Genome-wide Interaction Study Research Articles

Genome-Wide Interaction Study of Dietary Intake and Colorectal Cancer Risk in the UK Biobank

Candidate gene analysis approaches have shown that colorectal cancer (CRC) risk attributable to diet may differ according to genotype. A genome-wide approach further allows for the exploration of underlying pathways for associations between diet and CRC risk across the genome. To identify genetic variants that modify diet-CRC associations and to further explore the underlying pathways in the cause of CRC. This nested case-control study used data on White British participants from the prospective cohort UK Biobank. Participants were recruited between March 13, 2006, and October 1, 2010, and data were censored June 25, 2021. The average frequency intake of 11 dietary factors in the year preceding baseline was obtained via a touchscreen questionnaire. After quality control for more than 93 million variants of imputed genetic data, 4 122 345 variants remained. Colorectal cancer cases were identified according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision. Genome-wide interaction analysis was performed to test interactions between dietary factors and variants using a conditional logistic regression model. Summary statistics of interactions at the variant level were used to calculate empirical P values for interactions at gene and gene-set levels in gene-based and gene-set enrichment analyses. A total of 4686 participants with CRC (mean [SD] age, 60.7 [6.6] years; 2707 men [57.8%]) received a new diagnosis during a median of 12.4 years (IQR, 11.6-13.1 years) of follow-up. Once a case was detected, 3 matched controls were identified, for a total of 14 058 controls (mean [SD] age, 60.4 [6.6] years; 8121 men [57.8%]). A total of 324 variants were identified that interacted with diet consumption at the suggestive threshold (P < 1 × 10-5). In gene-based analysis, aggregation of multiple EPDR1 gene variants was found to interact with fish intake regarding CRC risk. Furthermore, gene-set enrichment analysis found that several sets of protein-coding genes, which were overrepresented with particular functions and pathways, interacted with the consumption of milk (ART), cheese (OR), tea (KRT), and alcohol (PRM and TNP). In this nested case-control study, the risk of CRC associated with fish intake was modified by multiple single-nucleotide polymorphisms of the EPDR1 gene. The findings further suggested possible functions and pathways that might link the consumption of milk, cheese, tea, and alcohol with CRC development.

A multidimensional social risk atlas of depression and anxiety: An observational and genome-wide environmental interaction study

A multidimensional social risk atlas of depression and anxiety: An observational and genome-wide environmental interaction study

Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.

statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions, there is a need for gene-environment interaction-specific methods that manipulate and use summary statistics. We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene-exposure and/or gene-covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate multiple exposures from multiple studies. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in genome-wide interaction study in the UK Biobank as well as by conducting a multi-exposure genome-wide interaction study meta-analysis in cohorts from the diabetes-focused ProDiGY consortium. These programs help to maximize the value of summary statistics from diverse and complex gene-environment interaction studies. REGEM and METAGEM are open-source projects freely available at https://github.com/large-scale-gxe-methods/REGEM and https://github.com/large-scale-gxe-methods/METAGEM.

A Genome-Wide Interaction Study of Erythrocyte ω-3 Polyunsaturated Fatty Acid Species and Memory in the Framingham Heart Study Offspring Cohort

BackgroundCognitive decline, and more specifically Alzheimer’s disease, continues to increase in prevalence globally, with few, if any, adequate preventative approaches. Several tests of cognition are utilized in the diagnosis of cognitive decline that assess executive function, short- and long-term memory, cognitive flexibility, and speech and motor control. Recent studies have separately investigated the genetic component of both cognitive health, using these measures, and circulating fatty acids. ObjectivesWe aimed to examine the potential moderating effect of main species of ω-3 polyunsaturated fatty acids (PUFAs) on an individual’s genetically conferred risk of cognitive decline. MethodsThe Offspring cohort from the Framingham Heart Study was cross-sectionally analyzed in this genome-wide interaction study (GWIS). Our sample included all individuals with red blood cell ω-3 PUFA, genetic, cognitive testing (via Trail Making Tests [TMTs]), and covariate data (N = 1620). We used linear mixed effects models to predict each of the 3 cognitive measures (TMT A, TMT B, and TMT D) by each ω-3 PUFA, single nucleotide polymorphism (SNP) (0, 1, or 2 minor alleles), ω-3 PUFA by SNP interaction term, and adjusting for sex, age, education, APOE ε4 genotype status, and kinship (relatedness). ResultsOur analysis identified 31 unique SNPs from 24 genes reaching an exploratory significance threshold of 1×10-5. Fourteen of the 24 genes have been previously associated with the brain/cognition, and 5 genes have been previously associated with circulating lipids. Importantly, 8 of the genes we identified, DAB1, SORCS2, SERINC5, OSBPL3, CPA6, DLG2, MUC19, and RGMA, have been associated with both cognition and circulating lipids. We identified 22 unique SNPs for which individuals with the minor alleles benefit substantially from increased ω-3 fatty acid concentrations and 9 unique SNPs for which the common homozygote benefits. ConclusionsIn this GWIS of ω-3 PUFA species on cognitive outcomes, we identified 8 unique genes with plausible biology suggesting individuals with specific polymorphisms may have greater potential to benefit from increased ω-3 PUFA intake. Additional replication in prospective settings with more diverse samples is needed.

