rs8051542

This paper applies a bias correction method for odds ratio estimates from GWAS discovery data, demonstrating that the 'winner's curse' affects initial effect size estimates. The authors applied conditional maximum likelihood estimation to correct bias in GWAS findings from multiple complex diseases (breast cancer, colorectal cancer, lung cancer, prostate cancer, type I and II diabetes) and show that bias-adjusted odds ratios are substantially more consistent with subsequent replication studies, with selection-adjusted confidence intervals providing better uncertainty quantification than uncorrected estimates.

Hemminki et al. (2010) conducted a case-control study of 1,415 German familial breast cancer patients and 1,830 controls to validate low-risk breast cancer susceptibility variants. The study found significant associations with FGFR2 (OR=1.43, p=1.24×10⁻¹²), TNRC9 (OR=1.33, p=1.54×10⁻⁷), and LSP1 variants. Notably, homozygous carriers showed higher risks: FGFR2 OR=2.05 and TNRC9 OR=1.62, while LSP1 showed a protective effect (OR=0.49) in homozygous carriers.

This comprehensive review article traces the evolution of pharmacogenetics from single-gene analysis to whole-genome approaches. It discusses validated pharmacogenetic biomarkers with clinical impact including CYP2D6, CYP2C9, CYP2C19, TPMT, DPD, VKORC1, UGT1A1, and ADRB1/ADRB2, providing examples of how genetic variants affect drug metabolism and response. The paper emphasizes the importance of integrating pharmacogenetic information into clinical practice and drug development.