Gene-environment Interactions Research Articles

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.

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

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.

New Advances in Attention-Deficit/Hyperactivity Disorder-like Dogs

Similar to humans, dogs could suffer an Attention-Deficit/Hyperactivity Disorder-like syndrome (ADHD-like). In fact, several studies highlight the use of dogs as a model for studying ADHD. This condition entails behavioral problems expressed through impulsivity, attention issues, hyperactivity, and/or aggression, compromising the quality of life for both the caregiver and the dog. The pathophysiology of ADHD-like is complex and is associated with dysregulation of various neurotransmitters such as serotonin and dopamine. The expression of ADHD-like behavior in dogs would appear to depend on a classical gene–environment interaction as is the case with many neurological disorders in humans. In addition to the described symptomatology, ADHD-like dogs can exhibit strong comorbidities with compulsive behaviors, aggressiveness, inappropriate elimination and fearfulness, in addition to epilepsy, foreign body ingestion, and pruritus. In spite of the fact that there is no veterinary consensus about the diagnosis of ADHD-like, some validated questionnaires could be helpful, but these cannot be used as a unique diagnostic tool. The use of drugs, such as fluoxetine, in addition to an adequate environmental enrichment, relaxation protocols, and behavior modification can achieve an adequate quality of life for both the dog and caregivers.

Genotype by environment interaction and stability analysis for harvest date in sugar beet cultivars

This research assessed the quantitative and qualitative reactions of commercially grown sugar beets to four different harvest dates and their yield stability. The study followed a split-plot design within a randomized complete block design over 3 years. The main plot involved 10 sugar beet cultivars, while the subplot involved four harvest dates: August 13 (HD1), September 7 (HD2), October 3 (HD3), and November 12 (HD4). The study found that environmental conditions, genotypes, and harvest dates significantly affected various traits of sugar beet. Yearly environmental variations and their interactions with genotypes and harvest dates had substantial impacts on all measured traits at the 1% probability level. Additive main effect and multiplicative interaction analysis based on white sugar yield indicated that genotype and environment's additive effects, as well as the genotype–environment interaction, were significant at 1% probability level. Shokoufa and Arya, which exhibit high white sugar yield (WSY) and low first interaction principal component (IPC1) values, are identified as desirable due to their stability across different environments. Among the harvest dates in different years, the fourth and third dates showed a higher yield than the total average. Perfekta and Ekbatan exhibited high specific adaptability. According to the multi-trait stability index, Arta, Arya and Sina were recognized as stable and superior across all measured traits.

Estimation of environment stability for fruit yield and capsaicin content by using two models in Capsicum chinense Jacq. (Ghost Pepper) with multi-year evaluation.

Capsicum chinense Jacq. (Ghost Pepper) is well-known for its high pungency and pleasant aroma. The recent years witnessed a significant decline in popularity of this important crop due to the use of inferior planting material and lack of elite lines. To maintain constant performance across a variety of settings, it is crucial to choose stable lines with high yield and capsaicin content, as these are the most promising traits of Ghost Pepper. In this study, 120 high-capsaicin genotypes were subjected to a 3-year (kharif 2017, 2018 and 2019) stability investigation utilizing two well-known stability methods: Eberhart-Russell (ER) and additive main effects and multiple interaction (AMMI). Three replications were used following Randomized Complete Block Design for 11 traits. The experiment soil was sandy loam with pH 4.9. Minimum and maximum temperature of 18.5 °C, 17.5 °C, 17.4 °C and 32.2 °C, 31.3 °C, 32.7 °C and rainfall of 1,781, 2,099, 1,972 mm respectively was recorded for the study period. The genotype-environment linear interaction (G×E Lin.) was highly significant for days to 50% flowering, capsaicin content, fruit length and girth, fruit yield per plant and number of fruits per plant at p < 0.005. G×E interaction for fruit yield and capsaicin content in AMMI-analysis of variance reported 67.07% and 71.51% contribution by IPCA-1 (interactive principal component axis) and 32.76% and 28.49% by IPCA-2, respectively. Eight genotypes were identified to be stable with high yield and capsaicin content. The identified stable lines can be opted for cultivation to reduce the impact of crop failure when grown in different macro-environments. Moreover, the pharmaceutical and spice sectors will also be benefitted from the lines with high capsaicin content. Further research assessing the lines' performance across various regions of India can provide a solid foundation for the crop's evaluation at national level.

