Gene-environment Interactions Research Articles

Decoding depression by exploring the exposome-genome edge amidst COVID-19 lockdown

Risk of depression increased in the general population after the COVID-19 pandemic outbreak. By examining the interplay between genetics and individual environmental exposures during the COVID-19 lockdown, we have been able to gain an insight as to why some individuals are more vulnerable to depression, while others are more resilient. This study, conducted on a Spanish cohort of 9218 individuals (COVICAT), includes a comprehensive non-genetic risk analysis, the exposome, complemented by a genomics analysis in a subset of 2442 participants. Depression levels were evaluated using the Hospital Anxiety and Depression Scale. Together with Polygenic Risk Scores (PRS), we introduced a novel score; Poly-Environmental Risk Scores (PERS) for non-genetic risks to estimate the effect of each cumulative score and gene-environment interaction. We found significant positive associations for PERSSoc (Social and Household), PERSLife (Lifestyle and Behaviour), and PERSEnv (Wider Environment and Health) scores across all levels of depression severity, and for PRSB (Broad depression) only for moderate depression (OR 1.2, 95% CI 1.03–1.40). On average OR increased 1.2-fold for PERSEnv and 1.6-fold for PERLife and PERSoc from mild to severe depression level. The complete adjusted model explained 16.9% of the variance. We further observed an interaction between PERSEnv and PRSB showing a potential mitigating effect. In summary, stressors within the social and behavioral domains emerged as the primary drivers of depression risk in this population, unveiling a mitigating interaction effect that should be interpreted with caution.

α-Synuclein Overexpression and the Microbiome Shape the Gut and Brain Metabolome in Mice.

Pathological forms of the protein α-synuclein contribute to a family of disorders termed synucleinopathies, which includes Parkinson's disease (PD). Most cases of PD are believed to arise from gene-environment interactions. Microbiome composition is altered in PD, and gut bacteria are causal to symptoms and pathology in animal models. To explore how the microbiome may impact PD-associated genetic risks, we quantitatively profiled nearly 630 metabolites from 26 biochemical classes in the gut, plasma, and brain of α-synuclein-overexpressing (ASO) mice with or without microbiota. We observe tissue-specific changes driven by genotype, microbiome, and their interaction. Many differentially expressed metabolites in ASO mice are also dysregulated in human PD patients, including amine oxides, bile acids and indoles. Notably, levels of the microbial metabolite trimethylamine N-oxide (TMAO) strongly correlate from the gut to the plasma to the brain, identifying a product of gene-environment interactions that may influence PD-like outcomes in mice. TMAO is elevated in the blood and cerebral spinal fluid of PD patients. These findings uncover broad metabolomic changes that are influenced by the intersection of host genetics and the microbiome in a mouse model of PD.

Association of embryonic inositol status with susceptibility to neural tube defects, metabolite profile, and maternal inositol intake

AbstractMaternal nutrition contributes to gene–environment interactions that influence susceptibility to common congenital anomalies such as neural tube defects (NTDs). Supplemental myo‐inositol (MI) can prevent NTDs in some mouse models and shows potential for prevention of human NTDs. We investigated effects of maternal MI intake on embryonic MI status and metabolism in curly tail mice, which are genetically predisposed to NTDs that are inositol‐responsive but folic acid resistant. Dietary MI deficiency caused diminished MI in maternal plasma and embryos, showing that de novo synthesis is insufficient to maintain MI levels in either adult or embryonic mice. Under normal maternal dietary conditions, curly tail embryos that developed cranial NTDs had significantly lower MI content than unaffected embryos, revealing an association between diminished MI status and failure of cranial neurulation. Expression of inositol‐3‐phosphate synthase 1, required for inositol biosynthesis, was less abundant in the cranial neural tube than at other axial levels. Supplemental MI or d‐chiro‐inositol (DCI) have previously been found to prevent NTDs in curly tail embryos. Here, we investigated the metabolic effects of MI and DCI treatments by mass spectrometry‐based metabolome analysis. Among inositol‐responsive metabolites, we noted a disproportionate effect on nucleotides, especially purines. We also found altered proportions of 5‐methyltetrahydrolate and tetrahydrofolate in MI‐treated embryos suggesting altered folate metabolism. Treatment with nucleotides or the one‐carbon donor formate has also been found to prevent NTDs in curly tail embryos. Together, these findings suggest that the protective effect of inositol may be mediated through the enhanced supply of nucleotides during neural tube closure.

