On study design in neuroimaging heritability analyses

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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).

Original languageEnglish (US)
Title of host publicationMedical Imaging 2014
Subtitle of host publicationImage Processing
PublisherSPIE
ISBN (Print)9780819498274
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
EventMedical Imaging 2014: Image Processing - San Diego, CA, United States
Duration: Feb 16 2014Feb 18 2014

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9034
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2014: Image Processing
CountryUnited States
CitySan Diego, CA
Period2/16/142/18/14

Keywords

  • Heritability
  • Imaging genetics
  • Power calculation
  • Statistical analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Koran, M. E., Li, B., Jahanshad, N., Thornton-Wells, T. A., Glahn, D. C., Thompson, P. M., Blangero, J., Nichols, T. E., Kochunov, P., & Landman, B. A. (2014). On study design in neuroimaging heritability analyses. In Medical Imaging 2014: Image Processing [90342P] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9034). SPIE. https://doi.org/10.1117/12.2043565