Methods for investigating gene-environment interactions in candidate pathway and genome-wide association studies

Association of hypertension and depression with mortality: an exploratory study with interaction and mediation models

BACKGROUND

The association of hypertension and depression with mortality has not been fully understood. We aimed to explore the possible independent or joint association of hypertension and depression with mortality. Their interaction effects on mortality and possible mediating role were also investigated.

METHODS

Associations of hypertension, depression, and their interaction with all-cause and cardiovascular disease (CVD) mortality were evaluated using multivariate Cox proportional hazards regression models. The mediation analysis was conducted with a Sobel test.

RESULTS

A total of 35152 participants were included in the final analysis. Hypertension and depression were independently associated with increased risk of all-cause and CVD mortality. The co-existence of hypertension and depression resulted in a 1.7-fold [95% confidence interval (CI): 1.3-2.1] increase in all-cause mortality and a 2.3-fold (95% CI: 1.4-3.7) increase in CVD mortality compared to those with neither of them. Hypertension and depression showed no significant multiplicative (P for interaction, 0.587) and additive interaction (P for relative excess risk of interaction, 0.243; P for Interaction on additive scale, 0.654) on all-cause mortality, as well as on CVD mortality. Depression did not mediate the relationship between hypertension and all-cause (Z=1.704, P=0.088) and CVD mortality (Z=1.547, P=0.122). Hypertension did not mediate the relationship between all-cause and CVD mortality as well.

CONCLUSION

Hypertension and depression were related to all-cause and CVD mortality independently and the co-existence of them increased the risk of mortality. However, there is no interaction effect of them on mortality, and hypertension or depression did not mediate the association of each other with mortality.

Epigenetic modifications in the development of bronchopulmonary dysplasia: a review

While perinatal medicine advancements have bolstered survival outcomes for premature infants, bronchopulmonary dysplasia (BPD) continues to threaten their long-term health. Gene-environment interactions, mediated by epigenetic modifications such as DNA methylation, histone modification, and non-coding RNA regulation, take center stage in BPD pathogenesis. Recent discoveries link methylation variations across biological pathways with BPD. Also, the potential reversibility of histone modifications fuels new treatment avenues. The review also highlights the promise of utilizing mesenchymal stem cells and their exosomes as BPD therapies, given their ability to modulate non-coding RNA, opening novel research and intervention possibilities. IMPACT: The complexity and universality of epigenetic modifications in the occurrence and development of bronchopulmonary dysplasia were thoroughly discussed. Both molecular and cellular mechanisms contribute to the diverse nature of epigenetic changes, suggesting the need for deeper biochemical techniques to explore these molecular alterations. The utilization of innovative cell-specific drug delivery methods like exosomes and extracellular vesicles holds promise in achieving precise epigenetic regulation.

Understanding and applying gene-environment interactions: a guide for nutrition professionals with an emphasis on integration in African research settings

Noncommunicable diseases (NCDs) are influenced by the interplay between genetics and environmental exposures, particularly diet. However, many healthcare professionals, including nutritionists and dietitians, have limited genetic background and, therefore, they may lack understanding of gene-environment interactions (GxEs) studies. Even researchers deeply involved in nutrition studies, but with a focus elsewhere, can struggle to interpret, evaluate, and conduct GxE studies. There is an urgent need to study African populations that bear a heavy burden of NCDs, demonstrate unique genetic variability, and have cultural practices resulting in distinctive environmental exposures compared with Europeans or Americans, who are studied more. Although diverse and rapidly changing environments, as well as the high genetic variability of Africans and difference in linkage disequilibrium (ie, certain gene variants are inherited together more often than expected by chance), provide unparalleled potential to investigate the omics fields, only a small percentage of studies come from Africa. Furthermore, research evidence lags behind the practices of companies offering genetic testing for personalized medicine and nutrition. We need to generate more evidence on GxEs that also considers continental African populations to be able to prevent unethical practices and enable tailored treatments. This review aims to introduce nutrition professionals to genetics terms and valid methods to investigate GxEs and their challenges, and proposes ways to improve quality and reproducibility. The review also provides insight into the potential contributions of nutrigenetics and nutrigenomics to the healthcare sphere, addresses direct-to-consumer genetic testing, and concludes by offering insights into the field's future, including advanced technologies like artificial intelligence and machine learning.

A Varying Coefficient Model to Jointly Test Genetic and Gene-Environment Interaction Effects

Most human traits are influenced by the interplay between genetic and environmental factors. Many statistical methods have been proposed to screen for gene-environment interaction (GxE) in the post genome-wide association study era. However, most of the existing methods assume a linear interaction between genetic and environmental factors toward phenotypic variations, which diminishes statistical power in the case of nonlinear GxE. In this paper, we present a flexible statistical procedure to detect GxE regardless of whether the underlying relationship is linear or not. By modeling the joint genetic and GxE effects as a varying-coefficient function of the environmental factor, the proposed model is able to capture dynamic trajectories of GxE. We employ a likelihood ratio test with a fast Monte Carlo algorithm for hypothesis testing. Simulations were conducted to evaluate validity and power of the proposed model in various settings. Real data analysis was performed to illustrate its power, in particular, in the case of nonlinear GxE.

A Genome-Wide Association Study and Genomic Prediction for Fiber and Sucrose Contents in a Mapping Population of LCP 85-384 Sugarcane

Sugarcane (Saccharum spp. hybrids) is an economically important crop for both sugar and biofuel industries. Fiber and sucrose contents are the two most critical quantitative traits in sugarcane breeding that require multiple-year and multiple-location evaluations. Marker-assisted selection (MAS) could significantly reduce the time and cost of developing new sugarcane varieties. The objectives of this study were to conduct a genome-wide association study (GWAS) to identify DNA markers associated with fiber and sucrose contents and to perform genomic prediction (GP) for the two traits. Fiber and sucrose data were collected from 237 self-pollinated progenies of LCP 85-384, the most popular Louisiana sugarcane cultivar from 1999 to 2007. The GWAS was performed using 1310 polymorphic DNA marker alleles with three models of TASSEL 5, single marker regression (SMR), general linear model (GLM) and mixed linear model (MLM), and the fixed and random model circulating probability unification (FarmCPU) of R package. The results showed that 13 and 9 markers were associated with fiber and sucrose contents, respectively. The GP was performed by cross-prediction with five models, ridge regression best linear unbiased prediction (rrBLUP), Bayesian ridge regression (BRR), Bayesian A (BA), Bayesian B (BB) and Bayesian least absolute shrinkage and selection operator (BL). The accuracy of GP varied from 55.8% to 58.9% for fiber content and 54.6% to 57.2% for sucrose content. Upon validation, these markers can be applied in MAS and genomic selection (GS) to select superior sugarcane with good fiber and high sucrose contents.

