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Results for “genotype by environment interactions”

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At least 19 recordsLinked to original sources
PubMed1977

The use of regression methods to study genotype-environment interactions: extending Griffing's model for diallel cross experiments and testing an empirical grouping method.

A model combining features of Griffing's diallel cross analysis with regression analysis for genotype-environment interactions is introduced using carp data of Moav et al. (1975) as an example. An analysis of variance based on this model provides information on the combining abilities of genetic effects and the interactions of these effects with environments from which inferences can readily be made on heterosis and heterosis-environment interactions. Applying the empirical grouping method of Lin and Thompson (1975) to these data (ignoring their diallel cross structure) established groups which were remarkably consistent with their members' crossing backgrounds.

Alleles↗
PubMed2025

Genotype by Environment Interactions in Gene Regulation Underlie the Response to Soil Drying in the Model Grass Brachypodium distachyon.

Gene expression is a quantitative trait under the control of genetic and environmental factors and their interaction, so-called genotype and environment (G × E). Understanding the mechanisms driving G × E is fundamental for ensuring stable crop performance across environments and for predicting the response of natural populations to climate change. Gene expression is regulated through complex molecular networks, yet the interactions between genotype and environment in gene regulation are rarely considered, particularly at the genome scale. Current frameworks and experimental designs often lack power to explicitly test network rewiring or to systematically compare regulatory networks. Here, we leverage a highly replicated RNA-sequencing dataset to model genome-scale gene expression variation between two natural accessions of the model grass Brachypodium distachyon and their response to soil drying. We first identified genotypic, environmental, and G × E effects on physiological, metabolic, and gene expression traits. We identify patterns of conservation-or variation-in gene coexpression networks and link these coexpression features to physiological traits. We further develop predictions of gene-gene interactions using causal inference and screen for interactions specific to-or with higher affinity in-a single genotype, treatment, or their interaction, G × E. Our analyses identify variation in candidate gene regulatory networks that may shape the evolution of environmental response in B. distachyon. We highlight the environmentally dependent regulatory control of several metabolic traits shown previously to play a role in drought acclimation. The framework presented here provides a scalable approach for more complex comparisons, particularly with the growing availability of large datasets from technologies such as single-cell transcriptomics.

Brachypodium↗
PubMed2026

Using high-dimensional environmental covariates to study genotype by environment interaction for reproductive traits in Duroc boars.

We investigated the potential of incorporating grid-cell-based environmental covariates (ECs) in the genetic evaluation of total sperm count (TSC), sperm motility (MOT), and sperm morphology (MOR) for Duroc boars. A total of 188,665 records derived from 3,684 genotyped boars, born between December 2018 and October 2024 and raised in three stud farms located in different U.S. states, were analyzed using multi-trait linear-threshold repeatability models. To account for genotype by environment interactions (GE), we constructed an interaction matrix as the Hadamard product of the genomic relationship matrix and an environmental (co)variance matrix. The environmental groups were defined in three ways: farm, farm-season, and farm-year-season. The (co)variance matrix was constructed based on daily ECs obtained from the NASA POWER database for each environmental group. Of all available ECs, those significantly associated with TSC, MOT, and MOR (temperature, relative humidity, atmospheric pressure, and wind speed and direction) were retained. We evaluated five models with different GE structures: M1 represented the baseline without accounting for GE, in M2 the GE included farm as environmental groups, in M3 the GE included farm-season as environmental groups, in M4 the GE included farm-year-season as environmental groups, and M5 involved M3 with an additional random effect of the farm-season. Estimates of heritability for TSC, MOT, and MOR ranged from 0.03 to 0.04, 0.05 to 0.08, and 0.04 to 0.08, respectively. Corresponding repeatability ranged from 0.15 to 0.23, 0.28 to 0.49, and 0.28 to 0.49. The proportion of phenotypic variance attributed to GE variance ranged from 0.00 to 0.32, 0.00 to 0.44, and 0.00 to 0.44. Lastly, estimates of genetic correlation, TSC-MOT, TSC-MOR, and MOT-MOR ranged from 0.27 to 0.31, 0.24 to 0.31, and 0.98 to 0.99, respectively, with minor differences across models. We assessed the predictive ability of models using the linear regression validation. Across traits and models, bias ranged from -0.05 to 0.02 standard deviations, slope varied from 0.88 to 0.99, the correlation ranged from 0.75 to 0.84, and accuracy from 0.41 to 0.53. Overall, building the GE matrix considering grid-cell-based ECs helped to account for GE, thereby reducing the proportion of phenotypic variance attributed to genetic components; however, it did not improve the validation metrics. Additional on-farm records for ECs may improve the model performance.

