Alzheimer's Disease: Early Alterations in Brain DNA Methylation at ANK1

© 2014 Nature America, Inc. All rights reserved. addressed addressed to P.L.D. ( General Hospital, Boston, USA. Massachusetts, McGill University, Montreal, Québec, Canada. Cambridge, USA. Massachusetts, Rush University Medical Center, Chicago, Illinois, USA. of Mayo Neuroscience, Clinic, Florida, Jacksonville, USA. USA. Massachusetts, Boston, USA. Massachusetts, 1 expressed differentially these that and associ pathology AD independently with also ated was regions methylated these in differentially found genes of an were transcription the changes that AD, of these feature that early AD, to relation in DNA levels altered methylation exhibited genome human the in dinucleotides CpG rela in We 71 discrete that found of AD neuropathology. burden to the tion methylated differentially were that genome the of regions identify to effort discovery gene rigorous statistically a out carried AD studying when considered carefully to be has that age increasing with genome the throughout sites many at methylation associations robust returned yet not have and conflicting sometimes are AD of studies inhibitors HDAC with patients AD of treatment off-label in and AD of systems model in (HDAC) deacetylases histone of manipulation the from comes AD influence a or factor diagnosis risk disease a as such individual, given a of experiences life the with correlated be could loci certain in DNA methylation in and individual an individuals in time across over stable variable highly both be can CpG dinucleotides certain at levels methylation DNA that emerging is Evidence genes connect to a known AD gene susceptibility network. changes may have a role in the onset of AD given that we observed them in subjects presymptomatic and that six of the validated in AD: Furthermore, we functionally validated these CpG associations and identified the nearby genes whose RNA expression was altered AD variants. susceptibility We validated 11 of the methylated differentially regions in an independent set of 117 subjects. associated significantly with the burden of AD pathology, including CpGs in the relation to Alzheimer’s disease (AD). We found that the level of methylation at 71 of the 415,848 interrogated CpGs was We used a collection of 708 collected prospectively autopsied brains to assess the methylation state of the brain’s DNA in Jonathan Mill E Bradley Bernstein Steven G Younkin Towfique Raj YuLei Philip L De Jager methylation at Alzheimer’s disease: early alterations in brain DNA nature nature Received 5 May; accepted 16 July; published online 17 August 2014; Program Program in Translational Genomics, NeuroPsychiatric Institute for the Departments Neurosciences, of Neurology and Psychiatry, Brigham and Women’s Hospital, ANK1 8 NEUR , , Matthew L Eaton 3 . The most compelling evidence that the epigenome may may epigenome the that evidence compelling most . The , , OSCI CDH23 1 4 [email protected] – , 4 3 5 University University of Exeter Medical School, University of Exeter, Exeter, UK. EN , , Joseph Replogle & David A Bennett 6– 1 9 C – 6 . Furthermore, there is a gradual increase in in increase gradual a is there Furthermore, . 3 , , , , Fanggeng Zou E , , Gyan Srivastava

2 DIP2A 2 , Harvard Harvard Medical School, Boston, USA. Massachusetts, 12 advance online publication online advance 10 , 13 Genetic Genetic Analysis Platform, Broad Institute, Cambridge, USA. Massachusetts, , , Alex Meissner 3 , , , 4 9 RHBDF2 , 1 5 , , Brendan T Keenan , . To date, results of epigenomic epigenomic of results date, To . 2 ) ) or D.A.B. ( . This suggests that differences differences that suggests This . ANK1 12 14 Epigenomics Epigenomics Program, Broad Institute, Cambridge, USA. Massachusetts, 1 Harvard Harvard Stem Cell Institute, Harvard University, Cambridge, USA. Massachusetts, should Correspondence be – 6 , , 3 , , Moshe Szyf 8 RPL13 , , Wendy Brodeur 1 [email protected] , 9 3 Computer Computer Science and Artificial Intelligence Laboratory (CSAIL), Institute Massachusetts of Technology, , , Katie Lunnon 7 Department Department of Neurology, Mayo Clinic, Florida, Jacksonville, USA. 9 , , , 12 SERPINF1 , , 14 BIN1 , , Nilufer Ertekin-Taner 8

