Gene-environment Interactions Research Articles

Gene-environment interactions in the influence of maternal education on adolescent neurodevelopment using ABCD study.

Maternal education was strongly correlated with adolescent brain morphology, cognitive performances, and mental health. However, the molecular basis for the effects of maternal education on the structural neurodevelopment remains unknown. Here, we conducted gene-environment-wide interaction study using the Adolescent Brain Cognitive Development cohort. Seven genomic loci with significant gene-environment interactions (G×E) on regional gray matter volumes were identified, with enriched biological functions related to metabolic process, inflammatory process, and synaptic plasticity. Additionally, genetic overlapping results with behavioral and disease-related phenotypes indicated shared biological mechanism between maternal education modified neurodevelopment and related behavioral traits. Finally, by decomposing the multidimensional components of maternal education, we found that socioeconomic status, rather than family environment, played a more important role in modifying the genetic effects on neurodevelopment. In summary, our study provided analytical evidence for G×E effects regarding adolescent neurodevelopment and explored potential biological mechanisms as well as social mechanisms through which maternal education could modify the genetic effects on regional brain development.

Marginal interaction test for detecting interactions between genetic marker sets and environment in genome-wide studies.

As human complex diseases are influenced by the interaction between genetics and the environment, identifying gene-environment interactions (G × E) is crucial for understanding disease mechanisms and predicting risk. Developing robust quantitative tools for G × E analysis can enhance the study of complex diseases. However, many existing methods that explore G × E focus on the interplay between an environmental factor and genetic variants, exclusively for common or rare variants. In this study, we developed MAGEIT_RAN and MAGEIT_FIX to identify interactions between an environmental factor and a set of genetic markers, including both rare and common variants, based on the MinQue for Summary statistics. The genetic main effects in MAGEIT_RAN and MAGEIT_FIX are modeled as random and fixed effects, respectively. Simulation studies showed that both tests had type I error under control, with MAGEIT_RAN being the most powerful test. Applying MAGEIT to a genome-wide analysis of gene-alcohol interactions on hypertension and seated systolic blood pressure in the Multi-Ethnic Study of Atherosclerosis revealed genes like EIF2AK2, CCNDBP1 and EPB42 influencing blood pressure through alcohol interaction. Pathway analysis identified one apoptosis and survival pathway involving PKR and two signal transduction pathways associated with hypertension and alcohol intake, demonstrating MAGEIT_RAN's ability to detect biologically relevant gene-environment interactions.

Advancements and limitations in polygenic risk score methods for genomic prediction: a scoping review.

This scoping review aims to identify and evaluate the landscape of Polygenic Risk Score (PRS)-based methods for genomic prediction from 2013 to 2023, highlighting their advancements, key concepts, and existing gaps in knowledge, research, and technology. Over the past decade, various PRS-based methods have emerged, each employing different statistical frameworks aimed at enhancing prediction accuracy, processing speed and memory efficiency. Despite notable advancements, challenges persist, including unrealistic assumptions regarding sample sizes and the polygenicity of traits necessary for accurate predictions, as well as limitations in exploring hyper-parameter spaces and considering environmental interactions. We included studies focusing on PRS-based methods for risk prediction that underwent methodological evaluations using valid approaches and released computational tools/software. Additionally, we restricted our selection to studies involving human participants that were published in English language. This review followed the standard protocol recommended by Joanna Briggs Institute Reviewer's Manual, systematically searching Ovid MEDLINE, Ovid Embase, Scopus and Web of Science databases. Additionally, searches included grey literature sources like pre-print servers such as bioRxiv, and articles recommended by experts to ensure comprehensive and diverse coverage of relevant records. This study identified 34 studies detailing 37 genomic prediction methods, the majority of which rely on linkage disequilibrium (LD) information and necessitate hyper-parameter tuning. Nine methods integrate functional/gene annotation, while 12 are suitable for cross-ancestry genomic prediction, with only one considering gene-environment (GxE) interaction. While some methods require individual-level data, most leverage summary statistics, offering flexibility. Despite progress, challenges remain. These include computational complexity and the need for large sample sizes for high prediction accuracy. Furthermore, recent methods exhibit varying effectiveness across traits, with absolute accuracies often falling short of clinical utility. Transferability across ancestries varies, influenced by trait heritability and diversity of training data, while handling admixed populations remains challenging. Additionally, the absence of standard error measurements for individual PRSs, crucial in clinical settings, underscores a critical gap. Another issue is the lack of customizable graphical visualization tools among current software packages. While genomic prediction methods have advanced significantly, there is still room for improvement. Addressing current challenges and embracing future research directions will lead to the development of more universally applicable, robust, and clinically relevant genomic prediction tools.