Animals↗
PubMed2025

Prediction of Australian wheat genotype by environment interactions and mega-environments.

Latent environmental effects of genotype by environment interactions could be predicted from observed environmental covariates. Predictions into the wider target population of environments revealed greater insights. Wheat is grown across a diverse range of environments in Australia with contrasting environmental constraints. Targeted breeding to optimise genotypes in target environments is hindered by large and ubiquitous genotype by environment interactions (GEI). Common GEI in multi-environment trial experiments, which sample the target population of environments, can be efficiently modelled using latent environmental effects from factor analytic mixed models. However, generalised prediction into the full target population of environments is difficult without a clear link to observed environmental covariates (ECs) that are defined from high-resolution weather and soil data. Here, we used a large wheat multi-environment trial dataset and demonstrated that latent environmental effects can be associated with and predicted from observed ECs. We found GEI-based environment classes could be defined by combinations of key ECs. Prediction of main and latent effects in a wider set of environments covering the full TPE across the Australian grain belt over 13 years revealed the complex trends of environmental effects and GEI over regional scales demonstrating high year-to-year variability. Regional environment types often shifted year-to-year. Cross-validation of forward genomic prediction into untested year environments demonstrated that increased accuracy is possible if estimated genetic effects are also accurate and ECs of new environments are known. These findings may guide Australian wheat breeders to better target specifically adapted material to mega-environments defined by static GEI while also considering broad adaptability and non-static GEI resulting from year-to-year variability.

Triticum↗
PubMed2025

Genomic prediction of agronomic traits in perennial ryegrass (Lolium perenne L.) and genotype x environment interactions at the limit of the species distribution.

KEY MESSAGE: Perennial ryegrass shows extensive genotype x environment interactions at the limit of its ecological niche. Accounting for GxE may improve prediction even when environmental and genetic samples are highly diverse. BACKGROUND: In breeding the aim is to identify and accumulate beneficial variants. However, detection of these variants may be challenging in the presence of extensive genotype x environment interactions (GxE). METHODS: The study assesses the performance of 264 diploid perennial ryegrass accessions in a multi-environment field trial. We investigate the extent of GxE, for yield (total dry matter) and persistence traits under environmental conditions experienced in Nordic and Baltic regions at the limit of the species distribution. Two different approaches to modelling GxE were tested and validated under three different breeding scenarios. RESULTS: Our analysis documented the presence of significant GxE for all traits. Validation showed improvements in prediction accuracy when accounting for GxE: up to 4% for yield when predicting in unobserved environments, and up to 22% and 9% for spring cover and winter kill, respectively, when predicting unobserved germplasm. Genome-wide-association-studies (GWAS) were utilized to detect genetic variants with marginal effects (environment-independent effect) and conditional effects (environment-dependent effects). Results showed the presence of large-effect genetic variants with marginal effects, in addition to few Quantitative Trait Loci (QTL) whose effects were adaptive under specific environmental conditions while neutral or deleterious under different environmental conditions. CONCLUSION: This study demonstrates the usefulness and limitations of genomic prediction models for predicting GxE in highly diverse samples and describes the extent of GxE at the limit of species distribution for perennial ryegrass. Our study points towards adaptive variation which may enhance persistence of perennial ryegrass populations in Nordic and Baltic growing conditions.