A tree-based gene-environment interaction analysis with rare features

Gene-environment (G-E) interaction analysis plays a critical role in understanding and modeling complex diseases. Compared to main-effect-only analysis, it is more seriously challenged by higher dimensionality, weaker signals, and the unique "main effects, interactions" variable selection hierarchy. In joint G-E interaction analysis under which a large number of G factors are analysed in a single model, effort tailored to rare features (e.g., SNPs with low minor allele frequencies) has been limited. Existing investigations on rare features have been mostly focused on marginal analysis, where various data aggregation techniques have been developed, and hypothesis testings have been conducted to identify significant aggregated features. However, such techniques cannot be extended to joint G-E interaction analysis. In this study, building on a very recent tree-based data aggregation technique, which has been developed for main-effect-only analysis, we develop a new G-E interaction analysis approach tailored to rare features. The adopted data aggregation technique allows for more efficient information borrowing from neighboring rare features. Similar to some existing state-of-the-art ones, the proposed approach adopts penalization for variable selection, regularized estimation, and respect of the variable selection hierarchy. Simulation shows that it has more accurate identification of important interactions and main effects than several competing alternatives. In the analysis of NFBC1966 study, the proposed approach leads to findings different from the alternatives and with satisfactory prediction and stability performance.

High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study

BACKGROUND

Consumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized.

OBJECTIVE

We aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk.

METHODS

We introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data recorded in free-living conditions. The proposed framework standardizes the handling of data irregularities; encodes contextual information regarding the underlying physiological state at any given time; and generates a set of 66 minimally redundant features across active, sedentary, and sleep states. We applied our approach to a multimodal data set, from the SingHEART study (NCT02791152), which comprises heart rate and step count time series from wearables, clinical screening profiles, and whole genome sequences from 692 healthy volunteers. We used machine learning to model nonlinear relationships between the high-resolution phenotypes on the one hand and clinical or genomic risk markers for blood pressure, lipid, weight and sugar abnormalities on the other. For each risk type, we performed model comparisons based on Brier scores to assess the predictive value of high-resolution features over and beyond typical baselines. We also qualitatively characterized the wearable phenotypes for participants who had actualized clinical events.

RESULTS

We found that the high-resolution features have higher predictive value than typical baselines for clinical markers of cardiometabolic disease risk: the best models based on high-resolution features had 17.9% and 7.36% improvement in Brier score over baselines based on age and gender and resting heart rate, respectively (P<.001 in each case). Furthermore, heart rate dynamics from different activity states contain distinct information (maximum absolute correlation coefficient of 0.15). Heart rate dynamics in sedentary states are most predictive of lipid abnormalities and obesity, whereas patterns in active states are most predictive of blood pressure abnormalities (P<.001). Moreover, in comparison with standard measures, higher resolution patterns in wearable heart rate recordings are better able to represent subtle physiological dynamics related to genomic risk for cardiometabolic disease (improvement of 11.9%-22.0% in Brier scores; P<.001). Finally, illustrative case studies reveal connections between these high-resolution phenotypes and actualized clinical events, even for borderline profiles lacking apparent cardiometabolic risk markers.

CONCLUSIONS

High-resolution digital phenotypes recorded by consumer wearables in free-living states have the potential to enhance the prediction of cardiometabolic disease risk and could enable more proactive and personalized health management.

Genome-wide variance quantitative trait locus analysis suggests small interaction effects in blood pressure traits

Genome-wide variance quantitative trait loci (vQTL) analysis complements genome-wide association study (GWAS) and has the potential to identify novel variants associated with the trait, explain additional trait variance and lead to the identification of factors that modulate the genetic effects. I conducted genome-wide analysis of the UK Biobank data and identified 27 vQTLs associated with systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP). The top single-nucleotide polymorphisms (SNPs) are enriched for expression QTLs (eQTLs) or splicing QTLs (sQTLs) annotated by GTEx, suggesting their regulatory roles in mediating the associations with blood pressure (BP). Of the 27 vQTLs, 14 are known BP-associated QTLs discovered by GWASs. The heteroscedasticity effects of the 13 novel vQTLs are larger than their genetic main effects, which were not detected by existing GWASs. The total R-squared of the 27 top SNPs due to variance heteroscedasticity is 0.28%, compared with 0.50% owing to their main effects. The overall effect size of the variance heteroscedasticity is small in GWAS SNPs compared with their main effects. For the 411, 384 and 285 GWAS SNPs associated with SBP, DBP and PP, respectively, their heteroscedasticity effects were 0.52%, 0.43%, and 0.16%, and their main effects were 5.13%, 5.61%, and 3.75%, respectively. The number and effects of the vQTLs are small, which suggests that the effects of gene-environment and gene-gene interactions are small. The main effects of the SNPs remain the major source of genetic variance for BP, which would probably be true for other complex traits as well.

Identifying Gene-Environment Interactions With Robust Marginal Bayesian Variable Selection

In high-throughput genetics studies, an important aim is to identify gene–environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal variable selection has not received much attention. In this study, we propose a novel marginal Bayesian variable selection method for G×E studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo (MCMC). The proposed method outperforms a number of alternatives in extensive simulation studies. The utility of the marginal robust Bayesian variable selection method has been further demonstrated in the case studies using data from the Nurse Health Study (NHS). Some of the identified main and interaction effects from the real data analysis have important biological implications.

Genome-wide gene-diet interaction analysis in the UK Biobank identifies novel effects on hemoglobin A1c

Diet is a significant modifiable risk factor for type 2 diabetes (T2D), and its effect on disease risk is under partial genetic control. Identification of specific gene-diet interactions (GDIs) influencing risk biomarkers such as glycated hemoglobin (HbA1c) is a critical step towards precision nutrition for T2D prevention, but progress has been slow due to limitations in sample size and accuracy of dietary exposure measurement. We leveraged the large UK Biobank (UKB) cohort and a diverse group of dietary exposures, including 30 individual dietary traits and 8 empirical dietary patterns, to conduct genome-wide interaction studies in ~340 000 European-ancestry participants to identify novel GDIs influencing HbA1c. We identified five variant-dietary trait pairs reaching genome-wide significance (P < 5 × 10-8): two involved dietary patterns (meat pattern with rs147678157 and a fruit & vegetable-based pattern with rs3010439) and three involved individual dietary traits (bread consumption with rs62218803, dried fruit consumption with rs140270534 and milk type [dairy vs. other] with 4:131148078_TAGAA_T). These were affected minimally by adjustment for geographical and lifestyle-related confounders, and four of the five variants lacked genetic main effects that would have allowed their detection in a traditional genome-wide association study for HbA1c. Notably, multiple loci near transient receptor potential subfamily M genes (TRPM2 and TRPM3) interacted with carbohydrate-containing food groups. These interactions were further characterized using non-European UKB subsets and alternative measures of glycaemia (fasting glucose and follow-up HbA1c measurements). Our results highlight GDIs influencing HbA1c for future investigation, while reinforcing known challenges in detecting and replicating GDIs.