Long-Term Air Pollution Exposure and Severity of Idiopathic Pulmonary Fibrosis: Data from the IPF-PRO Registry.

While exposure to air pollution is a known risk factor for adverse pulmonary outcomes, its impact in individuals with idiopathic pulmonary fibrosis (IPF) is less well understood. To investigate the effects of long-term exposure to air pollution on disease severity and progression in patients with IPF and to determine whether genomic factors, such as MUC5B promoter polymorphism or telomere length, modify these associations. We performed analyses at enrollment and after one year of follow-up in the IPF-PRO Registry, a prospective observational registry that enrolled individuals with IPF at 46 US sites from June 2014 to October 2018. Five-year average pollution exposures (PM2.5, NO2, O3) prior to enrollment date were estimated at participants' residential addresses with validated national spatio-temporal models. Multivariable regression models estimated associations between pollution exposure and physiologic measurements (FVC, DLCO, supplemental oxygen use at rest) and quality of life measurements (St. George's Respiratory Questionnaire [SGRQ], EuroQoL, Cough and Sputum Assessment Questionnaire) at enrollment. Cox proportional hazard models estimated associations between pollutants and a composite outcome of death, lung transplant, or >10% absolute decline in FVC % predicted in the year after enrollment. Models were adjusted for individual-level and spatial confounders, including proxies for disease onset. Gene-environment interactions with MUC5B and telomere length were assessed. Of 835 participants, 94% were non-Hispanic Whites, 76% were male, mean (SD) age was 70 (7.7) years. In fully adjusted analyses, higher PM2.5 exposure was associated with worse quality of life per SGRQ activity score (3.48 [95% confidence interval (CI) 0.64, 6.32] per 2µg/m3 PM2.5) and EuroQoL scores (-0.04 [95%CI -0.06, -0.01] per 2µg/m3 PM2.5), and lower FVC % predicted and lower DLCO% predicted at enrollment. Each 3 parts per billion difference in O3 exposure was associated with a 1.57% [95% CI 0.15, 2.98] higher FVC % predicted at enrollment, although this effect was attenuated in multi-pollutant models. There was no association between NO2 and enrollment measures, between pollution exposure and one-year outcomes, or evidence for gene-environment interactions. In the IPF-PRO Registry, long-term exposure to PM2.5 was associated with worse quality of life and lung function at enrollment, but not with short-term disease progression or mortality. There was no evidence of effect modification by interaction of genomic factors with pollution. The reason for the unexpected relationship between O3 exposure and higher FVC is unclear.

AMMI Biplot Analysis for Stability of Grain Yield in Foxtail Millet [Setaria italica (L.) P. Beauv.] Genotypes

Background: Foxtail millet cultivation in India’s NEH region holds promise due to its adaptation to diverse environments and high-quality grain. Studying G × E interaction in this region will guide breeding programs to develop foxtail millet varieties adapted to local conditions. The objective of this study was to find out foxtail millet genotypes that produce high yield in diverse environments and to identify ideal mega-environments using additive main effects and multiplicative interaction stability model analysis. Methods: In this study, 30 genotypes were evaluated at the Research Farm of the SAS, Nagaland University, Medziphema, India. The experiment was conducted during July 2022 to May 2023 involving four different environments. Two environments were rainfed and two were irrigated with weekly intervals. The experiment was conducted in randomized complete block design (RCBD) with three replications in all the environments. Result: Genotype-environment interactions significantly influenced grain yield across four environments, while replicates were non-significant. Pooled analysis revealed significant genotypic effects and seasonal impacts. AMMI analysis revealed significant effect on environmental and genotypic influences on grain yield and explained 23.47% and 27.94% variability respectively. The AMMI model effectively decomposed the intricate genotype-environment interaction into three principal components (PC1, PC2and PC3), explaining 54.5%, 28.7%and 16.8% of the interaction variance, respectively. AMMI model exposed genotypes viz G8, G9, G21and G22 as best performer and stable. AMMI stability value revealed G10 with highest stability (ASV = 0.34) followed by G15, G7, G29, G6and G13 in decreasing order indicating their consistent performance in grain yield across different environments.

