Gene-environment Interactions Research Articles

Genotype by environment interaction and stability study in bread wheat (Triticum aestivum L.) genotypes in Guji Zone, Southern Ethiopia

Genotype by environment interaction and stability study in bread wheat (Triticum aestivum L.) genotypes in Guji Zone, Southern Ethiopia

Gene-Environment Interaction Between CYP1B1 and Oral Contraception on Frontal Fibrosing Alopecia.

Frontal fibrosing alopecia (FFA) is an increasingly prevalent form of follicular lichen planus, causing irreversible hair loss predominantly in postmenopausal individuals. An earlier genome-wide meta-analysis of female FFA identified risk loci in genes implicated in self-antigen presentation and T-cell homeostasis, including HLA-B*07:02, ST3GAL1, and SEMA4B. However, CYP1B1, which is important for hormone metabolism, was also implicated with the substitution of serine for asparagine at position 453 (c.1358A>G, p.Asn453Ser) exhibiting a protective effect against FFA. Increasing understanding of genetic and environmental variables and their interactions will improve understanding of disease pathogenesis and has the potential to inform risk mitigation strategies. To investigate whether oral contraceptive pill (OCP) use modulates the protective effect of the common missense variant in CYP1B1 (c.1358A>G, p.Asn453Ser) on FFA risk. This gene-environment interaction study using a case-control design enrolled female patients with FFA from UK-based dermatology clinics. The patients were matched with unrelated age- and ancestry-matched female control individuals derived from UK Biobank in a 1:66 ratio, determined by the first 4 principal components from genome-wide genotypes. Data were collected from July 2015 to September 2017, and analyzed from October 2022 to December 2023. The main outcomes were the modulatory effect of OCP use on the contribution of the CYP1B1 missense variant to female FFA risk and a formal gene-environment interaction test evaluated by a logistic regression model with a multiplicative interaction term, under the assumptions of an additive genetic model interaction term, under the assumptions of an additive genetic model. Of the 489 female patients with FFA, the mean (SD) age was 65.8 (9.7) years, and 370 (75.7%) had a history of OCP use. Of the 34 254 age- and ancestry-matched control individuals, the mean (SD) age was 65.0 (8.4) years, and previous OCP use was reported in 31 177 (91.0%). An association between female FFA and the CYP1B1 risk allele was observed in individuals who reported OCP use (odds ratio, 1.90 [95% CI, 1.50-2.40]; P = 8.41 × 10-8) but not in those with no documented exposure to OCPs (odds ratio, 1.16 [95% CI, 0.82-1.64]; P = .39). A full gene-environment interaction model demonstrated a significant additive statistical interaction between c.1358A, p.453Asn, and history of OCP use on FFA risk (OR for interaction, 1.63 [95% CI, 1.07-2.46]; P = .02). This gene-environment interaction analysis suggests that the protective effect of the CYP1B1 missense variant on FFA risk might be mediated by exposure to OCPs. The allele that encodes an asparagine at position 453 of CYP1B1 was associated with increased odds of FFA only in participants with OCP history.

Joint Modeling of Gene-Environment Correlations and Interactions Using Polygenic Risk Scores in Case-Control Studies.

Polygenic risk scores (PRS) are rapidly emerging as a way to measure disease risk by aggregating multiple genetic variants. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using PRS in case control studies. We use a logistic-normal regression modeling framework to specify the disease risk and PRS distribution in the underlying population and propose joint inference across the two models using the retrospective likelihood of the case-control data. Extensive simulation studies demonstrate the flexibility of the method in trading-off bias and efficiency for the estimation of various model parameters compared to the standard logistic regression or a case-only analysis for gene-environment interactions, or a control-only analysis for gene-environment correlations. Finally using simulated case-control data sets within the UK Biobank study, we demonstrate the power of our method for its ability to recover results from the full prospective cohort for the detection of an interaction between long-term oral contraceptive use and PRS on the risk of breast cancer. This method is computationally efficient and implemented in a user-friendly R package.