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.

CLUE: Exact maximal reduction of kinetic models by constrained lumping of differential equations

MOTIVATION

Detailed mechanistic models of biological processes can pose significant challenges for analysis and parameter estimations due to the large number of equations used to track the dynamics of all distinct configurations in which each involved biochemical species can be found. Model reduction can help tame such complexity by providing a lower-dimensional model in which each macro-variable can be directly related to the original variables.

RESULTS

We present CLUE, an algorithm for exact model reduction of systems of polynomial differential equations by constrained linear lumping. It computes the smallest dimensional reduction as a linear mapping of the state space such that the reduced model preserves the dynamics of user-specified linear combinations of the original variables. Even though CLUE works with nonlinear differential equations, it is based on linear algebra tools, which makes it applicable to high-dimensional models. Using case studies from the literature, we show how CLUE can substantially lower model dimensionality and help extract biologically intelligible insights from the reduction.

AVAILABILITY

An implementation of the algorithm and relevant resources to replicate the experiments herein reported are freely available for download at https://github.com/pogudingleb/CLUE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

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.

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Background

Heterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification.

Results

Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation.

Conclusion

PheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.

Nordic OCD & Related Disorders Consortium: Rationale, design, and methods

Obsessive-compulsive disorder (OCD) is a debilitating psychiatric disorder, yet its etiology is unknown and treatment outcomes could be improved if biological targets could be identified. Unfortunately, genetic findings for OCD are lagging behind other psychiatric disorders. Thus, there is a pressing need to understand the causal mechanisms implicated in OCD in order to improve clinical outcomes and to reduce morbidity and societal costs. Specifically, there is a need for a large-scale, etiologically informative genetic study integrating genetic and environmental factors that presumably interact to cause the condition. The Nordic countries provide fertile ground for such a study, given their detailed population registers, national healthcare systems and active specialist clinics for OCD. We thus formed the Nordic OCD and Related Disorders Consortium (NORDiC, www.crowleylab.org/nordic), and with the support of NIMH and the Swedish Research Council, have begun to collect a large, richly phenotyped and genotyped sample of OCD cases. Our specific aims are geared toward answering a number of key questions regarding the biology, etiology, and treatment of OCD. This article describes and discusses the rationale, design, and methodology of NORDiC, including details on clinical measures and planned genomic analyses.

A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank

The etiology of most complex diseases involves genetic variants, environmental factors, and gene-environment interaction (G × E) effects. Compared with marginal genetic association studies, G × E analysis requires more samples and detailed measure of environmental exposures, and this limits the possible discoveries. Large-scale population-based biobanks with detailed phenotypic and environmental information, such as UK-Biobank, can be ideal resources for identifying G × E effects. However, due to the large computation cost and the presence of case-control imbalance, existing methods often fail. Here we propose a scalable and accurate method, SPAGE (SaddlePoint Approximation implementation of G × E analysis), that is applicable for genome-wide scale phenome-wide G × E studies. SPAGE fits a genotype-independent logistic model only once across the genome-wide analysis in order to reduce computation cost, and SPAGE uses a saddlepoint approximation (SPA) to calibrate the test statistics for analysis of phenotypes with unbalanced case-control ratios. Simulation studies show that SPAGE is 33-79 times faster than the Wald test and 72-439 times faster than the Firth's test, and SPAGE can control type I error rates at the genome-wide significance level even when case-control ratios are extremely unbalanced. Through the analysis of UK-Biobank data of 344,341 white British European-ancestry samples, we show that SPAGE can efficiently analyze large samples while controlling for unbalanced case-control ratios.

Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design

BACKGROUND AND AIMS

There is considerable interest in epidemiology to estimate an additive interaction effect between two risk factors in case-control studies. An additive interaction is defined as the differential reduction in absolute risk associated with one factor between different levels of the other factor. A stratified two-phase case-control design is commonly used in epidemiology to reduce the cost of assembling covariates. It is crucial to obtain valid estimates of the model parameters by accounting for the underlying stratification scheme to obtain accurate and precise estimates of additive interaction effects. The aim of this paper is to examine the properties of different methods for estimating model parameters and additive interaction effects under a stratified two-phase case-control design.

METHODS

Using simulations, we investigate the properties of three existing methods, namely stratum-specific offset, inverse-probability weighting, and multiple imputation for estimating model parameters and additive interaction effects. We also illustrate these properties using data from two published epidemiology studies.

RESULTS

Simulation studies show that the multiple imputation method performs well when both the true and analysis models are additive (i.e., does not include multiplicative interaction terms) but does not provide a discernible advantage over the offset method when the analysis models are non-additive (i.e., includes multiplicative interaction terms). The offset method exhibits the best overall properties when the analysis model contains multiplicative interaction effects.

CONCLUSION

When estimating additive interaction between risk factors in stratified two-phase case-control studies, we recommend estimating model parameters using multiple imputation when the analysis model is additive, and we recommend the offset method when the analysis model is non-additive.

Unravelling the genetic basis of schizophrenia and bipolar disorder with GWAS: A systematic review

OBJECTIVES

To systematically review findings of GWAS in schizophrenia (SZ) and in bipolar disorder (BD); and to interpret findings, with a focus on identifying independent replications.

METHOD

PubMed search, selection and review of all independent GWAS in SZ or BD, published since March 2011, i.e. studies using non-overlapping samples within each article, between articles, and with those of the previous review (Li et al., 2012).

RESULTS

From the 22 GWAS included in this review, the genetic associations surviving standard GWAS-significance were for genetic markers in the regions of ACSL3/KCNE4, ADCY2, AMBRA1, ANK3, BRP44, DTL, FBLN1, HHAT, INTS7, LOC392301, LOC645434/NMBR, LOC729457, LRRFIP1, LSM1, MDM1, MHC, MIR2113/POU3F2, NDST3, NKAPL, ODZ4, PGBD1, RENBP, TRANK1, TSPAN18, TWIST2, UGT1A1/HJURP, WHSC1L1/FGFR1 and ZKSCAN4. All genes implicated across both reviews are discussed in terms of their function and implication in neuropsychiatry.

CONCLUSION

Taking all GWAS to date into account, AMBRA1, ANK3, ARNTL, CDH13, EFHD1 (albeit with different alleles), MHC, PLXNA2 and UGT1A1 have been implicated in either disorder in at least two reportedly non-overlapping samples. Additionally, evidence for a SZ/BD common genetic basis is most strongly supported by the implication of ANK3, NDST3, and PLXNA2.

