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Identifying Candidate Genetic Associations with MRI-Derived AD-Related ROI via Tree-Guided Sparse Learning
- Source :
- IEEE/ACM Trans Comput Biol Bioinform
- Publication Year :
- 2018
-
Abstract
- Imaging genetics has attracted significant interests in recent studies. Traditional work has focused on mass-univariate statistical approaches that identify important single nucleotide polymorphisms (SNPs) associated with quantitative traits (QTs) of brain structure or function. More recently, to address the problem of multiple comparison and weak detection, multivariate analysis methods such as the least absolute shrinkage and selection operator (Lasso) are often used to select the most relevant SNPs associated with QTs. However, one problem of Lasso, as well as many other feature selection methods for imaging genetics, is that some useful prior information, e.g., the hierarchical structure among SNPs, are rarely used for designing a more powerful model. In this paper, we propose to identify the associations between candidate genetic features (i.e., SNPs) and magnetic resonance imaging (MRI)-derived measures using a tree-guided sparse learning (TGSL) method. The advantage of our method is that it explicitly models the complex hierarchical structure among the SNPs in the objective function for feature selection. Specifically, motivated by the biological knowledge, the hierarchical structures involving gene groups and linkage disequilibrium (LD) blocks as well as individual SNPs are imposed as a tree-guided regularization term in our TGSL model. Experimental studies on simulation data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) data show that our method not only achieves better predictions than competing methods on the MRI-derived measures of AD-related region of interests (ROIs) (i.e., hippocampus, parahippocampal gyrus, and precuneus), but also identifies sparse SNP patterns at the block level to better guide the biological interpretation.
- Subjects :
- Linkage disequilibrium
Multivariate analysis
Genotype
Imaging genetics
Computer science
Feature extraction
Quantitative Trait Loci
Feature selection
Neuroimaging
Machine learning
computer.software_genre
Hippocampus
Polymorphism, Single Nucleotide
Article
Linkage Disequilibrium
Machine Learning
Lasso (statistics)
Alzheimer Disease
Parietal Lobe
Genetics
Image Processing, Computer-Assisted
Humans
Computer Simulation
Genetic Predisposition to Disease
Brain Mapping
business.industry
Applied Mathematics
Brain
Bayes Theorem
Magnetic Resonance Imaging
Phenotype
Multiple comparisons problem
Parahippocampal Gyrus
Artificial intelligence
business
computer
Algorithms
Biotechnology
Genome-Wide Association Study
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- IEEE/ACM Trans Comput Biol Bioinform
- Accession number :
- edsair.doi.dedup.....0e2704d164dd172bfcff22ba23740112