Gene-environment Interactions Research Articles

Multivariate Analysis Techniques and Tolerance Indices for Detecting Bread Wheat Genotypes of Drought Tolerance

Drought stress is one of the biggest hardships in wheat cultivation because of the strong negative relationship between water deficit and crop yields owing to a lower grain weight, a shorter grain-filling period, a slower grain-filling rate, and reduced grain quality. Genotype–environment interaction (GEN:ENV) generates hardships in selecting wheat genotypes and ideotypes due to biased genetic estimates. Diverse strategies have been proposed to respond to the urgent need for concurrent improvements in yield performance and stability. This study’s purpose was to appraise genetic variation and GEN:ENV effects on yield and yield components to discover drought-stress-tolerant genotypes and ideotypes. This study evaluated 20 genotypes in three consecutive seasons under non-stressful and drought-stress conditions in a total of six ENVs. The broad-sense heritability ranged from 0.54 to 0.82 based on expected mean squares and ranged from 0.60 to 0.90 based on plot mean, but in the other three ways, it was usually greater than 0.90. The high values of (σgen:env2) revealed the effect that broad-sense heritability has on the expression of traits. G01, G03, G06, G07, G08, G10, G12, G13, G16, G17, and G18 were stable genotypes for grain yield (GY), according to additive main effects and a multiplicative interaction biplot for the six ENVs. Based on scores in the weighted average of absolute scores biplot (WAASB), G02, G04, G05, G08, G10, and G18 were selected as stable and high-performance for GY, and they were all selected as the best genotype groups using the WAASB-GY superiority index. From the results obtained from principal component analysis and hierarchical clustering and from the tolerance discrimination indices, G02, G04, G05, G18, and G19 are genotypes that produce a suitable yield under non-stressful and drought-stress conditions. In essence, combining approaches that take into consideration stability and high performance can contribute significantly to enhancing the reliability of recommendations for novel wheat genotypes.

Factor analytic selection tools and environmental feature-integration enable holistic decision-making in Eucalyptus breeding

Understanding the genotype-by-environment interaction (GEI) and considering it in the selection process is a sine qua non condition for the expansion of Brazilian eucalyptus silviculture. This study’s objective is to select high-performance and stable eucalyptus clones based on a novel selection index that considers the Factor Analytic Selection Tools (FAST) and the clone’s reliability. The investigation explores the nuances interplay of GEI and extends its insights by scrutinizing the relationship between latent factors and real environmental features. The analysis, conducted across seven trials in five Brazilian states involving 78 clones, employs FAST. The clonal selection was performed using an extended FAST index weighted by the clone’s reliability. Further insights about GEI emerge from the integration of factor loadings with 25 environmental features through a principal component analysis. Ten clones, distinguished by high performance, stability, and reliability, have been selected across the target population of environments. The environmental features most closely associated with factor loadings, encompassing air temperature, radiation, and soil characteristics, emerge as pivotal drivers of GEI within this dataset. This study contributes insights to eucalyptus breeders, equipping them to enhance decision-making by harnessing a holistic understanding-from the genotypes under evaluation to the diverse environments anticipated in commercial plantations.

Genotype-environment interaction for grain yield in maize (Zea mays L.) using the additive main effects and multiplicative interaction (AMMI) model.

Genotype-environment interaction consists of the different response of individual genotypes resulting from changing environmental conditions. Its significance is a phenomenon that makes the breeding process very difficult. On the one hand, the breeder expects stable genotypes, i.e., yielding similarly regardless of environmental conditions. On the other hand, selecting the best genotypes for each region is one of the key challenges for breeders and farmers. The aim of this study was to evaluate genotype-by-environment interaction for grain yield in new maize hybrids developed by Plant Breeding Smolice Co. Ltd., utilizing the additive main effects and multiplicative interaction (AMMI) model. The investigation involved 69 maize (Zea mays L.) hybrids, tested across five locations in a randomized complete block design with three replications. Grain yield varied from 8.76 t ha-1 (SMH_16417 in Smolice) to 16.89 t ha-1 (SMH_16043 in Płaczkowo), with a mean yield of 13.16 t ha-1. AMMI analysis identified significant effects of genotype, environment, and their interaction on grain yield. Analysis of variance indicated that 25.12% of the total variation in grain yield was due to environment factor, 35.20% to genotypic differences, and 21.18% to genotype by environmental interactions. Hybrids SMH_1706 and SMH_1707 are recommended for further breeding programs due to their high stability and superior average grain yield.

