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1.  A prospective study of polymorphisms of DNA repair genes XRCC1, XPD23 and APE/ref‐1 and risk of stroke in Linxian, China 
Background
Stroke is the leading cause of death in Linxian, China. Although there is evidence of DNA damage in experimental stroke, no data exist on DNA repair and stroke in human populations.
Aim
To assess the risk of stroke conferred by polymorphisms in the DNA repair genes, XRCC1, XPD23 and APE/ref‐1 in a cohort of individuals originally assembled as subjects in two cancer prevention trials in Linxian, China.
Methods
The subjects for this prospective study were sampled from a cohort of 4005 eligible subjects who were alive and cancer free in 1991 and had blood samples available for DNA extraction. Using real‐time Taqman analyses, all incident cases of stroke (n = 118) that developed from May 1996, and an age‐ and a sex‐stratified random sample (n = 454) drawn from all eligible subjects were genotyped. Cox proportional hazards models were used to estimate relative risks (RRs) and 95% CIs.
Results
No association was observed between polymorphisms in APE/ref‐1 codon 148 and XRCC1*6 codon 194, and stroke. Polymorphisms in XRCC1*10 codon 399 were associated with a significantly reduced risk of stroke (RR 0.59, 95% CI 0.36 to 0.96, p = 0.033), whereas XPD23 codon 312 was associated with a significantly increased risk of stroke (RR 2.18, 95% CI 1.14 to 4.17, p = 0.010).
Conclusions
Polymorphisms in DNA repair genes may be important in the aetiology of stroke. These data should stimulate research on DNA damage and repair in stroke.
doi:10.1136/jech.2006.048934
PMCID: PMC2653006  PMID: 17630376
2.  Decision Forest Analysis of 61 Single Nucleotide Polymorphisms in a Case-Control Study of Esophageal Cancer; a novel method 
BMC Bioinformatics  2005;6(Suppl 2):S4.
Background
Systematic evaluation and study of single nucleotide polymorphisms (SNPs) made possible by high throughput genotyping technologies and bioinformatics promises to provide breakthroughs in the understanding of complex diseases. Understanding how the millions of SNPs in the human genome are involved in conferring susceptibility or resistance to disease, or in rendering a drug efficacious or toxic in the individual is a major goal of the relatively new fields of pharmacogenomics. Esophageal squamous cell carcinoma is a high-mortality cancer with complex etiology and progression involving both genetic and environmental factors. We examined the association between esophageal cancer risk and patterns of 61 SNPs in a case-control study for a population from Shanxi Province in North Central China that has among the highest rates of esophageal squamous cell carcinoma in the world.
Methods
High-throughput Masscode mass spectrometry genotyping was done on genomic DNA from 574 individuals (394 cases and 180 age-frequency matched controls). SNPs were chosen from among genes involving DNA repair enzymes, and Phase I and Phase II enzymes.
We developed a novel adaptation of the Decision Forest pattern recognition method named Decision Forest for SNPs (DF-SNPs). The method was designated to analyze the SNP data.
Results
The classifier in separating the cases from the controls developed with DF-SNPs gave concordance, sensitivity and specificity, of 94.7%, 99.0% and 85.1%, respectively; suggesting its usefulness for hypothesizing what SNPs or combinations of SNPs could be involved in susceptibility to esophageal cancer. Importantly, the DF-SNPs algorithm incorporated a randomization test for assessing the relevance (or importance) of individual SNPs, SNP types (Homozygous common, heterozygous and homozygous variant) and patterns of SNP types (SNP patterns) that differentiate cases from controls. For example, we found that the different genotypes of SNP GADD45B E1122 are all associated with cancer risk.
Conclusion
The DF-SNPs method can be used to differentiate esophageal squamous cell carcinoma cases from controls based on individual SNPs, SNP types and SNP patterns. The method could be useful to identify potential biomarkers from the SNP data and complement existing methods for genotype analyses.
doi:10.1186/1471-2105-6-S2-S4
PMCID: PMC1637030  PMID: 16026601

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