Person:

Zhai, Rihong

GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM) and principal components analysis based approach (PCA) using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD) structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs) and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer.

Background: To study the association between functional single nucleotide polymorphisms (SNPs) in candidate genes from oxidative stress pathways, and risk of radiation pneumonitis (RP) in patients treated with thoracic radiation therapy (RT) for locally advanced lung cancer (LC).

Methods: We reviewed 136 patients treated with RT for LC between 2001 and 2007, and had prior genotyping of functional SNPs in oxidative stress genes including superoxide dismutase 2 (SOD2; rs4880) and methylenetetrahydrofolate reductase (MTHFR; rs1801131, rs1801133). RP events were retrospectively scored using the Common Terminology Criteria for Adverse Events, version 4.0. Cox proportional hazard regression was performed to identify clinical variables and genotypes associated with risk of grade ≥2 and grade ≥3 RP on univariate and multivariate analysis. P-values were corrected for multiple hypothesis testing.

Results: With a median follow-up of 21.4 months, the incidence of ≥grade 2 RP was 29% and ≥grade 3 RP was 14%. On multivariate analysis, after adjusting for clinical factors such as concurrent chemotherapy, and consolidation docetaxel, and lung dosimetric parameters such as V20 and mean lung dose, MTHFR genotype (rs1801131; AA versus AC/CC) was significantly associated with risk of ≥grade 2 RP (Hazard ratio [HR]: 0.37; 95% confidence interval [CI]: 0.18-0.76; p=0.006, corrected p=0.018) and ≥grade 3 RP (HR: 0.21; 95% CI: 0.06-0.70; p=0.01; corrected p=0.03). SOD2 genotype was not associated with RP.

Conclusions: Our study showed an association between MTHFR genotype and risk of clinically significant RP. Further study of MTHFR-related pathways may provide insight into the mechanisms behind RP.