Linganisha mbinu

	Utafiti wa GWAS unaoendeshwa na Bayesian katika Utafiti wa Kielimu	Uwekaji Nasibu wa Kimendeli
Nyanja	Bioinformatiki	Uhitimisho wa Kisababishi
Familia	Process / pipeline	Regression model
Mwaka wa asili	2013–2018 (educational attainment GWAS); Bayesian GWAS framework ~2001–2010	1997
Mwanzilishi	Social Science Genetic Association Consortium (SSGAC); Bayesian GWAS methods developed by Ter Braak, Meuwissen, and others	George Davey Smith
Aina	Statistical genomics pipeline	Genetic instrumental variable framework
Chanzo asilia	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.	Davey Smith, G., & Hemani, G. (2014). Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human Molecular Genetics, 23(R1), R89-R98.
Majina mbadala	Bayesian GWAS, Bayesian GWAS for educational attainment, B-GWAS, Bayesian polygenic GWAS	MR
Zinazohusiana	1	2
Muhtasari	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.