Gene-environment Interactions Research Articles

Maize green leaf area index dynamics: genetic basis of a new secondary trait for grain yield in optimal and drought conditions

Key messageGreen Leaf Area Index dynamics is a promising secondary trait for grain yield and drought tolerance. Multivariate GWAS is particularly well suited to identify the genetic determinants of the green leaf area index dynamics.Improvement of maize grain yield is impeded by important genotype-environment interactions, especially under drought conditions. The use of secondary traits, that are correlated with yield, more heritable and less prone to genotype-environment interactions, can increase breeding efficiency. Here, we studied the genetic basis of a new secondary trait: the green leaf area index (GLAI) dynamics over the maize life cycle. For this, we used an unmanned aerial vehicle to characterize the GLAI dynamics of a diverse panel in well-watered and water-deficient trials in two years. From the dynamics, we derived 24 traits (slopes, durations, areas under the curve), and showed that six of them were heritable traits representative of the panel diversity. To identify the genetic determinants of GLAI, we compared two genome-wide association approaches: a univariate (single-trait) method and a multivariate (multi-trait) method combining GLAI traits, grain yield, and precocity. The explicit modeling of correlation structure between secondary traits and grain yield in the multivariate mixed model led to 2.5 times more associations detected. A total of 475 quantitative trait loci (QTLs) were detected. The genetic architecture of GLAI traits appears less complex than that of yield with stronger-effect QTLs that are more stable between environments. We also showed that a subset of GLAI QTLs explains nearly one fifth of yield variability across a larger environmental network of 11 water-deficient trials. GLAI dynamics is a promising grain yield secondary trait in optimal and drought conditions, and the detected QTLs could help to increase breeding efficiency through a marker-assisted approach.

Understanding the Genetic and Non-genetic Interconnections in the Aetiology of Isolated Congenital Heart Disease: An Updated Review: Part 1.

Congenital heart disease (CHD) is the most frequently occurring birth defect. Majority of the earlier reviews focussed on the association of genetic factors with CHD. A few epidemiological studies provide convincing evidence for environmental factors in the causation of CHD. Although the multifactorial theory of gene-environment interaction is the prevailing explanation, explicit understanding of the biological mechanism(s) involved, remains obscure. Nonetheless, integration of all the information into one platform would enable us to better understand the collective risk implicated in CHD development. Great strides in novel genomic technologies namely, massive parallel sequencing, whole exome sequencing, multiomics studies supported by system-biology have greatly improved our understanding of the aetiology of CHD. Molecular genetic studies reveal that cardiac specific gene variants in transcription factors or signalling molecules, or structural proteins could cause CHD. Additionally, non-hereditary contributors such as exposure to teratogens, maternal nutrition, parental age and lifestyle factors also contribute to induce CHD. Moreover, DNA methylation and non-coding RNA are also correlated with CHD. Here, we inform that a complex combination of genetic, environmental and epigenetic factors interact to interfere with morphogenetic processes of cardiac development leading to CHD. It is important, not only to identify individual genetic and non-inherited risk factors but also to recognize which factors interact mutually, causing cardiac defects.

Genetic regulatory effects in response to a high-cholesterol, high-fat diet in baboons

Steady-state expression quantitative trait loci (eQTLs) explain only a fraction of disease-associated loci identified through genome-wide association studies (GWASs), while eQTLs involved in gene-by-environment (GxE) interactions have rarely been characterized in humans due to experimental challenges. Using a baboon model, we found hundreds of eQTLs that emerge in adipose, liver, and muscle after prolonged exposure to high dietary fat and cholesterol. Diet-responsive eQTLs exhibit genomic localization and genic features that are distinct from steady-state eQTLs. Furthermore, the human orthologs associated with diet-responsive eQTLs are enriched for GWAS genes associated with human metabolic traits, suggesting that context-responsive eQTLs with more complex regulatory effects are likely to explain GWAS hits that do not seem to overlap with standard eQTLs. Our results highlight the complexity of genetic regulatory effects and the potential of eQTLs with disease-relevant GxE interactions in enhancing the understanding of GWAS signals for human complex disease using non-human primate models.

Genetic Determinants of Thiazide-Induced Hyperuricemia, Hyperglycemia, and Urinary Electrolyte Disturbances - A Genome-Wide Evaluation of the UK Biobank.

