Exploring Monoallelic Methylation Using High-throughput Sequencing

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

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

5. Unmethylated and Methylatedpatches within a CpG island

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

7. Intermediate methylation levels at imprinted genes

8. Initial catalogue of Intermediate methylation sites Start Stop MRE MeDIP nearest gene Gene Chr1. . . . . . . . . . . . . . . . . . . chr22 . . . . . . . . . . . . . . . . Ting Wang, Washington University

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

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

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

12. Validation of monoallelic DNA methylation in POTEB

13. Searching for Monoallelic Methlylation Using Shotgun Bisulfite Sequencing • We expect streaks of 50±d% 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 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. Monoallelic Epigenomic Marks and Expression Distribution of the 62 SBS-ASM loci 1 0 0 4 7 9 16 MethylC-seq + RNA-seq Additional 25 loci MRE-seq + MeDIP-seq MethylC-seq + ChIP-seq

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