, 1 11 10– doi:10.1038/nn.378 , 3 , Charles , B Charles Epstein , , Jason Ernst 1 2 . . We and 10 4 - - , , Stacey Gabriel , 5 ). , , Jeremy Burgess SERPINF2 , To technically validate the nature of our data, we compared our our compared we data, our of nature the validate technically To AD of diagnosis pathologic a meeting of amyloid range pathology burden at a the time of death, with 60.8% of displayed subjects and cognitively declined subjects some study, the of course the Over population. older the of selection random a at were non-impaired the subjects cognitively study entry, we studied that Given death. of time the at donation brain include that aging of studies cohort prospective two (MAP), Project Aging and Memory the or (ROS) Study Order Religious the of part were subjects These glia. as such cells parenchymal other and populations neuronal ent differ of primarily composed tissue of piece a each profiled we from sample, matter gray the out dissected we that Given each individual. from obtained cortex prefrontal dorsolateral of profiles sample a and methylation beadset HumanMethylation450 These Illumina the using subjects. generated were 708 in dinucleotides CpG discrete 415,848 at measures methylation of consisted set data Our Description of subjects and data RESULTS AD network defined genetically susceptibility reported previously a to connected genes 3 Program Program in Medical and Population Genetics, Broad Institute, Cambridge, RHBDF2 6 3 5 , Institute Institute of Psychiatry, King’s College London, London, UK. 9 , , Cristin McCabe . . Our analyses suggest that these DNA methylation 12 6 ABCA7 , , , Julie A Schneider 10 7 , Lori , B Lori Chibnik , , High S Chai 6 , 7 , , Leonard C Schalkwyk and 1 11 3 . Department Department of Pharmacology and Therapeutics, and other loci BIN1 3 13 , , Anna Tang Department Department of Pathology, Massachusetts regions, which harbor known 8 6 Rush Rush Alzheimer’s Disease Center, , 7 , , Curtis Younkin 1 – 1 8 4 3 , ( , , Manolis Kellis

Supplementary Table 1a Supplementary 1 ,

4 t r a , 5 ,

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© 2014 Nature America, Inc. All rights reserved. significance; thus, significance; to enhance the clarity of the figure for the other chromosomes, these two chromosomes are not included in the figure. Not all genes (–log( in black. The next red circle reports the results of the association of RNA expression level of these genes to a diagnosis of AD in the Mayo clinic data set alleles, susceptibility are highlighted in red letters. The subset of the genes with differential mRNA expression in AD in the Mayo clinic data set is shown colored circle reports the names of genes found within 50 kb of each associated CpG (light blue letters). The DNA methylation levels to NP burden. Similarly, the first red circle reports the −log( 0–20), we report the results for each of the 71 associated CpGs found in 60 independent methylated differentially regions (DMR) from the analysis relating given genomic segment (range, 0–200 probes per 100 kb). The blue circle reports the results of the DNA methylation scan: using a −log( centromere highlighted in red. The next circle (green) reports the density of CpG probes successfully sampled by the Illumina beadset that are present in a presents the physical position along each chromosome (in Mb). The cytogenetic bands of each chromosome are presented in the first circle, with the RNA data. Each sector of this diagram presents summary results of the three different analyses in a chromosome. The perimeter of this circular figure Figure 1 sub four these in subjects): AD two and non-impaired (two jects DNA in of four sub brain the same samples the from generated data sequence methylation DNA genome-wide to data Illumina-derived t r a found in the associated regions are listed in the figure. For clarity, only a subset of genes are selected from loci significant in the discovery analysis.

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5 chr4 chr3 C E © 2014 Nature America, Inc. All rights reserved. significant inourdiscovery analysisandfoundinADsusceptibility lociarelistedinthe‘KnownADloci’. Chr, chromosome; pathoAD,pathologicdiagnosis ofAD. accounts forthetesting ofprobesgenome-wide.Listedareall genesfoundwithinasegment50kbupstream anddownstreamoftheassociated CpG.ThoseCpGsthatwere result ofalogisticregressionrelatingthe level ofmethylationagivenCpGtopathologicdiagnosisAD.Thethreshold ofgenome-widesignificanceis Model 3referstothesecondaryanalysis that includedavariablefortheestimatedproportionofneuronalcellsintissue andsurrogatevariables.TheADanalysisreportsthe The NPanalysisreportstheresultsofalinear regressionanalysisrelatingDNAmethylation leveltotheburdenofneuriticplaque.Model1 referstotheprimaryanalysisand cg02308560 cg22883290 cg05810363 cg05066959 cg03169557 cg19803550 cg22962123 cg16733298 cg15821544 cg23968456 cg11724984 CpG Table 1 target the be therefore may and region. given a in gene(s) expressed narrow CpGs differentially to methylated helped were differentially related it the and near genes region, change) the of this which down in a occurred that (transcriptional disease showing the effect by to region biological role methylated the meaningful differentially confirmed given further This a it subjects. of goals: non-AD two stage and accomplished AD in from strategy replicated obtained were mRNA that using the of 2 regions role the methylated validate functionally differentially to pathology significantly subjects. attempted of we set the AD 3, independent stage an replicated In in with 1 we stage 2, from CpGs correlate stage associated In levels Methods). methylation (Online regions which chromosomal for in screen methylation DNA a out carried ( stages three involves strategy analytic Our regions methylated differentially identify to study Discovery ( experiment our in quality data of assessments for lines profiled were that cell HapMap individuals from lymphoblastic and samples cortical our between profiles methylation DNA in differences more many found we expected, As subjects. older these of each of course life different very the despite in variation interindividual significant show not ( correlation of methylation levels for all possible subject pairs was 0.98 profiles across our subject population, we noted that the mean Pearson nature nature cg00621289 cg25594100 cg13076843 Supplementary Supplementary Fig. 1 Notably, when examining the nature of human cortical methylation

