Genome-wide Interaction Studies Research Articles

Interactions of the CSF3R polymorphism and early stress on future orientation: evidence for the differential model of stress-related growth.

This study aims to explore the concept of future orientation, which encompasses individuals' thoughts about the future, goal-setting, planning, response to challenges and behavioural adjustments in evolving situations. Often viewed as a psychological resource, future orientation is believed to be developed from psychological resilience. The study investigates the curvilinear relationship between childhood maltreatment and future orientation while examining the moderating effects of genotype. A total of 14,675 Chinese adults self-reported their experiences of childhood maltreatment and their future orientation. The influence of genetic polymorphism was evaluated through genome-wide interaction studies (GWIS; genome-wide association study [GWAS] using gene × environment interaction) and a candidate genes approach. Both GWAS and candidate genes analyses consistently indicated that rs4498771 and its linked single-nucleotide polymorphisms, located in the intergenic area surrounding CSF3R, significantly interacted with early trauma to influence future orientation. Nonlinear regression analyses identified a quadratic or cubic association between future orientation and childhood maltreatment across some genotypes. Specifically, as levels of childhood maltreatment increased, future orientation declined for all genotypes. However, upon reaching a certain threshold, future orientation exhibited a rebound in individuals with specific genotypes. The findings suggest that individuals with certain genotypes exhibit greater resilience to childhood maltreatment. Based on these results, we propose a new threshold model of stress-related growth.

Large-scale genome-wide interaction analyses on multiple cardiometabolic risk factors to identify age-specific genetic risk factors.

The genetic landscape of cardiometabolic risk factors has been explored extensively. However, insight in the effects of genetic variation on these risk factors over the life course is sparse. Here, we performed genome-wide interaction studies (GWIS) on different cardiometabolic risk factors to identify age-specific genetic risks. This study included 270,276 unrelated European-ancestry participants from the UK Biobank (54.2% women, a median age of 58 [interquartile range (IQR): 50, 63] years). GWIS models with interaction terms between genetic variants and age were performed on apolipoprotein B (ApoB), low-density lipoprotein-cholesterol (LDL-C), log-transformed triglycerides (TG), body mass index (BMI) and systolic blood pressure (SBP). Replication was subsequently performed in the Copenhagen General Population Study (CGPS) and the Estonian Biobank (EstBB). Multiple lead variants were identified to have genome-wide significant interactions with age (Pinteraction < 1e - 08). In detail, rs429358 (tagging APOE4) was identified for ApoB (Pinteraction = 9.0e - 14) and TG (Pinteraction = 5.4e - 16). Three additional lead variants were identified for ApoB: rs11591147 (R46L in PCSK9, Pinteraction = 3.9e - 09), rs34601365 (near APOB, Pinteraction = 8.4e - 09) and rs17248720 (near LDLR, Pinteraction = 2.0e - 09). Effect sizes of the identified lead variants were generally closer to the null with increasing age. No variant-age interactions were identified for LDL-C, SBP and BMI. The significant interactions of rs429358 with age on ApoB and TG were replicated in both CGPS and EstBB. The majority of genetic effects on cardiometabolic risk factors remain relatively constant over age, with the noted exceptions of specific genetic effects on ApoB and TG.

Applying an ELSI lens to real-world data and novel genomic insights for personalized mental healthcare.

Improving the understanding of the complex relationship between genetic predispositions, environmental influences, and sociocultural factors in the development and progression of mental illness is crucial for optimizing treatment efficacy and addressing longstanding health disparities. This paper discusses the ethical, legal, and social implications (ELSI) of recent advancements in biomedical research, particularly in genome-wide association studies (GWAS), phenome-wide association studies (PheWAS), and genome-wide environment interaction studies (GWEIS). Despite recent scientific progresses, challenges such as inadequate study methodology (e.g., correlational studies) and lack of diversity within study samples persist. Recent discoveries of several genetic variants of diseases, could augment and improve, or even challenge, existing understanding of the onset and management of mental illness. Leveraging real-world data (RWD), including electronic health record data (EHRs) focused on social determinant of health alongside biobank data, offers further opportunities to enhance the understanding of gene-environment interactions and inform efforts for reducing disparities in mental healthcare. Increased knowledge can support timely, holistic, evidence-based, and personalized care. Addressing ELSI considerations and maximizing the use of RWD is essential for advancing ethical and inclusive psychiatric genetics research, ultimately improving patient outcomes and promoting equitable access to evidence-based treatments.

