Exploring Monoallelic Methylation Using High-throughput Sequencing Cristian Coarfa, Ronald HarrisTing Wang, Aleksandar Milosavljevic, Joe Costello

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, OGeen 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

Imprinting

Non-imprinted monoallelic methylation

Cell type-specific methylation

Sites of inter-individual variation in methylation levelBiological importance of intermediate methylation levels

Methylated CpGsUnmethylated CpGsmethyl DNA immunoprecipitation(MeDIP)methylation-sensitive restriction digestion (MRE)~20 million reads/sample~100 million reads/sampleIGAII sequencingdata visualizationIllumina library constructioncombine parallel digests,ligate adapters,size-select 100-300 bpIP sonicated, adapter-ligatedDNA, size-select 100-300 bp

Unmethylated and Methylated patches within a CpG island

high MeDIP, no or low MREhigh MRE, no or low MeDIP12

Intermediate methylation levels at imprinted genes

Start Stop MRE MeDIP nearest gene GeneChr1. . . . . . . . . . . . . . . .... chr22 . . . . . . . . . . . . . . . .Initial catalogue of Intermediate methylation sites

Ting Wang, Washington University

Chr11153328115366671.034291.9069-205410HCCA2chr11194647519487870.776958.5443-18939LOC100133545chr11197514119774391.284587.55160H19chr11224568022505082.345199.4044-29211C11orf21chr11242074724232241.656529.51610KCNQ1

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)

MethylC-seq +ChIP-seqMethylC-seq + RNA-seqMRE-seq +MeDIP-seqMonoallelic Epigenomic Marks and Expression

Intermediate methylation levels in POTEB

Validation of monoallelic DNA methylation in POTEB

Searching for Monoallelic Methlylation Using Shotgun Bisulfite SequencingWe expect streaks of 50d% methylation ratiosUse 500bp windows tiling CpG IslandsCompute average CpG methylationCpG Islands1000 lociInfer distribution of methylation in 1000 lociSubselect 500bp windows tiling CpG IslandsIn the selected windows, search for allele specific methylation

