Gene-Environment Interaction: A Variable Selection Perspective

Using Quantitative Trait Locus Mapping and Genomic Resources to Improve Breeding Precision in Peaches: Current Insights and Future Prospects

Modern breeding technologies and the development of quantitative trait locus (QTL) mapping have brought about a new era in peach breeding. This study examines the complex genetic structure that underlies the morphology of peach fruits, paying special attention to the interaction between genome editing, genomic selection, and marker-assisted selection. Breeders now have access to precise tools that enhance crop resilience, productivity, and quality, facilitated by QTL mapping, which has significantly advanced our understanding of the genetic determinants underlying essential traits such as fruit shape, size, and firmness. New technologies like CRISPR/Cas9 and genomic selection enable the development of cultivars that can withstand climate change and satisfy consumer demands with unprecedented precision in trait modification. Genotype-environment interactions remain a critical challenge for modern breeding efforts, which can be addressed through high-throughput phenotyping and multi-environment trials. This work shows how combining genome-wide association studies and machine learning can improve the synthesis of multi-omics data and result in faster breeding cycles while preserving genetic diversity. This study outlines a roadmap that prioritizes the development of superior cultivars utilizing cutting-edge methods and technologies in order to address evolving agricultural and environmental challenges.

Gene-Environment Interaction: Small Deletions (DELs) and Transcriptomic Profiles in Non-Melanoma Skin Cancer (NMSC) and Potential Implications for Therapy

Arsenic (As) is a risk factor for non-melanoma skin cancer (NMSC). From a six-year follow-up study on 7000 adults exposed to As, we reported the associations of single-nucleotide variation in tumor tissue and gene expression. Here, we identify the associations of small deletions (DELs) and transcriptomic profiles in NMSC. Comparing the (a) NMSC tissue (n = 32) and corresponding blood samples from each patient, and (b) an independent set of non-lesional, healthy skin (n = 16) and paired blood, we identified NMSC-associated DELs. Differential expressions of certain gene pathways (TGF-β signaling pathway, IL-17 pathway, PD-L1 pathway, etc.) showed significant interactions with these somatic DELs and As exposure. In low-As-exposure cases, the DELs in APC were associated with the up-regulation of inflamed T-Cell-associated genes by a fold change (FC) of 8.9 (95% CI 4.5-17.6), compared to 5.7 (95% CI 2.9-10.8) without APC DELs; in high-As-exposure cases, the APC DELs were associated with an FC of 5.8 (95% CI 3.5-9.8) compared to 1.2 (95% CI -1.3 to 1.8) without APC DELs. We report, for the first time, the significant associations of somatic DELs (many in STR regions) in NMSC tissue and As exposure with many dysregulated gene pathways. These findings may help in selecting groups of patients for potential targeted therapy like PD-L1 inhibitors, IL-17 inhibitors, and TGF-β inhibitors in the future.

A two-phase epigenome-wide four-way gene-smoking interaction study of overall survival for early-stage non-small cell lung cancer

High-order interactions associated with non-small cell lung cancer (NSCLC) survival may elucidate underlying molecular mechanisms and identify potential therapeutic targets. Our previous work has identified a three-way interaction among pack-year of smoking (the number of packs of cigarettes smoked per day multiplied by the number of years the person has smoked) and two DNA methylation probes (cg05293407TRIM27 and cg00060500KIAA0226). However, whether a four-way interaction exists remains unclear. Therefore, we adopted a two-phase design to identify the four-way gene-smoking interactions by a hill-climbing strategy on the basis of the previously detected three-way interaction. One CpG probe, cg16658473SHISA9, was identified with FDR-q ≤ 0.05 in the discovery phase and P ≤ 0.05 in the validation phase. Meanwhile, the four-way interaction improved the discrimination ability for the prognostic prediction model, as indicated by the area under the receiver operating characteristic curve (AUC) for both 3- and 5-year survival. In summary, we identified a four-way interaction associated with NSCLC survival among pack-year of smoking, cg05293407TRIM27, cg00060500KIAA0226 and g16658473SHISA9, providing novel insights into the complex mechanisms underlying NSCLC progression.

