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Genetic clustering on the hippocampal surface for genome-wide association studies.

Author information

1
Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA.
2
Queensland Institute of Medical Research, Brisbane, Australia.
3
Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
4
Functional Magnetic Resonance Imaging Laboratory, School of Psychology, University of Queensland, Brisbane, Australia.
5
Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia.
6
KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, 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 (r(g)) 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.

PMID:
24579201
PMCID:
PMC4024454
[Indexed for MEDLINE]
Free PMC Article

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