Gene-environment interactions for complex traits: definitions, methodological requirements and challenges

Multi-trait analysis of gene-by-environment interactions in large-scale genetic studies

Identifying genotype-by-environment interaction (GEI) is challenging because the GEI analysis generally has low power. Large-scale consortium-based studies are ultimately needed to achieve adequate power for identifying GEI. We introduce Multi-Trait Analysis of Gene-Environment Interactions (MTAGEI), a powerful, robust, and computationally efficient framework to test gene-environment interactions on multiple traits in large data sets, such as the UK Biobank (UKB). To facilitate the meta-analysis of GEI studies in a consortium, MTAGEI efficiently generates summary statistics of genetic associations for multiple traits under different environmental conditions and integrates the summary statistics for GEI analysis. MTAGEI enhances the power of GEI analysis by aggregating GEI signals across multiple traits and variants that would otherwise be difficult to detect individually. MTAGEI achieves robustness by combining complementary tests under a wide spectrum of genetic architectures. We demonstrate the advantages of MTAGEI over existing single-trait-based GEI tests through extensive simulation studies and the analysis of the whole exome sequencing data from the UKB.

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.

Interactions between genetic risk for 21 neurodevelopmental and psychiatric disorders and sport activity on youth mental health

Childhood is a sensitive period where behavioral disturbances, determined by genetics and environmental factors including sport activity, may emerge and impact risk of mental illness in adulthood. We aimed to determine if participation in sports can mitigate genetic risk for neurodevelopmental and psychiatric disorders in youth. We analyzed 4975 unrelated European youth (ages 9-10) from the Adolescent Brain Cognitive Development Study. Our outcomes were eight Child Behavior Checklist (CBCL) scores, measured annually. Polygenic risk scores (PRSs) were calculated for 21 disorders, and sport frequency and type were summarized. PRSs and sport variables were tested for main effects and interactions against CBCL outcomes using linear models. Cross-sectionally, PRSs for attention-deficit/hyperactivity disorder and major depressive disorder were associated with increases in multiple CBCL outcomes. Participation in non-contact or team sports, as well as more frequent sport participation reduced all cross-sectional CBCL outcomes, whereas involvement in contact sports increased attention problems and rule-breaking behavior. Interactions revealed that more frequent exercise was significantly associated with less rule breaking behavior in individuals with high genetic risk for obsessive compulsive disorder. Associations with longitudinal CBCL outcomes demonstrated weaker effects. We highlight the importance of genetic context when considering sports as an intervention for early life behavioural problems.

A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets

Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 - 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data.

Gene-Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors

Asthma is a complex heterogeneous disease caused by gene-environment interactions. Although numerous genome-wide association studies have been conducted, these interactions have not been systemically investigated. We sought to identify genetic factors associated with the asthma phenotype in 66,857 subjects from the Health Examination Study, Cardiovascular Disease Association Study, and Korea Association Resource Study cohorts. We investigated asthma-associated gene-environment (smoking status) interactions at the level of single nucleotide polymorphisms, genes, and gene sets. We identified two potentially novel (SETDB1 and ZNF8) and five previously reported (DM4C, DOCK8, MMP20, MYL7, and ADCY9) genes associated with increased asthma risk. Numerous gene ontology processes, including regulation of T cell differentiation in the thymus (GO:0033081), were significantly enriched for asthma risk. Functional annotation analysis confirmed the causal relationship between five genes (two potentially novel and three previously reported genes) and asthma through genome-wide functional prediction scores (combined annotation-dependent depletion, deleterious annotation of genetic variants using neural networks, and RegulomeDB). Our findings elucidate the genetic architecture of asthma and improve the understanding of its biological mechanisms. However, further studies are necessary for developing preventive treatments based on environmental factors and understanding the immune system mechanisms that contribute to the etiology of asthma.

Genome-wide case-only analysis of gene-gene interactions with known Parkinson's disease risk variants reveals link between LRRK2 and SYT10

The effects of one genetic factor upon Parkinson's disease (PD) risk may be modified by other genetic factors. Such gene-gene interaction (G×G) could explain some of the 'missing heritability' of PD and the reduced penetrance of known PD risk variants. Using the largest single nucleotide polymorphism (SNP) genotype data set currently available for PD (18,688 patients), provided by the International Parkinson's Disease Genomics Consortium, we studied G×G with a case-only (CO) design. To this end, we paired each of 90 SNPs previously reported to be associated with PD with one of 7.8 million quality-controlled SNPs from a genome-wide panel. Support of any putative G×G interactions found was sought by the analysis of independent genotype-phenotype and experimental data. A total of 116 significant pairwise SNP genotype associations were identified in PD cases, pointing towards G×G. The most prominent associations involved a region on chromosome 12q containing SNP rs76904798, which is a non-coding variant of the LRRK2 gene. It yielded the lowest interaction p-value overall with SNP rs1007709 in the promoter region of the SYT10 gene (interaction OR = 1.80, 95% CI: 1.65-1.95, p = 2.7 × 10^-43). SNPs around SYT10 were also associated with the age-at-onset of PD in an independent cohort of carriers of LRRK2 mutation p.G2019S. Moreover, SYT10 gene expression during neuronal development was found to differ between cells from affected and non-affected p.G2019S carriers. G×G interaction on PD risk, involving the LRRK2 and SYT10 gene regions, is biologically plausible owing to the known link between PD and LRRK2, its involvement in neural plasticity, and the contribution of SYT10 to the exocytosis of secretory vesicles in neurons.

Genetic Basis of Inflammatory Demyelinating Diseases of the Central Nervous System: Multiple Sclerosis and Neuromyelitis Optica Spectrum

Demyelinating diseases alter myelin or the coating surrounding most nerve fibers in the central and peripheral nervous systems. The grouping of human central nervous system demyelinating disorders today includes multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) as distinct disease categories. Each disease is caused by a complex combination of genetic and environmental variables, many involving an autoimmune response. Even though these conditions are fundamentally similar, research into genetic factors, their unique clinical manifestations, and lesion pathology has helped with differential diagnosis and disease pathogenesis knowledge. This review aims to synthesize the genetic approaches that explain the differential susceptibility between these diseases, explore the overlapping clinical features, and pathological findings, discuss existing and emerging hypotheses on the etiology of demyelination, and assess recent pathogenicity studies and their implications for human demyelination. This review presents critical information from previous studies on the disease, which asks several questions to understand the gaps in research in this field.

FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity

Cellular exposure to free fatty acids (FFAs) is implicated in the pathogenesis of obesity-associated diseases. However, there are no scalable approaches to comprehensively assess the diverse FFAs circulating in human plasma. Furthermore, assessing how FFA-mediated processes interact with genetic risk for disease remains elusive. Here, we report the design and implementation of fatty acid library for comprehensive ontologies (FALCON), an unbiased, scalable, and multimodal interrogation of 61 structurally diverse FFAs. We identified a subset of lipotoxic monounsaturated fatty acids associated with decreased membrane fluidity. Furthermore, we prioritized genes that reflect the combined effects of harmful FFA exposure and genetic risk for type 2 diabetes (T2D). We found that c-MAF-inducing protein (CMIP) protects cells from FFA exposure by modulating Akt signaling. In sum, FALCON empowers the study of fundamental FFA biology and offers an integrative approach to identify much needed targets for diverse diseases associated with disordered FFA metabolism.

FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity

Cellular exposure to free fatty acids (FFA) is implicated in the pathogenesis of obesity-associated diseases. However, studies to date have assumed that a few select FFAs are representative of broad structural categories, and there are no scalable approaches to comprehensively assess the biological processes induced by exposure to diverse FFAs circulating in human plasma. Furthermore, assessing how these FFA- mediated processes interact with genetic risk for disease remains elusive. Here we report the design and implementation of FALCON (Fatty Acid Library for Comprehensive ONtologies) as an unbiased, scalable and multimodal interrogation of 61 structurally diverse FFAs. We identified a subset of lipotoxic monounsaturated fatty acids (MUFAs) with a distinct lipidomic profile associated with decreased membrane fluidity. Furthermore, we developed a new approach to prioritize genes that reflect the combined effects of exposure to harmful FFAs and genetic risk for type 2 diabetes (T2D). Importantly, we found that c-MAF inducing protein (CMIP) protects cells from exposure to FFAs by modulating Akt signaling and we validated the role of CMIP in human pancreatic beta cells. In sum, FALCON empowers the study of fundamental FFA biology and offers an integrative approach to identify much needed targets for diverse diseases associated with disordered FFA metabolism.

