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Identifying factors that affect treatment response is a central objective of clinical research, yet the role of common genetic variation remains largely unknown. Here, we develop a framework to study the genetic architecture of response to commonly prescribed drugs in large biobanks. We quantify treatment response heritability for statins, metformin, warfarin, and methotrexate in the UK Biobank. We find that genetic variation modifies the primary effect of statins on LDL cholesterol (9% heritable) as well as their side effects on hemoglobin A1c and blood glucose (10% and 11% heritable, respectively). We identify dozens of genes that modify drug response, which we replicate in a retrospective pharmacogenomic study. Finally, we find that polygenic score (PGS) accuracy varies up to 2-fold depending on treatment status, showing that standard PGSs are likely to underperform in clinical contexts.

Aims/hypothesis: Alcohol consumption has complex effects on diabetes and metabolic disease, but there is widespread heterogeneity within populations and the specific reasons are unclear. Genetic factors may play a role and warrant exploration. The aim of this study was to elucidate genetic variants modulating the impact of alcohol consumption on insulin sensitivity and pancreatic beta cell function within populations presenting normal glucose tolerance (NGT). Methods: We recruited 4194 volunteers in Nanjing, 854 in Jurong and an additional 5833 in Nanjing for Discovery cohorts 1 and 2 and a Validation cohort, respectively. We performed an OGTT on all participants, establishing a stringent NGT group, and then assessed insulin sensitivity and beta cell function. Alcohol consumption was categorised as abstinent, light-to-moderate (<210 g per week) or heavy (≥210 g per week). After excluding ineligible individuals, an exploratory genome-wide association study identified potential variants interacting with alcohol consumption in 1862 NGT individuals. These findings were validated in an additional cohort of 2169 NGT individuals. Cox proportional hazard regression was further employed to evaluate the effect of the interaction between the potential variants and alcohol consumption on the risk of type 2 diabetes within the UK Biobank cohort. Results: A significant correlation was observed between drinking levels and insulin sensitivity, accompanied by a consequent inverse relationship with insulin resistance and beta cell insulin secretion after adjusting for confounding factors in NGT individuals. However, no significant associations were noted in the disposition indexes. The interaction of variant rs56221195 with alcohol intake exhibited a pronounced effect on the liver insulin resistance index (LIRI) in the discovery set, corroborated in the validation set (combined p=1.32 × 10−11). Alcohol consumption did not significantly affect LIRI in rs56221195 wild-type (TT) carriers, but a strong negative association emerged in heterozygous (TA) and homozygous (AA) individuals. The rs56221195 variant also significantly interacts with alcohol consumption, influencing the total insulin secretion index INSR120 (the ratio of the AUC of insulin to glucose from 0 to 120 min) (p=2.06 × 10−9) but not disposition index. In the UK Biobank, we found a significant interaction between rs56221195 and alcohol consumption, which was linked to the risk of type 2 diabetes (HR 0.897, p=0.008). Conclusions/interpretation: Our findings reveal the effects of the interaction of alcohol and rs56221195 on hepatic insulin sensitivity in NGT individuals. It is imperative to weigh potential benefits and detriments thoughtfully when considering alcohol consumption across diverse genetic backgrounds. [ABSTRACT FROM AUTHOR]

Stress‐sensitivity (SS) is considered a psychobiological trait possibly resulting from the interaction of genetic and environmental factors (GxE). This study examined whether the interaction of SS‐related genetic markers with interview‐based dimensions of childhood adversity predicted longitudinal trajectories of low versus high SS. Participants were nonclinically‐ascertained young adults comprising normative and elevated scores on schizotypy. SS trajectories were defined in a previous report based on three prospective assessments (23.5, 25, 28 years‐old) of both retrospective (Perceived Stress Scale; PSS) and momentary (Experience Sampling Methodology; ESM) stress ratings. A total of n = 177 and n = 165 participants with PSS and ESM stress‐sensitivity trajectories, respectively, as well as genetic data, were included in the study. GxE effects between a SS Polygenic Risk Score (PRS‐SS) and a Genetic Risk Score of the Hypothalamic Pituitary Adrenal axis (GRS‐HPA) with childhood adversity dimensions (Intrafamilial Adversity, Threat and Deprivation) on SS trajectories were examined. Threat was the most consistent predictor of persistently high SS. PRS‐SS moderated the association of Threat with high‐PSS. GRS‐HPA moderated the effects of all adversity dimensions on high‐PSS. The interaction of PRS‐SS with Deprivation and GRS‐HPA with Intrafamilial Adversity predicted trajectories of momentary social stress, but the effects were driven by those with lower genetic susceptibility. Genetic‐HPA‐axis moderates the effects of all adversity dimensions on persistent SS trajectories, as well as PRS‐SS and Threat, particularly for retrospective stress measure. The findings highlight the complex interplay between GxE factors and suggest that PSS may better capture SS trait. Including biologically‐meaningful GRS indexing SS and adversity dimensions in future studies using comprehensive stress measures would enhance our knowledge on high SS susceptibility and its relationship with diverse psychopathological outcomes. [ABSTRACT FROM AUTHOR]

