Linganisha mbinu

	Bayesian epigenome-wide association study in educational research	Uwekaji Nasibu wa Kimendeli
Nyanja	Bioinformatiki	Uhitimisho wa Kisababishi
Familia	Process / pipeline	Regression model
Mwaka wa asili	EWAS framework ~2010–2011; Bayesian EWAS variants ~2013–2017; educational applications ~2015–present	1997
Mwanzilishi	Rakyan, Down, Balding, and Beck (conceptual EWAS framework); Bayesian extensions by multiple groups including Teschendorff and colleagues	George Davey Smith
Aina	Genomic association study with Bayesian inference	Genetic instrumental variable framework
Chanzo asilia	Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.	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 EWAS, Bayesian epigenome-wide scan, Bayesian methylation-wide association study, B-EWAS	MR
Zinazohusiana	3	2
Muhtasari	A Bayesian epigenome-wide association study (Bayesian EWAS) scans hundreds of thousands of DNA methylation sites across the genome to identify those statistically associated with an educational outcome — such as cognitive ability, attainment, or socioeconomic exposure during schooling. Unlike classical frequentist EWAS, the Bayesian framework incorporates prior biological knowledge to compute posterior probabilities of association, improving power and reducing false discoveries when applied to complex educational phenotypes.	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.