Abstract 13953: A Genome-Wide European Ancestry Study Identifies Coronary Artery Disease-Associated Loci Through Gene-Sex Hormone Interaction

Background: Genome-wide association studies (GWAS) have improved our understanding of the genetic basis of coronary artery disease (CAD). While sex-differences in CAD have been observed, little is known about the role of sex hormones in CAD genetics. Accounting for sex hormone levels may help identify CAD-risk loci and extend our knowledge of its genetic architecture. Methods: We performed a GWAS to identify CAD risk loci by incorporating gene-sex hormone interactions in 365,662 individuals of European ancestry enrolled in the UK Biobank (UKB). Genetic interactions of total testosterone (TT), bioavailable testosterone (BAT) and sex-hormone binding globulin (SHBG) were evaluated. GEM software was used to conduct sex-stratified genome-wide interaction analysis with prevalent CAD. Enrollment age and principal components 1-10 were model covariates. The online post-GWAS tool FUMA was used to annotate the biological functions of marginal, interaction, and joint effects summary statistics. Results: We identified 64 loci in males and 8 loci in females that reached genome-wide significance (p &lt; 5х10 -8 ) for CAD ( Table 1 ). Among males, 3 significant loci were identified for BAT, one for TT and one for SHBG using joint effect model that did not overlap with those identified with marginal effect. Among females, 5 significant loci were identified, all for TT using joint effect that that did not overlap with those identified with marginal effect. Of these 5 loci, two loci were independently identified with significant SNP-TT interaction. These loci were mapped close to the TRDN and THADA genes that have been implicated in congenital ventricular tachycardia and polycystic ovarian syndrome, respectively. Conclusion: This Genome-wide gene-sex hormone interaction study identified genomic-risk loci that may contribute to the differential CAD risk between men and women, which would otherwise not have been discovered in traditional GWAS only including marginal genetic effect.

A Case-Only Genome-Wide Interaction Study of Smoking and Bladder Cancer Risk: Results from the COBLAnCE Cohort.

Bladder cancer (BC) is the 6th most common cancer worldwide, with tobacco smoking considered as its main risk factor. Accumulating evidence has found associations between genetic variants and the risk of BC. Candidate gene-environment interaction studies have suggested interactions between cigarette smoking and NAT2/GSTM1 gene variants. Our objective was to perform a genome-wide association case-only study using the French national prospective COBLAnCE cohort (COhort to study BLAdder CancEr), focusing on smoking behavior. The COBLAnCE cohort comprises 1800 BC patients enrolled between 2012 and 2018. Peripheral blood samples collected at enrolment were genotyped using the Illumina Global Screening Array with a Multi-Disease drop-in panel. Genotyping data (9,719,614 single nucleotide polymorphisms (SNP)) of 1674, 1283, and 1342 patients were analyzed for smoking status, average tobacco consumption, and age at smoking initiation, respectively. A genome-wide association study (GWAS) was conducted adjusting for gender, age, and genetic principal components. The results suggest new candidate loci (4q22.1, 12p13.1, 16p13.3) interacting with smoking behavior for the risk of BC. Our results need to be validated in other case-control or cohort studies.

A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry

BackgroundGenome-wide studies of gene–environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer.MethodsAnalyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene–environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs.ResultsAssuming a 1 × 10–5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92–0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88–0.94).ConclusionsOverall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer.

