Gene-environment Interactions Research Articles

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.

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 (SPSS version 29.0). 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], 1.9 and 1.36, respectively), additive (OR, 1.24 and 1.14, respectively), and codominant (OR, 2 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.

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.

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.

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.

Genomic prediction for rust resistance in pea.

Genomic selection (GS) has become an indispensable tool in modern plant breeding, particularly for complex traits. This study aimed to assess the efficacy of GS in predicting rust (Uromyces pisi) resistance in pea (Pisum sativum), using a panel of 320 pea accessions and a set of 26,045 Silico-Diversity Arrays Technology (Silico-DArT) markers. We compared the prediction abilities of different GS models and explored the impact of incorporating marker × environment (M×E) interaction as a covariate in the GBLUP (genomic best linear unbiased prediction) model. The analysis included phenotyping data from both field and controlled conditions. We assessed the predictive accuracies of different cross-validation strategies and compared the efficiency of using single traits versus a multi-trait index, based on factor analysis and ideotype-design (FAI-BLUP), which combines traits from controlled conditions. The GBLUP model, particularly when modified to include M×E interactions, consistently outperformed other models, demonstrating its suitability for traits affected by complex genotype-environment interactions (GEI). The best predictive ability (0.635) was achieved using the FAI-BLUP approach within the Bayesian Lasso (BL) model. The inclusion of M×E interactions significantly enhanced prediction accuracy across diverse environments in GBLUP models, although it did not markedly improve predictions for non-phenotyped lines. These findings underscore the variability of predictive abilities due to GEI and the effectiveness of multi-trait approaches in addressing complex traits. Overall, our study illustrates the potential of GS, especially when employing a multi-trait index like FAI-BLUP and accounting for M×E interactions, in pea breeding programs focused on rust resistance.

Assessment of Essential and Toxic Element Levels in the Toenails of Children with Autism Spectrum Disorder.

Autism spectrum disorder (ASD) has become a global public health concern, impacting the quality of life. The question of gene-environment interaction in the emergence of ASD remains a subject of ongoing debate, and exploring its pathophysiology is thoroughly related to metals as a risk factor. Therefore, this study aims to assess the levels of toxic (Al, Cd, Hg, and Pb) and essential (Cr, Mn, Fe, Ni, Cu, Zn, and Se) elements in toenail samples collected in children with ASD and neurotypical children, by ICP-MS. Parallelly, we will discuss the use of toenails as an exposure indicator. The study involved 208 children aged 3 to 14 from Marrakech, Morocco. One hundred two were diagnosed with ASD and 106 were neurotypical children. Significant statistical differences in the concentration of Cr, Mn, and Fe were documented between the two groups. Higher levels of Pb in toenails compared to reference values have been reported. No association was established between concentrations of elements and age. Spearman correlation coefficients revealed a significantly different pattern of mutual dependence for toxic and essential elements between the two groups. The strongest positive correlations were found in the neurotypical group (Fe-Mn (ρ = 0.750), and Se-Zn (ρ = 0.800)). These results provide additional, although inconclusive, evidence on the probable role of element disturbance in the pathogenesis of ASD. Further studies should be performed to explore other nutritional, cultural, sociodemographic, environmental, and methodological factors that may impact the levels of these elements in the nails and their possible correlation with the incidence of ASD.

Genotype by environment interactions and stability for grain yield and other agronomic traits in selected sorghum genotypes in Ethiopia