Genotype by environment interaction and performance stability of common bean (Phaseolus vulgaris L.) cultivars grown in Dawuro zone, Southwestern Ethiopia

Abstract The performance of most crop genotypes varies significantly depending on their genotype–environment interactions. Providing information on high-yielding and well-adapted common bean genotypes in a given environment is crucial for small-holder farmers to boost their productivity. A field trial was conducted at the Gendo and Wara locations over two consecutive seasons to understand genotype–environment interactions on grain yield and to identify and select common bean genotypes that were better adapted and had high yield potential for production. Ten genotypes were evaluated for their performance using a randomized complete block design with three replications. The AMMI and GGE models were used to identify superior genotypes regarding their mean grain yield performance. The AMMI analysis of variance results showed that grain yield was significantly (p ≤ 0.05) influenced by genotype–environment interactions. This shows that genotypes varied in grain yield mean performance across environments, indicating the possibility of selecting suitable genotypes with specific and broad adaptability. The sum of squares factor indicated that grain yield variation was contributed by the genotype (61.0%), followed by the environment (29.80%) and the genotype–environment interaction effect (9.20%) of the total variation. The AMMI and GGE biplot analysis results showed that SER-119 and SER-125 were identified as the best genotypes regarding their mean grain yield performance. Additionally, the genotype selection index analysis result indicated that SER-119 and SER-125 were stable and consistently exhibited high mean performance in all evaluated settings. Therefore, SER-119 and SER-125 are prime candidates for production in various agroecologies suitable for common bean cultivation in Dawuro zones. The use of tools like GGE biplots and AMMI analysis helped visualize the genotype–environment interactions, aiding in the selection of appropriate genotypes for both specific and wide adaptability.

Gene-environment interactions in human health.

Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.

Organ transformation by environmental disruption of protein integrity and epigenetic memory in Drosophila.

Despite significant progress in understanding epigenetic reprogramming of cells, the mechanistic basis of "organ reprogramming" by (epi-)gene-environment interactions remained largely obscure. Here, we use the ether-induced haltere-to-wing transformations in Drosophila as a model for epigenetic "reprogramming" at the whole organism level. Our findings support a mechanistic chain of events explaining why and how brief embryonic exposure to ether leads to haltere-to-wing transformations manifested at the larval stage and on. We show that ether interferes with protein integrity in the egg, leading to altered deployment of Hsp90 and widespread repression of Trithorax-mediated establishment of active H3K4me3 chromatin marks throughout the genome. Despite this global reduction, Ubx targets and wing development genes preferentially retain higher levels of H3K4me3 that predisposes these genes for later up-regulation in the larval haltere disc, hence the wing-like outcome. Consistent with compromised protein integrity during the exposure, the penetrance of bithorax transformations increases by genetic or chemical reduction of Hsp90 function. Moreover, joint reduction in Hsp90 and trx gene dosage can cause bithorax transformations even without exposure to ether, supporting an underlying epistasis between Hsp90 and trx loss-of-functions. These findings implicate environmental disruption of protein integrity at the onset of histone methylation with altered epigenetic regulation of developmental patterning genes. The emerging picture provides a unique example wherein the alleviation of the Hsp90 "capacitor function" by the environment drives a morphogenetic shift towards an ancestral-like body plan. The morphogenetic impact of chaperone response during a major setup of epigenetic patterns may be a general scheme for organ reprogramming by environmental cues.

Investigating the dynamics of upland rice (Oryza sativa L.) in rainfed agroecosystems: an in-depth analysis of yield gap and strategic exploration for enhanced production

IntroductionAddressing the global demand for rice production necessitates innovative approaches to enhance upland rice yield in rainfed agroecosystems, considering the challenges posed by increasing population, limited land fertility, low productivity, and water availability.MethodsIn this study, our study investigated the impact of biochar and organic fertilizer on ten promising rice lines (G1 – G10) and two control (G11 – G12) cultivars under rainfed conditions. The experimental design used a split-plot design with four soil amendments as main plots, namely control, organic fertilizer, biochar, and biochar + organic fertilizer and 12 rice genotypes as subplot.ResultsThe absolute attainable yield gaps, differentiating organic and control (GAP1), biochar + organic and control (GAP2), and biochar and control (GAP3), ranged from 1.5 to 3.7 or increased of 91–580%, 0.8 to 3.5 (72–560%), and 0.6 to 2.58 tons/ha (58–472%), respectively. Notably, G2 + organic exhibited the highest positive absolute yield gap, ranging from 1.1 to 5.38 tons/ha, based on the yield gap matrix. Furthermore, genotype main effect plus genotype-environment interaction (GGE) biplot analysis identified G2 as the most promising rice line, displaying superior yield performance for cultivation in biochar and organic amended soils.DiscussionThese findings provide valuable insights for farmers, governments, and stakeholders, offering a roadmap to optimize rainfed areas for rice production, serving as practical guidance to enhance overall rice productivity in rainfed agroecosystems.