Genotype-by-environment interactions in chronic back pain

BACKGROUND CONTEXTChronic back pain (CBP) is a common debilitating condition with substantial societal impact. While understanding genotype-by-environment (GxE) interactions may be crucial to achieving the goals of personalized medicine, there are few large-scale studies investigating this topic for CBP. None of them systematically explore multiple CBP risk factors. PURPOSETo estimate the extent to which genetic effects on CBP are modified by known demographic and clinical risk factors. RESEARCH DESIGNCase-control study, genome-wide GxE interaction study. PATIENT SAMPLEData on up to 331,610 unrelated participants (57,881 CBP cases and 273,729 controls) from the UK Biobank cohort were used. UK Biobank is a prospective cohort with collected deep genetic and phenotypic data on approximately 500,000 individuals across the UK. OUTCOME MEASURESSelf-reported chronic back pain. METHODSWe applied a whole-genome approach to estimate the proportion of phenotypic variance explained by interactions between genotype and 12 known risk factors. We also analyzed if effects of common single-nucleotide polymorphisms on CBP are changed in presence of known risk factors. RESULTSThe results indicate a modest, if any, modification of genetic effects by examined risk factors in CBP. Our estimates suggest that detecting such weak effects would require a sample size of millions of individuals. CONCLUSIONSThe GxE interactions with examined common risk factors for CBP are either weak or absent. Interactions of such magnitude are unlikely to have the potential to inform and influence treatment strategies. Risk estimation models may use common genetic variation and the considered risk factors as independent predictors, without accounting for GxE.

Abstract 5224: Genome-wide interaction study of smoking and bladder cancer risk: results from the COBLAnCE cohort

Abstract Introduction: Bladder Cancer (BC) is the 6th most common cancer and the 9th leading cause of cancer death among men worldwide. Tobacco smoking is the main risk factor for BC. Accumulating evidence has found that genetic variants are also associated with the risk of BC. More insights into the biological mechanism of the disease have been gained through candidate gene-environment interaction studies, with the most known examples of the interactions between cigarette smoking and NAT2 and GSTM1 gene variants. Our objective was to perform a genome-wide association case-only study using the French national prospective COBLAnCE cohort (COhort to study BLAdder CancEr), while focusing on three important smoking behavior characteristics. Materials and Methods: The COBLAnCE cohort comprises 1,800 BC patients included within 14 centers in France between 2012 and 2018. Peripheral blood samples and complete history of tobacco smoking were collected at enrollment and genotyping was performed using the Illumina Global Screening Array with a Multi-Disease drop-in panel. After the quality control and imputation, 9,719,614 Single Nucleotide Polymorphisms (SNPs) were investigated. Genotyping data of 1,674, 1,283 and 1,342 patients were analyzed for smoking status, average tobacco consumption and age at smoking initiation, respectively. Genome-wide association study (GWAS) was conducted using logistic (for smoking status) and linear (for age at smoking initiation and average tobacco consumption, both log-transformed) regression models with each of the imputed SNPs as the independent variable adjusting for gender, age and first ten genetic principal components. Results: For smoking status, none of the SNPs reached the GWAS significant threshold of 5e-08, but results are suggesting tentative loci at 1p31.3 (rs114073636: OR [95%CI] = 0.31 [0.20-0.47], p = 8.68e-08; rs116571608: OR = 0.31 [0.20-0.47], p = 8.87e-08) and 4q22.1 (rs542541627: OR = 1.69 [1.39-2.06], p = 1.81e-07; rs1533294: OR = 1.63 [1.34-1.98], p = 1.01e-06). Analyses of age at smoking initiation revealed three significant SNPs: rs531756449, rs77186197 and rs78947799 with positive interaction associations (β (SE) = 0.3 (0.05), p = 8.26e-09; β = 0.29 (0.05), p = 3.74e-09; β = 0.31 (0.06), p = 3.97e-08, respectively). Only one SNP at 11q24.1, reached the significant threshold for the average tobacco consumption analyses: rs2714069 (β = 0.63 (0.11), p = 1.35e-08). The locus 16p13.3 seems to have protective variants for the interaction with average tobacco consumption: rs113683380 (β = -0.66 (0.12), p = 6.44e-08) and rs113590624 (β = -0.64 (0.12), p = 2.74e-07). Conclusion: Our study suggests new candidate loci interacting with smoking behavior for the risk of BC. Based on a case-only approach, our results need to be validated in a case-control or cohort study. A large-scale meta-analysis GWAS would be needed to identify SNPs with small Minor Allele Frequencies. Citation Format: Maryam Karimi, Sebastian Mendez-Pineda, Hélène Blanché, Anne Boland, Jean-François Deleuze, Xiang-Yu Meng, Karine Groussard, Thierry Lebret, Julia Bonastre, Yves Allory, François Radvanyi, Simone Benhamou, Stefan Michiels. Genome-wide interaction study of smoking and bladder cancer risk: results from the COBLAnCE cohort. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5224.

