DOI: 10.5061/dryad.nk98sf85p
title: Data from: Inferences from epigenetic information in an ecological context: A case study of DNA methylation and early-life environmental effects on zebra finches

description: DNA methylation (DNAm) is known to affect gene expression and has been
 suggested as a putative mechanism through which environmental factors can
 continuously shape phenotypic variation. DNAm data can be assessed using a
 variety of approaches, ranging from single-nucleotide resolution to the
 development of composite indexes, each providing unique insights.
 Utilizing whole-genome, longitudinal DNAm data from adult zebra finches
 raised in either small or large broods, we present a case study aimed at
 exploring how to utilize DNAm data to not only assess environmental
 effects on the epigenome but also to develop tools to directly measure
 those effects. Specifically, we (i) identified CpG sites where DNAm
 differed significantly between adult zebra finches raised in small and
 large broods, (ii) developed a phenotypic index using the methylation of
 the differentially methylated sites, and (iii) using an elastic net
 regression predicted brood size from methylation. Our findings suggest
 that early-life environment can lead to long-term differences in the DNAm
 of specific CpG sites and generate phenotypic variation. These methylation
 signatures can be leveraged to develop scalable tools to predict
 phenotypic quality and fitness outcomes as well as retroactively quantify
 stress.
description: # Data from: Inferences from epigenetic information in an ecological
 context: A case study of DNA methylation and early-life environmental
 effects on zebra finches Dataset DOI: 10.5061/dryad.nk98sf85p ##
 Description of the data and file structure DNA was extracted from zebra
 finch blood samples. Enzymatic Methyl-sequencing was used to quantify
 whole genome methylation.  ### Files and variables Raw data can be found
 in
 [https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1108628](https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1108628). Bioinformatics pipeline from raw sequencing data to .cov.gz methylation files can be found in [https://zenodo.org/records/12074859](https://zenodo.org/records/12074859) ### File descriptions **R-Script:** *PhilTrans_DNAm_BroodSize_R1.R* R code, including all plots and statistical analyses presented in the case study *Cov_ZF_2023_MalesnoW.rds* * StartPosition: position of the site * Endposition: position of site (“StartPosition” and “Endposition” should be the same) * MethylationPercentage: percentage of DNA methylation per site * CountMethylated: count of methylated reads per site * CountNonMethylated: count of unmethylated reads per site * Chromosome: the chromosome in which the site is located * Coverage: coverage per site (CountMethylated+CountNonMethylated) * Sample: sample ID *ZF_SampleInfo_Mass_R1.xlsx* Sheet = Info * BirdID: individual ID * ZV_ID: blood sample ID * Seq#: sequencing ID * Sample: Sample ID * Sexcode: sex (M or F) * StandRearNestSize: brood size (2=small, 6=large) * AgeYO: age at sampling (Young or Old, two observations per BirdID) * CollectionYear: year the blood sample was collected Sheet = Mass * BirdID: individual ID * HatchDate: hatching date * MassDate1st: date the first mass measurement was taken  * AgeDays1st: age at which the first mass measurement was taken  * Mass1st: first mass measurement (gr) * MassDate15: date the day 15 mass measurement was taken  * AgeDays15: age at which the day 15 mass measurement was taken  * Mass15: day 15 mass measurement (gr) "NA" indicates missing mass data Sheet = BirdID * Sample: arbitrary Sample ID for Methylkit * BirdID: individual ID * StandRearNestSize: brood size (2=small, 6=large) Sheet = Pos_per_chrom * Chromosome: chromosome * Unique_Pos_Count: number of CpG sites per chromosome after filtering Due to the size of the datasets and the computational demands of these analyses, we strongly recommend running the R workflows on a high‑performance computing (HPC) system rather than a standard desktop environment. Working with the ***"Cov_ZF_2023_MalesnoW.rds" *** is resource‑intensive and, in our tests, required approximately 400 GB of RAM and about 29 hours of runtime on an HPC system. ## Code/software R version 4.3.2 (2023-10-31 ucrt) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 19045) Matrix products: default locale: \[1] LC_COLLATE=English_Netherlands.utf8  LC_CTYPE=English_Netherlands.utf8    LC_MONETARY=English_Netherlands.utf8 LC_NUMERIC=C                         \[5] LC_TIME=English_Netherlands.utf8     time zone: Europe/Amsterdam tzcode source: internal attached base packages: \[1] grid      stats     graphics  grDevices utils     datasets  methods   base     other attached packages: \[1] ggVennDiagram_1.5.4 scales_1.4.0        ggrepel_0.9.6       rptR_0.9.23         sjPlot_2.9.0        tidyr_1.3.1         UpSetR_1.4.0        VennDiagram_1.7.3   \[9] futile.logger_1.4.3 AICcmodavg_2.3-4    ppcor_1.1           MASS_7.3-60         purrr_1.0.4         janitor_2.2.1       ggpubr_0.6.2        ggplot2_4.0.0       \[17] lmerTest_3.1-3      lme4_1.1-37         Matrix_1.6-1.1      dplyr_1.1.4         readxl_1.4.5        readr_2.1.5         loaded via a namespace (and not attached): \[1] psych_2.5.6          sjlabelled_1.2.0     tidyselect_1.2.1     farver_2.1.2         S7_0.2.0             timechange_0.3.0     lifecycle_1.0.4      survival_3.5-7       \[9] magrittr_2.0.3       compiler_4.3.2       rlang_1.1.5          tools_4.3.2          utf8_1.2.4           knitr_1.50           ggsignif_0.6.4       lambda.r_1.2.4       \[17] labeling_0.4.3       mnormt_2.1.1         bit_4.6.0            plyr_1.8.9           RColorBrewer_1.1-3   abind_1.4-8          withr_3.0.2          numDeriv_2016.8-1.1 \[25] datawizard_1.3.0     stats4_4.3.2         unmarked_1.5.0       xtable_1.8-4         insight_1.4.3        cli_3.6.4            crayon_1.5.3         reformulas_0.4.2     \[33] generics_0.1.4       tzdb_0.5.0           pbapply_1.7-4        minqa_1.2.8          stringr_1.6.0        splines_4.3.2        parallel_4.3.2       formatR_1.14         \[41] cellranger_1.1.0     vctrs_0.6.5          boot_1.3-28.1        VGAM_1.1-13          carData_3.0-5        car_3.1-3            hms_1.1.4            bit64_4.6.0-1       \[49] rstatix_0.7.3        Formula_1.2-5        glue_1.8.0           nloptr_2.2.1         cowplot_1.2.0        lubridate_1.9.4      stringi_1.8.7        gtable_0.3.6         \[57] ggeffects_2.3.1      tibble_3.2.1         pillar_1.11.1        R6_2.6.1             Rdpack_2.6.4         evaluate_1.0.5       vroom_1.6.6          lattice_0.21-9       \[65] futile.options_1.0.1 rbibutils_2.3        backports_1.5.0      broom_1.0.10         snakecase_0.11.1     Rcpp_1.1.0           gridExtra_2.3        nlme_3.1-163         \[73] xfun_0.52            mgcv_1.9-0           sjmisc_2.8.11        pkgconfig_2.0.3