Envirotype-based delineation of environmental effects and genotype × environment interactions in Indian soybean (Glycine max, L.)

Soybean is a rainfed crop grown across a wide range of environments in India. Its grain yield is a complex trait governed by many minor genes and influenced by environmental effects and genotype × environment interactions. In the current investigation, grain yield data of different sets of 41, 30 and 48 soybean genotypes evaluated during 2019, 2020 and 2021, respectively across 19 locations and twenty years’ data on 19 different climatic parameters at these locations was used to study the environmental effects on grain yield, to understand the genotype × environment interactions and to identify the mega-environments. Through analysis of variance (ANOVA), it was found that predominant portion of the variation was explained by environmental effects (E) (53.89, 54.86 and 60.56% during 2019, 2020 and 2021, respectively), followed by genotype × environment interactions (GEI) (31.29, 33.72 and 28.82% during 2019, 2020 and 2021, respectively). Principal Component Analysis (PCA) revealed that grain yield was positively associated with RH (Relative humidity at 2 m height), FRUE (Effect of temperature on radiation use efficiency), WSM (Wind speed at 2 m height) and RTA (Global solar radiation based on latitude and Julian day) and negatively associated with VPD (Deficit of vapour pressure), Trange (Daily temperature range), ETP (Evapotranspiration), SW (Insolation incident on a horizontal surface), n (Actual duration of sunshine) and N (Daylight hours). Identification of mega-environments is critical in enhancing the selection gain, productivity and varietal recommendation. Through envirotyping and genotype main effect plus genotype by environment interaction (GGE) biplot methods, nineteen locations across India were grouped into four mega-environments (MEs). ME1 included five locations viz., Bengaluru, Pune, Dharwad, Kasbe Digraj and Umiam. Eight locations—Anand, Amreli, Lokbharti, Bidar, Parbhani, Ranchi, Bhawanipatna and Raipur were included in ME2. Kota and Morena constitutes ME3, while Palampur, Imphal, Mojhera and Almora were included in ME4. Locations Imphal, Bidar and Raipur were found to be both discriminative and representative; these test locations can be utilized in developing wider adaptable soybean cultivars. Pune and Amreli were found to be high-yielding locations and can be used in large scale breeder seed production.

Residual networks without pooling layers improve the accuracy of genomic predictions.

Residual neural network genomic selection is the first GS algorithm to reach 35 layers, and its prediction accuracy surpasses previous algorithms. With the decrease in DNA sequencing costs and the development of deep learning, phenotype prediction accuracy by genomic selection (GS) continues to improve. Residual networks, a widely validated deep learning technique, are introduced to deep learning for GS. Since each locus has a different weighted impact on the phenotype, strided convolutions are more suitable for GS problems than pooling layers. Through the above technological innovations, we propose a GS deep learning algorithm, residual neural network for genomic selection (ResGS). ResGS is the first neural network to reach 35 layers in GS. In 15 cases from four public data, the prediction accuracy of ResGS is higher than that of ridge-regression best linear unbiased prediction, support vector regression, random forest, gradient boosting regressor, and deep neural network genomic prediction in most cases. ResGS performs well in dealing with gene-environment interaction. Phenotypes from other environments are imported into ResGS along with genetic data. The prediction results are much better than just providing genetic data as input, which demonstrates the effectiveness of GS multi-modal learning. Standard deviation is recommended as an auxiliary GS evaluation metric, which could improve the distribution of predicted results. Deep learning for GS, such as ResGS, is becoming more accurate in phenotype prediction.

Interaction Between Hypoxia-Inducible Factor 1-alpha Gene Polymorphism and Helicobacter pylori Infection on Gastric Cancer in a Chinese Tibetan Population.