Abstract P494: Gene-Level Gene-Environment Interaction Testing Reveals Rare Variants With Large Impacts on the Omega-3 Fatty Acid-Inflammation Relationship

Dietary omega-3 fatty acid (dN3FA) intake affects plasma N3FA (pN3FA) concentrations, circulating biomarkers of chronic inflammation such as high-sensitivity C-reactive protein (hsCRP), and ultimately cardiometabolic disease risk. Genetic variation may modify the N3FA-inflammation relationship, facilitating applications in precision medicine and possibly explaining variable intervention trial responses. Here, we explored the dual hypothesis that pN3FA mediates the dN3FA-hsCRP relationship and that genetic variants across the frequency spectrum modify this association. Regression models in the UK Biobank (N = 342,120) tested main effects of oily fish intake (a primary dietary source of N3FA; measured by food frequency questionnaire) on hsCRP and its mediation by pN3FA (measured by nuclear magnetic resonance; N = 188,701). Three genome-wide interaction studies tested modification of the dN3FA-hsCRP (“full”), dN3FA-pN3FA (“upstream”), and pN3FA-hsCRP (“downstream”) associations. Regression covariates included demographics, lifestyle indicators, socioeconomic measures, and genetic principal components. Variant-specific GWIS were followed by gene-level enrichment testing using the MAGMA tool. Replication of gene-based signals was conducted in the Women’s Genome Health Study (WGHS; N = 23,294) by testing the interaction of the top variant in each gene with total dN3FA impacting hsCRP. Oily fish intake associated most strongly with hsCRP compared to other questionnaire-based estimates of fish or fish oil intake, with statistical evidence that plasma N3FA mediated approximately 100% of this diet-inflammation relationship. While the “full” genetic interaction analysis did not produce any FDR-significant gene-level interactions, the “upstream” analysis uncovered nine genes, including a cluster containing the biologically-relevant fatty acid desaturases FADS1 and FADS2 , and the “downstream” analysis uncovered four genes. Primary variant interaction effect sizes were small (e.g., -0.02 std. dev. bN3FA / std. dev. fish / allele for the top FADS1 variant rs174560), but in-depth follow-up at these genes revealed allelic spectra including rare variants with substantially larger effects (e.g., -0.11 for a FADS1 missense variant with allele frequency of 0.14%, after conditioning on rs174560). WGHS results supported the overall robustness of the findings (consistent interaction effect signs at 8/10 loci), replicated specific signals (p = 0.04 for FADS1 rs174560), and implicated additional inflammatory biomarkers (e.g., fibrinogen and ICAM-1 for the top GLTSCR1 variant). In conclusion, we report (1) the novel epidemiological finding that pN3FA fully mediates the dN3FA-hsCRP relationship, and (2) specific genes with large-effect rare variants that modify the upstream (absorption) and downstream (inflammatory pathways) components of this relationship.

Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies

BackgroundNon-linear relationships at the genotype level are essential in understanding the genetic interactions of complex disease traits. Genome-wide association Studies (GWAS) have revealed statistical association of the SNPs in many complex diseases. As GWAS results could not thoroughly reveal the genetic background of these disorders, Genome-Wide Interaction Studies have started to gain importance. In recent years, various statistical approaches, such as entropy-based methods, have been suggested for revealing these non-additive interactions between variants. This study presents a novel prioritization workflow integrating two-step Random Forest (RF) modeling and entropy analysis after PLINK filtering. PLINK-RF-RF workflow is followed by an entropy-based 3-way interaction information (3WII) method to capture the hidden patterns resulting from non-linear relationships between genotypes in Late-Onset Alzheimer Disease to discover early and differential diagnosis markers.ResultsThree models from different datasets are developed by integrating PLINK-RF-RF analysis and entropy-based three-way interaction information (3WII) calculation method, which enables the detection of the third-order interactions, which are not primarily considered in epistatic interaction studies. A reduced SNP set is selected for all three datasets by 3WII analysis by PLINK filtering and prioritization of SNP with RF-RF modeling, promising as a model minimization approach. Among SNPs revealed by 3WII, 4 SNPs out of 19 from GenADA, 1 SNP out of 27 from ADNI, and 4 SNPs out of 106 from NCRAD are mapped to genes directly associated with Alzheimer Disease. Additionally, several SNPs are associated with other neurological disorders. Also, the genes the variants mapped to in all datasets are significantly enriched in calcium ion binding, extracellular matrix, external encapsulating structure, and RUNX1 regulates estrogen receptor-mediated transcription pathways. Therefore, these functional pathways are proposed for further examination for a possible LOAD association. Besides, all 3WII variants are proposed as candidate biomarkers for the genotyping-based LOAD diagnosis.ConclusionThe entropy approach performed in this study reveals the complex genetic interactions that significantly contribute to LOAD risk. We benefited from the entropy-based 3WII as a model minimization step and determined the significant 3-way interactions between the prioritized SNPs by PLINK-RF-RF. This framework is a promising approach for disease association studies, which can also be modified by integrating other machine learning and entropy-based interaction methods.