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

Chart1

0.04381694260.0055714741

0.04690808490.0039796243

0.04726356630.0049347342

0.04214772570.0071633238

0.03806741780.0084368036

0.03361617290.0079592486

0.03038592910.0074816937

0.02834577520.0101878383

0.02556374710.0062082139

0.02534736710.0087551735

0.02550192420.0105062082

0.02142161640.0084368036

0.01965966520.0081184336

0.01826865120.0093919134

0.0157030030.0066857689

0.0161357630.0078000637

0.01404924190.0078000637

0.01265822780.0073225088

0.01165360660.0074816937

0.01046351680.0070041388

0.01165360660.0079592486

0.00850064140.006049029

0.00877884420.0062082139

0.00710962740.0071633238

0.00571861330.0062082139

0.00737237450.0082776186

0.00633684180.0073225088

0.00625956320.0070041388

0.0056258790.0074816937

0.00499219490.0052531041

0.00644503180.0073225088

0.00468308060.0065265839

0.0048221820.0062082139

0.00451306780.0063673989

0.00443578920.0062082139

0.0042348650.0049347342

0.0041266750.0054122891

0.00403394070.005730659

0.00352390230.0054122891

0.00335388940.0049347342

0.00457489070.0078000637

0.00381756080.0054122891

0.00335388940.0070041388

0.00372482650.0055714741

0.00326115520.0068449538

0.00338480090.0074816937

0.00316842090.0071633238

0.00316842090.0065265839

0.00323024370.0062082139

0.00298295240.0052531041

0.0040184850.0071633238

0.00279748380.005889844

0.00361663650.0081184336

0.00315296520.0074816937

0.00346207940.0081184336

0.00333843370.0078000637

0.00306023090.0079592486

0.00316842090.0079592486

0.00289021810.006049029

0.00323024370.0066857689

0.00374028220.0093919134

0.00289021810.0101878383

0.00313750950.0098694683

0.00323024370.0095510984

0.00318387660.0133715377

0.00312205380.0100286533

0.00366300370.0085959885

0.00347753510.0120980579

0.00338480090.0113021331

0.00344662370.0114613181

0.00409576360.0128939828

0.00392575080.011620503

0.00449761210.0135307227

0.00462125780.0148042025

0.00386392790.0170327921

0.00547132190.017510347

0.00517766340.0167144222

0.0056258790.017510347

0.00590408190.0195797517

0.00649139890.0192613817

0.00874793280.0224450812

0.00834608430.0245144858

0.00913432560.0272206304

0.01103537810.0238777459

0.01163815090.0262655205

0.0142037990.0316778096

0.01540934450.0272206304

0.016939460.0289716651

0.01905689250.0248328558

0.01863958830.0245144858

0.01969057670.0194205667

0.01664580150.0122572429

0.01357011480.0093919134

0.00908795850.0049347342

0.00598136040.0033428844

0.00344662370.0028653295

0.00187014110.0007959249

0.00089643130.0003183699

0.00038639280.000159185

0.00142192550.0009551098

% of CpG Islands windows

% windows in 1000 loci

Percent methylation

% of windows

Average Methylation Scores over 500bp windows in CpG Islands and 1000 putative intermediate methylation loci

Methylation CpG Islands

Meth levelNumber of windows in CpG Islands% of CpG Islands windowsNumber of windows in 1000 loci% windows in 1000 loci