The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies

Data irregularity in cancer genomics studies has been widely observed in the form of outliers and heavy-tailed distributions in the complex traits. In the past decade, robust variable selection methods have emerged as powerful alternatives to the nonrobust ones to identify important genes associated with heterogeneous disease traits and build superior predictive models. In this study, to keep the remarkable features of the quantile LASSO and fully Bayesian regularized quantile regression while overcoming their disadvantage in the analysis of high-dimensional genomics data, we propose the spike-and-slab quantile LASSO through a fully Bayesian spike-and-slab formulation under the robust likelihood by adopting the asymmetric Laplace distribution (ALD). The proposed robust method has inherited the prominent properties of selective shrinkage and self-adaptivity to the sparsity pattern from the spike-and-slab LASSO (Roc̆ková and George, J Am Stat Associat, 2018, 113(521): 431-444). Furthermore, the spike-and-slab quantile LASSO has a computational advantage to locate the posterior modes via soft-thresholding rule guided Expectation-Maximization (EM) steps in the coordinate descent framework, a phenomenon rarely observed for robust regularization with nondifferentiable loss functions. We have conducted comprehensive simulation studies with a variety of heavy-tailed errors in both homogeneous and heterogeneous model settings to demonstrate the superiority of the spike-and-slab quantile LASSO over its competing methods. The advantage of the proposed method has been further demonstrated in case studies of the lung adenocarcinomas (LUAD) and skin cutaneous melanoma (SKCM) data from The Cancer Genome Atlas (TCGA).

DNA methylation at AHRR as a master predictor of smoke exposure and a biomarker for sleep and exercise

BACKGROUND

DNA methylation profiling may provide a more accurate measure of the smoking status than self-report and may be useful in guiding clinical interventions and forensic investigations. In the current study, blood DNA methylation profiles of nearly 800 Polish individuals were assayed using Illuminia EPIC and the inference of smoking from epigenetic data was explored. In addition, we focused on the role of the AHRR gene as a top marker for smoking and investigated its responsiveness to other lifestyle behaviors.

RESULTS

We found > 450 significant CpGs associated with cigarette consumption, and overrepresented in various biological functions including cell communication, response to stress, blood vessel development, cell death, and atherosclerosis. The model consisting of cg05575921 in AHRR (p = 4.5 × 10-32) and three additional CpGs (cg09594361, cg21322436 in CNTNAP2 and cg09842685) was able to predict smoking status with a high accuracy of AUC = 0.8 in the test set. Importantly, a gradual increase in the probability of smoking was observed, starting from occasional smokers to regular heavy smokers. Furthermore, former smokers displayed the intermediate DNA methylation profiles compared to current and never smokers, and thus our results indicate the potential reversibility of DNA methylation after smoking cessation. The AHRR played a key role in a predictive analysis, explaining 21.5% of the variation in smoking. In addition, the AHRR methylation was analyzed for association with other modifiable lifestyle factors, and showed significance for sleep and physical activity. We also showed that the epigenetic score for smoking was significantly correlated with most of the epigenetic clocks tested, except for two first-generation clocks.

CONCLUSIONS

Our study suggests that a more rapid return to never-smoker methylation levels after smoking cessation may be achievable in people who change their lifestyle in terms of physical activity and sleep duration. As cigarette smoking has been implicated in the literature as a leading cause of epigenetic aging and AHRR appears to be modifiable by multiple exogenous factors, it emerges as a promising target for intervention and investment.

Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies

Variable selection methods have been extensively developed for and applied to cancer genomics data to identify important omics features associated with complex disease traits, including cancer outcomes. However, the reliability and reproducibility of the findings are in question if valid inferential procedures are not available to quantify the uncertainty of the findings. In this article, we provide a gentle but systematic review of high-dimensional frequentist and Bayesian inferential tools under sparse models which can yield uncertainty quantification measures, including confidence (or Bayesian credible) intervals, p values and false discovery rates (FDR). Connections in high-dimensional inferences between the two realms have been fully exploited under the "unpenalized loss function + penalty term" formulation for regularization methods and the "likelihood function × shrinkage prior" framework for regularized Bayesian analysis. In particular, we advocate for robust Bayesian variable selection in cancer genomics studies due to its ability to accommodate disease heterogeneity in the form of heavy-tailed errors and structured sparsity while providing valid statistical inference. The numerical results show that robust Bayesian analysis incorporating exact sparsity has yielded not only superior estimation and identification results but also valid Bayesian credible intervals under nominal coverage probabilities compared with alternative methods, especially in the presence of heavy-tailed model errors and outliers.

Gene-environment interactions within a precision environmental health framework

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

Poor statistical power in population-based association study of gene interaction

BACKGROUND

Statistical epistasis, or "gene-gene interaction" in genetic association studies, means the nonadditive effects between the polymorphic sites on two different genes affecting the same phenotype. In the genetic association analysis of complex traits, nevertheless, the researchers haven't found enough clues of statistical epistasis so far.

METHODS

We developed a statistical model where the statistical epistasis was presented as an extra linkage disequilibrium between the polymorphic sites of different risk genes. The power of statistical test for identifying the gene-gene interaction was calculated and then compared in different hypothesis scenarios.

RESULTS

Our results show the statistical power increases with the increasing of interaction coefficient, relative risk, and linkage disequilibrium with genetic markers. However, the power of interaction discovery is much lower than that of regular single-site association test. When rigorous criteria were employed in statistical tests, the identification of gene-gene interaction became a very difficult task. Since the criterion of significance was given to be p-value ≤ 5.0 × 10-8, the same as that of many genome-wide association studies, there is little chance to identify the gene-gene interaction in all kind of circumstances.

CONCLUSIONS

The lack of epistasis tends to be an inevitable result caused by the statistical principles of methods in the genetic association studies and therefore is the inherent characteristic of the research itself.

Cauchy combination methods for the detection of gene-environment interactions for rare variants related to quantitative phenotypes

The characterization of gene-environment interactions (GEIs) can provide detailed insights into the biological mechanisms underlying complex diseases. Despite recent interest in GEIs for rare variants, published GEI tests are underpowered for an extremely small proportion of causal rare variants in a gene or a region. By extending the aggregated Cauchy association test (ACAT), we propose three GEI tests to address this issue: a Cauchy combination GEI test with fixed main effects (CCGEI-F), a Cauchy combination GEI test with random main effects (CCGEI-R), and an omnibus Cauchy combination GEI test (CCGEI-O). ACAT was applied to combine p values of single-variant GEI analyses to obtain CCGEI-F and CCGEI-R and p values of multiple GEI tests were combined in CCGEI-O. Through numerical simulations, for small numbers of causal variants, CCGEI-F, CCGEI-R and CCGEI-O provided approximately 5% higher power than the existing GEI tests INT-FIX and INT-RAN; however, they had slightly higher power than the existing GEI test TOW-GE. For large numbers of causal variants, although CCGEI-F and CCGEI-R exhibited comparable or slightly lower power values than the competing tests, the results were still satisfactory. Among all simulation conditions evaluated, CCGEI-O provided significantly higher power than that of competing GEI tests. We further applied our GEI tests in genome-wide analyses of systolic blood pressure or diastolic blood pressure to detect gene-body mass index (BMI) interactions, using whole-exome sequencing data from UK Biobank. At a suggestive significance level of 1.0 × 10-4, KCNC4, GAR1, FAM120AOS and NT5C3B showed interactions with BMI by our GEI tests.

Which Factors Influence Healthy Aging? A Lesson from the Longevity Village of Bama in China

A growing aging population is associated with increasing incidences of aging-related diseases and socioeconomic burdens. Hence, research into healthy longevity and aging is urgently needed. Longevity is an important phenomenon in healthy aging. The present review summarizes the characteristics of longevity in the elderly population in Bama, China, where the proportion of centenarians is 5.7-fold greater than the international standard. We examined the impact of genetic and environmental factors on longevity from multiple perspectives. We proposed that the phenomenon of longevity in this region is of high value for future investigations in healthy aging and aging-related disease and may provide guidance for fostering the establishment and maintenance of a healthy aging society.

Robust Bayesian variable selection for gene-environment interactions

Gene-environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.

Environmental Exposure Science and Human Health

Human health and environmental exposure form an inseparable binomial [...].

Springer: An R package for bi-level variable selection of high-dimensional longitudinal data

In high-dimensional data analysis, the bi-level (or the sparse group) variable selection can simultaneously conduct penalization on the group level and within groups, which has been developed for continuous, binary, and survival responses in the literature. Zhou et al. (2022) (PMID: 35766061) has further extended it under the longitudinal response by proposing a quadratic inference function-based penalization method in gene-environment interaction studies. This study introduces "springer," an R package implementing the bi-level variable selection within the QIF framework developed in Zhou et al. (2022). In addition, R package "springer" has also implemented the generalized estimating equation-based sparse group penalization method. Alternative methods focusing only on the group level or individual level have also been provided by the package. In this study, we have systematically introduced the longitudinal penalization methods implemented in the "springer" package. We demonstrate the usage of the core and supporting functions, which is followed by the numerical examples and discussions. R package "springer" is available at https://cran.r-project.org/package=springer.

Gene-environment interaction analysis via deep learning

Gene-environment (G-E) interaction analysis plays an important role in studying complex diseases. Extensive methodological research has been conducted on G-E interaction analysis, and the existing methods are mostly based on regression techniques. In many fields including biomedicine and omics, it has been increasingly recognized that deep learning may outperform regression with its unique flexibility (e.g., in accommodating unspecified nonlinear effects) and superior prediction performance. However, there has been a lack of development in deep learning for G-E interaction analysis. In this article, we fill this important knowledge gap and develop a new analysis approach based on deep neural network in conjunction with penalization. The proposed approach can simultaneously conduct model estimation and selection (of important main G effects and G-E interactions), while uniquely respecting the "main effects, interactions" variable selection hierarchy. Simulation shows that it has superior prediction and feature selection performance. The analysis of data on lung adenocarcinoma and skin cutaneous melanoma overall survival further establishes its practical utility. Overall, this study can advance G-E interaction analysis by delivering a powerful new analysis approach based on modern deep learning.