Fibrosis and Hepatocarcinogenesis: Role of Gene-Environment Interactions in Liver Disease Progression.

The liver is a complex organ that performs vital functions in the body. Despite its extraordinary regenerative capacity compared to other organs, exposure to chemical, infectious, metabolic and immunologic insults and toxins renders the liver vulnerable to inflammation, degeneration and fibrosis. Abnormal wound healing response mediated by aberrant signaling pathways causes chronic activation of hepatic stellate cells (HSCs) and excessive accumulation of extracellular matrix (ECM), leading to hepatic fibrosis and cirrhosis. Fibrosis plays a key role in liver carcinogenesis. Once thought to be irreversible, recent clinical studies show that hepatic fibrosis can be reversed, even in the advanced stage. Experimental evidence shows that removal of the insult or injury can inactivate HSCs and reduce the inflammatory response, eventually leading to activation of fibrolysis and degradation of ECM. Thus, it is critical to understand the role of gene-environment interactions in the context of liver fibrosis progression and regression in order to identify specific therapeutic targets for optimized treatment to induce fibrosis regression, prevent HCC development and, ultimately, improve the clinical outcome.

Geographic variation in developmental plasticity among populations of the canyon treefrog in response to temperature and pond‐drying

AbstractUnderstanding how species respond to environmental changes, particularly in the context of climate change, is crucial for biodiversity conservation. This study focuses on the plastic responses of canyon tree frog (Dryophytes arenicolor) larvae to variations in temperature and pond‐drying, examining potential consequences of climate change. Frog larvae serve as an excellent model due to their high responsiveness to environmental cues during development. We analysed the impact of temperature and pond‐drying on morphological and life‐history traits, via a common garden experiment with individuals from three distinct populations with different ecological conditions. The experiments revealed significant differences in responses among populations, indicating geographic variation in plasticity. Pond‐drying treatments led to reduced survival and reduction of morphological traits and growth, challenging the assumption that tadpoles have adaptive responses to drying conditions. In contrast, temperature treatments showed variable effects, with elevated temperatures generally favouring growth rates, reducing metamorphosis time, and having population‐specific morphological shifts. We emphasize the importance of considering both morphological and life‐history traits, as well as geographic variation, in assessing species' vulnerability to climate change. Furthermore, the integration of environmental standardized plasticity index (ESPI) and relative distances plasticity index (RDPI) in amphibian developmental plasticity will allow to quantify and compare plastic responses among populations and even other amphibian species in which these metrics are obtained in the future. Our results underscore the complexity of phenotypic plasticity, revealing genotype–environment interactions. These findings contribute valuable insights into the potential adaptability of D. arenicolor populations to ongoing climate changes, highlighting the need for comprehensive inter‐population studies for a more nuanced understanding of species' responses to environmental change, and suggest that certain populations may be more vulnerable to extinction or better equipped to handle climate change based on their ability to adapt to environmental change.

Sex and population differences in the cardiometabolic continuum: a machine learning study using the UK Biobank and ELSA-Brasil cohorts

BackgroundThe temporal relationships across cardiometabolic diseases (CMDs) were recently conceptualized as the cardiometabolic continuum (CMC), sequence of cardiovascular events that stem from gene-environmental interactions, unhealthy lifestyle influences, and metabolic diseases such as diabetes, and hypertension. While the physiological pathways linking metabolic and cardiovascular diseases have been investigated, the study of the sex and population differences in the CMC have still not been described.MethodsWe present a machine learning approach to model the CMC and investigate sex and population differences in two distinct cohorts: the UK Biobank (17,700 participants) and the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) (7162 participants). We consider the following CMDs: hypertension (Hyp), diabetes (DM), heart diseases (HD: angina, myocardial infarction, or heart failure), and stroke (STK). For the identification of the CMC patterns, individual trajectories with the time of disease occurrence were clustered using k-means. Based on clinical, sociodemographic, and lifestyle characteristics, we built multiclass random forest classifiers and used the SHAP methodology to evaluate feature importance.ResultsFive CMC patterns were identified across both sexes and cohorts: EarlyHyp, FirstDM, FirstHD, Healthy, and LateHyp, named according to prevalence and disease occurrence time that depicted around 95%, 78%, 75%, 88% and 99% of individuals, respectively. Within the UK Biobank, more women were classified in the Healthy cluster and more men in all others. In the EarlyHyp and LateHyp clusters, isolated hypertension occurred earlier among women. Smoking habits and education had high importance and clear directionality for both sexes. For ELSA-Brasil, more men were classified in the Healthy cluster and more women in the FirstDM. The diabetes occurrence time when followed by hypertension was lower among women. Education and ethnicity had high importance and clear directionality for women, while for men these features were smoking, alcohol, and coffee consumption.ConclusionsThere are clear sex differences in the CMC that varied across the UK and Brazilian cohorts. In particular, disadvantages regarding incidence and the time to onset of diseases were more pronounced in Brazil, against woman. The results show the need to strengthen public health policies to prevent and control the time course of CMD, with an emphasis on women.