028354.38%350.56%

130354.69%250.40%

230584.73%310.49%

327274.21%450.72%

424633.81%530.84%

521753.36%500.80%

619663.04%470.75%

718342.83%641.02%

816542.56%390.62%

916402.53%550.88%

1016502.55%661.05%

1113862.14%530.84%

1212721.97%510.81%

1311821.83%590.94%

1410161.57%420.67%

1510441.61%490.78%

169091.40%490.78%

178191.27%460.73%

187541.17%470.75%

196771.05%440.70%

207541.17%500.80%

215500.85%380.60%

225680.88%390.62%

234600.71%450.72%

243700.57%390.62%

254770.74%520.83%

264100.63%460.73%

274050.63%440.70%

283640.56%470.75%

293230.50%330.53%

304170.64%460.73%

313030.47%410.65%

323120.48%390.62%

332920.45%400.64%Looking at 500 bp windows with 30-80% methylation

342870.44%390.62%Rediscover 950 of the 1000 loci

352740.42%310.49%

362670.41%340.54%

372610.40%360.57%

382280.35%340.54%

392170.34%310.49%

402960.46%490.78%

412470.38%340.54%

422170.34%440.70%

432410.37%350.56%

442110.33%430.68%

452190.34%470.75%

462050.32%450.72%

472050.32%410.65%

482090.32%390.62%

491930.30%330.53%

502600.40%450.72%

511810.28%370.59%

522340.36%510.81%

532040.32%470.75%

542240.35%510.81%

552160.33%490.78%

561980.31%500.80%

572050.32%500.80%

581870.29%380.60%

592090.32%420.67%

602420.37%590.94%

611870.29%641.02%

622030.31%620.99%

632090.32%600.96%

642060.32%841.34%

652020.31%631.00%

662370.37%540.86%

672250.35%761.21%

682190.34%711.13%

692230.34%721.15%

702650.41%811.29%

712540.39%731.16%

722910.45%851.35%

732990.46%931.48%

742500.39%1071.70%

753540.55%1101.75%

763350.52%1051.67%

773640.56%1101.75%

783820.59%1231.96%

794200.65%1211.93%

805660.87%1412.24%

815400.83%1542.45%

825910.91%1712.72%

837141.10%1502.39%

847531.16%1652.63%

859191.42%1993.17%

869971.54%1712.72%

8710961.69%1822.90%

8812331.91%1562.48%

8912061.86%1542.45%

9012741.97%1221.94%

9110771.66%771.23%

928781.36%590.94%

935880.91%310.49%

943870.60%210.33%

952230.34%180.29%

961210.19%50.08%

97580.09%20.03%

98250.04%10.02%

100920.14%60.10%

647016282

&C&A

&CPage &P

Methylation CpG Islands

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

00

% of CpG Islands windows

% windows in 1000 loci

Percent methylation

% of windows

Average Methylation Scores over 500bp windows in CpG Islands and 1000 putative intermediate methylation loci

SBS-ASM loci

Imprinting TargetGenesChromosomeLocationMethylation Ratio Reference AlleleMethylation Ratio Alternative Allele

chr1:154451861-154454271PAQR6.2,BGLAP,SEMA4A,PMF1,SMG5,PAQR6,SLC25A44chr11544531460.03958224240.2182743041

chr6:31383220-31385505HLA-B,HLA-Cchr6313842010.10620915030.2990757138

chr19:54250034-54253309RUVBL2,CGB8,CGB7,CGB5,KCNA7,CGB2,CGB1,CGB,LHB,SNRP70.2,SNRP70,NTF5chr19542511000.16843971630.2415816327

chr1:154451861-154454271PAQR6.2,BGLAP,SEMA4A,PMF1,SMG5,PAQR6,SLC25A44chr11544529730.09942037760.2145667433

chr4:191171919-191174993TUBB4Q,DUX4C,FRG2chr41911733840.10714285710.225

chrY:11947004-11950328N/AchrY119493000.11085271320.22

chr9:66194587-66197092N/Achr9661954320.17111111110.2107142857

chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275753640.17801274990.2544820124

chr16:33870863-33874924LOC649159chr16338732810.08121468930.2108695652

chr20:31717472-31720650C20orf144,C20orf134,APBA2BP,PXMP4,CBFA2T2.3,PXMP4.2,CBFA2T2.2,E2F1,APBA2BP.2,CBFA2T2chr20317184920.17373737370.2343669251

chr16:33867738-33870988LOC649159chr16338689540.15299026590.2829022989

chr3:75802981-75805596N/Achr3758036280.13690255110.2073134022

chr10:46411453-46414301SYT15,SYT15.2,GPRIN2chr10464120670.14393939390.2083304853

chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275743840.16108084450.2547974307

chr20:26135638-26139348N/Achr20261372970.14018646040.2044407796

chr3:130246227-130249742RAB43,CCDC48,GP9chr31302483820.22916666670.0064935065

chr4:191097585-191100469FRG1,TUBB4Qchr41910986230.01158046960.3334500626

chr19:812930-815227C19orf22,PRG2.2,PRTN3,AZU1,PTBP1,PTBP1.4,CFD,THRAP5,PTBP1.2,ELA2,PTBP1.3chr198143880.26666666670.1265107212

chr6:108060856-108064386FLJ10159chr61080627880.0379629630.5191197691

chr16:33870863-33874924LOC649159chr16338723650.0468750.2941176471

chr3:75802981-75805596N/Achr37580449000.2941176471

chr4:191171919-191174993TUBB4Q,DUX4C,FRG2chr41911729570.05336134450.2712820513

chr17:28172533-28174999MYO1Dchr17281735890.16666666670.2222222222

chr3:75915941-75918448N/Achr3759171840.16203959970.2069444444

chr5:177344140-177346359,chr5:177342948-177345545LOC653316,PROP1,LOC653316,PROP1chr51773453410.1670292670.3183982684

