Preprint / Version 1

The ‘Obesity Knowledge Portal’: an AI-powered integrative platform for exploration of obesity genetics

This article is a preprint and has not been certified by peer review.

Authors

    Yifan Liang,  
    Yifan Liang
    • Fudan University image/svg+xml
    • Shanghai Key Laboratory of Metabolic Remodelling and Health, Institute of Metabolism and Integrative Biology, School of Basic Medical Sciences, Centre for Evolutionary Biology, Fudan University, Shanghai, 200438, China
    Shiqi Ao,  
    Shiqi Ao
    • Shanghai Key Laboratory of Metabolic Remodelling and Health, Institute of Metabolism and Integrative Biology, School of Basic Medical Sciences, Centre for Evolutionary Biology, Fudan University, Shanghai, 200438, China
    Wei Xu,   Baoguo Li,   Jun-feng Bi,   Tong-jin Zhao,   Peng Li,  
    Peng Li
    • State Key Laboratory of Metabolic Dysregulation & Prevention and Treatment of Esophageal Cancer, Tianjian Laboratory of Advanced Biomedical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450001, China
    John R. Speakman,  
    John R. Speakman
    • Shenzhen Key Laboratory of Metabolic Health, Center for Energy Metabolism and Reproduction, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
    • State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
    • Institute of Health Sciences, China Medical University, Shenyang, Liaoning, 110122, China
    • Faculty of Pharmacy, Shenzhen University of Advanced Technology, Shenzhen, 518107, Guangdong, China
    • School of Biological Sciences, University of Aberdeen, AB24 2TZ, Aberdeen, UK
    Guanlin Wang
    Guanlin Wang
Categories
Biology x AI
Keywords
obesity; knowledge portal; AI; large language model; GWAS; precision medicine

Abstract

Obesity is a chronic, multifactorial disease in which genetics plays a major role. Genome-wide association studies (GWAS) have identified thousands of loci associated with obesity and related traits, however, translating these findings into mechanistic understanding and clinical application remains challenging. Here we present an open-access and AI-powered web platform, Obesity Knowledge Portal (OKP, https://obesityknowledge.org), which covers 166,582 curated variant-trait associations mapped to 11,298 unique genes from published GWAS and UK Biobank summary statistics, a manually curated database of 49 clinical-stage obesity therapeutics with gene-level GWAS evidence annotation, and a domain-specific AI assistant built on Retrieval-Augmented Generation (RAG) of 10,198 peer-reviewed publications. The portal provides three core analytical capabilities: interactive exploration of gene- and variant-level associations across multiple datasets with cross-linking to clinical-stage drug targets; a genomic target prioritisation algorithm that combines statistical significance and variant density to prioritise novel candidates from genes not targeted by existing therapeutics, and literature-grounded interpretation of genetic and pharmacological queries through a scope-restricted LLM-driven contextualisation system. We demonstrate the portal’s utility through case studies at the FTO locus and an integrated pharmacogenomic analysis of the GLP-1/incretin axis. The OKP is freely available and aims to support the translation of genetic discoveries into therapeutic hypotheses for obesity.

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Liang, Y., Ao, S., Xu, W., Li, B., Bi, J.- feng, Zhao, T.- jin, Li, P., Speakman, J. R., & Wang, G. (2026). The ‘Obesity Knowledge Portal’: an AI-powered integrative platform for exploration of obesity genetics. LangTaoSha Preprint Server. https://doi.org/10.65215/LTSpreprints.2026.06.20.000276

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