Lack of association between TRPV1 gene polymorphisms and risk of migraine chronification: a case-control study and meta-analysis.

To confirm a previously reported association of TRPV1 rs8065080 with the risk of transformation from episodic (EM) to chronic migraine (CM) and to extend knowledge about the role of other TRPV1 single nucleotide polymorphisms (SNPs), we first investigated the impact of three TRPV1 SNPs (rs8065080, rs222747 and rs222749) on the risk of migraine chronification in a case-control study. A systematic review and meta-analysis were then conducted to summarize the accumulated findings. Genotyping of the selected TRPV1 SNPs was performed using TaqMan real-time PCR in 167 EM and 182 CM participants. Crude and adjusted odds ratios with associated 95% confidence intervals were calculated in the log-additive, dominant, and recessive genetic models. A comprehensive literature search was performed in PubMed, Web of Knowledge, Cochrane Library, and OpenGrey until February 2024. In our case-control study, no association was found between TRPV1 SNPs and the risk of migraine chronification, both in the unadjusted logistic regression models and after adjustment for confounding clinical variables. The results of the meta-analysis with a total of 241 participants with EM and 223 with CM confirmed no association between TRPV1 SNPs and the risk of migraine chronification in any of the genetic models tested. The results of the present case-control study and meta-analysis exclude a major role of TRPV1 rs8065080, rs222747, and rs222749 as risk factors for migraine chronification. However, further research is needed to investigate the gene-gene and gene-environment interactions of TRPV1 SNPs on the risk of transformation from episodic to chronic migraine.

Stability Analysis of Yield and Its Components in Snap Melon (Cucumis melo var momordica)

Snap melon, scientifically known as Cucumis melo L. var. momordica (Roxb.), is a crop native to India and belonging to Cucurbitaceae family. This study was carried out to assess the nature and magnitude of genotype-environment interaction and stability among some parameters in snap melon genotypes. Twenty five local cultivars along with three standard checks were evaluated in randomized block design with three replications at three environments during summer 2023 at Navsari. The Eberhart and Russell model of stability analysis was employed which has been proved to be a reliable model. The study revealed that the mean sum of squares due to genotypes when tested against pooled deviation were highly significant for all the traits studied. Environment ´ Genotype (linear) showed significant differences for all characteristics except fruit weight and yield per vine, indicating that prediction could be possible by considering individual genotypes for these traits. A significant non-linear component (pooled deviation) was observed for fruit weight and fruit yield per vine, suggesting that genotypes varied considerably in terms of stable performance for these traits. The G ´ E interaction was only significant for flesh thickness and yield per vine, with all other traits being non-significant. The genotypes T 14 (HUB 18) and T 17 (HUB 21) were relatively stable and can be further used as parents to develop a more robust stable genotype. We identified genotypes which could pave way introducing high performing snap melon varieties.

Exploring genotype by environment interaction in sunflower using genotype plus genotype by environment interaction (GGE) and best linear unbiased prediction (BLUP) approaches

