Genome-wide Gene-environment Interaction Analysis Research Articles

Genome-Wide Gene-Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk.

High red meat and/or processed meat consumption are established colorectal cancer risk factors. We conducted a genome-wide gene-environment (GxE) interaction analysis to identify genetic variants that may modify these associations. A pooled sample of 29,842 colorectal cancer cases and 39,635 controls of European ancestry from 27 studies were included. Quantiles for red meat and processed meat intake were constructed from harmonized questionnaire data. Genotyping arrays were imputed to the Haplotype Reference Consortium. Two-step EDGE and joint tests of GxE interaction were utilized in our genome-wide scan. Meta-analyses confirmed positive associations between increased consumption of red meat and processed meat with colorectal cancer risk [per quartile red meat OR = 1.30; 95% confidence interval (CI) = 1.21-1.41; processed meat OR = 1.40; 95% CI = 1.20-1.63]. Two significant genome-wide GxE interactions for red meat consumption were found. Joint GxE tests revealed the rs4871179 SNP in chromosome 8 (downstream of HAS2); greater than median of consumption ORs = 1.38 (95% CI = 1.29-1.46), 1.20 (95% CI = 1.12-1.27), and 1.07 (95% CI = 0.95-1.19) for CC, CG, and GG, respectively. The two-step EDGE method identified the rs35352860 SNP in chromosome 18 (SMAD7 intron); greater than median of consumption ORs = 1.18 (95% CI = 1.11-1.24), 1.35 (95% CI = 1.26-1.44), and 1.46 (95% CI = 1.26-1.69) for CC, CT, and TT, respectively. We propose two novel biomarkers that support the role of meat consumption with an increased risk of colorectal cancer. The reported GxE interactions may explain the increased risk of colorectal cancer in certain population subgroups.

Evaluate the effects of serum urate level on bone mineral density: a genome-wide gene-environment interaction analysis in UK Biobank cohort.

Serum urate is associated with BMD and may be a protective factor. However, the exact association and mechanism are still unclear. We performed a genome-wide gene-environmental interaction study (GWGEIS) to explore the interaction effects between gene and urate on BMD, using data from the UK Biobank cohort. A total of 4575 participants for femur total BMD, 4561 participants for L1-L4 BMD, and 237799 participants for heel BMD were included in the present study. Linear regression models were used to test for associations between urate and BMD (femur total BMD, L1-L4 BMD, heel BMD) by R software. GWGEIS was conducted by PLINK 2.0 using a generalize linear model, adjusted for age, sex, weight, smoking behavior, drinking behavior, physical activity and 10 principle components for population structure. Results showed that urate was positively associated with femur total BMD, L1-L4 BMD and heel BMD and similar findings were observed in both the male and female subgroups. GWGEIS identified 261 genome-wide significant (P < 5.00 × 10-8) SNP × urate interaction effects for femur total BMD (rs8192585 in NOTCH4, rs116080577 in PBX1, rs9409991 in COL5A1), 17 genome-wide significant SNP × urate interaction effects for heel BMD (rs145344540 in PDE11A and rs78485379 in DKK2), 17 suggestive genome-wide SNP × urate interaction effects (P < 1.00 × 10-5) for L1-L4 BMD (rs10977015 in PTPRD). We also detected genome-wide significant and suggestive SNP × urate interaction effects for BMD in both the male and female subgroups. This study reported several novel candidate genes, and strengthen the evidence of the interactive effects between gene and urate on the variations of BMD.

Maternal smoking during pregnancy and risks to depression and anxiety in offspring: An observational study and genome-wide gene-environment interaction analysis in UK biobank cohort

BackgroundMaternal smoking during pregnancy (MSDP) has been reported to be associated with increased anxiety and depression behaviors in offspring. However, there is still scant evidence to support the link between MSDP and anxiety/depression. MethodsUsing the subjects from the UK Biobank cohort (n = 371,903–432,881). Logistic regression analyses were first conducted to test the correlation between MSDP and anxiety/depression in offspring. Second, genome-wide gene-environment interaction study (GWGEIS) analyses were conducted by PLINK, using MSDP as environmental factor. Genetic correlation analysis of anxiety/depression and smoking was conducted by the LDSC software using the published genome-wide association study (GWAS) summary data of four smoking traits (n = 337,334–1,232,091), anxiety (n = 31,880) and depression (n = 490,359). Finally, pathway enrichment analysis was carried out to detect the pathway involved in the development of offspring anxiety caused by the interaction of MSDP × SNPs. ResultsObservational analyses showed that anxiety and depression status in offspring were significantly associated with MSDP (all p < 0.0001). Further GWEGI analyses observed significant MSDP-gene interaction effects at UNC80 gene for anxiety (p = 9.09 × 10−9). LDSC did not detect significant genetic correlation between anxiety and smoking traits. Pathway analysis identified 19 significant pathways for anxiety, such as MANALO_HYPOXIA_UP (FDR = 5.50 × 10−4), REACTOME_ADHERENS_JUNCTIONS_INTERACTIONS (FDR = 0.0304) and ONDER_CDH1_TARGETS_2_UP (FDR = 0.0371). ConclusionOur study results suggested the important impact of MDSP on the risk of anxiety in offspring, partly attributing to environment-gene interactions effects.