Abstract Background Although repeatedly confirmed, the molecular nature of gene-environment (GxE) interactions has rarely been investigated in the clinical context of mood disorders. This study assesses the relationship between HTR2A genetic variants and the modulatory effect of inflammation in a collective cohort of patients with major depressive disorder (MDD) and bipolar disorder (BD), as a unified group with two distinct phenotypes. Methods The study included 138 patients with acute mood episodes (BD = 83; MDD = 55). HTR2A rs6313 and rs6314 genotyping was performed while measuring platelet-derived indicators of inflammation (platelet count (PLT), mean platelet volume (MPV), plateletcrit, and platelet distribution width) and the MPV/PLT ratio. Results The HTR2A rs6313 variant is a significant predictor of the polarity phenotype in mood disorders, with the MPV/PLT ratio moderating this relationship, but only under low-inflammatory conditions. In more pronounced inflammatory states, genetic influences lose their predictive role. Conclusions To our knowledge, this is the first study to investigate the complex interplay between platelet-derived indicators of inflammation and HTR2A variants in the context of mood disorders. Without pro-inflammatory conditions, mood disorders seem to be more genetically determined. Under pro-inflammatory conditions, phenotypic presentation is less dependent on genetic factors. GxE interactions in mood disorders are multifaceted, context-dependent and relevant for assessing their clinical presentation and course.

Background: Although repeatedly confirmed, the molecular nature of gene-environment (GxE) interactions has rarely been investigated in the clinical context of mood disorders. This study assesses the relationship between HTR2A genetic variants and the modulatory effect of inflammation in a collective cohort of patients with major depressive disorder (MDD) and bipolar disorder (BD), as a unified group with two distinct phenotypes. Methods: The study included 138 patients with acute mood episodes (BD = 83; MDD = 55). HTR2A rs6313 and rs6314 genotyping was performed while measuring platelet-derived indicators of inflammation (platelet count (PLT), mean platelet volume (MPV), plateletcrit, and platelet distribution width) and the MPV/PLT ratio. Results: The HTR2A rs6313 variant is a significant predictor of the polarity phenotype in mood disorders, with the MPV/PLT ratio moderating this relationship, but only under low-inflammatory conditions. In more pronounced inflammatory states, genetic influences lose their predictive role. Conclusions: To our knowledge, this is the first study to investigate the complex interplay between platelet-derived indicators of inflammation and HTR2A variants in the context of mood disorders. Without pro-inflammatory conditions, mood disorders seem to be more genetically determined. Under pro-inflammatory conditions, phenotypic presentation is less dependent on genetic factors. GxE interactions in mood disorders are multifaceted, context-dependent and relevant for assessing their clinical presentation and course. [ABSTRACT FROM AUTHOR]

The diagnosis, treatment, and management of dementia provide significant challenges due to its chronic cognitive impairment. The complexity of this condition is further highlighted by the impact of gene-environment interactions. A recent strategy combines advanced genomics and precision medicine methods to explore the complex genetic foundations of dementia. Utilizing the most recent research in the field of neurogenetics, the importance of precise genetic data in explaining the variation seen in dementia patients can be investigated. Gene-environment interactions are important because they influence genetic susceptibilities and aid in the development and progression of dementia. Modified to each patient's genetic profile, precision medicine has the potential to detect groups at risk and make previously unheard-of predictions about the course of diseases. Precision medicine techniques have the potential to completely transform treatment and diagnosis methods. Targeted medications that target genetic abnormalities will probably appear, providing the possibility for more efficient and customized medical interventions. Investigating the relationship between genes and the environment may lead to preventive measures that would enable people to change their surroundings and minimize the risk of dementia, leading to the improved lifestyle of affected people. This paper provides a comprehensive overview of the genomic insights into dementia, emphasizing the pivotal role of precision medicine, and gene-environment interactions. [ABSTRACT FROM AUTHOR]

Over the last three decades, human genetics has gone from dissecting high-penetrance Mendelian diseases to discovering the vast and complex genetic etiology of common human diseases. In tackling this complexity, scientists have discovered the importance of numerous genetic processes – most notably functional regulatory elements – in the development and progression of these diseases. Simultaneously, scientists have increasingly used multiplex assays of variant effect to systematically phenotype the cellular consequences of millions of genetic variants. In this article, we argue that the context of genetic variants – at all scales, from other genetic variants and gene regulation to cell biology to organismal environment – are critical components of how we can employ genomics to interpret these variants, and ultimately treat these diseases. We describe approaches to extend existing experimental assays and computational approaches to examine and quantify the importance of this context, including through causal analytic approaches. Having a unified understanding of the molecular, physiological, and environmental processes governing the interpretation of genetic variants is sorely needed for the field, and this perspective argues for feasible approaches by which the combined interpretation of cellular, animal, and epidemiological data can yield that knowledge.

