4
Next-generation sequencing and other high-throughput technologies have made it feasible to characterize millions of sequence variations on large numbers of study participants. But when it comes to identifying a small number of these genetic features (or feature sets) that are associated with a disease trait, the investigator is faced with a formidable multiple-testing challenge. It can be thought of as a signal-tonoise problem, where the large number of unrelated genetic features tends to drown out the faint signal of the small number of biologically relevant features. The theoretical underpinnings of an emerging class of statistical methods for genomic studies, two-stage procedures for both gene-gene and gene-environment interactions have recently been described in a remarkable article (Dai et al., 2012). The key idea is that dimensionality of multiple testing in genomics can be reduced by screening features to be tested with an independent statistic in the same dataset, thereby mitigating the multipletesting problem and increasing power to detect effects. In other words, the noise is reduced, allowing the relevant signal to be more easily detected. These methods will likely gain importance as high-throughput technologies continue to yield exponentially increasing amounts of information per sample and per research dollar spent. Dai et al. couched their paper in the context of gene-environment interactions only. However, it is worth noting that the theoretical properties detailed by Dai et al. apply not just to the search for gene-environment interactions (GxE), but also to (epistatic) interactions between genetic variants (GxG), since in constructing these hypothesis tests, both “gene” and “environment” features are treated analogously as discrete or continuous variables in models designed to identify associations with a disease trait. A notable exception is when the approach depends on the environmental exposure being a randomized treatment, allowingadditional assumptions to be made. One such screening-testing interaction approach is designed for a case-control study where the investigator is interested in identifyingGxGorGxE pairs involved in interactions (Millstein et al., 2006;Murcray et al., 2009; Dai et al., 2012; Lewinger et al., 2013). There is an assumption that each pair of features considered is independent in the general population, and only if a dependence is found in the pooled case-control sample (the screening stage), is the pair tested in a formal model that includes an interaction term (the testing stage), e.g., logit(P[D]) = α+ β1∗SNP1+ β2 ∗SNP2+ β3∗SNP1∗SNP2, where β3 is the interaction parameter and D indicates disease. The interaction parameter can be testedaloneorinamultidegree-of-freedom testofoneorbothmaineffectstogetherwith the interaction, an approach that was generally found to bemore powerful (Millstein et al., 2006; Kraft et al., 2007). An important characteristic of the approach is that even if the independence assumption is not justified, type I error in the testing stage will still be properly controlled. This approach is perhaps more general and more powerful than previously appreciated. The screening procedure appears to be sensitive to both main effects and interactions, not just interactions, as claimed in prior work. The implication is that the approach is less specific to interactions and correspondingly more powerful when main effects are present. In fact, it may be capable of detecting weak interactions coupled with weak main effects. Some authors (Murcray et al., 2009; Dai et al., 2012; Lewinger et al., 2013) have attributed the statistical power of the screening procedure solely to an association in cases due to an interaction in the underlying population (non-zero β3, or more correctly, a departure from multiplicativity on a relative risk scale), as in the case-only interaction analysis (Piegorsch et al., 1994). According to this view, controls only contribute noise to the screening procedure because the factors are independent in this population. Further, if the two features contribute marginal disease risks and a multiplicative relative risk model describes their joint risk, then dependencies will not be induced among cases. The idea is that if there is independence in cases and independence in controls, then it should follow that there would be independence in the pooled case-control sample—but this is not necessarily the case. It has not been adequately appreciated that when cases and controls are pooled, main effects can contribute a substantial increase in power to capture disease-related feature pairs with the above screening procedure. Interestingly, the complex conditioning on disease status inherent in pooling of cases and controls can induce dependencies and thus increase power of the screening procedure when main effects are present. As proof of concept, consider the relatively simple relative risk model, log(P[D]) = λ+ β1∗SNP1+ β2∗SNP2+ β3 ∗SNP1∗SNP2, where exp (λ) is the baseline risk, the two SNPs have equal relative risks per allele, i.e., β1 = β2, there is a weak interaction (small β3), and equal
Loading PDF
3Genetic variants in PD-1 and its ligands, gene-gene and gene-environment interactions in allergic rhinitis.