Gene-environment interaction explains a part of missing heritability in human body mass index

Gene-environment (G×E) interaction could partially explain missing heritability in traits; however, the magnitudes of G×E interaction effects remain unclear. Here, we estimate the heritability of G×E interaction for body mass index (BMI) by subjecting genome-wide interaction study data of 331,282 participants in the UK Biobank to linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships–software for estimating SNP heritability from summary statistics (LDAK-SumHer) analyses. Among 14 obesity-related lifestyle factors, MET score, pack years of smoking, and alcohol intake frequency significantly interact with genetic factors in both analyses, accounting for the partial variance of BMI. The G×E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer are as follows: MET score, 0.45% (0.12) and 0.65% (0.24); pack years of smoking, 0.52% (0.13) and 0.93% (0.26); and alcohol intake frequency, 0.32% (0.10) and 0.80% (0.17), respectively. Moreover, these three factors are partially validated for their interactions with genetic factors in other obesity-related traits, including waist circumference, hip circumference, waist-to-hip ratio adjusted with BMI, and body fat percentage. Our results suggest that G×E interaction may partly explain the missing heritability in BMI, and two G×E interaction loci identified could help in understanding the genetic architecture of obesity.

TNS1 and NRXN1 Genes Interacting With Early-Life Smoking Exposure in Asthma-Plus-Eczema Susceptibility.

Numerous genes have been associated with allergic diseases (asthma, allergic rhinitis, and eczema), but they explain only part of their heritability. This is partly because most previous studies ignored complex mechanisms such as gene-environment (G-E) interactions and complex phenotypes such as co-morbidity. However, it was recently evidenced that the co-morbidity of asthma-plus-eczema appears as a sub-entity depending on specific genetic factors. Besides, evidence also suggest that gene-by-early life environmental tobacco smoke (ETS) exposure interactions play a role in asthma, but were never investigated for asthma-plus-eczema. To identify genetic variants interacting with ETS exposure that influence asthma-plus-eczema susceptibility. To conduct a genome-wide interaction study (GWIS) of asthma-plus-eczema according to ETS exposure, we applied a 2-stage strategy with a first selection of single nucleotide polymorphisms (SNPs) from genome-wide association meta-analysis to be tested at a second stage by interaction meta-analysis. All meta-analyses were conducted across 4 studies including a total of 5,516 European-ancestry individuals, of whom 1,164 had both asthma and eczema. Two SNPs showed significant interactions with ETS exposure. They were located in 2 genes, NRXN1 (2p16) and TNS1 (2q35), never reported associated and/or interacting with ETS exposure for asthma, eczema or more generally for allergic diseases. TNS1 is a promising candidate gene because of its link to lung and skin diseases with possible interactive effect with tobacco smoke exposure. This first GWIS of asthma-plus-eczema with ETS exposure underlines the importance of studying sub-phenotypes such as co-morbidities as well as G-E interactions to detect new susceptibility genes.

Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response.

Tobacco smoking is an established risk factor for colorectal cancer. However, genetically defined population subgroups may have increased susceptibility to smoking-related effects on colorectal cancer. A genome-wide interaction scan was performed including 33,756 colorectal cancer cases and 44,346 controls from three genetic consortia. Evidence of an interaction was observed between smoking status (ever vs. never smokers) and a locus on 3p12.1 (rs9880919, P = 4.58 × 10-8), with higher associated risk in subjects carrying the GG genotype [OR, 1.25; 95% confidence interval (CI), 1.20-1.30] compared with the other genotypes (OR <1.17 for GA and AA). Among ever smokers, we observed interactions between smoking intensity (increase in 10 cigarettes smoked per day) and two loci on 6p21.33 (rs4151657, P = 1.72 × 10-8) and 8q24.23 (rs7005722, P = 2.88 × 10-8). Subjects carrying the rs4151657 TT genotype showed higher risk (OR, 1.12; 95% CI, 1.09-1.16) compared with the other genotypes (OR <1.06 for TC and CC). Similarly, higher risk was observed among subjects carrying the rs7005722 AA genotype (OR, 1.17; 95% CI, 1.07-1.28) compared with the other genotypes (OR <1.13 for AC and CC). Functional annotation revealed that SNPs in 3p12.1 and 6p21.33 loci were located in regulatory regions, and were associated with expression levels of nearby genes. Genetic models predicting gene expression revealed that smoking parameters were associated with lower colorectal cancer risk with higher expression levels of CADM2 (3p12.1) and ATF6B (6p21.33). Our study identified novel genetic loci that may modulate the risk for colorectal cancer of smoking status and intensity, linked to tumor suppression and immune response. These findings can guide potential prevention treatments.

