Gene-environment Interactions Research Articles

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.

An adaptive dynamical system model for development of schizophrenia: Epigenetics and false memories

This paper introduces a fifth-order adaptive dynamical system model represented as a self-modelling network model to understand the role of epigenetic factors in developing schizophrenia, particularly about the occurrence of false memories. Schizophrenia, affecting a significant portion of the global population, presents complex challenges due to its symptoms across cognitive, emotional, and social domains. Central to this paper is the exploration of epigenetic influences on the development of schizophrenia, with a focus on how these epigenetic factors contribute to the generation of false memories, a notable symptom of the disorder. The model assumes that early adverse experiences significantly affect hippocampal development, which is crucial for memory processes. By employing a fifth-order adaptive dynamical system model represented as a self-modelling network, this paper examines the intricate dynamics between genetic predispositions, environmental factors, and epigenetic modifications in schizophrenia. The model’s sophistication allows for a nuanced understanding of the gene-environment interactions, particularly highlighting the role of early-life trauma in shaping lifelong cognitive outcomes. This approach offers a comprehensive framework for analyzing the multifaceted nature of schizophrenia, contributing to a deeper understanding of its etiology and progression.

Stability of anthocyanin, carotenoid and root yield of 10 sweet potato genotypes

ABSTRACT Sweet potato roots are rich in phytochemicals. The objective of this study was to evaluate the stability of number of storage roots, storage root dry weight, harvest index and anthocyanin and carotenoid contents of 10 sweet potato genotypes. Ten sweet potato genotypes were evaluated in a randomised complete block design with three replications at three locations in Surin province, including Rajamangala University of Technology Isan (RMUTI), Chomphra district and Sanom district during November 2022 to March 2023. Results indicated that the environment significantly influenced most traits, except for carotenoid content. Genotypes and genotype–environment interactions were significant for all traits. Number of storage roots was highest in genotypes found in RMUTI. Storage root yield and anthocyanin content was highest in genotypes found in Chomphra district, while harvest index and carotenoid content were highest in genotypes found in Sanom district. SR 2022/03 had the highest storage root yield across locations and in Chomphra district. KS 2022/07 had the highest harvest index and anthocyanin content across locations. SR 2022/10 had the highest anthocyanin content at RMUTI and also had the highest carotenoid content across locations. KS 2022/07 had the most stable storage root yield. SR 2022/01 had the most stable anthocyanin and carotenoid contents. Information on the levels and variability of phytochemicals in sweet potato enables breeders to select sweet potato genotypes with high and stable phytochemicals for sweet potato breeding.

КОМПЛЕКСНАЯ ОЦЕНКА ГЕНОТИПОВ LINUM USITATISSIMUM L. ПО ФЕНОТИПИЧЕСКИМ ПРИЗНАКАМ И СТАБИЛЬНОСТИ В УСЛОВИЯХ ПОДТАЕЖНОЙ АГРОЭКОЛОГИЧЕСКОЙ ЗОНЫ ТЮМЕНСКОЙ ОБЛАСТИ

The article presents the results of testing 12 hybrid combinations of fiber flax F3-F5 obtained based on interparietal crossings of four initial parental forms (Yarok, Grant, Alizee, Betertelsdorf 6884/60) for a set of morpho-biological parameters considering their ecological stability. Field research was carried out at the Biological Station of Tyumen State University "Lake Kuchak" (Nizhnetavdinsky District, Tyumen Region, 57°21' N, 66°04' E). Reliable differences (p&lt;0.05; p&lt;0.01) between them for all the studied traits were revealed. In the total phenotypic variability of the studied traits, the maximum influence of genotype-environment interactions (48.5%) was determined. A group of stable (bi=1.0, S2di=0, G1, G2, G3, G4, G5, G6, G8, G9), highly (bi&lt;1.0, S2di=0) and weakly responsive (bi&gt;1.0, S2di=0) hybrids was determined. Using the index (SI), the most stable of them in two groups were determined (SI=61.0-80.0% and 81.0-100.0%). According to the stability index (I), valuable flax genotypes were identified by plant height (G1, G2), number of capsules (G1, G2, G10), number of seeds in 1 capsule (G1, G8), and vegetation period (G1, G2, G8). Using the Kang rank criterion (RS), two groups by stability were identified, of which the first has the highest value for plant height (G1, G3, G5), inflorescence length (G1), number of capsules (G1, G6, G10, G11), number of seeds in 1 capsule (G8), fiber content (G1, G3, G5, G12), and vegetation period (G4). Hybrid combinations (G1, G2, G3, G5, G6, G7, G11) were selected, which are of the greatest practical interest for the selection process.