Identification of High Erucic Acid Brassica carinata Genotypes through Multi-Trait Stability Index

Brassica carinata is an important and native oilseed crop in Ethiopia. The seed oil from B.carinata attracts global attention for its various industrial applications, mainly due to its high erucic acid levels and its superior agronomic traits. Since the demand for high erucic acid from oilseed brassica has been increasing in the world market due to its wider applications in bio-industries, the breeding target of B. carinata has recently been focused on enhancing its erucic acid. Several high erucic acid B. carinata genotypes have been screened from the pre-breeding activities. Such genotypes, however, need to be tested for their stable performance, for their erucic acid level, and other desirable traits under different environments. The aim of this study was to identify high erucic acid B. carinata genotypes with stable performance in multiple desirable traits. Thirty-two B. carinata genotypes were grown in a randomized complete block design with three replications at three locations for two years. The genotypes were evaluated for nine desirable traits related to seed oil quality (erucic acid and oil content), seed yield, and other agronomic traits. The results showed that the proportion of genotype by environment interaction (GEI) was clearly observed in erucic acid, which led to a stability and mean performance analysis for selecting the most stable and best-performing genotypes for the desired traits. For such an analysis, we used the multi-trait stability index (MTSI) along with the weighted average of absolute score BLUPs (WAASB). As revealed from the MTSI, five genotypes (G13, G18, G10, G22 and G5) were identified as the most stable in erucic acid, oil content, seed yield, and other agronomic traits. The selected genotypes showed on average 45.7% erucic acid, 3185 kg ha−1 seed yield and 45.1% oil content with 4.3%, 25.8% and 6.9% positive selection gain, respectively. The negative selection gain of phonological traits and the plant height of the selected genotypes revealed their early maturity and their lower probability of being affected by lodging. Our findings demonstrated MTSI can be used to select high erucic acid B. carinata with a set of desirable traits, which would facilitate breeding efforts in developing novel and high erucic acid B. carinata varieties. Our results also showed that MTSI is an effective tool for selecting genotypes across different environments due to its unique ability to select multiple traits simultaneously.

Identification of High Erucic Acid Brassica carinata Genotypes through Multi-Trait Stability Index

Brassica carinata is an important and native oilseed crop in Ethiopia. The seed oil from B.carinata attracts global attention for its various industrial applications, mainly due to its high erucic acid levels and its superior agronomic traits. Since the demand for high erucic acid from oilseed brassica has been increasing in the world market due to its wider applications in bio-industries, the breeding target of B. carinata has recently been focused on enhancing its erucic acid. Several high erucic acid B. carinata genotypes have been screened from the pre-breeding activities. Such genotypes, however, need to be tested for their stable performance, for their erucic acid level, and other desirable traits under different environments. The aim of this study was to identify high erucic acid B. carinata genotypes with stable performance in multiple desirable traits. Thirty-two B. carinata genotypes were grown in a randomized complete block design with three replications at three locations for two years. The genotypes were evaluated for nine desirable traits related to seed oil quality (erucic acid and oil content), seed yield, and other agronomic traits. The results showed that the proportion of genotype by environment interaction (GEI) was clearly observed in erucic acid, which led to a stability and mean performance analysis for selecting the most stable and best-performing genotypes for the desired traits. For such an analysis, we used the multi-trait stability index (MTSI) along with the weighted average of absolute score BLUPs (WAASB). As revealed from the MTSI, five genotypes (G13, G18, G10, G22 and G5) were identified as the most stable in erucic acid, oil content, seed yield, and other agronomic traits. The selected genotypes showed on average 45.7% erucic acid, 3185 kg ha−1 seed yield and 45.1% oil content with 4.3%, 25.8% and 6.9% positive selection gain, respectively. The negative selection gain of phonological traits and the plant height of the selected genotypes revealed their early maturity and their lower probability of being affected by lodging. Our findings demonstrated MTSI can be used to select high erucic acid B. carinata with a set of desirable traits, which would facilitate breeding efforts in developing novel and high erucic acid B. carinata varieties. Our results also showed that MTSI is an effective tool for selecting genotypes across different environments due to its unique ability to select multiple traits simultaneously.

Epigenetic regulation by TET1 in gene-environmental interactions influencing susceptibility to congenital malformations.