Highlights

FALCON (Fatty Acid Library for Comprehensive ONtologies) enables multimodal profiling of 61 free fatty acids (FFAs) to reveal 5 FFA clusters with distinct biological effectsFALCON is applicable to many and diverse cell typesA subset of monounsaturated FAs (MUFAs) equally or more toxic than canonical lipotoxic saturated FAs (SFAs) leads to decreased membrane fluidityNew approach prioritizes genes that represent the combined effects of environmental (FFA) exposure and genetic risk for diseaseC-Maf inducing protein (CMIP) is identified as a suppressor of FFA-induced lipotoxicity via Akt-mediated signaling.

Using epigenomics to understand cellular responses to environmental influences in diseases

It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures. With the understanding that the interpretation of these associations is more complex than originally recognised, this model may be oversimplistic; therefore, adopting novel perspectives and experimental approaches when examining how environmental exposures are linked to phenotypes may prove worthwhile. In this review, we have chosen to use the example of nonalcoholic fatty liver disease (NAFLD), a common, complex human disease with strong environmental and genetic influences. We describe how epigenomic approaches combined with emerging functional genetic and single-cell genomic techniques are poised to generate new insights into the pathogenesis of environmentally influenced human disease phenotypes exemplified by NAFLD.

Rat Mammary carcinoma susceptibility 3 (Mcs3) pleiotropy, socioenvironmental interaction, and comparative genomics with orthologous human 15q25.1-25.2

Genome-wide association studies of breast cancer susceptibility have revealed risk-associated genetic variants and nominated candidate genes; however, the identification of causal variants and genes is often undetermined by genome-wide association studies. Comparative genomics, utilizing Rattus norvegicus strains differing in susceptibility to mammary tumor development, is a complimentary approach to identify breast cancer susceptibility genes. Mammary carcinoma susceptibility 3 (Mcs3) is a Copenhagen (COP/NHsd) allele that confers resistance to mammary carcinomas when introgressed into a mammary carcinoma susceptible Wistar Furth (WF/NHsd) genome. Here, Mcs3 was positionally mapped to a 7.2-Mb region of RNO1 spanning rs8149408 to rs107402736 (chr1:143700228-150929594, build 6.0/rn6) using WF.COP congenic strains and 7,12-dimethylbenz(a)anthracene-induced mammary carcinogenesis. Male and female WF.COP-Mcs3 rats had significantly lower body mass compared to the Wistar Furth strain. The effect on female body mass was observed only when females were raised in the absence of males indicating a socioenvironmental interaction. Furthermore, female WF.COP-Mcs3 rats, raised in the absence of males, did not develop enhanced lobuloalveolar morphologies compared to those observed in the Wistar Furth strain. Human 15q25.1-25.2 was determined to be orthologous to rat Mcs3 (chr15:80005820-82285404 and chr15:83134545-84130720, build GRCh38/hg38). A public database search of 15q25.1-25.2 revealed genome-wide significant and nominally significant associations for body mass traits and breast cancer risk. These results support the existence of a breast cancer risk-associated allele at human 15q25.1-25.2 and warrant ultrafine mapping of rat Mcs3 and human 15q25.1-25.2 to discover novel causal genes and variants.

BDNF val66met genotype is not associated with psychological distress: A cross-sectional study in Indonesian Pharmacy young adults

The number of mental disorders has been increasing but has yet to receive sufficient attention. In particular, healthcare students and professionals tend to have high stress burden. Finding the root cause of psychological distress is important to formulate a method for early detection and prevention. The association of brain-derived neurotrophic factor val66met polymorphism to neuropsychiatric disorders has been widely studied. To study the interplay between brain-derived neurotrophic factor val66met polymorphism and sociodemographic factors in the pathogenesis of psychological distress among Indonesian Pharmacy students. Level of psychological distress and sociodemographic profiling was collected by using the Kessler Psychological Distress Scale and sociodemographic questionnaires, respectively. Genotyping was performed using polymerase chain reaction-amplified refractory mutation system. Pearson's chi square and binomial logistic tests were used to evaluate the correlation. This study recruited 148 participants. The psychological distress levels of the participants were well (27.03%), mild (37.16%), moderate (25.00%), and severe (10.81%). Genotypic distributions were AA (25.67%), GA (50.68%), and GG (23.65%). No statistical significance between genotype and psychological distress was found in the study (P = .076). The sociodemographic factors also showed non significance, except for the source of tuition fee among women students (P = .049). Psychological distress is not affected by genotypic and sociodemographic factors. Further confirmatory research with larger and broader populations is required.

3DFAACTS-SNP: using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of type 1 diabetes (T1D) risk

Background

Genome-wide association studies (GWAS) have enabled the discovery of single nucleotide polymorphisms (SNPs) that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the identified variants lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. To address this problem, we developed a variant filtering workflow called 3DFAACTS-SNP to link genetic variants to target genes in a cell-specific manner. Here, we use 3DFAACTS-SNP to identify candidate SNPs and target genes associated with the loss of immune tolerance in regulatory T cells (Treg) in T1D.

Results

Using 3DFAACTS-SNP, we identified from a list of 1228 previously fine-mapped variants, 36 SNPs with plausible Treg-specific mechanisms of action. The integration of cell type-specific chromosome conformation capture data in 3DFAACTS-SNP identified 266 regulatory regions and 47 candidate target genes that interact with these variant-containing regions in Treg cells. We further demonstrated the utility of the workflow by applying it to three other SNP autoimmune datasets, identifying 16 Treg-centric candidate variants and 60 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of all known common (> 10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 9376 candidate variants and 4968 candidate target genes, generating a list of potential sites for future T1D or other autoimmune disease research.

Conclusions

We demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function, and illustrate the power of using cell type-specific multi-omics datasets to determine disease mechanisms. Our workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13072-022-00456-5.

Altered Cerebral Curvature in Preterm Infants Is Associated with the Common Genetic Variation Related to Autism Spectrum Disorder and Lipid Metabolism

Preterm births are often associated with neurodevelopmental impairment. In the critical developmental period of the fetal brain, preterm birth disrupts cortical maturation. Notably, preterm birth leads to alterations in the fronto-striatal and temporal lobes and the limbic region. Recent advances in MRI acquisition and analysis methods have revealed an integrated approach to the genetic influence on brain structure. Based on imaging studies, we hypothesized that the altered cortical structure observed after preterm birth is associated with common genetic variations. We found that the presence of the minor allele at rs1042778 in OXTR was associated with reduced curvature in the right medial orbitofrontal gyrus (p < 0.001). The presence of the minor allele at rs174576 in FADS2 (p < 0.001) or rs740603 in COMT (p < 0.001) was related to reduced curvature in the left posterior cingulate gyrus. This study provides biological insight into altered cortical curvature at term-equivalent age, suggesting that the common genetic variations related to autism spectrum disorder (ASD) and lipid metabolism may mediate vulnerability to early cortical dysmaturation in preterm infants.

Prevalence and risk factors for hyperhomocysteinemia: a population-based cross-sectional study from Hunan, China

OBJECTIVES

Hyperhomocysteinemia is an independent risk factor for cardiovascular diseases. We aimed to investigate the prevalence and risk factors for hyperhomocysteinemia, especially modifiable lifestyle factors, such as smoking behaviour and dietary factors.

DESIGN

Population-based cross-sectional study.

SETTING

Hunan Province, China PARTICIPANTS: A total of 4012 participants completed the study, between July 2013 and March 2014. The median age is 55 (interquartile range: 45-63) years, with 1644 males (41%) and 2368 females (59%).