Genetic variants in PD-1 and its ligands, gene-gene and gene-environment interactions in allergic rhinitis.
5Using Bayes model averaging to leverage both gene main effects and G × E interactions to identify genomic regions in genome-wide association studies.
Genome-wide association studies typically search for marginal associations between a single-nucleotide polymorphism (SNP) and a disease trait while gene-environment (G × E) interactions remain generally unexplored. More powerful methods beyond the simple case-control (CC) approach leverage either marginal effects or CC ascertainment to increase power. However, these potential gains depend on assumptions whose aptness is often unclear a priori. Here, we review G × E methods and use simulations to highlight performance as a function of main and interaction effects and the association of the two factors in the source population. Substantial variation in performance between methods leads to uncertainty as to which approach is most appropriate for any given analysis. We present a framework that (a) balances the robustness of a CC approach with the power of the case-only (CO) approach; (b) incorporates main SNP effects; (c) allows for incorporation of prior information; and (d) allows the data to determine the most appropriate model. Our framework is based on Bayes model averaging, which provides a principled statistical method for incorporating model uncertainty. We average over inclusion of parameters corresponding to the main and G × E interaction effects and the G-E association in controls. The resulting method exploits the joint evidence for main and interaction effects while gaining power from a CO equivalent analysis. Through simulations, we demonstrate that our approach detects SNPs within a wide range of scenarios with increased power over current methods. We illustrate the approach on a gene-environment scan in the USC Children's Health Study.
Read more18Describing time and age variations in the risk of radiation-induced solid tumour incidence in the Japanese atomic bomb survivors using generalized relative and absolute risk models.
Generalized relative and absolute risk models, in which various functions of time and age modify the excess relative or absolute risk of radiation-induced cancer, are fitted to the Japanese atomic bomb survivor cancer incidence data set. Among generalized relative risk models, those in which a product of powers of time since exposure and attained age modify the relative risk provide the best fit. There are indications that the Armitage-Doll model (in its formulation as a generalized relative risk model) provides a poor fit to the data, possibly in part because of increasing age-adjusted cancer incidence rates in the Japanese cohort. Generalized absolute risk models, and in particular models in which either powers of time since exposure and attained age, or powers of time since exposure and age at exposure modify the excess absolute risk, provide a superior fit to any of the generalized relative risk models for all solid cancer sites analysed together. When six cancer subtypes are examined separately, only for respiratory cancers does this finding remain true, and for two other sites (female breast cancer and thyroid cancer) the generalized relative risk model yields a better fit than the generalized absolute risk model.
Read moreGeneralized functional varying-index coefficient model for dynamic synergistic gene-environment interactions with binary longitudinal traits.
The genetic basis of complex traits involves the function of many genes with small effects as well as complex gene-gene and gene-environment interactions. As one of the major players in complex diseases, the role of gene-environment interactions has been increasingly recognized. Motivated by epidemiology studies to evaluate the joint effect of environmental mixtures, we developed a functional varying-index coefficient model (FVICM) to assess the combined effect of environmental mixtures and their interactions with genes, under a longitudinal design with quantitative traits. Built upon the previous work, we extend the FVICM model to accommodate binary longitudinal traits through the development of a generalized functional varying-index coefficient model (gFVICM). This model examines how the genetic effects on a disease trait are nonlinearly influenced by a combination of environmental factors. We derive an estimation procedure for the varying-index coefficient functions using quadratic inference functions combined with penalized splines. A hypothesis testing procedure is proposed to evaluate the significance of the nonparametric index functions. Extensive Monte Carlo simulations are conducted to evaluate the performance of the method under finite samples. The utility of the method is further demonstrated through a case study with a pain sensitivity dataset. SNPs were found to have their effects on blood pressure nonlinearly influenced by a combination of environmental factors.
