Uncovering methylation-dependent genetic effects on regulatory element function in diverse genomes

This project contains datasets related to:

Uncovering methylation-dependent genetic effects on regulatory element function in diverse genomes 
Rachel M. Petersen, Christopher M. Vockley, Amanda J. Lea

A preprint of this work can be found here: https://www.biorxiv.org/content/10.1101/2024.08.23.609412v1

Specifically, the data provided here are:

1) replicateinfo.txt contains metadata for each mSTARR-seq replicate, including replicate number, pool number, sample type (DNA vs RNA) and methylation status

2) rnadnacounts_400bpwin.txt contains a count matrix with the number of DNA and RNA reads falling within each 400 bp genomic window for each replicate. Columns are replicate names, rows are genomic windows.

3) Joint_genotyping.vcf contains results from joint genotyping analysis using DNA sequences generated in the current study from 25 individuals accessed through the 1000 Genomes Project.

4) ASE_data.zip contains

• ASE_totalcounts.txt: counts matrix of the total number of DNA and RNA reads in each replicate for each variant
• ASE_refcounts.txt: counts matrix of the number of DNA and RNA reads for the reference allele in each replicate for each variant
• ASE_mashr_inputsites.txt: sites that were tested for methylation-dependent allele-specific expression using mashr
• WASP_ASE_sites.txt: variant sites that were retained after using the WASP mappability pipeline (Van De Geijn et al. 2015)

5) model_results.zip contains 

• model1_methonly_results.txt: results from linear modeling to identify windows with regulatory function in the methylated condition
• model1_unmethonly_results.txt: results from linear modeling to identify windows with regulatory function in the methylated condition
• model2_mashr_results.txt: results from mashr analysis to identify windows with methylation-dependent regulatory function
• ASE_meth_results.txt: results from allele specific expression analysis to identify ASE in the methylated condition
• ASE_unmeth_results.txt: results from allele specific expression analysis to identify ASE in the unmethylated condition
• ASE_mashr_results.txt: results from mashr analysis to identify sites with methylation-dependent ASE

6) Comparison_datasets.zip contains

• Johnston_eLife_mSTARR_counts_K562.txt: counts matrix from Johnston et al. 2024, adapted to use 200 bp windows. Original dataset can be found here: https://zenodo.org/records/7949036#.ZGZ5UnbMJq9
• Lea_eLife_mSTARR_counts.txt: counts matrix from Lea et al. 2018

7) GWAS_EWAS_overlap_files.zip contains

• GWAShits_siteformat.txt: GWAS associations accessed through the NHGRI-EBI catalog in March 2024, formatted for use in R
• EWAS_Atlas_associations.tsv: EWAS associations accessed through the EWAS Open Platform Data Hub in March 2024
• EWAS_Atlas_probe_annotations.tsv: genomic locations of EWAS probes 
• ASE_mashr_GWASOverlap.bed: methylation-dependent genetic effect sites that are located within 400 bp of a GWAS hit (results of bedtools intersect)
• ASE_mashr_EWASOverlap.bed: methylation-dependent genetic effect sites that are located within 400 bp of a EWAS hit (results of bedtools intersect)
• blood_gwas_overlaps.rds: methylation-dependent genetic effects sites that are located within 400 bp of a GWAS hit for 20 quantitative immune-related blood traits from Pan-UK Biobank.