Lolium↗
PubMed2025

Haplotype stacking to improve stability of stripe rust resistance in wheat.

Genotype-by-environment interaction analysis and haplotype-level characterisation provide novel insights into the stability of stripe rust resistance. Breeding selection strategies are proposed to achieve rapid and stable genetic gains across environments. This study investigated stripe/yellow rust (YR) responses in the Vavilov wheat diversity panel evaluated across 11 field experiments conducted in Australia and Ethiopia during 2014-2021. Genotype-by-environment interaction (GEI) was analysed using a factor analytic (FA) model. Genotype-level selection was performed with overall performance (OP) and root-mean-square deviation (RMSD), which reflected average performance and stability of YR resistance across environments, respectively. Genomic estimated breeding values (GEBV) for these traits were calculated and compared with those from a multi-trait GBLUP model with average performance represented by the mean GEBV across environments and stability by the standard deviation of GEBV across environments. The FA-based and multi-trait GBLUP GEBV had high correlations. Haplotypes with large effects on OP and RMSD were identified using the local GEBV method. Favourable haplotypes were then used for stacking in breeding simulations, using the Vavilov collection as a base. Compared to truncation selection, optimal haplotype selection (OHS) using an artificial intelligence (AI)-based algorithm achieved longer-term genetic gains for both OP and RMSD (after many generations) by initially selecting founder parents that maximised favourable haplotypes. Simulations using YR responses from diverse environments that mimicked fluctuating environmental conditions across seasons were conducted to evaluate strategies for selection of YR resistance that is stable across years. Strategies which gave most weight to OP, but some weight to RMSD were optimal in these conditions, and substantially reduced variation of performance across years. This study provides useful information for breeding cultivars with both high YR resistance and high stability of resistance across environments.

Triticum↗
PubMed2017

Infection rate of Aedes aegypti mosquitoes with dengue virus depends on the interaction between temperature and mosquito genotype.

Dengue fever is the most prevalent arthropod-transmitted viral disease worldwide, with endemic transmission restricted to tropical and subtropical regions of different temperature profiles. Temperature is epidemiologically relevant because it affects dengue infection rates in Aedes aegypti mosquitoes, the major vector of the dengue virus (DENV). Aedes aegypti populations are also known to vary in competence for different DENV genotypes. We assessed the effects of mosquito and virus genotype on DENV infection in the context of temperature by challenging Ae. aegypti from two locations in Vietnam, which differ in temperature regimes, with two isolates of DENV-2 collected from the same two localities, followed by incubation at 25, 27 or 32°C for 10 days. Genotyping of the mosquito populations and virus isolates confirmed that each group was genetically distinct. Extrinsic incubation temperature (EIT) and DENV-2 genotype had a direct effect on the infection rate, consistent with previous studies. However, our results show that the EIT impacts the infection rate differently in each mosquito population, indicating a genotype by environment interaction. These results suggest that the magnitude of DENV epidemics may not only depend on the virus and mosquito genotypes present, but also on how they interact with local temperature. This information should be considered when estimating vector competence of local and introduced mosquito populations during disease risk evaluation.

Aedes↗
PubMed2025

Experimental Validation of Genome-Environment Associations in Arabidopsis.

