Population-based Association Analysis Of Candidate Polymorphism With Chronic Benzene Poisoning

Benzene-induced hemotoxicity and gentoxicity depends mainly on its metabolites. Chronic benzene poisoning (CBP) is associated with genetic polymorphisms. The association of genetic polymorphisms of toxicant-metabolizing enzymes genes, DNA repair genes, cell cycle control genes with CBP have been extensively studied by this group.There are some challenges in our studies. The first issue is that the results of our previous studies were achieved respectively on polymorphisms of toxicant-metabolizing enzymes gene, DNA repair gene, cell cycly control gene. These results may be biased due to confounding factors. The second issue is that interaction effects should be considered in the model while the main effect of single SNP may be smaller or absent. However the parameters to be estimated in Logistic regression model will increase exponentially with the number of SNP increase linearly. This will result in inaccurate parameter estimates for interaction effects. The third issue is that we are uncertain about the relative importance among all of polymorphism we have studied, though the selection of important variable is of great significance to achieve high prediction accuracy in estimating the probability of CBP.To resolve these issues, this study have analyzed the association of candidate polymorphisms with CBP comprehensively based on occupational epidemiological investigation and laboratory studies, including main effect, gene-gene interaction, gene-environment interaction. The statistics strategy is shown as follows. We controlled the false discovery rate (FDR) to adjust for multiple testing in single-locus method. Followed by SNP selection, Logistic regression model have been fitted to analyze main effect and 2-order interactions. Furthermore, we conducted the haplotypes associaton analysis with CBP while controlling confounding factor, such as smoking. Multifactor dimensionality reduction (MDR) was used to analyze high order interactions. Random forest was used to measure the relative importance of each polymorphism which may be associated with CBP. Based on variable importance, SNP could be screened for further studies according to variable importance.In order to elucidate the association of candidate polymorphism with CBP, a case-control study was designed and conducted.152 CBP patients and 152 workers without poisoning manifestations but occupationally exposed to benzene were investigated. The cumulative exposure level was estimated with method described by Dosmeci, and the lifestyles such as cigarette smoking and alcohol consumption were also explored. The results of occupational epidemiology showed that there was no statistic difference for distribution of sex, race, age, workage and cumulative exposure level in case and control groups, which indicated that it was comprehensive equilibrium between the case and control groups.According to mechanism of benzene toxicity, we have dected 3 pathway genetic polymorphisms. For toxicant-metabolizing enzymes genes,33 SNPs including CYP2E1, MPO, NQO1, GSTT1, GSTM1, GSTP1, EPHX1, EPHX2, UGT1A6, UGT1A7, SULT1A1, CYP1A1, and CYP2D6 were detected. For DNA repair genes,14 SNPs including hMTH1, hOGGl, hMYH, XPD, APE1, XRCC1, ADPRT, XRCC2, and XRCC3 were detected. For cell cycle control genes,13 SNPs including p53, p21, mdm2, gadd45a, and p14ARF were detected. Finally,51 SNPs (33 SNPs of toxicant-metabolizing enzymes,33 SNPs of DNA repair gene,8 SNPs of cell cycle control gene) were retained for further statistical analysis after excluding SNPs which had not variant allele or deviated form Hardy-Weinberg Equilibrium.Because of the larger number of SNP involved in the previous studies, interaction effect was introducd into Logistic regression model as the follows. Firstly, these SNPs with P value less than 0.05 were introduced into Logistic regression model as indepent variables while environmental factors treated as covariate. Then a Logistic regression model with forward stepwise was fitted to detect main effects and 2-order interactions. Finally, a backward stepwise Loggistic regression model was fitted again when we introduced these SNPs with interaction effect but no main effect in our previous studies.The results of Logistic regression are shown as follows. Main effects have not be detected for 40 SNPs. For toxicant-metabolizing enzymes gene, there were 3 SNPs of CYP2E1, GSTT1, GSM1,4 SNPs of MPO, NQO1 rs1131341 and rs1800566, EPHX1 rs2234922, rs1051741, rs2854451, rs3738047, EPHX2 rs151141,7 SNPs of UGT1A6, UGT1A7rsl 1692021, SULT1A1 rs9282861, CYP1A1 rs4646421, rs4646422, and rs1048943. For DNA repair gene, there were XPD rs 13181, APE1 rs 1130409, XRCC1 rs25487, ADPRT rs 1136410, XRCC3 rs861539. For cell cycle control gene, there were 3 SNPs of p53, p14ARF rs3731217, rs3088440, gadd45a rs581000, rs532446.Totally 11 SNPs were detected with main effects. For toxicant-metabolizing enzymes gene, there were GSTP1rs947894, EPHX1 rs1051740, CYP1A1 rs4646903, CYP2D6*10 rs1065852 and rs1135840. For DNA repair gene, there were MTH1 rs4866, hOGG1 rs1052133, hMYH rs3219489, XPD rs1799793, XRCC1 rs1799782. For cell cycle control gene, there were p21 rs1059234。