MAIN OUTCOME MEASURES

Homocysteine level were measured by the microplate enzyme immunoassay method. Hyperthomocysteinemia was defined as ≥15 µmol/L. Questionnaire was used to investigate potential risk factors of hyperhomocysteinemia. Crude odd ratio (OR) or adjusted OR with 95% CI were determined by using univariable or multivariable logistic regression models.

RESULTS

The prevalence of hyperhomocysteinemia is 35.4% (45.4% vs 28.5% for men, women, respectively). One-year increase in age is significantly associated with 2% higher risk of hyperhomocysteinemia (OR=1.02, 95% CI: 1.01 to 1.03). One unit increase of BMI is associated with 5% higher risk of hyperhomocysteinemia (OR=1.05, 95% CI: 1.03 to 1.07). Compared with the non-smoker, smoking participants have a 24% higher risk of hyperhomocysteinemia (OR=1.24, 95% CI: 1.006 to 1.53), while the risk for those quitting smoking are not significantly different (OR=1.14, 95% CI: 0.85 to 1.54). compared with those consuming fruit and vegetable at least once every day, those consuming less than once every day had a significantly higher risk of hyperhomocysteinemia (OR=1.29, 95% CI:1.11 to 1.50). In addition, we found there were significant sex interaction with education level or alcohol drinking on the risk of hyperhomocysteinemia (p_interaction <0.05).

CONCLUSIONS

Higher BMI and older age are potential risk factors for hyperhomocysteinemia. Current smoking but not quitting smoking is associated with higher risk of hyperhomocysteinemia. Fruit and vegetable consumption may have protective effect against hyperhomocysteinemia. Alcohol consumption or education level might interact to influence the risk of hyperhomocysteinemia.

A systematic review of gene-by-intervention studies of alcohol and other substance use

Alcohol and other substance use problems are common, and the efficacy of current prevention and intervention programs is limited. Genetics may contribute to differential effectiveness of psychosocial prevention and intervention programs. This paper reviews gene-by-intervention (G×I) studies of alcohol and other substance use, and implications for integrating genetics into prevention science. Systematic review yielded 17 studies for inclusion. Most studies focused on youth substance prevention, alcohol was the most common outcome, and measures of genotype were heterogeneous. All studies reported at least one significant G×I interaction. We discuss these findings in the context of the history and current state of genetics, and provide recommendations for future G×I research. These include the integration of genome-wide polygenic scores into prevention studies, broad outcome measurement, recruitment of underrepresented populations, testing mediators of G×I effects, and addressing ethical implications. Integrating genetic research into prevention science, and training researchers to work fluidly across these fields, will enhance our ability to determine the best intervention for each individual across development. With growing public interest in obtaining personalized genetic information, we anticipate that the integration of genetics and prevention science will become increasingly important as we move into the era of precision medicine.

Genome-wide sequencing-based identification of methylation quantitative trait loci and their role in schizophrenia risk

DNA methylation (DNAm) is an epigenetic regulator of gene expression and a hallmark of gene-environment interaction. Using whole-genome bisulfite sequencing, we have surveyed DNAm in 344 samples of human postmortem brain tissue from neurotypical subjects and individuals with schizophrenia. We identify genetic influence on local methylation levels throughout the genome, both at CpG sites and CpH sites, with 86% of SNPs and 55% of CpGs being part of methylation quantitative trait loci (meQTLs). These associations can further be clustered into regions that are differentially methylated by a given SNP, highlighting the genes and regions with which these loci are epigenetically associated. These findings can be used to better characterize schizophrenia GWAS-identified variants as epigenetic risk variants. Regions differentially methylated by schizophrenia risk-SNPs explain much of the heritability associated with risk loci, despite covering only a fraction of the genomic space. We provide a comprehensive, single base resolution view of association between genetic variation and genomic methylation, and implicate schizophrenia GWAS-associated variants as influencing the epigenetic plasticity of the brain.

The authors provide a comprehensive, single base resolution view of association between genetic variation and DNA methylation in human brain. They also show that heritability attributed to schizophrenia GWAS-associated variants reflects the epigenetic plasticity of the brain.

Genotype imputation in case-only studies of gene-environment interaction: validity and power

Case-only (CO) studies are a powerful means to uncover gene-environment (G × E) interactions for complex human diseases. Moreover, such studies may in principle also draw upon genotype imputation to increase statistical power even further. However, genotype imputation usually employs healthy controls such as the Haplotype Reference Consortium (HRC) data as an imputation base, which may systematically perturb CO studies in genomic regions with main effects upon disease risk. Using genotype data from 719 German Crohn Disease (CD) patients, we investigated the level of imputation accuracy achievable for single nucleotide polymorphisms (SNPs) with or without a genetic main effect, and with varying minor allele frequency (MAF). Genotypes were imputed from neighbouring SNPs at different levels of linkage disequilibrium (LD) to the target SNP using the HRC data as an imputation base. Comparison of the true and imputed genotypes revealed lower imputation accuracy for SNPs with strong main effects. We also simulated different levels of G × E interaction to evaluate the potential loss of statistical validity and power incurred by the use of imputed genotypes. Simulations under the null hypothesis revealed that genotype imputation does not inflate the type I error rate of CO studies of G × E. However, the statistical power was found to be reduced by imputation, particularly for SNPs with low MAF, and a gradual loss of statistical power resulted when the level of LD to the SNPs driving the imputation decreased. Our study thus highlights that genotype imputation should be employed with great care in CO studies of G × E interaction.

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.

Bullying Victimization and Life Satisfaction Among Rural Left-Behind Children in China: A Cross-Sectional Study

Objectives: This study aimed to evaluate the associations between bullying victimization and life satisfaction in primary school children and also investigate the interactive effects of left-behind status and bullying victimization on life satisfaction. Materials and Methods: Bullying victimization was measured using the Chinese version of the revised Olweus Bully/Victim Questionnaire. Life satisfaction was assessed using the Multidimensional Students' Life Satisfaction Scale (MSLSS). Life satisfaction is composed of five domains, namely, family, school, friends, environment, and self-satisfaction. Left-behind status of rural children was defined as one or both their parents migrating to working in cities. The data were analyzed using Mann-Whitney U tests, Chi-square tests, and multivariate linear and logistic regression analyses. Results: A total of 810 primary school children were involved, of which 8.5% reported bullying victimization, and 44.3% were left-behind children (LBC). We found that bullying victimization was negatively associated with all domains of life satisfaction (all p < 0.05). With further left-behind status-stratified analysis, we found that negative association between bullying victimization and friend satisfaction was more profound in the LBC group than in the non-LBC group [b(SE)= -0.133 (0.03) vs. -0.061 (0.026) for LBC and non-LBC, respectively, p < 0.05]. When further interaction analysis was conducted, we identified interaction effects between left-behind status and bullying victimization on friend satisfaction (p _interaction = 0.048). Similar interaction effect between bullying victimization and left-behind status on school satisfaction was also found (p _interaction = 0.004). Conclusions: Bullying victimization was associated with low life satisfaction (including lower family, friends, school, self, and environment satisfaction). There were significant interactions between left-behind status and bullying victimization on friend satisfaction, as well as school satisfaction. Left-behind status of children may exaggerate the impact of bullying victimization on friends/school satisfaction rating.

Gene-Environment Interaction: A Variable Selection Perspective

Gene-environment interactions have important implications for elucidating the genetic basis of complex diseases beyond the joint function of multiple genetic factors and their interactions (or epistasis). In the past, G × E interactions have been mainly conducted within the framework of genetic association studies. The high dimensionality of G × E interactions, due to the complicated form of environmental effects and the presence of a large number of genetic factors including gene expressions and SNPs, has motivated the recent development of penalized variable selection methods for dissecting G × E interactions, which has been ignored in the majority of published reviews on genetic interaction studies. In this article, we first survey existing studies on both gene-environment and gene-gene interactions. Then, after a brief introduction to the variable selection methods, we review penalization and relevant variable selection methods in marginal and joint paradigms, respectively, under a variety of conceptual models. Discussions on strengths and limitations, as well as computational aspects of the variable selection methods tailored for G × E studies, have also been provided.