Sunflower is important for food, feed and ornamentals, faces rising demand for quality oil. Identifying resilient high-yielding genotypes is essential. Multi-environmental trials and integrated approaches to evaluate genotype by environment interactions (GEI) enhance accuracy and comprehensiveness of breeding programs. This study assess the representativeness and discriminative power of tested environments; classify mega-environments for genotype recommendations and evaluate how GGE and BLUP methods identify stable and superior sunflower genotypes. The experiment used a randomized complete block design with three replications across six locations for two seasons. The pooled analysis of variance for GEI on seed yield showed significant variability (p < 0.05) with mean values from 1171 to 2564 kg ha−1, indicating inconsistent genotypes performance across environments. The study identified E11, E2, and E6 as key discriminating and representative environments and grouped test locations into four mega-environments. Genotypes G6, G1, and G4 are the most desirable based on stability and seed yield. E11 and G6 are identified as ideal environment and genotype, respectively. GGE biplot and BLUP methods consistently identify stable and ideal sunflower genotypes. However, GGE stability indices differ somewhat in assessing genotypes stability possibly due to low number of genotypes, testing environment, and year in the dataset. The study confirmed that simultaneous consideration of GGE and BLUP models for GEI analysis helps to select more stable and adaptable sunflower genotypes across environments. It also clarifies GEI pattern, identifying mega-environments, and aiding genotype selection in sunflower production. However, validating these mega-environments and exploring the genetic basis of adaptability and stability remains essential.

Gene-Environment Interactions Significantly Alter the Obesity Risk of SH2B1 rs7498665 Carriers.

Src homology 2 B adaptor protein 1 (SH2B1) gene and variants have been found to be associated with common obesity. We aimed to investigate the association between the common missense variant SH2B1 rs7498665 and common obesity risk as well as interactions with lifestyle variables in an Israeli population. An adult cohort (n=3,070; ≥18 years) with the SH2B1 rs7498665 variant and lifestyle, behavior (online questionnaire), and blood glucose data was analyzed. Associations between this variant, obesity risk (body mass index [BMI] ≥25 and ≥30 kg/m2), and interactions with behavioral and lifestyle factors (stress levels, eating habits score [EHS], physical activity [PA], and wine consumption) were investigated. Association and gene-environment interactions were analyzed using binary logistic regressions with interaction. SH2B1 rs7498665 carriers were significantly (P<0.05) more likely to be overweight (BMI ≥25 kg/m2) or obese (BMI ≥30 kg/m2) in recessive (odds ratio [OR], 1.90 and 1.36, respectively), additive (OR, 1.24 and 1.14, respectively), and codominant (OR, 2.00 and 1.41, respectively) genetic models. SH2B1 rs7498665 interacted with lifestyle and behavioral factors as well as glucose levels. PA and moderate wine consumption (1 to 3 drinks/week) reduced obesity risk (OR, 0.35 and 0.71, respectively). Conversely, carriers of two risk alleles who reported high stress levels, had ≥median EHS, and who had a fasting glucose level ≥90 mg/dL had a significantly increased obesity risk (OR, 3.63 and 5.82, respectively). Carrying SH2B1 rs7498665 significantly elevates the risk of obesity. Actionable lifestyle and behavioral factors significantly modulate the rs7498665 genetic predisposition to obesity; PA and moderate wine consumption attenuate the risk, while high stress, EHS, and fasting glucose level increase the obesity risk.

A framework for simulating genotype-by-environment interaction using multiplicative models

Key messageThe simulation of genotype-by-environment interaction using multiplicative models provides a general and scalable framework to generate realistic multi-environment datasets and model plant breeding programmes.Plant breeding has been historically shaped by genotype-by-environment interaction (GEI). Despite its importance, however, many current simulations do not adequately capture the complexity of GEI inherent to plant breeding. The framework developed in this paper simulates GEI with desirable structure using multiplicative models. The framework can be used to simulate a hypothetical target population of environments (TPE), from which many different multi-environment trial (MET) datasets can be sampled. Measures of variance explained and expected accuracy are developed to tune the simulation of non-crossover and crossover GEI and quantify the MET-TPE alignment. The framework has been implemented within the R package FieldSimR, and is demonstrated here using two working examples supported by R code. The first example embeds the framework into a linear mixed model to generate MET datasets with low, moderate and high GEI, which are used to compare several popular statistical models applied to plant breeding. The prediction accuracy generally increases as the level of GEI decreases or the number of environments sampled in the MET increases. The second example integrates the framework into a breeding programme simulation to compare genomic and phenotypic selection strategies over time. Genomic selection outperforms phenotypic selection by ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim $$\\end{document}50–70% in the TPE, depending on the level of GEI. These examples demonstrate how the new framework can be used to generate realistic MET datasets and model plant breeding programmes that better reflect the complexity of real-world settings, making it a valuable tool for optimising a wide range of breeding methodologies.

Gene‒Environment Interaction Between CAST Gene and Eye-Rubbing in the Chinese Keratoconus Cohort Study: A Case-Only Study.