chr4:4278000-4281740OTOP1,TMEM128,LYARchr442799560.09259259260.22237784

chr16:33870863-33874924LOC649159chr16338723580.10636363640.2026811789

chr3:75802981-75805596N/Achr3758045590.13043478260.2285714286

chr16:33867738-33870988LOC649159chr16338693110.10794905580.2075233467

chr16:33867738-33870988LOC649159chr16338699560.11957671960.2757554945

chr4:191175795-191178585TUBB4Q,DUX4C,FRG2chr41911774300.12747747750.2780509931

chr1:16845072-16848632NBPF1chr1168472240.17730923690.2102564103

chr19:60282717-60286792EPS8L1.3,PPP1R12C,TNNT1,EPS8L1.2,RDH13,GP6,EPS8L1chr19602854040.41666666670

chr20:26135638-26139348N/Achr20261379810.16471568350.255246592

chr16:33867738-33870988LOC649159chr16338687660.19797711210.2647193229

chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275745350.22048505370.1801493532

chr19:18839351-18843200UPF1,LASS1.2,LASS1,GDF1,COPE.3,COPE,COPE.2,DDX49chr19188415900.09615384620.2104166667

chr20:26135638-26139348N/Achr20261384430.13333333330.2864583333

chr3:75789310-75791885N/Achr3757901050.07501369540.2178603604

chr16:33870863-33874924LOC649159chr16338721860.24647495360.1625

chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275751690.18286384980.2408107898

chr4:42198-44744ZNF718,ZNF595chr4434280.14471544720.3188573655

chr17:28172533-28174999MYO1Dchr17281736200.43333333330.1145833333

chr9:67902472-67904732N/Achr9679035600.20256410260.1084002677

chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275750800.19893320760.4120905667

chr20:26135638-26139348N/Achr20261384360.250.1732098553

chr4:191171919-191174993TUBB4Q,DUX4C,FRG2chr41911731690.12105263160.2591489319

chr10:130397433-130399648N/Achr101303986530.23880597010.0454545455

chr16:33867738-33870988LOC649159chr16338699230.18103448670.3137566138

chr1:1086906-1091447TTLL10,TNFRSF18.3,C1orf159,TNFRSF4,TNFRSF18.2,TNFRSF18chr110899700.0370370370.2142857143

chr16:33870863-33874924LOC649159chr16338723160.2870370370.1429577465

chr1:991526-996181C1orf159,AGRINchr19929160.21505376340.1631205674

chr16:33867738-33870988LOC649159chr16338692030.22744708990.1696969697

chr13:24012604-24015429PARP4chr13240144680.21473063970.1599415205

chr4:191097585-191100469FRG1,TUBB4Qchr41910989890.16122802980.2071052989

chr12:131482773-131484987N/Achr121314828810.11694677870.2036458333

chr9:66194587-66197092N/Achr9661957720.13254707840.2039458217

chr19:60282717-60286792EPS8L1.3,PPP1R12C,TNNT1,EPS8L1.2,RDH13,GP6,EPS8L1chr19602852290.12916666670.2653333333

chrY:11947004-11950328N/AchrY119480220.21807044170.1744408369

chr19:46045291-46047525CYP2A7.2,CYP2A7,CYP2A6,EGLN2.2,EGLN2,EGLN2.3chr19460464460.01724137930.2162162162

chr20:26135638-26139348N/Achr20261373080.27083333330.1834654235

chr7:129916975-129921347MEST,COPG2,MEST.2,TSGA14,MEST.3chr71299200620.24619883040.1873308409

chr16:33870863-33874924LOC649159chr16338722240.18918918920.3263888889

chr9:66196371-66199460N/Achr9661981800.06232876710.242824875

chr9:34612512-34615081,chr9:34613586-34615793OPRS1.3,IL11RA.2,IL11RA,CNTFR,CCL27,GALT,CNTFR.2,OPRS1.2,DCTN3.2,C9orf23.2,C9orf23,ARID3C,OPRS1,DCTN3,OPRS1.3,IL11RA.2,IL11RA,CNTFR,CCL27,GALT,CNTFR.2,OPRS1.2,DCTN3.2,C9orf23.2,C9orf23,ARID3C,OPRS1,DCTN3chr9346137180.19357429720.2708333333