The etiology of neural tube defects (NTDs) involves complex gene-environmental interactions. Folic acid (FA) prevents NTDs, but the mechanisms remain poorly understood and at least 30% of human NTDs resist the beneficial effects of FA supplementation. Here, we identify the DNA demethylase TET1 as a nexus of folate-dependent one-carbon metabolism and genetic risk factors post-neural tube closure. We determine that cranial NTDs in Tet1 -/- embryos occur at two to three times higher penetrance in genetically heterogeneous than in homogeneous genetic backgrounds, suggesting a strong impact of genetic modifiers on phenotypic expression. Quantitative trait locus mapping identified a strong NTD risk locus in the 129S6 strain, which harbors missense and modifier variants at genes implicated in intracellular endocytic trafficking and developmental signaling. NTDs across Tet1 -/- strains are resistant to FA supplementation. However, both excess and depleted maternal FA diets modify the impact of Tet1 loss on offspring DNA methylation primarily at neurodevelopmental loci. FA deficiency reveals susceptibility to NTD and other structural brain defects due to haploinsufficiency of Tet1 . In contrast, excess FA in Tet1 -/- embryos drives promoter DNA hypermethylation and reduced expression of multiple membrane solute transporters, including a FA transporter, accompanied by loss of phospholipid metabolites. Overall, our study unravels interactions between modified maternal FA status, Tet1 gene dosage and genetic backgrounds that impact neurotransmitter functions, cellular methylation and individual susceptibilities to congenital malformations, further implicating that epigenetic dysregulation may underlie NTDs resistant to FA supplementation.

Genetic Factors Contributing to Respiratory Disorders in Brachycephalic Dog Breeds in Uganda

Purpose: To aim of the study was to analyze the genetic factors contributing to respiratory disorders in brachycephalic dog breeds. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Genetic factors are primary contributors to respiratory disorders in brachycephalic dog breeds, known for their flattened facial structure. Breeds like Bulldogs and Pugs exhibit narrowed nostrils, elongated soft palates, and other airway obstructions due to selective breeding for these distinctive traits. These anatomical abnormalities restrict airflow, leading to symptoms such as noisy breathing and exercise intolerance. Unique Contribution to Theory, Practice and Policy: Genetic inheritance theory, evolutionary genetics theory &amp; genotype-environment interaction theory may be used to anchor future studies on genetic factors contributing to respiratory disorders in brachycephalic dog breeds. Implement breeding programs that prioritize genetic health over physical appearance. Establish and enforce regulatory standards for breeding brachycephalic dogs that include mandatory genetic testing for respiratory disorder markers.

BHAFT: Bayesian heredity-constrained accelerated failure time models for detecting gene-environment interactions in survival analysis.

In addition to considering the main effects, understanding gene-environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high-dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity-constrained accelerated failure time (BHAFT) models for identifying main and interaction (M-I) effects with novel spike-and-slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high-dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa-bayesian/BHAFT.

Stability analysis of male sterile-derived pearl millet (Pennisetum glaucum) hybrids under variable growing condition

The identification and deployment of high-yielding pearl millet [Pennisetum glaucum (L.) R.Br.] hybrids adapted to various stress agro-ecologies are crucial for enhancing food and nutrition security in northern India. A study was carried out during the rainy (kharif) seasons of 2019 and 2020 at CCS Haryana Agricultural University, Hisar, Haryana in different growing situations (early-sown and late-sown patterns and rainfed to irrigated conditions) to investigate the genotype-environment interaction (GEI) of 59 pearl millet restorer lines, male sterile lines and their derived hybrids. Pooled analysis of variance for genotype, environment and GEI was significant for days to 50% flowering, seed yield and harvest index. These results indicate the significant differences among the genotypes, various environments and response of GEI. By combining the results of regression analysis and additive main effects and multiplicative interaction (AMMI) biplot, the genotypes HMS58 A1×EMRL-14/105, HMS54 A5×EMRL-14/127 and EMRL-15/109 exhibited high mean seed yield and high stability across environments. Among the tested environments, irrigated late-sown condition for seed yield and harvest index; and rainfed early-sown condition for days to 50% flowering had the highest discrimination ability. Hence, this study can help for the grouping of pearl millet potential hybrids and allow multi-year trials for identifying the best genotypes in the northern India.

Comparative Field Evaluation and Transcriptome Analysis Reveals that Chromosome Doubling Enhances Sheath Blight Resistance in Rice

Rice sheath blight, caused by Rhizoctonia solani Kihn (R. solani), poses a significant threat to rice production and quality. Autotetraploid rice, developed through chromosome doubling of diploid rice, holds great potential for enhancing biological and yield traits. However, its resistance to sheath blight in the field has remained unclear. In this study, the field resistance of 35 autotetraploid genotypes and corresponding diploids was evaluated across three environments from 2020 to 2021. The booting stage was optimal for inoculating period based on the inoculation and analysis of R. solani at five rice growth stages. We found autotetraploids generally exhibited lower disease scores than diploids, indicating enhanced resistance after chromosome doubling. Among the 35 genotypes, 16 (45.71%) displayed increased resistance, 2 (5.71%) showed decreased resistance, and 17 (48.57%) displayed unstable resistance in different sowing dates. All combinations of the genotype, environment and ploidy, including the genotype-environment-ploidy interaction, contributed significantly to field resistance. Chromosome doubling increased sheath blight resistance in most genotypes, but was also dependent on the genotype-environment interaction. To elucidate the enhanced resistance mechanism, RNA-seq revealed autotetraploid recruited more down-regulated differentially expressed genes (DEGs), additionally, more resistance-related DEGs, were down-regulated at 24 h post inoculation in autotetraploid versus diploid. The ubiquinone/terpenoid quinone and diterpenoid biosynthesis pathways may play key roles in ploidy-specific resistance mechanisms. In summary, our findings shed light on the understanding of sheath blight resistance mechanisms in autotetraploid rice.