Thiazide diuretics, widely used in hypertension, cause a variety of adverse reactions, including hyperglycemia, hyperuricemia, and electrolyte abnormalities. In this study, we aimed to identify genetic variants that interact with thiazide-use to increase the risk of these adverse reactions. Using UK Biobank data, we first performed genomewide variance quantitative trait locus (vQTL) analysis of ~ 6.2 million SNPs on 95,493 unrelated hypertensive White British participants (24,313 on self-reported bendroflumethiazide treatment at recruitment) for 2 blood (glucose and urate) and 2 urine (potassium and sodium) biomarkers. Second, we conducted direct gene-environment interaction (GEI) tests on the significant (P < 2.5 × 10-9) vQTLs, included a second UK Biobank cohort comprising 13,647 unrelated hypertensive White British participants (3,478 on thiazides other than bendroflumethiazide) and set significance at P = 0.05 divided by the number of vQTL SNPs tested for GEIs. The vQTL analysis identified eight statistically significant SNPs for blood glucose (5 SNPs) and serum urate (3 SNPs), with none being identified for the urinary biomarkers. Two of the SNPs (1 glucose SNP: CDKAL1 intron rs35612982, GEI P = 6.24 × 10-3; and 1 serum urate SNP: SLC2A9 intron rs938564, GEI P = 4.51 × 10-4) demonstrated significant GEI effects in the first, but not the second, cohort. Both genes are biologically plausible candidates, with the SLC2A9-mediated interaction having been previously reported. In conclusion, we used a two-stage approach to detect two biologically plausible genetic loci that can interact with thiazides to increase the risk of thiazide-associated biochemical abnormalities. Understanding how environmental exposures (including medications such as thiazides) and genetics interact, is an important step toward precision medicine and improved patient outcomes.

Interaction effect between low birthweight and resistin gene rs1862513 variant on insulin resistance and type 2 diabetes mellitus in adulthood: Toon Genome Study.

Gene-environment interactions are considered to critically influence type 2 diabetes mellitus development; however, the underlying mechanisms and specific interactions remain unclear. Given the increasing prevalence of low birthweight (LBW) influenced by the intrauterine environment, we sought to investigate genetic factors related to type 2 diabetes development in individuals with LBW. The interaction between 20 reported type 2 diabetes susceptibility genes and the development of type 2 diabetes in LBW (<2,500 g) individuals in a population-based Japanese cohort (n = 1,021) was examined by logistic regression and stratified analyses. Logistic regression analyses showed that only the G/G genotype at the rs1862513 locus of the resistin gene (RETN), an established initiator of insulin resistance, was closely related to the prevalence of type 2 diabetes in individuals with LBW. Age, sex and current body mass index-adjusted stratified analyses showed a significant interaction effect of LBW and the RETN G/G genotype on fasting insulin, homeostatic model assessment 2-insulin resistance, Matsuda index and the prevalence of type 2 diabetes (all P-values for interaction <0.05). The adjusted odds ratio for type 2 diabetes in the LBW + G/G genotype group was 7.33 (95% confidence interval 2.43-22.11; P = 0.002) compared with the non-LBW + non-G/G genotype group. Similar results were obtained after excluding the influence of malnutrition due to World War II. Simultaneous assessment of LBW and the RETN G/G genotype can more accurately predict the risk of future type 2 diabetes than assessing each of these factors alone, and provide management strategies, including early lifestyle intervention in LBW population.

Unveiling challenges in Mendelian randomization for gene-environment interaction.

Gene-environment (GxE) interactions play a crucial role in understanding the complex etiology of various traits, but assessing them using observational data can be challenging due to unmeasured confounders for lifestyle and environmental risk factors. Mendelian randomization (MR) has emerged as a valuable method for assessing causal relationships based on observational data. This approach utilizes genetic variants as instrumental variables (IVs) with the aim of providing a valid statistical test and estimation of causal effects in the presence of unmeasured confounders. MR has gained substantial popularity in recent years largely due to the success of genome-wide association studies. Many methods have been developed for MR; however, limited work has been done on evaluating GxE interaction. In this paper, we focus on two primary IV approaches: the two-stage predictor substitution and the two-stage residual inclusion, and extend them to accommodate GxE interaction under both the linear and logistic regression models for continuous and binary outcomes, respectively. Comprehensive simulation study and analytical derivations reveal that resolving the linear regression model is relatively straightforward. In contrast, the logistic regression model presents a considerably more intricate challenge, which demands additional effort.