Geographical adaptability for optimizing the recommendation of soybean cultivars in the Brazilian Cerrado

Yield multi-location trials associated to geostatistical techniques with environmental covariables can provide a better understanding of G x E interactions and, consequently, adaptation limits of soybean cultivars. Thus, the main objective of this study is understanding the environmental covariables effects on soybean adaptation, as well as predicting the adaptation of soybean under environmental variations and then recommend each soybean cultivar to favorable environments aiming maximize the average yield. The trials were carried out in randomized block design (RBD) with three replicates over three years, in 28 locations. Thirty-two genotypes (commercial and pre-commercial) representing different maturity groups (7.5–8.5) were evaluated in each trial were covering the Edaphoclimatic Region (REC) 401, 402 and 403. The covariables adopted as environmental descriptors were accumulated rainfall, minimum temperature, mean temperature, maximum temperature, photoperiod, relative humidity, soil clay content, soil water avaibility and altitude. After fitting means through Mixed Linear Model, the Regression-Kriging procedure was applied to spacialize the grain yield using environmental covariables as predictors. The covariables explained 32.54% of the GxE interaction, being the soil water avaibility the most important to the adaptation of soybean cultivars, contributing with 7.80%. Yield maps of each cultivar were obtained and, hence, the yield maximization map based on cultivar recommendation was elaborated.

Using eco‐evolutionary models to improve management of introgression in brown trout

AbstractThe management of intraspecific diversity in many species is usually disconnected from eco‐evolutionary processes happening in natura. A classic example is embodied in the problem of introgression in hybridized fish populations, wherein management practices are generally unaware of any selective process at work, and therefore generally rely on numbers (adding or removing individuals) to reduce introgression. Such an example can be observed in the French Alps, where native Mediterranea (MED) brown trout have been highly introgressed through decades of stocking with domesticated Atlantic (ATL) brown trout. Recently however, new results shed light on a potential selective mechanism that may affect differentially the fitness of MED and ATL genes depending on the environment (GxE interaction). Using a demogenetic agent‐based model able to account for such GxE interaction, we simulate a management scenario implemented in 2005 by some biodiversity managers and scientists, who attempted to restore the Mediterranea gene pool using translocation of near pure MED individuals in Atlantic‐dominated areas. We show that the model is unable to recreate the observed introgression dynamics if the GxE interaction is not included. This finding implies that (i) eco‐evolutionary mechanisms can have large effects on introgression dynamics on very short time scales and (ii) management of intraspecific diversity should increasingly rely on these natural mechanisms, so as to improve management targets and facilitate adaptation to rapid environmental changes.

Genomic analysis of genotype-environment interaction in age at first calving of Murrah buffaloes.