Stability of Blood DNA Methylation Across Two Timepoints in Three Cohorts

Background: DNA methylation mediates the gene–environment interactions, with implications for health and disease. Studies with sampling at more than one timepoint revealed the considerable variability of the blood methylome, but comprehensive resources on genome-wide methylation stability are still lacking. We aimed to identify methylation sites that remain the most stable across two timepoints in human whole blood. Methods: Publicly available blood DNA methylation data from three cohorts were analysed, which included methylation profiles at two timepoints >1 year apart. The cohorts included pre-/post-pubertal children (Illumina 450k array), the elderly (Illumina 450k array), and middle-aged adults with obesity (Illumina EPIC array). Two metrics were used for the stability assessment: the mean absolute difference (MAD) of beta values between two measurements and the intraclass correlation coefficient (ICC). We searched for probes demonstrating high stability (low MAD and high ICC) across the three cohorts. Data from 51 children, 86 elderly adults, and 120 middle-aged participants were re-analysed. Results: The median interquartile range (IQR) of the maximum (from three datasets) MAD was 2.1% (1.5–2.9%), and the median of the minimum ICC agreement coefficient was 0.053 (−0.077–0.304). The Pearson’s correlation coefficient for the ICC vs. maximum MAD was low (r = 0.34, p < 2.2 × 10−16). We found only 239 probes that were highly stable based on both the maximum MAD (<5th percentile, <0.01) and ICC criterion (>95th percentile, >0.74). Conclusions: The whole-blood DNA methylation profile, as measured using microarrays, is dynamic over >1 year, but contains a fraction of stable probes, most of which are related to genomic variation. A resource describing probe stability is made publicly available, with the intention to support biomarker studies and the investigation of early epigenetic programming. The absolute error and correlation are two complementary facets of probe stability that may be considered in further research, especially to determine the stability of probes in health and disease across different tissues and populations.

Intercontinental Insights into Autism Spectrum Disorder: A Synthesis of Environmental Influences and DNA Methylation

Abstract Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by a broad range of symptoms. The etiology of ASD is thought to involve complex gene-environment interactions, which are crucial to understanding its various causes and symptoms. DNA methylation is an epigenetic mechanism that potentially links genetic predispositions to environmental factors in the development of ASD. This review provides a global perspective on ASD, focusing on how DNA methylation studies may reveal gene-environment interactions characteristic of specific geographical regions. It delves into the role of DNA methylation in influencing the causes and prevalence of ASD in regions where environmental influences vary significantly. We also address potential explanations for the high ASD prevalence in North America, considering lifestyle factors, environmental toxins, and diagnostic considerations. Asian and European studies offer insights into endocrine-disrupting compounds (EDC), persistent organic pollutants (POP), maternal smoking, and their associations with DNA methylation alterations in ASD. In areas with limited data on DNA methylation and ASD, such as Africa, Oceania, and South America, we discuss prevalent environmental factors based on epidemiological studies. Additionally, the review integrates global and country-specific prevalence data from various studies, providing a comprehensive picture of the variables influencing ASD diagnoses over region and year of assessment. This prevalence data, coupled with regional environmental variables and DNA methylation studies, provides a perspective on the complexities of ASD research. Integrating global prevalence data, we underscore the need for a comprehensive global understanding of ASD’s complex etiology. Expanded research into epigenetic mechanisms of ASD is needed, particularly in underrepresented populations and locations, to enhance biomarker development for diagnosis and intervention strategies for ASD that reflect the varied environmental and genetic landscapes worldwide.

Genetic Polymorphisms in Obesity: How Variants in Lipid Metabolism Genes Contribute to Hyperlipidemia

Obesity is a complex metabolic disorder influenced by both environmental and genetic factors. Among the genetic contributors, polymorphisms in lipid metabolism genes play a significant role in regulating lipid profiles and promoting hyperlipidemia. This review aims to explore the impact of genetic variants on lipid metabolism in individuals with obesity, focusing on genes such as APOE, LPL, FTO, PCSK9, CETP, and FABP. These polymorphisms alter lipid processing, transportation, and storage, leading to aberrant lipid levels, which contribute to hyperlipidemia and subsequent cardiometabolic complications. We will also discuss the gene-environment interactions that modulate these genetic effects and review potential therapeutic interventions targeting lipid metabolism to manage hyperlipidemia in obese individuals. Keywords: Genetic polymorphisms, obesity, lipid metabolism, hyperlipidemia, gene-environment interactions, cardiometabolic disease

