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Data associated with publication: The genetic architecture of polygenic adaptation under a network-derived trait

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:doi10.48610/1a19d80&rft.title=Data associated with publication: The genetic architecture of polygenic adaptation under a network-derived trait&rft.identifier=RDM ID: 98cfe1ec-450f-4f0e-b1e1-81106db1537c&rft.publisher=The University of Queensland&rft.description=This paper investigates the evolutionary outcomes of populations adapting towards a phenotypic optimum when the phenotype is a polygenic trait described by either an additive quantitative genetics model or a simple gene regulatory network, the negative autoregulation (NAR) motif. Under the network model, the trait is constructed by solving an ordinary differential equation, which is modified by mutations at loci along the genome. Two treatments of the NAR are constructed, with different coefficients in the equation being modified by mutations. Simulations were carried out in a custom version of SLiM 4.1 available at https://github.com/nobrien97/SLiM/releases/tag/PolygenicNAR2024. 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.&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=2024&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 paper investigates the evolutionary outcomes of populations adapting towards a phenotypic optimum when the phenotype is a polygenic trait described by either an additive quantitative genetics model or a simple gene regulatory network, the negative autoregulation (NAR) motif. Under the network model, the trait is constructed by solving an ordinary differential equation, which is modified by mutations at loci along the genome. Two treatments of the NAR are constructed, with different coefficients in the equation being modified by mutations. Simulations were carried out in a custom version of SLiM 4.1 available at https://github.com/nobrien97/SLiM/releases/tag/PolygenicNAR2024. 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.

Issued: 04 12 2024

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

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