Polygenic Risk of Hypertriglyceridemia Is Modified by BMI

Background: Genetic risk scores (GRSs) have partially improved the understanding of the etiology of moderate hypertriglyceridemia (HTG), which until recently was mainly assessed by secondary predisposing causes. The main objective of this study was to assess whether this variability is due to the interaction between clinical variables and GRS. Methods: We analyzed 276 patients with suspected polygenic HTG. An unweighted GRS was developed with the following variants: c.724C > G (ZPR1 gene), c.56C > G (APOA5 gene), c.1337T > C (GCKR gene), g.19986711A > G (LPL gene), c.107 + 1647T > C (BAZ1B gene) and g.125478730A > T (TRIB gene). Interactions between the GRS and clinical variables (body mass index (BMI), diabetes mellitus, diet, physical activity, alcohol consumption, age and gender) were evaluated. Results: The GRS was associated with triglyceride (TG) concentrations. There was a significant interaction between BMI and GRS, with the intensity of the relationship between the number of alleles and the TG concentration being greater in individuals with a higher BMI. Conclusions: GRS is associated with plasma TG concentrations and is markedly influenced by BMI. This finding could improve the stratification of patients with a high genetic risk for HTG who could benefit from more intensive healthcare interventions.

Sparse group variable selection for gene-environment interactions in the longitudinal study

Penalized variable selection for high-dimensional longitudinal data has received much attention as it can account for the correlation among repeated measurements while providing additional and essential information for improved identification and prediction performance. Despite the success, in longitudinal studies, the potential of penalization methods is far from fully understood for accommodating structured sparsity. In this article, we develop a sparse group penalization method to conduct the bi-level gene-environment (G  × $\times $  E) interaction study under the repeatedly measured phenotype. Within the quadratic inference function framework, the proposed method can achieve simultaneous identification of main and interaction effects on both the group and individual levels. Simulation studies have shown that the proposed method outperforms major competitors. In the case study of asthma data from the Childhood Asthma Management Program, we conduct G  × $\times $  E study by using high-dimensional single nucleotide polymorphism data as genetic factors and the longitudinal trait, forced expiratory volume in 1 s, as the phenotype. Our method leads to improved prediction and identification of main and interaction effects with important implications.

Overlapping group screening for detection of gene-environment interactions with application to TCGA high-dimensional survival genomic data

BACKGROUND

In the context of biomedical and epidemiological research, gene-environment (G-E) interaction is of great significance to the etiology and progression of many complex diseases. In high-dimensional genetic data, two general models, marginal and joint models, are proposed to identify important interaction factors. Most existing approaches for identifying G-E interactions are limited owing to the lack of robustness to outliers/contamination in response and predictor data. In particular, right-censored survival outcomes make the associated feature screening even challenging. In this article, we utilize the overlapping group screening (OGS) approach to select important G-E interactions related to clinical survival outcomes by incorporating the gene pathway information under a joint modeling framework.

RESULTS

Simulation studies under various scenarios are carried out to compare the performances of our proposed method with some commonly used methods. In the real data applications, we use our proposed method to identify G-E interactions related to the clinical survival outcomes of patients with head and neck squamous cell carcinoma, and esophageal carcinoma in The Cancer Genome Atlas clinical survival genetic data, and further establish corresponding survival prediction models. Both simulation and real data studies show that our method performs well and outperforms existing methods in the G-E interaction selection, effect estimation, and survival prediction accuracy.

CONCLUSIONS

The OGS approach is useful for selecting important environmental factors, genes and G-E interactions in the ultra-high dimensional feature space. The prediction ability of OGS with the Lasso penalty is better than existing methods. The same idea of the OGS approach can apply to other outcome models, such as the proportional odds survival time model, the logistic regression model for binary outcomes, and the multinomial logistic regression model for multi-class outcomes.

Pharmacogenetics of Addiction Therapy

Drug addiction is a serious relapsing disease that has high costs to society and to the individual addicts. Treatment of these addictions is still in its nascency, with only a few examples of successful therapies. Therapeutic response depends upon genetic, biological, social, and environmental components. A role for genetic makeup in the response to treatment has been shown for several addiction pharmacotherapies with response to treatment based on individual genetic makeup. In this chapter, we will discuss the role of genetics in pharmacotherapies, specifically for cocaine, alcohol, and opioid dependences. The continued elucidation of the role of genetics should aid in the development of new treatments and increase the efficacy of existing treatments.

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.

An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data

Genomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate the data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNVs) and copy number variations (CNVs) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNVs and CNVs compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigation.

Integrating Multi–Omics Data for Gene-Environment Interactions

Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.