Read more65Gene-gene and gene-environment interactions: new insights into the prevention, detection and management of coronary artery disease
Despite the recent success of genome-wide association studies (GWASs) in identifying loci consistently associated with coronary artery disease (CAD), a large proportion of the genetic components of CAD and its metabolic risk factors, including plasma lipids, type 2 diabetes and body mass index, remain unattributed. Gene-gene and gene-environment interactions might produce a meaningful improvement in quantification of the genetic determinants of CAD. Testing for gene-gene and gene-environment interactions is thus a new frontier for large-scale GWASs of CAD. There are several anecdotal examples of monogenic susceptibility to CAD in which the phenotype was worsened by an adverse environment. In addition, small-scale candidate gene association studies with functional hypotheses have identified gene-environment interactions. For future evaluation of gene-gene and gene-environment interactions to achieve the same success as the single gene associations reported in recent GWASs, it will be important to pre-specify agreed standards of study design and statistical power, environmental exposure measurement, phenomic characterization and analytical strategies. Here we discuss these issues, particularly in relation to the investigation and potential clinical utility of gene-gene and gene-environment interactions in CAD.
Read more41Genotype-environment interaction in personality development: identical twins reared apart.
The focus of this study is to identify specific genotype-environment (GE) interactions as they contribute to individual differences in personality in later life. In behavioral genetics, GE interaction refers to the possibility that individuals of different genotypes may respond differently to specific environments. A sample of 99 pairs of identical twins reared apart, whose average age is 59 years, has been studied as part of the Swedish Adoption/Twin Study of Aging (SATSA). Hierarchical multiple regression was used to detect interactions between personality and environmental measures after the main effects of genotype and environment were removed. Analyses yield evidence for 11 significant interactions that provide the first evidence for GE interaction in human development using specific environmental measures. Thus, in addition to the main-effect contributions of heredity and environment, GE interactions contribute to individual differences in personality as measured in the second half of the life course.
Read moreAbstract 4730: Exploring the nature of prostate cancer SNP associations: Results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3)
Genome-wide association studies have identified more than 35 single nucleotide polymorphisms (SNPs) associated with prostate cancer risk. In order to characterize the associations by disease aggressiveness (stage and tumor grade) and to investigate gene-environment and gene-gene interactions that might increase our understanding of prostate cancer etiology, we genotyped 29 SNPs in 9,067 prostate cancer cases and 9,188 controls from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3). We confirmed prostate cancer associations for all SNPs (P-values ranging from 0.03 to 10−26) and found no evidence that the SNPs are differentially associated with aggressive and non-aggressive disease. We further tested each SNP for effect modification by age and family history. One SNP, rs6983267, showed a significant non-multiplicative interaction with age (P=7.3[[Unable to Display Character: ∙]]10−5) where a stronger risk association was observed in younger men. We observed no interaction between SNPs and family history. The prospective nature of cohorts allowed us to explore possible gene-environment interactions with environmental factors such as smoking, alcohol and BMI that have been inconsistently related to prostate cancer, and we hypothesized that hidden gene-environment interactions might explain previous weak associations. We observed no interaction between the SNPs and these factors. In addition, we found no evidence of SNP-SNP interactions (epistasis). Our findings for established prostate cancer SNPs did not differ across prostate cancer clinical subtypes, and we observed no significant gene-environment interactions. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4730.
Read more127Extrapolation of the Relative Risk of Radiogenic Neoplasms across Mouse Strains and to Man
We have examined two interrelated questions: is the susceptibility for radiogenic cancer related to the natural incidence, and are the responses of cancer induction by radiation described better by an absolute or a relative risk model. Also, we have examined whether it is possible to extrapolate relative risk estimates across species, from mice to humans. The answers to these questions were obtained from determinations of risk estimates for nine neoplasms in female and male C3Hf/Bd and C57BL/6 Bd mice and from data obtained from previous experiments with female BALB/c Bd and RFM mice. The mice were exposed to 137Cs gamma rays at 0.4 Gy/min to doses of 0, 0.5, 1.0, or 2.0 Gy. When tumors that were considered the cause of death were examined, both the control and induced mortality rates for the various tumors varied considerably among sexes and strains. The results suggest that in general susceptibility is determined by the control incidence. The relative risk model was significantly superior in five of the tumor types: lung, breast, liver, ovary, and adrenal. Both models appeared to fit myeloid leukemia and Harderian gland tumors, and neither provided good fits for thymic lymphoma and reticulum cell sarcoma. When risk estimates of radiation-induced tumors in humans and mice were compared, it was found that the relative risk estimates for lung, breast, and leukemia were not significantly different between humans and mice. In the case of liver tumors, mice had a higher risk than humans. These results indicate that the relative risk model is the appropriate approach for risk estimation for a number of tumors. The apparent concordance of relative risk estimates between humans and mice for the small number of cancers examined encourages us to undertake further studies.