MethylCallR : a comprehensive analysis framework for Illumina Methylation Beadchip

DNA methylation is a molecular process that mediates gene-environment interactions. Epigenome-wide association studies (EWAS) using the Illumina Human Methylation BeadChip are powerful tools for quantifying the relationship between DNA methylation and phenotypes. Recently, the Illumina Methylation EPICv2 BeadChip (EPICv2) was released, which includes new features, such as duplicated probes and changed probe names. Several published algorithms have been updated to address these features in EPICv2. However, appropriate EPICv2 preprocessing and integration with previous microarray versions remain complex. Therefore, MethylCallR, an open-source R package designed to provide standard procedures for performing EWAS using Illumina methylation microarrays including EPICv2, was developed. MethylCallR can be used to control duplicated probes in EPICv2, by using pre-set data implemented in MethylCallR or new customized data. MethylCallR includes a straightforward conversion function between different types of Illumina Human Methylation BeadChips. Using MethylCallR, potential outlier sample detection and statistical power estimation were conducted and used to select meaningful probes. Publicly available data was analyzed using MethylCallR and the findings were compared to that of a previous study.

The Val66Met variant of brain-derived neurotrophic factor is linked to reduced telomere length in a military population: a pilot study

In military populations, gene-environment interactions can influence performance and health outcomes. Brain-derived neurotrophic factor (BDNF) is a central nervous system protein that is important for neuronal function and synaptic plasticity. A BDNF single nucleotide polymorphism, rs6265, leads to an amino acid substitution of valine (Val) with methionine (Met) at codon 66 (Val66Met), which may influence an individual’s response to occupational stress, and predispose military members to psychological disorders. Telomere length (TL), a novel measure of biological aging, can be used as a biomarker of stress. Accordingly, telomere shortening may be a surrogate indicator of physiological weathering due to chronic disease and stressful life events. To increase our understanding about the potential effect of the Val66Met mutation on the human stress response, we evaluated the relationships between Val66Met, TL, and mental health symptoms in a military population. In this pilot study (N = 164), we observed an association between Val66Met and reduced TL (p = 0.048). There was no relationship between Val66Met and mental health symptoms. These results support the investigation of gene-environment interactions, and their potential influence on TL due to occupational stress such as military service.

Genetic markers and traditional risk factors in predicting atrial fibrillation in patients with arterial hypertension, focus on the renin-angiotensin-aldosterone system genes

BACKGROUND: Genetic and environmental factors are involved in the development of atrial fibrillation in arterial hypertension. This determines the relevance of studying gene-environment interactions in the occurrence of arrhythmia. AIM: To evaluate the contribution of the renin-angiotensin-aldosterone system genes polymorphisms to the susceptibility to atrial fibrillation in patients with arterial hypertension, and also to study the combined influence of these polymorphisms and environmental factors on the risk of arrhythmia. MATERIALS AND METHODS: The study included 60 patients with arterial hypertension and paroxysmal atrial fibrillation (study group), 60 patients with arterial hypertension without atrial fibrillation (comparison group-1) and 20 healthy volunteers (comparison group-2). Angiotensin-converting enzyme (ACE (I/D)) and angiotensin II type 1 receptor gene (AGTR1 (A1166C)) polymorphisms were analyzed by real-time polymerase chain reaction. RESULTS: Genotype II and allele I of the ACE gene (I/D) in patients with arterial hypertension and atrial fibrillation were significantly more frequent compared to patients with arterial hypertension without arrhythmia (χ² = 4.547; p = 0.03 and χ² = 4.818; p = 0.03 respectively). Carriage of genotype II in patients with arterial hypertension increased the chance of developing atrial fibrillation by 2.8 times (95% CI 1.19–7.18). The odds ratio (OR) for arrythmia development in patients with arterial hypertension and allele I was 1.8 (95% CI 1.10–3.07). The presence of obesity in patients with arterial hypertension in the presence of genotype II of the ACE gene (I/D) was associated with an increased risk of developing atrial fibrillation, compared with the genotype alone (OR = 4.16, 95% CI 1.16–19.87). A study of the A1166C polymorphism of the AGTR1 gene did not reveal a reliable significant relationship between its inheritance and the development of atrial fibrillation. CONCLUSION: Genotype II and allele I of the ACE gene (I/D) were statistically significantly more frequent in patients with arterial hypertension and atrial fibrillation. Carriage of genotype II and allele I of the ACE gene (I/D) increased the chance of developing atrial fibrillation in patients with arterial hypertension. Obesity had a significant effect on the susceptibility to atrial fibrillation in the presence of genotype II of the ACE gene (I/D) in hypertensive patients.

