ASanchez-Tojar / meta-analysis_IGEs

This is a repository that contains all code and data needed to reproduce the results of a meta-analytic study on indirect genetic effects

DOI: to be added via the Zenodo integration upon acceptance of this work in a scientific journal.

Meta-analysis of indirect genetic effects

This repository contains the data, code and other materials used in the following study:

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Francesca Santostefano*, Maria Moiron*, Alfredo Sánchez-Tójar*, David N. Fisher*. 2024. Indirect genetic effects increase the heritable variation available to selection and are largest for behaviours: a meta-analysis. Submitted. Preprint available here

*All authors contributed equally to this work

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The repository consists of an Rproject (meta-analysis_IGEs.Rproj) with 6 scripts and 3 folders containing the data either needed to run these scripts or created by the scripts, including the figures. For a detailed description of all the files, please see below. For any further information about this repository, please contact: Alfredo Sánchez-Tójar, email: [e-mail].

Information about scripts, folders and files within:

Scripts:

• 001_systematic_review.R : used to import the results of the systematic literature review conducted in Web of Science and Scopus.

  • Input: Web of Science and Scopus keyword search, and the 8 .bib files corresponding to the snowballing search

    • Web of Science keyword search results: WoS_IGEs_search.bib
    • Scopus keyword search results: Scopus_IGEs_search.bib
    • Snowballing search results: 8 .bib files found here
    • Deduplicated list of unique references to be formatted for importing into Rayyan

  • Output:

    • Preliminary list of unique references for further deduplication outside R.
    • Deduplicated full reference list to be imported into Rayyan for the title-and-abstract screening
    • R session information for reproducibility purposes

• 002_fulltext_screening.R : used to import the results of the title-and-abstract screening conducted using Rayyan and to generate the database needed to proceed with the full-text screening phase.

  • Input:

    • List of unique reference list with title-and-abstract decisions

  • Output:

    • Four full-text screening subsets (as .xlsx files) including the list of references assigned to each of the 4 observers. Files available here
    • R session information for reproducibility purposes

• 003_data_extraction.R : used to import the results of the full-text screening and to generate the databases needed to proceed to the data extraction phase.

  • Input:

    • List of unique reference list with title-and-abstract decisions
    • Final list of studies included after full-text screening
    • Final list of studies included after full-text screening and using animal models

  • Output:

    • Four data-extraction subsets (as .xlsx files) including the list of references assigned to each of the 4 observers. Files available here
    • Four data double-extraction subsets (as .xlsx files) including the list of references assigned to each of the 4 observers. Files available here

• 004_dataset_cleaning.R : used to clean the meta-analytic dataset and generate the version for the analyses that is imported by 005_data_analysis.r.

  • Input:

    • Four datasets with the full data extraction of each of the 4 observers. The four files are .csv and their name starts with 'Data from papers'. All available here)
    • Final list of studies included after full-text screening and using animal models
    • List of references created by this script before contacting authors, edited outside R, and imported back to be filled after having contacted the authors

  • Output:

    • List of references before contacting authors
    • Full list of references with data, including data obtained by author correspondence. This list will be imported by 005_data_analysis.r for the analyses

• 005_data_analysis.r : used to perform all the analyses.

  • Input:

    • Final dataset for analyses: the metadata providing a description for each of the variables is available here
    • Taxonomic data extracted from the Open Tree of Life created by this script and then imported for reproducibility purposes
    • Tree file created by this script and then imported for reproducibility purposes
    • Phylogenetic correlation matrix created by this script and then imported for reproducibility purposes

  • Output:

    • Taxonomic data extracted from the Open Tree of Life using the function 'tnrs_match_names()'
    • Tree file
    • Phylogenetic correlation matrix
    • Meta-analytic and meta-regression models. All available as .RData here and will be imported by 006_figures.r for generating the figures.
    • Model-specific datasets. All available as .csv here and will be imported by 006_figures.r for generating the figures.

• 006_figures.r : used to generate the all figures included in the main text and the supplementary materials (except Figure 1 from the main text).

  • Input:

    • Meta-analytic and meta-regression models from here
    • Model-specific datasets from here

  • Output:

    • All figures included in the main text and the supplementary materials (except Figure 1 from the main text)

Folders:

• literature_review:

  • systematic_search: contains 2 .bib files imported by 001_systematic_review.R
  • snowballing: contains 8 .bib files imported by 001_systematic_review.R
  • title_and_abstract_screening: containing the list of unique reference list with title-and-abstract decisions
  • fulltext_screening: containing all files generated by 003_data_extraction.R as well as the final list of references with all the full-text screening decisions
  • data_extraction: containing all files generated by 003_data_extraction.R

• data: contains all data files necessary for the following three scripts: 004_dataset_cleaning.R, 005_data_analysis.r and 006_figures.r

• figures: contains all figures generated by 006_figures.r and included in the main text and the supplementary materials (except Figure 1 from the main text)