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Genetic Clustering on the Hippocampal Surface for Genome-Wide Association Studies
Derrek P. Hibar1, Sarah E. Medland2, Jason L. Stein1, Sungeun Kim3, Li Shen3, Andrew J. Saykin3, Greig I. de Zubicaray4, Katie L. McMahon5, Grant W. Montgomery2, Nicholas G. Martin2, Margaret J. Wright2, Srdjan Djurovic6, Ingrid A. Agartz6, 7, Ole A. Andreassen6, and Paul M. Thompson1
1Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA
2Queensland Institute of Medical Research, Brisbane, Australia
3Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
4Functional Magnetic Resonance Imaging Laboratory, School of Psychology, University of Queensland, Brisbane, Australia
5Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
6KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
7Department of Psychiatry, Diakonhjemmet Hospital, Oslo, Norway
Abstract. Imaging genetics aims to discover how variants in the human genome influence brain measures derived from images. Genome-wide association scans (GWAS) can screen the genome for common differences in our DNA that relate to brain measures. In small samples, GWAS has low power as individual gene effects are weak and one must also correct for multiple comparisons across the genome and the image. Here we extend recent work on genetic clustering of images, to analyze surface-based models of anatomy using GWAS. We performed spherical harmonic analysis of hippocampal surfaces, automatically extracted from brain MRI scans of 1254 subjects. We clustered hippocampal surface regions with common genetic influences by examining genetic correlations (rg) between the normalized deformation values at all pairs of surface points. Using genetic correlations to cluster surface measures, we were able to boost effect sizes for genetic associations, compared to clustering with traditional phenotypic correlations using Pearson’s r.
Keywords: heritability, GWAS, clustering, hippocampus, 3D surfaces, imaging genetics
LNCS 8150, p. 690 ff.
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© Springer-Verlag Berlin Heidelberg 2013