Dataset Information

Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases.

ABSTRACT: Identification of causal variants and genes underlying genome-wide association study (GWAS) loci is essential to understand the biology of alcohol use disorder (AUD) and drinks per week (DPW). Multi-omics integration approaches have shown potential for fine mapping complex loci to obtain biological insights to disease mechanisms. In this study, we use multi-omics approaches, to fine-map AUD and DPW associations at single SNP resolution to demonstrate that rs56030824 on chromosome 11 significantly reduces SPI1 mRNA expression in myeloid cells and lowers risk for AUD and DPW. Our analysis also identifies MAPT as a candidate causal gene specifically associated with DPW. Genes prioritized in this study show overlap with causal genes associated with neurodegenerative disorders. Multi-omics integration analyses highlight, genetic similarities and differences between alcohol intake and disordered drinking, suggesting molecular heterogeneity that might inform future targeted functional and cross-species studies.

SUBMITTER: Kapoor M

PROVIDER: S-EPMC8379159 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:MotivationMolecular subtyping by integrative modeling of multi-omics and clinical data can help the identification of robust and clinically actionable disease subgroups; an essential step in developing precision medicine approaches.ResultsWe developed a novel outcome-guided molecular subgrouping framework, called Deep Multi-Omics Integrative Subtyping by Maximizing Correlation (DeepMOIS-MC), for integrative learning from multi-omics data by maximizing correlation between all input -omics views. DeepMOIS-MC consists of two parts: clustering and classification. In the clustering part, the preprocessed high-dimensional multi-omics views are input into two-layer fully connected neural networks. The outputs of individual networks are subjected to Generalized Canonical Correlation Analysis loss to learn the shared representation. Next, the learned representation is filtered by a regression model to select features that are related to a covariate clinical variable, for example, a survival/outcome. The filtered features are used for clustering to determine the optimal cluster assignments. In the classification stage, the original feature matrix of one of the -omics view is scaled and discretized based on equal frequency binning, and then subjected to feature selection using RandomForest. Using these selected features, classification models (for example, XGBoost model) are built to predict the molecular subgroups that were identified at clustering stage. We applied DeepMOIS-MC on lung and liver cancers, using TCGA datasets. In comparative analysis, we found that DeepMOIS-MC outperformed traditional approaches in patient stratification. Finally, we validated the robustness and generalizability of the classification models on independent datasets. We anticipate that the DeepMOIS-MC can be adopted to many multi-omics integrative analyses tasks.Availability and implementationSource codes for PyTorch implementation of DGCCA and other DeepMOIS-MC modules are available at GitHub (https://github.com/duttaprat/DeepMOIS-MC).Supplementary informationSupplementary data are available at Bioinformatics Advances online.

Dataset Information

Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets