Gene-environment interactions within a precision environmental health framework.

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Questionnaire-based exposome-wide association studies for common diseases in the Personalized Environment and Genes Study.

The exposome collectively refers to all exposures, beginning in utero and continuing throughout life, and comprises not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The exposome interacts with individual genetic and epigenetic characteristics to affect human health and disease, but large-scale studies that characterize the exposome and its relationships with human disease are limited. To address this gap, we used extensive questionnaire data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS, n = 9, 429) to evaluate exposure associations in relation to common diseases. We performed an exposome-wide association study (ExWAS) to examine single exposure models and their associations with 11 common complex diseases, namely allergic rhinitis, asthma, bone loss, fibroids, high cholesterol, hypertension, iron-deficient anemia, ovarian cysts, lower GI polyps, migraines, and type 2 diabetes. Across diseases, we found associations with lifestyle factors and socioeconomic status as well as asbestos, various dust types, biohazardous material, and textile-related exposures. We also found disease-specific associations such as fishing with lead weights and migraines. To differentiate between a replicated result and a novel finding, we used an AI-based literature search and database tool that allowed us to examine the current literature. We found both replicated findings, especially for lifestyle factors such as sleep and smoking across diseases, and novel findings, especially for occupational exposures and multiple diseases.

Interactive data sharing for multiple questionnaire-based exposome-wide association studies and exposome correlations in the Personalized Environment and Genes Study.

The correlations among individual exposures in the exposome, which refers to all exposures an individual encounters throughout life, are important for understanding the landscape of how exposures co-occur, and how this impacts health and disease. Exposome-wide association studies (ExWAS), which are analogous to genome-wide association studies (GWAS), are increasingly being used to elucidate links between the exposome and disease. Despite increased interest in the exposome, tools and publications that characterize exposure correlations and their relationships with human disease are limited, and there is a lack of data and results sharing in resources like the GWAS catalog. To address these gaps, we developed the PEGS Explorer web application to explore exposure correlations in data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS) that were rigorously calculated to account for differing data types and previously published results from ExWAS. Through globe visualizations, PEGS Explorer allows users to explore correlations between exposures found to be associated with complex diseases. The exposome data used for analysis includes not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The web application addresses the lack of accessible data and results sharing, a major challenge in the field, and enables users to put results in context, generate hypotheses, and, importantly, replicate findings in other cohorts. PEGS Explorer will be updated with additional results as they become available, ensuring it is an up-to-date resource in exposome science.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs).

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Race/ethnicity-stratified fine-mapping of the MHC locus reveals genetic variants associated with late-onset asthma.

Introduction: Asthma is a chronic disease of the airways that impairs normal breathing. The etiology of asthma is complex and involves multiple factors, including the environment and genetics, especially the distinct genetic architecture associated with ancestry. Compared to early-onset asthma, little is known about genetic predisposition to late-onset asthma. We investigated the race/ethnicity-specific relationship among genetic variants within the major histocompatibility complex (MHC) region and late-onset asthma in a North Carolina-based multiracial cohort of adults. Methods: We stratified all analyses by self-reported race (i.e., White and Black) and adjusted all regression models for age, sex, and ancestry. We conducted association tests within the MHC region and performed fine-mapping analyses conditioned on the race/ethnicity-specific lead variant using whole-genome sequencing (WGS) data. We applied computational methods to infer human leukocyte antigen (HLA) alleles and residues at amino acid positions. We replicated findings in the UK Biobank. Results: The lead signals, rs9265901 on the 5' end of HLA-B, rs55888430 on HLA-DOB, and rs117953947 on HCG17, were significantly associated with late-onset asthma in all, White, and Black participants, respectively (OR = 1.73, 95%CI: 1.31 to 2.14, p = 3.62 × 10-5; OR = 3.05, 95%CI: 1.86 to 4.98, p = 8.85 × 10-6; OR = 19.5, 95%CI: 4.37 to 87.2, p = 9.97 × 10-5, respectively). For the HLA analysis, HLA-B*40:02 and HLA-DRB1*04:05, HLA-B*40:02, HLA-C*04:01, and HLA-DRB1*04:05, and HLA-DRB1*03:01 and HLA-DQB1 were significantly associated with late-onset asthma in all, White, and Black participants. Conclusion: Multiple genetic variants within the MHC region were significantly associated with late-onset asthma, and the associations were significantly different by race/ethnicity group.

Pharmacogenomic Analyses Implicate B Cell Developmental Status and MKL1 as Determinants of Sensitivity toward Anti-CD20 Monoclonal Antibody Therapy.

