Find research datasets worth reusing

opencc-zeroMar 2024View details →

Directional epistasis is common in morphological divergence

Epistasis is often portrayed as unimportant in evolution. While random patterns of epistasis may have limited effects on the response to selection, systematic directional epistasis can have substantial effects on evolutionary dynamics. Directional epistasis occurs when allelic substitutions that change a trait also modify the effects of allelic substitutions at other loci in a systematic direction. In this case, trait evolution may induce correlated changes in allelic effects and effective genetic variance (evolvability) that modify further evolution. Although theory thus suggests a potentially important role for directional epistasis in evolution, we still lack empirical evidence about its prevalence and magnitude. Using a new framework to estimate systematic patterns of epistasis from line-crosses experiments, we quantify its effects on 197 size-related traits from diverging natural populations in 24 animal and 17 plant species. We show that directional epistasis is common and tends to become stronger with increasing morphological divergence. In animals, most traits displayed negative directionality toward larger size, suggesting that epistasis constraints reducing evolvability toward larger size may be common. Dominance was also common and did not systematically alter the effects of epistasis.

publicMar 2024View details →

Directional epistasis is common in morphological divergence

Open the record for dataset details and reuse information.

publicFeb 2020View details →

Epistasis, inbreeding depression and the evolution of self-fertilization

Open the record for dataset details and reuse information.

publicMar 2020View details →

Epistasis, inbreeding depression and the evolution of self-fertilization

Open the record for dataset details and reuse information.

opencc-zeroJun 2020View details →

Data from: Genotype-by-genotype epistasis for exploratory behavior in D. simulans

Social interactions can influence the expression and underlying genetic basis of many traits. Yet, empirical investigations of indirect genetic effects (IGEs) and genotype-by-genotype epistasis—quantitative genetics parameters representing the role of genetic variation in a focal individual and its interacting partners in producing the observed trait values—are still scarce. Studying this social plasticity is notoriously challenging when individuals interact in groups, rather than (simpler) dyads. Here, we investigate the genetic architecture of social plasticity for exploratory behavior, one of the most intensively-studied behaviors in recent decades. Using isofemale lines of D. simulans, we measured genotypes both alone, and in social groups representing a mix of two genotypes. We found that females adjusted their exploratory behavior based on the behavior of others in the group, representing social plasticity. However, the direction of this plasticity depended on the identity of group members: focal individuals adjusted their exploratory behavior to match that of group members who were the same genotype as the focal, but, changed their exploratory behavior to differentiate from partner-genotype group members. Exploratory behavior also depended on the identities of both genotypes that composed the group. Together, these findings demonstrate genotype-by-genotype epistasis for exploratory behavior both within and among groups.

zenodo36/100

Epistasis at the SARS-CoV-2 RBD Interface and the Propitiously Boring Implications for Vaccine Escape

This repository includes:   SI Appendix GISAID Acknowledgements An example resfile RosettaScripts .xml files Complex conformations (50 each) for WT/Delta/Gamma/Omicron; antibody (NAb) and receptor (ACE2)

opencc-by-4.0Aug 2021View details →

opencc-zeroNov 2022View details →

Data for: Testing for fitness epistasis in a transplant experiment identifies a candidate adaptive locus in Timema stick insects

Identifying the genetic basis of adaptation is a central goal of evolutionary biology. However, identifying genes and mutations affecting fitness remains challenging because a large number of traits and variants can influence fitness. Selected phenotypes can also be difficult to know a priori, complicating top-down genetic approaches for trait mapping that involve crosses or genome-wide association studies. In such cases, experimental genetic approaches, where one maps fitness directly and attempts to infer the traits involved afterward, can be valuable. Here, we re-analyse data from a transplant experiment involving Timema stick insects, where five physically clustered SNPs associated with cryptic body colouration were shown to interact to affect survival. Our analysis covers a larger genomic region than past work and revealed a locus previously not identified as associated with survival. This locus resides near a gene, Punch (Pu), involved in pteridine pigments production, implying that it could be associated with an unmeasured colouration trait. However, by combining previous and newly obtained phenotypic data, we show that this trait is not eye or body colouration. We discuss the implications of our results for the discovery of traits, genes, and mutations associated with fitness in other systems, as well as for supergene evolution.

publicNov 2022View details →

Data for: Testing for fitness epistasis in a transplant experiment identifies a candidate adaptive locus in Timema stick insects

Open the record for dataset details and reuse information.