Joint Analysis of Multiple Interaction Parameters in Genetic Association Studies

With growing human genetic and epidemiologic data, there has been increased interest for the study of gene-by-environment (G-E) interaction effects. Still, major questions remain on how to test jointly a large number of interactions between multiple SNPs and multiple exposures. In this study, we first compared the relative performance of four fixed-effect joint analysis approaches using simulated data, considering up to 10 exposures and 300 SNPs: (1) omnibus test, (2) multi-exposure and genetic risk score (GRS) test, (3) multi-SNP and environmental risk score (ERS) test, and (4) GRS-ERS test. Our simulations explored both linear and logistic regression while considering three statistics: the Wald test, the Score test, and the likelihood ratio test (LRT). We further applied the approaches to three large sets of human cohort data (n = 37,664), focusing on type 2 diabetes (T2D), obesity, hypertension, and coronary heart disease with smoking, physical activity, diets, and total energy intake. Overall, GRS-based approaches were the most robust, and had the highest power, especially when the G-E interaction effects were correlated with the marginal genetic and environmental effects. We also observed severe miscalibration of joint statistics in logistic models when the number of events per variable was too low when using either the Wald test or LRT test. Finally, our real data application detected nominally significant interaction effects for three outcomes (T2D, obesity, and hypertension), mainly from the GRS-ERS approach. In conclusion, this study provides guidelines for testing multiple interaction parameters in modern human cohorts including extensive genetic and environmental data.

Transferring entropy to the realm of GxG interactions

Genome-wide association studies are moving to genome-wide interaction studies, as the genetic background of many diseases appears to be more complex than previously supposed. Thus, many statistical approaches have been proposed to detect gene-gene (GxG) interactions, among them numerous information theory-based methods, inspired by the concept of entropy. These are suggested as particularly powerful and, because of their nonlinearity, as better able to capture nonlinear relationships between genetic variants and/or variables. However, the introduced entropy-based estimators differ to a surprising extent in their construction and even with respect to the basic definition of interactions. Also, not every entropy-based measure for interaction is accompanied by a proper statistical test. To shed light on this, a systematic review of the literature is presented answering the following questions: (1) How are GxG interactions defined within the framework of information theory? (2) Which entropy-based test statistics are available? (3) Which underlying distribution do the test statistics follow? (4) What are the given strengths and limitations of these test statistics?

A linear mixed model framework for gene-based gene-environment interaction tests in twin studies

Interaction between genes and environments (G×E) can be well investigated in families due to the shared genes and environment among family members. However, the majority of the current tests of G×E interaction between a set of variants and an environment are only suitable for studies with unrelated subjects. In this paper, we extend several G×E interaction tests to a linear mixed model framework to study interaction between a set of correlated environments and a candidate gene in families. The correlated environments can either be modeled separately or jointly in one model. We demonstrate theoretically that the tests developed by modeling correlated environments separately are valid and present a computationally fast alternative to detect G×E interaction in families. For either strategy, we propose treating the genetic main effects as a random effect to reduce the number of main-effect parameters and thus improve the power to detect interactions. Additionally, we propose a generalization of a test of interaction that adaptively sums the interactions using a sequential algorithm. This generalized set of tests, referred to as the sequential algorithm for the sum of powered score (Seq-SPU) family of tests, can be expressed as a weighted version of the SPU. We find that the adaptive version of our test, Seq-aSPU, can outperform aSPU in cases where the interactions effects are in opposite directions. We applied these methods to the Minnesota Center for Twin and Family Research data set and found one significant gene in interaction with four psychosocial environmental factors affecting the alcohol consumption among the twins.

The effect of age and gender on the genetic regulation of serum 25-hydroxyvitamin D - the FIN-D2D population-based study

In addition to sunlight and dietary sources, several genes in the metabolic pathway of vitamin D affect serum 25-hydroxyvitamin D (25OHD) concentration. It is not known whether this genetic regulation is influenced by host characteristics. We investigated the effect of age and gender on the genetic regulation of serum 25OHD concentration. In total, 2868 Finnish men and women aged 45-74 years participated in FIN-D2D population-based health survey in 2007. Of the 2822 participants that had serum 25OHD concentration available, 2757 were successfully genotyped. Age and gender-dependent association of SNPs with serum 25OHD concentration was studied in 10 SNPs with previously found association with vitamin D metabolites. Associations of 3 SNPs with serum 25OHD concentration were dependent on age with greater effects on younger (≤60 y) than older (>60 y) adults (rs10783219 in VDR, rs12512631 in GC and rs3794060 in NADSYN1/DHCR7; p_interaction = 0.03, 0.02 and 0.01, respectively). The results suggested a novel association between serum 25OHD concentration and rs8082391 in STAT5B gene in men but not in women (p_interaction = 0.01). After multiple testing correction with false discovery rate method, two age-dependent interactions (rs3794060 in NADSYN1/DHCR7 gene and rs12512631 in GC gene) remained statistically significant. This is the first study to suggest that genetic regulation of serum 25OHD concentration is age-dependent. Our results also indicated a novel association between serum 25OHD concentration and SNP in STAT5B gene in men. The results need to be confirmed in future studies preferably in a larger sample.

Grapevine field experiments reveal the contribution of genotype, the influence of environment and the effect of their interaction (G×E) on the berry transcriptome

Changes in the performance of genotypes in different environments are defined as genotype × environment (G×E) interactions. In grapevine (Vitis vinifera), complex interactions between different genotypes and climate, soil and farming practices yield unique berry qualities. However, the molecular basis of this phenomenon remains unclear. To dissect the basis of grapevine G×E interactions we characterized berry transcriptome plasticity, the genome methylation landscape and within-genotype allelic diversity in two genotypes cultivated in three different environments over two vintages. We identified, through a novel data-mining pipeline, genes with expression profiles that were: unaffected by genotype or environment, genotype-dependent but unaffected by the environment, environmentally-dependent regardless of genotype, and G×E-related. The G×E-related genes showed different degrees of within-cultivar allelic diversity in the two genotypes and were enriched for stress responses, signal transduction and secondary metabolism categories. Our study unraveled the mutual relationships between genotypic and environmental variables during G×E interaction in a woody perennial species, providing a reference model to explore how cultivated fruit crops respond to diverse environments. Also, the pivotal role of vineyard location in determining the performance of different varieties, by enhancing berry quality traits, was unraveled.

Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence

There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.

Genetic determinants of serum 25-hydroxyvitamin D concentration during pregnancy and type 1 diabetes in the child

Objective

The in utero environment plays an important role in shaping development and later life health of the fetus. It has been shown that maternal genetic factors in the metabolic pathway of vitamin D associate with type 1 diabetes in the child. In this study we analyzed the genetic determinants of serum 25-hydroxyvitamin D (25OHD) concentration during pregnancy in mothers whose children later developed type 1 diabetes and in control mothers.