Read more67Genetic programming neural networks: A powerful bioinformatics tool for human genetics
Genetic programming neural networks: A powerful bioinformatics tool for human genetics
17Analysis of Indian post-rainy sorghum multi-location trial data reveals complexity of genotype × environment interaction
SUMMARYSorghum [Sorghum bicolor (L.) Moench] grown in India is of two adaptive types: rainy and post-rainy. The post-rainy sorghum is predominantly consumed by humans. While releasing new cultivars through multi-location testing, major emphasis is given to the superiority of new cultivars over existing cultivars, with very little emphasis on the genotype × environment interaction (GEI). To understand the complexity of GEI in post-rainy sorghum testing location trials, the multi-location evaluation data of two post-rainy seasons (2009/10 and 2010/11) under the All India Coordinated Sorghum Improvement Project were analysed. In both years, location explained the highest proportion of total sum of squares followed by the GEI effect and main effect of genotype. Additive main effects and multiplicative interaction (AMMI), stability values (ASV) and genotype + genotype × environment interaction (GGE) instability values recorded high correlation resulting in identification of the best performing cultivars. However, the rank correlations were lower, though still significant. A mixture of crossover and non-crossover GEI was a common occurrence in both years. ‘Which-won-where’ analysis suggested the existence of four possible mega-environments (ME) among post-rainy testing locations, with a few non-informative locations within ME. Mega-environments are characterized by soil type, rainfall pattern and moisture conservation practices. The present study indicated the possibility of reducing the number of test locations by eliminating non-representative highly correlated locations and suggested the need to breed for location-specific genotypes rather than genotypes with wider adaptability.
Read more
25Dissecting the Genotype × Environment Interaction for Potato Tuber Yield and Components
In the Mediterranean Basin, potato (Solanum tuberosum L.) is a major staple crop, the yield performance of which can vary dramatically based on pedo-climatic conditions and genotype. Hence, dissecting the genotype × environment interaction (GEI) in this region is mandatory for the setup of high-yielding and stable potato genotypes, also considering its importance for local potato breeding and the development of protected geographical identifications. Therefore, this research evaluated the marketable tuber yield (MY) and several yield components of five potato genotypes (Arizona, Generosa, Levante, Paradiso, and Vogue) over 4 years (2019, 2020, 2021, and 2022) and three locations in Sicily (Southern Italy) by additive main effects and multiplicative interactions (AMMI) and genotype main effects + genotype × environment interaction (GGE) biplot analyses. From combined ANOVA emerged a high significance of GEI, with the environment that provided the most considerable extent of variation for the most of the productive traits. The AMMI and GGE analyses identified Arizona as the best leading genotype in the studied area by virtue of its high productivity (44.5 t ha−1 of mean MY) coupled with stability, followed by Generosa (46.5 t ha−1). Ideal environments (location × year) were highly dependent on the productive trait, but most of them belonged to Acireale, characterized by fertile soils. According to our results, this approach could be recommended for breeding programs and commercial cultivation in the studied regions, along with the setup of potato protected geographical identifications.