The Influence of DNA Repair Genes and Prenatal Tobacco Exposure on Risk of Childhood Acute Lymphoblastic Leukemia-A Gene-Environment Interaction Study.

Acute lymphoblastic leukemia (ALL) is the most common type of cancer among children. Tobacco exposure during gestation has been investigated as a potential risk factor, but its role remains undefined. Given tobacco's toxicological profile as a DNA damaging agent, we examined the impact of DNA repair gene variability as a source of vulnerability to tobacco exposure risk for ALL. Leveraging demographic and genotype data from two large California-based ALL epidemiology studies, we used logistic regression, MinimumP (MinP) statistical method and permutation tests to examine interactions between DNA repair genes and prenatal tobacco exposure. We found statistically significant interactions between prenatal tobacco exposure and DNA repair genes RECQL (minP= 1.00x 10-4, FDR-P value = 1.86x 10-2) and TDG (minP= 1.00x 10-4, FDR-P value = 1.86 x 10-2) regarding childhood ALL risk. Notable interactions in the homologous recombination pathway were observed among Latino children, while non-Latino White children displayed significant interactions in the base excision repair and nucleotide excision repair pathways. Our study highlights the significance of DNA repair genes and pathways when evaluating environmental exposure to tobacco smoke, suggesting that genetic variability within these pathways could impact vulnerability in the development of childhood ALL. This study highlights the significant impact of genetic variation interacting with prenatal tobacco exposure on ALL risk. Further research is needed to understand these interactions and their implications for ALL etiology. Expanding studies to other gene-environment interactions will aid in developing targeted prevention, diagnosis, and treatment strategies for pediatric oncology.

Social isolation intensifies adgrl3.1-related externalizing and internalizing behaviors in zebrafish

Externalizing disorders (EDs) are characterized by outward-directed behaviors such as aggression and hyperactivity. They are influenced by gene-environment interactions, yet our understanding of the genetic predispositions and environmental contexts that give rise to them is incomplete. Additionally, people with EDs often exhibit comorbid internalizing symptoms, which can complicate the clinical presentation and treatment strategies. Following on from our previous studies, we examined genes x environment interaction as a risk factor for EDs by looking at internalizing and externalizing behaviors after social isolation. Specifically, we subjected adgrl3.1 knockout zebrafish — characterized by hyperactivity and impulsivity — to a 2-week social isolation protocol. We subsequently assessed the impact on anxiety-like behavior, abnormal repetitive behaviors, working memory, and social interactions. Genotype-specific additive effects emerged, with socially isolated adgrl3.1 knockout fish exhibiting intensified comorbid phenotypes, including increased anxiety, abnormal repetitive behaviors, reduced working memory, and altered shoaling, when compared to WT fish. The findings demonstrate that genetic predispositions interact with environmental stressors, such as social isolation, to exacerbate both externalizing and internalizing symptoms. This underlines the necessity for comprehensive diagnostic and intervention strategies.

Adequate sleep duration accentuates the effect of glucagon-like peptide-1 receptor variant on HbA1c: A gene-environment interaction study

BackgroundBoth glucagon-like peptide-1 receptor (GLP1R) agonists and lifestyle modifications are widely adopted in managing glycemia. However, the joint effects of GLP1R agonists with lifestyle on glycemic traits have not been evaluated. MethodsThis gene-environment study tested the interaction between GLP1R-rs10305492 variant, consistent with the effect of GLP1R agonist therapies, and four lifestyle factors (diet, physical activity, sleep duration, and chronotype) for glucose and glycated hemoglobin (HbA1c) levels among 263,846 UK Biobank participants. Linear regression models were conducted to evaluate the effects of the rs10305492 and lifestyle factors on glucose and HbA1c levels. ResultsGLP1R-rs10305492-AA/AG genotype combined a healthy diet, regular physical activity, adequate sleep duration, or morning chronotype were associated with lower glucose and HbA1c levels (all P for trend < 0.001). A synergistic effect was found between rs10305492 and sleep duration on HbA1c, suggesting a recommended adequate sleep duration (7–8 h/day) may amplify the HbA1c lowering effect of GLP1R agonists. Joint effects of the rs10305492 and adequate sleep were associated with a 26 % reduced risk of hyperglycemia (>7.8 mmol/L) risk and a 22 % lower of high HbA1c (>39 mmol/mol or 5.7 %). ConclusionsCombining GLP1R agonists with adequate sleep may provide additional benefits for glycemic control in clinical practice.