Study design

474 mothers of type 1 diabetic children and 348 mothers of non-diabetic children were included in the study. We previously selected 7 single nucleotide polymorphisms (SNPs) in four genes in the metabolic pathway of vitamin D vitamin based on our previously published data demonstrating an association between genotype and serum 25OHD concentration. In this re-analysis, possible differences in strength in the association between the SNPs and serum 25OHD concentration in mothers of type 1 diabetic and non-diabetic children were investigated. Serum 25OHD concentrations were previously shown to be similar between the mothers of type 1 diabetic and non-diabetic children and vitamin D deficiency prevalent in both groups.

Results

Associations between serum 25OHD concentration and 2 SNPs, one in the vitamin D receptor (VDR) gene (rs4516035) and one in the group-specific component (GC) gene (rs12512631), were stronger during pregnancy in mothers whose children later developed type 1 diabetes than in mothers whose children did not (p_interaction = 0.03, 0.02, respectively).

Conclusions

We show for the first time that there are differences in the strength of genetic determinants of serum 25OHD concentration during pregnancy between the mothers of type 1 diabetic and non-diabetic children. Our results emphasize that the in utero environment including maternal vitamin D metabolism should be important lines of investigation when searching for factors that lead to early programming of type 1 diabetes.

Identifying gene-environment interactions for prognosis using a robust approach

For many complex diseases, prognosis is of essential importance. It has been shown that, beyond the main effects of genetic (G) and environmental (E) risk factors, gene-environment (G × E) interactions also play a critical role. In practical data analysis, part of the prognosis outcome data can have a distribution different from that of the rest of the data because of contamination or a mixture of subtypes. Literature has shown that data contamination as well as a mixture of distributions, if not properly accounted for, can lead to severely biased model estimation. In this study, we describe prognosis using an accelerated failure time (AFT) model. An exponential squared loss is proposed to accommodate data contamination or a mixture of distributions. A penalization approach is adopted for regularized estimation and marker selection. The proposed method is realized using an effective coordinate descent (CD) and minorization maximization (MM) algorithm. The estimation and identification consistency properties are rigorously established. Simulation shows that without contamination or mixture, the proposed method has performance comparable to or better than the nonrobust alternative. However, with contamination or mixture, it outperforms the nonrobust alternative and, under certain scenarios, is superior to the robust method based on quantile regression. The proposed method is applied to the analysis of TCGA (The Cancer Genome Atlas) lung cancer data. It identifies interactions different from those using the alternatives. The identified markers have important implications and satisfactory stability.

Multiple Gene-Environment Interactions on the Angiogenesis Gene-Pathway Impact Rectal Cancer Risk and Survival

Characterization of gene-environment interactions (GEIs) in cancer is limited. We aimed at identifying GEIs in rectal cancer focusing on a relevant biologic process involving the angiogenesis pathway and relevant environmental exposures: cigarette smoking, alcohol consumption, and animal protein intake. We analyzed data from 747 rectal cancer cases and 956 controls from the Diet, Activity and Lifestyle as a Risk Factor for Rectal Cancer study. We applied a 3-step analysis approach: first, we searched for interactions among single nucleotide polymorphisms on the pathway genes; second, we searched for interactions among the genes, both steps using Logic regression; third, we examined the GEIs significant at the 5% level using logistic regression for cancer risk and Cox proportional hazards models for survival. Permutation-based test was used for multiple testing adjustment. We identified 8 significant GEIs associated with risk among 6 genes adjusting for multiple testing: TNF (OR = 1.85, 95% CI: 1.10, 3.11), TLR4 (OR = 2.34, 95% CI: 1.38, 3.98), and EGR2 (OR = 2.23, 95% CI: 1.04, 4.78) with smoking; IGF1R (OR = 1.69, 95% CI: 1.04, 2.72), TLR4 (OR = 2.10, 95% CI: 1.22, 3.60) and EGR2 (OR = 2.12, 95% CI: 1.01, 4.46) with alcohol; and PDGFB (OR = 1.75, 95% CI: 1.04, 2.92) and MMP1 (OR = 2.44, 95% CI: 1.24, 4.81) with protein. Five GEIs were associated with survival at the 5% significance level but not after multiple testing adjustment: CXCR1 (HR = 2.06, 95% CI: 1.13, 3.75) with smoking; and KDR (HR = 4.36, 95% CI: 1.62, 11.73), TLR2 (HR = 9.06, 95% CI: 1.14, 72.11), EGR2 (HR = 2.45, 95% CI: 1.42, 4.22), and EGFR (HR = 6.33, 95% CI: 1.95, 20.54) with protein. GEIs between angiogenesis genes and smoking, alcohol, and animal protein impact rectal cancer risk. Our results support the importance of considering the biologic hypothesis to characterize GEIs associated with cancer outcomes.

The NOD2-Smoking Interaction in Crohn's Disease is likely Specific to the 1007fs Mutation and may be Explained by Age at Diagnosis: A Meta-Analysis and Case-Only Study

Background

NOD2 and smoking are risk factors for Crohn's disease. We meta-analyzed NOD2-smoking interactions in Crohn's disease (Phase 1), then explored the effect of age at diagnosis on NOD2-smoking interactions (Phase 2).

Methods

Phase 1: MEDLINE and EMBASE were searched for studies (n = 18) providing data on NOD2 and smoking in Crohn's disease. NOD2-smoking interactions were estimated using odds ratios (ORs) and 95% confidence intervals (CIs) calculated using random effects models. Phase 2: A case-only study compared the proportion of smokers and carriers of the 1007 fs variant across ages at diagnosis (≤ 16, 17–40, > 40 years).

Findings

Phase 1: Having ever smoked was less common among carriers of the 1007 fs variant of NOD2 (OR 0.74, 95%CI:0.66–0.83). There was no interaction between smoking and the G908R (OR 0.96, 95%CI:0.82–1.13) or the R702W variant (OR 0.89, 95%CI:0.76–1.05). Phase 2: The proportion of patients (n = 627) carrying the 1007 fs variant decreased with age at diagnosis (≤ 16 years: 15%; 17–40: 12%; > 40: 3%; p = 0.003). Smoking was more common in older patients (≤ 16 years: 4%; 17–40: 48%; > 40: 71%; p < 0.001).

Interpretation

The negative NOD2-smoking interaction in Crohn's disease is specific to the 1007 fs variant. However, opposing rates of this variant and smoking across age at diagnosis may explain this negative interaction.

•

There is a negative interaction between NOD2 smoking in Crohn's disease and it is specific to the 1007fs variant.

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With increasing age, the prevalence of the 1007fs variant decreases and exposure to cigarette smoke increases.

•

Contrasting trends in the 1007fs variant and cigarette smoking may explain the negative NOD2-smoking interaction.

