Detecting gene-by-smoking interactions in a genome-wide association study of early-onset coronary heart disease using random forests

Functional, Cellular, and Molecular Remodeling of the Heart under Influence of Oxidative Cigarette Tobacco Smoke

Passive and active chronic cigarette smoking (CS) remains an international epidemic and a key risk factor for cardiovascular disease (CVD) development. CS-induced cardiac damage is divided into two major and interchangeable mechanisms: (1) direct adverse effects on the myocardium causing smoking cardiomyopathy and (2) indirect effects on the myocardium by fueling comorbidities such as atherosclerotic syndromes and hypertension that eventually damage and remodel the heart. To date, our understanding of cardiac remodeling following acute and chronic smoking exposure is not well elucidated. This manuscript presents for the first time the RIMD (oxidative stress (R), inflammation (I), metabolic impairment (M), and cell death (D)) detrimental cycle concept as a major player in CS-induced CVD risks and direct cardiac injury. Breakthroughs and latest findings in the field with respect to structural, functional, cellular, and molecular cardiac remodeling following chronic smoking exposure are summarized. This review also touches the genetics/epigenetics of smoking as well as the smoker's paradox and highlights the most currently prominent pharmacological venues to mitigate CS-induced adverse cardiac remodeling.

Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes

The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints.

Multivariate Methods for Genetic Variants Selection and Risk Prediction in Cardiovascular Diseases

Over the last decade, high-throughput genotyping and sequencing technologies have contributed to major advancements in genetics research, as these technologies now facilitate affordable mapping of the entire genome for large sets of individuals. Given this, genome-wide association studies are proving to be powerful tools in identifying genetic variants that have the capacity to modify the probability of developing a disease or trait of interest. However, when the study’s goal is to evaluate the effect of the presence of genetic variants mapping to specific chromosomes regions on a specific phenotype, the candidate loci approach is still preferred. Regardless of which approach is taken, such a large data set calls for the establishment and development of appropriate analytical methods in order to translate such knowledge into biological or clinical findings. Standard univariate tests often fail to identify informative genetic variants, especially when dealing with complex traits, which are more likely to result from a combination of rare and common variants and non-genetic determinants. These limitations can partially be overcome by multivariate methods, which allow for the identification of informative combinations of genetic variants and non-genetic features. Furthermore, such methods can help to generate additive genetic scores and risk stratification algorithms that, once extensively validated in independent cohorts, could serve as useful tools to assist clinicians in decision-making. This review aims to provide readers with an overview of the main multivariate methods for genetic data analysis that could be applied to the analysis of cardiovascular traits.

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.

Gene-environment interactions in genome-wide association studies: current approaches and new directions

BACKGROUND

Complex psychiatric traits have long been thought to be the result of a combination of genetic and environmental factors, and gene-environment interactions are thought to play a crucial role in behavioral phenotypes and the susceptibility and progression of psychiatric disorders. Candidate gene studies to investigate hypothesized gene-environment interactions are now fairly common in human genetic research, and with the shift toward genome-wide association studies, genome-wide gene-environment interaction studies are beginning to emerge.

METHODS

We summarize the basic ideas behind gene-environment interaction, and provide an overview of possible study designs and traditional analysis methods in the context of genome-wide analysis. We then discuss novel approaches beyond the traditional strategy of analyzing the interaction between the environmental factor and each polymorphism individually.

RESULTS

Two-step filtering approaches that reduce the number of polymorphisms tested for interactions can substantially increase the power of genome-wide gene-environment studies. New analytical methods including data-mining approaches, and gene-level and pathway-level analyses, also have the capacity to improve our understanding of how complex genetic and environmental factors interact to influence psychologic and psychiatric traits. Such methods, however, have not yet been utilized much in behavioral and mental health research.

CONCLUSIONS

Although methods to investigate gene-environment interactions are available, there is a need for further development and extension of these methods to identify gene-environment interactions in the context of genome-wide association studies. These novel approaches need to be applied in studies of psychology and psychiatry.

Current Genetics and Epigenetics of Smoking/Tobacco-Related Cardiovascular Disease

Genetic and epigenetic factors are of great importance in cardiovascular biology and disease. Tobacco-smoking, one of the most important cardiovascular risk factors, is itself partially determined by genetic background and is associated with altered epigenetic patterns. This could render the genetics and epigenetics of smoking-related cardiovascular disease a textbook example of environmental epigenetics and modern approaches to multimodal data analysis. A pronounced association of smoking-related methylation patterns in the F2RL3 gene with prognosis in patients with stable coronary heart disease has recently been described. Nonetheless, surprisingly little concrete knowledge on the role of specific genetic variants and epigenetic modifications in the development of cardiovascular diseases in people who smoke has been accumulated. Beyond the current knowledge, the present review briefly outlines some chief challenges and priorities for moving forward in this field.

Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data

Background

Due to the low statistical power of individual markers from a genome-wide association study (GWAS), detecting causal single nucleotide polymorphisms (SNPs) for complex diseases is a challenge. SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity.

Methods

We aim to detect type 2 diabetes (T2D) causal SNP combinations from a GWAS dataset with optimal filtration and to discover the biological meaning of the detected SNP combinations. Optimal filtration can enhance the statistical power of SNP combinations by comparing the error rates of SNP combinations from various Bonferroni thresholds and p-value range-based thresholds combined with linkage disequilibrium (LD) pruning. T2D causal SNP combinations are selected using random forests with variable selection from an optimal SNP dataset. T2D causal SNP combinations and genome-wide SNPs are mapped into functional modules using expanded gene set enrichment analysis (GSEA) considering pathway, transcription factor (TF)-target, miRNA-target, gene ontology, and protein complex functional modules. The prediction error rates are measured for SNP sets from functional module-based filtration that selects SNPs within functional modules from genome-wide SNPs based expanded GSEA.

Results

A T2D causal SNP combination containing 101 SNPs from the Wellcome Trust Case Control Consortium (WTCCC) GWAS dataset are selected using optimal filtration criteria, with an error rate of 10.25%. Matching 101 SNPs with known T2D genes and functional modules reveals the relationships between T2D and SNP combinations. The prediction error rates of SNP sets from functional module-based filtration record no significance compared to the prediction error rates of randomly selected SNP sets and T2D causal SNP combinations from optimal filtration.

Conclusions

We propose a detection method for complex disease causal SNP combinations from an optimal SNP dataset by using random forests with variable selection. Mapping the biological meanings of detected SNP combinations can help uncover complex disease mechanisms.

Correction for population stratification in random forest analysis

BACKGROUND

Population structure (PS), including population stratification and admixture, is a significant confounder in genome-wide association studies (GWAS), as it may produce spurious associations. Random forest (RF) has been increasingly applied in GWAS data analysis because of its advantage in analysing high dimensional genetic data. RF creates importance measures for single nucleotide polymorphisms (SNPs), which are helpful for feature selections. However, if PS is not appropriately corrected, RF tends to give high importance to disease-unrelated SNPs with different frequencies of allele or genotype among subpopulations, leading to inaccurate results.

METHODS

In this study, the authors propose to correct for the confounding effect of PS by including the information of PS in RF analysis. The correction procedure starts by extracting the information of PS using EIGENSTRAT or multi-dimensional scaling clustering procedure from a large number of structure inference SNPs. Phenotype and genotypes adjusted by the information of PS are then used as the outcome and predictors in RF analysis.

RESULTS

Extensive simulations indicate that the importance measure of the causal SNP is increased following the PS correction. By analysing a real dataset, the proposed correction removes the spurious association between the lactase gene and height.

CONCLUSION

The authors propose a simple method to correct for PS in RF analysis on GWAS data. Further studies in real GWAS datasets are required to validate the robustness of the proposed approach.

Challenges and opportunities in genome-wide environmental interaction (GWEI) studies

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.

Random forests for genetic association studies

The Random Forests (RF) algorithm has become a commonly used machine learning algorithm for genetic association studies. It is well suited for genetic applications since it is both computationally efficient and models genetic causal mechanisms well. With its growing ubiquity, there has been inconsistent and less than optimal use of RF in the literature. The purpose of this review is to breakdown the theoretical and statistical basis of RF so that practitioners are able to apply it in their work. An emphasis is placed on showing how the various components contribute to bias and variance, as well as discussing variable importance measures. Applications specific to genetic studies are highlighted. To provide context, RF is compared to other commonly used machine learning algorithms.

