Additional file 2 of Allele-specific DNA methylation is increased in cancers and its dense mapping in normal plus neoplastic cells increases the yield of disease-associated regulatory SNPs
Creators: name: Do, Catherine
name: Dumont, Emmanuel L. P.
name: Salas, Martha
name: Castano, Angelica
name: Huthayfa Mujahed
name: Maldonado, Leonel
name: Arunjot Singh
name: DaSilva-Arnold, Sonia C.
name: Bhagat, Govind
name: Soren Lehman
name: Christiano, Angela M.
name: Madhavan, Subha
name: Nagy, Peter L.
name: Green, Peter H. R.
name: Feinman, Rena
name: Trimble, Cornelia
name: Illsley, Nicholas P.
name: Marder, Karen
name: Honig, Lawrence
name: Monk, Catherine
name: Goy, Andre
name: Kar Chow
name: Goldlust, Samuel
name: Kaptain, George
name: Siegel, David
name: Tycko, Benjamin
Publisher: name: figshare
Year: 2020
Subjects: subject: 29999 Physical Sciences not elsewhere classified
subject: FOS: Physical sciences
subject: FOS: Physical sciences
subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Developmental Biology
subject: Cancer
subject: Plant Biology
subject: Computational Biology

description: Additional file 2: Figure S1. Flow charts and diagram of computational and analytical approaches in this study. Figure S2. Summary of sample types and numbers and yield of informative SNPs. Figure S3. PCA of the combined WGBS and SureSelect methyl-seq data and series-wide overlap between ASM loci detected by the two methods. Figure S4. Distribution of ASM shows a high proportion of rare or private ASM in both cancer and normal samples and a significant increase in per-sample ASM in the cancers. Figure S5. Comparison of results from individual DNA samples analyzed by two WGBS library construction kits at two sequencing facilities, or by SureSelect and WGBS. Figure S6. Example of a chromosome region illustrating consistency between SureSelect methyl-seq, WGBS, and targeted bisulfite sequencing. Figure S7. Validations of ASM DMRs in disease-associated chromosomal regions: rs10411630 and multiple sclerosis. Figure S8. Validation of ASM DMRs in disease-associated chromosomal regions: rs2427290 and colorectal cancer. Figure S9. Validation of ASM DMRs in disease-associated chromosomal regions: rs2283639 and non-small cell lung carcinoma. Figure S10. Validations of ASM DMRs spanning a range of ASM ranks. Figure S11. Kernel density plots of methylation levels showing global hypomethylation and decrease in the percentage of highly methylated CpGs in cancers. Figure S12. Replication of the findings using WGBS from a single facility. Figure S13. Allele-specific losses of methylation leading to ASM in cancers. Figure S14. Kernel density plots of methylation level distributions showing statistically enriched instances of allele-specific gains of methylation in cancers. Figure S15. Shared ASM loci in cancer and non-cancer have similar ASM magnitude. Figure S16. Correlations between allelic TF binding affinity scores and ASM magnitude in the 4 classes of ASM loci. Figure S17. Examples of ASM DMRs in chromatin deserts. Figure S18. Models for inter-individual variability and allele-switching at ASM loci. Figure S19. The percentage of ASM loci that show switching behavior in cancers is smaller when considering only loci for which ASM is also detected in non-cancer samples. Figure S20. Examples of haplotype blocks defined by stringent and lenient parameters. Figure S21. Utility of D’ and R-square parameters for assessing candidate disease-associated rSNPs. Figure S22. Additional examples of mechanistically informative disease associated ASM index SNPs: autoimmune and neuropsychiatric. Figure S23. Additional examples of mechanistically informative disease associated ASM index SNPs: breast cancer and lymphoma. Figure S24. ASM loci displayed as annotated genome browser tracks.