We reviewed 18 studies evaluating NOD2-smoking interactions in Crohn's disease. Only the 1007fs variant interacted with smoking. Smokers with this mutation were less likely to develop Crohn's disease. We then conducted a study of 627 patients with Crohn's disease, which showed that the 1007fs variant was common in young patients and rare in older patients, whereas smoking was more common among older patients. The decreasing prevalence of 1007fs mutation and increasing exposure to smoking as age of diagnosis advances may explain the negative interaction between NOD2 and smoking observed in our meta-analysis. Our study highlights the challenges of identifying gene-environment interactions.

Statistical interactions and Bayes estimation of log odds in case-control studies

This paper is concerned with the estimation of the logarithm of disease odds (log odds) when evaluating two risk factors, whether or not interactions are present. Statisticians define interaction as a departure from an additive model on a certain scale of measurement of the outcome. Certain interactions, known as removable interactions, may be eliminated by fitting an additive model under an invertible transformation of the outcome. This can potentially provide more precise estimates of log odds than fitting a model with interaction terms. In practice, we may also encounter nonremovable interactions. The model must then include interaction terms, regardless of the choice of the scale of the outcome. However, in practical settings, we do not know at the outset whether an interaction exists, and if so whether it is removable or nonremovable. Rather than trying to decide on significance levels to test for the existence of removable and nonremovable interactions, we develop a Bayes estimator based on a squared error loss function. We demonstrate the favorable bias-variance trade-offs of our approach using simulations, and provide empirical illustrations using data from three published endometrial cancer case-control studies. The methods are implemented in an R program, and available freely at http://www.mskcc.org/biostatistics/~satagopj .

Bayesian logistic regression in detection of gene-steroid interaction for cancer at PDLIM5 locus

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene- steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (P< 0.05); especially, SNP rs6532496 revealed the strongest association with cancer (P = 6.84 × 10⁻³); while the next best signal was rs951613 (P = 7.46 × 10⁻³). Classic logistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene-steroid interaction effects (OR=2.18, 95% CI=1.31-3.63 with P = 2.9 × 10⁻³ for rs6532496 and OR=2.07, 95% CI=1.24-3.45 with P = 5.43 × 10⁻³ for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR=2.26, 95% CI=1.2-3.38 for rs6532496 and OR=2.14, 95% CI=1.14-3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene-steroid interaction effects (P < 0.05); whereas 13 SNPs showed gene-steroid interaction effects without main effect on cancer. SNP rs4634230 revealed the strongest gene-steroid interaction effect (OR=2.49, 95% CI=1.5-4.13 with P = 4.0 × 10⁻⁴ based on the classic logistic regression and OR=2.59, 95% CI=1.4-3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

Pathway-Driven Approaches of Interaction between Oxidative Balance and Genetic Polymorphism on Metabolic Syndrome

Despite evidences of association between basic redox biology and metabolic syndrome (MetS), few studies have evaluated indices that account for multiple oxidative effectors for MetS. Oxidative balance score (OBS) has indicated the role of oxidative stress in chronic disease pathophysiology. In this study, we evaluated OBS as an oxidative balance indicator for estimating risk of MetS with 6414 study participants. OBS is a multiple exogenous factor score for development of disease; therefore, we investigated interplay between oxidative balance and genetic variation for development of MetS focusing on biological pathways by using gene-set-enrichment analysis. As a result, participants in the highest quartile of OBS were less likely to be at risk for MetS than those in the lowest quartile. In addition, persons in the highest quartile of OBS had the lowest level of inflammatory markers including C-reactive protein and WBC. With GWAS-based pathway analysis, we found that VEGF signaling pathway, glutathione metabolism, and Rac-1 pathway were significantly enriched biological pathways involved with OBS on MetS. These findings suggested that mechanism of angiogenesis, oxidative stress, and inflammation can be involved in interaction between OBS and genetic variation on risk of MetS.

The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers

BACKGROUND

Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers, and cancer-related traits.

METHODS

The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers, and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background.

RESULTS

The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis.

CONCLUSIONS

Results from these analyses will enable researchers to identify new susceptibility loci, perform fine-mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental, and lifestyle-related exposures.

IMPACT

Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. Cancer Epidemiol Biomarkers Prev; 26(1); 126-35. ©2016 AACR.

Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions

BACKGROUND

Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other "-omics" and interaction data.

SCOPE OF REVIEW

1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other "-omics" and interaction data.

MAJOR CONCLUSIONS

To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other "-omics" data and interaction can better explain gene functions.

GENERAL SIGNIFICANCE

Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.

Nutrigenomics in the modern era

The concept that interactions between nutrition and genetics determine phenotype was established by Garrod at the beginning of the 20th century through his ground-breaking work on inborn errors of metabolism. A century later, the science and technologies involved in sequencing of the human genome stimulated development of the scientific discipline which we now recognise as nutritional genomics (nutrigenomics). Much of the early hype around possible applications of this new science was unhelpful and raised expectations, which have not been realised as quickly as some would have hoped. However, major advances have been made in quantifying the contribution of genetic variation to a wide range of phenotypes and it is now clear that for nutrition-related phenotypes, such as obesity and common complex diseases, the genetic contribution made by SNP alone is often modest. There is much scope for innovative research to understand the roles of less well explored types of genomic structural variation, e.g. copy number variants, and of interactions between genotype and dietary factors, in phenotype determination. New tools and models, including stem cell-based approaches and genome editing, have huge potential to transform mechanistic nutrition research. Finally, the application of nutrigenomics research offers substantial potential to improve public health e.g. through the use of metabolomics approaches to identify novel biomarkers of food intake, which will lead to more objective and robust measures of dietary exposure. In addition, nutrigenomics may have applications in the development of personalised nutrition interventions, which may facilitate larger, more appropriate and sustained changes in eating (and other lifestyle) behaviours and help to reduce health inequalities.

The Genetic Architecture of Noise-Induced Hearing Loss: Evidence for a Gene-by-Environment Interaction

The discovery of environmentally specific genetic effects is crucial to the understanding of complex traits, such as susceptibility to noise-induced hearing loss (NIHL). We describe the first genome-wide association study (GWAS) for NIHL in a large and well-characterized population of inbred mouse strains, known as the Hybrid Mouse Diversity Panel (HMDP). We recorded auditory brainstem response (ABR) thresholds both pre and post 2-hr exposure to 10-kHz octave band noise at 108 dB sound pressure level in 5–6-wk-old female mice from the HMDP (4–5 mice/strain). From the observation that NIHL susceptibility varied among the strains, we performed a GWAS with correction for population structure and mapped a locus on chromosome 6 that was statistically significantly associated with two adjacent frequencies. We then used a “genetical genomics” approach that included the analysis of cochlear eQTLs to identify candidate genes within the GWAS QTL. In order to validate the gene-by-environment interaction, we compared the effects of the postnoise exposure locus with that from the same unexposed strains. The most significant SNP at chromosome 6 (rs37517079) was associated with noise susceptibility, but was not significant at the same frequencies in our unexposed study. These findings demonstrate that the genetic architecture of NIHL is distinct from that of unexposed hearing levels and provide strong evidence for gene-by-environment interactions in NIHL.