Interactions between environmental factors and polymorphisms in angiogenesis pathway genes in esophageal adenocarcinoma risk: a case-only study

BACKGROUND

Gastroesophageal reflux disease (GERD), higher body mass index (BMI), smoking, and genetic variants in angiogenic pathway genes have been individually associated with increased risk of esophageal adenocarcinoma. However, how angiogenic gene polymorphisms and environmental factors jointly affect esophageal adenocarcinoma development remains unclear.

METHODS

By using a case-only design (n = 335), the authors examined interactions between 141 functional/tagging angiogenic single nucleotide polymorphisms (SNPs) and environmental factors (GERD, BMI, smoking) in modulating esophageal adenocarcinoma risk. Gene-environment interactions were assessed by a 2-step approach. First, the authors applied random forest to screen for important SNPs that had either main or interaction effects. Second, they used case-only logistic regression to assess the effects of gene-environment interactions on esophageal adenocarcinoma risk, adjusting for covariates and false-discovery rate.

RESULTS

Random forest analyses identified 3 sets of SNPs (17 SNPs-GERD, 26 SNPs-smoking, and 34 SNPs-BMI) that had the highest importance scores. In subsequent logistic regression analyses, interactions between 2 SNPs (rs2295778 of HIF1AN, rs13337626 of TSC2) and GERD, 2 SNPs (rs2295778 of HIF1AN, rs2296188 of VEGFR1) and smoking, and 7 SNPs (rs2114039 of PDGRFA, rs2296188 of VEGFR1, rs11941492 of VEGFR1, rs17708574 of PDGFRB, rs7324547 of VEGFR1, rs17619601 of VEGFR1, and rs17625898 of VEGFR1) and BMI were significantly associated with esophageal adenocarcinoma development (all false-discovery rates ≤0.10). Moreover, these interactions tended to have SNP dose-response effects for increased esophageal adenocarcinoma risk with increasing number of combined risk genotypes.

CONCLUSIONS

These findings suggest that genetic variations in angiogenic genes may modify esophageal adenocarcinoma susceptibility through interactions with environmental factors in an SNP dose-response manner.

Interactions between genetic polymorphisms in the apoptotic pathway and environmental factors on esophageal adenocarcinoma risk

How genetic variations in apoptosis pathway interact with environmental factors to contribute to esophageal adenocarcinoma (EA) risk has not been comprehensively investigated. We conducted a case-only analysis in 335 Caucasian EA patients that were genotyped for 242 single nucleotide polymorphisms (SNPs) in 43 apoptotic genes. Gene-environment interactions were assessed using a two-step approach. First, random forest algorithm was used to screen for the potential interacting markers. Next, we used case-only logistic regression model to estimate the effects of gene-environment interactions on EA risk. Four SNPs (PERP rs648802; PIK3CA rs4855094, rs7644468 and TNFRSF1A rs4149579) had significant interaction with gastroesophageal reflux disease (GERD). The presence of variant alleles in TP53BP1 rs560191, CASP7 rs7907519 or BCL2 rs12454712 enhanced the risk of smoking by 2.08-2.58 times [interaction odds ratio (ORi)=2.08-2.58, adjusted P-value (Padj)=0.02-0.04]. Compared with patients carrying ≤1 risk genotype, the risk of GERD on EA was increased in persons with two (ORi=1.89, Padj=0.016) or ≥3 (ORi=4.30, Padj<0.0001) risk genotypes. Compared with cases with ≤1 risk genotype, smoking-associated EA risk increased by 3.15 times when ≥2 risk genotypes were present (ORi=3.15, Padj<0.0001). In conclusion, interactions among apoptotic SNPs and GERD or smoking play an important role in EA development.

Detecting gene-environment interactions in genome-wide association data

Despite the importance of gene-environment (GxE) interactions in the etiology of common diseases, little work has been done to develop methods for detecting these types of interactions in genome-wide association study data. This was the focus of Genetic Analysis Workshop 16 Group 10 contributions, which introduced a variety of new methods for the detection of GxE interactions in both case-control and family-based data using both cross-sectional and longitudinal study designs. Many of these contributions detected significant GxE interactions. Although these interactions have not yet been confirmed, the results suggest the importance of testing for interactions. Issues of sample size, quantifying the environmental exposure, longitudinal data analysis, family-based analysis, selection of the most powerful analysis method, population stratification, and computational expense with respect to testing GxE interactions are discussed.