Age at first calving (AFC) is a measure of sexual maturity associated with the start of productive life of dairy animals. Additionally, a lower AFC reduces the generation interval and early culling of females. However, AFC has low heritability, making it a trait highly influenced by environmental factors. In this scenario, one way to improve the reproductive performance of buffalo cows is to select robust animals according to estimated breeding value (EBV) using models that include genotype-environment interaction (GEI) with the application of reaction norm models (RNMs). This can be achieved by understanding the genomic basis related to GEI of AFC. Thus, in this study, we aimed to predict EBV considering GEI via the RNM and identify candidate genes related to this component in dairy buffaloes through genome-wide association studies (GWAS). We used 1795 AFC records from three Murrah buffalo herds and formed environmental gradients (EGs) from contemporary group solutions obtained from genetic analysis of 270-day cumulative milk yield. Heritability estimates ranged from 0.15 to 0.39 along the EG. GWAS of the RNM slope parameter identified important genomic regions. The genomic window that explained the highest percentage of genetic variance of the slope (0.67%) was located on BBU1. After functional analysis, five candidate genes were detected, involved in two biological processes. The results suggested the existence of a GEI for AFC in Murrah buffaloes, with reclassification of animals when different environmental conditions were considered. The inclusion of genomic information increased the accuracy of breeding values for the intercept and slope of the reaction norm. GWAS analysis suggested that important genes associated with the AFC reaction norm slope were possibly also involved in biological processes related to lipid metabolism and immunity.

Emerging Role of Environmental Epitranscriptomics and RNA Modifications in Parkinson's Disease.

Environmental risk factors and gene-environment interactions play a critical role in Parkinson's disease (PD). However, the relatively large contribution of environmental risk factors in the overwhelming majority of PD cases has been widely neglected in the field. A "PD prevention agenda" proposed in this journal laid out a set of research priorities focused on preventing PD through modification of environmental risk factors. This agenda includes a call for preclinical studies to employ new high-throughput methods for analyzing transcriptomics and epigenomics to provide a deeper understanding of the effects of exposures linked to PD. Here, we focus on epitranscriptomics as a novel area of research with the potential to add to our understanding of the interplay between genes and environmental exposures in PD. Both epigenetics and epitranscriptomics have been recognized as potential mediators of the complex relationship between genes, environment, and disease. Multiple studies have identified epigenetic alterations, such as DNA methylation, associated with PD and PD-related exposures in human studies and preclinical models. In addition, recent technological advancements have made it possible to study epitranscriptomic RNA modifications, such as RNA N6-methyladenosine (m6A), and a handful of recent studies have begun to explore epitranscriptomics in PD-relevant exposure models. Continued exploration of epitranscriptomic mechanisms in environmentally relevant PD models offers the opportunity to identify biomarkers, pre-degenerative changes that precede symptom onset, and potential mitigation strategies for disease prevention and treatment.

Genotype-by-environment interaction and stability analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes using AMMI and GGE biplot analyses

Bread wheat is a vital staple crop worldwide; including in Ethiopia, but its production is prone to various environmental constraints and yield reduction associated with adaptation. To identify adaptable genotypes, a total of 12 bread wheat genotypes (G1 to G12) were evaluated for their genotype-environment interaction (GEI) and stability across three different environments for two years using Additive Main Effect and Multiplicative Interaction (AMMI) and genotype main effect plus genotype-by-environment interaction (GGE) biplots analysis. GEI is a common phenomenon in crop improvement and is of significant importance in genotype assessment and recommendation. According to combined analysis of variance, grain yield was considerably impacted by environments, genotypes, and GEI. AMMI and GGE biplots analysis also provided insights into the performance and stability of the genotypes across diverse environmental conditions. Among the 12 genotypes, G6 was selected by AMMI biplot analysis as adaptive and high-yielding genotype; G5 and G7 demonstrated high stability and minimal interaction with the environment, as evidenced by their IPCA1 values. G7 was identified as the most stable and high-yielding genotype. The GGE biplot's polygon view revealed that the highest grain yield was obtained from G6 in environment three (E3). E3 was selected as the ideal environment by the GGE biplot. The top three stable genotypes identified by AMMI stability value (ASV) were G5, G7, and G10, while the most stable genotype determined by Genotype Selection Index (GSI) was G7. Even though G6 was a high yielder, it was found to be unstable according to ASV and ranked third in stability according to GSI. Based on the study's findings, the GGE biplot genotype view for grain yield identified Tay genotype (G6) to be the most ideal genotype due to its high grain yield and stability in diverse environments. G7 showed similar characteristics and was also stable. These findings provide valuable insights to breeders and researchers for selecting high-yielding and stable, as well as high-yielding specifically adapted genotypes.

