Empirical hierarchical bayes approach to gene-environment interactions: development and application to genome-wide association studies of lung cancer in TRICL

An Evaluation of Gene-Diet Interaction Statistical Methods and Discovery of rs7175421-Whole Grain Interaction in Lung Cancer

Dietary factors show different effects on genetically diverse populations. Scientific research uses gene-environment interaction models to study the effects of dietary factors on genetically diverse populations for lung cancer risk. However, previous study designs have not investigated the degree of type I error inflation and, in some instances, have not corrected for multiple testing. Using a motivating investigation of diet-gene interaction and lung cancer risk, we propose a training and testing strategy and perform real-world simulations to select the appropriate statistical methods to reduce false-positive discoveries. The simulation results show that the unconstrained maximum likelihood (UML) method controls the type I error better than the constrained maximum likelihood (CML). The empirical Bayesian (EB) method can compete with the UML method in achieving statistical power and controlling type I error. We observed a significant interaction between SNP rs7175421 with dietary whole grain in lung cancer prevention, with an effect size (standard error) of -0.312 (0.112) for EB estimate. SNP rs7175421 may interact with dietary whole grains in modulating lung cancer risk. Evaluating statistical methods for gene-diet interaction analysis can help balance the statistical power and type I error.

RDoC and translational perspectives on the genetics of trauma-related psychiatric disorders

Individuals with a history of child abuse are at high risk for depression, anxiety disorders, aggressive behavior, and substance use problems. The goal of this paper is to review studies of the genetics of these stress-related psychiatric disorders. An informative subset of studies that examined candidate gene by environment (GxE) predictors of these psychiatric problems in individuals maltreated as children is reviewed, together with extant genome wide association studies (GWAS). Emerging findings on epigenetic changes associated with adverse early experiences are also reviewed. Meta-analytic support and replicated findings are evident for several genetic risk factors; however, extant research suggests the effects are pleiotropic. Genetic factors are not associated with distinct psychiatric disorders, but rather diverse clinical phenotypes. Research also suggests adverse early life experiences are associated with changes in gene expression of multiple known candidate genes, genes involved in DNA transcription and translation, and genes necessary for brain circuitry development, with changes in gene expression reported in key brain structures implicated in the pathophysiology of psychiatric and substance use disorders. The finding of pleiotropy highlights the value of using the Research Domain Criteria (RDoC) framework in future studies of the genetics of stress-related psychiatric disorders, and not trying simply to link genes to multifaceted clinical syndromes, but to more limited phenotypes that map onto distinct neural circuits. Emerging work in the field of epigenetics also suggests that translational studies that integrate numerous unbiased genome-wide approaches will help to further unravel the genetics of stress-related psychiatric disorders.

Effect of occupational exposures on lung cancer susceptibility: a study of gene-environment interaction analysis

BACKGROUND

Occupational exposures are known risk factors for lung cancer. Role of genetically determined host factors in occupational exposure-related lung cancer is unclear.

METHODS

We used genome-wide association (GWA) data from a case-control study conducted in 6 European countries from 1998 to 2002 to identify gene-occupation interactions and related pathways for lung cancer risk. GWA analysis was performed for each exposure using logistic regression and interaction term for genotypes, and exposure was included in this model. Both SNP-based and gene-based interaction P values were calculated. Pathway analysis was performed using three complementary methods, and analyses were adjusted for multiple comparisons. We analyzed 312,605 SNPs and occupational exposure to 70 agents from 1,802 lung cancer cases and 1,725 cancer-free controls.

RESULTS

Mean age of study participants was 60.1 ± 9.1 years and 75% were male. Largest number of significant associations (P ≤ 1 × 10(-5)) at SNP level was demonstrated for nickel, brick dust, concrete dust, and cement dust, and for brick dust and cement dust at the gene-level (P ≤ 1 × 10(-4)). Approximately 14 occupational exposures showed significant gene-occupation interactions with pathways related to response to environmental information processing via signal transduction (P < 0.001 and FDR < 0.05). Other pathways that showed significant enrichment were related to immune processes and xenobiotic metabolism.

CONCLUSION

Our findings suggest that pathways related to signal transduction, immune process, and xenobiotic metabolism may be involved in occupational exposure-related lung carcinogenesis.

IMPACT

Our study exemplifies an integrative approach using pathway-based analysis to demonstrate the role of genetic variants in occupational exposure-related lung cancer susceptibility. Cancer Epidemiol Biomarkers Prev; 24(3); 570-9. ©2015 AACR.

Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression

Accounting for gene-environment (G×E) interactions in complex trait association studies can facilitate our understanding of genetic heterogeneity under different environmental exposures, improve the ability to discover susceptible genes that exhibit little marginal effect, provide insight into the biological mechanisms of complex diseases, help to identify high-risk subgroups in the population, and uncover hidden heritability. However, significant G×E interactions can be difficult to find. The sample sizes required for sufficient power to detect association are much larger than those needed for genetic main effects, and interactions are sensitive to misspecification of the main-effects model. These issues are exacerbated when working with binary phenotypes and rare variants, which bear less information on association. In this work, we present a similarity-based regression method for evaluating G×E interactions for rare variants with binary traits. The proposed model aggregates the genetic and G×E information across markers, using genetic similarity, thus increasing the ability to detect G×E signals. The model has a random effects interpretation, which leads to robustness against main-effect misspecifications when evaluating G×E interactions. We construct score tests to examine G×E interactions and a computationally efficient EM algorithm to estimate the nuisance variance components. Using simulations and data applications, we show that the proposed method is a flexible and powerful tool to study the G×E effect in common or rare variant studies with binary traits.

Modification of the association between PM10 and lung function decline by cadherin 13 polymorphisms in the SAPALDIA cohort: a genome-wide interaction analysis

BACKGROUND

Both air pollution and genetic variation have been shown to affect lung function. Their interaction has not been studied on a genome-wide scale to date.

OBJECTIVES

We aimed to identify, in an agnostic fashion, genes that modify the association between long-term air pollution exposure and annual lung function decline in an adult population-based sample.

METHODS

A two-stage genome-wide interaction study was performed. The discovery (n = 763) and replication (n = 3,896) samples were derived from the multi-center SAPALDIA cohort (Swiss Cohort Study on Air Pollution and Lung Disease in Adults). Annual rate of decline in the forced mid-expiratory flow (FEF25-75%) was the main end point. Multivariate linear regression analyses were used to identify potential multiplicative interactions between genotypes and 11-year cumulative PM10 exposure.

RESULTS

We identified a cluster of variants intronic to the CDH13 gene as the only locus with genome-wide significant interactions. The strongest interaction was observed for rs2325934 (p = 8.8 × 10(-10)). Replication of the interaction between this CDH13 variant and cumulative PM10 exposure on annual decline in FEF25-75% was successful (p = 0.008). The interaction was not sensitive to adjustment for smoking or body weight.

CONCLUSIONS

CDH13 is functionally linked to the adipokine adiponectin, an inflammatory regulator. Future studies need to confirm the interaction and assess how the result relates to previously observed interactions between air pollution and obesity on respiratory function.

Gene-age interactions in blood pressure regulation: a large-scale investigation with the CHARGE, Global BPgen, and ICBP Consortia

Although age-dependent effects on blood pressure (BP) have been reported, they have not been systematically investigated in large-scale genome-wide association studies (GWASs). We leveraged the infrastructure of three well-established consortia (CHARGE, GBPgen, and ICBP) and a nonstandard approach (age stratification and metaregression) to conduct a genome-wide search of common variants with age-dependent effects on systolic (SBP), diastolic (DBP), mean arterial (MAP), and pulse (PP) pressure. In a two-staged design using 99,241 individuals of European ancestry, we identified 20 genome-wide significant (p ≤ 5 × 10(-8)) loci by using joint tests of the SNP main effect and SNP-age interaction. Nine of the significant loci demonstrated nominal evidence of age-dependent effects on BP by tests of the interactions alone. Index SNPs in the EHBP1L1 (DBP and MAP), CASZ1 (SBP and MAP), and GOSR2 (PP) loci exhibited the largest age interactions, with opposite directions of effect in the young versus the old. The changes in the genetic effects over time were small but nonnegligible (up to 1.58 mm Hg over 60 years). The EHBP1L1 locus was discovered through gene-age interactions only in whites but had DBP main effects replicated (p = 8.3 × 10(-4)) in 8,682 Asians from Singapore, indicating potential interethnic heterogeneity. A secondary analysis revealed 22 loci with evidence of age-specific effects (e.g., only in 20 to 29-year-olds). Age can be used to select samples with larger genetic effect sizes and more homogenous phenotypes, which may increase statistical power. Age-dependent effects identified through novel statistical approaches can provide insight into the biology and temporal regulation underlying BP associations.