publicMay 2025View details →

Testing for age- and sex- specific mitonuclear epistasis in Drosophila

Open the record for dataset details and reuse information.

publicJun 2020View details →

Data from: Genotype-by-genotype epistasis for exploratory behavior in D. simulans

Open the record for dataset details and reuse information.

opencc-zeroDec 2019View details →

Data from: Effect of epistasis and environment on flowering time of barley reveals novel flowering-delaying QTL allele

Flowering time is a complex trait and has key role in crop yield and adaptation to environmental stressors such as heat and drought. The aim of this study was to better understand interconnected dynamic of epistasis and environment and look for novel regulators. For this purpose we investigated 534 spring barley MAGIC DH lines for flowering time at various environments. Analysis of QTL, epistatic interaction, QTL × environment (Q×E) and epistasis × environment (E×E) interactions were performed with single SNP and haplotype approaches. In total, 18 QTL and 2,420 epistatic interactions were detected including intervals harboring major genes such as Ppd-H1, Vrn-H1, Vrn-H3 and denso/swd1. Epistatic interactions found in field and semi-controlled conditions were distinctive. Q×E and E×E interactions revealed that temperature influenced flowering time by triggering different interactions between known and newly-detected regulators. A novel flowering-delaying QTL allele could be identified on chromosome 1H (named "HvHeading") that was shown to be engaged in epistatic and environment interactions. Results suggest that investigating epistasis, environment and their interactions, rather than only single QTL is an effective approach for detecting novel regulators. We assume that barley can adapt the time of flowering to the environment through alternative routes within the pathway.

opencc-zeroJun 2022View details →

Genome-wide signatures of synergistic epistasis during parallel adaptation in a Baltic Sea copepod

The role of epistasis in adaptive evolution has remained an unresolved problem dating back to the Evolutionary Synthesis. This role is now being revisited due to its relevance for polygenic adaptation. In the absence of epistasis, polygenic adaptation is predicted to result in non-parallel evolution, because repeated selection could act on subsets of effectively redundant alleles. However, positive epistatic interactions among adaptive alleles would make the alleles non-redundant and selection for particular allelic combinations could drive parallel evolution. The inability to address this fundamental question might arise from traditional approaches lacking the power to capture the genomic architecture and dynamics of polygenic adaptation. To address this problem, we employed a replicated and controlled evolution experiment using the copepod Eurytemora affinis to elucidate the evolutionary response architecture to rapid salinity decline, a predicted consequence of global climate change in higher latitudes. Based on time-resolved pooled whole-genome sequencing, we uncovered a remarkably parallel response, despite polygenic adaptation involving over 1000 loci across ten replicate selection lines. Interestingly, single-nucleotide polymorphism (SNP) frequencies converged during the experiment, far beyond expectations, resulting in replicate lines sharing 93.1% of selected alleles. Using simulations, we found that this polygenic parallelism was consistent with synergistic epistasis among alleles responding in concert across replicate lines, a phenomenon that may be common for selection on complex physiological traits. Furthermore, we found that the same SNPs with signatures of selection in the laboratory also exhibited signatures of selection across a natural salinity gradient in the Baltic Sea. Our study provides the first experimental evidence that polygenic adaptation can actually be highly repeatable at the genomic level, given the presence of synergistic epistasis among the loci under selection.

zenodo32/100

Co-GWAS unveils the genetic architecture of inter-individual epistasis affecting biomass and disease severity in wheat binary mixtures

