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Data associated with publication: O'Brien et al. "The distribution of fitness effects during adaptive walks using a simple genetic network"

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/f3850b0&rft.title=Data associated with publication: O'Brien et al. The distribution of fitness effects during adaptive walks using a simple genetic network&rft.identifier=RDM ID: 321eb5b0-68a5-11ee-8106-a3d31968420c&rft.publisher=The University of Queensland&rft.description=This paper investigates the behaviour of adaptive walks to a phenotypic optimum when the phenotype is described by 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. Simulations were carried out in a custom version of SLiM 3.7.1 available at https://github.com/nobrien97/SLiM/releases/tag/AdaptiveWalks2023. 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), and per-locus heterozygosity (describing the per-locus heterozygosity over time).&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=2023&rft_rights= https://guides.library.uq.edu.au/deposit-your-data/license-reuse-data-agreement&rft_subject=eng&rft_subject=BIOLOGICAL SCIENCES&rft_subject=Evolutionary biology&rft_subject=Genetics not elsewhere classified&rft_subject=Genetics&rft_subject=Statistical and quantitative genetics&rft_subject=Bioinformatics and computational biology&rft.type=dataset&rft.language=English Access the data
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s4395747@student.uq.edu.au
School of Biological Sciences

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This paper investigates the behaviour of adaptive walks to a phenotypic optimum when the phenotype is described by 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. Simulations were carried out in a custom version of SLiM 3.7.1 available at https://github.com/nobrien97/SLiM/releases/tag/AdaptiveWalks2023. 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), and per-locus heterozygosity (describing the per-locus heterozygosity over time).

Issued: 18 10 2023

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Subjects
Biological Sciences | Bioinformatics and Computational Biology | Evolutionary Biology | Genetics | Genetics Not Elsewhere Classified | Statistical and Quantitative Genetics | eng |

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The distribution of fitness effects during adaptive walks using a simple genetic network

local : UQ:6b464ef

O’Brien, Nicholas L. V., Holland, Barbara, Engelstädter, Jan and Ortiz-Barrientos, Daniel (2024). The distribution of fitness effects during adaptive walks using a simple genetic network. PLOS Genetics, 20 (5) e1011289, e1011289. doi: 10.1371/journal.pgen.1011289

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local : UQ:289097

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