Cultivating sustainable futures: multi-environment evaluation and seed yield stability of faba bean (Vicia faba L.) genotypes by using different stability parameters in Ethiopia

Faba bean is an important legume crop with significant potential to contribute to sustainable agricultural systems and food security in Ethiopia. Despite its importance, the crop is prone to various biotic and abiotic constraints that can reduce seed yield and affect its stability and adaptability. To identify stable and adaptable genotypes, 10 faba bean genotypes were evaluated at three locations over two growing seasons using different stability parameters. Genotype-by-environment interaction (GGE biplot) and additive main effect and multiplicative interaction (AMMI) analyses are the statistical methods used to evaluate crop genotype performance across different environments to identify high-performing, stable, and adaptable genotypes and to highlight preferable environments for genotype differentiation. This study utilized cultivar superiority, regression coefficients, and deviations from regression parameters that provide valuable insights into genotype performance under varying environmental conditions. This approach helps to identify robust cultivars that can thrive across different agricultural settings and challenges, ultimately contributing to improved crop production and food security. The results revealed that G9, G8, and G7 are the three most stable and adaptable genotypes. These faba bean genotypes showed greater resilience to environmental changes and improved suitability for sustainable production, making them better options for local farmers. They also bolster resilience against climate variability and ultimately ensure agricultural sustainability. The AMMI model indicated that the genotype-environment interaction (GEI) significantly influences seed yield. These findings provide crucial insights into the genetic potential of faba bean genotypes that can help breeding programs to develop high-yielding, adaptable, and stable varieties for the region and other areas with similar agro-ecological conditions.

Cyclic and pseudo-cyclic electron pathways play antagonistic roles during nitrogen deficiency in Chlamydomonas reinhardtii.

Nitrogen (N) scarcity frequently constrains global biomass productivity. N deficiency halts cell division, downregulates photosynthetic electron transfer, and enhances carbon storage. However, the molecular mechanism downregulating photosynthesis during N deficiency and its relationship with carbon storage are not fully understood. Proton Gradient Regulator-like 1 (PGRL1) controlling cyclic electron flow (CEF) and Flavodiiron proteins (FLV) involved in pseudo-CEF (PCEF) are major players in the acclimation of photosynthesis. To determine the role of PGRL1 or FLV in photosynthesis under N deficiency, we measured photosynthetic electron transfer, oxygen gas exchange, and carbon storage in Chlamydomonas reinhardtii pgrl1 and flvB knockout mutants. Under N deficiency, pgrl1 maintained higher net photosynthesis and O2 photoreduction rates and higher levels of Cytochrome b6f and PSI compared to the control and flvB. The photosynthetic activity of flvB and pgrl1 flvB double mutants decreased in response to N deficiency, similar to the control strains. Furthermore, the preservation of photosynthetic activity in pgrl1 was accompanied by an increased accumulation of triacylglycerol in certain genetic backgrounds but not others, highlighting the importance of gene-environment interaction in determining traits such as oil content. Our results suggest that in the absence of PGRL1-controlled CEF, FLV-mediated PCEF maintains net photosynthesis at a high level and that CEF and PCEF play antagonistic roles during N deficiency. They further illustrate how a strain's nutrient status and genetic makeup can affect regulation of photosynthetic energy conversion in relation to carbon storage and provide additional strategies for improving lipid productivity in algae.

PNPLA3 I148M Interacts With Environmental Triggers to Cause Human Disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD) affects up to 30% of Western populations. While obesity is a recognized risk factor, MASLD does not develop in all obese individuals, highlighting the need to understand genetic and environmental interactions. The PNPLA3 I148M variant has been identified as a key genetic risk factor, significantly increasing the likelihood of MASLD development and progression. We reviewed current literature on the role of PNPLA3 I148M in MASLD, focusing on gene-environment interactions involving diet, physical activity, obesity, and insulin resistance. We included studies analysing ethnic differences in PNPLA3 I148M prevalence and its association with MASLD. Additionally, we reviewed data on how PNPLA3 I148M influences the response to therapies, including lipid-lowering medications and GLP-1 agonists. The PNPLA3 I148M variant markedly heightens MASLD risk, particularly in Hispanic populations, where a higher prevalence of MASLD is observed. Lifestyle factors such as high sugar intake, alcohol consumption, and physical inactivity exacerbate MASLD risk among I148M carriers. Evidence shows that insulin resistance amplifies MASLD risk associated with the I148M variant, especially in non-diabetic individuals. Moreover, the PNPLA3 I148M variant interacts with other genetic loci, further modifying MASLD risk and disease course. The variant also influences treatment response, with variability observed in effectiveness of lipid-lowering therapies and GLP-1 agonists among carriers. The interplay between PNPLA3 I148M and environmental factors underscores the need for personalized MASLD prevention and treatment strategies. Targeting both genetic and lifestyle contributors may enhance MASLD management, offering a tailored approach to reducing disease burden.