The editorial in "Frontiers in Neuroscience" explores the long-lasting neurobehavioral effects of early-life events, emphasizing the impact of gene-environment interactions on brain development, physiology, and behavior. Various studies discussed in the editorial highlight how early-life experiences, such as exposure to environmental toxins or traumatic stress, can have significant effects on cognitive functions, aggression, structural plasticity, and white matter integrity in both animals and humans. The research underscores the importance of understanding how different early-life factors interact to shape long-term outcomes and offers insights into potential therapeutic strategies to mitigate adverse effects and promote resilience in vulnerable populations. [Extracted from the article]

Abstract Genetic variants associated with increased liver fat and volume have been reported, but whether physical activity (PA) can attenuate the impact of genetic susceptibility to these traits is poorly understood. We aimed to investigate whether higher PA modify genetic impact on liver-related traits in the UK Biobank cohort. PA was self-reported, while magnetic resonance images were used to estimate liver fat (n = 27,243) and liver volume (n = 24,752). Metabolic dysfunction-associated liver disease (MASLD) and chronic liver disease (CLD) were diagnosed using ICD-9 and ICD-10 codes. Ten liver fat and eleven liver volume-associated genetic variants were selected and unweighted genetic-risk scores for liver fat (GRSLF) and liver volume (GRSLV) were computed. Linear regression analyses were performed to explore interactions between GRSLF/ GRSLV and PA in relation to liver-related traits. Association between GRSLF and liver fat was not different among lower (β = 0.063, 95% CI 0.041–0.084) versus higher PA individuals (β = 0.065, 95% CI 0.054–0.077, p interaction = 0.62). The association between the GRSLV and liver volume was not different across different PA groups (p interaction = 0.71). Similarly, PA did not modify the effect of GRSLF and GRSLV on MASLD or CLD. Our findings show that physical activity and genetic susceptibility to liver-related phenotypes seem to act independently, benefiting all individuals regardless of genetic risk.

The epigenome is the suite of interacting chemical marks and molecules that helps to shape patterns of development, phenotypic plasticity and gene regulation, in part due to its responsiveness to environmental stimuli. There is increasing interest in understanding the functional and evolutionary importance of this sensitivity under ecologically realistic conditions. Observations that epigenetic variation abounds in natural populations have prompted speculation that it may facilitate evolutionary responses to rapid environmental perturbations, such as those occurring under climate change. A frequent point of contention is whether epigenetic variants reflect genetic variation or are independent of it. The genome and epigenome often appear tightly linked and interdependent. While many epigenetic changes are genetically determined, the converse is also true, with DNA sequence changes influenced by the presence of epigenetic marks. Understanding how the epigenome, genome and environment interact with one another is therefore an essential step in explaining the broader evolutionary consequences of epigenomic variation. Drawing on results from experimental and comparative studies carried out in diverse plant and animal species, we synthesize our current understanding of how these factors interact to shape phenotypic variation in natural populations, with a focus on identifying similarities and differences between taxonomic groups. We describe the main components of the epigenome and how they vary within and between taxa. We review how variation in the epigenome interacts with genetic features and environmental determinants, with a focus on the role of transposable elements (TEs) in integrating the epigenome, genome and environment. And we look at recent studies investigating the functional and evolutionary consequences of these interactions. Although epigenetic differentiation in nature is likely often a result of drift or selection on stochastic epimutations, there is growing evidence that a significant fraction of it can be stably inherited and could therefore contribute to evolution independently of genetic change. [ABSTRACT FROM AUTHOR]

This study aimed to explore the association of maternal diet, infant MTHFR gene polymorphisms, and their interactions with the risk of ventricular septal defects (VSDs). This case–control study recruited 448 mothers of VSD children and 620 mothers of healthy counterparts. Multivariableadjusted logistic regression models were constructed to examine the association between maternal dietary habits during the first trimester of gestation, MTHFR gene polymorphisms, and VSD. Gene–environment interaction effects were analyzed through logistic regression models, with false discovery rate p-value (FDR_p) < 0.05. Maternal excessive intake of fermented bean curd (OR = 2.00, 95%CI: 1.59–2.52), corned foods (OR = 2.23, 1.76–2.84), fumatory foods (OR = 1.75, 1.37–2.23), grilled foods (OR = 1.34, 1.04–1.72), and fried foods (OR = 1.80, 1.42–2.27) was associated with an increased risk of VSD. Regular intake of fish and shrimp (OR = 0.42, 0.33–0.53), fresh eggs (OR = 0.58, 0.44–0.75), soy products (OR = 0.69, 0.56–0.85), and dairy products (OR = 0.71, 0.59–0.85) was found to reduce the occurrence of VSD. Moreover, MTHFR gene polymorphisms at rs2066470 (homozygous: OR = 4.28, 1.68–10.90), rs1801133 (homozygous: OR = 2.28, 1.39–3.74), and rs1801131 (heterozygous: OR = 1.75, 1.24–2.47; homozygous: OR = 3.45, 1.50–7.95) elevated offspring susceptibility to VSDs. Furthermore, significant interactions of MTHFR polymorphisms with maternal dietary habits were observed, encompassing corned foods, fermented bean curd, fried foods, and grilled foods. Maternal dietary habits; MTHFR polymorphisms at rs2066470, rs1801131, and rs1801133; and their interactions were significantly associated with the occurrence of VSDs in offspring. [ABSTRACT FROM AUTHOR]

