Co-sparse reduced-rank regression for association analysis between imaging phenotypes and genetic variants
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Summary
An iterative algorithm based on a group primal dual-active set formulation to detect simultaneously important genetic variants and imaging phenotypes efficiently and precisely via non-convex penalty and may be a valuable statistical toolbox for imaging genetic studies.
- Type
- article
- Published
- 2020-07-19
- Cited by
- 12
- References
- 39
- OpenAlex
- https://openalex.org/W32683450
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:220654098
Keywords
Subject (documents), Witness, Political science, Law, Criminology
References
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- Discovering genetic associations with high-dimensional neuroimaging phenotypes: a sparse reduced-rank regression approach
- Learning regulatory programs by threshold SVD regression
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- Identification of novel quantitative traits-associated susceptibility loci for APOE ε 4 non-carriers of Alzheimer's disease.
- Measuring temporal morphological changes robustly in brain MR images via 4-dimensional template warping
- The Structure of Haplotype Blocks in the Human Genome
- A nonparametric method for automatic correction of intensity nonuniformity in MRI data
- Imaging Genetics and Psychiatric Disorders
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- Haploview: analysis and visualization of LD and haplotype maps
Cited by
- Deep-gated recurrent unit and diet network-based genome-wide association analysis for detecting the biomarkers of Alzheimer's disease
- A review ofimaging genetics in Alzheimer's disease.
- Stability Approach to Regularization Selection for Reduced-Rank Regression
- The association between genetic variations and morphology‐based brain networks changes in Alzheimer's disease
- Genetic architecture of hippocampus subfields volumes in Alzheimer’s disease
- Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases
- Quantized Low-Rank Multivariate Regression With Random Dithering
- Identification of genetic basis of brain imaging by group sparse multi-task learning leveraging summary statistics
- Nonparametric multi-task regression under group sparsity and low-rank structures
- Computation and resource efficient genome-wide association analysis for large-scale imaging studies
- Incomplete multimodal association analysis based on modality-specific graph constraint and dual-level self-representation learning for AD-related biomarker detection
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