To investigate the impact of four single nucleotide polymorphisms (SNPs) of the HIF1α gene and its interaction with Helicobacter pylori (H. pylori) infection on susceptibility to gastric cancer (GC).Logistic regression was used to test the relationship between four SNPs of HIF1α gene and the susceptibility of GC. A generalized multifactor dimensionality reduction (GMDR) model was used to assess the HIF1α gene-H. pylori infection interaction.Logistic regression analysis indicated that both the rs11549465-CT genotype and the T allele were associated with an increased risk of GC, adjusted OR (95% CI) were 1.63 (1.09-2.20) (CT vs. CC) and 1.70 (1.13-2.36) (T vs. C), respectively. We also found that both the rs11549467-A allele and rs11549467-GA genotype were associated with an increased risk of GC, and adjusted OR (95% CI) were 2.21 (1.61-2.86) (GA vs. GG), 2.13 (1.65-2.65) (A vs. G), respectively. However, no statistically significant impact of rs2057482 or rs1957757 on risk of GC was found. The GMDR model indicated a statistically significant two-dimensional model combination (including rs11549467 and H. pylori infection). The selected model had testing balanced accuracy of 0.60 and the best cross-validation consistencies of 10/10 (p = 0.0107). Compared with H. pylori infection negative participants with rs11549467-GG genotype, H. pylori positive participants with the rs11549467-GA genotype had the highest GC risk, the OR (95% CI) was 3.04 (1.98-4.12).The rs11549467-A allele and rs11549467-GA genotype was associated with increased GC risk. Additionally, the gene-environment interaction between HIF-1α-rs11549467 and H. pylori infection was also correlated with an increased risk of GC.

Nutrition, Other Environmental Influences, and Genetics in the Determination of Human Stature.

Linear growth during three distinct stages of life determines attained stature in adulthood: namely, in utero, early postnatal life, and puberty and the adolescent period. Individual host factors, genetics, and the environment, including nutrition, influence attained human stature. Each period of physical growth has its specific biological and environmental considerations. Recent epidemiologic investigations reveal a strong influence of prenatal factors on linear size at birth that in turn influence the postnatal growth trajectory. Although average population height changes have been documented in high-income regions, stature as a complex human trait is not well understood or easily modified. This review summarizes the biology of linear growth and its major drivers including nutrition from a life-course perspective, the genetics of programmed growth patterns or height, and gene-environment interactions that determine human stature in toto over the life span. Implications for public health interventions and knowledge gaps are discussed.

P36 Environmental triggers of psoriasis and relationship to disease severity: insights from the mySkin study

Abstract Introduction and aims Psoriasis is a common debilitating inflammatory skin condition with an established genetic basis. Despite a wealth of genetic research on psoriasis susceptibility, epidemiological studies characterizing environmental triggers of disease onset are limited. Our aim is to identify self-reported triggers of psoriasis onset, and investigate the relationship between triggers and subsequent disease severity. Methods Self-reported data from people with a clinician-confirmed diagnosis of psoriasis were collected through the online mySkin survey. mySkin was launched UK-wide in June 2023 and all surveys completed by 6 December 2023 were analysed. Measures of disease severity at the time of survey completion included Patient Global Assessment and Dermatology Life Quality Index. The association between triggers of psoriasis onset and disease severity was analysed using one-way Anova tests. Results Of 529 participants, 93.4% were of White ethnicity, 64.8% were female and their average age was 51.1 years. Overall, 49.9% reported a family history of psoriasis and 55% (n = 289) of participants reported at least 1 trigger of psoriasis onset. Of those who reported a trigger, the most commonly reported trigger was stress (n = 167, 57.8%), followed by infection (n = 85, 29.4%) and low mood (n = 23, 7.9%). For participants who selected multiple triggers, there was considerable coselection of stress, low mood, climate (pollution, weather) and lifestyle (weight gain, alcohol, smoking) factors. A greater proportion of participants with a family history of psoriasis reported climate and hormonal (pregnancy, childbirth, menopause) triggers of psoriasis onset, compared with those without a family history (73% vs. 26% and 69% vs. 31%, respectively). There was no significant association between triggers of psoriasis onset and disease severity (P = 0.76). Conclusions These self-reported data highlight the importance of stress in psoriasis onset. The co-occurrence of different triggers, including in those with vs. without a family history of psoriasis, underscores the complex aetiology of psoriasis. Further characterization of gene–environment interactions is warranted.