Background: Ecological and genomic attributes of populations can provide two orthologous perspectives on the biological profiles associated with local adaptation. The ability of organisms to track suitable habitats (ecological adaptability) and of populations to shift allele frequencies (adaptive potential) are prerequisite for population sustainability. Aims: Many contemporary populations are threatened by habitat loss (ecological vulnerability) and a lack of adaptive potential (evolutionary vulnerability). Technical advances provide new opportunities to address these challenges in biological conservation: Future habitat shifts can be predicted by ecological niche modelling and adaptive genetic diversity can be discerned using genome sequence data. Together, these two approaches illuminate the local adaptation profile and help identify the environmental and genomic conditions that should maximize evolutionary fitness. Materials and Methods: Here, we reviewed the primary literature to identify key studies that utilize both whole‐genome resequencing (WGR) and ecological niche modelling (ENM) in an effort to envisage future research directions that may benefit conservation efforts. Results: We identified ways to integrate different approaches, such as ENM‐informed adaptive genomics and adaptive genomics‐informed ENMs, that can be used to delineate and conserve local adaptation profiles. Discussion: Integrative approaches can identify adaptive characteristics, vulnerable populations subject to environmental changes, and the patterns of local adaptation from geographic and genomic analyses. We discuss future research directions, limitations and their potential solutions with suggestions for collaborative workflows. Conclusion: The integration of WGR and ENM is promising with their continuous advancement. An integrative approach can be used to evaluate eco‐evolutionary attributes, at both organismal and molecular levels, that can be used to help conserve local adaptation profiles. [ABSTRACT FROM AUTHOR]

The Chinese keratoconus (CKC) cohort study is a population-based longitudinal prospective cohort study in the Chinese population involving a clinical database and biobanks. This ongoing study focuses on the prevention of KC progression and is the first to involve the effect of gene‒environment interactions on KC progression. The CKC cohort is hospital-based and dynamic and was established in Zhengzhou, China; KC patients (n = 1114) from a large geographical area were enrolled from January 2019 to June 2023, with a mean age of 22.23 years (6‒57 years). Demographic details, socioeconomic characteristics, lifestyle, disease history, surgical history, family history, and visual and social function data are being collected using questionnaires. General physical examination, eye examination, biological specimen collection, and first-degree relative data were collected and analyzed in the present study. The primary focus of the present study was placed on gene, environment and the effect of gene‒environment interactions on KC progression. The follow-up of the CKC cohort study is expected to include data collection at 3 months, 6 months, and 1 year after the initial examination and then at the annual follow-up examinations. The first follow-up of the CKC cohort study was recorded. A total of 918 patients completed the follow-up by June 1, 2023, with a response rate of 82.40%. Aside from the younger age of patients who were followed up, no significant differences were found between patients who were followed up and patients who were not. [ABSTRACT FROM AUTHOR]

Background The essential hypertension phenotype results from an interplay between genetic and environmental factors. The influence of lifestyle exposures such as excess adiposity, alcohol consumption, tobacco use, diet, and activity patterns on blood pressure (BP) is well established. Additionally, polygenic risk scores for BP traits are associated with clinically significant phenotypic variation. However, interactions between genetic and environmental risk factors in hypertension morbidity and mortality are poorly characterized. Methods and Results We used genotype and phenotype data from up to 49 234 participants from the HUNT (Trøndelag Health Study) to model gene–environment interactions between genome‐wide polygenic risk scores for systolic BP and diastolic BP and 125 environmental exposures. Among the 125 environmental exposures assessed, 108 and 100 were independently associated with SBP and DBP, respectively. Of these, 12 interactions were identified for genome‐wide PRSs for systolic BP and 4 for genome‐wide polygenic risk scores for diastolic BP, 2 of which were overlapping (P

