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Proceedings Paper

On study design in neuroimaging heritability analyses
Author(s): Mary Ellen Koran; Bo Li; Neda Jahanshad; Tricia A. Thornton-Wells; David C. Glahn; Paul M. Thompson; John Blangero; Thomas E. Nichols; Peter Kochunov; Bennett A. Landman
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Paper 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).

Paper Details

Date Published: 21 March 2014
PDF: 6 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90342P (21 March 2014); doi: 10.1117/12.2043565
Show Author Affiliations
Mary Ellen Koran, Vanderbilt Univ. (United States)
Bo Li, Vanderbilt Univ. (United States)
Neda Jahanshad, Univ. of California, Los Angeles (United States)
Tricia A. Thornton-Wells, Vanderbilt Univ. (United States)
David C. Glahn, Yale Univ. (United States)
Paul M. Thompson, Univ. of California, Los Angeles (United States)
John Blangero, Texas Biomedical Research Institute (United States)
Thomas E. Nichols, The Univ. of Warwick (United Kingdom)
Peter Kochunov, Maryland Psychiatric Research Ctr. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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