Statistical methods for imaging genetic data Public Deposited

Downloadable Content

Download PDF
Last Modified
  • March 21, 2019
  • Lin, Ja-an
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
  • More and more large-scale imaging genetic studies are being widely conducted to collect a rich set of imaging, genetic, and clinical data in order to detect susceptibility genes for complexly inherited diseases including common mental disorders (e.g., schizophrenia) and neurodegenerative disorders, among many others. However, the development of statistical and computational methods for the joint analysis of complex imaging phenotypes, genetic data, and clinical data has fallen seriously behind the technological advances. The aim this work is to develop three statistical approaches called Projection Regression Method (PRM) and functional mixed effects model (FMEM) for the joint analysis of high-dimensional imaging data with a set of genetic markers. In PRM, it generalizes a statistical method based on the principal component of heritability for association analysis in genetic studies of complex multivariate phenotypes. The key components of the PRM include an estimation procedure for extracting several principal directions of multivariate phenotypes relating to covariates and a test procedure based on wild-bootstrap method for testing for the association between the weighted multivariate phenotype and explanatory variables. Simulation studies and an imaging genetic dataset are used to examine the finite sample performance of the PRM. In FMEM, to accommodate the correlation structure of the marker set, we model the genetic effects as population-shared random effects with a common variance component (VC), whereas to accommodate spatial feature in imaging data, we spatially model varying associations between imaging measures in a three-dimensional (3D) volume (or 2D surface) with a set of covariates and the genetic random effects. We develop a two-stage estimation procedure to spatially and adaptively estimate the varying coefficient functions, while preserving its edges among different piecewise-smooth regions. To test hypothesis of interest, we provide two test statistics with well-controlled type I error and better power comparing to traditional voxel-based approach. Simulation studies and a real data analysis of the Alzheimer's Disease Neuroimage Initiative (ADNI) show that FMEM significantly outperforms voxel-based approaches in terms of identification of activation regions.
Date of publication
Resource type
Rights statement
  • In Copyright
  • Zhu, Hongtu
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill
Graduation year
  • 2013

This work has no parents.