ABSTRACT Objective This pilot study investigated whether single nucleotide polymorphisms (SNP) in the NOX5 gene (NADPH oxidase 5) are associated with the type 2 diabetes (T2D) risk. Subjects and methods A total of 1579 patients with T2D and 1627 age- and sex-matched healthy subjects were recruited for this study. Genotyping of common SNPs, namely rs35672233, rs3743093, rs2036343, rs311886, and rs438866, was performed using the MassArray-4 system. Results SNP rs35672233 was associated with an increased risk of T2D (OR = 1.67, 95% CI 1.29-2.17, FDR = 0.003). The H3 haplotype (rs35672233T-rs3743093G-rs2036343A-rs311886C-rs438866C) increased T2D risk (OR = 1.65, 95% CI 1.27-2.13, FDR = 0.001). The rs35672233 polymorphism and H3 haplotype were found to have an association with T2D risk only in subjects with a body mass index greater than 25 kg/m2 (FDR < 0.01). Environmental risk factors, such as chronic psycho-emotional stress, sedentary lifestyle, high-calorie diet and SNP rs35672233 were jointly associated with T2D susceptibility. A haplotype comprising the allele rs35672233-C and conferring protection against T2D, was associated with elevated levels of antioxidants such as total glutathione and uric acid, as well as reduced levels of two-hour postprandial glucose in the plasma of patients. The NOX5 polymorphisms showed no associations with diabetic complications. Conclusion The present study is the first to establish associations between polymorphisms in NOX5 and the risk of type 2 diabetes mellitus, and provides a new line of evidence for the crucial role of oxidative stress-related genes in disease susceptibility.

BackgroundGenome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer.MethodsAnalyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs.ResultsAssuming a 1 × 10-5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability

Metabolic dysfunction-associated steatotic liver disease (MASLD), described as the most prominent cause of chronic liver disease worldwide, has emerged as a significant public health issue, posing a considerable challenge for most countries. Endocrine-disrupting chemicals (EDCs), commonly found in daily use items and foods, are able to interfere with nuclear receptors (NRs) and disturb hormonal signaling and mitochondrial function, leading, among other metabolic disorders, to MASLD. EDCs have also been proposed to cause transgenerationally inherited alterations leading to increased disease susceptibility. In this review, we are focusing on the most prominent linking pathways between EDCs and MASLD, their role in the induction of epigenetic transgenerational inheritance of the disease as well as up-to-date practices aimed at reducing their impact. [ABSTRACT FROM AUTHOR]

The discovery of a pathogenic variant in the alpha-synuclein (SNCA) gene in the Contursi kindred in 1997 indisputably confirmed a genetic cause in a subset of Parkinson's disease (PD) patients. Currently, pathogenic variants in one of the seven established PD genes or the strongest known risk factor gene, GBA1, are identified in ∼15% of PD patients unselected for age at onset and family history. In this Debate article, we highlight multiple avenues of research that suggest an important - and in some cases even predominant - role for genetics in PD aetiology, including familial clustering, high rates of monogenic PD in selected populations, and complete penetrance with certain forms. At first sight, the steep increase in PD prevalence exceeding that of other neurodegenerative diseases may argue against a predominant genetic etiology. Notably, the principal genetic contribution in PD is conferred by pathogenic variants in LRRK2 and GBA1 and, in both cases, characterized by an overall late age of onset and age-related penetrance. In addition, polygenic risk plays a considerable role in PD. However, it is likely that, in the majority of PD patients, a complex interplay of aging, genetic, environmental, and epigenetic factors leads to disease development. [ABSTRACT FROM AUTHOR]

The relationship between nutrition and brain health is intricate. Studies suggest that nutrients during early life impact not only human physiology but also mental health. Although the exact molecular mechanisms that depict this relationship remain unclear, there are indications that environmental factors such as eating, lifestyle habits, stress, and physical activity, influence our genes and modulate their function by epigenetic mechanisms to shape mental health outcomes. Epigenetic mechanisms act as crucial link between genes and environmental influences, proving that non-genetic factors could have enduring effects on the epigenome and influence health trajectories. We review studies that demonstrated an epigenetic mechanism of action of nutrition on mental health, focusing on the role of specific micronutrients during critical stages of brain development. The methyl-donor micronutrients of the one-carbon metabolism, such as choline, betaine, methionine, folic acid, VitB6 and VitB12 play critical roles in various physiological processes, including DNA and histone methylation. These micronutrients have been shown to alter gene function and susceptibility to diseases including mental health and metabolic disorders. Understanding how micronutrients influence metabolic genes in humans can lead to the implementation of early nutritional interventions to reduce the risk of developing metabolic and mental health disorders later in life. [ABSTRACT FROM AUTHOR]

Genetically associated phenotypic variability has been widely observed across organisms and traits, including in humans. Both gene-gene and gene-environment interactions can lead to an increase in genetically associated phenotypic variability. Therefore, detecting the underlying genetic variants, or variance Quantitative Trait Loci (vQTLs), can provide novel insights into complex traits. Established approaches to detect vQTLs apply different methodologies from variance-only approaches to mean-variance joint tests, but a comprehensive comparison of these methods is lacking. Here, we review available methods to detect vQTLs in humans, carry out a simulation study to assess their performance under different biological scenarios of gene-environment interactions, and apply the optimal approaches for vQTL identification to gene expression data. Overall, with a minor allele frequency (MAF) of less than 0.2, the squared residual value linear model (SVLM) and the deviation regression model (DRM) are optimal when the data follow normal and non-normal distributions, respectively. In addition, the Brown–Forsythe (BF) test is one of the optimal methods when the MAF is 0.2 or larger, irrespective of phenotype distribution. Additionally, a larger sample size and more balanced sample distribution in different exposure categories increase the power of BF, SVLM, and DRM. Our results highlight vQTL detection methods that perform optimally under realistic simulation settings and show that their relative performance depends on the phenotype distribution, allele frequency, sample size, and the type of exposure in the interaction model underlying the vQTL. [ABSTRACT FROM AUTHOR]

