Methods have been proposed for pathway analysis [26], and one of the

Methods have been proposed for pathway AN 3199 cost analysis [26], and one of the commonly used method is gene set enrichment analysis (GSEA) [16]. Briefly, three steps are used for pathway analysis in GSEA. First, individual-SNP association analysis is conducted to determine the effect for each SNP. Second, the representative SNP with the lowest P value is mapped to each gene, and all genes are assigned to predefined biological pathways. Finally, all genes are ranked by their significance, and then are to be evaluated whether a particular group of genes is enriched at the top of the ranked list by chance. As a result, a cluster of biological related SNPs which appeared in the top list may be potentially associated with disease as integration. In a large-scale GWAS of lung cancer in 23977191 Han Chinese population, we have already validated suggestive SNPs with a P value #1.061024 in independent populations and found five new lung cancer risk-related loci with effect size (odds ratio) ranging from 0.84 to 1.35 at a genome-wide significance level [3,4]. To further deeply understand the genetics mechanism of lung cancer and identify the crucial pathway in lung carcinogens, we Docosahexaenoyl ethanolamide price currently performed a two-stage pathway analysis using GSEA method based on our existing GWAS data in Han Chinese population. In stage 1, we screened all available pathways in Nanjing study using 1,473 cases and 1,962 controls. In stage 2, the pathways with P values #0.05 and FDR #0.50 were validated in Beijing study using 858 cases and 1,115 controls.HWE in either the Nanjing or Beijing study samples. We removed samples with call rate ,95 , ambiguous gender, familial relationships, extreme heterozygosity rate and outliers. Finally, a total of 2,331 cases and 3,077 controls (Nanjing study: 1,473 cases and 1,962 controls; Beijing study: 858 cases and 1,115 controls) with 570,373 SNPs were remained in subsequent pathway analysis.Pathway Data ConstructionWe collected pathways from two public resources: KEGG and BioCarta database (URL: http://www.biocarta.com/). Pathways containing genes from 10 to 200 were included in this study. This gene number range was considered appropriate to reduce the multiple-comparison issue and to avoid testing overly narrow or broad functional gene categories [22]. Pathway overlap was defined as the percentage of shared genes to total ones of two pathways [14].Statistical AnalysisLogistic regression model with adjustment for age, gender, packyear of smoking and the first four principal components derived from EIGENSTRAT 3.0 [31] was used to evaluate the association significance of each SNP using GLM package executed in R software (version 2.14.0; The R Foundation for Statistical Computing). SNPs were assigned to a gene if they located within 50 kb downstream or upstream of the gene. The significance of each gene was derived from the representative SNP. All genes were assigned to pathways. Then the association between lung cancer risk and each pathway was evaluated by GenGen software [16] using the weighted Kolmogorov-Smirnov-like running sum statistic (denoted by enrichment score, ES), which reflected the over-representation of a cluster of genes within this pathway at the top of the entire ranked list of genes in the genome. We randomly shuffled the case-control status for 1,000 times, and repeated these above steps to get the permuted pathway association results. Thus, the normalized ES after adjusted for different sizes of genes, could be acquired via the perm.Methods have been proposed for pathway analysis [26], and one of the commonly used method is gene set enrichment analysis (GSEA) [16]. Briefly, three steps are used for pathway analysis in GSEA. First, individual-SNP association analysis is conducted to determine the effect for each SNP. Second, the representative SNP with the lowest P value is mapped to each gene, and all genes are assigned to predefined biological pathways. Finally, all genes are ranked by their significance, and then are to be evaluated whether a particular group of genes is enriched at the top of the ranked list by chance. As a result, a cluster of biological related SNPs which appeared in the top list may be potentially associated with disease as integration. In a large-scale GWAS of lung cancer in 23977191 Han Chinese population, we have already validated suggestive SNPs with a P value #1.061024 in independent populations and found five new lung cancer risk-related loci with effect size (odds ratio) ranging from 0.84 to 1.35 at a genome-wide significance level [3,4]. To further deeply understand the genetics mechanism of lung cancer and identify the crucial pathway in lung carcinogens, we currently performed a two-stage pathway analysis using GSEA method based on our existing GWAS data in Han Chinese population. In stage 1, we screened all available pathways in Nanjing study using 1,473 cases and 1,962 controls. In stage 2, the pathways with P values #0.05 and FDR #0.50 were validated in Beijing study using 858 cases and 1,115 controls.HWE in either the Nanjing or Beijing study samples. We removed samples with call rate ,95 , ambiguous gender, familial relationships, extreme heterozygosity rate and outliers. Finally, a total of 2,331 cases and 3,077 controls (Nanjing study: 1,473 cases and 1,962 controls; Beijing study: 858 cases and 1,115 controls) with 570,373 SNPs were remained in subsequent pathway analysis.Pathway Data ConstructionWe collected pathways from two public resources: KEGG and BioCarta database (URL: http://www.biocarta.com/). Pathways containing genes from 10 to 200 were included in this study. This gene number range was considered appropriate to reduce the multiple-comparison issue and to avoid testing overly narrow or broad functional gene categories [22]. Pathway overlap was defined as the percentage of shared genes to total ones of two pathways [14].Statistical AnalysisLogistic regression model with adjustment for age, gender, packyear of smoking and the first four principal components derived from EIGENSTRAT 3.0 [31] was used to evaluate the association significance of each SNP using GLM package executed in R software (version 2.14.0; The R Foundation for Statistical Computing). SNPs were assigned to a gene if they located within 50 kb downstream or upstream of the gene. The significance of each gene was derived from the representative SNP. All genes were assigned to pathways. Then the association between lung cancer risk and each pathway was evaluated by GenGen software [16] using the weighted Kolmogorov-Smirnov-like running sum statistic (denoted by enrichment score, ES), which reflected the over-representation of a cluster of genes within this pathway at the top of the entire ranked list of genes in the genome. We randomly shuffled the case-control status for 1,000 times, and repeated these above steps to get the permuted pathway association results. Thus, the normalized ES after adjusted for different sizes of genes, could be acquired via the perm.