The repository contains all the data and scripts required to perform the analysis and generate the figures and tables presented in the article: "Co-GWAS unveils the genetic architecture of inter-individual epistasis affecting biomass and disease severity in wheat binary mixtures". 0. DATA FOLDER: 0_Data This folder contains the raw phenotypic and genotypic data. I. FILE PREPARATION FOLDER: 1_FilePrep_Design_Fig The R script prepares the phenotypic and genotypic data for analysis and generates the kinship matrix. It also creates a figure illustrating the pairs of phenotyped genotypes. II. PHENOTYPIC ANALYSIS  FOLDER: 2_GeneticEffects_PhenoCorr_Residus The R script tests the significance of genetic effects, calculates the proportion of phenotypic variance explained for each phenotype, and checks for correlations between phenotypes. It also controls the residuals in the models. III. A. DGE-BASED GWAS FOLDER: 3_DGE_GWAS This folder contains three subfolders for each phenotype (GWAS_DGE_B, GWAS_DGE_N, GWAS_DGE_P). For example, the pycnidia folder contains the script 3.A_GWAS_DGE_P_AsREML_cluster.R, which performs DGE-based GWAS for the pycnidia phenotype. III. B. Analysis of GWAS Results for DGE FOLDER: 3_DGE_GWAS Within the same folder, the script 3.B_Results_GWAS_DGE_P.Rmd combines the result files and produces Manhattan plots and plots of significant SNPs. IV. A. IGE-BASED GWAS FOLDER: 4_IGE_GWAS This folder contains three subfolders for each phenotype (GWAS_IGE_B, GWAS_IGE_N, GWAS_IGE_P). For example, the pycnidia folder contains the script 4.A_GWAS_IGE_P_AsREML_cluster.R, which performs IGE-based GWAS for the pycnidia phenotype. IV. B. Analysis of GWAS Results for IGE FOLDER: 4_IGE_GWAS Within the same folder, the script 4.B_Results_GWAS_IGE_P.Rmd combines the result files and produces Manhattan plots and plots of significant SNPs. V. PREPARATION OF FILES FOR CO-GWAS FOLDER: 5_FilePrep_coGWAS The script 5.A_FilePrep_coGWAS.Rmd prepares the phenotypic and genotypic data for analysis after SNP pruning. The script 5.B_FilePrep_SNP_Pruning.R performs SNP pruning. The script 5.C_Plot_SNP_Pruning_position.R plots the positions of SNPs before and after pruning. VI. CO-GWAS FOLDER: 6_coGWAS This folder contains three subfolders for each phenotype (coGWAS_B, coGWAS_N, and coGWAS_P).  For example, the pycnidia folder contains the script 6.A_coGWAS_DGEIGE_P_Sommer_cluster.R, which performs the co-GWAS for the pycnidia phenotype. The other scripts in this folder combine the result files. VII. HEATMAPS AND QQPLOT OF CO-GWAS RESULTS FOLDER: 7_Heatmaps_qqplots_coGWAS This folder contains three subfolders for each phenotype (Heatmaps_qqplots_B, Heatmaps_qqplots_N, and Heatmaps_qqplots_P). For example, the pycnidia subfolder includes two scripts: 7.A_coGWAS_P_Heatmaps.R generates heatmaps for the pycnidia phenotype and 7.B_coGWAS_P_qqplots.R produces the QQ plot. VIII. BOXPLOTS - 3D PLOTS - PHYSICAL MAPS FOLDER: 8_3Dplots_PhysicalMaps_Boxplots_coGWAS This folder contains three subfolders for each phenotype (Boxplots_PhysicalMaps_3Dplots_B, Boxplots_PhysicalMaps_3Dplots_N, and Boxplots_PhysicalMaps_3Dplots_P). For example, the pycnidia folder contains the script 8_coGWAS_P_Boxplots_PhysicalMaps_3Dplots.R, which generates 3D plots, boxplots and physical maps for the significant interactions.  IX. CIRCULAR PLOTS   FOLDER: 9_CircularPlots_coGWAS This folder contains three subfolders for each phenotype (CircularPlots_B, CircularPlots_N, and CircularPlots_P). For example, the pycnidia folder contains the script 9_coGWAS_P_CircularPlot.R, which generates the necessary files to create the circular plot.  The script circos.conf creates the circular plot.  X. GO ENRICHMENT ANALYSIS  FOLDER: 10_GOterms_coGWAS This folder contains three subfolders for each phenotype (GOenrichments_B, GOenrichments_N, and GOenrichments_P). For example, the pycnidia folder contains the script 10_coGWAS_P_GOenrichments.Rmd, which generates GO enrichment plots.

opencc-by-4.0Oct 2024View details →

ClinicalTrials.gov32/100

Genome-wide Epistasis for Cardiovascular Severity in Marfan Study

ClinicalTrials.gov study NCT06257004. IPD Sharing: Not stated. Countries: 1. Publications: 9.

restrictedIPD-UNDECIDEDFeb 2026View details →

publicAug 2019View details →

Data from: Environment changes epistasis to alter trade-offs along alternative evolutionary paths

Open the record for dataset details and reuse information.

publicDec 2017View details →

Data from: Diminishing-returns epistasis decreases adaptability along an evolutionary trajectory

Open the record for dataset details and reuse information.

publicApr 2018View details →

Data from: GxG epistasis in growth and condition and the maintenance of genetic polymorphism in Gambusia holbrooki

Open the record for dataset details and reuse information.