chr3:75799803-75802846N/Achr3758013150.18928284410.2144067797

chr16:33870863-33874924LOC649159chr16338717580.22580645160.0962803726

chr16:33870863-33874924LOC649159chr16338739020.16176470590.2222222222

chr16:33870863-33874924LOC649159chr16338722150.17647058820.2266666667

chr20:26135638-26139348N/Achr20261379300.14939100580.2029379347

chr20:26135638-26139348N/Achr20261382550.00568181820.2317997842

chr3:75802981-75805596N/Achr3758042790.06572769950.2496151996

chr14:103706827-103709413N/Achr141037081940.06060606060.2078651685

chr16:33867738-33870988LOC649159chr16338691080.17094017090.2272727273

chr2:91139907-91142695N/Achr2911408740.25870646770.161352657

chr20:26135638-26139348N/Achr20261381730.16913214990.4047619048

chr16:33870863-33874924LOC649159chr16338730340.20913461540.1516946803

chr16:33870863-33874924LOC649159chr16338736730.26121794870.1907078429

chr16:33867738-33870988LOC649159chr16338686390.18540669860.3181584088

chr19:669098-672953C19orf21,PALM,FSTL3,PRSSL1,PALM.2chr196703220.16170634920.31

chr16:33867738-33870988LOC649159chr16338684830.11342592590.2352941176

chr2:91139907-91142695N/Achr2911407930.24132007230.1624223602

chr16:33870863-33874924LOC649159chr16338738130.24862313170.1631763129

chrY:11947004-11950328N/AchrY119485630.20217917680.1696648966

chr4:1396446-1399014CRIPAK,KIAA1530chr413974720.1235104670.2711926962

chr9:99022031-99025780ZNF322B,KIAA1529chr9990238830.09670781890.2673076923

chr4:191175001-191177268,chr4:191175795-191178585TUBB4Q,DUX4C,FRG2,TUBB4Q,DUX4C,FRG2chr41911768860.0686274510.2083333333

chr16:33867738-33870988LOC649159chr16338686240.19175036810.2360421472

chr16:33867738-33870988LOC649159chr16338684730.18032006920.3076923077

chr8:145174273-145181015EXOSC4,CYC1,GRINA,PARP10,GPAA1,OPLAH,GRINA.2,SPATC1,SHARPINchr81451797830.10666666670.2076923077

chr20:26135638-26139348N/Achr20261371080.03098958330.2066381374

chr20:26135638-26139348N/Achr20261381580.07708333330.2397435897

chr16:33867738-33870988LOC649159chr16338690010.16216216220.2054263566

chr17:1423797-1426009SLC43A2,PITPNAchr1714254620.19675925930.2660493827

chr16:33867738-33870988LOC649159chr163386949100.2427606178

chr4:191178728-191182892DUX4.3,TUBB4Q,DUX4.4,DUX4C,FRG2chr41911800710.34482758620.1964512028

chr16:33870863-33874924LOC649159chr16338735630.31250.1593873099

chr8:145672846-145675739PPP1R16A,CYHR1,VPS28.2,NFKBIL2,MFSD3,MGC70857,GPT,FOXH1,LRRC14,LRRC24,KIAA1688,RECQL4,KIFC2,VPS28chr81456735080.21851851850.1568181818

chr4:191097585-191100469FRG1,TUBB4Qchr41910992530.05950812470.2038690476

chr5:178352831-178355943GRM6,ZNF454chr51783538750.156250.2072463768

chr15:19219795-19222683N/Achr15192215210.01315789470.249021164

chr9:66196371-66199460N/Achr9661981260.03282828280.2253278123

chr14:105014466-105017745CRIP1,C14orf80,CRIP2,MTA1,TMEM121chr141050150840.06724063240.3024680604

