ArticleNeuroImage2015
FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data.
Article in NeuroImage, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Computation and resource efficient genome-wide association analysis for large-scale imaging studies.Nature communications · 2026Article
- Computation and resource efficient genome-wide association analysis for large-scale imaging studies.medRxiv : the preprint server for health sciences · 2025Article
- S-GMAS: Genome-Wide Mediation Analysis With Brain Subcortical Shape Mediators.Human brain mapping · 2025Article
- Leveraging multimodal neuroimaging and GWAS for identifying modality-level causal pathways to Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- A multivariate to multivariate approach for voxel-wise genome-wide association analysis.Statistics in medicine · 2024Article
- FPLS-DC: functional partial least squares through distance covariance for imaging genetics.Bioinformatics (Oxford, England) · 2024Article
- Statistical Learning Methods for Neuroimaging Data Analysis with Applications.Annual review of biomedical data science · 2023Review
- Neuroimaging feature extraction using a neural network classifier for imaging genetics.BMC bioinformatics · 2023Article
- Bayesian nonparametric method for genetic dissection of brain activation region.Frontiers in neuroscience · 2023Article
- Genome-wide association studies of brain imaging data via weighted distance correlation.Bioinformatics (Oxford, England) · 2020Article
- L2RM: Low-rank Linear Regression Models for High-dimensional Matrix Responses.Journal of the American Statistical Association · 2020Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Incorporating spatial-anatomical similarity into the VGWAS framework for AD biomarker detection.Bioinformatics (Oxford, England) · 2019Article
- Multivariate pattern analysis of the neural correlates of smoking cue attentional bias.Pharmacology, biochemistry, and behavior · 2019Article
- A Review of Statistical Methods in Imaging Genetics.The Canadian journal of statistics = Revue canadienne de statistique · 2019Article
- Group sparse reduced rank regression for neuroimaging genetic study.World wide web · 2019Article
- A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis.Proceedings of SPIE--the International Society for Optical Engineering · 2019Article
- Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model.IEEE transactions on bio-medical engineering · 2019Article
- Spatial correlations exploitation based on nonlocal voxel-wise GWAS for biomarker detection of AD.NeuroImage. Clinical · 2019Article
- TPRM: TENSOR PARTITION REGRESSION MODELS WITH APPLICATIONS IN IMAGING BIOMARKER DETECTION.The annals of applied statistics · 2018Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
More and more large-scale imaging genetic studies are being widely conducted to collect a rich set of imaging, genetic, and clinical data to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. Several major big-data challenges arise from testing genome-wide (NC>12 million known variants) associations with signals at millions of locations (NV~10(6)) in the brain from thousands of subjects (n~10(3)). The aim of this paper is to develop a Fast Voxelwise Genome Wide Association analysiS (FVGWAS) framework to efficiently carry out whole-genome analyses of whole-brain data. FVGWAS consists of three components including a heteroscedastic linear model, a global sure independence screening (GSIS) procedure, and a detection procedure based on wild bootstrap methods. Specifically, for standard linear association, the computational complexity is O (nNVNC) for voxelwise genome wide association analysis (VGWAS) method compared with O ((NC+NV)n(2)) for FVGWAS. Simulation studies show that FVGWAS is an efficient method of searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. Finally, we have successfully applied FVGWAS to a large-scale imaging genetic data analysis of ADNI data with 708 subjects, 193,275voxels in RAVENS maps, and 501,584 SNPs, and the total processing time was 203,645s for a single CPU. Our FVGWAS may be a valuable statistical toolbox for large-scale imaging genetic analysis as the field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing.
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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.