Gene-environment interactions between clonal hematopoiesis of indeterminate potential and air pollution in non-small cell lung cancer among non-smokers.

10514 Background: Small particulate matter air pollution (≤ 2.5µm diameter; PM2.5) is a recognized driver of non-small cell lung cancer (NSCLC) among non-smokers. PM2.5 recruits pro-inflammatory macrophages to the lungs, which facilitate the clonal expansion of mutated epithelial cells and eventual transformation to NSCLC. Clonal hematopoiesis of indeterminate potential (CHIP) is a common age-related condition caused by the acquisition of somatic mutations in hematopoietic stem cells. CHIP and its accompanying pro-inflammatory phenotype have been linked with various age-related diseases, including incident NSCLC; however, the mechanisms underlying this relationship are unknown. In this study, we aimed to explore the interaction between CHIP and exposure to PM2.5 in the development of incident NSCLC. Methods: This study was conducted using data from participants in the UK Biobank (n = 451,095). CHIP status was determined from peripheral blood whole exome sequencing data, and defined as the presence of a somatic driver mutation in the blood at variant allele frequency (VAF) ≥2% (PMID: 36652671). Incident NSCLC was determined from UK cancer registry data, and PM2.5 exposure was determined based on ambient regional measures from 2010. Cox proportional hazard models were used to evaluate associations between CHIP, PM2.5, and NSCLC. Proteomics data was measured using OLINK proteomics in a subset of UK Biobank participants (n = 44,625). Results: CHIP was prevalent in 3.4% of UK Biobank participants (n = 15,633; never smokers: 3.0%, n = 6,916/227,133). There were 1983 incident cases of NSCLC (never smokers: n = 307). CHIP status was associated with incident NSCLC when adjusting for smoking status (HR = 1.73, 95% CI: 1.48–2.02) and analyzing exclusively non-smokers (HR = 2.01, 95% CI: 1.34-3.00). PM2.5 levels were not associated with NSCLC in non-smokers (HR = 1.00, 95% CI: 0.89–1.12 per µg/m3 increase in PM2.5); however, there was a significant interaction between PM2.5 and CHIP (HR = 1.46, 95% CI: 1.02–2.11, pint = 0.04), suggesting that it is the interplay between the two that drives incident NSCLC risk. We found no association between PM2.5 levels and increased CHIP prevalence or CHIP VAF. PM2.5 and CHIP were also found to interact to increase systemic inflammatory markers C-reactive peptide (pint = 0.01) and IL-6 (pint = 0.002). Conclusions: PM2.5 and CHIP act as a novel gene x environment interaction pair that play a key role in NSCLC etiology among non-smokers. Rather than acting in isolation to increase risk of NSCLC, PM2.5 intensifies the relationship between CHIP and NSCLC, presumably by exacerbating systemic inflammation and the hyper-inflammatory lung microenvironment. With PM2.5 levels in the UK among the lowest in the world, we posit that the CHIP x PM2.5 interaction contributes substantially to the global burden of NSCLC in non-smokers.