Index of Multiple Deprivation Contributed to Common Psychiatric Disorders: An Observational and Genome-Wide Gene-Environment Interaction Analysis in UK Biobank Cohort

Index of Multiple Deprivation Contributed to Common Psychiatric Disorders: An Observational and Genome-Wide Gene-Environment Interaction Analysis in UK Biobank Cohort

Identifying blood pressure loci whose effects are modulated by multiple lifestyle exposures.

Although multiple lifestyle exposures simultaneously impact blood pressure (BP) and cardiovascular health, most analysis so far has considered each single lifestyle exposure (e.g., smoking) at a time. Here, we exploit gene-multiple lifestyle exposure interactions to find novel BP loci. For each of 6,254 Framingham Heart Study participants, we computed lifestyle risk score (LRS) value by aggregating the risk of four lifestyle exposures (smoking, alcohol, education, and physical activity) on BP. Using the LRS, we performed genome-wide gene-environment interaction analysis in systolic and diastolic BP using the joint 2 degree of freedom (DF) and 1 DF interaction tests. We identified one genome-wide significant (p < 5 × 10-8 ) and 11 suggestive (p < 1 × 10-6 ) loci. Gene-environment analysis using single lifestyle exposures identified only one of the 12 loci. Nine of the 12 BP loci detected were novel. Loci detected by the LRS were located within or nearby genes with biologically plausible roles in the pathophysiology of hypertension, including KALRN, VIPR2, SNX1, and DAPK2. Our results suggest that simultaneous consideration of multiple lifestyle exposures in gene-environment interaction analysis can identify additional loci missed by single lifestyle approaches.

Genome-wide gene-environment interaction analysis of pesticide exposure and risk of Parkinson's disease

IntroductionGenetic factors and environmental exposures, including pesticides, contribute to the risk of Parkinson's disease (PD). There have been few studies of gene and pesticide exposure interactions in PD, and all of the prior studies used a candidate gene approach. MethodsWe performed the first genome-wide gene-environment interaction analysis of pesticide exposure and risk of Parkinson's disease. Analyses were performed using data on >700,000 single nucleotide polymorphisms (SNPs) in 364 discordant sibling pairs. In addition to testing for SNP-pesticide interaction effects, we also performed exploratory analyses of gene-pesticide interactions at the gene level. ResultsNone of the gene-environment interaction results were significant after genome-wide correction for multiple testing (α = 1.5E-07 for SNP-level tests; α = 2.1E-06 for gene-level tests). Top results in the SNP-level tests provided suggestive evidence (P < 5.0E-06) that the effect of pesticide exposure on PD risk may be modified by SNPs in the ERCC6L2 gene (P = 2.4E-06), which was also supported by suggestive evidence in the gene-level analysis (P = 4.7E-05). None of the candidate genes assessed in prior studies of gene-pesticide interactions reached statistical support in this genome-wide screen. ConclusionAlthough no significant interactions were identified, several of the genes with suggestive evidence of gene-environment interaction effects have biological plausibility for PD risk. Further investigation of the role of those genes in PD risk, particularly in the context of pesticide exposure, in large and carefully recruited samples is warranted.