Opportunities for Gene and Environment Research in Cancer: An Updated Review of NCI's Extramural Grant Portfolio

BACKGROUND

The study of gene-environment (GxE) interactions is a research priority for the NCI. Previously, our group analyzed NCI's extramural grant portfolio from fiscal years (FY) 2007 to 2009 to determine the state of the science in GxE research. This study builds upon our previous effort and examines changes in the landscape of GxE cancer research funded by NCI.

METHODS

The NCI grant portfolio was examined from FY 2010 to 2018 using the iSearch application. A time-trend analysis was conducted to explore changes over the study interval.

RESULTS

A total of 107 grants met the search criteria and were abstracted. The most common cancer types studied were breast (19.6%) and colorectal (18.7%). Most grants focused on GxE using specific candidate genes (69.2%) compared with agnostic approaches using genome-wide (26.2%) or whole-exome/whole-genome next-generation sequencing (NGS) approaches (19.6%); some grants used more than one approach to assess genetic variation. More funded grants incorporated NGS technologies in FY 2016-2018 compared with prior FYs. Environmental exposures most commonly examined were energy balance (46.7%) and drugs/treatment (40.2%). Over the time interval, we observed a decrease in energy balance applications with a concurrent increase in drug/treatment applications.

CONCLUSIONS

Research in GxE interactions has continued to concentrate on common cancers, while there have been some shifts in focus of genetic and environmental exposures. Opportunities exist to study less common cancers, apply new technologies, and increase racial/ethnic diversity.

IMPACT

This analysis of NCI's extramural grant portfolio updates previous efforts and provides a review of NCI grant support for GxE research.

Variable selection methods for identifying predictor interactions in data with repeatedly measured binary outcomes

Introduction:

Identifying predictors of patient outcomes evaluated over time may require modeling interactions among variables while addressing within-subject correlation. Generalized linear mixed models (GLMMs) and generalized estimating equations (GEEs) address within-subject correlation, but identifying interactions can be difficult if not hypothesized a priori. We evaluate the performance of several variable selection approaches for clustered binary outcomes to provide guidance for choosing between the methods.

Methods:

We conducted simulations comparing stepwise selection, penalized GLMM, boosted GLMM, and boosted GEE for variable selection considering main effects and two-way interactions in data with repeatedly measured binary outcomes and evaluate a two-stage approach to reduce bias and error in parameter estimates. We compared these approaches in real data applications: hypothermia during surgery and treatment response in lupus nephritis.

Results:

Penalized and boosted approaches recovered correct predictors and interactions more frequently than stepwise selection. Penalized GLMM recovered correct predictors more often than boosting, but included many spurious predictors. Boosted GLMM yielded parsimonious models and identified correct predictors well at large sample and effect sizes, but required excessive computation time. Boosted GEE was computationally efficient and selected relatively parsimonious models, offering a compromise between computation and parsimony. The two-stage approach reduced the bias and error in regression parameters in all approaches.

Conclusion:

Penalized and boosted approaches are effective for variable selection in data with clustered binary outcomes. The two-stage approach reduces bias and error and should be applied regardless of method. We provide guidance for choosing the most appropriate method in real applications.

Cohort Differences in the Associations of Selected Candidate Genes With Risk of All-Cause Mortality at Advanced Ages

Considerable efforts have been made to identify the genetic basis of human longevity, with only limited progress. One important drawback of current genetic studies is the limited knowledge of gene-environment interaction. Using 2 cohorts of long-lived individuals born in 1905 and 1915 in Denmark, we performed survival analysis to estimate risk of mortality for major candidate genes of aging and longevity and their cohort effects. Through statistical modeling that combines individual genetic and survival information with cohort-specific survival data, we estimated the relative risks of mortality from ages 95 to 103 years associated with genetic variants in apolipoprotein E (APOE), forkhead box class O3a, clusterin, and phosphatidylinositol binding clathrin assembly protein. Our analysis estimated a decreased risk of carrying the APOE$\varepsilon $4 allele (change in risk = -0.403, 95% confidence interval (CI): -0.831, 0.021; P = 0.040) in men of the later cohort, although the allele itself was harmful to survival across sexes (relative risk = 1.161, 95% CI: 1.027, 1.345; P = 0.026). We also estimated a cohort effect of increased risk for the minor allele of rs3851179 in phosphatidylinositol binding clathrin assembly protein with borderline significance (change in risk = 0.165, 95% CI: -0.010, 0.331; P = 0.052) in women. Our estimated significant cohort effect on APOE$\varepsilon $4 is indicative of the interplay between the gene and the changing environment that modulates survival at extreme ages.

Unravelling the complex genetics of common kidney diseases: from variants to mechanisms

Genome-wide association studies (GWAS) have identified hundreds of loci associated with kidney-related traits such as glomerular filtration rate, albuminuria, hypertension, electrolyte and metabolite levels. However, these impressive, large-scale mapping approaches have not always translated into an improved understanding of disease or development of novel therapeutics. GWAS have several important limitations. Nearly all disease-associated risk loci are located in the non-coding region of the genome and therefore, their target genes, affected cell types and regulatory mechanisms remain unknown. Genome-scale approaches can be used to identify associations between DNA sequence variants and changes in gene expression (quantified through bulk and single-cell methods), gene regulation and other molecular quantitative trait studies, such as chromatin accessibility, DNA methylation, protein expression and metabolite levels. Data obtained through these approaches, used in combination with robust computational methods, can deliver robust mechanistic inferences for translational exploitation. Understanding the genetic basis of common kidney diseases means having a comprehensive picture of the genes that have a causal role in disease development and progression, of the cells, tissues and organs in which these genes act to affect the disease, of the cellular pathways and mechanisms that drive disease, and of potential targets for disease prevention, detection and therapy.

A gene-diet interaction-based score predicts response to dietary fat in the Women's Health Initiative

BACKGROUND

Although diet response prediction for cardiometabolic risk factors (CRFs) has been demonstrated using single genetic variants and main-effect genetic risk scores, little investigation has gone into the development of genome-wide diet response scores.

OBJECTIVE

We sought to leverage the multistudy setup of the Women's Health Initiative cohort to generate and test genetic scores for the response of 6 CRFs (BMI, systolic blood pressure, LDL cholesterol, HDL cholesterol, triglycerides, and fasting glucose) to dietary fat.

METHODS

A genome-wide interaction study was undertaken for each CRF in women (n ∼ 9000) not participating in the dietary modification (DM) trial, which focused on the reduction of dietary fat. Genetic scores based on these analyses were developed using a pruning-and-thresholding approach and tested for the prediction of 1-y CRF changes as well as long-term chronic disease development in DM trial participants (n ∼ 5000).

RESULTS

Only 1 of these genetic scores, for LDL cholesterol, predicted changes in the associated CRF. This 1760-variant score explained 3.7% (95% CI: 0.09, 11.9) of the variance in 1-y LDL cholesterol changes in the intervention arm but was unassociated with changes in the control arm. In contrast, a main-effect genetic risk score for LDL cholesterol was not useful for predicting dietary fat response. Further investigation of this score with respect to downstream disease outcomes revealed suggestive differential associations across DM trial arms, especially with respect to coronary heart disease and stroke subtypes.

CONCLUSIONS

These results lay the foundation for the combination of many genome-wide gene-diet interactions for diet response prediction while highlighting the need for further research and larger samples in order to achieve robust biomarkers for use in personalized nutrition.

CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits

While genome-wide association studies are an established method of identifying genetic variants associated with disease, environment-wide association studies (EWAS) highlight the contribution of nongenetic components to complex phenotypes. However, the lack of high-throughput quality control (QC) pipelines for EWAS data lends itself to analysis plans where the data are cleaned after a first-pass analysis, which can lead to bias, or are cleaned manually, which is arduous and susceptible to user error. We offer a novel software, CLeaning to Analysis: Reproducibility-based Interface for Traits and Exposures (CLARITE), as a tool to efficiently clean environmental data, perform regression analysis, and visualize results on a single platform through user-guided automation. It exists as both an R package and a Python package. Though CLARITE focuses on EWAS, it is intended to also improve the QC process for phenotypes and clinical lab measures for a variety of downstream analyses, including phenome-wide association studies and gene-environment interaction studies. With the goal of demonstrating the utility of CLARITE, we performed a novel EWAS in the National Health and Nutrition Examination Survey (NHANES) (N overall Discovery=9063, N overall Replication=9874) for body mass index (BMI) and over 300 environment variables post-QC, adjusting for sex, age, race, socioeconomic status, and survey year. The analysis used survey weights along with cluster and strata information in order to account for the complex survey design. Sixteen BMI results replicated at a Bonferroni corrected p < 0.05. The top replicating results were serum levels of g-tocopherol (vitamin E) (Discovery Bonferroni p: 8.67x10^-12, Replication Bonferroni p: 2.70x10^-9) and iron (Discovery Bonferroni p: 1.09x10^-8, Replication Bonferroni p: 1.73x10^-10). Results of this EWAS are important to consider for metabolic trait analysis, as BMI is tightly associated with these phenotypes. As such, exposures predictive of BMI may be useful for covariate and/or interaction assessment of metabolic-related traits. CLARITE allows improved data quality for EWAS, gene-environment interactions, and phenome-wide association studies by establishing a high-throughput quality control infrastructure. Thus, CLARITE is recommended for studying the environmental factors underlying complex disease.

Interaction between lifestyle behaviors and genetic polymorphism in SCAP gene on blood pressure among Chinese children

BACKGROUNDS

Previous studies had revealed that sterol regulatory element-binding protein (SREBP) cleavage-activating protein (SCAP) rs12487736 polymorphism was associated with blood pressure (BP), but whether rs12487736 could interact with lifestyle behaviors on BP is unknown.

METHODS

A case-control study with 1092 Chinese children was conducted.

RESULTS

We found an interaction between rs12487736 and high calorie foods intake (fried chips/cakes/cookies) on systolic blood pressure (SBP) (P_interaction = 0.027), and rs12487736 was associated with SBP in the subgroup having high calorie foods at least once in the last week (b = 2.19, P = 0.025), but not in the subgroup not having high calorie foods. Also, interaction between protein intake (meat/fish/soy beans/egg) and rs12487736 on diastolic BP (DBP) was identified (P_interaction = 0.049); rs12487736 was associated with DBP in the subgroup consuming protein (meat/fish/soy beans/egg) <twice/day (b = 3.38, P = 0.014), but not in the subgroup ≥twice/day. There is combined effect between rs12487736 and physical activity on DBP. In the subgroup who were inactive (physical activity <1 h/day), rs12487736 was significantly associated with DBP (b = 3.27, P = 0.046), but not in the active group (physical activity ≥1 h/day). Similar combined effect between rs12487736 and soft drink was found.

CONCLUSIONS

Interactions or combined effects between SCAP and lifestyle behaviors on BP support the importance of promoting a healthy lifestyle in the children genetically predisposed to higher BP.

Genetically modulated educational attainment and coronary disease risk

AIMS

Genetic disposition and lifestyle factors are understood as independent components underlying the risk of multiple diseases. In this study, we aim to investigate the interplay between genetics, educational attainment-an important denominator of lifestyle-and coronary artery disease (CAD) risk.

METHODS AND RESULTS

Based on the effect sizes of 74 genetic variants associated with educational attainment, we calculated a 'genetic education score' in 13 080 cases and 14 471 controls and observed an inverse correlation between the score and risk of CAD [P = 1.52 × 10-8; odds ratio (OR) 0.79, 95% confidence interval (CI) 0.73-0.85 for the higher compared with the lowest score quintile]. We replicated in 146 514 individuals from UK Biobank (P = 1.85 × 10-6) and also found strong associations between the 'genetic education score' with 'modifiable' risk factors including smoking (P = 5.36 × 10-23), body mass index (BMI) (P = 1.66 × 10-30), and hypertension (P = 3.86 × 10-8). Interestingly, these associations were only modestly attenuated by adjustment for years spent in school. In contrast, a model adjusting for BMI and smoking abolished the association signal between the 'genetic education score' and CAD risk suggesting an intermediary role of these two risk factors. Mendelian randomization analyses performed with summary statistics from large genome-wide meta-analyses and sensitivity analysis using 1271 variants affecting educational attainment (OR 0.68 for the higher compared with the lowest score quintile; 95% CI 0.63-0.74; P = 3.99 × 10-21) further strengthened these findings.

CONCLUSION

Genetic variants known to affect educational attainment may have implications for a health-conscious lifestyle later in life and subsequently affect the risk of CAD.

Combined effects of the rs9810888 polymorphism in calcium voltage-gated channel subunit alpha1 D (CACNA1D) and lifestyle behaviors on blood pressure level among Chinese children

Backgrounds

A recent GWAS Study found a new locus (rs9810888 in CACNA1D) was associated with blood pressure (BP) in Chinese adults. But whether the association exists in children is unknown. Whether lifestyle behaviors could interact with rs9810888 on BP is not clear. This study aimed to identify the association between rs9810888 and BP in children, and also explore the gene-lifestyle interaction.

Methods

A case-control study was conducted among 2030 Chinese children aged 7 to 18 years. Genotyping was conducted by using the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Lifestyle behaviors were investigated with questionnaire.

Results

With adjustment for age, age square, sex, study group and body mass index (BMI), rs9810888 was significantly associated with diastolic BP (DBP) (b = 1.69, p = 0.021) and mean arterial BP (MAP) (b = 1.56, p = 0.010). Stratified analysis showed that the rs9810888 GG genotype carriers had higher DBP than GT/TT carriers (b = 3.78, p = 0.023) in the subgroup having protein intake (meat/fish/soybeans/egg) <twice/day, but not in the subgroup ≥twice/day. In addition, in the subgroup with screen time≥2h/day, the rs9810888 GG genotype carriers had higher DBP than GT/TT carriers (b = 5.49, p = 0.012), but not in the subgroup with screen time<2h/day. For MAP, in the subgroup having either fruits or vegetables < twice/day, the rs9810888 GG genotype carriers had higher MAP than GT/TT carriers (b = 2.64, p = 0.037), but not in the subgroup with both fruits and vegetables intake ≥twice/day. Additionally, in the subgroup with screen time≥2h/day, the rs9810888 GG genotype carriers had higher MAP than GT/TT carriers (b = 4.80, p = 0.017), but not in those with screen time<2h/day.

Conclusions

The CACNA1D rs9810888 polymorphism was significantly associated with DBP and MAP. In addition, unhealthy lifestyle behaviors and rs9810888 GG genotype had combined effects on BP level among Chinese children.

Polygenic risk for neuropsychiatric disease and vulnerability to abnormal deep grey matter development

Neuropsychiatric disease has polygenic determinants but is often precipitated by environmental pressures, including adverse perinatal events. However, the way in which genetic vulnerability and early-life adversity interact remains obscure. We hypothesised that the extreme environmental stress of prematurity would promote neuroanatomic abnormality in individuals genetically vulnerable to psychiatric disorders. In 194 unrelated infants (104 males, 90 females), born before 33 weeks of gestation (mean gestational age 29.7 weeks), we combined Magnetic Resonance Imaging with a polygenic risk score (PRS) for five psychiatric pathologies to test the prediction that: deep grey matter abnormalities frequently seen in preterm infants are associated with increased polygenic risk for psychiatric illness. The variance explained by the PRS in the relative volumes of four deep grey matter structures (caudate nucleus, thalamus, subthalamic nucleus and lentiform nucleus) was estimated using linear regression both for the full, mixed ancestral, cohort and a subsample of European infants. Psychiatric PRS was negatively associated with lentiform volume in the full cohort (β = −0.24, p = 8 × 10⁻⁴) and a European subsample (β = −0.24, p = 8 × 10⁻³). Genetic variants associated with neuropsychiatric disease increase vulnerability to abnormal lentiform development after perinatal stress and are associated with neuroanatomic changes in the perinatal period.

