An epigenetic clock (DNA methylation age) is a predictor for biological age based on the level of DNA methylation.

Follow instructions from the DNA methylation age calculator (Horvath, 2013) tutorial here: https://dnamage.genetics.ucla.edu/sites/all/files/tutorials/TUTORIALonlineCalculator.pdf. I reccomend reading through this and Horvath (2013) before running below scripts.

DNA methylation ages calculated include:

• pan tissue clock (Horvath 2013)
• blood based epigenetic clocks (Hannum et al., 2013)
• skin and blood clock (Horvath et al., 2018)
• estimators of phenotypic age (Levine et al., 2017)
• mortality risk DNAmGrimAge (Lu et al., 2019a)
• estimator of telomere length: DNAmTL (Lu et al., 2019b)
• epigenetic estimators of plasma proteins e.g. DNAmPAI1 (Lu et al., 2019a)
• DNAm estimate of smoking pack years(DNAmPACKYRS) (Lu et al., 2019a)

ALSPAC participants (n = ~900) had DNA methylation measured in umbilical cord blood and peripheral blood from age 7 year, and age 15 or 17 years clinic visits using Illumina Infinium HumanMethylation450 BeadChip (450 K) array (Relton et al., 2015). Final sample sizes for DNA methylation ages are:

• age 0 years: n = 906
• age 7 years: n = 971
• age 15 years: n = 718
• age 17 years: n = 254

# Change directory to the location you want to calculate DNAm ages in and have cloned this repo.
cd /Users/aes/Library/CloudStorage/OneDrive-UniversityofEdinburgh/PhD/Studies/grimAge_ALSPAC

# Check the dependencies
cat environment.yml

# Create the conda environment (use --force if overwriting existing evironment eg. to add more dependencies)
conda env create --prefix ./env --file environment.yml --force

# Activate the environment and start R studio
conda activate ./env

# Include an "&" after RStudio so even if the terminal closes instance to RStudio remains open
/Applications/RStudio.app/Contents/MacOS/RStudio & 

# Once in R studio check R.home() and .libPaths() point to the conda environment

Source Scripts/DNAm_prep.R to create compressed csv files to upload to the DNA methylation age calculator. You may need to change relative paths in this script to wherever the ALSPAC server is mapped to on your computer. If it takes too long on your local computer please see Scripts/eddie.md for running this script on Eddie. Briefly, this script:

1. Reads in DNAm betas from ALSPAC/data/genomics/B3421/methylation/B3421/betas/data.Robj

2. Subsets CpGs to those used by the DNA methylation age calculator listed in datMiniAnnotation3.csv(https://dnamage.genetics.ucla.edu/sites/all/files/tutorials/datMiniAnnotation3.csv). There is 30,084 CpGs in datMiniAnnotation3.csv. ALSPAC contains 28,463 of these CpGs. For the remaining CpGs with no data in ALSPAC have rows filled in with NA, as specified by the tutorial.

3. Saves zip compressed csv files for each DNAm collection time point (birth, age 7, 15 and 17 years). In these files each row is a CpG and each column is a participant.

   • ALSPAC/data/genomics/B3421/methylation/B3421/grimage_ALSPAC/Input/betas_0.zip
   • ALSPAC/data/genomics/B3421/methylation/B3421/grimage_ALSPAC/Input/betas_7.zip
   • ALSPAC/data/genomics/B3421/methylation/B3421/grimage_ALSPAC/Input/betas_15.zip
   • ALSPAC/data/genomics/B3421/methylation/B3421/grimage_ALSPAC/Input/betas_17.zip

4. Creates annotation files (.csv) for each respective beta file. These contain column names: Subject, Age, Female for each participant. Each row is a participant (in the same order as the columns in the beta files).