Exploring the role of genetic variability and exposure to bisphenols and parabens on excess body weight in Spanish children

Gene-environment interaction studies are emerging as a promising tool to shed light on the reasons for the rapid increase in excess body weight (overweight and obesity). We aimed to investigate the influence of several polymorphisms on excess weight in Spanish children according to a short- and long-term exposure to bisphenols and parabens, combining individual approach with the joint effect of them. This case-control study included 144 controls and 98 cases children aged 3-12 years. Thirty SNPs in genes involved in obesity-related pathways, xenobiotic metabolism and hormone systems were genotyped using the GSA microchip technology and qPCRs with Taqman® probes. Levels of bisphenols and parabens in urine and hair were used to assess short- and long-term exposure, respectively, via UHPLC-MS/MS system. LEPR rs9436303 was identified as a relevant risk variant for excess weight (ORDom:AAvsAG+GG=2.65, p<0.001), and this effect persisted across exposure-stratified models. For long-term exposure, GPX1 rs1050450 was associated with increased excess weight at low single paraben exposure (ORGvsA=2.00, p=0.028, p-interaction=0.016), whereas LEPR rs1137101 exhibited a protective function at high co-exposure (ORDom:AAvsAG+GG=0.17, p=0.007, p-interaction=0.043). ESR2 rs3020450 (ORDom:GGvsAG+AA=5.17, p=0.020, p-interaction=0.028) and CYP2C19 rs4244285 (ORDom:GGvsAG+AA=3.54, p=0.039, p-interaction=0.285) were identified as predisposing variants at low and high co-exposure, respectively. In short-term exposure, higher odds were observed for INSIG2 rs7566605 at high bisphenol exposure (ORCvsG=2.97, p=0.035, p-interaction=0.017) and for GSTP1 rs1695 at low levels (ORDom:AAvsAG+GG=5.38, p=0.016, p-interaction=0.016). At low and medium co-exposure, SH2B1 rs7498665 (ORAvsG=0.17, p=0.015, p-interaction=0.085) and MC4R rs17782313 (ORAvsG=0.10, p=0.023, p-interaction=0.045) displayed a protective effect, whereas ESR2 rs3020450 maintained its contributing role (ORGvsA=3.12, p=0.030, p-interaction=0.010). Our findings demonstrate for the first time that understanding the genetic variation in excess weight and how the level of exposure to bisphenols and parabens might interact with it, is crucial for a more in-depth comprehension of the complex polygenic and multifactorial aetiology of overweight and obesity.

α-synuclein overexpression and the microbiome shape the gut and brain metabolome in mice

Pathological forms of α-synuclein contribute to synucleinopathies, including Parkinson’s disease (PD). Most cases of PD arise from gene-environment interactions. Microbiome composition is altered in PD, and gut bacteria are causal to symptoms in animal models. We quantitatively profiled nearly 630 metabolites in the gut, plasma, and brain of α-synuclein-overexpressing (ASO) mice, compared to wild-type (WT) animals, and comparing germ-free (GF) to specific pathogen-free (SPF) animals (n = 5 WT-SPF; n = 6 ASO-SPF; n = 6 WT-GF; n = 6 ASO-GF). Many differentially expressed metabolites in ASO mice are also dysregulated in human PD patients, including amine oxides, bile acids and indoles. The microbial metabolite trimethylamine N-oxide (TMAO) strongly correlates from the gut to the plasma to the brain in mice, notable since TMAO is elevated in the blood and cerebrospinal fluid of PD patients. These findings uncover broad metabolomic changes that are influenced by the intersection of host genetics and microbiome in a mouse model of PD.