An Analytic Approach Using Candidate Gene Selection and Logic Forest to Identify Gene by Environment Interactions (G × E) for Systemic Lupus Erythematosus in African Americans

Development and progression of many human diseases, such as systemic lupus erythematosus (SLE), are hypothesized to result from interactions between genetic and environmental factors. Current approaches to identify and evaluate interactions are limited, most often focusing on main effects and two-way interactions. While higher order interactions associated with disease are documented, they are difficult to detect since expanding the search space to all possible interactions of p predictors means evaluating 2^p − 1 terms. For example, data with 150 candidate predictors requires considering over 10⁴⁵ main effects and interactions. In this study, we present an analytical approach involving selection of candidate single nucleotide polymorphisms (SNPs) and environmental and/or clinical factors and use of Logic Forest to identify predictors of disease, including higher order interactions, followed by confirmation of the association between those predictors and interactions identified with disease outcome using logistic regression. We applied this approach to a study investigating whether smoking and/or secondhand smoke exposure interacts with candidate SNPs resulting in elevated risk of SLE. The approach identified both genetic and environmental risk factors, with evidence suggesting potential interactions between exposure to secondhand smoke as a child and genetic variation in the ITGAM gene associated with increased risk of SLE.

No link between season of birth and subsequent development of Graves' disease or toxic nodular goitre: a nationwide Danish register-based study

BACKGROUND

Season of birth, an exogenous indicator of early life environment, has been linked with a higher risk of adverse health outcomes such as autoimmune thyroiditis, multiple sclerosis and schizophrenia later in life. Whether the development and cause of hyperthyroidism is influenced by season of birth is unclarified. We aimed, at a nationwide level, to investigate whether season of birth influences the risk of hyperthyroidism due to Graves' disease (GD) and/or toxic nodular goitre (TNG).

METHOD

Register-based nationwide cohort study. By record-linkage between Danish health registers, 36,087 and 20,537 patients with GD and TNG, respectively, were identified. Each case was matched with four controls without thyroid disease, according to age and sex. Differences in month-of-birth across the year were evaluated by the Walter-Elwood test. Hazard ratios, for the risk of GD and TNG in individuals born in a certain month or season of the year, were calculated using Cox regression models.

RESULTS

Neither for GD nor for TNG could we demonstrate a significant difference in birth rate across months or seasons of the year (Walter-Elwood's test; X2 = 5.92 and X2 = 1.27, p = 0.052 and p=0.53, respectively).

CONCLUSION

Irrespective of its cause, our findings do not support the hypothesis that season of birth is significantly related to the development of hyperthyroidism.

Network Analysis as a Grand Unifier in Biomedical Data Science

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

Effects of the Ser326Cys Polymorphism in the DNA Repair OGG1 Gene on Cancer, Cardiovascular, and All-Cause Mortality in the PREDIMED Study: Modulation by Diet

BACKGROUND

Oxidatively induced DNA damage, an important factor in cancer etiology, is repaired by oxyguanine glycosylase 1 (OGG1). The lower repair capacity genotype (homozygote Cys326Cys) in the OGG1-rs1052133 (Ser326Cys) polymorphism has been associated with cancer risk. However, no information is available in relation to cancer mortality, other causes of death, and modulation by diet.

OBJECTIVE

Our aim was to evaluate the association of the OGG1-rs1052133 with total, cancer, and cardiovascular disease (CVD) mortality and to analyze its modulation by the Mediterranean diet, focusing especially on total vegetable intake as one of the main characteristics of this diet.

DESIGN

Secondary analysis in the PREDIMED (Prevención con Dieta Mediterránea) trial is a randomized, controlled trial conducted in Spain from 2003 to 2010.

PARTICIPANTS/SETTING

Study participants (n=7,170) were at high risk for CVD and were aged 55 to 80 years.

INTERVENTION

Participants were randomly allocated to two groups with a Mediterranean diet intervention or a control diet. Vegetable intake was measured at baseline.

MAIN OUTCOME MEASURES

Main outcomes were all-cause, cancer, and CVD mortality after a median follow-up of 4.8 years.

STATISTICAL ANALYSES

Multivariable-adjusted Cox regression models were fitted.

RESULTS

Three hundred eighteen deaths were detected (cancer, n=127; CVD, n=81; and other, n=110). Cys326Cys individuals (prevalence 4.2%) presented higher total mortality rates than Ser326-carriers (P=0.009). The multivariable-adjusted hazard ratio for Cys326Cys vs Ser326-carriers was 1.69 (95% CI 1.09 to 2.62; P=0.018). This association was greater for CVD mortality (P=0.001). No relationship was detected for cancer mortality in the whole population (hazard ratio 1.07; 95% CI 0.47 to 2.45; P=0.867), but a significant age interaction (P=0.048) was observed, as Cys326Cys was associated with cancer mortality in participants <66.5 years (P=0.029). Recessive effects limited our ability to investigate Cys326Cys×diet interactions for cancer mortality. No statistically significant interactions for total or CVD mortality were found for the Mediterranean diet intervention. However, significant protective interactions for CVD mortality were found for vegetable intake (hazard ratio interaction per standard deviation 0.42; 95% CI 0.18 to 0.98; P=0.046).

CONCLUSIONS

In this population, the Cys326Cys-OGG1 genotype was associated with all-cause mortality, mainly CVD instead of cancer mortality. Additional studies are needed to provide further evidence on its dietary modulation.

Machine learning shows association between genetic variability in PPARG and cerebral connectivity in preterm infants

Preterm birth affects 11% of births globally; 35% of infants develop long-term neurocognitive problems, and prematurity leads to the loss of 75 million disability adjusted life years per annum worldwide. Imaging studies have shown that these infants have extensive alterations in brain development, but little is known about the molecular or cellular mechanisms involved. This imaging genetics study found a strong association between abnormal cerebral connectivity and variability in the PPARG gene, implicating PPARG signaling in abnormal white-matter development in preterm infants and suggesting a tractable new target for therapeutic research.

Preterm infants show abnormal structural and functional brain development, and have a high risk of long-term neurocognitive problems. The molecular and cellular mechanisms involved are poorly understood, but novel methods now make it possible to address them by examining the relationship between common genetic variability and brain endophenotype. We addressed the hypothesis that variability in the Peroxisome Proliferator Activated Receptor (PPAR) pathway would be related to brain development. We employed machine learning in an unsupervised, unbiased, combined analysis of whole-brain diffusion tractography together with genomewide, single-nucleotide polymorphism (SNP)-based genotypes from a cohort of 272 preterm infants, using Sparse Reduced Rank Regression (sRRR) and correcting for ethnicity and age at birth and imaging. Empirical selection frequencies for SNPs associated with cerebral connectivity ranged from 0.663 to zero, with multiple highly selected SNPs mapping to genes for PPARG (six SNPs), ITGA6 (four SNPs), and FXR1 (two SNPs). SNPs in PPARG were significantly overrepresented (ranked 7–11 and 67 of 556,000 SNPs; P < 2.2 × 10⁻⁷), and were mostly in introns or regulatory regions with predicted effects including protein coding and nonsense-mediated decay. Edge-centric graph-theoretic analysis showed that highly selected white-matter tracts were consistent across the group and important for information transfer (P < 2.2 × 10⁻¹⁷); they most often connected to the insula (P < 6 × 10⁻¹⁷). These results suggest that the inhibited brain development seen in humans exposed to the stress of a premature extrauterine environment is modulated by genetic factors, and that PPARG signaling has a previously unrecognized role in cerebral development.

Genotype × Environment Interaction in Smoking Behaviors: A Systematic Review

INTRODUCTION

There has been rapid growth in research exploring gene-environment interaction (G×E) contributing to smoking behaviors. Yet, no systematic review exists to date.

METHODS

This article aims to review evidence on the contribution of G×E to the risk of smoking. Through a search of electronic databases (ie, Google Scholar, PubMed, ScienceDirect, and Elsevier) up until May 2014, 16 studies of G×E focused on smoking behaviors were identified. These studies were compared in terms of: research design and sample studied, measure of smoking behavior and environments used, genes explored, and G×E in relation to these factors.