Fetal-neonatal neurologists (FNNs) consider diagnostic, therapeutic, and prognostic decisions strengthened by interdisciplinary collaborations. Biosocial perspectives of the woman's health influence evaluations of maternalplacental-fetal (MPF) triad, neonate, and child. A dual cognitive process integrates "fast thinking-slow thinking" to reach shared decisions that minimize bias and maintain trust. Assessing the science of uncertainty with uncertainties in science improves diagnostic choices across the developmental-aging continuum. Three case vignettes highlight challenges that illustrate this approach. The first maternal-fetal dyad involved a woman who had been recommended to terminate her pregnancy based on an incorrect diagnosis of an encephalocele. A meningocele was subsequently identified when she sought a second opinion with normal outcome for her child. The second vignette involved two pregnancies during which fetal cardiac rhabdomyoma was identified, suggesting tuberous sclerosis complex (TSC). One woman sought an out-of-state termination without confirmation using fetal brain MRI or postmortem examination. The second woman requested pregnancy care with postnatal evaluations. Her adult child experiences challenges associated with TSC sequelae. The third vignette involved a prenatal diagnosis of an open neural tube defect with arthrogryposis multiplex congenita. The family requested prenatal surgical closure of the defect at another institution at their personal expense despite receiving a grave prognosis. The subsequent Management of Myelomeningocele Study (MOMS) would not have recommended this procedure. Their adult child requires medical care for global developmental delay, intractable epilepsy, and autism. These three evaluations involved uncertainties requiring shared clinical decisions among all stakeholders. Falsely negative or misleading positive interpretation of results reduced chances for optimal outcomes. FNN diagnostic skills require an understanding of dynamic gene-environment interactions affecting reproductive followed by pregnancy exposomes that influence the MPF triad health with fetal neuroplasticity consequences. Toxic stressor interplay can impair the neural exposome, expressed as anomalous and/or destructive fetal brain lesions. Functional improvements or permanent sequelae may be expressed across the lifespan. Equitable and compassionate healthcare for women and families require shared decisions that preserve pregnancy health, guided by person-specific racial-ethnic, religious, and bio-social perspectives. Applying developmental origins theory to neurologic principles and practice supports a brain health capital strategy for all persons across each generation. [ABSTRACT FROM AUTHOR]

Climate change is a change in the usual weather found in a place. The climate change has a major impact not only on natural disasters of the Earth but also on human health. The climate crisis is then no longer a future concern. It includes both the global warming driven by human emissions of greenhouse gases (GHG), and the resulting large-scale shifts in weather patterns. Global warming can occur from a variety of causes, both natural and human induced. The primary GHG in Earth's atmosphere, listed in decreasing order of average global mole fraction, are: water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3). Today, scientists around the world continue to try and solve the puzzle of climate change. It is clear that to address climate change, the amount of CO2 released into the atmosphere by industrial process has to be reduced because once it is added to the atmosphere, it can continue to affect climate for thousands of years. For such a purpose, an approach to intervention using expression vectors for any protein targeting to the cell plasma membrane via the glycosylphosphatidylinositol, GPI, anchor is suggested. The resulting GPI-anchored proteins would be useful for studying intermolecular interactions, especially gene-environment interactions, in investigating the potential impact of any chemical compounds on any genes of interest and could be used for carbonic anhydrase (CA)-based CO2-capture (environmental application). This approach would be crucial not only for capturing CO2via GPI and CA but also for the production of CA enzyme as well as its stabilization and therefore useful for combating the global warming of climate change. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Rapid advancements in high-throughput biological techniques have facilitated the generation of highdimensional omics datasets, which have provided a solid foundation for precision medicine and prognosis prediction. Nonetheless, the problem of missing heritability persists. To solve this problem, it is essential to explain the genetic structure of disease incidence risk and prognosis by incorporating interactions. The development of the Bayesian theory has provided new approaches for developing models for interaction identification and estimation. Several Bayesian models have been developed to improve the accuracy of model and identify the main effect, gene-environment (G×E) and gene-gene (G×G) interactions. Studies based on singlenucleotide polymorphisms (SNPs) are significant for the exploration of rare and common variants. Models based on the effect heredity principle and group-based models are relatively flexible and do not require strict constraints when dealing with the hierarchical structure between the main effect and interactions (M-I). These models have a good interpretability of biological mechanisms. Machine learningbased Bayesian approaches are highly competitive in improving prediction accuracy. These models provide insights into the mechanisms underlying the occurrence and progression of complex diseases, identify more reliable biomarkers, and develop higher predictive accuracy. In this paper, we provide a comprehensive review of these Bayesian approaches. [ABSTRACT FROM AUTHOR]