Epigenome-Wide Association Studies of COPD and Lung Function: A Systematic Review.

Chronic Obstructive Pulmonary Disease (COPD) results from gene-environment interactions over the lifetime. These interactions are captured by epigenetic changes, such as DNA methylation. This systematic review synthesizes evidence from epigenome-wide association studies (EWAS) related to COPD and lung function. Systematic literature search on PubMed, Embase and CINAHL databases, identified 1947 articles that investigated epigenetic changes associated with COPD/lung function; 17 of them met our eligibility criteria from which data was manually extracted. Differentially methylated positions (DMPs) and/or annotated genes, were considered replicated if identified by ≥2 studies with a p<1 x 10-4. Ten studies profiled DNA methylation changes in blood and 7 in respiratory samples, including surgically resected lung tissue (n=3), small airways epithelial brushings (n=2), bronchoalveolar lavage (n=1) and sputum (n=1). Main results showed: (1) high variability in study design, covariates and effect sizes, which prevented a formal meta-analysis; (2) in blood samples, 51 DMPs were replicated in relation to lung function and 12 related to COPD; (3) in respiratory samples, 42 DMPs were replicated in relation to COPD but none in relation to lung function; and, (4) in COPD vs. control studies, 123 genes (2.6% of total) were shared between ≥1 blood and ≥1 respiratory sample and associated with chronic inflammation, ion transport and coagulation. There is high heterogeneity across published COPD/lung function EWAS studies. A few genes (n=123; 2.6%) were replicated in blood and respiratory samples, suggesting that blood can recapitulate some changes in respiratory tissues. These findings have implications for future research.

Epistasis in neurotransmitter receptors linked to posttraumatic stress disorder and major depressive disorder comorbidity in traumatized Chinese.

Posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) comorbidity occurs through exposure to trauma with genetic susceptibility. Neuropeptide-Y (NPY) and dopamine are neurotransmitters associated with anxiety and stress-related psychiatry through receptors. We attempted to explore the genetic association between two neurotransmitter receptor systems and the PTSD-MDD comorbidity. Four groups were identified using latent profile analysis (LPA) to examine the patterns of PTSD and MDD comorbidity among survivors exposed to earthquake-related trauma: low symptoms, predominantly depression, predominantly PTSD, and PTSD-MDD comorbidity. NPY2R (rs4425326), NPY5R (rs11724320), DRD2 (rs1079597), and DRD3 (rs6280) were genotyped from 1,140 Chinese participants exposed to earthquake-related trauma. Main, gene-environment interaction (G × E), and gene-gene interaction (G × G) effects for low symptoms, predominantly depression, and predominantly PTSD were tested using a multinomial logistic model with PTSD-MDD comorbidity as a reference. The results demonstrated that compared to PTSD-MDD comorbidity, epistasis (G × G) NPY2R-DRD2 (rs4425326 × rs1079597) affects low symptoms (β = -0.66, OR = 0.52 [95% CI: 0.32-0.84], p = 0.008, pperm = 0.008) and predominantly PTSD (β = -0.56, OR = 0.57 [95% CI: 0.34-0.97], p = 0.037, pperm = 0.039), while NPY2R-DRD3 (rs4425326 × rs6280) impacts low symptoms (β = 0.82, OR = 2.27 [95% CI: 1.26-4.10], p = 0.006, pperm = 0.005) and predominantly depression (β = 1.08, R = 2.95 [95% CI: 1.55-5.62], p = 0.001, pperm = 0.001). The two G × G effects are independent. NPY and dopamine receptor genes are related to the genetic etiology of PTSD-MDD comorbidity, whose specific mechanisms can be studied at multiple levels.