chr3:75802981-75805596N/Achr3758036870.07633477630.270339676

chr17:21159584-21162005MAP2K3,MAP2K3.2chr17211609780.07807296870.233531746

chr4:191178728-191182892DUX4.3,TUBB4Q,DUX4.4,DUX4C,FRG2chr41911808500.21800519050.1850461236

chr4:191097585-191100469FRG1,TUBB4Qchr41910991750.08101103690.2075831025

chr18:12766496-12769113PTPN2.2,PTPN2.3,PTPN2chr18127679480.02127659570.2173202614

chr2:132825786-132828789N/Achr21328269030.16331168830.25

chr3:75802981-75805596N/Achr37580421400.2359085359

chr17:28172533-28174999MYO1Dchr17281738770.05122865440.2465607957

chr16:33870863-33874924LOC649159chr16338720020.20265936510.1065667503

chrY:11947004-11950328N/AchrY119485100.20179195560.1829223889

chr16:33870863-33874924LOC649159chr16338736110.38271604940.0223880597

chr19:7838263-7841898LRRC8E,FLJ22184,EVI5L,MAP2K7,SNAPC2chr1978393050.2318840580.1553366174

chr15:19346666-19350003POTE15chr15193481120.25833333330.1798245614

chr16:33870863-33874924LOC649159chr16338724920.07499795920.228880074

chr1:154451861-154454271PAQR6.2,BGLAP,SEMA4A,PMF1,SMG5,PAQR6,SLC25A44chr11544529930.22087301590.150615659

chr16:33870863-33874924LOC649159chr16338725630.20183841150.1737012987

chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275747680.17077625570.3514244874

chr3:75802981-75805596N/Achr3758037350.04344476210.3671643706

chr17:28172533-28174999MYO1Dchr17281739320.27516596530.1471453043

chr16:33870863-33874924LOC649159chr16338735370.27195910970.1737784416

chr1:154451861-154454271PAQR6.2,BGLAP,SEMA4A,PMF1,SMG5,PAQR6,SLC25A44chr11544530850.17348448210.2089833153

chrY:11947004-11950328N/AchrY119479680.22561904760.1986111111

chr16:33870863-33874924LOC649159chr16338727000.21362368140.1713352007

chr16:33867738-33870988LOC649159chr16338695270.19736842110.3588804053

chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275754680.12711864410.2036931818

chr18:12766496-12769113PTPN2.2,PTPN2.3,PTPN2chr18127678410.23684210530.1541666667

chr22:18088081-18096311TBX1.2,TBX1,SEPT5,GP1BB,TBX1.3chr22180946060.36271929820.0782828283

chr6:144369609-144372540PLAGL1.2,PLAGL1chr61443706610.16282051280.2258907759

chr5:115325249-115328036,chr5:115324506-115326992AP3S1.2,AP3S1,FLJ90650,AP3S1.2,AP3S1,FLJ90650chr511532637400.2106481481