AbstractEnvironmental changes pose major impacts on the performance of crop genotypes with important implications for crop improvement strategies. Hence, breeders pay attention to the effects of genotype by environment interaction (GEI) to mine genetic resources and select adapted genotypes. Twenty sorghum genotypes selected from a large collection of Ethiopian sorghum landraces and two improved varieties were evaluated using a randomized complete block design with three replications at eight locations representing different environmental conditions in Ethiopia. The study aimed at assessing GEI and identifying stable and high‐yielding genotypes of sorghum for grain yield and major agronomic traits. Analysis of variance and additive main effect and multiplicative interaction (AMMI) revealed highly significant (p ≤ 0.001) variance due to genotypes, environments, and GEI among all traits except for days to maturity. Plant height, days to maturity, panicle width, panicle weight, and grain yield were highly affected by environment and GEI, while days to flowering, panicle length, and 1000‐grain weight were mainly affected by genotypic variations. The data also suggest the importance of considering GEI in screening for high‐yielding and stable sorghum genotypes across environments. Among testing sites, Chawaka, Gute, and Uke were ideal environments for grain yield and Asosa was the most discriminative environment. Three genotypes (ETSL100808, Merera, and ETSL100474) were superior and stable across test environments for grain yield and related traits. Overall, based on mean grain yield and disease reaction, AMMI, GGE (genotype and genotype by environment interaction) biplot, and regression models, ETSL100808 was the most stable, high‐yielding, and disease‐tolerant sorghum genotype, suggesting its potential both in breeding program, as donor of traits, and for direct release as a variety.

Decoding Cancer Risk: Understanding Gene-Environment Interactions in Cancer Development

Decoding Cancer Risk: Understanding Gene-Environment Interactions in Cancer Development

Genetic Mutation Due to Pollution

This research delves into the intricate relationship between environmental pollution and human genetics, elucidating the multifaceted mechanisms through which pollutants exert genotoxic effects and impact health outcomes. Through a synthesis of biochemical, genomic, and epidemiological evidence, we elucidate how pollutants induce DNA damage, disrupt epigenetic regulation, and compromise cellular repair mechanisms, leading to a spectrum of adverse health effects including cancer, developmental abnormalities, and reproductive disorders. Leveraging multidisciplinary approaches, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, we uncover the molecular underpinnings of pollution-induced genetic mutations, unveiling complex gene-environment interactions and signaling pathways implicated in disease pathogenesis. Population-level genomic surveillance emerges as a pivotal tool for monitoring genotoxic burden, informing evidence-based interventions, and advancing environmental justice. Looking forward, interdisciplinary collaboration and innovative research strategies hold promise for mitigating genotoxic risk and safeguarding human genetic integrity amidst the challenges posed by environmental pollution.

A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations.

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes. Investigating these genes' functional implications shed light on neurological, thyroidal, bone metabolism, and hematopoietic pathways that necessitate future investigation for blood pressure management that caters to sleep health lifestyle. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausible nature of distinct influences of both sleep duration extremes in cardiovascular health. Several of our loci are specific towards a particular population background or sex, emphasizing the importance of addressing heterogeneity entangled in gene-environment interactions, when considering precision medicine design approaches for blood pressure management.

GIS-based G × E modeling of maize hybrids through enviromic markers engineering.

Through enviromics, precision breeding leverages innovative geotechnologies to customize crop varieties to specific environments, potentially improving both crop yield and genetic selection gains. In Brazil's four southernmost states, data from 183 distinct geographic field trials (also accounting for 2017-2021) covered information on 164 genotypes: 79 phenotyped maize hybrid genotypes for grain yield and their 85 nonphenotyped parents. Additionally, 1342 envirotypic covariates from weather, soil, sensor-based, and satellite sources were collected to engineer 10 K synthetic enviromic markers via machine learning. Soil, radiation light, and surface temperature variations remarkably affect differential genotype yield, hinting at ecophysiological adjustments including evapotranspiration and photosynthesis. The enviromic ensemble-based random regression model showcases superior predictive performance and efficiency compared to the baseline and kernel models, matching the best genotypes to specific geographic coordinates. Clustering analysis has identified regions that minimize genotype-environment (G × E) interactions. These findings underscore the potential of enviromics in crafting specific parental combinations to breed new, higher-yielding hybrid crops. The adequate use of envirotypic information can enhance the precision and efficiency of maize breeding by providing important inputs about the environmental factors that affect the average crop performance. Generating enviromic markers associated with grain yield can enable a better selection of hybrids for specific environments.

