1. Exploring Monoallelic Methylation Using High-throughput Sequencing Cristian Coarfa, Ronald Harris Ting Wang, Aleksandar Milosavljevic, Joe Costello

2. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications Harris RA, Wang T, Coarfa C, Nagarajan RP, Hong C, Downey S, Johnson BE, Delaney A, Zhao Y, Olshen A, Ballinger T, Zhou X, Fosberg KJ, Gu J, Echipare L, O’Geen H, Lister R, Pelizzola M, Xi Y, Epstein CB, Bernstein BE, Hawkins RD, Ren B, Chung WY, Gu H, Bock C, Gnirke A, Zhang MQ, Haussler D, Ecker JR, Li W, Farnham PJ, Waterland RA, Meissner A, Marra MA, Hirst M, Milosavljevic A, Costello JF. In press, Nature Biotechnology

3. 1.Imprinting 2.Non-imprinted monoallelic methylation 3.Cell type-specific methylation 4.Sites of inter-individual variation in methylation level Biological importance of intermediate methylation levels

4. Methylated CpGs Unmethylated CpGs methyl DNA immunoprecipitation ( MeDIP ) methylation-sensitive restriction digestion ( MRE ) ~20 million reads/sample~100 million reads/sample IGAII sequencing data visualization Illumina library construction 5’ CpG islands are unmethylated 3’ CpG island is partially methylated Methylated Unmethylated combine parallel digests, ligate adapters, size-select 100-300 bp IP sonicated, adapter-ligated DNA, size-select 100-300 bp

5. Unmethylated and Methylated patches within a CpG island

6. high MeDIP, no or low MRE high MRE, no or low MeDIP 1 2 high MRE and MeDIP (uniform) high MRE and MeDIP (patch Methylation) 3 4

7. Intermediate methylation levels at imprinted genes

8. Chr11153328115366671.034291.9069-205410HCCA2 chr11194647519487870.776958.5443-18939LOC100133545 chr11197514119774391.284587.55160H19 chr11224568022505082.345199.4044-29211C11orf21 chr11242074724232241.656529.51610KCNQ1 Start Stop MRE MeDIP nearest gene Gene Chr1................ chr22................ Initial catalogue of Intermediate methylation sites Ting Wang, Washington University

9. Using Genetic Variation to Detect Monoallelic Epigenomic and Transcription States H1 cell line 1.Monoallelic DNA methylation (MRE and MeDIP) 2.Monoallelic expression (MethylC-seq and RNA-seq) 3.Monoallelic Histone H3K4me3 (MethylC-seq and Chip-seq)

10. MethylC-seq + ChIP-seq MethylC-seq + RNA-seq MRE-seq + MeDIP-seq Monoallelic Epigenomic Marks and Expression 34 39 21 4 1 0

11. CpG islands MRE-seq 1 MeDIP-seq 1 MRE-seq 2 MeDIP-seq 2 Bisulfite POTEB Intermediate methylation levels in POTEB chr15:19346666-19350003 G 9 A 30 Location Medip Allele Count MRE Allele Count

12. Validation of monoallelic DNA methylation in POTEB

13. Searching for Monoallelic Methlylation Using Shotgun Bisulfite Sequencing We expect streaks of 50±  methylation ratios Use 500bp windows tiling CpG Islands Compute average CpG methylation –CpG Islands –1000 loci Infer distribution of methylation in 1000 loci Subselect 500bp windows tiling CpG Islands In the selected windows, search for allele specific methylation

14. Average methylation over 500 bp window in CpG Islands and 1000 loci

15. Parameter Search Experimented with various lower and upper bounds for methylation Guidelines Discover as many of the 1000 loci Reduce the overall number of 500bp windows Lower Bound Upper Bound Number of 500bp windows Number of 500bp windows overlapping 1000 loci % of 500bp windows overlapping 1000 loci 1000 loci overlapped 10702479328510.114992135950 10802806038770.138168211989 10903667755120.15028492999 20701408423450.166500994926 20801735133710.19428275977 20902596850060.192775724990 3070940319120.20333936884 30801267029380.231886346958 30902128745730.21482595979 30-80 rediscovers 958 of loci, at the highest specificity

16. Incorporating Genetic Variation Search for allele-specific methylation Look only into the 30-80% methylation loci overlapping with CpG Islands Use het SNPs Check for those that separate reads into different methylation states One allele >20% Other allele <20% Other thresholding methods possible

17. Results Found 6295 heterozygous sites 586 sites have allele specific methylation Overlap with 62 of the 1000 loci –37 of the loci discovered using pairs of assays –25 new loci

18. MethylC-seq + ChIP-seq MethylC-seq + RNA-seq MRE-seq + MeDIP-seq Monoallelic Epigenomic Marks and Expression Distribution of the 62 SBS-ASM loci 7 9 1 4 16 0 0 Additional 25 loci

19. Breast Tissue Allele specific methylation Determine informative heterozygous SNPs Loci with monoallelic MRE-seq and MeDIP-seq

20. Breast Tissue Multiple cell types –Different epigenotypes –Same genotype Identify monoallelic events –Constitutional –Tissue specific Cell types for four individuals –Conserved monoallelic marks –Individual specific monoallelic marks

21. Integrate Array-based and Seq-based methods Collaboration with Leo Schalkwyk and Jonathan Mill, King’s College, UK Investigate same breast tissue samples Insight –Cost –Results # of ASM loci Distribution of ASM loci identified by each method –Suggestions for designing future studies

22. Acknowledgements NIEHS/NIDA: Joni Rutter, Tanya Barrett, Fred Tyson, Christine Colvis EDACC: R. Alan Harris, Cristian Coarfa, Yuanxin Xi, Wei Li, Robert A. Waterland, Aleksandar Milosavljevic UCSF/GSC REMC: Raman Nagarajan, Chibo Hong, Sara Downey, Brett E. Johnson, Allen Delaney, Yongjun Zhao, Marco Marra, Martin Hirst, Joseph Costello –UCSC: Tracy Ballinger, David Haussler –Washington University: Xin Zhou, Maximiliaan Schillebeeckx, Ting Wang –UCD: Lorigail Echipare, Henriette O’Geen, Peggy J. Farnham UCSD REMC: Ryan Lister, Mattia Pelizzola, Bing Ren, Joseph Ecker –Cold Spring Harbor: Wen-Yu Chung, Michael Q. Zhang Broad REMC: Hongcang Gu, Christoph Bock, Andreas Gnirke, Chuck Epstein, Brad Bernstein, Alexander Meissner