Selection of Durum Wheat and SSR Markers for Organic Farming in Central Italy Using AMMI Analysis

Durum wheat is one of the main crops in the Mediterranean region, which is characterized as the hotspot of climate change, with large year-to-year weather fluctuations. Although chemical input reduction in agriculture is strongly demanded, as well as healthy food, there is still a lack of stable and high-yielding crop varieties specifically adapted for organic conditions. This study evaluates the performance of fifteen durum wheat varieties in terms of suitability for organic farming in central Italy and assesses the impact of the genotype–environment interaction (GEI) on productive and quality traits. Variety performance was evaluated in field experiments over four successive seasons. In addition, a genotypic diversity analysis of 38 microsatellites associated with traits important for organic farming was performed. The AMMI (additive main effects and multiplicative interaction) stability analysis revealed that the best and most stable genotype regarding quality traits, such as thousand-kernel weight, protein content, and test weight was the ancient variety, Senatore Cappelli. The most stable and high yield was determined for the Fuego, Iride, and Mv-Pelsodur genotypes. Moreover, SSR markers that could be used for plant breeding, targeting organic farming systems based on molecular markers and GEI results, were identified.

Genomic characterization, phylogenetic and expression analysis of foraging gene in Apis mellifera

The genomic characterization of the foraging gene and its expression analysis are required to better understand the behavior of honey bees (Apis mellifera). The present study performed a genome-wide characterization of the foraging gene, analyzing its physicochemical properties, phylogenetic features, and expression. An in silico analysis was carried out to characterize the foraging gene and the motifs and conserved domains of the encoded protein to predict its physicochemical properties. Moreover, a phylogenetic analysis of the foraging gene was performed in different species using MEGAX. The relative expression of the foraging gene was determined using qRT-PCR in two groups of forager bee samples (incoming and outgoing bees) during two seasons (five times per day). In addition, the queen effect was evaluated in another experiment. The results revealed that foraging gene expression and bee traffic were influenced by the interaction of season and daytime. The daily foraging traffic and transcription level of the foraging gene were the same in both seasons. The traffic of bees and the transcription abundance of the foraging gene were the highest in the middle and at the end of the day in the first and second seasons, respectively. Furthermore, the mRNA expression of the foraging gene was relatively higher in incoming bees than in outgoing bees. The queen also had a significant effect on the outgoing bees. We conclude that gene–environment interactions affect the foraging behavior of bees through the modulation of the foraging gene transcription.

Gene-environment interaction elicits dystonia-like features and impaired translational regulation in a DYT-TOR1A mouse model

DYT-TOR1A dystonia is the most common monogenic dystonia characterized by involuntary muscle contractions and lack of therapeutic options. Despite some insights into its etiology, the disease's pathophysiology remains unclear. The reduced penetrance of about 30% suggests that extragenetic factors are needed to develop a dystonic phenotype. In order to systematically investigate this hypothesis, we induced a sciatic nerve crush injury in a genetically predisposed DYT-TOR1A mouse model (DYT1KI) to evoke a dystonic phenotype. Subsequently, we employed a multi-omic approach to uncover novel pathophysiological pathways that might be responsible for this condition. Using an unbiased deep-learning-based characterization of the dystonic phenotype showed that nerve-injured DYT1KI animals exhibited significantly more dystonia-like movements (DLM) compared to naive DYT1KI animals. This finding was noticeable as early as two weeks following the surgical procedure. Furthermore, nerve-injured DYT1KI mice displayed significantly more DLM than nerve-injured wildtype (wt) animals starting at 6 weeks post injury. In the cerebellum of nerve-injured wt mice, multi-omic analysis pointed towards regulation in translation related processes. These observations were not made in the cerebellum of nerve-injured DYT1KI mice; instead, they were localized to the cortex and striatum. Our findings indicate a failed translational compensatory mechanisms in the cerebellum of phenotypic DYT1KI mice that exhibit DLM, while translation dysregulations in the cortex and striatum likely promotes the dystonic phenotype.