RESULTS

Thirteen of 16 studies (81.2%) found at least one significant G×E association. Wide variation in analytic methods was found across studies, especially with respect to the phenotypes of interest, environmental measures used, and tests conducted to estimate G×E. Heterogeneity across studies made it difficult to compare findings and evaluate the strength of evidence for G×E.

CONCLUSIONS

G×E research on smoking contains studies that are methodologically different, making it difficult to assess the current state of the evidence. To decrease heterogeneity, we offer recommendations related to: (1) choice of measurement for environmental variables, (2) testing and reporting of main and interaction effects, (3) treatment of covariates, (4) reporting gene-environment correlation, and (5) conducting sensitivity analyses and checking for scaling artifacts. Continued study is needed to identify mechanisms by which genes and environmental factors combine to influence smoking behaviors.

IMPLICATIONS

No comprehensive review of G×E studies of smoking behavior has previously been published. The present article seeks to fill this gap by providing a comprehensive review of: how G×E has been defined, how twin and molecular genetic methodologies have been used to test for G×E, and which genes and environmental factors are associated with smoking behaviors. Variations in methodological approaches make it difficult to interpret and summarize findings, so recommendations for future research are provided as a means to more easily compare and replicate findings across studies.

Effects of dopaminergic genes, prenatal adversities, and their interaction on attention-deficit/hyperactivity disorder and neural correlates of response inhibition

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is often accompanied by impaired response inhibition; both have been associated with aberrant dopamine signalling. Given that prenatal exposure to alcohol or smoking is known to affect dopamine-rich brain regions, we hypothesized that individuals carrying the ADHD risk alleles of the dopamine receptor D4 (DRD4) and dopamine transporter (DAT1) genes may be especially sensitive to their effects.

METHODS

Functional MRI data, information on prenatal adversities and genetic data were available for 239 adolescents and young adults participating in the multicentre ADHD cohort study NeuroIMAGE (average age 17.3 yr). We analyzed the effects of DRD4 and DAT1, prenatal exposure to alcohol and smoking and their interactions on ADHD severity, response inhibition and neural activity.

RESULTS

We found no significant gene × environment interaction effects. We did find that the DRD4 7-repeat allele was associated with less superior frontal and parietal brain activity and with greater activity in the frontal pole and occipital cortex. Prenatal exposure to smoking was also associated with lower superior frontal activity, but with greater activity in the parietal lobe. Further, those exposed to alcohol had more activity in the lateral orbitofrontal cortex, and the DAT1 risk variant was associated with lower cerebellar activity.

LIMITATIONS

Retrospective reports of maternal substance use and the cross-sectional study design restrict causal inference.

CONCLUSION

While we found no evidence of gene × environment interactions, the risk factors under investigation influenced activity of brain regions associated with response inhibition, suggesting they may add to problems with inhibiting behaviour.

Lifestyle Cardiovascular Risk Score, Genetic Risk Score, and Myocardial Infarction in Hispanic/Latino Adults Living in Costa Rica

BACKGROUND

A lifestyle cardiovascular risk score (LCRS) and a genetic risk score (GRS) have been independently associated with myocardial infarction (MI) in Hispanics/Latinos. Interaction or joint association between these scores has not been examined. Thus, our aim was to assess interactive and joint associations between LCRS and GRS, and each individual lifestyle risk factor, on likelihood of MI.

METHODS AND RESULTS

Data included 1534 Costa Rican adults with nonfatal acute MI and 1534 matched controls. The LCRS used estimated coefficients as weights for each factor: unhealthy diet, physical inactivity, smoking, elevated waist:hip ratio, low/high alcohol intake, low socioeconomic status. The GRS included 14 MI-associated risk alleles. Conditional logistic regressions were used to calculate adjusted odds ratios. The odds ratios for MI were 2.72 (2.33, 3.17) per LCRS unit and 1.13 (95% CI 1.06, 1.21) per GRS unit. A significant joint association for highest GRS tertile and highest LCRS tertile and odds of MI was detected (odds ratio=5.43 [3.71, 7.94]; P<1.00×10-7), compared to both lowest tertiles. The odds ratios were 1.74 (1.22, 2.49) under optimal lifestyle and unfavorable genetic profile, and 5.02 (3.46, 7.29) under unhealthy lifestyle but advantageous genetic profile. Significant joint associations were observed for the highest GRS tertile and the highest of each lifestyle component risk category. The interaction term was nonsignificant (P=0.33).

CONCLUSIONS

Lifestyle risk factors and genetics are jointly associated with higher odds of MI among Hispanics/Latinos. Individual and combined lifestyle risk factors showed stronger associations. Efforts to improve lifestyle behaviors could help prevent MI regardless of genetic susceptibility.

Possible relationship between common genetic variation and white matter development in a pilot study of preterm infants

BACKGROUND

The consequences of preterm birth are a major public health concern with high rates of ensuing multisystem morbidity, and uncertain biological mechanisms. Common genetic variation may mediate vulnerability to the insult of prematurity and provide opportunities to predict and modify risk.

OBJECTIVE

To gain novel biological and therapeutic insights from the integrated analysis of magnetic resonance imaging and genetic data, informed by prior knowledge.

METHODS

We apply our previously validated pathway-based statistical method and a novel network-based method to discover sources of common genetic variation associated with imaging features indicative of structural brain damage.

RESULTS

Lipid pathways were highly ranked by Pathways Sparse Reduced Rank Regression in a model examining the effect of prematurity, and PPAR (peroxisome proliferator-activated receptor) signaling was the highest ranked pathway once degree of prematurity was accounted for. Within the PPAR pathway, five genes were found by Graph Guided Group Lasso to be highly associated with the phenotype: aquaporin 7 (AQP7), malic enzyme 1, NADP(+)-dependent, cytosolic (ME1), perilipin 1 (PLIN1), solute carrier family 27 (fatty acid transporter), member 1 (SLC27A1), and acetyl-CoA acyltransferase 1 (ACAA1). Expression of four of these (ACAA1, AQP7, ME1, and SLC27A1) is controlled by a common transcription factor, early growth response 4 (EGR-4).

CONCLUSIONS

This suggests an important role for lipid pathways in influencing development of white matter in preterm infants, and in particular a significant role for interindividual genetic variation in PPAR signaling.

Oxytocin receptor polymorphism and childhood social experiences shape adult personality, brain structure and neural correlates of mentalizing

INTRODUCTION

The oxytocin system is involved in human social behavior and social cognition such as attachment, emotion recognition and mentalizing (i.e. the ability to represent mental states of oneself and others). It is shaped by social experiences in early life, especially by parent-infant interactions. The single nucleotid polymorphism rs53576 in the oxytocin receptor (OXTR) gene has been linked to social behavioral phenotypes.

METHOD

In 195 adult healthy subjects we investigated the interaction of OXTR rs53576 and childhood attachment security (CAS) on the personality traits "adult attachment style" and "alexithymia" (i.e. emotional self-awareness), on brain structure (voxel-based morphometry) and neural activation (fMRI) during an interactive mentalizing paradigm (prisoner's dilemma game; subgroup: n=163).

RESULTS

We found that in GG-homozygotes, but not in A-allele carriers, insecure childhood attachment is - in adulthood - associated with a) higher attachment-related anxiety and alexithymia, b) higher brain gray matter volume of left amygdala and lower volumes in right superior parietal lobule (SPL), left temporal pole (TP), and bilateral frontal regions, and c) higher mentalizing-related neural activity in bilateral TP and precunei, and right middle and superior frontal gyri. Interaction effects of genotype and CAS on brain volume and/or function were associated with individual differences in alexithymia and attachment-related anxiety. Interactive effects were in part sexually dimorphic.

CONCLUSION

The interaction of OXTR genotype and CAS modulates adult personality as well as brain structure and function of areas implicated in salience processing and mentalizing. Rs53576 GG-homozygotes are partially more susceptible to childhood attachment experiences than A-allele carriers.

Review of the Gene-Environment Interaction Literature in Cancer: What Do We Know?

BACKGROUND

Risk of cancer is determined by a complex interplay of genetic and environmental factors. Although the study of gene-environment interactions (G×E) has been an active area of research, little is reported about the known findings in the literature.

