ArticleNeuroImage2017
FGWAS: Functional genome wide association analysis.
Article in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sex-stratified genome-wide association meta-analysis of major depressive disorder.Nature communications · 2025Pooled it
- Shape-Based Partially Linear Single-Index Cox Model for Alzheimer's Disease Conversion.Statistics in medicine · 2026Article
- 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
- Accelerating Heritability, Genetic Correlation, and Genome-Wide Association Imaging Genetic Analyses in Complex Pedigrees.Human brain mapping · 2024Article
- 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
- A PARTIALLY FUNCTIONAL LINEAR REGRESSION FRAMEWORK FOR INTEGRATING GENETIC, IMAGING, AND CLINICAL DATA.The annals of applied statistics · 2024Article
- Article
- Statistical Learning Methods for Neuroimaging Data Analysis with Applications.Annual review of biomedical data science · 2023Review
- Genetic overlap between cortical brain morphometry and frontotemporal dementia risk.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Minimax Powerful Functional Analysis of Covariance Tests: with Application to Longitudinal Genome-Wide Association Studies.Scandinavian journal of statistics, theory and applications · 2023Article
- Constructing a polygenic risk score for childhood obesity using functional data analysis.Econometrics and statistics · 2023Article
- Editorial: Modern statistical learning strategies in imaging genetics, volume II.Frontiers in neuroscience · 2023Article
- Bayesian nonparametric method for genetic dissection of brain activation region.Frontiers in neuroscience · 2023Article
- Article
- Article
- Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage.BMC bioinformatics · 2021Article
- Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.Nucleic acids research · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs.
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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.