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111 results for “genotype by environment”
Data from: Genotype-by-environment interactions for seminal fluid expression and sperm competitive ability
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Data from: Genetic parameters in subtropical pine F1 hybrids: heritabilities, between-trait correlations and genotype-by-environment interactions
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Data from: Predicting genotypes environmental range from genome-environment associations
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Data from: Divergence in DNA photorepair efficiency among genotypes from contrasting UV radiation environments in nature
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Data from: Investigating the production of sexual resting structures in a plant pathogen reveals unexpected self-fertility and genotype-by-environment effects
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A method for identifying environmental stimuli and genes responsible for genotype-by-environment interactions from a large-scale multi-environment data set
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Data from: Male-specific genotype by environment interactions influence viability selection acting on a sexually selected inversion system in the seaweed fly, Coelopa frigida.
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Data from: Stage-specific genotype-by-environment interactions for cold and heat hardiness in Drosophila melanogaster.
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Data from: What drives selection on flowering time? An experimental manipulation of the inherent correlation between genotype and environment
The optimal timing of the seasonal switch from somatic growth to reproduction can depend on an individual's condition at reproduction, the quality of the environment in which it will reproduce, or both. In annual plants, vegetative size (a function of age at flowering) affects resources available for seed production, while exposure to mutualists, antagonists, and abiotic stresses in the environment (functions of Julian date of flowering), influences success in converting resources into offspring. The inherent tight correlation between age, size, and environment obscures their independent fitness contributions. We isolated the fitness effects of these factors by experimentally manipulating the correlation between age at flowering and date of flowering in Brassica rapa. We staggered the planting dates of families with differing ages at flowering to produce experimental populations in which age at flowering and date of flowering were positively-, negatively-, or uncorrelated. In all populations, plants with an early date of flowering produced more seed than those flowering late, regardless of age or size at flowering onset. The temporal environment was thus the principal driver of selection on flowering time, but its importance relative to that of age and size varied with the presence/absence of herbivores and seed predators.
Data from: Quantifying genotype x environment effects in long-term common wheat yield trials from an agroecologically diverse production region
Multienvironment trials (METs) are used to investigate the performance of crop genotypes. To efficiently generate reliable performance estimates, the magnitude and patterns of genotype × environment interaction (G×E) in MET data must be known. We quantified G×E in fall-planted common wheat (Triticum aestivum L.) in California, with the goal of increasing the reliability and efficiency of statewide variety testing activities. Linear mixed models and the genotype main effects plus G×E interaction effects (GGE) biplot method were used to analyze MET data for 211 common wheat genotypes, the MET consisted of 9 locations and 14 seasons. The representativeness and discriminating power of the MET locations were tested, and estimates of the optimum number of test locations were made. The analyses did not find evidence for significant, repeatable, crossover G×E. The genotype and G×E effects were of a similar magnitude, and the G×E effects were relatively strong compared with other sources of variance but were dominated by seasonal effects, with potentially repeatable genotype-by-location (G×L) effects being relatively weak. The GGE analyses did not detect repeatable G×L patterns across seasons. As a result, we conclude that the cereal production regions of California consist of a single, but unstable, mega-environment for common wheat grain yield. The test location evaluation found few significant differences between test locations in terms of how well they represent the target production environment. We estimate that the number of test locations could be reduced while maintaining trial accuracy, which would improve the resource use efficiency of statewide trial activities without sacrificing information about variety-specific common wheat yield performance.
Data from: The interaction between genotype and juvenile and adult density environment in shaping multidimensional reaction norms of behaviour
1. Both juvenile (Ej) and adult (Ea) environment can alter developmental trajectories, independently or interactively (as environment by environment interaction; Ej×Ea), to shape behaviour in later life. However, within a population, the developmental response of behaviours to environments can vary among genotypes (G×E×E, multidimensional behavioural plasticity). 2. Here we use a full-sibling, split-brood experimental design and random regression model to study genetic variation in behavioural plasticity across juvenile/adult density conditions in four behavioural traits of male water striders Tenagogerris euphrosyne: exploration, dispersal, same-sex sexual behaviour and remounting attempts. 3. Our results showed that both juvenile and adult density affected the expression of behaviours, and that there was modest level of genetic variation in all behaviour traits. In contrast, there was little genetic variation in behavioural plasticity across density conditions at different life stages (G×Ej, G×Ea or G×Ej×Ea) observed in all traits other than same-sex sexual behaviour. 4. We suggest that experiences at different life stages can interact to affect behavioural expression, but the magnitude of G×Ej, G×Ea or G×Ej×Ea interactions depends on the specific traits. 5. Lack of genetic variation in multidimensional behavioural plasticity may be due to strong selection across environments, high prevalence of environmental heterogeneity and/or modest genetic variance. 6. Our results highlight the complex ways in which plasticity shapes the behaviours and life-histories of organisms.