Risk factors associated with age at onset of Parkinson’s disease in the UK Biobank

Substantial evidence shown that the age at onset (AAO) of Parkinson’s disease (PD) is a major determinant of clinical heterogeneity. However, the mechanisms underlying heterogeneity in the AAO remain unclear. To investigate the risk factors with the AAO of PD, a total of 3156 patients with PD from the UK Biobank were included in this study. We evaluated the effects of polygenic risk scores (PRS), nongenetic risk factors, and their interaction on the AAO using Mann–Whitney U tests and regression analyses. We further identified the genes interacting with nongenetic risk factors for the AAO using genome-wide environment interaction studies. We newly found physical activity (P < 0.0001) was positively associated with AAO and excessive daytime sleepiness (P < 0.0001) was negatively associated with AAO, and reproduced the positive associations of smoking and non-steroidal anti-inflammatory drug intake and the negative association of family history with AAO. In the dose-dependent analyses, smoking duration (P = 1.95 × 10−6), coffee consumption (P = 0.0150), and tea consumption (P = 0.0008) were positively associated with AAO. Individuals with higher PRS had younger AAO (P = 3.91 × 10−5). In addition, we observed a significant interaction between the PRS and smoking for AAO (P = 0.0316). Specifically, several genes, including ANGPT1 (P = 7.17 × 10−7) and PLEKHA6 (P = 4.87 × 10−6), may influence the positive relationship between smoking and AAO. Our data suggests that genetic and nongenetic risk factors are associated with the AAO of PD and that there is an interaction between the two.

Averaged versus individualized: pragmatic N-of-1 design as a method to investigate individual treatment response

Heterogeneous treatment effects represent a major issue for medicine as they undermine reliable inference and clinical decision-making. To overcome the issue, the current vision of precision and personalized medicine acknowledges the need to control individual variability in response to treatment. In this paper, we argue that gene-treatment-environment interactions (G × T × E) undermine inferences about individual treatment effects from the results of both genomics-based methodologies—such as genome-wide association studies (GWAS) and genome-wide interaction studies (GWIS)—and randomized controlled trials (RCTs). Then, we argue that N-of-1 trials can be a solution to overcome difficulties in handling individual variability in treatment response. Although this type of trial has been suggested as a promising strategy to assess individual treatment effects, it nonetheless has limitations that limit its use in everyday clinical practice. We analyze the existing variability within the designs of N-of-1 trials in terms of a continuum where each design prioritizes epistemic and pragmatic considerations. We then support wider use of the designs located at the pragmatic end of the explanatory-pragmatic continuum.

A genome-wide by PM10 exposure interaction study for blood pressure in Korean adults

