Bioscience Methods 2026, Vol.17, No.3, 153-168 http://bioscipublisher.com/index.php/bm 165 differences between studies are likely to reflect inconsistencies in statistical definitions rather than true biological variation. This issue is particularly pronounced in multi-population or cross-environment comparisons, and thus harmonizing analytical frameworks is essential to avoid misleading interpretations. Based on these considerations, a more integrative conceptual framework can be proposed: SNP heritability is not an intrinsic biological constant of a trait, but rather a statistical function dependent on specific models, data structures, and LD patterns. This perspective is especially important for reinterpreting the “missing heritability” problem. Traditional explanations often attribute low SNP heritability to unobserved genetic variation, whereas in reality, model assumptions and LD mismatches can also lead to systematic underestimation (Yang et al., 2015). Overall, this study not only reveals structural differences among estimation methods but also highlights a fundamental issue: the numerical value of heritability has no independent meaning outside its statistical definition. Only when its estimation context and model conditions are clearly specified can the results be scientifically interpretable. This perspective provides a more robust analytical framework for future genetic studies and contributes to improving the comparability and methodological consistency of research findings. Author Contributions Xuanjun Fang conducted this study, including literature review, data analysis, and the drafting 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 Program of the National Natural Science Foundation of China (Grant No. 30490254). References Bulik-Sullivan B.K., Loh P.R., Finucane H.K., Ripke S., Yang J., Schizophrenia Working Group of the Psychiatric Genomics Consortium, Patterson N., Daly M.J., Price A.L., and Neale B.M., 2015, LD score regression distinguishes confounding from polygenicity in genome-wide association studies, Nature Genetics, 47: 291-295. https://doi.org/10.1038/ng.3211 Bycroft C., Freeman C., Petkova D., Band G., Elliott L.T., Sharp, K., Motyer A., Vukcevic D., Delaneau O., O'Connell J., Cortes A., Welsh S., Young A., Effingham M., McVean G., Leslie S., Allen N., Donnelly P., and Marchini J., 2018, The UK Biobank resource with deep phenotyping and genomic data, Nature, 562(7726): 203-209. https://doi.org/10.1038/s41586-018-0579-z Fang X.J., 2026, Genome-wide relationship matrix-based heritability estimation: statistical interpretation, comparability, and practical diagnostics in the GCTA-GREML framework, Computational Molecular Biology, 16(1): 11-20. Fang X.J., and Wu W.R., 2026, Evolution of statistical genetic paradigms: from linkage analysis and candidate gene strategies to GWAS, Molecular Plant Breeding, 24(9): 2817-2829. Ge T., Chen C.Y., Neale B.M., Sabuncu M.R., and Smoller J.W., 2018, Correction: Phenome-wide heritability analysis of the UK Biobank, PLOS Genetics 14(2): e1007228. https://doi.org/10.1371/journal.pgen.1007228 Hou K., Burch K.S., Majumdar A., Shi H., Mancuso N., Wu Y., Sankararaman S., and Pasaniuc B., 2019, Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture, Nature genetics, 51(8): 1244-1251. https://doi.org/10.1038/s41588-019-0465-0 Ni G., Moser G., Schizophrenia Working Group of the Psychiatric Genomics Consortium, Wray N., and Lee S., 2018, Estimation of genetic correlation via linkage disequilibrium score regression and genomic restricted maximum likelihood, The American Journal of Human Genetics, 102(6): 1185-1194. https://doi.org/10.1101/194019 Rawlik K., Canela-Xandri O., Woolliams J., and Tenesa A., 2020, SNP heritability: What are we estimating? BioRxiv, pp.1-18. https://doi.org/10.1101/2020.09.15.276121 Schoeler T., Speed D., Porcu E., Pirastu N., Pingault J.B., and Kutalik Z., 2023, Participation bias in the UK Biobank distorts genetic associations and downstream analyses, Nature Human Behaviour, 7(7): 1216-1227. https://doi.org/10.1038/s41562-023-01579-9 Speed D., and Balding D.J., 2019, SumHer better estimates the SNP heritability of complex traits from summary statistics, Nature genetics, 51(2): 277-284. https://doi.org/10.1038/s41588-018-0279-5 Speed D., Holmes J., and Balding D.J., 2020, Evaluating and improving heritability models using summary statistics, Nature Genetics, 52(4): 458-462. https://doi.org/10.1038/s41588-020-0600-y Yang J., Benyamin B., McEvoy B.P., Gordon S., Henders A.K., Nyholt D.R., Madden P.A., Heath A.C., Martin N.G., Montgomery G.W., Goddard M.E., and Visscher P.M., 2010, Common SNPs explain a large proportion of the heritability for human height, Nature genetics, 42(7): 565-569. https://doi.org/10.1038/ng.608
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