Monoclonal antibody (mAb) therapy directed against CD20 is an important tool in the treatment of B cell disorders. However, variable patient response and acquired resistance remain important clinical challenges. To identify genetic factors that may influence sensitivity to treatment, the cytotoxic activity of three CD20 mAbs: rituximab; ofatumumab; and obinutuzumab, were screened in high-throughput assays using 680 ethnically diverse lymphoblastoid cell lines (LCLs) followed by a pharmacogenomic assessment. GWAS analysis identified several novel gene candidates. The most significant SNP, rs58600101, in the gene MKL1 displayed ethnic stratification, with the variant being significantly more prevalent in the African cohort and resulting in reduced transcript levels as measured by qPCR. Functional validation of MKL1 by shRNA-mediated knockdown of MKL1 resulted in a more resistant phenotype. Gene expression analysis identified the developmentally associated TGFB1I1 as the most significant gene associated with sensitivity. qPCR among a panel of sensitive and resistant LCLs revealed immunoglobulin class-switching as well as differences in the expression of B cell activation markers. Flow cytometry showed heterogeneity within some cell lines relative to surface Ig isotype with a shift to more IgG+ cells among the resistant lines. Pretreatment with prednisolone could partly reverse the resistant phenotype. Results suggest that the efficacy of anti-CD20 mAb therapy may be influenced by B cell developmental status as well as polymorphism in the MKL1 gene. A clinical benefit may be achieved by pretreatment with corticosteroids such as prednisolone followed by mAb therapy.

RYK Gene Expression Associated with Drug Response Variation of Temozolomide and Clinical Outcomes in Glioma Patients.

Temozolomide (TMZ) chemotherapy is an important tool in the treatment of glioma brain tumors. However, variable patient response and chemo-resistance remain exceptionally challenging. Our previous genome-wide association study (GWAS) identified a suggestively significant association of SNP rs4470517 in the RYK (receptor-like kinase) gene with TMZ drug response. Functional validation of RYK using lymphocytes and glioma cell lines resulted in gene expression analysis indicating differences in expression status between genotypes of the cell lines and TMZ dose response. We conducted univariate and multivariate Cox regression analyses using publicly available TCGA and GEO datasets to investigate the impact of RYK gene expression status on glioma patient overall (OS) and progression-free survival (PFS). Our results indicated that in IDH mutant gliomas, RYK expression and tumor grade were significant predictors of survival. In IDH wildtype glioblastomas (GBM), MGMT status was the only significant predictor. Despite this result, we revealed a potential benefit of RYK expression in IDH wildtype GBM patients. We found that a combination of RYK expression and MGMT status could serve as an additional biomarker for improved survival. Overall, our findings suggest that RYK expression may serve as an important prognostic or predictor of TMZ response and survival for glioma patients.

MKX-AS1 Gene Expression Associated with Variation in Drug Response to Oxaliplatin and Clinical Outcomes in Colorectal Cancer Patients.

Oxaliplatin (OXAL) is a commonly used chemotherapy for treating colorectal cancer (CRC). A recent genome wide association study (GWAS) showed that a genetic variant (rs11006706) in the lncRNA gene MKX-AS1 and partnered sense gene MKX could impact the response of genetically varied cell lines to OXAL treatment. This study found that the expression levels of MKX-AS1 and MKX in lymphocytes (LCLs) and CRC cell lines differed between the rs11006706 genotypes, indicating that this gene pair could play a role in OXAL response. Further analysis of patient survival data from the Cancer Genome Atlas (TCGA) and other sources showed that patients with high MKX-AS1 expression status had significantly worse overall survival (HR = 3.2; 95%CI = (1.17-9); p = 0.024) compared to cases with low MKX-AS1 expression status. Alternatively, high MKX expression status had significantly better overall survival (HR = 0.22; 95%CI = (0.07-0.7); p = 0.01) compared to cases with low MKX expression status. These results suggest an association between MKX-AS1 and MKX expression status that could be useful as a prognostic marker of response to OXAL and potential patient outcomes in CRC.

Questionnaire-Based Polyexposure Assessment Outperforms Polygenic Scores for Classification of Type 2 Diabetes in a Multiancestry Cohort.