Blood pressure (BP) is a typical complex trait, and the genetic susceptibility of individuals to changes in BP induced by air pollution exposure is different. Although interactions of exposure to air pollutants with several candidate genes have been identified, genome-wide interaction studies (GWISs) are needed to understand the association between them with BP. Therefore, we aimed to discover the unique genetic loci for BP that interact with exposure to air pollutants in Korean adults. We ultimately included 1868 participants in the discovery step and classified them into groups of those with low-to-moderate exposure and high exposure to average annual concentration of particulate matter with an aerodynamic diameter ≤ 10 μm (PM10). Because none of the single nucleotide polymorphisms (SNPs) achieved a genome-wide level of significance of pint < 5 × 10–8 for either systolic BP (SBP) or diastolic BP (DBP), we considered the top 10 ranking SNPs for each BP trait. To validate these suggestive SNPs, we finally selected six genetic variants for SBP and five variants for DBP, respectively. In a replication result for SBP, only one SNP (rs12914147) located in an intergenic region of the NR2F2 showed a significant interaction. We also identified several genetic susceptibility loci (e.g., CHST11, TEK, and ITGA1) implicated in candidate mechanisms such as inflammation and oxidative stress in the discovery step, although their interaction effects were not replicated. Our study reports the first GWIS finding to our knowledge, and the association between exposure to PM10 and BP levels may be determined in part by several newly discovered genetic suggestive loci, including NR2F2.

Diversity and genetic architecture of agro-morphological traits in a core collection of European traditional tomato.

European traditional tomato varieties have been selected by farmers given their consistent performance and adaptation to local growing conditions. Here we developed a multipurpose core collection, comprising 226 accessions representative of the genotypic, phenotypic, and geographical diversity present in European traditional tomatoes, to investigate the basis of their phenotypic variation, gene×environment interactions, and stability for 33 agro-morphological traits. Comparison of the traditional varieties with a modern reference panel revealed that some traditional varieties displayed excellent agronomic performance and high trait stability, as good as or better than that of their modern counterparts. We conducted genome-wide association and genome-wide environment interaction studies and detected 141 quantitative trait loci (QTLs). Out of those, 47 QTLs were associated with the phenotype mean (meanQTLs), 41 with stability (stbQTLs), and 53 QTL-by-environment interactions (QTIs). Most QTLs displayed additive gene actions, with the exception of stbQTLs, which were mostly recessive and overdominant QTLs. Both common and specific loci controlled the phenotype mean and stability variation in traditional tomato; however, a larger proportion of specific QTLs was observed, indicating that the stability gene regulatory model is the predominant one. Developmental genes tended to map close to meanQTLs, while genes involved in stress response, hormone metabolism, and signalling were found within regions affecting stability. A total of 137 marker-trait associations for phenotypic means and stability were novel, and therefore our study enhances the understanding of the genetic basis of valuable agronomic traits and opens up a new avenue for an exploitation of the allelic diversity available within European traditional tomato germplasm.

1469-P: Gene-Level Gene–Mediterranean Diet Interaction Detection Influencing Inflammatory and Glycemic Biomarkers in 137,000 UK Biobank Participants

Adherence to a Mediterranean diet pattern strongly reduces type 2 diabetes risk on average, but there is substantial heterogeneity in the long-term glycemic and inflammatory response to dietary changes, likely due in part to genetic factors. Using a genome-wide interaction association analysis in the UK Biobank (UKB) cohort, we sought to prioritize and characterize genes contributing to this heterogeneity in the Mediterranean diet-risk factor relationship. We studied an unrelated, multi-ancestry UKB subset who completed at least one 24-hour diet questionnaire, had available glycemic (hemoglobin A1c; HbA1c) and inflammatory (high-sensitivity C-reactive protein; hsCRP) biomarkers measured in blood, and were free of major disease at baseline (N = 136,880). A nine-item, point-based Mediterranean diet score (MDS) based on food group and nutrient intakes was calculated. We conducted a series of genome-wide interaction studies (GWIS) to test for significant variant-diet interactions: one for the MDS itself and one for each sub-component. Variant-level results were aggregated by gene using the MAGMA tool. Gene-level MDS interaction results revealed two genes with false discovery rate &amp;lt; 0.1 for HbA1c: LRRC24 (p = 3.2×10−6) and CCDC40 (p = 3.3×10−6). Gene-level sub-component-specific analysis showed that these two interactions were primarily driven by alcohol and fish intake, respectively, and further prioritized an interaction between intake of nuts and LIN9 gene variants impacting HbA1c (p = 8.8×10−8). Follow-up analysis at the top variant in LIN9, rs9729447, showed a substantial attenuation of the negative nut intake-HbA1c association in major allele carriers and revealed a stronger interaction effect size in individuals who completed at least two dietary questionnaires. Our GWIS analysis suggests that there are locus-specific interactors with Mediterranean diet patterns influencing glycemic and inflammatory health. Disclosure K.E.Westerman: None. J.B.Meigs: Consultant; Quest Diagnostics. A.Manning: None. Funding National Institutes of Health (K01DK133637)

