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| 贝叶斯全基因组关联研究（Bayesian GWAS）在教育研究中的应用× | 孟德尔随机化× | |
|---|---|---|
| 领域≠ | 生物信息学 | 因果推断 |
| 方法族≠ | Process / pipeline | Regression model |
| 起源年份≠ | 2013–2018 (educational attainment GWAS); Bayesian GWAS framework ~2001–2010 | 1997 |
| 提出者≠ | Social Science Genetic Association Consortium (SSGAC); Bayesian GWAS methods developed by Ter Braak, Meuwissen, and others | George Davey Smith |
| 类型≠ | Statistical genomics pipeline | Genetic instrumental variable framework |
| 开创性文献≠ | Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., ... & Cesarini, D. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50(8), 1112–1121. DOI ↗ | Davey Smith, G., & Hemani, G. (2014). Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human Molecular Genetics, 23(R1), R89-R98. DOI ↗ |
| 别名≠ | Bayesian GWAS, Bayesian GWAS for educational attainment, B-GWAS, Bayesian polygenic GWAS | MR |
| 相关≠ | 1 | 2 |
| 摘要≠ | Bayesian genome-wide association study (Bayesian GWAS) applies Bayesian statistical models to millions of single-nucleotide polymorphisms (SNPs) to identify genetic variants associated with educational outcomes such as years of schooling or cognitive test scores. Unlike classical frequentist GWAS, Bayesian approaches assign prior distributions over effect sizes, enabling more principled handling of the polygenic architecture typical of educational traits, shrinkage of small effects, and direct posterior probability estimates for variant inclusion. | Mendelian randomization is a method for estimating causal effects of exposures on outcomes using genetic variants as instrumental variables. Introduced by George Davey Smith in the 1990s, it exploits Mendel's law of segregation to remove confounding bias. It has become a cornerstone technique in epidemiological causal inference. |
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