Abstract 1454: Interaction of obesity loci with energy intake and physical activity on the risk of colorectal cancer: Evidence from UK Biobank

Abstract Colorectal cancer is one of the common cancers worldwide. Obesity and physical activity are well-established risk factors for colorectal cancer. Studies have shown that as a consequence of energy metabolism, imbalance between energy intake and physical activity can lead to obesity. In order to precisely examine the close interrelationship between obesity, energy intake, and physical activity, we conducted a genome-wide interaction study of obesity with energy intake and physical activity. We also evaluated the interaction between obesity susceptibility loci with energy intake and physical activity for colorectal cancer using the nested case-control design. We analyzed data on participants in the UK Biobank. Genome-wide interaction analyses of obesity with energy intake and physical activity was performed on 65,514 and 250,369 participants who have information on genotype, body mass index, and energy intake/physical activity, respectively. We assessed the effect of obesity loci in the participants comprised 3523 colorectal cancer cases and 10,522 matched controls. The logistic regression model was used for finding obesity loci under a p-value threshold of 10-4. Odds ratios (ORs) and 95% confidence intervals (CIs) for colorectal cancer were estimated using a conditional logistic regression model. We found 21 loci and 44 loci of obesity with energy intake and physical activity, respectively. We also observed significant interaction of obesity loci with energy intake and physical activity for colorectal cancer. The obesity loci interacting with energy intake, including rs10828896 and rs7099260, were significantly interacted with physical activity for colorectal cancer. Rs426345 and rs12551684, which were the obesity loci interacting with physical activity, were significantly interacted with energy intake for colorectal cancer. The results of this study may provide evidence for bioenergetics in colorectal cancer carcinogenesis. Citation Format: Sooyoung Cho, Aesun Shin. Interaction of obesity loci with energy intake and physical activity on the risk of colorectal cancer: Evidence from UK Biobank [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1454.

Little Contribution of Genome-Wide Gene-Diet Interactions to Cardiometabolic Risk Factors in the UK Biobank

ObjectivesGenome-guided dietary recommendations have long been a goal of precision nutrition and have a substantial evidence base in well-controlled animal experiments. However, it remains unclear how much these gene-diet interactions (GxDs) affect disease risk in human populations. Understanding the contribution of GxDs to variability in disease risk factors is thus critical to guide scientific effort as well as prioritize specific dietary exposures for study. We set out to quantify the genome-wide contribution of GxDs to the heritability of multiple cardiometabolic risk factors across a wide range of dietary traits.MethodsThe study population consisted of unrelated, European-ancestry individuals from the UK Biobank with dietary data available from one or more 24-hour recall questionnaires (N = 122,945). We conducted a genome-wide GxD interaction study for each combination of 12 cardiometabolic risk factors (e.g., lipids and glycemic traits) and 20 dietary exposures (macronutrients, micronutrients, and food groups). We then used the resulting genome-wide summary statistics as input to the regression-based GxEsum method. GxEsum estimates the contribution of genetic main effects or interaction effects to heritability, expressed as a fraction of the outcome trait variability with associated confidence intervals.ResultsNo diet-trait pair passed a Bonferroni significance threshold, though 13 pairs were nominally significant (p < 0.05). The strongest of these was the gene-by-whole grain interaction, explaining 1.1% of the variability in systolic blood pressure (p = 0.002). GxD contributions to heritability (mean of 0.1% variance explained across all diet-trait pairs) were much lower than those from genetic main effects (mean of 16.8% variance explained). Finally, we saw substantial attenuation of heritability estimates when regressions adjusted for GxD terms for the remaining dietary components; this reinforces conclusions from non-dietary studies and indicates the importance of adjusting for “interaction confounders” in nutrigenetic studies.ConclusionsOur results show that it is difficult to find meaningful GxD signal for common traits, even in a large and homogeneous population. This may indicate a true lack of GxD effects or a need for improved (e.g., longitudinal or biomarker-based) dietary behavior estimation.Funding SourcesNHLBI.