Brain-derived neurotrophic factor gene rs925946 associates with Israeli females' obesity predisposition: An interaction between genetics, eating habits, and physical inactivity

The global obesity pandemic presents a pressing health challenge, with an increasing prevalence shaped by an intricate interplay of genetics and environment. Brain-derived neurotrophic factor (BDNF) plays a pivotal role in regulating feeding behavior and energy expenditure. BDNF single nucleotide polymorphisms have been linked to obesity risk. We hypothesized that BDNF rs925946 is positively associated with obesity susceptibility in the Israeli population. We aimed to study BDNF rs925946 association with obesity susceptibility and its interaction with environmental factors, including eating habits, sugar-sweetened beverages, and physical activity. A data cohort of 4668 Israeli adults (≥18 years, Jewish) was analyzed. Participants' genotypic data for the BDNF rs925946 and lifestyle and eating behavior questionnaire data were analyzed for the association between obesity predisposition and gene–environment interactions. Female (n = 3259) BDNF rs925946 T-allele carriers had an elevated obesity odd (odds ratio [OR] = 1.2; 95% confidence interval [CI], 1.03–1.4, P = .02). BDNF rs925946 genotype interacted significantly with physical inactivity, sugar-sweetened beverage consumption, and eating habits score to enhance obesity odds (OR = 1.4; 95% CI, 1.14–1.7; OR = 1.54, 95% CI, 1.1–2.15; and OR = 1.4; 95% CI, 1.2–2.11, respectively). Our data demonstrated a significant association between BDNF rs925946 T-allele female carriers and a higher obesity predisposition, affected by modifiable lifestyle factors.

The interaction effects of zinc and polygenic risk score with benzo[a]pyrene exposure on lung cancer risk: A prospective case-cohort study among Chinese populations

The relationship of exposure to benzo[a]pyrene (BaP) with lung cancer risk has been firmly established, but whether this association could be modified by other environmental or genetic factors remains to be explored. To investigate whether and how zinc (Zn) and genetic predisposition modify the association between BaP and lung cancer, we performed a case-cohort study with a 5.4-year median follow-up duration, comprising a representative subcohort of 1399 participants and 359 incident lung cancer cases. The baseline concentrations of benzo[a]pyrene diol epoxide-albumin adduct (BPDE-Alb) and Zn were quantified. We also genotyped the participants and computed the polygenic risk score (PRS) for lung cancer. Our findings indicated that elevated BPDE-Alb and PRS were linked to increased lung cancer risk, with the HR (95%CI) of 1.54 (1.36, 1.74) per SD increment in ln-transformed BPDE-Alb and 1.27 (1.14, 1.41) per SD increment in PRS, but high plasma Zn level was linked to a lower lung cancer risk [HR (95%CI)=0.77 (0.66, 0.91) per SD increment in ln-transformed Zn]. There was evidence of effect modification by Zn on BaP-lung cancer association (P for multiplicative interaction = 0.008). As Zn concentrations increased from the lowest to the highest tertile, the lung cancer risk per SD increment in ln-transformed BPDE-Alb decreased from 2.07 (1.48, 2.89) to 1.33 (0.90, 1.95). Additionally, we observed a significant synergistic interaction of BPDE-Alb and PRS [RERI (95%CI) = 0.85 (0.03, 1.67)], with 42% of the incident lung cancer cases among individuals with high BPDE-Alb and high PRS attributable to their additive effect [AP (95%CI) = 0.42 (0.14, 0.69)]. This study provided the first prospective epidemiological evidence that Zn has protective effect against BaP-induced lung tumorigenesis, whereas high genetic risk can enhance the harmful effect of BaP. These findings may provide novel insight into the environment-environment and environment-gene interaction underlying lung cancer development, which may help to develop prevention and intervention strategies to manage BaP-induced lung cancer.

Parametric and Non-Parametric Measures to Identify Stable and Adaptable Cotton (Gossypium Hirsutum L.) Genotypes

ABSTRACT The study, comprised seventeen genotypes, was conducted during 2016 to 2019 growing seasons. The design was Randomized Complete Block Design (RCBD) with three replications to identify adaptable cotton genotypes. Parametric and non-parametric measures, principal component analysis (PCA) and correlation among the ranks of the parameters computed using R software. The environment, genotype and genotype-environment interaction contributed 79.58, 3.2 and 15.46% of the variation, respectively, and were significant (p < 0.001). The highest (1510.7 kg/ha) and lowest (1080.1 kg/ha) seed cotton yield was recorded from G12 and G8 genotypes respectively. Coefficients of determination (R2) and superiority index (Pi) recognized G13, G11, G10 and G11, G12, G7 as the top three stable genotypes correspondingly. G10 was selected three times as superior by stability variance (σ2), wricke’s ecovalence (Wi) and deviation from regression (S2di). Seed cotton yield positively correlated with GAI, Kang’s rank-sum (KRS), Huhn and Nassar S(1), S(6) S(3) while negatively correlated with Thennarasu stability measures NP(2), NP(3) and NP(4 . The first, second and third clusters of the PCA biplot comprised, 4, 4 and 10 parameters respectively. G12, with highest seed cotton yield and good stability, was the best genotype and recommended for variety verification.