OBJECTIVE: Environmental exposures may have greater predictive power for type 2 diabetes than polygenic scores (PGS). Studies examining environmental risk factors, however, have included only individuals with European ancestry, limiting the applicability of results. We conducted an exposome-wide association study in the multiancestry Personalized Environment and Genes Study to assess the effects of environmental factors on type 2 diabetes. RESEARCH DESIGN AND METHODS: Using logistic regression for single-exposure analysis, we identified exposures associated with type 2 diabetes, adjusting for age, BMI, household income, and self-reported sex and race. To compare cumulative genetic and environmental effects, we computed an overall clinical score (OCS) as a weighted sum of BMI and prediabetes, hypertension, and high cholesterol status and a polyexposure score (PXS) as a weighted sum of 13 environmental variables. Using UK Biobank data, we developed a multiancestry PGS and calculated it for participants. RESULTS: We found 76 significant associations with type 2 diabetes, including novel associations of asbestos and coal dust exposure. OCS, PXS, and PGS were significantly associated with type 2 diabetes. PXS had moderate power to determine associations, with larger effect size and greater power and reclassification improvement than PGS. For all scores, the results differed by race. CONCLUSIONS: Our findings in a multiancestry cohort elucidate how type 2 diabetes odds can be attributed to clinical, genetic, and environmental factors and emphasize the need for exposome data in disease-risk association studies. Race-based differences in predictive scores highlight the need for genetic and exposome-wide studies in diverse populations.

Association of distance to swine concentrated animal feeding operations with immune-mediated diseases: An exploratory gene-environment study.

BACKGROUND: Concentrated animal feeding operations (CAFOs) are a source of environmental pollution and have been associated with a variety of health outcomes. Immune-mediated diseases (IMD) are characterized by dysregulation of the normal immune response and, while they may be affected by gene and environmental factors, their association with living in proximity to a CAFO is unknown. OBJECTIVES: We explored gene, environment, and gene-environment (GxE) relationships between IMD, CAFOs, and single nucleotide polymorphisms (SNPs) of prototypical xenobiotic response genes AHR, ARNT, and AHRR and prototypical immune response gene PTPN22. METHODS: The exposure analysis cohort consisted of 6,464 participants who completed the Personalized Environment and Genes Study Health and Exposure Survey and a subset of 1,541 participants who were genotyped. We assessed the association between participants' residential proximity to a CAFO in gene, environment, and GxE models. We recombined individual associations in a transethnic model using METAL meta-analysis. RESULTS: In White participants, ARNT SNP rs11204735 was associated with autoimmune diseases and rheumatoid arthritis (RA), and ARNT SNP rs1889740 was associated with RA. In a transethnic genetic analysis, ARNT SNPs rs11204735 and rs1889740 and PTPN22 SNP rs2476601 were associated with autoimmune diseases and RA. In participants living closer than one mile to a CAFO, the log-distance to a CAFO was associated with autoimmune diseases and RA. In a GxE interaction model, White participants with ARNT SNPs rs11204735 and rs1889740 living closer than eight miles to a CAFO had increased odds of RA and autoimmune diseases, respectively. The transethnic model revealed similar GxE interactions. CONCLUSIONS: Our results suggest increased risk of autoimmune diseases and RA in those living in proximity to a CAFO and a potential role of the AHR-ARNT pathway in conferring risk. We also report the first association of ARNT SNPs rs11204735 and rs1889740 with RA. Our findings, if confirmed, could allow for novel genetically-targeted or other preventive approaches for certain IMD.

The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS).

BACKGROUND: Autoimmune (AI) diseases appear to be a product of genetic predisposition and environmental triggers. Disruption of the skin barrier causes exacerbation of psoriasis/eczema. Oxidative stress is a mechanistic pathway for pathogenesis of the disease and is also a primary mechanism for the detrimental effects of air pollution. METHODS: We evaluated the association between autoimmune skin diseases (psoriasis or eczema) and air pollutant mixtures in 9060 subjects from the Personalized Environment and Genes Study (PEGS) cohort. Pollutant exposure data on six criteria air pollutants are publicly available from the Center for Air, Climate, and Energy Solutions and the Atmospheric Composition Analysis Group. For increased spatial resolution, we included spatially cumulative exposure to volatile organic compounds from sites in the United States Environmental Protection Agency Toxic Release Inventory and the density of major roads within a 5 km radius of a participant's address from the United States Geological Survey. We applied logistic regression with quantile g-computation, adjusting for age, sex, diagnosis with an autoimmune disease in family or self, and smoking history to evaluate the relationship between self-reported diagnosis of an AI skin condition and air pollution mixtures. RESULTS: Only one air pollution variable, sulfate, was significant individually (OR = 1.06, p = 3.99E-2); however, the conditional odds ratio for the combined mixture components of PM2.5 (black carbon, sulfate, sea salt, and soil), CO, SO2, benzene, toluene, and ethylbenzene is 1.10 (p-value = 5.4E-3). SIGNIFICANCE: While the etiology of autoimmune skin disorders is not clear, this study provides evidence that air pollutants are associated with an increased prevalence of these disorders. The results provide further evidence of potential health impacts of air pollution exposures on life-altering diseases. SIGNIFICANCE AND IMPACT STATEMENT: The impact of air pollution on non-pulmonary and cardiovascular diseases is understudied and under-reported. We find that air pollution significantly increased the odds of psoriasis or eczema in our cohort and the magnitude is comparable to the risk associated with smoking exposure. Autoimmune diseases like psoriasis and eczema are likely impacted by air pollution, particularly complex mixtures and our study underscores the importance of quantifying air pollution-associated risks in autoimmune disease.

Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study.

While multiple factors are associated with cardiovascular disease (CVD), many environmental exposures that may contribute to CVD have not been examined. To understand environmental effects on cardiovascular health, we performed an exposome-wide association study (ExWAS), a hypothesis-free approach, using survey data on endogenous and exogenous exposures at home and work and data from health and medical histories from the North Carolina-based Personalized Environment and Genes Study (PEGS) (n = 5015). We performed ExWAS analyses separately on six cardiovascular outcomes (cardiac arrhythmia, congestive heart failure, coronary artery disease, heart attack, stroke, and a combined atherogenic-related outcome comprising angina, angioplasty, atherosclerosis, coronary artery disease, heart attack, and stroke) using logistic regression and a false discovery rate of 5%. For each CVD outcome, we tested 502 single exposures and built multi-exposure models using the deletion-substitution-addition (DSA) algorithm. To evaluate complex nonlinear relationships, we employed the knockoff boosted tree (KOBT) algorithm. We adjusted all analyses for age, sex, race, BMI, and annual household income. ExWAS analyses revealed novel associations that include blood type A (Rh-) with heart attack (OR[95%CI] = 8.2[2.2:29.7]); paint exposures with stroke (paint related chemicals: 6.1[2.2:16.0], acrylic paint: 8.1[2.6:22.9], primer: 6.7[2.2:18.6]); biohazardous materials exposure with arrhythmia (1.8[1.5:2.3]); and higher paternal education level with reduced risk of multiple CVD outcomes (stroke, heart attack, coronary artery disease, and combined atherogenic outcome). In multi-exposure models, trouble sleeping and smoking remained important risk factors. KOBT identified significant nonlinear effects of sleep disorder, regular intake of grapefruit, and a family history of blood clotting problems for multiple CVD outcomes (combined atherogenic outcome, congestive heart failure, and coronary artery disease). In conclusion, using statistics and machine learning, these findings identify novel potential risk factors for CVD, enable hypothesis generation, provide insights into the complex relationships between risk factors and CVD, and highlight the importance of considering multiple exposures when examining CVD outcomes.

High-throughput screening and genome-wide analyses of 44 anticancer drugs in the 1000 Genomes cell lines reveals an association of the NQO1 gene with the response of multiple anticancer drugs.

Cancer patients exhibit a broad range of inter-individual variability in response and toxicity to widely used anticancer drugs, and genetic variation is a major contributor to this variability. To identify new genes that influence the response of 44 FDA-approved anticancer drug treatments widely used to treat various types of cancer, we conducted high-throughput screening and genome-wide association mapping using 680 lymphoblastoid cell lines from the 1000 Genomes Project. The drug treatments considered in this study represent nine drug classes widely used in the treatment of cancer in addition to the paclitaxel + epirubicin combination therapy commonly used for breast cancer patients. Our genome-wide association study (GWAS) found several significant and suggestive associations. We prioritized consistent associations for functional follow-up using gene-expression analyses. The NAD(P)H quinone dehydrogenase 1 (NQO1) gene was found to be associated with the dose-response of arsenic trioxide, erlotinib, trametinib, and a combination treatment of paclitaxel + epirubicin. NQO1 has previously been shown as a biomarker of epirubicin response, but our results reveal novel associations with these additional treatments. Baseline gene expression of NQO1 was positively correlated with response for 43 of the 44 treatments surveyed. By interrogating the functional mechanisms of this association, the results demonstrate differences in both baseline and drug-exposed induction.

Extending the lymphoblastoid cell line model for drug combination pharmacogenomics.

Combination drug therapies have become an integral part of precision oncology, and while evidence of clinical effectiveness continues to grow, the underlying mechanisms supporting synergy are poorly understood. Immortalized human lymphoblastoid cell lines (LCLs) have been proven as a particularly useful, scalable and low-cost model in pharmacogenetics research, and are suitable for elucidating the molecular mechanisms of synergistic combination therapies. In this review, we cover the advantages of LCLs in synergy pharmacogenomics and consider recent studies providing initial evidence of the utility of LCLs in synergy research. We also discuss several opportunities for LCL-based systems to address gaps in the research through the expansion of testing regimens, assessment of new drug classes and higher-order combinations, and utilization of integrated omics technologies.