Abstract Background Genome-wide studies of gene–environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer. Methods Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene–environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs. Results Assuming a 1 × 10–5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability

Fetal-neonatal neurologists (FNNs) consider diagnostic, therapeutic, and prognostic decisions strengthened by interdisciplinary collaborations. Bio-social perspectives of the woman’s health influence evaluations of maternal-placental-fetal (MPF) triad, neonate, and child. A dual cognitive process integrates “fast thinking-slow thinking” to reach shared decisions that minimize bias and maintain trust. Assessing the science of uncertainty with uncertainties in science improves diagnostic choices across the developmental-aging continuum. Three case vignettes highlight challenges that illustrate this approach. The first maternal-fetal dyad involved a woman who had been recommended to terminate her pregnancy based on an incorrect diagnosis of an encephalocele. A meningocele was subsequently identified when she sought a second opinion with normal outcome for her child. The second vignette involved two pregnancies during which fetal cardiac rhabdomyoma was identified, suggesting tuberous sclerosis complex (TSC). One woman sought an out-of-state termination without confirmation using fetal brain MRI or postmortem examination. The second woman requested pregnancy care with postnatal evaluations. Her adult child experiences challenges associated with TSC sequelae. The third vignette involved a prenatal diagnosis of an open neural tube defect with arthrogryposis multiplex congenita. The family requested prenatal surgical closure of the defect at another institution at their personal expense despite receiving a grave prognosis. The subsequent Management of Myelomeningocele Study (MOMS) would not have recommended this procedure. Their adult child requires medical care for global developmental delay, intractable epilepsy, and autism. These three evaluations involved uncertainties requiring shared clinical decisions among all stakeholders. Falsely negative or misleading positive interpretation of results reduced chances for optimal outcomes. FNN diagnostic skills require an understanding of dynamic gene-environment interactions affecting reproductive followed by pregnancy exposomes that influence the MPF triad health with fetal neuroplasticity consequences. Toxic stressor interplay can impair the neural exposome, expressed as anomalous and/or destructive fetal brain lesions. Functional improvements or permanent sequelae may be expressed across the lifespan. Equitable and compassionate healthcare for women and families require shared decisions that preserve pregnancy health, guided by person-specific racial-ethnic, religious, and bio-social perspectives. Applying developmental origins theory to neurologic principles and practice supports a brain health capital strategy for all persons across each generation.

This study aimed to investigate the relationships between maternal FA supplementation and nine single-nucleotide variants of the GATA4 gene in non-chromosomal CHD and further explore the gene–environment interactions associated with CHD. A total of 585 CHD patients and 600 controls were recruited in the case–control study. Maternal FA (FA-containing multivitamin) supplementation information and nine polymorphisms of the GATA4 gene were collected in this study. Adjusted ORs (aOR) and their 95% confidence intervals (CIs) were calculated using proper statistical methods to analyze the relationships between the two main exposures of interest with respect to CHD. After adjusting the suspicious confounding factors, a significantly increased risk for CHD in offspring was found with non-FA supplementation before/during the pregnancy to CHD in offspring (aOR = 1.58, 95% CI: 1.01–2.48). We suggested taking FA supplementation before/during the pregnancy to prevent CHD in offspring, especially in the preconception period (aOR = 0.53, 95% CI: 0.32–0.90). The genetic results showed that the polymorphisms of rs4841588, rs12458, and rs904018 under specific genotypes and genetic models were significantly related to CHD. The gene–environment interaction between rs10108052 and FA supplementation before/during pregnancy could increase the risk of CHD (aOR = 5.38, 95% CI: 1.67–17.09, Pinteraction = 0.004). Relationships between maternal FA supplementation and specific polymorphisms of the GATA4 gene, as well as the gene–environment interaction, were significantly associated with CHD in offspring. [ABSTRACT FROM AUTHOR]