Cancer Diagnosis by Gene-Environment Interactions via Combination of SMOTE-Tomek and Overlapped Group Screening Approaches with Application to Imbalanced TCGA Clinical and Genomic Data

The complexity of cancer development involves intricate interactions among multiple biomarkers, such as gene-environment interactions. Utilizing microarray gene expression profile data for cancer classification is anticipated to be effective, thus drawing considerable interest in the fields of bioinformatics and computational biology. Due to the characteristics of genomic data, problems of high-dimensional interactions and noise interference do exist during the analysis process. When building cancer diagnosis models, we often face the dilemma of model adaptation errors due to an imbalance of data types. To mitigate the issues, we apply the SMOTE-Tomek procedure to rectify the imbalance problem. Following this, we utilize the overlapping group screening method alongside a binary logistic regression model to integrate gene pathway information, facilitating the identification of significant biomarkers associated with clinically imbalanced cancer or normal outcomes. Simulation studies across different imbalanced rates and gene structures validate our proposed method’s effectiveness, surpassing common machine learning techniques in terms of classification prediction accuracy. We also demonstrate that prediction performance improves with SMOTE-Tomek treatment compared to no imbalance treatment and SMOTE treatment across various imbalance rates. In the real-world application, we integrate clinical and gene expression data with prior pathway information. We employ SMOTE-Tomek and our proposed methods to identify critical biomarkers and gene-environment interactions linked to the imbalanced binary outcomes (cancer or normal) in patients from the Cancer Genome Atlas datasets of lung adenocarcinoma and breast invasive carcinoma. Our proposed method consistently achieves satisfactory classification accuracy. Additionally, we have identified biomarkers indicative of gene-environment interactions relevant to cancer and have provided corresponding estimates of odds ratios. Moreover, in high-dimensional imbalanced data, for achieving good prediction results, we recommend considering the order of balancing processing and feature screening.

Assessing the yield and nutrient potential of horse gram mutants (Macrotyloma uniflorum Lam. Verdc.) an underutilized legume through a multi-environment-based experiment

The agronomic stability and nutritional importance of 30 (Test genotypes: 29 + Check: 1 = 30) promising horse gram mutants were evaluated in this multi-environment-based experiment (MEE). Attempts were made to (i) identify stable mutants for agronomic traits through AMMI and GGE biplot models, (ii) quantify nutritional traits, (iii) understand the linkage between yield and nutritional traits, and (iv) estimate physical (PP) and cooking properties (CP) of selected genotypes to fix their food-chain usability. The ANOVA of the pooled data exhibited significant differences among environments (E), genotypes (G), and GxE interaction. The combined AMMI and GGE results helped to identify a few good-yielding and stable genotypes (GYSM) (G1, G25, G3, and G27). The yield advantages of these GYSMs over the parent PAIYUR 2 are 42.99%, 34.63%, 28.68%, and 30.59% respectively. The nutrient profiling of mutants revealed (i) a significant coefficient of variation for macronutrients (fat: 29.98%; fibre: 20.72%, and protein: 5.01%), (ii) a good range of variation for micronutrients, and (iii) helped to identify macro (MaNSM) and micro nutrient-specific mutants (MiNSM). The relationship analysis between yield and nutrient traits ascertained that yield had (i) positivity with protein (r2 = 0.69) and negativity for micronutrients except for Mn (r2 = 0.63), Cu (r2 = 0.46), and B (r2 = 0.01) in GYSM, (ii) positivity with protein and fibre in MaNSM, and (iii) negativity with micronutrients in MiNSM. Of the GYSM, G1 and G25 offer scope for commercial exploitation, and their PP and CP analyses revealed that G1 can be used for pastry and baked product preparation while G25 for weaning foods. Cooking time exhibited positivity with seed size parameters and negativity with water absorption capacity (r2 = − 0.53). An LC–MS–MS-based amino acid (AA) fractionation study showed the effect of induced mutagenesis on the contents of amino acids and also revealed the significance of horse gram for its lysine and methionine contents.