REFERENCES
1. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. Hallmarks of aging: an expanding universe. Cell. 2023;186:243-78.
2. Moqri M, Herzog C, Poganik JR, et al. Biomarkers of aging for the identification and evaluation of longevity interventions. Cell. 2023;186:3758-75.
3. Chen C, Wang J, Pan D, et al. Applications of multi‐omics analysis in human diseases. MedComm. 2023;4:e315.
4. Zhang S, Wang Z, Wang Y, et al. A metabolomic profile of biological aging in 250,341 individuals from the UK biobank. Nat Commun. 2024;15:8081.
5. Li G, Zhu X, Xiao F, et al. A multi-omics molecular landscape of 30 tissues in aging female rhesus macaques. Nat Methods. 2025;22:2658-69.
6. Li Y, Wang Q, Xuan Y, et al. Investigation of human aging at the single-cell level. Ageing Res Rev. 2024;101:102530.
7. Wen J, Nodzak C, Shi X. QTL analysis beyond eQTLs. In: Shi XM, editor. eQTL analysis. New York, NY: Springer US; 2019. pp. 201-10.
8. Gai K, Li W, Li J, et al. Genetic and molecular factors underlying human longevity and epigenetic aging. NPJ Aging. 2026;12:84.
9. Chen M, Zhu X, Hang L, et al. Alternative polyadenylation in aging and aging-related diseases. Aging Dis. 2026.
10. Zhang J, Zhao H. eQTL studies: from bulk tissues to single cells. J Genet Genomics. 2023;50:925-33.
11. Yang C, Gorijala P, Timsina J, et al. European and African ancestry-specific plasma protein-QTL and metabolite-QTL analyses identify ancestry-specific T2D effector proteins and metabolites. Nat Commun. 2025;16:7412.
12. Aguet F, Anand S, Ardlie KG, et al. The GTEx consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369:1318-30.
13. Long E, García-Closas M, Chanock SJ, Camargo MC, Banovich NE, Choi J. The case for increasing diversity in tissue-based functional genomics datasets to understand human disease susceptibility. Nat Commun. 2022;13:2907.
14. Forde A, Hemani G, Ferguson J. Simulated sample splitting approach to address biases due to instrument selection and participant overlap in two-sample Mendelian Randomization studies. PLoS Genet. 2026;22:e1011949.
15. Wang K. Interval estimate of causal effect in summary data based Mendelian randomization in the presence of winner's curse. Genet Epidemiol. 2024;48:74-84.
16. Liu Z, Shen J, Barfield R, Schwartz J, Baccarelli AA, Lin X. Large-scale hypothesis testing for causal mediation effects with applications in genome-wide epigenetic studies. J Am Stat Assoc. 2021;117:67-81.
17. Yashin AI, Zhbannikov I, Arbeeva L, et al. Pure and confounded effects of causal SNPs on longevity: insights for proper interpretation of research findings in GWAS of populations with different genetic structures. Front Genet. 2016;7:188.
18. Zhu Z, Zhang F, Hu H, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48:481-7.
19. Zhu Z, Zheng Z, Zhang F, et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun. 2018;9:224.
20. Burgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res. 2023;4:186.
21. Jiang T, Gill D, Butterworth AS, Burgess S. An empirical investigation into the impact of winner’s curse on estimates from Mendelian randomization. Int J Epidemiol. 2023;52:1209-19.
22. Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two‐sample Mendelian randomization. Genet Epidemiol. 2016;40:597-608.
23. Wu Y, Qi T, Wray NR, Visscher PM, Zeng J, Yang J. Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes. Cell Genomics. 2023;3:100344.
24. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512-25.
25. Graham AC, Bellou E, Harwood JC, et al. Human longevity and Alzheimer’s disease variants act via microglia and oligodendrocyte gene networks. Brain. 2025;148:969-84.
26. Ma S, Ji Z, Zhang B, et al. Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging. Cell. 2024;187:7025-44.e34.
27. Yusri K, Kumar S, Fong S, Gruber J, Sorrentino V. Towards healthy longevity: comprehensive insights from molecular targets and biomarkers to biological clocks. Int J Mol Sci. 2024;25:6793.
28. Zhang L, Li T, Miao X, Ding L, Wang S, Wang Y. Aspirin suppresses NFκB1 expression and inactivates cAMP signaling pathway to treat atherosclerosis. Biofactors. 2019;45:343-54.
29. Van Der Wel T, Hilhorst R, Den Dulk H, et al. Chemical genetics strategy to profile kinase target engagement reveals role of FES in neutrophil phagocytosis. Nat Commun. 2020;11:3216.
30. Najm M, Azencott C, Playe B, Stoven V. Drug target identification with machine learning: how to choose negative examples. Int J Mol Sci. 2021;22:5118.
31. Hao Y, Quinnies K, Realubit R, Karan C, Tatonetti NP. Tissue‐specific analysis of pharmacological pathways. CPT Pharmacom Syst Pharma. 2018;7:453-63.
32. Yu Z, Wang Y, Zhang Y, et al. PersADE: a database of personalized adverse drug events and their underlying molecular mechanisms. Nucleic Acids Res. 2026;54:D1556-68.
33. Tong L, Ge Y, Yang S. Enhancing the confidence of potential targets enriched by similarity-centric models: the crucial role of the similarity threshold. Front Pharmacol. 2025;16:1574540.
34. De La Fuente J, Serrano G, Veleiro U, et al. Towards a more inductive world for drug repurposing approaches. Nat Mach Intell. 2025;7:495-508.
35. Rask-Andersen M, Masuram S, Schiöth HB. The druggable genome: evaluation of drug targets in clinical trials suggests major shifts in molecular class and indication. Annu Rev Pharmacol Toxicol. 2014;54:9-26.
36. Tanoli Z, Seemab U, Scherer A, Wennerberg K, Tang J, Vähä-Koskela M. Exploration of databases and methods supporting drug repurposing: a comprehensive survey. Brief Bioinform. 2021;22:1656-78.





