rs3817198

This pooled analysis of 872 women from two studies (Susan G. Komen Tissue Bank and BREAST Stamp Project) investigated the association between 62 established breast cancer susceptibility SNPs and terminal duct lobular unit (TDLU) involution, a breast cancer risk factor. Six SNPs (9.7%) showed nominal associations with at least one TDLU measure: rs616488 (PEX14), rs11242675 (FOXQ1), and rs6001930 (MKL1) with higher TDLU count (P=0.047, 0.045, 0.031); rs1353747 (PDE4D) and rs6472903 (8q21.11) with higher acini count per TDLU (P=0.007, 0.027); and rs1353747 (PDE4D) and rs204247 (RANBP9) with higher epithelial content (P=0.024, 0.017). Overall, breast cancer susceptibility SNPs showed limited enrichment for associations with TDLU involution.

A meta-analysis of 72 studies comprising 108,248 cases and 161,472 controls found that the TERT rs2736100 T>G polymorphism increases overall cancer risk by 16-39% (OR = 1.17-1.39 across genetic models). Stratified analysis showed strongest associations with lung cancer (OR = 1.24-1.60), and also elevated risk for thyroid cancer, bladder cancer, glioma, myeloproliferative neoplasms, and acute myeloid leukemia, with decreased colorectal cancer risk.

A hospital-based case-control study in rural and urban India (1,204 cases; 1,212 controls) examined genetic and lifestyle risk factors for breast cancer. Four SNPs in FGFR2 (rs1219648, rs2420946, rs2981575, rs2981582) showed positive associations with breast cancer (ORs 1.32-1.47). Additional SNPs in obesity and metabolic genes (rs374748 in FBN2, rs2922763 in HNF4G, rs2116830 in KCNMA1, rs11121832 in MTHFR, rs16886165 in MAP3K1, rs11594610 in TCF7L2, rs2274459 in MLN) were associated with increased breast cancer risk. Waist-to-hip ratio ≥0.95 showed strong association (OR 3.78; 95% CI 2.92-4.89), and women living first 20 years in rural areas showed protective effect (OR 0.77).

Population-based case-control study of 1,484 breast cancer cases and 1,307 controls examining 13 GWAS-identified SNPs for breast cancer susceptibility. Confirmed associations for 7 SNPs (rs13387042, rs4973768, rs10941679, rs2981582, rs3817198, rs3803662, rs6504950), with women in the highest quintile of a polygenic risk score having 2.2-fold increased breast cancer risk (95% CI: 1.67-2.88) compared to the lowest quintile. No significant interactions were detected between genetic loci and reproductive/menstrual risk factors.

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 case-control study examined whether seven breast cancer susceptibility alleles (in FGFR2, TNRC9, MAP3K1, LSP1, and chromosomal regions 8q24 and 2q35) were associated with epithelial ovarian cancer risk. The pooled analysis of 1,383 ovarian cancer cases and 1,804 controls found no significant associations between these variants and ovarian cancer risk, with OR estimates for FGFR2 rs1219648 of 1.06 (95% CI=0.95-1.18) and rs2981582 of 1.04 (95% CI=0.93-1.15), suggesting that breast cancer risk alleles may be specific to breast cancer.

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.