Comparison of additive main effect–multiplicative interaction model and factor analytic model for genotypes ordination from multi‐environment trials

AbstractAn additive main effects and multiplicative interaction (AMMI) model is used to explore the genotype × environment interaction (GEI) in complete multi‐environmental trials. This model orders genotypes (G) according to their performance across environments (E) on a vectorial plane generated by the first two axes of a principal component analysis (AMMI‐biplot). Alternatively, interaction terms can be regarded as random effects, which can be predicted from linear mixed models using a factor analytic (FA) covariance structure for the GEI terms. Here, an FA‐biplot was obtained by plotting the G and E scores derived from the FA mixed model with complete and incomplete data. The aim of this work was to compare AMMI‐biplot with FA‐biplot for balanced data and then show the impact of the imbalance on the FA‐biplot. The G ordinations were assessed in four scenarios generated using datasets of 3 consecutive years obtained from comparative wheat trials conducted under a complete random block design in different environments across the Argentine network of cultivar assessment. For each scenario, G with the lowest performance in the third year were deleted, one by one, from all sites to generate a scenario with missing G. Although we used different statistical procedures to obtain AMMI‐biplot and FA‐biplot, they showed the same interaction pattern in the case of up to 50% of G dropped from all E in the last year of the multiyear trials. We conclude that the FA‐biplot yields a robust G ordination even when with incomplete datasets.

Translation, validation, and comparison of genetic knowledge scales in Greek and German

Introduction: Advances in biosciences have significantly expanded our knowledge and capabilities in medicine and technology. Genetic tests can now predict hereditary predisposition or susceptibility to diseases, while gene-editing tools like CRISPR/Cas enable easy repair of disease genes in both somatic and germline cells, ensuring permanent genome correction. Despite these advancements, there is a shortage of valid instruments for studying the knowledge about these technologies. To fill this gap, our study aims to translate and validate various scales to effectively measure the public’s knowledge of genetics.Methods: A convenience sample of N = 567 (Germany n = 317, Greece n = 250) participants completed a Google Forms questionnaire between December 2022 and June 2023, which included the General Knowledge of Genes and Heredity (GKGH), Knowledge about Gene-Environment Interaction (KGEI), and Knowledge of Modern Genetics and Genomics (KMGG) questionnaires. Analyses included internal consistency, structural validity, construct validity, and retest reliability with a subset of n = 72 (DE) and n = 50 (GR). Correlation analyses and group differences were evaluated for gender, education, religiosity, age, prior experience with genetic testing, and preferences toward potential providers of genetic testing. This study used the STROBE checklist for reporting.Results: The GKGH exhibited low values in internal consistency and item analysis, along with a ceiling effect within the German group. However, it demonstrated good values in retest and construct validity. In the Greek group, all properties were highly satisfactory. The KMGG consistently displayed excellent properties across all analyses, whereas the KGEI only showed convincing results in construct validity and item analysis.Discussion: The GKGH and KMGG demonstrated strong psychometric properties with varying difficulty levels dependent on the sample, with the German sample demonstrating a notably higher understanding of genetic technologies. Despite displaying acceptable properties, the KGEI fell short of measuring what its title suggests. Participants’ level of education showed a significant correlation with knowledge of genetic technologies, and only in the Greek sample did experiences with genetic tests influence knowledge. Preferences regarding availability of genetic testing are comparable between the two countries, with variations influenced by factors such as age, gender and religiosity.

A Novel Macrophage Subpopulation Conveys Increased Genetic Risk of Coronary Artery Disease.

Coronary artery disease (CAD), the leading cause of death worldwide, is influenced by both environmental and genetic factors. Although over 250 genetic risk loci have been identified through genome-wide association studies, the specific causal variants and their regulatory mechanisms are still largely unknown, particularly in disease-relevant cell types like macrophages. We utilized single-cell RNA-seq and single-cell multiomics approaches in primary human monocyte-derived macrophages to explore the transcriptional regulatory network involved in a critical pathogenic event of coronary atherosclerosis-the formation of lipid-laden foam cells. The relative genetic contribution to CAD was assessed by partitioning disease heritability across different macrophage subpopulations. Meta-analysis of single-cell RNA-seq data sets from 38 human atherosclerotic samples was conducted to provide high-resolution cross-referencing to macrophage subpopulations in vivo. We identified 18 782 cis-regulatory elements by jointly profiling the gene expression and chromatin accessibility of >5000 macrophages. Integration with CAD genome-wide association study data prioritized 121 CAD-related genetic variants and 56 candidate causal genes. We showed that CAD heritability was not uniformly distributed and was particularly enriched in the gene programs of a novel CD52-hi lipid-handling macrophage subpopulation. These CD52-hi macrophages displayed significantly less lipoprotein accumulation and were also found in human atherosclerotic plaques. We investigated the cis-regulatory effect of a risk variant rs10488763 on FDX1, implicating the recruitment of AP-1 and C/EBP-β in the causal mechanisms at this locus. Our results provide genetic evidence of the divergent roles of macrophage subsets in atherogenesis and highlight lipid-handling macrophages as a key subpopulation through which genetic variants operate to influence disease. These findings provide an unbiased framework for functional fine-mapping of genome-wide association study results using single-cell multiomics and offer new insights into the genotype-environment interactions underlying atherosclerotic disease.