Abstract 4154: Pathway-based approach to genome-wide gene-environment interaction analysis for occupational exposures in lung cancer susceptibility

Abstract Background: Occupational exposures are known risk factors for lung cancer with asbestos, silica, polycyclic aromatic hydrocarbons and heavy metals being the major contributors. Few studies have addressed the role of genetically determined host factors in the pathogenesis of lung cancer associated with exposure to these agents. We used data from a genome-wide association (GWA) analysis to identify gene-occupation interactions and related pathways associated with increased lung cancer risk. Methods: For our analysis, we used data from a previously published GWA analysis based on a case-control study conducted in six countries in Central and Eastern Europe from 1998 to 2002. Genotyping on samples from study participants was performed using Illumina Sentrix HumanHap300 BeadChip. Occupational exposure data was collected by trained interviewers and coders using semi-structured questionnaires. Single marker association analysis was performed using PLINK 1.07 for each exposure using logistic regression after adjusting for age, gender, smoking history, country/center and risk score calculated using history of exposure to other occupational exposures. Interaction term for genotypes and exposure was included in this model. Pathway analysis was performed using gene-set enrichment analyses approach. False discovery rate (FDR) was calculated to adjust for multiple comparisons. We analyzed 312,605 SNPs and occupational exposure to 70 agents from 1802 lung cancer cases and 1725 cancer-free controls. Results: Mean age of study participants was 60.1 ± 9.1 years and 75% were male. We found SNPs showing gene-environment interaction p-values &amp;lt; 1 x 10-5 for asbestos, diesel and kerosene, coal products, silica, oil mists and metals/alloys. Pathway analysis identified pathways related to response to environmental information processing via signal transduction as being most significant. More than 15 occupational exposures including asbestos, chromate, gasoline and diesel/kerosene showed significant gene-occupation interactions with signal transduction pathways (p&amp;lt;0.001, FDR&amp;lt;0.05), especially for MAPK, GPCR, TNFR and WNT signaling. Gasoline and diesel/kerosene gene-interaction analysis also showed enrichment in pathways related to immune processes (Toll-like receptor signaling pathway, FDR 0.05 and T cell activation, FDR 0.07). Diesel and petrol engine emissions showed significant gene interaction association with xenobiotic metabolism pathway, which regulates metabolism of carcinogens (FDR 0.008 and 0.04 respectively). Conclusion: Our findings suggest that pathways involved in signal transduction, immune process and xenobiotic metabolism may be involved in lung cancer pathogenesis in interaction with occupational carcinogens. Further studies are needed to elucidate these molecular pathways. Citation Format: Jyoti Malhotra, Samantha Sartori, Paul Brennan, Paolo Boffetta. Pathway-based approach to genome-wide gene-environment interaction analysis for occupational exposures in lung cancer susceptibility. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4154. doi:10.1158/1538-7445.AM2014-4154

546 Genome-Wide Gene-Environment Interaction Analysis of Aspirin and Non-Steroidal Anti-Inflammatory Drug Use and Colorectal Cancer Identifies Loci At Chromosomes 12p12.3 and 15q25.2

Aim: To quantitatively assess complex cancer cell phenotypes after gene inhibition by RNAi interference (RNAi), and assess the effect of these distinct morphologies on cell viability and cell motility using semi-automated image-based high-throughput screening. Method: GOhTRT cells were seeded and treated with the siRNA Human Druggable Genome Library (Dharmacon) by reverse transfection. Cells immunostained for DNA, tubulin and actin were imaged with the InCell Analyzer 1000 and processed using the InCell Analyzer software, CellHTS2 and RNAither. Statistical z-score analysis was performed on the combined A-T metric (F-actin area-α-tubulin area). The effect of RNAi knockdown on cell viability and cell motility were assessed using MTT cell proliferation assay and scratch wound assay. Results:127 high confidence hits (Z-factor>2) was refined to six genes (RRM2, ITGB8, GPS1, SPRY1, NOL1, MYO9B) on the basis of distinct morphologies, reproducible metrics, and functional pathway analysis. In siRRM2 cells nuclear displacement (ND) and A-T area was 1.598±0.076; p <0.0001 and 285.70±35.48; p<0.05, respectively. siGPS1 cells had an ND and A-T area of 0.845±0.036; p =0.0369 and 266.201±25.629; p =0.0326, respectively. Silencing of GPS1, MYO9B and SPRY1 increased the rate of migration in a scratch wound assay, with 86.98%±3.097%, 75.78%±5.454% and 72.97%±5.463% (p =0.0022) respectively. No significant difference in cell viability existed for siGPS1 (0.9037 ± 0.06575; p = 0.1905) and siSPRY1 (0.9088 ± 0.09849; p =0.2985), suggesting that wound closure is by virtue of migratory signalling and not proliferation. Cell viability was decreased considerably in siRRM2 cells (0.2492±0.02798; p <0.0001) and siMYO9B (0.4048±0.04663; p <0.0001) in comparison to siNT cells (1.046±0.07712). Discussion:In summary, this approach successfully identified genes regulating oesophageal cancer cell cytoskeletal remodelling and metastasis using in-vitro assays, some of which are already associated with metastasis in literature and database searches. Further mechanistic studies and gene pathway analysis of candidate genes will provide novel therapeutic targets which can be utilised to block the spread of cancer in oesophageal adenocarcinoma patients.

