This dataset is associated with the manuscript: Intraspecific diversity is critical to population-level risk assessments
DOI: https://doi.org/10.5061/dryad.5tb2rbpck

Change log

April 22, 2025: In this version, the authors have uploaded the variant call format (VCF) file along with its associated index file that allows users to download the full set of variant sites identified across the whole genomes of all 20 Daphnia magna clones used in the study.

General Description

This dataset contains the raw and processed data, the variant call format (VCF) file, as well as the R scripts necessary to reproduce the statistical analyses and figures presented in the manuscript. The data pertain to phenotypic and genomic variation in Daphnia, analyzed under different experimental treatments.

File Structure

The dataset includes:

• Eight CSV files containing phenotypic and genomic data
• Two R script files used for statistical analyses and figure generation
• One VCF file storing the full set of variant sites identified across the whole genomes of all 20 Daphnia magna clones used in the study
• One VCF index file included alongside the compressed VCF file to enable rapid access to variant data by genomic position, facilitating efficient downstream analyses
• This README file describing the dataset and its contents

Data Files and Descriptions

Each CSV file is structured in a tabular format with proper headings, including relevant variables such as clone ID, treatment conditions, and measured phenotypes. Below is a detailed breakdown of each file:

Phenotypic Data

1. Survival_210129.csv
   • Contains survival data across experimental treatments.
   • Variables:
     • Clone: Unique identifier for each clone
     • Treatment: Experimental condition applied
     • Replicate: Replicate number
     • Day X: Survival on Day 0, 7, and 14 (1 = alive, 0 = dead)
2. Neonates_210226.csv
   • Contains reproductive output data.
   • Variables:
     • Clone: Unique identifier for each clone
     • Treatment: Experimental condition applied
     • Replicate: Replicate number
     • Neonates: Number of offspring produced (unit: count)
     • Fbrood: Number of neonates produced in the first brood recorded per replicate
     • Tbroods: Total number of broods recorded throughout the entire experiment per replicate
     • Missing Values: Some values in Fbrood, Tbroods, and neonates columns are missing. These reflect cases where Daphnia mothers never reproduced or other constraints (e.g., the Daphnia replicate died).
3. DaphniaGrow_ResponseMeans_210407.csv
   • Summarized response means for various phenotypic traits.
   • Variables:
     • Clone: Unique identifier for each clone
     • Treatment: Experimental condition applied
     • Trait: Phenotypic trait measured
     • Mean_Response: Mean value of the measured trait (e.g., mean survival on Day 7, mean length on Day 14, etc.)

Genomic Data

1. DaphniaBenchtop_FunctionSNPDifferences.csv
   • Contains SNP functional annotations and their differences among groups.
   • Variables:
     • Clone: Unique identifier for each clone
     • Missing Values: Some SNPs have missing effect annotations, indicating regions where functional effects were not determined.
2. DaphniaBenchtop_MCGeneVariantCounts_230817.csv
   • Contains gene-level variant counts from sequencing data.
   • Variables:
     • ID: Unique sample identifier
     • TotalVariant: Total number of variants detected in the gene
     • nRefHom: Number of homozygous reference alleles in the sample
     • nNonRefHom: Number of homozygous non-reference alleles in the sample
     • nHets: Number of heterozygous alleles in the sample
     • nIndels: Number of insertions and deletions detected in the sample
3. DaphBenchtopSNPmatrixDifferences.csv
   • SNP matrix indicating presence/absence or frequency differences across experimental groups.
   • Variables:
     • Clone: Identifier for each sample
     • Missing Values: Some SNPs have missing allele counts, likely due to sequencing depth limitations.
4. GenomicPCAOutput.csv
   • Principal component analysis (PCA) results from genomic data.
   • Variables:
     • Clone: Unique identifier for each clone
     • PC1, PC2, PC3: Principal component scores
5. DMOrderLetters.csv
   • Statistical comparison output, containing grouping information from post-hoc tests.
   • Variables:
     • Comparison: Pairwise comparison identifier
     • Order_Letter: Letter groupings from statistical analysis

Code Files

1. DaphniaRiskAssessment_RCode_240606.R
   • This script performs all statistical analyses and generates figures found in the manuscript.
   • Dependencies: Requires all CSV files for data input.
2. Daphnia_Bootstrap_230826.R
   • This script conducts bootstrap resampling analyses for certain statistical comparisons.
   • Dependencies: Uses phenotypic data CSV files.

Variant Call Format Files

1. Shahmohamadlooetal_2024_daphnia_geneticvariance.vcf.gz
   • This file contains the full set of variant sites identified across the whole genomes of all 20 *Daphnia magna *clones used in the study, enabling population-level comparisons of genomic variation and facilitating genotype–phenotype association analyses under toxin exposure.
   • Dependencies: All VCF files for data input.
2. Shahmohamadlooetal_2024_daphnia_geneticvariance_index
   • This file is included alongside the compressed VCF file to enable rapid access to variant data by genomic position, facilitating efficient downstream analyses.
   • Dependencies: All VCF files for data input.

Software and Reproducibility

• All statistical analyses were performed using R version 4.2.2.
• Required R packages: (Ensure these are installed before running scripts)
  • ggplot2 for visualization
  • lme4 for mixed models
  • dplyr for data manipulation
  • vegan for multivariate analyses
• Ensure all CSV files are in the working directory before running the scripts.

Handling of Missing Data

Some variables in the dataset contain missing values due to experimental constraints or data collection limitations. These missing values have been left as empty cells to ensure compatibility with analysis scripts. Users should handle these appropriately during their analyses. The specific files and columns with missing values are documented in the file descriptions above.