Data from: Inferring the potentially complex genetic architectures of adaptation, sexual dimorphism, and genotype by environment interactions by partitioning of mean phenotypes.
Genetic architecture fundamentally affects the way that traits evolve. However, the mapping of genotype to phenotype includes complex interactions with the environment or even the sex of an organism that can modulate the expressed phenotype. Line cross analysis is a powerful quantitative genetics method to infer genetic architecture by analyzing the mean phenotype value of two diverged strains and a series of subsequent crosses and backcrosses. However, it has been difficult to account for complex interactions with the environment or sex within this framework. We have developed extensions to line cross analysis that allow for gene by environment and gene by sex interactions. Using extensive simulations studies and reanalysis of empirical data, we show that our approach can account for both unintended environmental variation when crosses cannot be reared in a common garden and can be used to test for the presence of gene by environment or gene by sex interactions. In analyses that fail to account for environmental variation between crosses we find that line cross analysis has low power and high false positive rates. However, we illustrate that accounting for environmental variation allows for the inference of adaptive divergence, and that accounting for sex differences in phenotypes allows practitioners to infer the genetic architecture of sexual dimorphism.
Genotype by environment interactions for chronic wasting disease in farmed U.S. white-tailed deer
<p>Despite the implementation of enhanced management practices, chronic wasting disease (CWD) in U.S. white-tailed deer (<em>Odocoileus virginianus</em>; hereafter WTD) continues to expand geographically. Herein, we perform the largest genome-wide association analysis (GWAA) to date for CWD (n = 412 CWD-positive; n = 758 CWD-non-detect) using a custom Affymetrix Axiom® single nucleotide polymorphism (SNP) array (n = 121,010 SNPs), and confirm that differential susceptibility to CWD is a highly heritable (h<sup>2</sup> = 0.611 ± 0.056) polygenic trait in farmed U.S. WTD, but with greater trait complexity than previously appreciated. We also confirm <em>PRNP</em> codon 96 (G96S) as having the largest effects on risk (<em>P</em> ≤ 3.19E-08; Phenotypic Variance Explained ≥ 0.025) across three U.S. regions (Northeast, Midwest, South). However, 20 CWD-positive WTD possessing codon 96SS genotypes were also observed, including one that was lymph node and obex positive. Beyond <em>PRNP</em>, we also detected 23 significant SNPs (<em>P</em>-value ≤ 5E-05) implicating ≥ 24 positional candidate genes; many of which have been directly implicated in Parkinson's, Alzheimer's, and prion diseases. Genotype-by-environment (GxE) interaction GWAA revealed a SNP in the lysosomal enzyme gene <em>ARSB</em> as having the most significant regional heterogeneity of effects on CWD (<em>P</em> ≤ 3.20E-06); with increasing copy number of the minor allele increasing susceptibility to CWD in the Northeast and Midwest; but with opposite effects in the South. In addition to <em>ARSB</em>, 38 significant GxE SNPs (<em>P</em>-value ≤ 5E-05) were also detected, thereby implicating ≥ 36 positional candidate genes; the majority of which have also been associated with aspects of Parkinson's, Alzheimer's, and prion diseases.</p>
Data for "Individual, but not population asymmetries, are modulated by social environment and genotype in Drosophila melanogaster"
<p>Dataset and scripts for the manuscript: "Individual, but not population asymmetries, are modulated by social environment and genotype in <em>Drosophila melanogaster". </em>We upload:</p> <p>1.Data. This directory contains the raw tracking data used for the analysis and the calibration files required by Flytracker for each strain of the Drosophila Genetic Reference Panel (RAL-69, RAL-136, RAL-338, RAL-535, RAL-796).<br> The format of the dataset consists in the output of tracking the (x,y) position and related features (e.g. velocity, distance) of flies in a circular arena using the software Flytracker - http://www.vision.caltech.edu/Tools/FlyTracker/ - For each video we have a trak.mat file and a feat.mat file. For instance, for a video in which we tracked the DGRP line RAL-136 (d338), with a pair of males (mm), trial 3 (t5) we had as outputs d338mmt5joined-feat.mat and d338mmt5joined-track.mat . You can find the description of the output here: http://www.vision.caltech.edu/Tools/FlyTracker/documentation.html</p> <p>2.Load_data _and_circling_scripts. This directory contains a MATLAB file with the instructions on how to load datasets and how to compute the circling index.</p> <p>3.Heatmap. This directory contains the instructions (readme_heatmap.txt) and scripts to (a) fix a bug in the feat files ouput by Flytracker using the calibration files and the script feat_compute_revised.m (b) plot the heatmap of the position and distance between partner flies in a dyad.</p>