chr16:33867738-33870988LOC649159chr16338694450.22294372290.138996139

&C&"Times New Roman,Regular"&12&A

&C&"Times New Roman,Regular"&12Page &P

SBS-ASM genes

AGRIN

AP3S1

AP3S1.2

APBA2BP

APBA2BP.2

ARID3C

AZU1

BCCIP.3

BGLAP

C14orf80

C19orf21

C19orf22

C1orf159

C20orf134

C20orf144

C9orf23

C9orf23.2

CBFA2T2

CBFA2T2.2

CBFA2T2.3

CCDC48

CCL27

CFD

CGB

CGB1

CGB2

CGB5

CGB7

CGB8

CNTFR

CNTFR.2

COPE

COPE.2

COPE.3

COPG2

CRIP1

CRIP2

CRIPAK

CYC1

CYHR1

CYP2A6

CYP2A7

CYP2A7.2

DCTN3

DCTN3.2

DDX49

DHX32

DUX4.3

DUX4.4

DUX4C

E2F1

EGLN2

EGLN2.2

EGLN2.3

ELA2

EPS8L1

EPS8L1.2

EPS8L1.3

EVI5L

EXOSC4

FANK1

FLJ10159

FLJ22184

FLJ90650

FOXH1

FRG1

FRG2

FSTL3

GALT

GDF1

GP1BB

GP6

GP9

GPAA1

GPRIN2

GPT

GRINA

GRINA.2

GRM6

HLA-B

HLA-C

IL11RA

IL11RA.2

KCNA7

KIAA1529

KIAA1530

KIAA1688

KIFC2

LASS1

LASS1.2

LHB

LOC649159

LOC653316

LRRC14

LRRC24

LRRC8E

LYAR

MAP2K3

MAP2K3.2

MAP2K7

MEST

MEST.2

MEST.3

MFSD3

MGC70857

MTA1

MYO1D

N/A

NBPF1

NFKBIL2

NTF5

OPLAH

OPRS1

OPRS1.2

OPRS1.3

OTOP1

PALM

PALM.2

PAQR6

PAQR6.2

PARP10

PARP4

PITPNA

PLAGL1

PLAGL1.2

PMF1

POTE15

PPP1R12C

PPP1R16A

PRG2.2

PROP1

PRSSL1

PRTN3

PTBP1

PTBP1.2

PTBP1.3

PTBP1.4

PTPN2

PTPN2.2

PTPN2.3

PXMP4

PXMP4.2

RAB43

RDH13

RECQL4

RUVBL2

SEMA4A

SEPT5

SHARPIN

SLC25A44

SLC43A2

SMG5

SNAPC2

SNRP70

SNRP70.2

SPATC1

SYT15

SYT15.2

TBX1

TBX1.2

TBX1.3

THRAP5

TMEM121

TMEM128

TNFRSF18

TNFRSF18.2

TNFRSF18.3

TNFRSF4

TNNT1

TSGA14

TTLL10

TUBB4Q

UPF1

VPS28

VPS28.2

ZNF322B

ZNF454

ZNF595

ZNF718

&C&"Times New Roman,Regular"&12&A

&C&"Times New Roman,Regular"&12Page &P

Parameter SearchExperimented with various lower and upper bounds for methylationGuidelinesDiscover as many of the 1000 loci Reduce the overall number of 500bp windows30-80 rediscovers 958 of loci, at the highest specificity

Incorporating Genetic VariationSearch for allele-specific methylationLook only into the 30-80% methylation loci overlapping with CpG IslandsUse het SNPsCheck for those that separate reads into different methylation states One allele >20%Other allele

ResultsFound 6295 heterozygous sites 586 sites have allele specific methylationOverlap with 62 of the 1000 loci37 of the loci discovered using pairs of assays25 new loci

MethylC-seq +ChIP-seqMethylC-seq + RNA-seqMRE-seq +MeDIP-seqMonoallelic Epigenomic Marks and ExpressionDistribution of the 62 SBS-ASM loci

Additional25 loci

Breast Tissue Allele specific methylationDetermine informative heterozygous SNPs Loci with monoallelic MRE-seq and MeDIP-seq

Breast Tissue Multiple cell typesDifferent epigenotypesSame genotypeIdentify monoallelic events ConstitutionalTissue specificCell types for four individualsConserved monoallelic marksIndividual specific monoallelic marks

Integrate Array-based and Seq-based methodsCollaboration with Leo Schalkwyk and Jonathan Mill, Kings College, UKInvestigate same breast tissue samples

InsightCostResults# of ASM lociDistribution of ASM loci identified by each methodSuggestions for designing future studies

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 OGeen, 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

*maybe 20 M reads per lane (optimal), range ~ 10 - 20 M reads/laneUSing all CpG islands genome wide here, correct?

Not sure I understand the cutoffs. Can you elaborate more about what the >20,