A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits

Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into disease mechanisms, explain sources of heritability, and improve genetic risk prediction. While large biobanks with genetic and deep phenotypic data hold promise for obtaining novel insights into GxE, our understanding of GxE architecture in complex traits remains limited. We introduce a method to estimate the proportion of trait variance explained by GxE (GxE heritability) and additive genetic effects (additive heritability) across the genome and within specific genomic annotations. We show that our method is accurate in simulations and computationally efficient for biobank-scale datasets. We applied our method to common array SNPs (MAF ≥1%), fifty quantitative traits, and four environmental variables (smoking, sex, age, and statin usage) in unrelated white British individuals in the UK Biobank. We found 68 trait-E pairs with significant genome-wide GxE heritability (p<0.05/200) with a ratio of GxE to additive heritability of ≈6.8% on average. Analyzing ≈8 million imputed SNPs (MAF ≥0.1%), we documented an approximate 28% increase in genome-wide GxE heritability compared to array SNPs. We partitioned GxE heritability across minor allele frequency (MAF) and local linkage disequilibrium (LD) values, revealing that, like additive allelic effects, GxE allelic effects tend to increase with decreasing MAF and LD. Analyzing GxE heritability near genes highly expressed in specific tissues, we find significant brain-specific enrichment for body mass index (BMI) and basal metabolic rate in the context of smoking and adipose-specific enrichment for waist-hip ratio (WHR) in the context of sex.

Maternal behaviours disrupted by Gprasp2 deletion modulate neurodevelopmental trajectory in progeny

Autism spectrum disorders (ASDs) are known to present sex-specific differences. At the same time, understanding how maternal behaviours are affected by pathogenic mutations is crucial to translate research efforts since rearing may recursively modulate neurodevelopment phenotype of the progeny. In this work, we focused on the effects of Gprasp2 deletion in females and its impact in progeny care and development. Female mice, wild-type (WT), Gprasp2+/− (HET) or Gprasp2−/− (KO) mutants and their progeny were used and behavioural paradigms targeting anxiety, memory, maternal care, and other social behaviours were performed. Analysis of communication was carried out through daily recordings of ultrasonic vocalizations in isolated pups and cross-fostering experiments were performed to understand the effect of maternal genotype in pup development. We found that Gprasp2−/− females presented striking impairments in social and working memory. Females also showed disruptions in maternal care, as well as physiological and molecular alterations in the reproductive system and hypothalamus, such as the structure of the mammary gland and the expression levels of oxytocin receptor (OxtR) in nulliparous versus primiparous females. We observed alterations in pup communication, particularly a reduced number of calls in Gprasp2 KO pups, which resulted from an interaction effect of the dam and pup genotype. Cross-fostering mutant pups with wild-type dams rescued some of the early defects shown in vocalizations, however, this effect was not bidirectional, as rearing WT pups with Gprasp2−/− dams was not sufficient to induce significant phenotypical alterations. Our results suggest Gprasp2 mutations perturb social and working memory in a sex-independent manner, but impact female-specific behaviours towards progeny care, female physiology, and gene expression. These changes in mutant dams contribute to a disruption in early stages of progeny development. More generally, our results highlight the need to better understand GxE interactions in the context of ASDs, when female behaviour may present a contributing factor in postnatal neurodevelopmental trajectory.

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

Variability of polygenic prediction for body mass index in Africa

BackgroundPolygenic prediction studies in continental Africans are scarce. Africa’s genetic and environmental diversity pose a challenge that limits the generalizability of polygenic risk scores (PRS) for body mass index (BMI) within the continent. Studies to understand the factors that affect PRS variability within Africa are required.MethodsUsing the first multi-ancestry genome-wide association study (GWAS) meta-analysis for BMI involving continental Africans, we derived a multi-ancestry PRS and compared its performance to a European ancestry-specific PRS in continental Africans (AWI-Gen study) and a European cohort (Estonian Biobank). We then evaluated the factors affecting the performance of the PRS in Africans which included fine-mapping resolution, allele frequencies, linkage disequilibrium patterns, and PRS-environment interactions.ResultsPolygenic prediction of BMI in continental Africans is poor compared to that in European ancestry individuals. However, we show that the multi-ancestry PRS is more predictive than the European ancestry-specific PRS due to its improved fine-mapping resolution. We noted regional variation in polygenic prediction across Africa’s East, South, and West regions, which was driven by a complex interplay of the PRS with environmental factors, such as physical activity, smoking, alcohol intake, and socioeconomic status.ConclusionsOur findings highlight the role of gene-environment interactions in PRS prediction variability in Africa. PRS methods that correct for these interactions, coupled with the increased representation of Africans in GWAS, may improve PRS prediction in Africa.

