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 (BD 2 ) problem, presents major computational and theoretical challenges for existing analytical methods. Besides the high-dimensional nature of BD 2 , 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.
Original language | English (US) |
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Pages (from-to) | 108-131 |
Number of pages | 24 |
Journal | Canadian Journal of Statistics |
Volume | 47 |
Issue number | 1 |
DOIs | |
State | Published - Mar 2019 |
Externally published | Yes |
Keywords
- Group sparse regression
- JEL Code: Primary 62F15
- multivariate high-dimensional regression
- neuroimaging phenotype
- reduced rank regression
- secondary 62G10
- single nucleotide polymorphism
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty