Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report

Ethical, Legal, and Social Implications of Gene-Environment Interaction Research

Many complex disorders are impacted by the interplay of genetic and environmental factors. In gene-environment interactions (GxE), an individual's genetic and epigenetic makeup impacts the response to environmental exposures. Understanding GxE can impact health at the individual, community, and population levels. The rapid expansion of GxE research in biomedical studies for complex diseases raises many unique ethical, legal, and social implications (ELSIs) that have not been extensively explored and addressed. This review article builds on discussions originating from a workshop held by the National Institute of Environmental Health Sciences (NIEHS) and the National Human Genome Research Institute (NHGRI) in January 2022, entitled: "Ethical, Legal, and Social Implications of Gene-Environment Interaction Research." We expand upon multiple key themes to inform broad recommendations and general guidance for addressing some of the most unique and challenging ELSI in GxE research. Key takeaways include strategies and approaches for establishing sustainable community partnerships, incorporating social determinants of health and environmental justice considerations into GxE research, effectively communicating and translating GxE findings, and addressing privacy and discrimination concerns in all GxE research going forward. Additional guidelines, resources, approaches, training, and capacity building are required to further support innovative GxE research and multidisciplinary GxE research teams.

SCAMPI: A scalable statistical framework for genome-wide interaction testing harnessing cross-trait correlations

Family-based heritability estimates of complex traits are often considerably larger than their single-nucleotide polymorphism (SNP) heritability estimates. This discrepancy may be due to non-additive effects of genetic variation, including variation that interacts with other genes or environmental factors to influence the trait. Variance-based procedures provide a computationally efficient strategy to screen for SNPs with potential interaction effects without requiring the specification of the interacting variable. While valuable, such variance-based tests consider only a single trait and ignore likely pleiotropy among related traits that, if present, could improve power to detect such interaction effects. To fill this gap, we propose SCAMPI (Scalable Cauchy Aggregate test using Multiple Phenotypes to test Interactions), which screens for variants with interaction effects across multiple traits. SCAMPI is motivated by the observation that SNPs with pleiotropic interaction effects induce genotypic differences in the patterns of correlation among traits. By studying such patterns across genotype categories among multiple traits, we show that SCAMPI has improved performance over traditional univariate variance-based methods. Like those traditional variance-based tests, SCAMPI permits the screening of interaction effects without requiring the specification of the interaction variable and is further computationally scalable to biobank data. We employed SCAMPI to screen for interacting SNPs associated with four lipid-related traits in the UK Biobank and identified multiple gene regions missed by existing univariate variance-based tests. SCAMPI is implemented in software for public use.

Influence of structural racism on cancer health disparities: Tailoring measures relevant to multiple myeloma

This commentary highlights a need for comprehensive measures of structural racism tailored to cancer health disparities, in particular Black-White disparities in multiple myeloma (MM). Recent political and social calls and advances in the ability to quantitate structural racism have led to rapidly growing research on the health consequences of structural racism. However, to date, most studies have used unidimensional measures of structural racism that do not capture cumulative influences or enable the identification of factors most responsible for driving disparities. Furthermore, measures may not reflect aspects of structural racism most relevant to underlying disease processes and risks. This study proposes a multifaceted approach to measuring structural racism relevant to MM that includes comprehensive, disease- and at-risk population-tailored social and environmental data and biomarkers of susceptibility and progression related to underlying biological changes associated with structural racism. Such novel measures of structural racism may improve the ability to assess the influence of structural racism on cancer health disparities, which may advance understanding of disease etiology and differences observed by racialized groups.

Decoding Cancer Risk: Understanding Gene-Environment Interactions in Cancer Development

While lifestyle choices or behavioral patterns remain the most significant factors influencing cancer risk, environmental exposure to certain chemicals, both manufactured and natural, may also contribute to an individual's likelihood of developing cancer. This interplay of factors, coupled with an aging demographic and shifting lifestyle patterns, has led to an increasing prevalence of cancer in recent years. This study examines the environmental and behavioral factors that contribute to anomalies in the immune system and increase the risk of developing cancer. Significant environmental and occupational factors include the contamination of air and water, exposure to radiation, contact with harmful microorganisms and pathogens, and workplace exposure to carcinogens such as asbestos, certain chemicals, and industrial pollutants. Behavioral factors, such as food, physical activity, stress, substance misuse, and sleep patterns, have a substantial impact on immunological function and the likelihood of developing cancer. For example, pollutants like benzene and arsenic can disrupt immune function and raise the risk of developing cancer. Similarly, lifestyle variables such as inactivity and poor nutrition have been linked to an increased risk of cancer. Long-term stress and substance abuse can also decrease immunological responses, increasing the risk of developing cancer. The review underlines the complexities of examining gene-environment interactions, as well as the importance of using several perspectives to fully comprehend these pathways. Future investigations should emphasize improved methodology and larger sample sizes. Public health campaigns should aim to reduce human exposure to cancer-causing compounds known as carcinogens while also encouraging the adoption of healthy behaviors and habits. Tailored preventive approaches that account for individual genetic vulnerabilities have the potential to improve cancer prevention and treatment.

Identification of Pancreatic Cancer Germline Risk Variants With Effects That Are Modified by Smoking

PURPOSE

Pancreatic cancer (PC) is a deadly disease most often diagnosed in late stages. Identification of high-risk subjects could both contribute to preventative measures and help diagnose the disease at earlier timepoints. However, known risk factors, assessed independently, are currently insufficient for accurately stratifying patients. We use large-scale data from the UK Biobank (UKB) to identify genetic variant-smoking interaction effects and show their importance in risk assessment.

METHODS

We draw data from 15,086,830 genetic variants and 315,512 individuals in the UKB. There are 765 cases of PC. Crucially, robust resampling corrections are used to overcome well-known challenges in hypothesis testing for interactions. Replication analysis is conducted in two independent cohorts totaling 793 cases and 570 controls. Integration of functional annotation data and construction of polygenic risk scores (PRS) demonstrate the additional insight provided by interaction effects.

RESULTS

We identify the genome-wide significant variant rs77196339 on chromosome 2 (per minor allele odds ratio in never-smokers, 2.31 [95% CI, 1.69 to 3.15]; per minor allele odds ratio in ever-smokers, 0.53 [95% CI, 0.30 to 0.91]; P = 3.54 × 10-8) as well as eight other loci with suggestive evidence of interaction effects (P < 5 × 10-6). The rs77196339 region association is validated (P < .05) in the replication sample. PRS incorporating interaction effects show improved discriminatory ability over PRS of main effects alone.

CONCLUSION

This study of genome-wide germline variants identified smoking to modify the effect of rs77196339 on PC risk. Interactions between known risk factors can provide critical information for identifying high-risk subjects, given the relative inadequacy of models considering only main effects, as demonstrated in PRS. Further studies are necessary to advance toward comprehensive risk prediction approaches for PC.

Alzheimer's Disease Prevention and Treatment Based on Population-Based Approaches

The development of effective prevention and treatment strategies for Alzheimer's disease (AD) and dementia is hindered by limited knowledge of the underlying biological and environmental causes. While certain genetic factors have been associated with AD, and various lifestyle and environmental factors have been linked to dementia risk, the interactions between genes and the environment are not yet fully understood. To identify new avenues for dementia prevention, coordinated global efforts are needed to utilize existing cohorts and resources effectively and efficiently. This chapter provides an overview of current research on risk and protective factors for AD and dementia and discusses the opportunities and challenges associated with population-based approaches.

The Spatial-Temporal Dimension of Oncological Prevalence and Mortality in Romania

The objective of this study was to identify spatial disparities in the distribution of cancer hotspots within Romania. Additionally, the research aimed to track prevailing trends in cancer prevalence and mortality according to a cancer type. The study covered the timeframe between 2008 and 2017, examining all 3,181 territorial administrative units. The analysis of spatial distribution relied on two key parameters. The first parameter, persistence, measured the duration for which cancer prevalence exceeded the 75th percentile threshold. Cancer prevalence refers to the total number of individuals in a population who have been diagnosed with cancer at a specific time point, including both newly diagnosed cases (occurrence) and existing cases. The second parameter, the time continuity of persistence, calculated the consecutive months during which cancer prevalence consistently surpassed the 75th percentile threshold. Notably, persistence of elevated values was also evident in lowland regions, devoid of any discernible direct connection to environmental conditions. In conclusion, this work bears substantial relevance to regional health policies, by aiding in the formulation of prevention strategies, while also fostering a deeper comprehension of the socioeconomic and environmental factors contributing to cancer.

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

BACKGROUND

Genome-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.

METHODS

Analyses 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.

RESULTS

Assuming 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).

CONCLUSIONS

Overall, 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.

Early-life farm exposures and eczema among adults in the Agricultural Lung Health Study

Background

Several studies conducted in Europe have suggested a protective association between early-life farming exposures and childhood eczema or atopic dermatitis; few studies have examined associations in adults.

Objectives

To investigate associations between early-life exposures and eczema among 3217 adult farmers and farm spouses (mean age 62.8 years) in a case-control study nested within an US agricultural cohort.

Methods

We used sampling-weighted logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (95%CIs) for associations between early-life exposures and self-reported doctor-diagnosed eczema (273 cases) and polytomous logistic regression to estimate ORs (95%CIs) for a 4-level outcome combining information on eczema and atopy (specific IgE≥0.35). Additionally, we explored genetic and gene-environment associations with eczema.

Results

Although early-life farming exposures were not associated with eczema overall, several early-life exposures were associated with a reduced risk of having both eczema and atopy. Notably, results suggest stronger protective associations among individuals with both eczema and atopy than among those with either atopy alone or eczema alone. For example, ORs (95%CIs) for having a mother who did farm work while pregnant were 1.01 (0.60-1.69) for eczema alone and 0.80 (0.65-0.99) for atopy alone, but 0.54 (0.33-0.80) for having both eczema and atopy. A genetic risk score based on previously identified atopic dermatitis variants was strongly positively associated with eczema, and interaction testing suggested protective effects of several early-life farming exposures only in individuals at lower genetic risk.

Conclusions

In utero and childhood farming exposures are associated with decreased odds of having eczema with atopy in adults.

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Three-way interaction effects of early life stress, positive parenting and FKBP5 in the development of depressive symptoms in a general population

FKBP5 gene–environment interaction (cG × E) studies have shown diverse results, some indicating significant interaction effects between the gene and environmental stressors on depression, while others lack such results. Moreover, FKBP5 has a potential role in the diathesis stress and differential susceptibility theorem. The aim of the present study was to evaluate whether a cG × E interaction effect of FKBP5 single-nucleotide polymorphisms (SNPs) or haplotype and early life stress (ELS) on depressive symptoms among young adults was moderated by a positive parenting style (PASCQ^pos), through the frameworks of the diathesis stress and differential susceptibility theorem. Data were obtained from the Survey of Adolescent Life in Västmanland Cohort Study, including 1006 participants and their guardians. Data were collected during 2012, when the participants were 13 and 15 years old (Wave I: DNA), 2015, when participants were 16 and 18 years old (Wave II: PASCQ^pos, depressive symptomology and ELS) and 2018, when participants were 19 and 21 years old (Wave III: depressive symptomology). Significant three-way interactions were found for the FKBP5 SNPs rs1360780, rs4713916, rs7748266 and rs9394309, moderated by ELS and PASCQ^pos, on depressive symptoms among young adults. Diathesis stress patterns of interaction were observed for the FKBP5 SNPs rs1360780, rs4713916 and rs9394309, and differential susceptibility patterns of interaction were observed for the FKBP5 SNP rs7748266. Findings emphasize the possible role of FKBP5 in the development of depressive symptoms among young adults and contribute to the understanding of possible differential susceptibility effects of FKBP5.

Gene-Environment Interactions Relevant to Estrogen and Risk of Breast Cancer: Can Gene-Environment Interactions Be Detected Only among Candidate SNPs from Genome-Wide Association Studies?

In this study we aim to examine gene-environment interactions (GxEs) between genes involved with estrogen metabolism and environmental factors related to estrogen exposure. GxE analyses were conducted with 1970 Korean breast cancer cases and 2052 controls in the case-control study, the Seoul Breast Cancer Study (SEBCS). A total of 11,555 SNPs from the 137 candidate genes were included in the GxE analyses with eight established environmental factors. A replication test was conducted by using an independent population from the Breast Cancer Association Consortium (BCAC), with 62,485 Europeans and 9047 Asians. The GxE tests were performed by using two-step methods in GxEScan software. Two interactions were found in the SEBCS. The first interaction was shown between rs13035764 of NCOA1 and age at menarche in the GE|2df model (p-2df = 1.2 × 10-3). The age at menarche before 14 years old was associated with the high risk of breast cancer, and the risk was higher when subjects had homozygous minor allele G. The second GxE was shown between rs851998 near ESR1 and height in the GE|2df model (p-2df = 1.1 × 10-4). Height taller than 160 cm was associated with a high risk of breast cancer, and the risk increased when the minor allele was added. The findings were not replicated in the BCAC. These results would suggest specificity in Koreans for breast cancer risk.

Using Genetic Marginal Effects to Study Gene-Environment Interactions with GWAS Data

Gene-environment interactions (GxE) play a central role in the theoretical relationship between genetic factors and complex traits. While genome wide GxE studies of human behaviors remain underutilized, in part due to methodological limitations, existing GxE research in model organisms emphasizes the importance of interpreting genetic associations within environmental contexts. In this paper, we present a framework for conducting an analysis of GxE using raw data from genome wide association studies (GWAS) and applying the techniques to analyze gene-by-age interactions for alcohol use frequency. To illustrate the effectiveness of this procedure, we calculate genetic marginal effects from a GxE GWAS analysis for an ordinal measure of alcohol use frequency from the UK Biobank dataset, treating the respondent's age as the continuous moderating environment. The genetic marginal effects clarify the interpretation of the GxE associations and provide a direct and clear understanding of how the genetic associations vary across age (the environment). To highlight the advantages of our proposed methods for presenting GxE GWAS results, we compare the interpretation of marginal genetic effects with an interpretation that focuses narrowly on the significance of the interaction coefficients. The results imply that the genetic associations with alcohol use frequency vary considerably across ages, a conclusion that may not be obvious from the raw regression or interaction coefficients. GxE GWAS is less powerful than the standard "main effect" GWAS approach, and therefore require larger samples to detect significant moderated associations. Fortunately, the necessary sample sizes for a successful application of GxE GWAS can rely on the existing and on-going development of consortia and large-scale population-based studies.

Opportunities for Gene and Environment Research in Cancer: An Updated Review of NCI's Extramural Grant Portfolio

BACKGROUND

The study of gene-environment (GxE) interactions is a research priority for the NCI. Previously, our group analyzed NCI's extramural grant portfolio from fiscal years (FY) 2007 to 2009 to determine the state of the science in GxE research. This study builds upon our previous effort and examines changes in the landscape of GxE cancer research funded by NCI.

METHODS

The NCI grant portfolio was examined from FY 2010 to 2018 using the iSearch application. A time-trend analysis was conducted to explore changes over the study interval.

RESULTS

A total of 107 grants met the search criteria and were abstracted. The most common cancer types studied were breast (19.6%) and colorectal (18.7%). Most grants focused on GxE using specific candidate genes (69.2%) compared with agnostic approaches using genome-wide (26.2%) or whole-exome/whole-genome next-generation sequencing (NGS) approaches (19.6%); some grants used more than one approach to assess genetic variation. More funded grants incorporated NGS technologies in FY 2016-2018 compared with prior FYs. Environmental exposures most commonly examined were energy balance (46.7%) and drugs/treatment (40.2%). Over the time interval, we observed a decrease in energy balance applications with a concurrent increase in drug/treatment applications.

CONCLUSIONS

Research in GxE interactions has continued to concentrate on common cancers, while there have been some shifts in focus of genetic and environmental exposures. Opportunities exist to study less common cancers, apply new technologies, and increase racial/ethnic diversity.

IMPACT

This analysis of NCI's extramural grant portfolio updates previous efforts and provides a review of NCI grant support for GxE research.

Impact of Gene-Environment Interactions on Cancer Development

Several epidemiological and experimental studies have demonstrated that many human diseases are not only caused by specific genetic and environmental factors but also by gene-environment interactions. Although it has been widely reported that genetic polymorphisms play a critical role in human susceptibility to cancer and other chronic disease conditions, many single nucleotide polymorphisms (SNPs) are caused by somatic mutations resulting from human exposure to environmental stressors. Scientific evidence suggests that the etiology of many chronic illnesses is caused by the joint effect between genetics and the environment. Research has also pointed out that the interactions of environmental factors with specific allelic variants highly modulate the susceptibility to diseases. Hence, many scientific discoveries on gene-environment interactions have elucidated the impact of their combined effect on the incidence and/or prevalence rate of human diseases. In this review, we provide an overview of the nature of gene-environment interactions, and discuss their role in human cancers, with special emphases on lung, colorectal, bladder, breast, ovarian, and prostate cancers.

Incorporating multiple sets of eQTL weights into gene-by-environment interaction analysis identifies novel susceptibility loci for pancreatic cancer

It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene-by-environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data-adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case-Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome-wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking-related pancreatic cancer susceptibility genes, TRIP10 and KDM3A, were identified. The aGEw test is implemented in an R package aGE.

Polygenic approaches to detect gene-environment interactions when external information is unavailable

The exploration of 'gene-environment interactions' (G × E) is important for disease prediction and prevention. The scientific community usually uses external information to construct a genetic risk score (GRS), and then tests the interaction between this GRS and an environmental factor (E). However, external genome-wide association studies (GWAS) are not always available, especially for non-Caucasian ethnicity. Although GRS is an analysis tool to detect G × E in GWAS, its performance remains unclear when there is no external information. Our 'adaptive combination of Bayes factors method' (ADABF) can aggregate G × E signals and test the significance of G × E by a polygenic test. We here explore a powerful polygenic approach for G × E when external information is unavailable, by comparing our ADABF with the GRS based on marginal effects of SNPs (GRS-M) and GRS based on SNP × E interactions (GRS-I). ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when single-nucleotide polymorphisms main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. Furthermore, we apply these methods to Taiwan Biobank data. ADABF and GRS-M identified gene × alcohol and gene × smoking interactions on blood pressure (BP). BP-increasing alleles elevate more BP in drinkers (smokers) than in nondrinkers (nonsmokers). This work provides guidance to choose a polygenic approach to detect G × E when external information is unavailable.

Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers

Genome-wide association studies (GWAS) have led to the identification of hundreds of susceptibility loci across cancers, but the impact of further studies remains uncertain. Here we analyse summary-level data from GWAS of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) and underlying effect-size distribution. All cancers show a high degree of polygenicity, involving at a minimum of thousands of loci. We project that sample sizes required to explain 80% of GWAS heritability vary from 60,000 cases for testicular to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores (PRS), compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that PRS have potential for risk stratification for cancers of breast, colon and prostate, but less so for others because of modest heritability and lower incidence.

Fast computation of genome-metagenome interaction effects

Motivation

Association studies have been widely used to search for associations between common genetic variants observations and a given phenotype. However, it is now generally accepted that genes and environment must be examined jointly when estimating phenotypic variance. In this work we consider two types of biological markers: genotypic markers, which characterize an observation in terms of inherited genetic information, and metagenomic marker which are related to the environment. Both types of markers are available in their millions and can be used to characterize any observation uniquely.

Objective

Our focus is on detecting interactions between groups of genetic and metagenomic markers in order to gain a better understanding of the complex relationship between environment and genome in the expression of a given phenotype.

Contributions

We propose a novel approach for efficiently detecting interactions between complementary datasets in a high-dimensional setting with a reduced computational cost. The method, named SICOMORE, reduces the dimension of the search space by selecting a subset of supervariables in the two complementary datasets. These supervariables are given by a weighted group structure defined on sets of variables at different scales. A Lasso selection is then applied on each type of supervariable to obtain a subset of potential interactions that will be explored via linear model testing.

Results

We compare SICOMORE with other approaches in simulations, with varying sample sizes, noise, and numbers of true interactions. SICOMORE exhibits convincing results in terms of recall, as well as competitive performances with respect to running time. The method is also used to detect interaction between genomic markers in Medicago truncatula and metagenomic markers in its rhizosphere bacterial community.

Software availability

An R package is available [4], along with its documentation and associated scripts, allowing the reader to reproduce the results presented in the paper.

From human genome epidemiology to systems epidemiology: current progress and future perspective

The recent progress in human genome epidemiology (HuGE) is already having a profound impact on the practice of medicine and public health. First, the success of genome-wide association studies has greatly expanded the direction and content of epidemiological researches, including revealing new genetic mechanisms of complex diseases, identifying new targets for therapeutic interventions, and improving application in early screening of high-risk populations. At the same time, large-scale genomic studies make it possible to efficiently explore the gene-environment interactions, which will help better understand the biological pathways of complex diseases and identify individuals who may be more susceptible to diseases. Additionally, the emergence of systems epidemiology aims to integrate multi-omics together with epidemiological data to create a systems network that can comprehensively characterize the diverse range of factors contributing to disease development. These progress will help to apply HuGE findings into practice to improve the health of individuals and populations.

Familial cancer risk in family members and spouses of patients with early-onset head and neck cancer

BACKGROUND

Reported patterns of familial aggregation of head and neck cancer (HNC) vary greatly, with many studies hampered by the limited number of subjects.

METHODS

Altogether 923 early-onset (≤40 years old) HNC probands, their first-degree relatives, spouses, and siblings' offspring were ascertained. Cumulative risk and standardized incidence ratios (SIRs) were estimated.

RESULTS

Of all early-onset HNC families, only 21 (2.3%) had familial HNC cancers at any age and less than five familial early onset HNC cancers among first-degree relatives. The cumulative risk of HNC for siblings by age 60 (0.52%) was at population level (0.33%). No increased familial risk of early-onset HNC could be discerned in family members (SIR 2.68, 95% CI 0.32-9.68 for first-degree relatives).

CONCLUSIONS

Our study indicates that the cumulative and relative familial risk of early-onset HNC is modest in the Finnish population and, at most, only a minor proportion of early-onset HNCs are due solely to inherited genetic mutations.

Semiparametric Bayesian variable selection for gene-environment interactions

Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Study of gene-environment (G×E) interactions is important for elucidating the disease etiology. Existing Bayesian methods for G×E interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. Many studies have shown the advantages of penalization methods in detecting G×E interactions in "large p, small n" settings. However, Bayesian variable selection, which can provide fresh insight into G×E study, has not been widely examined. We propose a novel and powerful semiparametric Bayesian variable selection model that can investigate linear and nonlinear G×E interactions simultaneously. Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main-effects-only case within the Bayesian framework. Spike-and-slab priors are incorporated on both individual and group levels to identify the sparse main and interaction effects. The proposed method conducts Bayesian variable selection more efficiently than existing methods. Simulation shows that the proposed model outperforms competing alternatives in terms of both identification and prediction. The proposed Bayesian method leads to the identification of main and interaction effects with important implications in a high-throughput profiling study with high-dimensional SNP data.

Comparison of Radiation Dose Reconstruction Methods to Investigate Late Adverse Effects of Radiotherapy for Childhood Cancer: A Report from the Childhood Cancer Survivor Study

Quantification of radiation dose to normal tissue during radiotherapy is critical for assessing risk for radiotherapy-related late effects, including subsequent neoplasms (SNs). Case-control studies of SNs typically reconstruct absorbed radiation dose to the specific SN location using individual treatment parameters. A simplified method estimates the maximum prescribed target dose to the body region in which the SN arises. We compared doses and risk estimates from these methods using data from case-control studies of subsequent brain tumors (64 cases, 244 controls) and breast cancer (94 cases, 358 controls) nested within the Childhood Cancer Survivor Study (≥5-year survivors of childhood cancer diagnosed 1970-1986). The weighted kappa statistic [95% confidence interval (CI)] evaluating agreement between categorical (>0-9.9/10-19.9/20-29.9/≥30 Gy) body-region and tumor location-specific doses was 0.95 (0.91-0.98) for brain and 0.76 (0.69-0.82) for breast. The body-region and location-specific doses were assigned to the same dose category for a smaller proportion of patients treated with fields delivering a heterogeneous dose across the tissue of interest (e.g., partial brain field = 57.1%; mantle field = 61.3%) than patients treated with fields delivering a more homogeneous dose (e.g., whole brain field = 100%). Excess odds ratios per Gy (95% CI) from conditional logistic regression were 1.25 (0.33-6.33) and 1.20 (0.31-6.14) for brain tumors and 0.21 (0.05-0.77) and 0.10 (0.02-0.44) for breast cancer, using location-specific and body-region doses, respectively. We observed that body-region doses can approximate location-specific doses when the tissue of interest is clearly in the radiation field or outside the treated body region. Agreement is lower when there is greater ambiguity of SN location relative to the treatment field.

Identification of gene-environment interactions with marginal penalization

Gene-environment (G-E) interaction analysis has been extensively conducted for complex diseases. In marginal analysis, the common practice is to conduct likelihood-based (and other "standard") estimation with each marginal model, and then select significant G-E interactions and main effects based on p values and multiple comparisons adjustment. One limitation of this approach is that the identification results often do not respect the "main effects, interactions" hierarchy, which has been stressed in recent G-E interaction analyses. There is some recent effort tackling this problem, however, with very complex formulations. Another limitation of the common practice is that it may not perform well when regularization is needed, for example, because of "non-normal" distributions. In this article, we propose a marginal penalization approach which adopts a novel penalty to directly tackle the aforementioned problems. The proposed approach has a framework more coherent with that of the recently developed joint analysis methods and an intuitive formulation, and can be effectively realized. In simulation, it outperforms the popular significance-based analysis and simple penalization-based alternatives. Promising findings are made in the analysis of a single-nucleotide polymorphism and a gene expression data.

Epigenetics Predicts Serum 25-Hydroxyvitamin D Response to Vitamin D3 Supplementation in African Americans

SCOPE

The effects of vitamin D₃ supplementations on circulating 25-hydroxyvitamin D [25(OH)D] are varied. The hypothesis that the baseline DNA methylation plays a role in the serum 25(OH)D response to vitamin D₃ supplementation is tested.

METHODS AND RESULTS

A randomized clinical trial is first conducted among 64 African Americans, who are randomly assigned to a placebo or a 16-week treatment of 600, 2000, and 4000 IU d^-1 of vitamin D₃ supplements. Expected serum 25(OH)D concentrations at posttest are estimated by intervention, age, gender, body mass index, baseline 25(OH)D concentrations, and seasonal variations. The 25(OH)D response is categorized into a high-response group when the actual 25(OH)D concentrations at posttest are higher than expected, and a low-response group otherwise. The 25(OH)D response is associated with baseline methylation levels of CYP family and VDR genes (raw p < 0.05). At a genome-wide level, the baseline methylation level of cg07873128 (OSBPL5) that regulates cholesterol balance and calcium homeostasis is higher in the low-response group (false discovery rate = 0.028).

CONCLUSIONS

The baseline methylation levels of CYP family and VDR modulate 25(OH)D response. In addition, the hypermethylation of cg07873128 at the baseline, which is located in the imprinted gene OSBPL5, may reduce the serum 25(OH)D response to vitamin D₃ supplementation.

Gene-environment interactions and colorectal cancer risk: An umbrella review of systematic reviews and meta-analyses of observational studies

The cause of colorectal cancer (CRC) is multifactorial, involving both genetic variants and environmental risk factors. We systematically searched the MEDLINE, EMBASE, China National Knowledge Infrastructure (CNKI) and Wanfang databases from inception to December 2016, to identify systematic reviews and meta-analyses of observational studies that investigated gene-environment (G×E) interactions in CRC risk. Then, we critically evaluated the cumulative evidence for the G×E interactions using an extension of the Human Genome Epidemiology Network's Venice criteria. Overall, 15 articles reporting systematic reviews of observational studies on 89 G×E interactions, 20 articles reporting meta-analyses of candidate gene- or single-nucleotide polymorphism-based studies on 521 G×E interactions, and 8 articles reporting 33 genome-wide G×E interaction analyses were identified. On the basis of prior and observed scores, only the interaction between rs6983267 (8q24) and aspirin use was found to have a moderate overall credibility score as well as main genetic and environmental effects. Though 5 other interactions were also found to have moderate evidence, these interaction effects were tenuous due to the lack of main genetic effects and/or environmental effects. We did not find highly convincing evidence for any interactions, but several associations were found to have moderate strength of evidence. Our conclusions are based on application of the Venice criteria which were designed to provide a conservative assessment of G×E interactions and thus do not include an evaluation of biological plausibility of an observed joint effect.

A powerful and data-adaptive test for rare-variant-based gene-environment interaction analysis

As whole-exome/genome sequencing data become increasingly available in genetic epidemiology research consortia, there is emerging interest in testing the interactions between rare genetic variants and environmental exposures that modify the risk of complex diseases. However, testing rare-variant-based gene-by-environment interactions (GxE) is more challenging than testing the genetic main effects due to the difficulty in correctly estimating the latter under the null hypothesis of no GxE effects and the presence of neutral variants. In response, we have developed a family of powerful and data-adaptive GxE tests, called "aGE" tests, in the framework of the adaptive powered score test, originally proposed for testing the genetic main effects. Using extensive simulations, we show that aGE tests can control the type I error rate in the presence of a large number of neutral variants or a nonlinear environmental main effect, and the power is more resilient to the inclusion of neutral variants than that of existing methods. We demonstrate the performance of the proposed aGE tests using Pancreatic Cancer Case-Control Consortium Exome Chip data. An R package "aGE" is available at http://github.com/ytzhong/projects/.

Translational research on reserve against neurodegenerative disease: consensus report of the International Conference on Cognitive Reserve in the Dementias and the Alzheimer's Association Reserve, Resilience and Protective Factors Professional Interest Area working groups

BACKGROUND

The concept of reserve was established to account for the observation that a given degree of neurodegenerative pathology may result in varying degrees of symptoms in different individuals. There is a large amount of evidence on epidemiological risk and protective factors for neurodegenerative diseases and dementia, yet the biological mechanisms that underpin the protective effects of certain lifestyle and physiological variables remain poorly understood, limiting the development of more effective preventive and treatment strategies. Additionally, different definitions and concepts of reserve exist, which hampers the coordination of research and comparison of results across studies.

DISCUSSION

This paper represents the consensus of a multidisciplinary group of experts from different areas of research related to reserve, including clinical, epidemiological and basic sciences. The consensus was developed during meetings of the working groups of the first International Conference on Cognitive Reserve in the Dementias (24-25 November 2017, Munich, Germany) and the Alzheimer's Association Reserve and Resilience Professional Interest Area (25 July 2018, Chicago, USA). The main objective of the present paper is to develop a translational perspective on putative mechanisms underlying reserve against neurodegenerative disease, combining evidence from epidemiological and clinical studies with knowledge from animal and basic research. The potential brain functional and structural basis of reserve in Alzheimer's disease and other brain disorders are discussed, as well as relevant lifestyle and genetic factors assessed in both humans and animal models.

CONCLUSION

There is an urgent need to advance our concept of reserve from a hypothetical model to a more concrete approach that can be used to improve the development of effective interventions aimed at preventing dementia. Our group recommends agreement on a common dictionary of terms referring to different aspects of reserve, the improvement of opportunities for data sharing across individual cohorts, harmonising research approaches across laboratories and groups to reduce heterogeneity associated with human data, global coordination of clinical trials to more effectively explore whether reducing epidemiological risk factors leads to a reduced burden of neurodegenerative diseases in the population, and an increase in our understanding of the appropriateness of animal models for reserve research.

A Selective Review of Multi-Level Omics Data Integration Using Variable Selection

High-throughput technologies have been used to generate a large amount of omics data. In the past, single-level analysis has been extensively conducted where the omics measurements at different levels, including mRNA, microRNA, CNV and DNA methylation, are analyzed separately. As the molecular complexity of disease etiology exists at all different levels, integrative analysis offers an effective way to borrow strength across multi-level omics data and can be more powerful than single level analysis. In this article, we focus on reviewing existing multi-omics integration studies by paying special attention to variable selection methods. We first summarize published reviews on integrating multi-level omics data. Next, after a brief overview on variable selection methods, we review existing supervised, semi-supervised and unsupervised integrative analyses within parallel and hierarchical integration studies, respectively. The strength and limitations of the methods are discussed in detail. No existing integration method can dominate the rest. The computation aspects are also investigated. The review concludes with possible limitations and future directions for multi-level omics data integration.

Interaction between the microbiome and TP53 in human lung cancer

Background

Lung cancer is the leading cancer diagnosis worldwide and the number one cause of cancer deaths. Exposure to cigarette smoke, the primary risk factor in lung cancer, reduces epithelial barrier integrity and increases susceptibility to infections. Herein, we hypothesize that somatic mutations together with cigarette smoke generate a dysbiotic microbiota that is associated with lung carcinogenesis. Using lung tissue from 33 controls and 143 cancer cases, we conduct 16S ribosomal RNA (rRNA) bacterial gene sequencing, with RNA-sequencing data from lung cancer cases in The Cancer Genome Atlas serving as the validation cohort.

Results

Overall, we demonstrate a lower alpha diversity in normal lung as compared to non-tumor adjacent or tumor tissue. In squamous cell carcinoma specifically, a separate group of taxa are identified, in which Acidovorax is enriched in smokers. Acidovorax temporans is identified within tumor sections by fluorescent in situ hybridization and confirmed by two separate 16S rRNA strategies. Further, these taxa, including Acidovorax, exhibit higher abundance among the subset of squamous cell carcinoma cases with TP53 mutations, an association not seen in adenocarcinomas.

Conclusions

The results of this comprehensive study show both microbiome-gene and microbiome-exposure interactions in squamous cell carcinoma lung cancer tissue. Specifically, tumors harboring TP53 mutations, which can impair epithelial function, have a unique bacterial consortium that is higher in relative abundance in smoking-associated tumors of this type. Given the significant need for clinical diagnostic tools in lung cancer, this study may provide novel biomarkers for early detection.

Electronic supplementary material

The online version of this article (10.1186/s13059-018-1501-6) contains supplementary material, which is available to authorized users.

Case-only approach to identifying markers predicting treatment effects on the relative risk scale

Retrospectively measuring markers on stored baseline samples from participants in a randomized controlled trial (RCT) may provide high quality evidence as to the value of the markers for treatment selection. Originally developed for approximating gene-environment interactions in the odds ratio scale, the case-only method has recently been advocated for assessing gene-treatment interactions on rare disease endpoints in randomized clinical trials. In this article, the case-only approach is shown to provide a consistent and efficient estimator of marker by treatment interactions and marker-specific treatment effects on the relative risk scale. The prohibitive rare-disease assumption is no longer needed, broadening the utility of the case-only approach. The case-only method is resource-efficient as markers only need to be measured in cases only. It eliminates the need to model the marker's main effect, and can be used with any parametric or nonparametric learning method. The utility of this approach is illustrated by an application to genetic data in the Women's Health Initiative (WHI) hormone therapy trial.

Testing for gene-environment interaction under exposure misspecification

Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further, we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties.

Epigenome-Wide Association Study of Dietary Fiber Intake in African American Adolescents

SCOPE

Low fiber intake is associated with increased risk for cardiovascular disease (CVD) and cancer. However, the underlying mechanisms are not well understood. Two hypotheses are tested: 1) dietary fiber would be associated with DNA methylation levels; 2) those DNA methylation changes would be associated with visceral adiposity and inflammation. Also the possibility that the associations between fiber and DNA methylation levels might be confounded with folic acid intake as sensitivity analysis are explored.

METHODS AND RESULTS

An epigenome-wide association study is conducted using Illumina 450K Bead-Chip on leukocyte DNA in 284 African American adolescents. Linear regression is performed to identify differentially methylated CpG sites associated with fiber. The methylation levels of 3 CpG sites (cg15200711, cg19462022, and cg07035602) in LPCAT1 and RASA3 genes are associated with fiber (false discovery rate [FDR] < 0.05) after adjustment for covariates including folic acid. The methylation levels of cg07035602 and cg19462022 are also associated with visceral adiposity and inflammation.

CONCLUSIONS

The data show that DNA methylation levels at LPCAT1 and RASA3 genes are associated with dietary fiber intake as well as with adiposity and inflammation. Future studies are warranted to determine whether epigenetic regulation may underlie the beneficial effects of fiber intake on adiposity and inflammation.

Additive varying-coefficient model for nonlinear gene-environment interactions

Gene-environment (G×E) interaction plays a pivotal role in understanding the genetic basis of complex disease. When environmental factors are measured continuously, one can assess the genetic sensitivity over different environmental conditions on a disease trait. Motivated by the increasing awareness of gene set based association analysis over single variant based approaches, we proposed an additive varying-coefficient model to jointly model variants in a genetic system. The model allows us to examine how variants in a gene set are moderated by an environment factor to affect a disease phenotype. We approached the problem from a variable selection perspective. In particular, we select variants with varying, constant and zero coefficients, which correspond to cases of G×E interaction, no G×E interaction and no genetic effect, respectively. The procedure was implemented through a two-stage iterative estimation algorithm via the smoothly clipped absolute deviation penalty function. Under certain regularity conditions, we established the consistency property in variable selection as well as effect separation of the two stage iterative estimators, and showed the optimal convergence rates of the estimates for varying effects. In addition, we showed that the estimate of non-zero constant coefficients enjoy the oracle property. The utility of our procedure was demonstrated through simulation studies and real data analysis.

The Biosocial Approach to Human Development, Behavior, and Health Across the Life Course

Social and biological phenomena are widely recognized as determinants of human development, health, and socioeconomic attainments across the life course, but our understanding of the underlying pathways and processes remains limited. To address this gap, we define the "biosocial approach" as one that conceptualizes the biological and social as mutually constituting, and that draws on models and methods from the biomedical and social/behavioral sciences. By bringing biology into the social sciences, we can illuminate mechanisms through which socioeconomic, psychosocial, and other contextual factors shape human development and health. Human biology is a social biology, and biological measures can therefore identify aspects of social contexts that are harmful, as well as beneficial, with respect to well-being. By bringing social science concepts and study designs to biology and biomedicine, we encourage an epistemological shift that foregrounds social/contextual factors as important determinants of human biology and health. The biosocial approach also underscores the importance of the life course, as assessments of both biological and social features throughout human development over time, and across generations, are needed to achieve a full understanding of social and physical well-being. We conclude with a brief review of the papers in the volume, which showcase the value of a biosocial approach to understanding the pathways linking social stratification, biology, and health across the life course.

Reproductive risk factors in breast cancer and genetic hormonal pathways: a gene-environment interaction in the MCC-Spain project

BACKGROUND

Reproductive factors are well known risk factors for breast cancer; however, little is known about how genetic variants in hormonal pathways interact with that relationship.

METHODS

One thousand one hundred thirty nine cases of breast cancer in women and 1322 frequency-matched controls were compared. Genetic variants in hormonal pathways (identified in the Kyoto Encyclopedia of Genes and Genomes) were screened according to their relationship with breast cancer using the Cochran-Armitage statistic. Information on reproductive factors was obtained using a face-to-face questionnaire. The interaction among the selected genetic variants and reproductive factors was tested with logistic regression.

RESULTS

Concerning C allele in rs2229712, compared to nulliparity in non-carriers the ORs for 1-2 and > 2 deliveries were 0.48 (0.28-0.81) and 0.34 (0.19-0.59), and in C carriers they were 0.92 (0.42-1.98) and 0.71 (0.31-1.61). Similar results were found in women carrying the C allele in rs1269851. Carriers of Allele T in rs35652107 and allele C in rs6018027 had the delivery number effect more pronounced.

CONCLUSIONS

The number of deliveries had a dose-response protective effect on breast cancer; women carrying C allele in rs2229712 did not benefit from this protective effect.

Research Strategies for Nutritional and Physical Activity Epidemiology and Cancer Prevention

Very large international and ethnic differences in cancer rates exist, are minimally explained by genetic factors, and show the huge potential for cancer prevention. A substantial portion of the differences in cancer rates can be explained by modifiable factors, and many important relationships have been documented between diet, physical activity, and obesity, and incidence of important cancers. Other related factors, such as the microbiome and the metabolome, are emerging as important intermediary components in cancer prevention. It is possible with the incorporation of newer technologies and studies including long follow-up and evaluation of effects across the life cycle, additional convincing results will be produced. However, several challenges exist for cancer researchers; for example, measurement of diet and physical activity, and lack of standardization of samples for microbiome collection, and validation of metabolomic studies. The United States National Cancer Institute convened the Research Strategies for Nutritional and Physical Activity Epidemiology and Cancer Prevention Workshop on June 28-29, 2016, in Rockville, Maryland, during which the experts addressed the state of the science and areas of emphasis. This current paper reflects the state of the science and priorities for future research. Cancer Epidemiol Biomarkers Prev; 27(3); 233-44. ©2017 AACR.

Screening for gene-environment (G×E) interaction using omics data from exposed individuals: an application to gene-arsenic interaction

Identifying gene-environment interactions is a central challenge in the quest to understand susceptibility to complex, multi-factorial diseases. Developing an understanding of how inter-individual variability in inherited genetic variation alters the effects of environmental exposures will enhance our knowledge of disease mechanisms and improve our ability to predict disease and target interventions to high-risk sub-populations. Limited progress has been made identifying gene-environment interactions in the epidemiological setting using existing statistical approaches for genome-wide searches for interaction. In this paper, we describe a novel two-step approach using omics data to conduct genome-wide searches for gene-environment interactions. Using existing genome-wide SNP data from a large Bangladeshi cohort study specifically designed to assess the effect of arsenic exposure on health, we evaluated gene-arsenic interactions by first conducting genome-wide searches for SNPs that modify the effect of arsenic on molecular phenotypes (gene expression and DNA methylation features). Using this set of SNPs showing evidence of interaction with arsenic in relation to molecular phenotypes, we then tested SNP-arsenic interactions in relation to skin lesions, a hallmark characteristic of arsenic toxicity. With the emergence of additional omics data in the epidemiologic setting, our approach may have the potential to boost power for genome-wide interaction research, enabling the identification of interactions that will enhance our understanding of disease etiology and our ability to develop interventions targeted at susceptible sub-populations.

Screening for gene-environment (G×E) interaction using omics data from exposed individuals: an application to gene-arsenic interaction

Identifying gene-environment interactions is a central challenge in the quest to understand susceptibility to complex, multi-factorial diseases. Developing an understanding of how inter-individual variability in inherited genetic variation alters the effects of environmental exposures will enhance our knowledge of disease mechanisms and improve our ability to predict disease and target interventions to high-risk sub-populations. Limited progress has been made identifying gene-environment interactions in the epidemiological setting using existing statistical approaches for genome-wide searches for interaction. In this paper, we describe a novel two-step approach using omics data to conduct genome-wide searches for gene-environment interactions. Using existing genome-wide SNP data from a large Bangladeshi cohort study specifically designed to assess the effect of arsenic exposure on health, we evaluated gene-arsenic interactions by first conducting genome-wide searches for SNPs that modify the effect of arsenic on molecular phenotypes (gene expression and DNA methylation features). Using this set of SNPs showing evidence of interaction with arsenic in relation to molecular phenotypes, we then tested SNP-arsenic interactions in relation to skin lesions, a hallmark characteristic of arsenic toxicity. With the emergence of additional omics data in the epidemiologic setting, our approach may have the potential to boost power for genome-wide interaction research, enabling the identification of interactions that will enhance our understanding of disease etiology and our ability to develop interventions targeted at susceptible sub-populations.

Multiancestry Study of Gene-Lifestyle Interactions for Cardiovascular Traits in 610 475 Individuals From 124 Cohorts: Design and Rationale

BACKGROUND

Several consortia have pursued genome-wide association studies for identifying novel genetic loci for blood pressure, lipids, hypertension, etc. They demonstrated the power of collaborative research through meta-analysis of study-specific results.

METHODS AND RESULTS

The Gene-Lifestyle Interactions Working Group was formed to facilitate the first large, concerted, multiancestry study to systematically evaluate gene-lifestyle interactions. In stage 1, genome-wide interaction analysis is performed in 53 cohorts with a total of 149 684 individuals from multiple ancestries. In stage 2 involving an additional 71 cohorts with 460 791 individuals from multiple ancestries, focused analysis is performed for a subset of the most promising variants from stage 1. In all, the study involves up to 610 475 individuals. Current focus is on cardiovascular traits including blood pressure and lipids, and lifestyle factors including smoking, alcohol, education (as a surrogate for socioeconomic status), physical activity, psychosocial variables, and sleep. The total sample sizes vary among projects because of missing data. Large-scale gene-lifestyle or more generally gene-environment interaction (G×E) meta-analysis studies can be cumbersome and challenging. This article describes the design and some of the approaches pursued in the interaction projects.

CONCLUSIONS

The Gene-Lifestyle Interactions Working Group provides an excellent framework for understanding the lifestyle context of genetic effects and to identify novel trait loci through analysis of interactions. An important and novel feature of our study is that the gene-lifestyle interaction (G×E) results may improve our knowledge about the underlying mechanisms for novel and already known trait loci.

Barrett's esophagus transits to a cancer condition via potential biomarkers

AIM

In this study, the transcriptome profile of Barrett's esophagus (BE) was examined for identification potential related biomarkers in view of interacting charactering.

BACKGROUND

Since BE is known as a precursor of esophageal cancer, the molecular studies of this condition could be essential.

METHODS

Gene expression data of BE in comparison with normal cases, GSE34619 was retrieved from Gene Expression Omnibus. Differentially expressed genes (DEGs) were determined applying GEO2R online software. The DEGs then were analyzed in terms of centrality properties via constructing an interaction network.

RESULTS

The data indicate that there are two sets of hub-bottlenecks panels with distinguishable values in BE. The first group shows that BE is very susceptible to develop cancer, and the second one implied on central characteristic of some DEGs as previously were also reported for BE pathogenicity. In addition, these genes are also implicated in cancer shift from certain conditions.

CONCLUSION

On the whole, taking together these findings explain and support the cancerous origin of BE and introduced a panel of nominated biomarkers that could be more specific for BE rather than other types of esophageal problems. However, a complementary study to support this claim is suggested.

Population-Based Approaches to Alzheimer's Disease Prevention

Progress in prevention and treatment of Alzheimer's disease (AD) and dementia is hampered by the restricted understanding of the biological and environmental causes underlying pathophysiology. It is widely accepted that certain genetic factors are associated with AD and a number of lifestyle and other environmental characteristics have also been linked to dementia risk. However, interactions between genes and the environment are not yet well understood, and coordinated global action is required to utilize existing cohorts and other resources effectively and efficiently to identify new avenues for dementia prevention. This chapter provides a brief summary of current research on risk and protective factors and opportunities and challenges in relation to population-based approaches are discussed.

Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures

Identification of gene-environment (G × E) interactions associated with disease phenotypes has posed a great challenge in high-throughput cancer studies. The existing marginal identification methods have suffered from not being able to accommodate the joint effects of a large number of genetic variants, while some of the joint-effect methods have been limited by failing to respect the "main effects, interactions" hierarchy, by ignoring data contamination, and by using inefficient selection techniques under complex structural sparsity. In this article, we develop an effective penalization approach to identify important G × E interactions and main effects, which can account for the hierarchical structures of the 2 types of effects. Possible data contamination is accommodated by adopting the least absolute deviation loss function. The advantage of the proposed approach over the alternatives is convincingly demonstrated in both simulation and a case study on lung cancer prognosis with gene expression measurements and clinical covariates under the accelerated failure time model.