Natural variations of heterosis-related allele-specific expression genes in promoter regions lead to allele-specific expression in maize

BackgroundHeterosis has successfully enhanced maize productivity and quality. Although significant progress has been made in delineating the genetic basis of heterosis, the molecular mechanisms underlying its genetic components remain less explored. Allele-specific expression (ASE), the imbalanced expression between two parental alleles in hybrids, is increasingly being recognized as a factor contributing to heterosis. ASE is a complex process regulated by both epigenetic and genetic variations in response to developmental and environmental conditions.ResultsIn this study, we explored the differential characteristics of ASE by analyzing the transcriptome data of two maize hybrids and their parents under four light conditions. On the basis of allele expression patterns in different hybrids under various conditions, ASE genes were divided into three categories: bias-consistent genes involved in basal metabolic processes in a functionally complementary manner, bias-reversal genes adapting to the light environment, and bias-specific genes maintaining cell homeostasis. We observed that 758 ASE genes (ASEGs) were significantly overlapped with heterosis quantitative trait loci (QTLs), and high-frequency variations in the promoter regions of heterosis-related ASEGs were identified between parents. In addition, 10 heterosis-related ASEGs participating in yield heterosis were selected during domestication.ConclusionsThe comprehensive analysis of ASEGs offers a distinctive perspective on how light quality influences gene expression patterns and gene-environment interactions, with implications for the identification of heterosis-related ASEGs to enhance maize yield.

Erosive Tooth Wear, Wine Intake, and Genetic Variation in COMT and MMP2.

Erosive tooth wear (ETW) is a multifactorial condition of increasing prevalence in the younger population. This study aimed to explore the association between different ETW phenotypes with MMP2 and COMT single-nucleotide variants, and selected environmental factors. Saliva samples, erosive wear and dental caries experience data, and dietary/behavioral information from 16-18-year-old patients (n= 747) were used. Genotypes were obtained and phenotypes were further analyzed considering diet and behavioral data, using logistic regression as implemented in PLINK, with an alpha of 0.05. When comparing individuals' ETW-free with those with mild ETW, an association was found with COMT rs6269 (p = 0.02). The comparison between ETW-free individuals with individuals with severe ETW also showed an association with COMT rs6269 under the recessive model (p = 0.03). Logistic regression showed that in the presence of less common alleles of MMP2 rs9923304 and COMT rs6269, ETW were more likely to occur when individuals drank wine. The GG genotype of COMT rs6269 was associated with the presence of lower (p = 0.02) and higher (p = 0.02) caries experience when individuals with ETW only in enamel were compared with individuals with ETW involving dentin. The results support a role of genes in ETW, with wine consumption being identified as a significant modulator, suggesting that gene-environment interactions may contribute to the development of erosive tooth wear.

Gene - maltreatment interplay in adult ADHD symptoms: main role of a gene-environment correlation effect in a Brazilian population longitudinal study.

