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Data associated with publication: Polygenic adaptation is constrained by variation in genetic network structure

The University of Queensland
Associate Professor Jan Engelstaedter (Aggregated by) Associate Professor Jan Engelstaedter (Aggregated by) Mr Nicholas O'Brien (Aggregated by) Mr Nicholas O'Brien (Aggregated by) Professor Daniel Ortiz-Barrientos (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/ed8140e&rft.title=Data associated with publication: Polygenic adaptation is constrained by variation in genetic network structure&rft.identifier=RDM ID: 42bdbed1-8031-4f21-b623-a19bc2731bcd&rft.publisher=The University of Queensland&rft.description=This dataset investigates the evolutionary outcomes of populations adapting towards a multivariate phenotypic optimum when the phenotype is controlled by one of five simple gene regulatory network motifs: negative or positive autoregulation, type 1 coherent or incoherent feedforward loops or a feedforward/feedback hybrid. The phenotype is constructed by solving an ordinary differential equation, which is modified by mutations at loci along the genome. Traits are various measurements of the solution over time. Simulations were carried out in a custom version of SLiM 4.3 available at https://github.com/nobrien97/SLiM/releases/tag/MultiMotifs2025. This dataset contains csv files measuring trait responses (detailing how the mean population trait value changed during adaptation), mutation information (allelic effect sizes, origin times, and frequencies), linkage disequilibrium, epistasis strength, and genetic variance, as well as characteristics of each motif's fitness landscape (ruggedness and holeyness).&rft.creator=Associate Professor Jan Engelstaedter&rft.creator=Associate Professor Jan Engelstaedter&rft.creator=Mr Nicholas O'Brien&rft.creator=Mr Nicholas O'Brien&rft.creator=Professor Daniel Ortiz-Barrientos&rft.creator=Professor Daniel Ortiz-Barrientos&rft.date=2025&rft_rights= https://guides.library.uq.edu.au/deposit-your-data/license-reuse-data-agreement&rft_subject=eng&rft_subject=BIOLOGICAL SCIENCES&rft_subject=Biological network analysis&rft_subject=Bioinformatics and computational biology&rft_subject=Statistical and quantitative genetics&rft_subject=Evolutionary biology&rft.type=dataset&rft.language=English Access the data
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s4395747@student.uq.edu.au
School of the Environment

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This dataset investigates the evolutionary outcomes of populations adapting towards a multivariate phenotypic optimum when the phenotype is controlled by one of five simple gene regulatory network motifs: negative or positive autoregulation, type 1 coherent or incoherent feedforward loops or a feedforward/feedback hybrid. The phenotype is constructed by solving an ordinary differential equation, which is modified by mutations at loci along the genome. Traits are various measurements of the solution over time. Simulations were carried out in a custom version of SLiM 4.3 available at https://github.com/nobrien97/SLiM/releases/tag/MultiMotifs2025. This dataset contains csv files measuring trait responses (detailing how the mean population trait value changed during adaptation), mutation information (allelic effect sizes, origin times, and frequencies), linkage disequilibrium, epistasis strength, and genetic variance, as well as characteristics of each motif's fitness landscape (ruggedness and holeyness).

Issued: 18 06 2025

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Biological Sciences | Bioinformatics and Computational Biology | Biological Network Analysis | Evolutionary Biology | Statistical and Quantitative Genetics | eng |

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local : UQ:289097

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