A genome-wide gene-environment interaction analysis for tobacco smoke and lung cancer susceptibility

Tobacco smoke is the major environmental risk factor underlying lung carcinogenesis. However, approximately one-tenth smokers develop lung cancer in their lifetime indicating there is significant individual variation in susceptibility to lung cancer. And, the reasons for this are largely unknown. In particular, the genetic variants discovered in genome-wide association studies (GWAS) account for only a small fraction of the phenotypic variations for lung cancer, and gene-environment interactions are thought to explain the missing fraction of disease heritability. The ability to identify smokers at high risk of developing cancer has substantial preventive implications. Thus, we undertook a gene-smoking interaction analysis in a GWAS of lung cancer in Han Chinese population using a two-phase designed case-control study. In the discovery phase, we evaluated all pair-wise (591 370) gene-smoking interactions in 5408 subjects (2331 cases and 3077 controls) using a logistic regression model with covariate adjustment. In the replication phase, promising interactions were validated in an independent population of 3023 subjects (1534 cases and 1489 controls). We identified interactions between two single nucleotide polymorphisms and smoking. The interaction P values are 6.73 × 10(-) (6) and 3.84 × 10(-) (6) for rs1316298 and rs4589502, respectively, in the combined dataset from the two phases. An antagonistic interaction (rs1316298-smoking) and a synergetic interaction (rs4589502-smoking) were observed. The two interactions identified in our study may help explain some of the missing heritability in lung cancer susceptibility and present strong evidence for further study of these gene-smoking interactions, which are benefit to intensive screening and smoking cessation interventions.

Gene-environment interaction effects on lung function- a genome-wide association study within the Framingham heart study

BackgroundPrevious studies in occupational exposure and lung function have focused only on the main effect of occupational exposure or genetics on lung function. Some disease-susceptible genes may be missed due to their low marginal effects, despite potential involvement in the disease process through interactions with the environment. Through comprehensive genome-wide gene-environment interaction studies, we can uncover these susceptibility genes. Our objective in this study was to explore gene by occupational exposure interaction effects on lung function using both the individual SNPs approach and the genetic network approach.MethodsThe study population comprised the Offspring Cohort and the Third Generation from the Framingham Heart Study. We used forced expiratory volume in one second (FEV1) and ratio of FEV1 to forced vital capacity (FVC) as outcomes. Occupational exposures were classified using a population-specific job exposure matrix. We performed genome-wide gene-environment interaction analysis, using the Affymetrix 550 K mapping array for genotyping. A linear regression-based generalized estimating equation was applied to account for within-family relatedness. Network analysis was conducted using results from single-nucleotide polymorphism (SNP)-level analyses and from gene expression study results.ResultsThere were 4,785 participants in total. SNP-level analysis and network analysis identified SNP rs9931086 (Pinteraction =1.16 × 10-7) in gene SLC38A8, which may significantly modify the effects of occupational exposure on FEV1. Genes identified from the network analysis included CTLA-4, HDAC, and PPAR-alpha.ConclusionsOur study implies that SNP rs9931086 in SLC38A8 and genes CTLA-4, HDAC, and PPAR-alpha, which are related to inflammatory processes, may modify the effect of occupational exposure on lung function.

Genome-wide association analyses of esophageal squamous cell carcinoma in Chinese identify multiple susceptibility loci and gene-environment interactions

We conducted a genome-wide association study (GWAS) and a genome-wide gene-environment interaction analysis of esophageal squamous-cell carcinoma (ESCC) in 2,031 affected individuals (cases) and 2,044 controls with independent validation in 8,092 cases and 8,620 controls. We identified nine new ESCC susceptibility loci, of which seven, at chromosomes 4q23, 16q12.1, 17q21, 22q12, 3q27, 17p13 and 18p11, had a significant marginal effect (P=1.78×10(-39) to P=2.49×10(-11)) and two of which, at 2q22 and 13q33, had a significant association only in the gene-alcohol drinking interaction (gene-environment interaction P (PG×E)=4.39×10(-11) and PG×E=4.80×10(-8), respectively). Variants at the 4q23 locus, which includes the ADH cluster, each had a significant interaction with alcohol drinking in their association with ESCC risk (PG×E=2.54×10(-7) to PG×E=3.23×10(-2)). We confirmed the known association of the ALDH2 locus on 12q24 to ESCC, and a joint analysis showed that drinkers with both of the ADH1B and ALDH2 risk alleles had a fourfold increased risk for ESCC compared to drinkers without these risk alleles. Our results underscore the direct genetic contribution to ESCC risk, as well as the genetic contribution to ESCC through interaction with alcohol consumption.