Data from: Genotype-by-environment interactions for cuticular hydrocarbon expression in Drosophila simulans
Genotype-by-environment interactions (G x Es) describe genetic variation for phenotypic plasticity. Recent interest in the role of these interactions in sexual selection has identified G x Es across a diverse range of species and sexual traits. Additionally, theoretical work predicts that G x Es in sexual traits could help to maintain genetic variation, but could also disrupt the reliability of these traits as signals of mate quality. However, empirical tests of these theoretical predictions are scarce. We reared iso-female lines of Drosophila simulans across two axes of environmental variation (diet and temperature) in a fully factorial design and tested for G x Es in the expression of cuticular hydrocarbons (CHCs), a multivariate sexual trait in this species. We find sex-specific environmental, genetic and G x E effects on CHC expression, with G x Es for diet in both male and female CHC profile and a G x E for temperature in females. We also find some evidence for ecological crossover in these G x Es, and by quantifying variance components, genetic correlations and heritabilities, we show the potential for these G x Es to help maintain genetic variation and cause sexual signal unreliability in D. simulans CHC profiles.
Genotype-by-environment interactions for precopulatory mate guarding in a lek-mating insect
In sexually reproducing species males often experience strong pre- and postcopulatory sexual selection leading to a wide variety of male adaptations. One example is mate guarding, where males prevent females from mating with other males either before or after they (will) have mated themselves. In case social conditions vary short-term and in an unpredictable manner and if there is genetic variation in plasticity of mate guarding (i.e. genotype-by-environment interaction, G x E), adaptive behavioral plasticity in mate guarding may evolve. Here, we test for genetic variation in the plasticity of precopulatory mate guarding behavior in the lek-mating lesser wax moth Achroia grisella. When offered two females in rapid succession, virgin males of this species usually copulate around 10-20 min with the first female. With the second female, however, they engage in copulation posture for many hours until they have produced another spermatophore, an unusual behavior among insects possibly functioning as precopulatory mate guarding. Previous studies showed the mating latency with the second female to be shorter under higher perceived sperm competition risk. We accordingly measured the mate guarding behavior of males from six inbred lines under either elevated perceived male-male competition risk or under control conditions allowing us to test for G x E interactions. We found significant inbred line-by-competitor treatment interactions on mating latency and copulation duration with the second female suggesting genetic variation in the degree of behavioral plasticity. However, we found no significant G x E interaction on the sum of mating latency and copulation duration. Our results suggest a potential for adaptive evolution of mate guarding plasticity in natural populations of lek-mating species. Future studies using selection experiments and experimental evolution approaches in laboratory populations, or comparisons of multiple natural populations will be helpful to study under which conditions plasticity in male mate guarding behavior evolves.
Data from: The interaction between genotype and juvenile and adult density environment in shaping multidimensional reaction norms of behaviour
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Data from: Genotype × environment interaction in the allometry of body, genitalia, and signal traits in Enchenopa treehoppers (Hemiptera: Membracidae)
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Data from: Plastic multicellular development of Myxococcus xanthus: genotype-environment interactions in a physical gradient
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Data from: Sex-specific genotype-by-environment interactions for cuticular hydrocarbon expression in decorated crickets, Gryllodes sigillatus: implications for the evolution of signal reliability
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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