Uncovering Multi-Omics Profiles of Population Heterogeneity: A Cluster-Based Bayesian Approach

Understanding the heterogeneous nature of genetic effects is critical for advancing our knowledge of the genetic architecture of complex traits and developing personalized management strategies. However, existing methods often rely on pre-specified modifying variables to model this heterogeneity, limiting their ability to capture effects driven by complex or unobserved factors. Here, we propose MOCHA (Multi-Omics Clustering for Heterogeneous Association), a novel Bayesian analytical paradigm that identifies latent population subgroups with distinct genetic effects directly from multi-omics data, without requiring a priori variable specification. Extensive simulations confirm that MOCHA accurately identifies the underlying clustering structure, demonstrates superior performance in identifying and ranking features with cluster-specific effects, and provides reliable parameter estimates. Applying MOCHA to genomic and transcriptomic data from the IMAGEN study, we identified two distinct neurodevelopmental clusters associated with adolescent inhibitory control. Post-hoc characterization of these clusters provided novel insights into the mechanisms of brain plasticity, demonstrating the method's practical utility and interpretability.

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Publication Details

Journal
medRxiv
Published
2025-12-20
DOI
https://doi.org/10.64898/2025.12.19.25342631
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
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article

Uncovering Multi-Omics Profiles of Population Heterogeneity: A Cluster-Based Bayesian Approach

Runye Shi, Herta Flor, Gowland Pc, Xiaolei Lin et al.
medRxiv
Genetic Associations and Epidemiology
article

Uncovering Multi-Omics Profiles of Population Heterogeneity: A Cluster-Based Bayesian Approach

Runye Shi, Herta Flor, Gowland Pc, Xiaolei Lin, E. Artiges, Nick Ortiz, T. Banaschewski, Günter Schumann, Gareth John Barker, Martinot Jl, Ziyang Xiong, Luise Poustka, Nilakshi Vaidya, Michael N. Smolka, Sylvane Desrivières, Andreas Heinz, Jianfeng Feng, Henrik Walter, Sarah Hohmann, Marie-Laure Paillère Martinot, Dimitri Papadopoulos Orfanos, Arun L.W. Bokde, Robert Whelan
article en

Abstract

Understanding the heterogeneous nature of genetic effects is critical for advancing our knowledge of the genetic architecture of complex traits and developing personalized management strategies. However, existing methods often rely on pre-specified modifying variables to model this heterogeneity, limiting their ability to capture effects driven by complex or unobserved factors. Here, we propose MOCHA (Multi-Omics Clustering for Heterogeneous Association), a novel Bayesian analytical paradigm that identifies latent population subgroups with distinct genetic effects directly from multi-omics data, without requiring a priori variable specification. Extensive simulations confirm that MOCHA accurately identifies the underlying clustering structure, demonstrates superior performance in identifying and ranking features with cluster-specific effects, and provides reliable parameter estimates. Applying MOCHA to genomic and transcriptomic data from the IMAGEN study, we identified two distinct neurodevelopmental clusters associated with adolescent inhibitory control. Post-hoc characterization of these clusters provided novel insights into the mechanisms of brain plasticity, demonstrating the method's practical utility and interpretability.

medRxiv
University of Vermont (US), Centre National de la Recherche Scientifique (FR), University of Nottingham (GB), Inserm (FR), Wellcome Centre for Human Neuroimaging (GB), University of Mannheim (DE), King's College London (GB), Trinity College Dublin (IE), Heidelberg University (DE), Fudan University (CN), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), University Hospital Heidelberg (DE), Humboldt-Universität zu Berlin (DE), Sorbonne Université (FR), Assistance Publique – Hôpitaux de Paris (FR), Central Institute of Mental Health (DE), Psychiatry Research Trust (GB), CEA Paris-Saclay (FR), Laboratoire de Biologie et Pharmacologie Appliquée (FR), Huashan Hospital (CN), Shanghai Center for Brain Science and Brain-Inspired Technology (CN), Klinik und Poliklinik für Psychotherapie und Psychosomatik (DE), Technische Universität Dresden (DE), Charité - Universitätsmedizin Berlin (DE)
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Openalex Percentile: Top 26%
Genetic Associations and Epidemiology
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