Heat stress on dairy cattle

Climate change significantly impacts the production, health, welfare, and survival of farm animals. The prolonged heat stress is a major concern in tropical and subtropical regions. Heat tolerance enables livestock to maintain thermal balance under extreme climatic conditions, thus ensuring productivity and animal welfare. There is a feasibility of genetic selection for heat tolerance to improve production and reproduction traits. This genetic selection may provide enduring advantages in maintaining productive and reproductive performances as heat stress is a heritable trait. To identify and integrate traits associated with thermal tolerance into breeding programs, it is imperative to have a robust animal performance records and meteorological data. Temperature humidity index (THI) is a widely used biomarker for assessing heat stress in dairy cows, as it combines air temperature and relative humidity. In regions with sparse weather station coverage, NASA POWER satellite data is a viable alternative for obtaining meteorological information to calculate THI and evaluate heat stress impacts. There are multiple models for calculating THI and those focusing on humidity being more effective in humid climates. Genetic models, particularly reaction norm models based on random regressions, were introduced for estimating genetic parameters for heat tolerance in cattle. These models help to understand genotype-environment interactions and non-linear relationship between THI and milk yield reduction. The continued genetic selection for milk production without considering heat tolerance traits increases susceptibility to heat stress, emphasizing a need for comprehensive selection objectives. This review underscores the importance of incorporating heat tolerance traits into genetic selection for dairy cattle and highlights various biomarkers and genetic models used to estimate heat tolerance. Incorporating these traits into breeding goals is essential to improve thermotolerance while maintaining productivity, bolstering resilience of cattle to climate change.

Effects of Co-Exposure to Benzene, Toluene, and Xylene, Polymorphisms of microRNA Genes, and Their Interactions on Genetic Damage in Chinese Petrochemical Workers.

Benzene, toluene, and xylene (BTX) co-exist in human environments, yet their individual and combined effects on genetic damage at low exposure levels are not fully understood. Additionally, single nucleotide polymorphisms in microRNAs (mirSNPs) might be involved in cancer etiology by affecting the related early health damage. To investigate the influence of BTX exposure, mirSNPs, and their interactions on genetic damage, we conducted a cross-sectional study in 1083 Chinese petrochemical workers, quantifying the BTX cumulative exposure levels and multiple genetic damage biomarkers. Additionally, we genotyped multiple common mirSNPs. Benzene and a BTX mixture were positive associated with the olive tail moment (OTM) and tail DNA% (p < 0.05). Higher levels of toluene and xylene enhanced the association of benzene with genetic damage levels. Genotypes and/or mutant allele counts of miR-4482-related rs11191980, miR-4433-related rs136547, miR-27a-related rs2594716, miR-3130-related rs725980, and miR-3928-related rs878718 might significantly influence genetic damage levels. Stronger effect estimates of benzene/BTX exposure were found in carriers of miR-196a-2-related rs11614913 heterozygotes and of wild homozygotes of miR-1269b-related rs12451747, miR-612-related rs12803915, and miR-4804-related rs266437. Our findings provide further support of the involvement of BTX co-exposure, mirSNPs, and their gene-environment interactions in determining the severity of DNA strand break in a complex manner.

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

Haploinsufficiency and Alzheimer's Disease: The Possible Pathogenic and Protective Genetic Factors.

Alzheimer's disease (AD) is a complex neurodegenerative disorder influenced by various genetic factors. In addition to the well-established amyloid precursor protein (APP), Presenilin-1 (PSEN1), Presenilin-2 (PSEN2), and apolipoprotein E (APOE), several other genes such as Sortilin-related receptor 1 (SORL1), Phospholipid-transporting ATPase ABCA7 (ABCA7), Triggering Receptor Expressed on Myeloid Cells 2 (TREM2), Phosphatidylinositol-binding clathrin assembly protein (PICALM), and clusterin (CLU) were implicated. These genes contribute to neurodegeneration through both gain-of-function and loss-of-function mechanisms. While it was traditionally thought that heterozygosity in autosomal recessive mutations does not lead to disease, haploinsufficiency was linked to several conditions, including cancer, autism, and intellectual disabilities, indicating that a single functional gene copy may be insufficient for normal cellular functions. In AD, the haploinsufficiency of genes such as ABCA7 and SORL1 may play significant yet under-explored roles. Paradoxically, heterozygous knockouts of PSEN1 or PSEN2 can impair synaptic plasticity and alter the expression of genes involved in oxidative phosphorylation and cell adhesion. Animal studies examining haploinsufficient AD risk genes, such as vacuolar protein sorting-associated protein 35 (VPS35), sirtuin-3 (SIRT3), and PICALM, have shown that their knockout can exacerbate neurodegenerative processes by promoting amyloid production, accumulation, and inflammation. Conversely, haploinsufficiency in APOE, beta-secretase 1 (BACE1), and transmembrane protein 59 (TMEM59) was reported to confer neuroprotection by potentially slowing amyloid deposition and reducing microglial activation. Given its implications for other neurodegenerative diseases, the role of haploinsufficiency in AD requires further exploration. Modeling the mechanisms of gene knockout and monitoring their expression patterns is a promising approach to uncover AD-related pathways. However, challenges such as identifying susceptible genes, gene-environment interactions, phenotypic variability, and biomarker analysis must be addressed. Enhancing model systems through humanized animal or cell models, utilizing advanced research technologies, and integrating multi-omics data will be crucial for understanding disease pathways and developing new therapeutic strategies.

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.