Lessons Learned From Past Gene-Environment Interaction Successes

Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G×E) has been a focus of research for several years. In this article, select examples of G×E from the literature are described to highlight different approaches and underlying principles related to the success of these studies. These examples can be broadly categorized as studies of single metabolism genes, genes in complex metabolism pathways, ranges of exposure levels, functional approaches and model systems, and pharmacogenomics. Some studies illustrated the success of studying exposure metabolism for which candidate genes can be identified. Moreover, some G×E successes depended on the availability of high-quality exposure assessment and longitudinal measures, study populations with a wide range of exposure levels, and the inclusion of ethnically and geographically diverse populations. In several examples, large population sizes were required to detect G×Es. Other examples illustrated the impact of accurately defining scale of the interactions (i.e., additive or multiplicative). Last, model systems and functional approaches provided insights into G×E in several examples. Future studies may benefit from these lessons learned.

Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.

Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies

A growing number and increasing diversity of factors are available for epidemiological studies. These measures provide new avenues for discovery and prevention, yet they also raise many challenges for adoption in epidemiological investigations. Here, we evaluate 1) designs to investigate diseases that consider heterogeneous and multidimensional indicators of exposure and behavior, 2) the implementation of numerous methods to capture indicators of exposure, and 3) the analytical methods required for discovery and validation. We find that case-control studies have provided insights into genetic susceptibility but are insufficient for characterizing complex effects of environmental factors on disease development. Prospective and two-phase designs are required but must balance extended data collection with follow-up of study participants. We discuss innovations in assessments including the microbiome; mass spectrometry and metabolomics; behavioral assessment; dietary, physical activity, and occupational exposure assessment; air pollution monitoring; and global positioning and individual sensors. We claim the the availability of extensive correlated data raises new challenges in disentangling specific exposures that influence cancer risk from among extensive and often correlated exposures. In conclusion, new high-dimensional exposure assessments offer many new opportunities for environmental assessment in cancer development. Cancer Epidemiol Biomarkers Prev; 26(9); 1370-80. ©2017 AACR.

Approaches to detect genetic effects that differ between two strata in genome-wide meta-analyses: Recommendations based on a systematic evaluation

Genome-wide association meta-analyses (GWAMAs) conducted separately by two strata have identified differences in genetic effects between strata, such as sex-differences for body fat distribution. However, there are several approaches to identify such differences and an uncertainty which approach to use. Assuming the availability of stratified GWAMA results, we compare various approaches to identify between-strata differences in genetic effects. We evaluate type I error and power via simulations and analytical comparisons for different scenarios of strata designs and for different types of between-strata differences. For strata of equal size, we find that the genome-wide test for difference without any filtering is the best approach to detect stratum-specific genetic effects with opposite directions, while filtering for overall association followed by the difference test is best to identify effects that are predominant in one stratum. When there is no a priori hypothesis on the type of difference, a combination of both approaches can be recommended. Some approaches violate type I error control when conducted in the same data set. For strata of unequal size, the best approach depends on whether the genetic effect is predominant in the larger or in the smaller stratum. Based on real data from GIANT (>175 000 individuals), we exemplify the impact of the approaches on the detection of sex-differences for body fat distribution (identifying up to 10 loci). Our recommendations provide tangible guidelines for future GWAMAs that aim at identifying between-strata differences. A better understanding of such effects will help pinpoint the underlying mechanisms.

Interaction between genes and macronutrient intake on the risk of developing type 2 diabetes: systematic review and findings from European Prospective Investigation into Cancer (EPIC)-InterAct

Background: Gene-diet interactions have been reported to contribute to the development of type 2 diabetes (T2D). However, to our knowledge, few examples have been consistently replicated to date.Objective: We aimed to identify existing evidence for gene-macronutrient interactions and T2D and to examine the reported interactions in a large-scale study.Design: We systematically reviewed studies reporting gene-macronutrient interactions and T2D. We searched the MEDLINE, Human Genome Epidemiology Network, and WHO International Clinical Trials Registry Platform electronic databases to identify studies published up to October 2015. Eligibility criteria included assessment of macronutrient quantity (e.g., total carbohydrate) or indicators of quality (e.g., dietary fiber) by use of self-report or objective biomarkers of intake. Interactions identified in the review were subsequently examined in the EPIC (European Prospective Investigation into Cancer)-InterAct case-cohort study (n = 21,148, with 9403 T2D cases; 8 European countries). Prentice-weighted Cox regression was used to estimate country-specific HRs, 95% CIs, and P-interaction values, which were then pooled by random-effects meta-analysis. A primary model was fitted by using the same covariates as reported in the published studies, and a second model adjusted for additional covariates and estimated the effects of isocaloric macronutrient substitution.Results: Thirteen observational studies met the eligibility criteria (n < 1700 cases). Eight unique interactions were reported to be significant between macronutrients [carbohydrate, fat, saturated fat, dietary fiber, and glycemic load derived from self-report of dietary intake and circulating n-3 (ω-3) polyunsaturated fatty acids] and genetic variants in or near transcription factor 7-like 2 (TCF7L2), gastric inhibitory polypeptide receptor (GIPR), caveolin 2 (CAV2), and peptidase D (PEPD) (P-interaction < 0.05). We found no evidence of interaction when we tried to replicate previously reported interactions. In addition, no interactions were detected in models with additional covariates.Conclusions: Eight gene-macronutrient interactions were identified for the risk of T2D from the literature. These interactions were not replicated in the EPIC-InterAct study, which mirrored the analyses undertaken in the original reports. Our findings highlight the importance of independent replication of reported interactions.

Loss of Cardioprotective Effects at the ADAMTS7 Locus as a Result of Gene-Smoking Interactions

BACKGROUND

Common diseases such as coronary heart disease (CHD) are complex in etiology. The interaction of genetic susceptibility with lifestyle factors may play a prominent role. However, gene-lifestyle interactions for CHD have been difficult to identify. Here, we investigate interaction of smoking behavior, a potent lifestyle factor, with genotypes that have been shown to associate with CHD risk.

METHODS

We analyzed data on 60 919 CHD cases and 80 243 controls from 29 studies for gene-smoking interactions for genetic variants at 45 loci previously reported to be associated with CHD risk. We also studied 5 loci associated with smoking behavior. Study-specific gene-smoking interaction effects were calculated and pooled using fixed-effects meta-analyses. Interaction analyses were declared to be significant at a P value of <1.0×10-3 (Bonferroni correction for 50 tests).

RESULTS

We identified novel gene-smoking interaction for a variant upstream of the ADAMTS7 gene. Every T allele of rs7178051 was associated with lower CHD risk by 12% in never-smokers (P=1.3×10-16) in comparison with 5% in ever-smokers (P=2.5×10-4), translating to a 60% loss of CHD protection conferred by this allelic variation in people who smoked tobacco (interaction P value=8.7×10-5). The protective T allele at rs7178051 was also associated with reduced ADAMTS7 expression in human aortic endothelial cells and lymphoblastoid cell lines. Exposure of human coronary artery smooth muscle cells to cigarette smoke extract led to induction of ADAMTS7. CONCLUSIONS: Allelic variation at rs7178051 that associates with reduced ADAMTS7 expression confers stronger CHD protection in never-smokers than in ever-smokers. Increased vascular ADAMTS7 expression may contribute to the loss of CHD protection in smokers.