Childhood maltreatment correlates with attention-deficit/hyperactivity disorder (ADHD) in previous research. The interaction between ADHD genetic predisposition and maltreatment's impact on ADHD symptom risk remains unclear. We aimed to elucidate this relationship by examining the interplay between a polygenic score for ADHD (ADHD-PGS) and childhood maltreatment in predicting ADHD symptoms during young adulthood. Using data from the 2004 Pelotas (Brazil) birth cohort comprising 4231 participants, we analyzed gene-environment interaction (GxE) and correlation (rGE). We further explored rGE mechanisms through mediation models. ADHD symptoms were assessed at age 18 via self-report (Adult Self Report Scale - ASRS) and mother-reports (Strength and Difficulties Questionnaire - SDQ). The ADHD-PGS was derived from published ADHD GWAS meta-analysis. Physical and psychological child maltreatment was gauged using the Parent-Child Conflict Tactics Scale (CTSPC) at ages 6 and 11, with a mean score utilized as a variable. The ADHD-PGS exhibited associations with ADHD symptoms on both ASRS (β = 0.53; 95% CI: 0.03; 1.03, p = 0.036), and SDQ (β = 0.20; 95% CI: 0.08; 0.32, p = 0.001) scales. The total mean maltreatment score was associated with ADHD symptoms using both scales [(βASRS = 0.51; 95% CI: 0.26;0.77) and (βSDQ = 0.24; 95% CI: 0.18;0.29)]. The ADHD-PGS was associated with total mean maltreatment scores (β = 0.09; 95% CI: 0.01; 0.17; p = 0.030). Approximately 47% of the total effect of ADHD-PGS on maltreatment was mediated by ADHD symptoms at age 6. No evidence supported gene-environment interaction in predicting ADHD symptoms. Our findings underscore the significant roles of genetics and childhood maltreatment as predictors for ADHD symptoms in adulthood, while also indicating a potential evocative mechanism through gene-environment correlation.

Genotype by environment interaction and stability analysis of three agronomic traits in Kersting's groundnut (Macrotyloma geocarpum) using factor analytic modeling and environmental covariates

AbstractUnderstanding genotype by environment interaction (GEI) represents a challenge in Kersting's groundnut [Macrotyloma geocarpum (Harms) Maréchal and Baudet] breeding for selecting high‐performing and stable lines across environments. Here, we investigated GEI and stability in Kersting's groundnut using factor analytic (FA) based linear mixed models and environmental covariates. A total of 375 accessions were evaluated across 3 years (2017, 2018, and 2019) and two locations (Sékou and Savè) in Benin, generating five environments (E1, E2, E3, E4, and E5). The traits measured included days to 50% flowering (DFF), grain yield (YLD), and 100‐seed weight (HSW). The study generated multi‐environment values for grain yield and its components in Kersting's groundnut. The genetic correlations between pairs of environments ranged from −0.71 to 0.99. The genetic correlations between YLD and HSW indicated positive and moderate to high correlations in all environments. The FA analysis revealed that FA2 structure accounted for 93.9% of the genetic variability in DFF with factor 1 accounting for more than 90% of the environments variations. Two factors explained 87% of the genetic variance in grain yield, and 70% of the environments variability were clustered by factor 1. For HSW, two factors explained 85% of the genetic variance of the environments, and factor 1 accounted for 72.7%. Combining environmental covariates to FA models revealed that precipitation, temperature, and growth cycle duration were highly correlated to the environmental loadings of factor 1. Relative humidity and solar radiation showed moderate to high correlations with factor 2 loadings. Those covariates explained the high GEI among environments clustered by a given factor. Precipitations and temperatures affected the variations in grain yield. Finally, based on latent regression analysis, the accessions AF202, AF221, AF223, AF225, and AF256 were identified as accessions combining best performance for grain yield, early flowering, and 100‐seed weight, showing adaptability across environments and stability to some environments.

Impact of PD-L1 gene polymorphisms and interactions with cooking with solid fuel exposure on tuberculosis.

Introduction Given that PD-L1 is a crucial immune checkpoint in regulating T cell responses, the aim of this study was to explore the impact of PD-L1 gene polymorphism and its interaction with cooking with solid fuel on susceptibility to tuberculosis (TB) in Chinese Han populations. Methods A total of 503 TB patients and 494 healthy controls were enrolled in this case-control study. Mass spectrometry (MS) technology was applied to genotype rs2297136 and rs4143815 of PD-L1 genes. The associations between SNPs and TB were assessed using unconditional logistic regression analysis. Marginal structural linear odds models were used to estimate the gene-environment interactions. Results Compared with genotype CC, genotypes GG and CG + GG at rs4143815 locus were significantly associated with susceptibility to TB (OR: 3.074 and 1.506, respectively, P<0.05). However, no statistical association was found between rs2297136 SNP and TB risk. Moreover, the relative excess risk of interaction (RERI) between rs4143815 of the PD-L1 gene and cooking with solid fuel was 2.365 (95% CI 1.922,2.809), suggesting positive interactions with TB susceptibility. Conclusion The rs4143815 polymorphism of the PD-L1 gene was associated with susceptibility to TB in Chinese Han populations. There were significantly positive interactions between rs4143815 and cooking with solid fuel.