Keratoconus (KC), characterized by progressive corneal protrusion and thinning, is a complex disease influenced by the combination of genetic and environmental factors. The purpose of this study was to explore potential gene‒environment interaction between the calpastatin (CAST) gene and eye-rubbing in KC. A case-only study including 930 patients (676 patients with eye-rubbing and 254 patients without eye-rubbing) from the Chinese Keratoconus (CKC) cohort study was performed in the present study. Genotyping of single nucleotide polymorphism (SNP) was conducted using the Illumina Infinium Human Asian Screening Array (ASA) Beadchip. The gene‒environment interactions between CAST gene and eye-rubbing were analyzed using PLINK version 1.90. The interactions between CAST genotypes and eye-rubbing were analyzed by logistic regression models. The SNP-SNP-environment interactions were analyzed using generalized multifactor dimensionality reduction (GMDR). Three SNPs in CAST gene, namely, rs26515, rs27991, and rs9314177, reached the significance threshold for interactions (defined as P < 2.272 × 10-3). Notably, the minor alleles of these three SNPs exhibited negative interactions with eye-rubbing in KC. The results of logistic regression models revealed that the minor allele homozygotes and heterozygotes of rs26515, rs27991, and rs9314177 also exhibited negative interactions with eye-rubbing. Furthermore, GMDR analysis revealed the significant SNP-SNP-environment interactions among rs26515, rs27991, rs9314177, and eye-rubbing in KC. This study identified rs26515, rs27991, and rs9314177 in CAST gene existed gene-environment interactions with eye-rubbing in KC, which is highly important for understanding the underlying biological mechanisms of KC and guiding precision prevention and proper management.

Assessing genotype-environment interactions in Atlantic salmon reared in freshwater loch and recirculating systems.

The interest in recirculating aquaculture systems (RAS) is growing due to their benefits such as increased productivity, better control over animal care, reduced environmental effects, and less water consumption. However, in some regions of the world, traditional aquaculture methods remain prevalent, and selective breeding has often been designed for performance within these systems. Therefore, it is important to evaluate how current fish populations fare in RAS to guide future breeding choices. In a commercial setting, we explore the genetic structure of growth characteristics, measure genotype-environment interactions (GxE) in salmon smolts, and examine genetic markers related to growth in freshwater lochs and RAS. Young salmon were raised together until they reached the parr stage, after which they were divided equally between freshwater net-pens and RAS. After an 8-week period, we sampled fish from each environment and genotyped them. Our findings revealed that fish reared in RAS were generally smaller in weight and length but exhibited a higher condition factor and uniformity. We found a notably smaller component of unexplained variance in the RAS, leading to higher heritability estimates. We observed a low GxE effect for length and condition factor, but significant re-ranking for whole-body weight, as well as noticeable differences in trait associations across environments. Specifically, a segment of chromosome 22 was found to be linked with the condition factor in the RAS population only. Results suggests that if the use of RAS continues to expand, the efficiency of existing commercial populations may not reach its full potential unless breeding programs specific to RAS are implemented.

Styrene and ethylbenzene exposure and type 2 diabetes mellitus: A longitudinal gene–environment interaction study

Styrene and ethylbenzene (S/EB) are identified as hazardous air contaminants that raise significant concerns. The association between S/EB exposure and the incidence of type 2 diabetes mellitus (T2DM), and the interaction between genes and environment, remains poorly understood. Our study consisted of 2,219 Chinese adults who were part of the Wuhan-Zhuhai cohort. A follow-up assessment was conducted after six years. Exposure to S/EB was quantified by determining the concentrations of urinary biomarkers of exposure to S/EB (UBE-S/EB; urinary phenylglyoxylic acid level plus urinary mandelic acid level). Logistic regression models were constructed to investigate the relations of UBE-S/EB and genetic risk score (GRS) with T2DM prevalence and incidence. The interaction effects of UBE-S/EB and GRS on T2DM were investigated on multiplicative and additive scales. UBE-S/EB was dose-dependently and positively related to T2DM prevalence and incidence. Participants with high levels of UBE-S/EB [relative risk (RR) ​= ​1.930, 95% confidence interval (CI): 1.157–3.309] or GRS (1.943, 1.110–3.462) demonstrated the highest risk of incident T2DM, in comparison to those with low levels of UBE-S/EB or GRS. Significant additive interaction between UBE-S/EB and GRS on T2DM incidence was discovered with relative excess risk due to interaction (95% CI) of 0.178 (0.065–0.292). The RR (95% CI) of T2DM incidence was 2.602 (1.238–6.140) for individuals with high UBE-S/EB and high GRS, compared to those with low UBE-S/EB and low GRS. This study presented the initial evidence that S/EB exposure was significantly related to increased risk of T2DM incidence, and the relationship was interactively aggravated by genetic predisposition.

