon, K. L., de Zubicaray, Greig I., Martin, N. G., Wright, M. J., & Thompson, P. M. (2013) Bivariate genome-wide association study of genetically correlated neuroimaging phenotypes from DTI and MRI through a seemingly unrelated regression model. In Shen, Li, Tianming, Liu, Yap, Pew-Thian, Huang, Heng, Shen, Dinggang, & Westin, Carl-Fredrik (Eds.) Multimodal Brain Image Analysis: Third International Workshop, MBIA 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, Proceedings, Springer International Publishing, Nagoya, Japan, pp. 189-201.

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Large multisite efforts (e.g., the ENIGMA Consortium), have shown that neuroimaging traits including tract integrity (from DTI fractional anisotropy, FA) and subcortical volumes (from T1-weighted scans) are highly heritable and promising phenotypes for discovering genetic variants associated with brain structure. However, genetic correlations (rg) among measures from these different modalities for mapping the human genome to the brain remain unknown. Discovering these correlations can help map genetic and neuroanatomical pathways implicated in development and inherited risk for disease. We use structural equation models and a twin design to find rg between pairs of phenotypes extracted from DTI and MRI scans. When controlling for intracranial volume, the caudate as well as related measures from the limbic system - hippocampal volume - showed high rg with the cingulum FA. Using an unrelated sample and a Seemingly Unrelated Regression model for bivariate analysis of this connection, we show that a multivariate GWAS approach may be more promising for genetic discovery than a univariate approach applied to each trait separately.

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ID Code: 85776
Item Type: Conference Paper
Refereed: Yes
Keywords: bivariate analysis, brain connectivity, genetic correlation, GWAS, Neuroimaging genetics
DOI: 10.1007/978-3-319-02126-3_19
ISBN: 9783319021263
ISSN: 0302-9743
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Copyright Owner: Copyright 2013 Springer International Publishing
Deposited On: 01 Sep 2015 02:20
Last Modified: 03 Sep 2015 04:42

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