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

Hibar, D. P., Medland, S. E., Stein, J. L., Kim, S., Shen, L., Saykin, A. J., de Zubicaray, G. I., McMahon, K. L., Montgomery, G. W., Martin, N. G., Wright, M. J., Djurovic, S., Agartz, I. A., Andreassen, O. A., & Thompson, P. M. (2013) Genetic clustering on the hippocampal surface for genome-wide association studies. In Mori, Kensaku, Sakuma, Ichiro, Sato, Yoshinobu, Barillot, Christian, & Navab, Nassir (Eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II, Springer Berlin Heidelberg, Nagoya, Japan, pp. 690-697.

View at publisher

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.

Impact and interest:

1 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 85766
Item Type: Conference Paper
Refereed: Yes
DOI: 10.1007/978-3-642-40763-5_85
ISBN: 9783642407635
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-Verlag Berlin Heidelberg
Deposited On: 09 Oct 2015 02:56
Last Modified: 21 Oct 2015 02:24

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page