10 types of interaction effects were detected as follows: CYP2E1 rs3813867 with EPHX1 rs3738047, EPHX1 rs3738047 with alcohol consumption, GSTP1 rs947894 with alcohol consumption, CYP1A1 rs4646903 with CYP2D6 rs1135840, hMTHl rs4866 with XRCC1 rsl799782, hOGG1 rs1052133 with hMYH rs3219489, hMYH rs3219489 with cigarette smoking, XRCC1 rs1799782 with APE1 rs1130409, APE1 rsl 130409 with alcohol consumption and hOGG1 rs1052133 with XPD rsl799793.Compared to our previous results, there were some differences. In the present study, we had 2 SNP showing main effect, i.e. GSTP1 rs947894 and hMYH rs3219489 which were not included in our previous results. Furthermore, we have detected 4 types of interactions that were not included in our previous results, i.e. CYP2E1 rs3813867 with EPHX1 rs3738047, XRCC1 rsl 799782 with APE1 rs1130409, CYP1A1 rs4646903 with CYP2D6 rs1135840, hMTHl rs4866 with XRCC1 rs1799782. The previous positive results of interactions between NQO1 rs1800566 and cigarette smoking or alcohol consumption disappeared in the present study.After controlling confounding factor such as smoking, the haplotype associaton analysis with CBP detected an association between haplotype of CYP2D6*10 with CBP the same as our previous results. The individuals with CC haplotype would be more susceptible to CBP than TC haplotype. Contrary to previous results, the haplotype of EPHX1, UGT1A6, CYP1A1, and XRCC1 did not show any association with CBP either in the present study. For toxicant-metabolizing enzymes genes, we detected a positive 3-order interaction based on MDR model. It is the 3-factor combination of CYP1A1 rs4646903, CYP2D6 rs1065852 and CYP2D6 rs1135840.3 types of individuals would be classified to high risk group, i.e. the individuals with CC genotype of CYP2D6 rs1135840, TC or CC genotype of CYP1A1 rs4646903, CT or CC genotype of CYP2D6 rs1065852, the individuals with CC genotype of CYP2D6 rs1135840, TT genotype of CYP1A1 rs4646903, CT or CC genotype of CYP2D6 rs1065852, and the individuals with CG or GG genotype of CYP2D6 rs1135840, TT genotype of CYP1A1 rs4646903, CT or CC genotype of CYP2D6 rs1065852 would be classified as high risk group. The other 5 types of individuals would be classified to low risk group.For DNA repair genes and cell cycle genes, we had not detected high-order interaction more than 2-order based on MDR model. The 2-factor combination of hOGGl rs1052133 and XPD rsl7997933 was the best 2-order interaction with the highest prediction accuracy. The individuals with GG genotype of hOGG1 rs1052133 and GG genotype of XPD rs1799793 would be classified to high risk group. The other 3 types of individual would be classified to low risk group.When 3 pathway genetic polymorphisms were considered, the best n-factor combination was the same as the result of toxicant-metabolizing enzymes gene, namely the 3-facotrs combination of CYP1A1 rs4646903, CYP2D6 rs1065852 and CYP2D6 rs1135840.According to the variable importance score analyzed by Random forest model while 3 pathway genetic polymorphisms were considering, the 25 top important SNPs or environmental factors are shown as follows, EPHX1 rs1051740, hOGGl rs1052133, CYP2D6 rs1065852, CYP1A1 rs4646903, CYP2D6 rs1135840, p21 rs1059234, XPD rs1799793, P53 rsl7878362, hMYH rs3219489, hMTHl rs4866, GSTP1 rs947894, EPHX1 rs1051741, CYP2E1 96-bp insert, MPO rs7208693, EPHX1 rs3738047, XRCC1 rs25487, SULT1A1 rs9282861, cigarette smoking, UGT1A6 rs6759892, UGT1A7 rs11692021, XRCC1 rs1779782, NQO1 rs1800566, XPD rs13181, UGT1A6 rs2070959, and GSTT1. For the most important SNP, EPHX1 rs1051740, its role of CBP should be further studied. Except for p53 rs17878362, the top 2 to top 11 important SNP had been detected in Logistic regression, and most of them did not shown main effects but interaction actions. For these undetectable SNP in Logistic regression limited by small sample size, Random forest could be used to test its relative importance and SNPs would be screened for further study. Based on our present study, we found that the results of association of polymorphisms related to toxicant-metabolizing enzymes genes, DNA repair genes, cell cycle genes with CBP were consistent with our previous results. These results further supported our previous results. The present results on haplotype association, high-order interaction, and relative importance of SNPs were a beneficial supplement to our previous results.In conclusion, we found that the polymorphisms of toxicant-metabolizing enzymes genes, DNA repair genes, cell cycle genes were associated with CBP. Gene-environment interaction, gene-gene interaction were important mechanism to genetic polymorphism of CBP. Results in our study will provide theoretical evidence for health surveillance and screening effective biomarkers of susceptibility. In this present study, the main effects or interaction effects for most of SNPs were undetectable. The reasons may be attributed to the faint or absent effects of these SNPs on CBP, and limited sample size which would lead to low power of test. Our future research will focus on the screen of SNPs based on statistical variable selection method and biological mechanism, and expansion of the sample size.