Read more15Population attributable risk associated with lung cancer induced by residential radon in Canada: Sensitivity to relative risk model and radon probability density function choices: In memory of Professor Jan M. Zielinski
Population attributable risk associated with lung cancer induced by residential radon in Canada: Sensitivity to relative risk model and radon probability density function choices: In memory of Professor Jan M. Zielinski
Read more43Polymorphisms of STAT-6, STAT-4 and IFN- γ genes and the risk of asthma in Chinese population
Polymorphisms of STAT-6, STAT-4 and IFN- γ genes and the risk of asthma in Chinese population
Abstract 3742: Gene-environment Interactions in esophageal adenocarcinoma risk: A case-only analysis
The incidence of esophageal adenocarcinoma (EA) has increased approximately 500% in Western countries over the last four decades. The rapidly increasing incidence and sporadically development feature of EA suggest that gene-environment (G-E) interactions dominate the etiology. However, how G-E contributes to EA carcinogenesis is still poorly understood. We used a case-only approach to test the effect of G-E interactions in the occurrence of EA. 1330 functional and/or tagging SNPs selected from 354 cancer-associated genes were genotyped in 335 Caucasian EA cases. G-E (gastroesophageal reflux symptoms, GERD; BMI; and smoking) interactions were assessed by a two-step approach. First, we applied random forest (RF) to screen for important SNPs that had main effects as well as interaction effects. Second, we used case-only logistic regression (LR) models to estimate the G-E interaction effect, adjusting for covariates and false-discovery rate (FDR). RF analyses identified three sets of SNPs (94 SNPs-GERD, 38 SNPs-smoking, and 44 SNPs-BMI, respectively) that had the highest importance scores and lowest classification error rates. Further case-only LR analysis revealed that multiple interaction markers were significantly associated with EA risk: GERD*rs2237051 of EGF(OR = 1.39, P = 3.50E-07), GERD*rs2440 of XRCC5 (OR = 1,80, P = 0.0008), GERD*rs2237051*rs2440 (OR = 1.85, P = 0.004), smoking*rs10842514 of KRAS (OR = 0.85, P = 0.012), BMI*rs2305742 of IL12RB1 (OR = 0.84, P = 0.003), BMI*rs11568820 of VDR (OR = 0.97, P = 0.0005), BMI*rs11244142 of ABL1 (OR = 1.47, P = 2.09E-05), BMI*rs2800975 of XPA*rs743572 of CYP17A (OR = 3.62, P = 0.0009), and BMI*rs11244142*rs20547 of IL13 (OR = 1.89, P = 0.023). These results indicate that genetic variability may contribute to EA development through interactions with environmental factors. Our data also suggest that G-E interactions may be G- and E-specific in modulating the risk of EA. Supported by grants: NIH grants CA92824, CA74386, CA90578, and CA119650); Flight Attendant Medical Research Institute (FAMRI) grant 062459_YCSA; the Kevin Jackson Memorial Fund and Alan Brown Chair of Molecular Genomics. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3742. doi:10.1158/1538-7445.AM2011-3742
Read more7English
Genotype by environment interaction (GEI) is a major complication in plant breeding. An assessment of genotypes across diversified environment and season is important for releasing of varieties. The objective of the study was to evaluate the effects of GEI in fourteen NERICA rice genotype and their adaptation in two years at three locations in the production year 2009 and 2010. The trial was laid out in randomized complete block design with three replications. The combined analysis of variance revealed significant (P<0.01) environments, genotypes and genotype by environment interaction effect and environment captured 91.4% of the total variability. The additive main and multiplicative interaction further explained the genotype by environment interaction by decomposing in to two significant interaction principal components. The fourteen rice genotypes were best explained by the additive main effects and multiplicative interaction (AMMI) 1 that gives model fitness of 98.1%. The polygon view of the Genotype and genotype by environment interaction (GGE) bipolt analysis showed that the environments fall in to two sectors and the genotype NERICA 4 was wining genotypes in all of the environment except E4 (Gomma, 2010). The additive main effect and multiplicative interaction biplot 1 and polygon view of the GGE (Genotype and genotype by environment interaction) biplot showed that the genotype NERICA 4 were consistently higher yielder in all the environments in the study. Key words: Additive main effects and multiplicative interaction (AMMI), Genotype and genotype by environment interaction (GGE), grain yield, upland rice.
Read more
Copyright 2026 Cactus Communications. All rights reserved.