Key Considerations and Methods in the Study of Gene-Environment Interactions

With increased involvement of genetic data in most epidemiological investigations, gene-environment (G × E) interactions now stand as a topic, which must be meticulously assessed and thoroughly understood. The level, mode, and outcomes of interactions between environmental factors and genetic traits have the capacity to modulate disease risk. These must, therefore, be carefully evaluated as they have the potential to offer novel insights on the "missing heritability problem", reaching beyond our current limitations. First, we review a definition of G × E interactions. We then explore how concepts such as the early manifestation of the genetic components of a disease, the heterogeneity of complex traits, the clear definition of epidemiological strata, and the effect of varying physiological conditions can affect our capacity to detect (or miss) G × E interactions. Lastly, we discuss the shortfalls of regression models to study G × E interactions and how other methods such as the ReliefF algorithm, pattern recognition methods, or the LASSO (Least Absolute Shrinkage and Selection Operator) method can enable us to more adequately model G × E interactions. Overall, we present the elements to consider and a path to follow when studying genetic determinants of disease in order to uncover potential G × E interactions.

A perspective on interaction effects in genetic association studies

The identification of gene–gene and gene–environment interaction in human traits and diseases is an active area of research that generates high expectation, and most often lead to high disappointment. This is partly explained by a misunderstanding of the inherent characteristics of standard regression‐based interaction analyses. Here, I revisit and untangle major theoretical aspects of interaction tests in the special case of linear regression; in particular, I discuss variables coding scheme, interpretation of effect estimate, statistical power, and estimation of variance explained in regard of various hypothetical interaction patterns. Linking this components it appears first that the simplest biological interaction models—in which the magnitude of a genetic effect depends on a common exposure—are among the most difficult to identify. Second, I highlight the demerit of the current strategy to evaluate the contribution of interaction effects to the variance of quantitative outcomes and argue for the use of new approaches to overcome this issue. Finally, I explore the advantages and limitations of multivariate interaction models, when testing for interaction between multiple SNPs and/or multiple exposures, over univariate approaches. Together, these new insights can be leveraged for future method development and to improve our understanding of the genetic architecture of multifactorial traits.

GENOME-WIDE ASSOCIATION STUDY (GWAS) AND GENOME-WIDE BY ENVIRONMENT INTERACTION STUDY (GWEIS) OF DEPRESSIVE SYMPTOMS IN AFRICAN AMERICAN AND HISPANIC/LATINA WOMEN

BACKGROUND

Genome-wide association studies (GWAS) have made little progress in identifying variants linked to depression. We hypothesized that examining depressive symptoms and considering gene-environment interaction (GxE) might improve efficiency for gene discovery. We therefore conducted a GWAS and genome-wide by environment interaction study (GWEIS) of depressive symptoms.

METHODS

Using data from the SHARe cohort of the Women's Health Initiative, comprising African Americans (n = 7,179) and Hispanics/Latinas (n = 3,138), we examined genetic main effects and GxE with stressful life events and social support. We also conducted a heritability analysis using genome-wide complex trait analysis (GCTA). Replication was attempted in four independent cohorts.

RESULTS

No SNPs achieved genome-wide significance for main effects in either discovery sample. The top signals in African Americans were rs73531535 (located 20 kb from GPR139, P = 5.75 × 10(-8) ) and rs75407252 (intronic to CACNA2D3, P = 6.99 × 10(-7) ). In Hispanics/Latinas, the top signals were rs2532087 (located 27 kb from CD38, P = 2.44 × 10(-7) ) and rs4542757 (intronic to DCC, P = 7.31 × 10(-7) ). In the GEWIS with stressful life events, one interaction signal was genome-wide significant in African Americans (rs4652467; P = 4.10 × 10(-10) ; located 14 kb from CEP350). This interaction was not observed in a smaller replication cohort. Although heritability estimates for depressive symptoms and stressful life events were each less than 10%, they were strongly genetically correlated (rG = 0.95), suggesting that common variation underlying self-reported depressive symptoms and stressful life event exposure, though modest on their own, were highly overlapping in this sample.

CONCLUSIONS

Our results underscore the need for larger samples, more GEWIS, and greater investigation into genetic and environmental determinants of depressive symptoms in minorities.

Genome-wide gene-environment interactions on quantitative traits using family data

Gene-environment interactions may provide a mechanism for targeting interventions to those individuals who would gain the most benefit from them. Searching for interactions agnostically on a genome-wide scale requires large sample sizes, often achieved through collaboration among multiple studies in a consortium. Family studies can contribute to consortia, but to do so they must account for correlation within families by using specialized analytic methods. In this paper, we investigate the performance of methods that account for within-family correlation, in the context of gene-environment interactions with binary exposures and quantitative outcomes. We simulate both cross-sectional and longitudinal measurements, and analyze the simulated data taking family structure into account, via generalized estimating equations (GEE) and linear mixed-effects models. With sufficient exposure prevalence and correct model specification, all methods perform well. However, when models are misspecified, mixed modeling approaches have seriously inflated type I error rates. GEE methods with robust variance estimates are less sensitive to model misspecification; however, when exposures are infrequent, GEE methods require modifications to preserve type I error rate. We illustrate the practical use of these methods by evaluating gene-drug interactions on fasting glucose levels in data from the Framingham Heart Study, a cohort that includes related individuals.

The Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort study: assessment of environmental exposures

The Canadian Healthy Infant Longitudinal Development birth cohort was designed to elucidate interactions between environment and genetics underlying development of asthma and allergy. Over 3600 pregnant mothers were recruited from the general population in four provinces with diverse environments. The child is followed to age 5 years, with prospective characterization of diverse exposures during this critical period. Key exposure domains include indoor and outdoor air pollutants, inhalation, ingestion and dermal uptake of chemicals, mold, dampness, biological allergens, pets and pests, housing structure, and living behavior, together with infections, nutrition, psychosocial environment, and medications. Assessments of early life exposures are focused on those linked to inflammatory responses driven by the acquired and innate immune systems. Mothers complete extensive environmental questionnaires including time-activity behavior at recruitment and when the child is 3, 6, 12, 24, 30, 36, 48, and 60 months old. House dust collected during a thorough home assessment at 3-4 months, and biological specimens obtained for multiple exposure-related measurements, are archived for analyses. Geo-locations of homes and daycares and land-use regression for estimating traffic-related air pollution complement time-activity-behavior data to provide comprehensive individual exposure profiles. Several analytical frameworks are proposed to address the many interacting exposure variables and potential issues of co-linearity in this complex data set.