While converging evidence suggests that both environmental and genetic factors underlie variations in diurnal cortisol, the extent to which these sources of influence vary according to socioeconomic status (SES) has seldom been investigated, particularly in adolescence. To investigate whether a distinct genetic and environmental contribution to youth's diurnal cortisol secretion emerges according to family SES and whether the timing of these experiences matters. Participants were 592 twin pairs, who mostly came from middle-income and intact families and for whom SES was measured in childhood and adolescence. Diurnal cortisol was assessed at age 14 at awakening, 30 min later, in the afternoon and evening over four nonconsecutive days. SES–cortisol phenotypic associations were specific to the adolescence period. Specifically, higher awakening cortisol levels were detected in wealthier backgrounds, whereas higher cortisol awakening response (CAR) and diurnal changes were present at both ends of the SES continuum. Moreover, smaller genetic contributions emerged for awakening cortisol in youth from poorer compared to wealthier backgrounds. The results suggest that the relative contribution of inherited factors to awakening cortisol secretion may be enhanced or suppressed depending on the socio-family context, which may help to decipher the mechanisms underlying later adjustment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Caregivers shape the rearing environment of their young. Consequently, offspring traits are influenced by the genes of their caregivers via indirect genetic effects (IGEs). However, the extent to which IGEs are modulated by environmental factors, other than the genotype of social partners (i.e. intergenomic epistasis), remains an open question. Here we investigate how brood are influenced by the genotype of their caregivers in the clonal raider ant, Ooceraea biroi , a species in which the genotype, age and number of both caregivers and brood can be experimentally controlled. First, we used four clonal lines to establish colonies that differed only in the genotype of caregivers and measured effects on foraging activity, as well as IGEs on brood phenotypes. In a second experiment, we tested whether these IGEs are conditional on the age and number of caregivers. We found that caregiver genotype affected the feeding and foraging activity of colonies, and influenced the rate of development, survival, body size, and caste fate of brood. Caregiver genotype interacted with other factors to influence the rate of development and survival of brood, demonstrating that IGEs can be conditional. Thus, we provide an empirical example of phenotypes being influenced by IGE-by-environment interactions beyond intergenomic epistasis, highlighting that IGEs of caregivers/parents are alterable by factors other than their brood's/offspring's genotype. [ABSTRACT FROM AUTHOR]

Gene–environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy‐tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike‐and‐slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single‐nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives. [ABSTRACT FROM AUTHOR]

The aim of this pilot study was to investigate whether single nucleotide polymorphisms (SNP) in the gene encoding the catalytic subunit of glutamate cysteine ligase (GCLC) are associated with the risk and clinical features of psoriasis. A total of 944 unrelated individuals, including 474 patients with a diagnosis of psoriasis and 470 healthy controls, were recruited for the study. Six common SNPs in the GCLC gene were genotyped using the MassArray-4 system. Polymorphisms rs648595 (OR = 0.56, 95% CI 0.35–0.90; Pperm = 0.017) and rs2397147 (OR = 0.54, 95% CI 0.30–0.98; Pperm = 0.05) were associated with susceptibility to psoriasis in males. In the male group, diplotype rs2397147-C/C × rs17883901-G/G was associated with a decreased risk of psoriasis (FDR-adjusted p = 0.014), whereas diplotype rs6933870-G/G × rs17883901-G/G (FDR-adjusted p = 0.045) showed an association with an increased disease risk in females. The joint effects of SNPs with tobacco smoking (rs648595 and rs17883901) and alcohol abuse (rs648595 and rs542914) on psoriasis risk were observed (Pperm ≤ 0.05). We also found multiple sex-independent associations between GCLC gene polymorphisms and various clinical features such as earlier disease onset, the psoriatic triad, and specific localizations of skin lesions. The present study is the first to show that polymorphisms of the GCLC gene are significantly associated with the risk of psoriasis and related to its clinical features. [ABSTRACT FROM AUTHOR]

The development of genomic technologies over the past decades has enabled identification of genetic variants responsible of disease; occasionally however, protective rare variants emerged. Verweij et al have recently reported genetic variants in CIDEB gene that are protective from liver injury. Here, we briefly summarise the recent findings on the impact of CIDEB variants on liver disease, while emphasizing how phenotype-genotype studies tailored for the identification of "protective" mutations might direct development of prevention and therapeutic strategies for common diseases. [ABSTRACT FROM AUTHOR]

Arterial hypertension is the most frequent cardiovascular risk factor all over the world, and it is one of the leading drivers of the risk of cardiovascular events and death. It is a complex trait influenced by heritable and environmental factors. To date, the World Health Organization estimates that 1.28 billion adults aged 30–79 years worldwide have arterial hypertension (defined by European guidelines as office systolic blood pressure ≥ 140 mmHg or office diastolic blood pressure ≥ 90 mmHg), and 7.1 million die from this disease. The molecular genetic basis of primary arterial hypertension is the subject of intense research and has recently yielded remarkable progress. In this review, we will discuss the genetics of arterial hypertension. Recent studies have identified over 900 independent loci associated with blood pressure regulation across the genome. Comprehending these mechanisms not only could shed light on the pathogenesis of the disease but also hold the potential for assessing the risk of developing arterial hypertension in the future. In addition, these findings may pave the way for novel drug development and personalized therapeutic strategies.

Genetic studies provide valuable information to assess if the effect of genetic variants varies by the nongenetic "environmental" variables, what is traditionally defined to be gene-environment interaction (GxE). A common complication is that multiple disease states present with the same set of symptoms, and hence share the clinical diagnosis. Because (a) disease states might have distinct genetic bases; and (b) frequencies of the disease states within the clinical diagnosis vary by the environmental variables, analyses of association with the clinical diagnosis as an outcome variable might result in false positive or false negative findings. We develop estimates for this setting to be able to assess GxE in a case-only study and we compare the case-control and case-only estimates. We report extensive simulation studies that evaluate empirical properties of the estimates and show the application to a study of Alzheimer's disease.

A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2-degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10-6) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P < 5 × 10-8 using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models.