Genotype-by-environment interactions govern fitness changes associated with adaptive mutations in two-component response systems.

Introduction: Two-component response systems (TCRS) are the main mechanism by which prokaryotes acclimate to changing environments. These systems are composed of a membrane bound histidine kinase (HK) that senses external signals and a response regulator (RR) that activates transcription of response genes. Despite their known role in acclimation, little is known about the role TCRS play in environmental adaptation. Several experimental evolution studies have shown the acquisition of mutations in TCRS during adaptation, therefore here we set out to characterize the adaptive mechanism resulting from these mutations and evaluate whether single nucleotide changes in one gene could induce variable genotype-by-environment (GxE) interactions. Methods: To do this, we assessed fitness changes and differential gene expression for four adaptive mutations in cusS, the gene that encodes the HK CusS, acquired by Escherichia coli during silver adaptation. Results: Fitness assays showed that as the environment changed, each mutant displayed a unique fitness profile with greatest fitness in the original selection environment. RNAseq then indicated that, in ± silver nitrate, each mutant induces a primary response that upregulates cusS, its RR cusR, and constitutively expresses the target response genes cusCFBA. This then induces a secondary response via differential expression of genes regulated by the CusR through TCRS crosstalk. Finally, each mutant undergoes fitness tuning through unique tertiary responses that result in gene expression patterns specific for the genotype, the environment and optimized for the original selection conditions. Discussion: This three-step response shows that different mutations in a single gene leads to individualized phenotypes governed by unique GxE interactions that not only contribute to transcriptional divergence but also to phenotypic plasticity.

AMMI and GGE biplot analysis for genotype × environment interactions affecting the yield and quality characteristics of sugar beet.

Sugar beet, an important sugar crop, contributes significantly to the world's sugar production. However, genotype-environment interactions (GEI) often affect the quality characteristics of sugar beet. Hence, understanding the effects of GEI on sugar beet quality can aid in identifying high-quality genotypes that can adapt to different environments. Traditional variance analysis can only be used to examine the yield of a variety and not its specific adaptability to specific conditions. Therefore, more comprehensive analytical methods are required to evaluate the characteristics of the variety under specific environments. Additive main effects and multiplicative interaction (AMMI) and genotype main effect and genotype × environment interaction (GGE) biplot models can be employed to comprehensively evaluate different varieties and address the drawbacks associated with a single evaluation method. Moreover, these models also allow us to explore new varieties more objectively and comprehensively. In this study, the adaptability and stability of 16 sugar beet varieties, in terms of yield and sugar content, were evaluated using AMMI and GGE biplot analysis in seven pilot projects undertaken in 2022. In the assessment of a small but significant proportion of the total GEI variance for the two qualitative traits (yield and sugar content), 80.58% of the variance was explained by the cumulative contribution of IPC1, IPC2, and IPC3. AMMI and GGE biplots clearly highlighted that KWS4207 (G3) exhibited high and stable quality. They also demonstrated that the experiments in Jalaid Banner (Inner Mongolia) (E7) were the most representative. Together, the results suggested that the comprehensive application of AMMI and GGE biplot analysis allowed for a more comprehensive, scientific, and effective evaluation of sugar beet varieties across different regions. The findings offer a theoretical basis for sugar beet breeding and could guide the rational design of experiments for testing new varieties of sugar beet.

How shared versus unshared parental contribution to child risk influences guilt responses.

The extent to which parents experience guilt related to their child's health may depend on their perceptions of their contribution to these outcomes. The impact of the child's "other" biological parent's (OBP) contribution to child health on guilt responses is understudied. Some models posit a diffusion-of-responsibility process, while others favor a heightened-risk-heightened-guilt model. The present study examines how perceived OBP contribution to child risk affects guilt among a sample of parents with self-reported overweight. Parents who perceived their child's OBP to also have overweight experienced more guilt for passing down genetic and family environment-based obesity risk to their child, which suggests that perceptions of shared risk contribution promote guilt-related outcomes. Additionally, risk information endorsing a gene-environment interaction liability framing was the most responsive to OBP weight status. These results support a heightened-risk-heightened-guilt process. Future work should consider guilt when developing child health interventions to avoid undesirable emotional outcomes among parents.