Identifying the genetic basis of local adaptation is a key goal in evolutionary biology. Allele frequency clines along environmental gradients, known as genotype-environment associations (GEA), are often used to detect potential loci causing local adaptation but are rarely followed by experimental validation. Here, we tested loci identified in three moisture-related GEA studies on Arabidopsis. We studied 42 GEA-identified genes using t-DNA knockout lines under drought and tested effects on flowering time, an adaptive trait, and genotype-by-environment (GxE) interactions for performance and fitness. In total, 16/42 genes had significant effects on traits involved in local adaptation or performance responses to the environment. We found that wrky38 mutants had significant GxE effects for fitness; lsd1 plants had a significant GxE effect for flowering time, and 11 genes showed flowering time effects with no drought interaction. However, most GEA candidates did not exhibit GxE. In the follow-up experiments, wrky38 caused decreased stomatal conductance and specific leaf area under drought, indicating potentially adaptive drought avoidance. Additionally, GEA identified natural putative LoF variants of WRKY38 associated with dry environments, as well as alleles associated with variation in LSD1 expression. While only a few GEA-identified genes were validated for GxE interactions for fitness, we likely overlooked some genes because experiments might not well represent natural environments and t-DNA insertions might not well represent natural alleles. Nevertheless, GEAs apparently identified some genes contributing to local adaptation. GEA and follow-up experiments are straightforward to implement in model systems and demonstrate prospects for GEA discovery of new local adaptations.

Arabidopsis↗
PubMed2026

Genome-wide association study combined with multi-assay phenotyping identifies a novel anthracnose resistance locus in apple.

BACKGROUND: Apple anthracnose, a disease complex that includes Glomerella leaf spot (GLS) and bitter rot caused by Colletotrichum species, is a major disease affecting apple production worldwide. In this study, we combined multi-year field evaluations with controlled inoculation assays to identify genomic regions associated with anthracnose resistance in apple. RESULTS: A total of 440 apple genotypes, including 411 F₁ progenies derived from six parental crosses and 29 cultivars, were evaluated under natural orchard conditions and through artificial fruit and leaf inoculation assays using wound and non-wound methods. Disease severity varied substantially between years, particularly under contrasting environmental conditions, indicating strong genotype-by-environment interactions. Genome-wide association analysis (GWAS) using field-derived disease severity scores from 2019 identified a significant quantitative trait locus (QTL) on chromosome 15 (~ 31.8 Mb) associated with reduced anthracnose severity. This locus was distinct from the previously reported Rgls/MdTNL1 region on chromosome 15 (~ 2-5 Mb), suggesting the presence of a novel resistance-associated locus. In contrast, no genome-wide significant associations were detected from artificial inoculation datasets. CONCLUSIONS: These findings demonstrate the importance of field-based, multi-environment phenotyping for detecting field-relevant resistance loci and improving understanding of the genetic architecture underlying anthracnose resistance in apple.

Malus↗
PubMed2026

From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement.

Sugarcane (Saccharum spp.) underpins global sugar and bioenergy supply and is increasingly valued as a renewable biomass feedstock. Sustained improvement in commercial traits and resilience is constrained by long breeding cycles, clonal propagation, multi-stage testing, and a highly polyploid, heterozygous, and frequently aneuploid genome with substantial non-additive genetic variation. Genomic selection has demonstrated value for predicting elite-clone performance, yet its operational use remains limited at earlier decision points, including family selection, parent evaluation, and cross design. This review examines the biological, statistical, and genomic factors that shape these decisions, with emphasis on the Australian breeding context based on progeny assessment trials (PATs), clonal assessment trials (CATs), and final assessment trials (FATs). We evaluate challenges arising from family plot means, the use of different full-sib samples as nominal family replicates, spatial heterogeneity, competition, genotype-by-environment interaction, and the partitioning of additive and non-additive effects. We also assess the integration of pedigree and genomic relationship, genotype representation, allele-dosage estimation, aneuploidy, genomic prediction models, and training-population design. We then consider genomic prediction of cross performance and constrained mate allocation as approaches for improving expected family performance, accounting for cross-specific non-additive effects and managing relatedness. We propose a decision-centred framework that links family and clonal data across breeding stages, tracks the propagation of information and uncertainty, and supports parent recycling and cross allocation. We conclude with a practical research agenda for stage-integrated mixed-model and single-step analyses that connect early family evaluation with genomic prediction and cross-level decision support in sugarcane breeding.