The interaction of early life factors and depression-associated loci affecting the age at onset of the depression

Multiple previous studies explored the associations between early life factors and the age at onset of the depression. However, they only focused on the influence of environmental or genetic factors, without considering the interactions between them. Based on previous genome-wide association study (GWAS) data, we first calculated polygenic risk score (PRS) for depression. Regression analyses were conducted to assess the interacting effects of depression PRS and 5 early life factors, including felt hated by family member (N = 40,112), physically abused by family (N = 40,464), felt loved (N = 35633), and sexually molested (N = 41,595) in childhood and maternal smoking during pregnancy (N = 38,309), on the age at onset of the depression. Genome-wide environment interaction studies (GWEIS) were then performed to identify the genes interacting with early life factors for the age at onset of the depression. In regression analyses, we observed significant interacting effects of felt loved as a child and depression PRS on the age at onset of depression in total sample (β = 0.708, P = 5.03 × 10−3) and males (β = 1.421, P = 7.64 × 10−4). GWEIS identified a novel candidate loci interacting with felt loved as a child at GSAP (rs2068031, P = 4.24 × 10–8) and detected several genes with suggestive significance association, such as CMYA5 (rs7343, P = 2.03 × 10–6) and KIRREL3 (rs535603, P = 4.84 × 10–6) in males. Our results indicate emotional care in childhood may affect the age at onset of depression, especially in males, and GSAP plays an important role in their interaction.

Genome-Wide Interaction Study of Late-Onset Asthma With Seven Environmental Factors Using a Structured Linear Mixed Model in Europeans.

Asthma is among the most common chronic diseases worldwide, creating a substantial healthcare burden. In late-onset asthma, there are wide global differences in asthma prevalence and low genetic heritability. It has been suggested as evidence for genetic susceptibility to asthma triggered by exposure to multiple environmental factors. Very few genome-wide interaction studies have identified gene-environment (G×E) interaction loci for asthma in adults. We evaluated genetic loci for late-onset asthma showing G×E interactions with multiple environmental factors, including alcohol intake, body mass index, insomnia, physical activity, mental status, sedentary behavior, and socioeconomic status. In gene-by-single environment interactions, we found no genome-wide significant single-nucleotide polymorphisms. However, in the gene-by-multi-environment interaction study, we identified three novel and genome-wide significant single-nucleotide polymorphisms: rs117996675, rs345749, and rs17704680. Bayes factor analysis suggested that for rs117996675 and rs17704680, body mass index is the most relevant environmental factor; for rs345749, insomnia and alcohol intake frequency are the most relevant factors in the G×E interactions of late-onset asthma. Functional annotations implicate the role of these three novel loci in regulating the immune system. In addition, the annotation for rs117996675 supports the body mass index as the most relevant environmental factor, as evidenced by the Bayes factor value. Our findings help to understand the role of the immune system in asthma and the role of environmental factors in late-onset asthma through G×E interactions. Ultimately, the enhanced understanding of asthma would contribute to better precision treatment depending on personal genetic and environmental information.

Gene-lifestyle interactions in the genomics of human complex traits.

The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries for three lipids and four blood pressure traits, while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. We performed simulations to characterize these limitations and to provide the community with guidelines for future G×E studies.

Scaling-up a fragment-based protein-protein interaction method using a human reference interaction set.

Protein–protein interactions (PPIs) are essential in understanding numerous aspects of protein function. Here, we significantly scaled and modified analyses of the recently developed all‐vs‐all sequencing (AVA‐Seq) approach using a gold‐standard human protein interaction set (hsPRS‐v2) containing 98 proteins. Binary interaction analyses recovered 20 of 47 (43%) binary PPIs from this positive reference set (PRS), comparing favorably with other methods. However, the increase of 20× in the interaction search space for AVA‐Seq analysis in this manuscript resulted in numerous changes to the method required for future use in genome‐wide interaction studies. We show that standard sequencing analysis methods must be modified to consider the possible recovery of thousands of positives among millions of tested interactions in a single sequencing run. The PRS data were used to optimize data scaling, auto‐activator removal, rank interaction features (such as orientation and unique fragment pairs), and statistical cutoffs. Using these modifications to the method, AVA‐Seq recovered >500 known and novel PPIs, including interactions between wild‐type fragments of tumor protein p53 and minichromosome maintenance complex proteins 2 and 5 (MCM2 and MCM5) that could be of interest in human disease.