A Large-Scale Genome-Wide Gene-Gene Interaction Study of Lung Cancer Susceptibility in Europeans With a Trans-Ethnic Validation in Asians

IntroductionAlthough genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G × G) interactions that may influence the risk of non-small cell lung cancer (NSCLC). MethodsLeveraging a total of 445,221 European-descent participants from the International Lung Cancer Consortium OncoArray project, Transdisciplinary Research in Cancer of the Lung and UK Biobank, we performed a large-scale genome-wide G × G interaction study on European NSCLC risk by a series of analyses. First, we used BiForce to evaluate and rank more than 58 billion G × G interactions from 340,958 single-nucleotide polymorphisms (SNPs). Then, the top interactions were further tested by demographically adjusted logistic regression models. Finally, we used the selected interactions to build lung cancer screening models of NSCLC, separately, for never and ever smokers. ResultsWith the Bonferroni correction, we identified eight statistically significant pairs of SNPs, which predominantly appeared in the 6p21.32 and 5p15.33 regions (e.g., rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.17, p = 6.57 × 10−13; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.17, p = 2.43 × 10−13; rs2858859HLA-DQA1 and rs9275572HLA-DQA2, ORinteraction = 1.15, p = 2.84 × 10−13; rs2853668TERT and rs62329694CLPTM1L, ORinteraction = 0.73, p = 2.70 × 10−13). Notably, even with much genetic heterogeneity across ethnicities, three pairs of SNPs in the 6p21.32 region identified from the European-ancestry population remained significant among an Asian population from the Nanjing Medical University Global Screening Array project (rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.13, p = 0.008; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.11, p = 5.23 × 10−4; rs3135369BTNL2 and rs9271300HLA-DQA1, ORinteraction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk score and G × G information score was remarkable in lung cancer risk stratification. ConclusionsImportant G × G interactions were identified and enriched in the 5p15.33 and 6p21.32 regions, which may enhance lung cancer screening models.

Genome-wide interaction analysis of menopausal hormone therapy use and breast cancer risk among 62,370 women

Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values < 5 × 10–8 as genome-wide significant, and p-values < 1 × 10–5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants. None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values < 1 × 105. The strongest evidence was found for rs4674019 (p-value = 2.27 × 10–7), which showed genome-wide significant interaction (p-value = 3.8 × 10–8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen–progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT–breast cancer risk association.

Interaction between cigarette smoking and genetic polymorphisms on the associations with age of natural menopause and reproductive lifespan: the Singapore Chinese Health Study.