Saccharum↗
PubMed2026

Divergent Biological Consequences of APOE Isoforms Across Industrialized and Non-Industrial Environments.

The apolipoprotein ε4 (APOE ε4) isoform directly alters cholesterol and immune biology and is associated with an increased risk of neurodegenerative and cardiometabolic disease in industrialized settings; nevertheless, APOE ε4-which is ancestral in humans-has persisted over evolutionary time. One potential explanation is that the costs and benefits of APOE ε4 were significantly different in the environments in which humans evolved compared to those we experience today. In support, previous work has suggested that living in a high pathogen environment, engaging in high levels of physical activity, or eating a low fat diet can dampen the detrimental effects of APOE ε4, and has revealed positive effects for fertility. However, direct tests of whether APOE isoforms are associated with different biological outcomes in non-industrial versus industrialized contexts are lacking. Working with the Turkana of Kenya and the Orang Asli of Peninsular Malaysia-two Indigenous groups in which individuals of shared ancestry span a continuum of subsistence, non-industrial to urban, industrialized lifestyles-we investigated how APOE genotypes impact cholesterol, immunological, and reproductive traits and tested for genotype x environment (GxE) interactions. First, we confirmed established genotype effects across lifestyles, showing that more APOE ε4 alleles are associated with higher total cholesterol, higher LDL cholesterol, and lower HDL cholesterol. Second, we tested for lifestyle interactions, finding lifestyle-dependent effects of genotype on innate immune biomarkers in the Orang Asli but not Turkana. Finally, we show that more APOE ε4 alleles are correlated with an extended reproductive lifespan, however this effect is relatively weak, is not consistent across populations, and does not correspond with a higher reproductive output. Together, our study provides evidence that industrialized environments can modify the biology of APOE ε4; however, we find that APOE ε4 is not universally beneficial in non-industrial contexts, highlighting the role of local environmental variation in determining its specific costs and benefits.

Journal Article↗
PubMed1977

The time and duration of meiosis.

Ever since meiosis was recognized as a process there has been a continuing interest in its temporal aspects. Two main types of meiotic timing experiments have been conducted: first, experiments to estimate the duration of meiosis (and sometimes its stages); second, experiments to locate the sensitive stage(s) when exposure of meiocytes to various treatments can affect meiotic chromosome behaviour (e.g. pairing or recombination). Such experiments have played an important role in increasing our understanding of the meiotic process. The duration of meiosis has been estimated in about 70 organisms, including two prokaryotes (yeast and Chlamydomonas) and the following eukaryotes: 1 Basidiomycete (Coprinus lagopus), 2 Gymnosperms (Larix decidua and Thuja plicata gracilis). at least 39 angiosperms, and at least 26 animal species. The duration of female meiosis has been estimated in far fewer species than male meiosis. However, estimates of the duration of female meiosis are available for 6 angiosperms. Drosophila melanogaster, Xenopus laevis, and several mammals. Comparison of these data shows that the duration of meiosis is one of the most variable aspects of the meiotic process, ranging from less than 6 h in yeast to more than 40 years in the human female. Developmental holds at different stages of meiosis are common in plants and animals, and inevitably prolong the meiotic division. However, even among species without developmental holds, the duration of meiosis is very variable. For instance, in animals it ranges from about 1-2 days in male Drosophila melanogaster to more than 24 days in male Homo sapiens and several Orthopterans. Despite the large variation in the duration of meiosis three generalizations can be made: (i) first prophase is always very long compared with the remaining meiotic stages, (ii) the rate of meiotic development is very slow compared with the rate of development in dividing somatic meristem cells of the same organisms under the same conditions, (iii) the duration of meiosis is characteristic of the genotype and species. Four main factors have been recognized which effect or determine the duration of meiosis, namely (1) environmental factors (e.g. temperature); (2) nuclear DNA content; (3) ploidy level of the organism; and, (4) the genotype. Because nuclear DNA content plays a major role in determining the duration of meiosis, it has been suggested that DNA influences the rate of meiotic development in two ways: first through its informational content (the genotype), and second indirectly by the physical and mechanical effects of its mass independently of its informational content (i.e. the nucleotype). Thus, the observed duration of meiosis is the result of a complex genotype-nucleotype-environment interaction. With the obvious exception of variation caused by developmental holds, changes in the duration of meiosis usually involve proportional changes in the durations of all its stages...