Gene-environment interaction in expertise acquisition: Practice effects on musical expertise vary by polygenic scores for cognitive performance

Expert performance is associated with practice, partly because of causal effects of practice on skill (i.e., learning). However, the practice-expertise association is also influenced by a complex interplay between genes and environment including partly overlapping genetic influences. The importance of cognitive ability in the practice-expertise association is less well understood. Therefore, we first examined whether genetic predisposition for cognitive performance, operationalized as a polygenic score, is associated with music practice and expertise. Next, we tested whether there is evidence for gene × environment interaction, i.e., whether effects of practice on expertise differ depending on an individual's genetic predisposition for cognitive performance. Polygenic scores for cognitive performance (PGScp) and multi-trait cognitive performances, including educational attainment and mathematical performances (PGScps) were calculated for approximately 3800 genotyped Swedish individuals with information available on their cumulative amount of music practice, musical achievement, and musical auditory discrimination. We found that higher PGScp and PGScps were associated with higher levels of achievement, musical auditory discrimination, and more practice, although the association with practice weakened when controlling for education. Music practice was linked to both expertise outcomes, and the effect sizes of these associations varied depending on an individual's PGScp and PGScps (with the exception of PGScp for musical auditory discrimination). These results suggest genetic pleiotropy between cognitive performance and musical expertise. Additionally, they reveal the presence of G × E interaction in skill acquisition, as effects of practice on musical expertise are stronger for individuals with a higher genetic predisposition for cognitive performance.

The Impact of Rheumatoid Arthritis on First Nations and How We Can Work With Communities to Prevent It.

Rheumatoid arthritis (RA) is prevalent in many Indigenous North American First Nations (FN) and tends to be seropositive, familial, and disabling, as well as associated with highly unfavorable outcomes such as early mortality. The risk of developing RA is based on a perfect storm of gene-environment interactions underpinning this risk. The gene-environment interactions include a high frequency of shared epitope encoding HLA alleles, particularly HLA-DRB1*1402, in the background population, and prevalent predisposing environmental factors such as smoking and periodontal disease. Together, these provide a compelling rationale for an RA prevention agenda in FN communities. Our research team has worked in partnership with several FN communities to prospectively follow the first-degree relatives of FN patients with RA, with the aim of better understanding the preclinical stages of RA in this population. We have focused on specific features of the anticitrullinated protein antibodies (ACPA) and other proteomic biomarkers as predictors of future development of RA. These studies have now led us to consider interventions having a favorable risk-benefit ratio if applied at a stage prior to a hypothetical "point of no return," when the autoimmunity potentially becomes irreversible. Based on a supportive mouse model and available human studies of curcumin, omega-3, and vitamin D supplements, we are undertaking studies where we screen communities using dried blood spot technology adapted for the detection of ACPA, and then enrolling ACPA-positive individuals in studies that use a combination of these supplements. These studies are guided by shared decision-making principles.

Application of AMMI and GGE Biplot for Genotype by Environment Interaction and Yield Stability Analysis in Potato Genotypes Grown in Dawuro Zone, Ethiopia

The performance of most crop genotypes is greatly influenced by the interactions between genotype and environment. Providing information on well-adapted and high-yielding potato genotypes in a given environment is paramount for small-holder farmers to enhance their productivity. The study aimed to understand GEI effect on tuber yield, and identify and select widely or specifically adapted high-yielding potato genotypes for production. Eleven genotypes were evaluated using a randomized complete block design with three replications. The results of the AMMI analysis of variance showed that tuber yield significantly (p < 0.05) influenced genotype-environment interaction. This reveals that genotypes exhibit varying mean performance in tuber yield across different environments. The sum squared results showed that genotype (62.40%) and environment (26.73%) were the main contributors to tuber yield variation, while the genotype-environment interaction effect (10.87%) contributed least to then tuber yield total variation. The AMMI, GGE biplot, and GSI analysis revealed Gudanie and Gorebella as superior genotypes in tuber yield, demonstrating high mean performance across tested environments. Thus, Gudanie and Gorebella have been chosen as the most broadly adaptable genotypes for production in all potato-growing agroecologies in the Dawuro zone. The GGE and AMMI biplot provided the genotype-by-environment interaction association among environments and genotypes.