Vitamin D and the Risks of Depression and Anxiety: An Observational Analysis and Genome-Wide Environment Interaction Study.

Previous studies have suggested that vitamin D (VD) was associated with psychiatric diseases, but efforts to elucidate the functional relevance of VD with depression and anxiety from genetic perspective have been limited. Based on the UK Biobank cohort, we first calculated polygenic risk score (PRS) for VD from genome-wide association study (GWAS) data of VD. Linear and logistic regression analysis were conducted to evaluate the associations of VD traits with depression and anxiety traits, respectively. Then, using individual genotype and phenotype data from the UK Biobank, genome-wide environment interaction studies (GWEIS) were performed to identify the potential effects of gene × VD interactions on the risks of depression and anxiety traits. In the UK Biobank cohort, we observed significant associations of blood VD level with depression and anxiety traits, as well as significant associations of VD PRS and depression and anxiety traits. GWEIS identified multiple candidate loci, such as rs114086183 (p = 4.11 × 10−8, LRRTM4) for self-reported depression status and rs149760119 (p = 3.88 × 10−8, GNB5) for self-reported anxiety status. Our study results suggested that VD was negatively associated with depression and anxiety. GWEIS identified multiple candidate genes interacting with VD, providing novel clues for understanding the biological mechanism potential associations between VD and psychiatric disorders.

LncRNA DINOR is a virulence factor and global regulator of stress responses in Candida auris.

The emergent fungal pathogen Candida auris exhibits high resistance to antifungal drugs and environmental stresses, impeding treatment and decontamination1-3. The fungal factors mediating this stress tolerance are largely unknown. In the present study, we performed piggyBac, transposon-mediated, genome-wide mutagenesis and genetic screening in C. auris, and identified a mutant that grew constitutively in the filamentous form. Mapping the transposon insertion site revealed the disruption of a long non-coding RNA, named DINOR for DNA damage-inducible non-coding RNA. Deletion of DINOR caused DNA damage and an upregulation of genes involved in morphogenesis, DNA damage and DNA replication. The DNA checkpoint kinase Rad53 was hyperphosphorylated in dinorΔ mutants, and deletion of RAD53 abolished DNA damage-induced filamentation. DNA-alkylating agents, which cause similar filamentous growth, induced DINOR expression, suggesting a role for DINOR in maintaining genome integrity. Upregulation of DINOR also occurred during exposure to the antifungal drugs caspofungin and amphotericin B, macrophages, H2O2 and sodium dodecylsulfate, indicating that DINOR orchestrates multiple stress responses. Consistently, dinorΔ mutants displayed increased sensitivity to these stresses and were attenuated for virulence in mice. Moreover, genome-wide genetic interaction studies revealed links between the function of DINOR and TOR signalling, an evolutionarily conserved pathway that regulates the stress response. Identification of the mechanism(s) by which DINOR regulates stress responses in C. auris may provide future opportunities for the development of therapeutics.

Genetic interactions with stressful environments in depression and addiction.

Stress is the most important proximal precipitant of depression, yet most large genome-wide association studies (GWAS) do not include stress as a variable. Here, we review how gene × environment (G × E) interaction might impede the discovery of genetic factors, discuss two examples of G × E interaction in depression and addiction, studies incorporating high-stress environments, as well as upcoming waves of genome-wide environment interaction studies (GWEIS). We discuss recent studies which have shown that genetic distributions can be affected by social factors such as migrations and socioeconomic background. These distinctions are not just academic but have practical consequences. Owing to interaction with the environment, genetic predispositions to depression should not be viewed as unmodifiable destiny. Patients may genetically differ not just in their response to drugs, as in the now well-recognised field of pharmacogenetics, but also in how they react to stressful environments and how they are affected by behavioural therapies.