METHODS

To examine the state of the science in G×E research in cancer, we performed a systematic review of published literature using gene-environment or pharmacogenomic flags from two curated databases of genetic association studies, the Human Genome Epidemiology (HuGE) literature finder and Cancer Genome-Wide Association and Meta Analyses Database (CancerGAMAdb), from January 1, 2001, to January 31, 2011. A supplemental search using HuGE was conducted for articles published from February 1, 2011, to April 11, 2013. A 25% sample of the supplemental publications was reviewed.

RESULTS

A total of 3,019 articles were identified in the original search. From these articles, 243 articles were determined to be relevant based on inclusion criteria (more than 3,500 interactions). From the supplemental search (1,400 articles identified), 29 additional relevant articles (1,370 interactions) were included. The majority of publications in both searches examined G×E in colon, rectal, or colorectal; breast; or lung cancer. Specific interactions examined most frequently included environmental factors categorized as energy balance (e.g., body mass index, diet), exogenous (e.g., oral contraceptives) and endogenous hormones (e.g., menopausal status), chemical environment (e.g., grilled meats), and lifestyle (e.g., smoking, alcohol intake). In both searches, the majority of interactions examined were using loci from candidate genes studies and none of the studies were genome-wide interaction studies (GEWIS). The most commonly reported measure was the interaction P-value, of which a sizable number of P-values were considered statistically significant (i.e., <0.05). In addition, the magnitude of interactions reported was modest.

CONCLUSION

Observations of published literature suggest that opportunity exists for increased sample size in G×E research, including GWAS-identified loci in G×E studies, exploring more GWAS approaches in G×E such as GEWIS, and improving the reporting of G×E findings.

Latent trajectories of adolescent antisocial behavior: Serotonin transporter linked polymorphic region (5-HTTLPR) genotype influences sensitivity to perceived parental support

Although prevailing theories of antisocial behavior (ASB) emphasize distinct developmental trajectories, few studies have explored gene–environment interplay underlying membership in empirically derived trajectories. To improve knowledge about the development of overt (e.g., aggression) and covert (e.g., delinquency) ASB, we tested the association of the 44-base pair promoter polymorphism in the serotonin transporter linked polymorphic region gene (5-HTTLPR), perceived parental support (e.g., closeness and warmth), and their interaction with ASB trajectories derived using latent class growth analysis in 2,558 adolescents followed prospectively into adulthood from the National Longitudinal Study of Adolescent Health. Three distinct trajectories emerged for overt (low desisting, adolescent peak, and late onset) and covert ASB (high stable, low stable, and nonoffending). Controlling for sex, parental support inversely predicted membership in the adolescent-peak overt ASB trajectory (vs. low desisting), but was unrelated to class membership for covert ASB. Furthermore, the 5-HTTLPR genotype significantly moderated the association of parental support on overt ASB trajectory membership. It is interesting that the pattern of Gene × Environment interaction differed by trajectory class: whereas short allele carriers were more sensitive to parental support in predicting the late-onset trajectory, the long/long genotype functioned as a potential “plasticity genotype” for the adolescent-peak trajectory group. We discuss these preliminary findings in the context of the differential susceptibility hypothesis and discuss the need for future studies to integrate gene–environment interplay and prospective longitudinal designs.

Interactions between breast cancer susceptibility loci and menopausal hormone therapy in relationship to breast cancer in the Breast and Prostate Cancer Cohort Consortium

Current use of menopausal hormone therapy (MHT) has important implications for postmenopausal breast cancer risk, and observed associations might be modified by known breast cancer susceptibility loci. To provide the most comprehensive assessment of interactions of prospectively collected data on MHT and 17 confirmed susceptibility loci with invasive breast cancer risk, a nested case-control design among eight cohorts within the NCI Breast and Prostate Cancer Cohort Consortium was used. Based on data from 13,304 cases and 15,622 controls, multivariable-adjusted logistic regression analyses were used to estimate odds ratios (OR) and 95 % confidence intervals (CI). Effect modification of current and past use was evaluated on the multiplicative scale. P values <1.5 × 10(-3) were considered statistically significant. The strongest evidence of effect modification was observed for current MHT by 9q31-rs865686. Compared to never users of MHT with the rs865686 GG genotype, the association between current MHT use and breast cancer risk for the TT genotype (OR 1.79, 95 % CI 1.43-2.24; P interaction = 1.2 × 10(-4)) was less than expected on the multiplicative scale. There are no biological implications of the sub-multiplicative interaction between MHT and rs865686. Menopausal hormone therapy is unlikely to have a strong interaction with the common genetic variants associated with invasive breast cancer.

Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification

The number of methods for genome-wide testing of gene-environment (G-E) interactions continues to increase, with the aim of discovering new genetic risk factors and obtaining insight into the disease-gene-environment relationship. The relative performance of these methods, assessed on the basis of family-wise type I error rate and power, depends on underlying disease-gene-environment associations, estimates of which may be biased in the presence of exposure misclassification. This simulation study expands on a previously published simulation study of methods for detecting G-E interactions by evaluating the impact of exposure misclassification. We consider 7 single-step and modular screening methods for identifying G-E interaction at a genome-wide level and 7 joint tests for genetic association and G-E interaction, for which the goal is to discover new genetic susceptibility loci by leveraging G-E interaction when present. In terms of statistical power, modular methods that screen on the basis of the marginal disease-gene relationship are more robust to exposure misclassification. Joint tests that include main/marginal effects of a gene display a similar robustness, which confirms results from earlier studies. Our results offer an increased understanding of the strengths and limitations of methods for genome-wide searches for G-E interaction and joint tests in the presence of exposure misclassification.

BAX and BCL-2 polymorphisms, as predictors of proliferative vitreoretinopathy development in patients suffering retinal detachment: the Retina 4 project

PURPOSE

To compare the distribution of BCL-2 -938C>A (rs2279115) and BAX -248G>A (rs4645878) genotypes among European subjects undergoing rhegmatogenous retinal detachment (RRD) surgery in relation to the further development of proliferative vitreoretinopathy (PVR).

METHODS

A case-control gene association study, as a part of Retina 4 project, was designed. rs2279115 and rs4645878 polymorphisms were analysed in 555 samples from patients with RRD (134 with PVR secondary to surgery). Proportions of genotypes and AA homozygous groups of BCL-2 and BAX polymorphisms between subsamples were analysed in two phases. Genotypic and allelic frequencies were compared in global sample and in subsamples.

RESULTS

BAX: Differences were observed in the genotype frequencies and in AA carriers between controls and cases in the global series. The odds ratio (OR) of A carriers in the global sample was 1.7 (95% CI: 1.23-2.51). Proportions of genotypes in Spain + Portugal were significant different. The OR of A carriers from Spain and Portugal was 1.8 (95% CI: 1.11-2.95). BCL-2: No significant differences were observed in genotype frequencies. However, proportions of genotypes in Spain + Portugal were significant. A protective effect (OR: 0.6 95% CI: 0.43-0.96) was found in A carriers from Spain and Portugal.

CONCLUSIONS

Results suggest that A allele of rs4645878 could be a biomarker of high risk of developing PVR in patients undergoing RD surgery. The possible role of BCL-2 (inhibitor of necroptosis pathway) as a possible new target in PVR prophylaxis should be investigated.

Developing Primary Intervention Strategies to Prevent Allergic Disease

Allergic diseases are a major cause of morbidity in the developed world, now affecting up to 40 % of the population with no evidence that this is abating. If anything, the prevalence of early onset allergic diseases such as eczema and food allergy appears to be still increasing. This is almost certainly due to the changing modern environment and lifestyle factors, acting to promote immune dysfunction through early perturbations in immune maturation, immune tolerance and regulation. This early propensity to inflammation may also have implications for the rising risk of other inflammatory non-communicable diseases (NCDs) later in life. Identifying risk factors and pathways for preventing early onset immune disease like allergy is likely to have benefits for many aspects of human health, particularly as many NCDs share similar risk factors. This review focuses on recent advances in primary intervention strategies for promoting early immune health and preventing allergic disease, highlighting the current evidence-based guidelines where applicable and areas requiring further investigation.