DOI: 10.5061/dryad.n4h3672
title: Data from: Age estimation in a long-lived seabird (Ardenna tenuirostris) using DNA methylation-based biomarkers

description: Age structure is a fundamental aspect of animal population biology. Age is
 strongly related to individual physiological condition, reproductive
 potential and mortality rate. Currently, there are no robust molecular
 methods for age estimation in birds. Instead, individuals must be ringed
 as chicks to establish known-age populations, which is a labour intensive
 and expensive process. The estimation of chronological age using DNA
 methylation is emerging as a robust approach in mammals including humans,
 mice and some non-model species. Here we quantified DNA methylation in
 whole blood samples from a total of 71 known-age Short-tailed shearwaters
 (Ardenna tenuirostris) using digital restriction enzyme analysis of
 methylation (DREAM). The DREAM method measures DNA methylation levels at
 thousands of CpG dinucleotides throughout the genome. We identified seven
 CpG sites with DNA methylation levels that correlated with age. A model
 based on these relationships estimated age with a mean difference of 2.8
 years to known age, based on validation estimates from models created by
 repeated sampling of training and validation data subsets. Longitudinal
 observation of individuals re-sampled over 1 or 2 years generally showed
 an increase in estimated age (6/7 cases). For the first time, we have
 shown that epigenetic changes with age can be detected in a wild bird.
 This approach should be of broad interest to researchers studying age
 biomarkers in non-model species and will allow identification of markers
 that can be assessed using targeted techniques for accurate age estimation
 in large population studies.
description: Adjusted DNAm data for
 known-age animals.This file contains the
 run adjusted DNAm data that was used for lasso analysis, creating
 regressions and other figure creation. It contains DNA methylation values
 for N=63 known age individuals across 2338 CpGs. Known age, sex and run
 number are all included.knownage_noreplicates_adjDNAm_data.csvSequence count data for all CpGsThis file contains the count of sequences, e.g. the number of restriction enzyme cut sites of the same sequence, that was used to calculate DNAm scores for all unfiltered CpGs. This is a final count of "CCGGG" (methylated) + "GGG" (unmethylated) sequence ends. This value can be used to filter CpG sites based on 'read depth'.methcount_data_long.csvFasta filtering and DNAm scoring pipelineThis file takes in maxee filtered and dereplicated sample fasta files. Samples are then checked for the correct sequence site “CCGGG” or “GGG” at the beginning of each sequence. Samples are trimmed to be the same length and dereplicated using USEARCH10. A database of each set of unique sequences of CCGGG or GGG starting sites is created e.g. a methylated and unmethylated database. Samples are compared against these two databases and counts recorded to generate scores between 0-1. DNAm scores can then be further filtered on read depth, standard deviation or output as needed.fasta_filtering.rSeabird epigenetic age assay - variable selectionThis R script uses the data frame of DNAm values from the pipeline script as an input to the glmnet package to select for markers based on lambda 1se or min. First, samples are adjusted for any between run difference and the cv.glmnet function is used to penalise the large set of variables against known age. Any selected variables are then visualised (and filtered if needed) using simple linear regression. The final selection of markers are then used to train and test a multiple linear regression.SEAA_lasso_combined_dream.R