Accounting for genetic effect heterogeneity in fine-mapping and improving power to detect gene-environment interactions with SharePro

Classical gene-by-environment interaction (GxE) analysis can be used to characterize genetic effect heterogeneity but has a high multiple testing burden in the context of genome-wide association studies (GWAS). We adapt a colocalization method, SharePro, to account for effect heterogeneity in fine-mapping and identify candidates for GxE analysis with reduced multiple testing burden. SharePro demonstrates improved power for both fine-mapping and GxE analysis compared to existing methods as well as well-controlled false type I error in simulations. Using smoking status stratified GWAS summary statistics, we identify genetic effects on lung function modulated by smoking status that are not identified by existing methods. Additionally, using sex stratified GWAS summary statistics, we characterize sex differentiated genetic effects on fat distribution. In summary, we have developed an analytical framework to account for effect heterogeneity in fine-mapping and subsequently improve power for GxE analysis. The SharePro software for GxE analysis is openly available at https://github.com/zhwm/SharePro_gxe.

Mood disorders and 5-HTR2A genetic variants – the moderator effect of inflammation on expression of affective polarity phenotype

BackgroundAlthough 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.MethodsThe 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.ResultsThe 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.ConclusionsTo 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.

Dopamine in Sports: A Narrative Review on the Genetic and Epigenetic Factors Shaping Personality and Athletic Performance.

This narrative review examines the relationship between dopamine-related genetic polymorphisms, personality traits, and athletic success. Advances in sports genetics have identified specific single nucleotide polymorphisms (SNPs) in dopamine-related genes linked to personality traits crucial for athletic performance, such as motivation, cognitive function, and emotional resilience. This review clarifies how genetic variations can influence athletic predisposition through dopaminergic pathways and environmental interactions. Key findings reveal associations between specific SNPs and enhanced performance in various sports. For example, polymorphisms such as COMT Val158Met rs4680 and BDNF Val66Met rs6265 are associated with traits that could benefit performance, such as increased focus, stress resilience and conscientiousness, especially in martial arts. DRD3 rs167771 is associated with higher agreeableness, benefiting teamwork in sports like football. This synthesis underscores the multidimensional role of genetics in shaping athletic ability and advocates for integrating genetic profiling into personalized training to optimize performance and well-being. However, research gaps remain, including the need for standardized training protocols and exploring gene-environment interactions in diverse populations. Future studies should focus on how genetic and epigenetic factors can inform tailored interventions to enhance both physical and psychological aspects of athletic performance. By bridging genetics, personality psychology, and exercise science, this review paves the way for innovative training and performance optimization strategies.

Gene-gene and gene-environment interaction in coronary artery disease risk

Abstract Coronary Artery Disease (CAD) is a lethal illness that kills millions of individuals each year worldwide. This disorder is multifactorial, resulting in the complex interplay of genetic, epigenetic, and environmental factors. Exploring gene-gene and gene-environment interactions is essential to understanding the aetiology of common complex diseases like CAD. Aim We proposed investigating the interaction between two genetic variants robustly associated with CAD by GWAS (MTHFR rs1801133 and APOE rs7412/rs429358) in cardiovascular susceptibility. After that, consider whether there was a new interaction between this genetic association and environment (hypertension, dyslipidemia and smoking). Methods A case-control study enrolled 3,157 individuals, 1,721 coronary patients (with at least 70% stenosis of one or more major coronary arteries or its primary branches on the coronary angiography) and 1,436 controls without CAD. Traditional risk factors were investigated, and the two polymorphisms were performed by TaqMan real-time PCR. Bivariate analysis was used to study genotypic proportions between CAD and non-CAD patients with respective odds ratios (OR). Logistic regression analysis displayed the interaction between the two genetic variants each other and with some important environmental risk factors. Results In bivariate analysis, the genetic models were studied for both genes, being the dominant model the one which showed significance for CAD. In MTHFR, 57.8% of the patients and 53.4% of the controls presented the CT+TT genotype (OR=1.20; p=0.013). In APOE, 26.5% of the patients and 22.3% of the controls had E2E4;E3E4+E4E4 (OR=1.26; p=0.006). The first multivariate logistic regression showed a synergistic interaction between the two genetic variants (OR=1.50; p=0.001). After adjusting for conventional risk factors, the logistic regression analysis entered the MTHFR rs1801133 and APOE rs7412/rs429358, showing a combined interaction effect between those genes and smoking status with an OR of 3.44 (p&amp;lt;0.0001), with hypertension (OR=1.55; p=0.002) and with dyslipidemia (OR=1.71; p&amp;lt;0.0001). Conclusion According to our results, the genetic interaction between APOE and MTHFR synergistically affected the susceptibility to CAD. This interaction, linked to conventional risk factors, consistently increased the risk of CAD in our population, particularly in the group of genetic carriers who did not quit smoking.