   • ALSPAC/data/genomics/B3421/methylation/B3421/grimage_ALSPAC/Input/annotation_0.csv
   • ALSPAC/data/genomics/B3421/methylation/B3421/grimage_ALSPAC/Input/annotation_7.csv
   • ALSPAC/data/genomics/B3421/methylation/B3421/grimage_ALSPAC/Input/annotation_15.csv
   • ALSPAC/data/genomics/B3421/methylation/B3421/grimage_ALSPAC/Input/annotation_17.csv

Create an account to submit methylation data (https://dnamage.genetics.ucla.edu/new). Upload zip compressed csv files. Check the boxes which say "Normalize Data" and "Advanced Analysis" - see tutorial doc (link at top) for more info on this. Upload corresponding age annotation file. An email is sent when analysis has run with a file for DNA methylation ages and age acceleration values, see the tutorial document for details on output. Output saved to: ALSPAC/data/genomics/B3421/methylation/B3421/grimage_ALSPAC/Output/scores_*.csv

Cell count esimates were calculated using methods from Houseman et al. (2012). Run Scripts/age_acc.R to calculate age acceleration (AgeAccelerationResidual = residuals(lm(DNAmGrimAge~Age + WBC estimates))). These residuals are saved as an additonal column called DNAmGrimAgeAdjWBCAge. Currently these residuals/age acceleration is calculated only for GrimAge. Could improve this by adjusting for batch effects too (if available in ALSPAC?)

Output saved to: ALSPAC/data/genomics/B3421/methylation/B3421/grimage_ALSPAC/Output/scores_acceleration_*.csv

• Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14(10):R115 PMID: 24138928

• Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y, Deconde R, Chen M, Rajapakse I, Friend S, Ideker T, Zhang K (2013) Genomewide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013 Jan 24;49(2):359-67.

• Horvath S, Oshima J, Martin GM, Lu AT, Quach A, Cohen H, Felton S, Matsuyama M, Lowe D, Kabacik S, Wilson JG, Reiner AP, Maierhofer A, Flunkert J, Aviv A, Hou L, Baccarelli AA, Li Y, Stewart JD, Whitsel EA, Ferrucci L, Matsuyama S, Raj K. (2018) Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies. Aging (Albany NY). 2018 Jul 26;10(7):1758-1775. doi: 10.18632/aging.101508. PMID: 30048243 PMCID: PMC6075434

• Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y, Whitsel EA, Wilson JG, Reiner AP, Aviv A, Lohman K, Liu Y, Ferrucci L, Horvath S. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018 Apr 18;10(4):573-591. doi: 10.18632/aging.101414. PMID: 29676998 PMCID: PMC5940111

• Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, Hou L, Baccarelli AA, Li Y, Stewart JD, Whitsel EA, Assimes TL, Ferrucci L, Horvath S. (2019). DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY). 2019 Jan 21;11(2):303-327. doi: 10.18632/aging.101684. PMID: 30669119 PMCID: PMC6366976

• Lu AT, Seeboth A, Tsai PC, Sun D, Quach A, Reiner AP, Kooperberg C, Ferrucci L, Hou L, Baccarelli AA, Li Y, Harris SE, Corley J, Taylor A, Deary IJ, Stewart JD, Whitsel EA, Assimes TL, Chen W, Li S, Mangino M, Bell JT, Wilson JG, Aviv A, Marioni RE, Raj K Horvath S (2019) DNA methylation-based estimator of telomere length. Aging (Albany NY). 2019 Aug 18;11(16):5895-5923. doi: 10.18632/aging.102173. PMID: 31422385 PMCID: PMC6738410

• Caroline L Relton, Tom Gaunt, Wendy McArdle, Karen Ho, Aparna Duggirala, Hashem Shihab, Geoff Woodward, Oliver Lyttleton, David M Evans, Wolf Reik, Yu-Lee Paul, Gabriella Ficz, Susan E Ozanne, Anil Wipat, Keith Flanagan, Allyson Lister, Bastiaan T Heijmans, Susan M Ring, George Davey Smith, Data Resource Profile: Accessible Resource for Integrated Epigenomic Studies (ARIES), International Journal of Epidemiology, Volume 44, Issue 4, August 2015, Pages 1181–1190, https://doi.org/10.1093/ije/dyv072

• Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke JK, Kelsey KT (2012) DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 2012, 13:86 doi:10.1186/1471-2105-13-86