Plant Molecular Phenology and Climate Feedbacks Mediated by BVOCs.

Climate change profoundly affects the timing of seasonal activities of organisms, known as phenology. The impact of climate change is not unidirectional; it is also influenced by plant phenology as plants modify atmospheric composition and climatic processes. One important aspect of this interaction is the emission of biogenic volatile organic compounds (BVOCs), which link the Earth's surface, atmosphere, and climate. BVOC emissions exhibit significant diurnal and seasonal variations and are therefore considered essential phenological traits. To understand the dynamic equilibrium arising from the interplay between plant phenology and climate, this review presents recent advances in comprehending the molecular mechanisms underpinning plant phenology and its interaction with climate. We provide an overview of studies investigating molecular phenology, genome-wide gene expression analyses conducted in natural environments, and how these studies revolutionize the concept of phenology, shifting it from observable traits to dynamic molecular responses driven by gene-environment interactions. We explain how this knowledge can be scaled up to encompass plant populations, regions, and even the globe by establishing connections between molecular phenology, changes in plant distribution, species composition, and climate.

Time-dependent gene-environment interactions are essential drivers of asthma initiation and persistence.

Asthma is a clinical syndrome caused by heterogeneous underlying mechanisms with some of them having a strong genetic component. It is known that up to 82% of atopic asthma has a genetic background with the rest being influenced by environmental factors that cause epigenetic modification(s) of gene expression. The interaction between the gene(s) and the environment has long been regarded as the most likely explanation of asthma initiation and persistence. Lately, much attention has been given to the time frame the interaction occurs since the host response (immune or biological) to environmental triggers, differs at different developmental ages. The integration of the time variant into asthma pathogenesis is appearing to be equally important as the gene(s)-environment interaction. It seems that, all three factors should be present to trigger the asthma initiation and persistence cascade. Herein, we introduce the importance of the time variant in asthma pathogenesis and emphasize the long-term clinical significance of the time-dependent gene-environment interactions in childhood.

Effect of high-resolution satellite and UAV imagery plot pixel resolution in wheat crop yield prediction

ABSTRACT Accurate crop performance assessment and yield prediction in plant breeding programmes can aid decision-making to improve productivity and product quality during crop selection and management. Grain yield is a complex trait, which is a function of the genotype-environment interaction. While using digital remote sensing traits to assess crop performance and predict yield, the characteristics of the sensing tools and approaches can influence prediction performance. In this study, two sensing scales, an unmanned aerial vehicle (UAV) equipped with a ten-band multispectral camera and high-resolution (~0.31 m) WorldView-3 satellite imagery, were used to monitor spring and winter wheat breeding trails in two growing seasons (2020 and 2021). The breeding plots were planted in three different plot sizes (about 1.5 × 5.0 m, 3.0 × 11.0 m, and 4.5 × 11.0 m in spring wheat, and about 1.5 × 3.0 m, 3.0 × 7.3 m, and 4.5 × 7.3 m in winter wheat), with each having 12 varieties and three replications per variety. The spectral and vegetation indices (VI) were extracted from the datasets, and machine learning models for yield prediction (partial least squares regression, least absolute shrinkage selector operator regression, and random forest regression) were evaluated. With multiscale approaches, a moderate to strong correlation of VI data between high-resolution satellite and UAV data (0.42 ≤ r ≤ 0.99, p < 0.01) was found in most cases. The yield prediction accuracies using the extracted data from the high-resolution satellite (6.26 ≤ RMSE% ≤ 25.49; 5.11 ≤ MAE% ≤ 20.95; 0.17 ≤ r ≤ 0.78) and UAV imagery (5.53 ≤ RMSE% ≤ 17.20; 4.28 ≤ MAE% ≤ 14.20; 0.43 ≤ r ≤ 0.92) were also comparable. These findings inform the applications of high-resolution satellite imagery in breeding programmes, considering that the plot size would influence yield prediction accuracies.