Effect of Genotype × Environment Interactions on the Yield and Stability of Sugarcane Varieties in Ecuador: GGE Biplot Analysis by Location and Year

Yield and stability are desirable characteristics that crops need to have high agronomic value; sugarcane stands out globally due to its diverse range of products and by-products. However, genotype-environment (G × E) interactions can affect the overall performance of a crop. The objective of this study is to identify genotypes with the highest yield and stability, as well as to understand their independent and interactive effects. A collection of 10 sugarcane varieties was evaluated, including Colombian, Dominican, Ecuadorian lines, and a group of clones planted across five different locations from 2018 to 2020. A two-way ANOVA along with the GGE biplot technique were used to analyze yield and stability. The ANOVA model shows highly significant effects in all cases (p &lt; 0.001) except for the genotype by year and sector interaction (G × Y × S); however, the decomposition by sectors reveals a significant triple interaction in sector 04 (p &lt; 0.05). The GGE biplot model accounted for up to 74.77% of the total variance explained in its PC1 and PC2 components. It also highlighted the group of clones as having the highest yield and environmental instability, and the Ecuadorian varieties EC-07 and EC-08 as having the best yield-stability relationship. We conclude that the combined results of the ANOVA and GGE biplot models provide a more synergistic and effective evaluation of sugarcane varieties, offering theoretical and practical bases for decision-making in the selection of specific varieties.

Interaction of Th17 differentiations-related gene polymorphisms and environmental factors contributing to the disease classification, complications, and surgical risks of Crohn's disease in the Chinese Han population.

Few studies have been conducted on gene-environment interactions in the Chinese population with Crohn's disease (CD). We aimed to investigate the association between single nucleotide polymorphisms (SNPs) on the T helper 17 (Th17) cell and CD susceptibility/performance in Chinese individuals. We conducted a case-control and case-only study at the Peking Union Medical College Hospital. Four SNPs related to the Th17 cell pathway genes were prioritized, including rs2284553 (interferon gamma receptor 2), rs7517847 (interleukin 23 receptor), rs7773324 (interferon regulatory factor 4), and rs4263839 (tumor necrosis factor superfamily 15). SNP frequency was calculated, and gene-environment interaction was assessed by multifactor dimensionality reduction analysis. Altogether 159 CD patients and 316 healthy controls were included. All analyzed SNPs were found in Hardy-Weinberg equilibrium (P > 0.05). The frequency of rs2284553-A allele and rs4263839-A allele were lower in CD patients compared with controls (P < 0.05). While the rs4263839-A allele was more prevalent in ileocolonic CD patients than in those with isolated small intestinal or colonic disease (P = 0.035). Gene-environment interactions revealed associations between rs2284553 and breastfeeding, sunshine exposure, and fridge-stored food, affecting age at diagnosis, intestinal involvement, and intestinal stricture. Interaction of rs4263839 and breastfeeding influenced small intestinal lesions and intestinalstricture in CD. This study provided information on the genetic background in Chinese CD patients. Incorporating these SNPs into predictive models may improve risk assessment and outcome prediction. Gene-environment interaction contributes to the understanding of CD pathogenesis.

Genotype by Environment Interaction and Stability Analysis for Grain Yield in White Seeded Tef [&amp;lt;i&amp;gt;Eragrostis tef&amp;lt;/i&amp;gt; (zucc.)Trotter] Genotypes in Western Oromia, Ethiopia

Tef [&amp;lt;i&amp;gt;Eragrostis tef (Zucc.) Trotter &amp;lt;/i&amp;gt;L.] is a most important cereal crop in Ethiopia in terms of production, consumption and cash. The study was carried out to investigate grain yield stability and genotype by environment interaction for 18 genotypes conducted in the potential high land areas of Western Oromia, Ethiopia for two consecutive years (2020 to 2021) using Randomized Complete Block Experimental Design with three replications. The study of variance for grain yield using the AMMI model indicated highly significant variation for genotypes, environment, and genotype-environment interactions. Environment accounted for 18.7% of the variance in grain yield, 17.9% for genotypes, and 61.5% for genotypes. The first IPCA component accounted for 47.9% of the interaction effect and revealed the two models were fit. Genotypes G15, G10, G4, G1, and G3 had the lowest AMMI stability value (ASV), indicating stability; genotypes G16, G14, G9, G7, G2, and G5 had the highest ASV value, indicating instability. From over all analysis genotype G1 and G3, showed a high mean grain yield, lowest GSI, ASV and stable compared to other genotypes in the study. As a result, G1 and G3 were identified as the best genotypes for future breeding programs and potential release in Western Oromia, Ethiopia&amp;apos;s highlands.