Genetic architecture of colorectal cancer

Colorectal cancer (CRC) is a complex disease that develops as a consequence of both genetic and environmental risk factors. A small proportion (3-5%) of cases arise from hereditary syndromes predisposing to early onset CRC as a result of mutations in over a dozen well defined genes. In contrast, CRC is predominantly a late onset 'sporadic' disease, developing in individuals with no obvious hereditary syndrome. In recent years, genome wide association studies have discovered that over 40 genetic regions are associated with weak effects on sporadic CRC, and it has been estimated that increasingly large genome wide scans will identify many additional novel genetic regions. Subsequent experimental validations have identified the causally related variant(s) in a limited number of these genetic regions. Further biological insight could be obtained through ethnically diverse study populations, larger genetic sequencing studies and development of higher throughput functional experiments. Along with inherited variation, integration of the tumour genome may shed light on the carcinogenic processes in CRC. In addition to summarising the genetic architecture of CRC, this review discusses genetic factors that modify environmental predictors of CRC, as well as examples of how genetic insight has improved clinical surveillance, prevention and treatment strategies. In summary, substantial progress has been made in uncovering the genetic architecture of CRC, and continued research efforts are expected to identify additional genetic risk factors that further our biological understanding of this disease. Subsequently these new insights will lead to improved treatment and prevention of colorectal cancer.

"Missing" G x E Variation Controls Flowering Time in Arabidopsis thaliana

Understanding how genetic variation interacts with the environment is essential for understanding adaptation. In particular, the life cycle of plants is tightly coordinated with local environmental signals through complex interactions with the genetic variation (G x E). The mechanistic basis for G x E is almost completely unknown. We collected flowering time data for 173 natural inbred lines of Arabidopsis thaliana from Sweden under two growth temperatures (10°C and 16°C), and observed massive G x E variation. To identify the genetic polymorphisms underlying this variation, we conducted genome-wide scans using both SNPs and local variance components. The SNP-based scan identified several variants that had common effects in both environments, but found no trace of G x E effects, whereas the scan using local variance components found both. Furthermore, the G x E effects appears to be concentrated in a small fraction of the genome (0.5%). Our conclusion is that G x E effects in this study are mostly due to large numbers of allele or haplotypes at a small number of loci, many of which correspond to previously identified flowering time genes.

Gene-environment interactions in the study of asthma in the postgenomewide association studies era

PURPOSE OF REVIEW

Asthma is a complex disease characterized by an intricate interplay of both heritable and environmental factors. Understanding the mechanisms through which genes and environment interact represents one of the major challenges for pulmonary researchers. This review provides an overview of the recently published literature on gene-environment (G × E) interactions in asthma, with a special focus on the new methodological developments in the postgenomewide association studies (GWAS) era.

RECENT FINDINGS

Most recent studies on G × E interaction in asthma used a candidate-gene approach. Candidate-gene studies considering exposure to outdoor air pollutants showed significant interactions mainly with variants in the GSTP1 gene on asthma in children. G × E studies on passive and active smoking, including one genomewide interaction study, identified novel genes of susceptibility to asthma and a time-dependent effect of maternal smoking. Other recent studies on asthma found interactions between candidate genes and occupational allergen exposure and several domestic exposures such as endotoxin and gas cooking. New methods were developed to efficiently estimate G × E interaction in GWAS, and a pathway-based strategy to select an enriched gene-set for G × E studies has recently been proposed.

SUMMARY

The G × E studies presented in this review offer a good example on how candidate-gene approaches can complement and help in validating GWAS findings.

Gene-environment interactions in inflammatory bowel disease pathogenesis

PURPOSE OF REVIEW

Inflammatory bowel disease (IBD) has long been known to have genetic risk factors because of increased prevalence in the relatives of affected individuals. However, genome-wide association studies have only explained limited heritability in IBD. The observed globally rising incidence of IBD has implicated the role of environmental factors. The hidden unexplained heritability remains to be explored.

RECENT FINDINGS

Recent aggregate evidence has highlighted the extent and nature of host genome-microbiome associations, a key next step in understanding the mechanisms of pathogenesis in IBD. An individual's gut microbiota is shaped not only by genetic but also by environmental factors like diet. Minimizing exposure of the intestinal lumen to selected food items has shown to prolong the remission state of IBD. Among a genetically susceptible host, the shift of gut microbiota (or 'dysbiosis') can lead to increasing the susceptibility to IBD. With the advances in high-throughput large-scale 'omics' technologies in combination with creative data mining and system biology-based network analyses, the complexity of biological functional networks behind the cause of IBD has become more approachable. Therefore, the hidden heritability in IBD has become more explainable, and can be attributable to the changing environmental factors, epigenetic modifications, and gene-host microbial ('in-vironmental') or gene-extrinsic environmental interactions.

SUMMARY

This review discusses the perspectives of relevance to clinical translation with emphasis on gene-environment interactions. No doubt, the use of system-based approaches will lead to the development of alternative, and hopefully better, diagnostic, prognostic, and monitoring tools in the management of IBD.

Deciphering Genome Environment Wide Interactions Using Exposed Subjects Only

The recent successes of genome-wide association studies (GWAS) have renewed interest in genome environment wide interaction studies (GEWIS) to discover genetic factors that modulate penetrance of environmental exposures to human diseases. Indeed, gene-environment interactions (G × E), which have not been emphasized in the GWAS era, could be a source contributing to the missing heritability, a major bottleneck limiting continuing GWAS successes. In this manuscript, we describe a design and analytic strategy to focus on G × E using only exposed subjects, dubbed as e-GEWIS. Operationally, an e-GEWIS analysis is equivalent to a GWAS analysis on exposed subjects only, and it has actually been used in some earlier GWAS without being explicitly identified as such. Through both analytics and simulations, e-GEWIS has been shown better efficiency than the usual cross-product-based analysis of G × E interaction with both cases and controls (cc-GEWIS), and they have comparable efficiency to case-only analysis of G × E (c-GEWIS), with potentially smaller sample sizes. The formalization of e-GEWIS here provides a theoretical basis to legitimize this framework for routine investigation of G × E, for more efficient G × E study designs, and for improvement of reproducibility in replicating GEWIS findings. As an illustration, we apply e-GEWIS to a lung cancer GWAS data set to perform a GEWIS, focusing on gene and smoking interaction. The e-GEWIS analysis successfully uncovered positive genetic associations on chromosome 15 among current smokers, suggesting a gene-smoking interaction. Although this signal was detected earlier, the current finding here serves as a positive control in support of this e-GEWIS strategy.