ArticleNeuroImage2017
Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies.
Article in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 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
- A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders.American journal of human genetics · 2024Article
- Decoding Genetic Markers of Multiple Phenotypic Layers Through Biologically Constrained Genome-To-Phenome Bayesian Sparse Regression.Frontiers in molecular medicine · 2022Article
- Super-variants identification for brain connectivity.Human brain mapping · 2021Article
- Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!Computational and structural biotechnology journal · 2021Review
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- A Review of Statistical Methods in Imaging Genetics.The Canadian journal of statistics = Revue canadienne de statistique · 2019Article
- Spatial correlations exploitation based on nonlocal voxel-wise GWAS for biomarker detection of AD.NeuroImage. Clinical · 2019Article
- A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis.Neuroinformatics · 2018Article
- Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations show that the L2R2 model outperforms several other competing methods. We apply the L2R2 model to investigate the effect of single nucleotide polymorphisms (SNPs) on the top 10 and top 40 previously reported Alzheimer disease-associated genes. We also identify associations between the interactions of these SNPs with patient age and the tissue volumes of 93 regions of interest from patients' brain images obtained from the Alzheimer's Disease Neuroimaging Initiative.
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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.