Establishment and comprehensive characterization of a novel dark-reared zebrafish model for myopia studies

Myopia is predicted to impact approximately 5 billion people by 2050, necessitating mechanistic understanding of its development. Myopia results from dysregulated genetic mechanisms of emmetropization, caused by over-exposure to aberrant visual environments; however, these genetic mechanisms remain unclear. Recent human genome-wide association studies have identified a range of novel myopia-risk genes. To facilitate large-scale in vivo mechanistic examination of gene-environment interactions, this study aims to establish a myopia model platform that allows efficient environmental and genetic manipulations. We established an environmental zebrafish myopia model by dark-rearing. Ocular biometrics including relative ocular refraction were quantified using optical coherence tomography images. Spatial vision was assessed using optomotor response (OMR). Retinal function was analyzed via electroretinography (ERG). Myopia-associated molecular contents or distributions were examined using RT-qPCR or immunohistochemistry. Our model produces robust phenotypic changes, showing myopia after 2 weeks of dark-rearing, which were recoverable within 2 weeks after returning animals to normal lighting. 2-week dark-reared zebrafish have reduced spatial-frequency tuning function. ERG showed reduced photoreceptor and bipolar cell function (a- and b-waves) after only 2 days of dark-rearing, which worsened after 2 weeks of dark-rearing. We also found dark-rearing-induced changes to expression of myopia-risk genes, including egr1, vegfaa, vegfab, rbp3, gjd2a and gjd2b, inner retinal distribution of EFEMP1, TIMP2 and MMP2, as well as transiently reduced PSD95 density in the inner plexiform layer. Coupled with the gene editing tools available for zebrafish, our environmental myopia model provides an excellent platform for large-scale investigation of gene-environment interactions in myopia development.

Factor-Analytic Variance-Covariance Structures for Prediction Into a Target Population of Environments.

Finlay-Wilkinson regression is a popular method for modeling genotype-environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor-analytic variance-covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi-environment trials for the purpose of making predictions in a target population of environments. We investigate the implication of random versus fixed effects assumptions, starting from basic analysis-of-variance models, then moving on to factor-analytic models and considering the transition to models involving observable environmental covariates, which promise to provide more accurate and targeted predictions than models with latent environmental variables.

Unravelling the genetic basis and regulation networks related to fibre quality improvement using chromosome segment substitution lines in cotton.

The elucidation of genetic architecture and molecular regulatory networks underlying complex traits remains a significant challenge in life science, largely due to the substantial background effects that arise from epistasis and gene-environment interactions. The chromosome segment substitution line (CSSL) is an ideal material for genetic and molecular dissection of complex traits due to its near-isogenic properties; yet a comprehensive analysis, from the basic identification of substitution segments to advanced regulatory network, is still insufficient. Here, we developed two cotton CSSL populations on the Gossypium hirsutum background, representing wide adaptation and high lint yield, with introgression from G. barbadense, representing superior fibre quality. We sequenced 99 CSSLs that demonstrated significant differences from G. hirsutum in fibre, and characterized 836 dynamic fibre transcriptomes in three crucial developmental stages. We developed a workflow for precise resolution of chromosomal substitution segments; the genome sequencing revealed substitutions collectively representing 87.25% of the G.barbadense genome. Together, the genomic and transcriptomic survey identified 18 novel fibre-quality-related quantitative trait loci with high genetic contributions and the comprehensive landscape of fibre development regulation. Furthermore, analysis determined unique cis-expression patterns in CSSLs to be the driving force for fibre quality alteration; building upon this, the co-expression regulatory network revealed biological relationships among the noted pathways and accurately described the molecular interactions of GhHOX3, GhRDL1 and GhEXPA1 during fibre elongation, along with reliable predictions for their interactions with GhTBA8A5. Our study will enhance more strategic employment of CSSL in crop molecular biology and breeding programmes.