Joint modeling of gene-environment correlations and interactions using polygenic risk scores in case-control studies.

Polygenic risk scores (PRSs) are rapidly emerging as a way to measure disease risk by aggregating multiple genetic variants. Understanding the interplay of the PRS with environmental factors is critical for interpreting and applying PRSs in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using the 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 2 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 with 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 the 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.

Application of Next-Generation Sequencing (NGS) Techniques for Selected Companion Animals.

Next-Generation Sequencing (NGS) techniques have revolutionized veterinary medicine for cats and dogs, offering insights across various domains. In veterinary parasitology, NGS enables comprehensive profiling of parasite populations, aiding in understanding transmission dynamics and drug resistance mechanisms. In infectious diseases, NGS facilitates rapid pathogen identification, characterization of virulence factors, and tracking of outbreaks. Moreover, NGS sheds light on metabolic processes by elucidating gene expression patterns and metabolic pathways, essential for diagnosing metabolic disorders and designing tailored treatments. In autoimmune diseases, NGS helps identify genetic predispositions and molecular mechanisms underlying immune dysregulation. Veterinary oncology benefits from NGS through personalized tumor profiling, mutation analysis, and identification of therapeutic targets, fostering precision medicine approaches. Additionally, NGS plays a pivotal role in veterinary genetics, unraveling the genetic basis of inherited diseases and facilitating breeding programs for healthier animals. Physiological investigations leverage NGS to explore complex biological systems, unraveling gene-environment interactions and molecular pathways governing health and disease. Application of NGS in treatment planning enhances precision and efficacy by enabling personalized therapeutic strategies tailored to individual animals and their diseases, ultimately advancing veterinary care for companion animals.

Secondhand tobacco smoke and risk of atrial fibrillation: an observational epidemiologic and gene-environment interaction analysis

Abstract Background Second-hand tobacco smoke (SHS) is related to coronary artery disease and increases susceptibility to arrhythmia inducibility including atrial fibrillation (AF). However, epidemiologic evidence on the relationship between SHS and the risk of incident AF remains controversial. Purpose To evaluate the effect of SHS exposure on the long-term risk of AF and assess whether gene-environment interaction (synergism) exists by utilising the population-based data from the UK biobank. Methods We performed a nationwide population-based cohort study using the UK Biobank database. Participants with previous AF or current smokers were excluded. Participants were categorised into SHS-exposure and non-exposure groups. The risk of incident AF was assessed using the multivariable Cox proportional hazards model. We also evaluated the synergistic effect of SHS exposure and the polygenic risk score (PRS) of AF. Results Out of 400,493 participants, AF occurred in 23,471 (5.9%) during a median follow-up of 12.5 years. The SHS-exposure group (mean exposure 2.2 ± 5.2 hours/week) had a higher proportion of male participants with a younger age. The risk of incident AF was significantly increased in the SHS-exposure group (multivariable-adjusted hazard ratio [aHR] 1.06, 95% confidence interval 1.03-1.10, P &amp;lt;0.001) compared to the non-exposure group, suggesting a dose-dependent manner (aHR of severe SHS-exposure group, 1.11 [1.03-1.20], P = 0.005). The risk of AF increases regardless of the exposure location (home, outside, or workplace). When SHS-exposure was combined with the high genetic risk of AF, significant additive interaction was observed on the risk AF (Synergy Index of SHS-exposure with 3rdtertile of AF PRS, 1.13 [1.02-1.25], P = 0.024). Conclusion Exposure to SHS is a significant predictor for an increased risk of AF, with a synergistic effect when combined with genetic predisposition. Minimising SHS exposure, especially in individuals with a heightened genetic susceptibility to AF, may hold potential benefits for reducing the future risk of AF.