Plant Gene and Trait 2026, Vol.17, No.3, 156-172 http://genbreedpublisher.com/index.php/pgt 170 Although this general strategy is applicable across biological systems, its implementation must be adapted to the characteristics of the data. In human studies, ancestry matching and LD structure play a central role in interpretation, whereas in plant systems, environmental variation, genomic complexity, and gene copy number introduce additional challenges. These differences do not alter the overall framework but influence how individual steps are carried out and weighted. Looking forward, advances in epigenomics, single-cell technologies, and multimodal datasets will enable the relationships between genetic variation and phenotypic outcomes to be examined across multiple biological layers. Integrating these data into existing analytical frameworks will allow regulatory pathways to be characterized with greater precision. Coupled with high-throughput experimental approaches, such developments have the potential to establish a more continuous link between data-driven inference and mechanistic validation, ultimately advancing the translation of statistical findings into actionable biological insights. Author Contributions Xuanjun Fang conducted this study, including literature review, data analysis, and the writing and revision of the manuscript. The author has read and approved the final version of the manuscript. Acknowledgements This work was supported by a Major Project of the National Natural Science Foundation of China (Grant No. 30490254). References Barfield R., Feng H., Gusev A., Wu L., Zheng W., Pasaniuc B., and Kraft P., 2018, Transcriptome‐wide association studies accounting for colocalization using Egger regression, Genetic Epidemiology, 42(5): 418-433. https://doi.org/10.1002/gepi.22131 Bhattacharya A., Li Y., and Love M.I., 2021, MOSTWAS: multi-omic strategies for transcriptome-wide association studies, PLoS Genetics, 17(3): e1009398. https://doi.org/10.1371/journal.pgen.1009398 Boix C.A., James B.T., Park Y. P., Meuleman W., and Kellis M., 2021, Regulatory genomic circuitry of human disease loci by integrative epigenomics, Nature, 590(7845): 300-307. https://doi.org/10.1038/s41586-020-03145-z Bryois J., Calini D., Macnair W., Foo L., Urich E., Ortmann W., Iglesias V.A., Selvaraj S., Nutma E., Marzin M., Amor S., Williams A., Castelo-Branco G., Menon V., De Jager P., and Malhotra D., 2022, Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders, Nature Neuroscience, 25(8): 1104-1112. https://doi.org/10.1038/s41593-022-01128-z Colomé-Tatché M., and Theis F.J., 2018, Statistical single cell multi-omics integration, Current Opinion in Systems Biology, 7: 54-59. https://doi.org/10.1016/j.coisb.2018.01.003 De Leeuw C., Werme J., Savage J.E., Peyrot W.J., and Posthuma D., 2023, On the interpretation of transcriptome-wide association studies, PLoS Genetics, 19(9): e1010517. https://doi.org/10.1371/journal.pgen.1010921 Evans P., Nagai T., Konkashbaev A., Zhou D., Knapik E.W., and Gamazon E.R., 2024, Transcriptome‐wide association studies (TWAS): methodologies, applications, and challenges, Current Protocols, 4(2): e981. https://doi.org/10.1002/cpz1.981 Fagny M., Paulson J.N., Kuijjer M.L., Sonawane A.R., Chen C.Y., Lopes-Ramos C.M., Glass K., Quackenbush J., and Platig J., 2017, Exploring regulation in tissues with eQTL networks, Proceedings of the National Academy of Sciences, 114(37): E7841-E7850. https://doi.org/10.1073/pnas.1707375114 Fang X.J., 2026, A hierarchical inference framework for multi-trait genetics integrating genomic SEM, PLEIO, and Primo, Tree Genetics and Molecular Breeding, 16(1): xx-xx. Foley C.N., Staley J.R., Breen P.G., Sun B.B., Kirk P.D., Burgess S., and Howson J.M., 2021, A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits, Nature Communications, 12(1): 764. https://doi.org/10.1038/s41467-020-20885-8 Gleason K.J., Yang F., and Chen L.S., 2021, A robust two‐sample transcriptome‐wide Mendelian randomization method integrating GWAS with multi‐tissue eQTL summary statistics, Genetic Epidemiology, 45(4): 353-371. https://doi.org/10.1002/gepi.22380 Hemani G., Bowden J., and Davey Smith G., 2018, Evaluating the potential role of pleiotropy in Mendelian randomization studies, Human Molecular Genetics, 27(R2): R195-R208. https://doi.org/10.1093/hmg/ddy163
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