Genome-wide gene-environment interaction analysis for asbestos exposure in lung cancer susceptibility.

Asbestos exposure is a known risk factor for lung cancer. Although recent genome-wide association studies (GWASs) have identified some novel loci for lung cancer risk, few addressed genome-wide gene–environment interactions. To determine gene–asbestos interactions in lung cancer risk, we conducted genome-wide gene–environment interaction analyses at levels of single nucleotide polymorphisms (SNPs), genes and pathways, using our published Texas lung cancer GWAS dataset. This dataset included 317 498 SNPs from 1154 lung cancer cases and 1137 cancer-free controls. The initial SNP-level P -values for interactions between genetic variants and self-reported asbestos exposure were estimated by unconditional logistic regression models with adjustment for age, sex, smoking status and pack-years. The P- value for the most significant SNP rs13383928 was 2.17×10 –6 , which did not reach the genome-wide statistical significance. Using a versatile gene-based test approach, we found that the top significant gene was C7orf54 , located on 7q32.1 ( P = 8.90×10 –5 ). Interestingly, most of the other significant genes were located on 11q13. When we used an improved gene-set-enrichment analysis approach, we found that the Fas signaling pathway and the antigen processing and presentation pathway were most significant (nominal P < 0.001; false discovery rate < 0.05) among 250 pathways containing 17 572 genes. We believe that our analysis is a pilot study that first describes the gene–asbestos interaction in lung cancer risk at levels of SNPs, genes and pathways. Our findings suggest that immune function regulation-related pathways may be mechanistically involved in asbestos-associated lung cancer risk. Abbreviations:CIconfidence intervalEenvironmentFDRfalse discovery rateGgeneGSEAgene-set-enrichment analysisGWASgenome-wide association studiesi-GSEAimproved gene-set-enrichment analysis approachORodds ratioSNPsingle nucleotide polymorphism

Abstract LB-441: The genome wide gene-environment interaction analysis for asbestos exposure on lung cancer susceptibility

Abstract Asbestos exposure is a known risk factor for lung cancer. Although recent genome wide association studies (GWAS) have identified a few novel loci for lung cancer risk, little is known about genome wide gene-environment interactions and lung cancer risk. To determine genetic variations involved in the interactions of gene-asbestos exposure on lung cancer risk, we conducted genome wide gene-environment interaction analyses at levels of single nucleotide polymorphisms (SNPs), genes and pathways, using the Texas lung cancer GWAS dataset. This dataset included 307,870 SNPs from 1,154 lung cancer cases and 1,137 cancer-free controls. The initial SNP-level p values for interactions between genetic variants and self-reported asbestos exposure were estimated by unconditional logistic regression models adjusted for age, sex, smoking status and pack-years. The p value for the most significant SNP rs12706823 was 1.37 × 10–4 and did not reach the genome-wide statistical significance. Using a versatile gene-based test approach to estimate the interactions at the gene level, we found the top significant gene was RNF121and was located on 11q13.4 (p value = 4 × 10–4). Interestingly, most of the genes with top significant p values were located in 11q13. When we used an improved gene set enrichment analysis approach (i-GSEA), however, we found a total of 249 pathways containing 16,079 genes, of which the Fas signaling pathway was the most significant pathway, significantly involved in the interactions between genetic variations and self-reported asbestos exposure (nominal p value &amp;lt; 0.001; false discovery rate (FDR) &amp;lt; 0.05). We believe our analysis is the first to comprehensively describe the genome wide gene-asbestos exposure interaction on lung cancer risk at the levels of SNPs, genes and pathways. Our findings suggested that the current designed GWAS may have a limited power to detect gene-environment interactions at the SNP or gene levels, but the pathway-based approach may be more powerful in performing genome wide gene-environment analyses. (This study was supported by NIH grants R01 ES 11740, R01CA 131274, R01CA055769, R01CA127219, R01CA121197 and P30CA016672) Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-441. doi:10.1158/1538-7445.AM2011-LB-441