Are there genetic variants that interact with smoking to reduce reproductive lifespan in East-Asian women? Our study corroborates several recently identified genetic loci associated with reproductive lifespan and highlights specific genetic predispositions that may interact with smoking status to adversely affect reproductive lifespan in East-Asian women. Epidemiological data as well as evaluations on genetic predisposition to smoke indicate on the importance of smoking in adverse effects on reproductive lifespan in women. However, there are no previous smoking and gene interaction studies for reproductive traits in East-Asian women. This population-based prospective cohort study comprised 11643 East-Asian Chinese women with overlapping genome-wide genotyping and reproductive data. We performed a genome-wide association study for reproductive lifespan in women (n = 11643) from the Singapore Chinese Health Study (SCHS) and carried out a genome-wide interaction study to identify loci that interacted with smoking status to affect age of natural menopause and reproductive-time. Two known loci associated with menopause, rs113430717 (near HMCES, chromosome 3, Pmeta = 5.72 × 10-15) and rs3020136 (near RAD21, chromosome 8, Pmeta = 1.38 × 10-8) were observed beyond genome-wide levels of association with age at menopause in this study. For reproductive lifespan, the genome-wide association observed at rs79784106 (chromosome 3, Pmeta = 5.05 × 10-12) was in linkage disequilibrium with the menopause lead single-nucleotide polymorphism (SNP) (rs113430717). Four additional loci, first reported to be associated with menopause, were also associated with reproductive lifespan in our study (PAdj between 7.42 × 10-5 to 4.51 × 10-3). A significant interaction was observed between smoking and an East-Asian specific SNP, rs140146885, for reduced reproductive lifespan, per copy of the minor C allele (beta = -1.417 years, Pinteraction = 2.31 × 10-10). This interaction was successfully replicated in additional independent samples (beta = -1.389 years, Pinteraction = 6.78 × 10-3). Another known variant associated with menopause, rs11031006 (near FSHB), was also observed to interact with smoking status to reduce age at menopause in our dataset (beta = -0.450 years, Padj = 0.042). The modest sample size of the replication datasets used likely affected the statistical power to firmly replicate all identified novel loci observed in our smoking interaction analyses. Age of natural menopause and reproductive lifespan have clear genetic predispositions with distinct ethnic differences, and they may be adversely truncated by lifestyle factors such as smoking, which can pose a significant impact on the reproductive lifespan and future health outcomes in women. The Singapore Chinese Health Study is funded by the National Medical Research Council, Singapore (NMRC/CIRG/1456/2016), National Institutes of Health (R01 CA144034 and UM1 CA182876) and National Research Foundation, Singapore (Project Number 370062002). W.-P.K. is supported by the National Medical Research Council, Singapore (MOH-CSASI19nov-0001). The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. The authors do not report conflicts of interest. N/A.

A genome-wide gene-based gene-environment interaction study of breast cancer in more than 90,000 women.

Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene-environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this. We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P<0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test. After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE=4.44×10-6). In this transcriptome-informed genome-wide gene-environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk. Our study suggests a limited role of gene-environment interactions in breast cancer risk.

Genome-Wide Interaction Study of Late-Onset Asthma With Seven Environmental Factors Using a Structured Linear Mixed Model in Europeans.

Asthma is among the most common chronic diseases worldwide, creating a substantial healthcare burden. In late-onset asthma, there are wide global differences in asthma prevalence and low genetic heritability. It has been suggested as evidence for genetic susceptibility to asthma triggered by exposure to multiple environmental factors. Very few genome-wide interaction studies have identified gene-environment (G×E) interaction loci for asthma in adults. We evaluated genetic loci for late-onset asthma showing G×E interactions with multiple environmental factors, including alcohol intake, body mass index, insomnia, physical activity, mental status, sedentary behavior, and socioeconomic status. In gene-by-single environment interactions, we found no genome-wide significant single-nucleotide polymorphisms. However, in the gene-by-multi-environment interaction study, we identified three novel and genome-wide significant single-nucleotide polymorphisms: rs117996675, rs345749, and rs17704680. Bayes factor analysis suggested that for rs117996675 and rs17704680, body mass index is the most relevant environmental factor; for rs345749, insomnia and alcohol intake frequency are the most relevant factors in the G×E interactions of late-onset asthma. Functional annotations implicate the role of these three novel loci in regulating the immune system. In addition, the annotation for rs117996675 supports the body mass index as the most relevant environmental factor, as evidenced by the Bayes factor value. Our findings help to understand the role of the immune system in asthma and the role of environmental factors in late-onset asthma through G×E interactions. Ultimately, the enhanced understanding of asthma would contribute to better precision treatment depending on personal genetic and environmental information.

Genome-wide Interaction Study Implicates VGLL2 and Alcohol Exposure and PRL and Smoking in Orofacial Cleft Risk.

Non-syndromic cleft lip with or without cleft palate (NSCL/P) is a common birth defect, affecting approximately 1 in 700 births. NSCL/P has complex etiology including several known genes and environmental factors; however, known genetic risk variants only account for a small fraction of the heritability of NSCL/P. It is commonly suggested that gene-by-environment (G×E) interactions may help explain some of the “missing” heritability of NSCL/P. We conducted a genome-wide G×E interaction study in cases and controls of European ancestry with three common maternal exposures during pregnancy: alcohol, smoking, and vitamin use using a two-stage design. After selecting 127 loci with suggestive 2df tests for gene and G x E effects, 40 loci showed significant G x E effects after correcting for multiple tests. Notable interactions included SNPs of 6q22 near VGLL2 with alcohol and 6p22.3 near PRL with smoking. These interactions could provide new insights into the etiology of CL/P and new opportunities to modify risk through behavioral changes.