Koran, Mary Ellen, Li, Bo, Jahanshad, Neda, Thornton-Wells, Tricia A., Glahn, David C., Thompson, Paul M., Blangero, John, Nichols, Thomas E., Kochunov, Peter and Landman, Bennett A. (2014) On study design in neuroimaging heritability analyses. In: Medical Imaging 2014: Image Processing, San Diego, California, USA, 15 Feb 2014. Published in: Proceedings of SPIE 9034, Medical Imaging 2014: Image Processing, Volume 9034 doi:10.1117/12.2043565 ISSN 0277-786X.

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Abstract

Imaging genetics is an emerging methodology that combines genetic information with imaging-derived metrics to understand how genetic factors impact observable structural, functional, and quantitative phenotypes. Many of the most well-known genetic studies are based on Genome-Wide Association Studies (GWAS), which use large populations of related or unrelated individuals to associate traits and disorders with individual genetic factors. Merging imaging and genetics may potentially lead to improved power of association in GWAS because imaging traits may be more sensitive phenotypes, being closer to underlying genetic mechanisms, and their quantitative nature inherently increases power. We are developing SOLAR-ECLIPSE (SE) imaging genetics software which is capable of performing genetic analyses with both large-scale quantitative trait data and family structures of variable complexity. This program can estimate the contribution of genetic commonality among related subjects to a given phenotype, and essentially answer the question of whether or not the phenotype is heritable. This central factor of interest, heritability, offers bounds on the direct genetic influence over observed phenotypes. In order for a trait to be a good phenotype for GWAS, it must be heritable: at least some proportion of its variance must be due to genetic influences. A variety of family structures are commonly used for estimating heritability, yet the variability and biases for each as a function of the sample size are unknown. Herein, we investigate the ability of SOLAR to accurately estimate heritability models based on imaging data simulated using Monte Carlo methods implemented in R. We characterize the bias and the variability of heritability estimates from SOLAR as a function of sample size and pedigree structure (including twins, nuclear families, and nuclear families with grandparents).

Item Type:	Conference Item (Paper)
Divisions:	Faculty of Science, Engineering and Medicine > Science > Statistics
Journal or Publication Title:	Proceedings of SPIE 9034, Medical Imaging 2014: Image Processing
Publisher:	S P I E - International Society for Optical Engineering
ISSN:	0277-786X
Book Title:	Medical Imaging 2014: Image Processing
Official Date:	21 March 2014
Dates:	Date Event 21 March 2014 Published
Volume:	Volume 9034
DOI:	10.1117/12.2043565
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Conference Paper Type:	Paper
Title of Event:	Medical Imaging 2014: Image Processing
Type of Event:	Conference
Location of Event:	San Diego, California, USA
Date(s) of Event:	15 Feb 2014
Related URLs:	Organisation

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