Race and smoking status associated with paclitaxel drug response in patient-derived lymphoblastoid cell lines.

The use of ex-vivo model systems to provide a level of forecasting for in-vivo characteristics remains an important need for cancer therapeutics. The use of lymphoblastoid cell lines (LCLs) is an attractive approach for pharmacogenomics and toxicogenomics, due to their scalability, efficiency, and cost-effectiveness. There is little data on the impact of demographic or clinical covariates on LCL response to chemotherapy. Paclitaxel sensitivity was determined in LCLs from 93 breast cancer patients from the University of North Carolina Lineberger Comprehensive Cancer Center Breast Cancer Database to test for potential associations and/or confounders in paclitaxel dose-response assays. Measures of paclitaxel cell viability were associated with patient data included treatment regimens, cancer status, demographic and environmental variables, and clinical outcomes. We used multivariate analysis of variance to identify the in-vivo variables associated with ex-vivo dose-response. In this unique dataset that includes both in-vivo and ex-vivo data from breast cancer patients, race (P = 0.0049) and smoking status (P = 0.0050) were found to be significantly associated with ex-vivo dose-response in LCLs. Racial differences in clinical dose-response have been previously described, but the smoking association has not been reported. Our results indicate that in-vivo smoking status can influence ex-vivo dose-response in LCLs, and more precise measures of covariates may allow for more precise forecasting of clinical effect. In addition, understanding the mechanism by which exposure to smoking in-vivo effects ex-vivo dose-response in LCLs may open up new avenues in the quest for better therapeutic prediction.

The influence of Neanderthal alleles on cytotoxic response.

Various studies have shown that people of Eurasian origin contain traces of DNA inherited from interbreeding with Neanderthals. Recent studies have demonstrated that these Neanderthal variants influence a range of clinically important traits and diseases. Thus, understanding the genetic factors responsible for the variability in individual response to drug or chemical exposure is a key goal of pharmacogenomics and toxicogenomics, as dose responses are clinically and epidemiologically important traits. It is well established that ethnic and racial differences are important in dose response traits, but to our knowledge the influence of Neanderthal ancestry on response to xenobiotics is unknown. Towards this aim, we examined if Neanderthal ancestry plays a role in cytotoxic response to anti-cancer drugs and toxic environmental chemicals. We identified common Neanderthal variants in lymphoblastoid cell lines (LCLs) derived from the globally diverse 1000 Genomes Project and Caucasian cell lines from the Children's Hospital of Oakland Research Institute. We analyzed the effects of these Neanderthal alleles on cytotoxic response to 29 anti-cancer drugs and 179 environmental chemicals at varying concentrations using genome-wide data. We identified and replicated single nucleotide polymorphisms (SNPs) from these association results, including a SNP in the SNORD-113 cluster. Our results also show that the Neanderthal alleles cumulatively lead to increased sensitivity to both the anti-cancer drugs and the environmental chemicals. Our results demonstrate the influence of Neanderthal ancestry-informative markers on cytotoxic response. These results could be important in identifying biomarkers for personalized medicine or in dissecting the underlying etiology of dose response traits.

Pharmacogenetic Analysis of the Model-Based Pharmacokinetics of Five Anti-HIV Drugs: How Does This Influence the Effect of Aging?

Analysis of aging and pharmacogenetics (PGx) on antiretroviral pharmacokinetics (PKs) could inform precision dosing for older human HIV-infected patients. Seventy-four participants receiving either atazanavir/ritonavir (ATV/RTV) or efavirenz (EFV) with tenofovir/emtricitabine (TFV/FTC) provided PK and PGx information. Aging-PGx-PK association and interaction analyses were conducted using one-way analysis of variance (ANOVA), multiple linear regression, and Random Forest ensemble methods. Our analyses associated unbound ATV disposition with multidrug resistance protein (MRP)4, RTV with P-glycoprotein (P-gp), and EFV with cytochrome P450 (CYP)2B6 and MRP4 genetic variants. The clearance and cellular distribution of TFV were associated with P-gp, MRP2, and concentrative nucleoside transporters (CNTs), and FTC parameters were associated with organic cation transporters (OCTs) and MRP2 genetic variants. Notably, p16INK4a expression, a cellular aging marker, predicted EFV and FTC PK when genetic factors were adjusted. Both age and p16INK4a expression interacted with PGx on ATV and TFV disposition, implying potential dose adjustment based on aging may depend on genetic background.