ArticleGenomics, proteomics & bioinformatics2023
inMTSCCA: An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics.
Article in Genomics, proteomics & bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Intersection of Genetics and Neuroimaging: A Systematic Review of Imaging Genetics in Neurological Disease for Personalized Treatment.Journal of molecular neuroscience : MN · 2025Pooled it
- From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research.Artificial intelligence review · 2026Article
- A Two-Stage Framework for Early Detection and Subtype Identification of Alzheimer's Disease Through Multimodal Biomarker Extraction and Improved GCN.Brain sciences · 2026Article
- Deep adversarial learning identifies ADHD-specific associations between apoptotic genes and white matter microstructure in frontal-striatum-cerebellum circuit.Translational psychiatry · 2025Article
- Trustworthy causal biomarker discovery: a multiomics brain imaging genetics-based approach.Bioinformatics (Oxford, England) · 2025Article
- Mutual-assistance learning for trustworthy biomarker discovery and disease prediction.Briefings in bioinformatics · 2025Article
- Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
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Authors and funding
7 authors.
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Abstract
Identifying genetic risk factors for Alzheimer's disease (AD) is an important research topic. To date, different endophenotypes, such as imaging-derived endophenotypes and proteomic expression-derived endophenotypes, have shown the great value in uncovering risk genes compared to case-control studies. Biologically, a co-varying pattern of different omics-derived endophenotypes could result from the shared genetic basis. However, existing methods mainly focus on the effect of endophenotypes alone; the effect of cross-endophenotype (CEP) associations remains largely unexploited. In this study, we used both endophenotypes and their CEP associations of multi-omic data to identify genetic risk factors, and proposed two integrated multi-task sparse canonical correlation analysis (inMTSCCA) methods, i.e., pairwise endophenotype correlation-guided MTSCCA (pcMTSCCA) and high-order endophenotype correlation-guided MTSCCA (hocMTSCCA). pcMTSCCA employed pairwise correlations between magnetic resonance imaging (MRI)-derived, plasma-derived, and cerebrospinal fluid (CSF)-derived endophenotypes as an additional penalty. hocMTSCCA used high-order correlations among these multi-omic data for regularization. To figure out genetic risk factors at individual and group levels, as well as altered endophenotypic markers, we introduced sparsity-inducing penalties for both models. We compared pcMTSCCA and hocMTSCCA with three related methods on both simulation and real (consisting of neuroimaging data, proteomic analytes, and genetic data) datasets. The results showed that our methods obtained better or comparable canonical correlation coefficients (CCCs) and better feature subsets than benchmarks. Most importantly, the identified genetic loci and heterogeneous endophenotypic markers showed high relevance. Therefore, jointly using multi-omic endophenotypes and their CEP associations is promising to reveal genetic risk factors. The source code and manual of inMTSCCA are available at https://ngdc.cncb.ac.cn/biocode/tools/BT007330.
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