Chinese Medical Journal · 8 authors, 5 centres
This summary was generated by AI from a single paper. It has not been reviewed by a clinician and is not clinical advice. Verify against the source before acting on it.
This narrative review summarizes methods for integrating genomic and proteomic data to study obesity, focusing on overlap, colocalization, Mendelian randomization, and proteome-wide association studies. It concludes that while such integrative approaches help identify obesity-associated proteins and potential drug targets, the field is limited by current pQTL study scales and tissue diversity.
This narrative review summarizes published methods and findings from integrating genomic and proteomic data for obesity research. It describes four main strategies: overlap analysis, colocalization analysis, Mendelian randomization (MR), and proteome-wide association studies (PWAS). The review identifies 17 eligible papers, with overlap analysis being the most common (nine papers). It highlights that integrated analyses have identified obesity-associated proteins like leptin and follistatin. The authors discuss the limitations of current pQTL studies, which are constrained by smaller sample sizes compared to large GWAS (e.g., the largest BMI GWAS has ~700,000 participants) and a lack of population and tissue diversity. Implications call for larger, more diverse prospective cohort studies to validate findings and translate them into prevention and treatment strategies.