Genome-wide gene-diet interaction analysis in the UK Biobank identifies novel effects on hemoglobin A1c.

Diet is a significant modifiable risk factor for type 2 diabetes (T2D), and its effect on disease risk is under partial genetic control. Identification of specific gene-diet interactions (GDIs) influencing risk biomarkers such as glycated hemoglobin (HbA1c) is a critical step towards precision nutrition for T2D prevention, but progress has been slow due to limitations in sample size and accuracy of dietary exposure measurement. We leveraged the large UK Biobank (UKB) cohort and a diverse group of dietary exposures, including 30 individual dietary traits and 8 empirical dietary patterns, to conduct genome-wide interaction studies in ~340 000 European-ancestry participants to identify novel GDIs influencing HbA1c. We identified five variant-dietary trait pairs reaching genome-wide significance (P < 5 × 10-8): two involved dietary patterns (meat pattern with rs147678157 and a fruit & vegetable-based pattern with rs3010439) and three involved individual dietary traits (bread consumption with rs62218803, dried fruit consumption with rs140270534 and milk type [dairy vs. other] with 4:131148078_TAGAA_T). These were affected minimally by adjustment for geographical and lifestyle-related confounders, and four of the five variants lacked genetic main effects that would have allowed their detection in a traditional genome-wide association study for HbA1c. Notably, multiple loci near transient receptor potential subfamily M genes (TRPM2 and TRPM3) interacted with carbohydrate-containing food groups. These interactions were further characterized using non-European UKB subsets and alternative measures of glycaemia (fasting glucose and follow-up HbA1c measurements). Our results highlight GDIs influencing HbA1c for future investigation, while reinforcing known challenges in detecting and replicating GDIs.

Genome-wide gene-environment interactions in neuroticism: an exploratory study across 25 environments

Gene-environment interactions (GxE) are often suggested to play an important role in the aetiology of psychiatric phenotypes, yet so far, only a handful of genome-wide environment interaction studies (GWEIS) of psychiatric phenotypes have been conducted. Representing the most comprehensive effort of its kind to date, we used data from the UK Biobank to perform a series of GWEIS for neuroticism across 25 broadly conceptualised environmental risk factors (trauma, social support, drug use, physical health). We investigated interactions on the level of SNPs, genes, and gene-sets, and computed interaction-based polygenic risk scores (PRS) to predict neuroticism in an independent sample subset (N = 10,000). We found that the predictive ability of the interaction-based PRSs did not significantly improve beyond that of a traditional PRS based on SNP main effects from GWAS, but detected one variant and two gene-sets showing significant interaction signal after correction for the number of analysed environments. This study illustrates the possibilities and limitations of a comprehensive GWEIS in currently available sample sizes.

Bromodomain protein BRDT directs \u0394Np63 function and super-enhancer activity in a subset of esophageal squamous cell carcinomas

Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal cancer with a particularly high prevalence in certain geographical regions and a poor prognosis with a 5-year survival rate of 15–25%. Despite numerous studies characterizing the genetic and transcriptomic landscape of ESCC, there are currently no effective targeted therapies. In this study, we used an unbiased screening approach to uncover novel molecular precision oncology targets for ESCC and identified the bromodomain and extraterminal (BET) family member bromodomain testis-specific protein (BRDT) to be uniquely expressed in a subgroup of ESCC. Experimental studies revealed that BRDT expression promotes migration but is dispensable for cell proliferation. Further mechanistic insight was gained through transcriptome analyses, which revealed that BRDT controls the expression of a subset of ΔNp63 target genes. Epigenome and genome-wide occupancy studies, combined with genome-wide chromatin interaction studies, revealed that BRDT colocalizes and interacts with ΔNp63 to drive a unique transcriptional program and modulate cell phenotype. Our data demonstrate that these genomic regions are enriched for super-enhancers that loop to critical ΔNp63 target genes related to the squamous phenotype such as KRT14, FAT2, and PTHLH. Interestingly, BET proteolysis-targeting chimera, MZ1, reversed the activation of these genes. Importantly, we observed a preferential degradation of BRDT by MZ1 compared with BRD2, BRD3, and BRD4. Taken together, these findings reveal a previously unknown function of BRDT in ESCC and provide a proof-of-concept that BRDT may represent a novel therapeutic target in cancer.