A review of statistical methods in imaging genetics
Abstract
With the rapid growth of modern technology, many biomedical studies are being conducted to collect massive datasets with volumes of multi‐modality imaging, genetic, neurocognitive and clinical information from increasingly large cohorts. Simultaneously extracting and integrating rich and diverse heterogeneous information in neuroimaging and/or genomics from these big datasets could transform our understanding of how genetic variants impact brain structure and function, cognitive function and brain‐related disease risk across the lifespan. Such understanding is critical for diagnosis, prevention and treatment of numerous complex brain‐related disorders (e.g., schizophrenia and Alzheimer's disease). However, the development of analytical methods for the joint analysis of both high‐dimensional imaging phenotypes and high‐dimensional genetic data, a big data squared (BD2) problem, presents major computational and theoretical challenges for existing analytical methods. Besides the high‐dimensional nature of BD2, various neuroimaging measures often exhibit strong spatial smoothness and dependence and genetic markers may have a natural dependence structure arising from linkage disequilibrium. We review some recent developments of various statistical techniques for imaging genetics, including massive univariate and voxel‐wise approaches, reduced rank regression, mixture models and group sparse multi‐task regression. By doing so, we hope that this review may encourage others in the statistical community to enter into this new and exciting field of research. The Canadian Journal of Statistics 47: 108–131; 2019 © 2019 Statistical Society of Canada
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 25, 2019
- Source ID
- 10.1002/cjs.11487
Entities
People
- Farouk S. Nathoo
- For The Alzheimer’s Disease Neuroimaging Initiative*
- Hongtu Zhu
- Linglong Kong
Organizations
- Cancer Prevention and Research Institute of Texas
- National Institutes of Health
- National Science Foundation
- Natural Sciences and Engineering Research Council
- United States Department of Defense
- University of Alberta
- University of Texas at Austin
- University of Victoria