Animals↗
PubMed2024

Genomic selection for tolerance to aluminum toxicity in a synthetic population of upland rice.

Over half of the world's arable land is acidic, which constrains cereal production. In South America, different rice-growing regions (Cerrado in Brazil and Llanos in Colombia and Venezuela) are particularly affected due to high aluminum toxicity levels. For this reason, efforts have been made to breed for tolerance to aluminum toxicity using synthetic populations. The breeding program of CIAT-CIRAD is a good example of the use of recurrent selection to increase productivity for the Llanos in Colombia. In this study, we evaluated the performance of genomic prediction models to optimize the breeding scheme by hastening the development of an improved synthetic population and elite lines. We characterized 334 families at the S0:4 generation in two conditions. One condition was the control, managed with liming, while the other had high aluminum toxicity. Four traits were considered: days to flowering (FL), plant height (PH), grain yield (YLD), and zinc concentration in the polished grain (ZN). The population presented a high tolerance to aluminum toxicity, with more than 72% of the families showing a higher yield under aluminum conditions. The performance of the families under the aluminum toxicity condition was predicted using four different models: a single-environment model and three multi-environment models. The multi-environment models differed in the way they integrated genotype-by-environment interactions. The best predictive abilities were achieved using multi-environment models: 0.67 for FL, 0.60 for PH, 0.53 for YLD, and 0.65 for ZN. The gain of multi-environment over single-environment models ranged from 71% for YLD to 430% for FL. The selection of the best-performing families based on multi-trait indices, including the four traits mentioned above, facilitated the identification of suitable families for recombination. This information will be used to develop a new cycle of recurrent selection through genomic selection.

Oryza↗
PubMed1978

[Intelligence : individual differences, genetic factors, environmental factors and interaction between the genotype and the environment (author's transl)].

The authors undertook a review of published work on the sources of variation in individual differences in intelligence. They should : 1) that methods of quantitative genetics concerning intelligence are not applicable to human populations ; 2) that the results of studies on adoptions and on twins do not permit one to estimate the respective roles of environment and heredity ; 3) that this division of variance had no heuristic value in the study of human intelligence.

Animals↗
PubMed2026

Grain protein and yield stability study in rainfed durum wheat RILs.

Developing durum wheat cultivars with stable grain yield across diverse environments remains a key breeding objective. This study evaluated 118 recombinant inbred lines (RILs) derived from a cross between the drought-adapted cultivar 'Zardak' (Triticum durum) and the landrace 'Iran-249' (T. turanicum) with desirable seed characteristics, across four heterogeneous rainfed environments in Italy and Iran. The assessment focused on grain yield (GY) and grain protein content (GPC) stability. Combined analysis of variance revealed significant (p&#x2009;<&#x2009;0.01) effects for genotype, environment, and their interaction for both traits. Line ZD-050 showed the highest GY (3.91 t ha&#x207b;&#xb9;), while ZD-032 had the highest GPC (14.27%). Stability analysis using parametric and non-parametric methods, along with AMMI and GGE biplot modeling, identified ZD-050 as among the most promising genotypes according to yield-integrating and dynamic-stability approaches. This line showed high grain yield in methods such as the Superiority Index and Kang's rank-sum, although stability rankings differed across the used methods. This line maintained superior yield, demonstrated broad adaptability across environments, and had moderate protein levels, identifying it as an optimal candidate for breeding programs targeting yield stability and wide adaptation under rainfed conditions.

Triticum↗