ArticleNeuroImage2010
Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach.
Article in NeuroImage, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 1 of them a synthesis that pooled it.
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Who cites it
110 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers.Brain imaging and behavior · 2014Pooled it
- Lateral Ventricle Enlargement and Cortical Thinning in Idiopathic Normal-pressure Hydrocephalus Patients.Scientific reports · 2018Trial
- A multivariate to multivariate approach for voxel-wise genome-wide association analysis.Statistics in medicine · 2024Article
- Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods.Behavior genetics · 2024Review
- FPLS-DC: functional partial least squares through distance covariance for imaging genetics.Bioinformatics (Oxford, England) · 2024Article
- Neurogenetics of Brain Connectivity: Current Approaches to the Study (Review).Sovremennye tekhnologii v meditsine · 2024Review
- Integrating Multimodal Neuroimaging and Genetics: A Structurally-Linked Sparse Canonical Correlation Analysis Approach.IEEE journal of translational engineering in health and medicine · 2024Article
- Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease.Human brain mapping · 2023Article
- Neuroimaging feature extraction using a neural network classifier for imaging genetics.BMC bioinformatics · 2023Article
- Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Partially Observed Dynamic Tensor Response Regression.Journal of the American Statistical Association · 2023Article
- Bayesian nonparametric method for genetic dissection of brain activation region.Frontiers in neuroscience · 2023Article
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- LARGE-SCALE MULTIVARIATE SPARSE REGRESSION WITH APPLICATIONS TO UK BIOBANK.The annals of applied statistics · 2022Article
- Article
- Predictive classification of Alzheimer's disease using brain imaging and genetic data.Scientific reports · 2022Article
- Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis.Current genomics · 2021Review
- Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop.Circulation. Cardiovascular imaging · 2021Article
- Predicting future cognitive decline with hyperbolic stochastic coding.Medical image analysis · 2021Article
- Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage.BMC bioinformatics · 2021Article
50 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
There is growing interest in performing genome-wide searches for associations between genetic variants and brain imaging phenotypes. While much work has focused on single scalar valued summaries of brain phenotype, accounting for the richness of imaging data requires a brain-wide, genome-wide search. In particular, the standard approach based on mass-univariate linear modelling (MULM) does not account for the structured patterns of correlations present in each domain. In this work, we propose sparse reduced rank regression (sRRR), a strategy for multivariate modelling of high-dimensional imaging responses (measurements taken over regions of interest or individual voxels) and genetic covariates (single nucleotide polymorphisms or copy number variations), which enforces sparsity in the regression coefficients. Such sparsity constraints ensure that the model performs simultaneous genotype and phenotype selection. Using simulation procedures that accurately reflect realistic human genetic variation and imaging correlations, we present detailed evaluations of the sRRR method in comparison with the more traditional MULM approach. In all settings considered, sRRR has better power to detect deleterious genetic variants compared to MULM. Important issues concerning model selection and connections to existing latent variable models are also discussed. This work shows that sRRR offers a promising alternative for detecting brain-wide, genome-wide associations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.