NEUR CpGs CpGs associated with amyloid burden: validated CpGs and CpGs in known AD loci 19 17 16 10 17 21 17 16 12 Chr 2 8 7 7 1 OSCI 127800646 121890864 74475270 41519308 89598950 27153605 19127132 43473840 73521631 47855916 74475294 Position 1071176 1637391 4786943 EN (bp) C ), suggesting that that majority of CpG sites did E

advance online publication online advance Supplementary Fig. 2 Fig. Supplementary NP burden Estimate model 1 2.19 4.41 2.95 2.69 4.86 4.36 2.75 3.52 4.97 3.02 3.15 2.35 1.7 3.5 3.06 3.06 × 10 3.73 3.73 × 10 3.68 × 10 7.13 × 10 1.12 1.12 × 10 1.17 × 10 3.97 × 10 3.99 3.99 × 10 1.04 1.04 × 10 6.48 × 10 2.54 × 10 1.68 × 10 5.24 × 10 4.76 × 10 P Fig. Fig. −8 −8 −7 −7 −10 −10 −14 −10 −11 −9 −8 −9 −8 −8 NP burden

). methylation levels levels methylation Estimate model 3 1 3.62 4.44 2.86 2.78 4.88 3.08 3.96 5.11 4.79 4.48 4.22 2.29 4.48 5.06 ). In stage 1, we we 1, stage In ). Discovery study

2.45 2.45 × 10 8.97 × 10 8.11 8.11 × 10 7.56 × 10 3.36 3.36 × 10 1.60 × 10 8.79 × 10 1.32 × 10 8.09 × 10 8.97 × 10 9.75 × 10 2.18 2.18 × 10 3.33 × 10 5.99 × 10 Known Known AD loci P −12 −8 −9 −13 −9 −13 −9 −11 −9 −11 −8 −11 −13 −8

technical or other artifacts. Of the 137 CpGs discovered in the the in discovered CpGs 137 the Of artifacts. cryptic other capture or may and do technical that confounders data known with methylation the correlate in not structure capture surrogate as that well as variables neurons of proportion the vari the captures that included able that analysis secondary a performed associated we CpGs, conservatively most the on only focus to However, sis. ( AD to related not was sample each in found neurons of the As proportion measured. was that CpG each of testing the for correct and hypotheses multiple of testing the for account to con strategy but simple, servative, this chose we unknown, currently is in genome the methylation of units independent functionally of number exact standard a on correction Bonferroni a ing impos by CpGs tested 415,848 all of testing the for accounts cance Table ( pathology NP of burden the with associated were AD of diagnosis a for criteria neuropathologic which of meet some pathology, NP of range a display individuals intact cognitively as subject, deceased a of brain the of state the captures better burden NP neuropathology. disease Alzheimer’s of measure a key (NP), plaques amyloid of neuritic burden the with correlates of methylation level whose CpGs autosomal we identified segment and were highly correlated in their level of methylation. methylation. chromosomal of level same their in the in correlated highly found were and were segment CpGs 71 these of Some con ( analysis more the secondary in servative significant remained CpGs 71 analysis, primary Estimate P 13.7 15.9 11.3 11.2 18.6 19 17.5 14.9 10.8 12.9 18.9 16.8 16.7 In the primary analysis of our cortical methylation profiles (stage 1), = 0.08), we did not include this as a term in the primary analy primary the in term a as this include not did we 0.08), = AD 9.32 1 and and 1.77 1.77 × 10 1.83 × 10 3.36 × 10 4.88 × 10 1.67 1.67 × 10 5.56 × 10 9.76 × 10 1.46 × 10 1.45 × 10 1.27 × 10 6.81 6.81 × 10 3.98 × 10 1.83 × 10 5.81 × 10 Supplementary Table 2 Table Supplementary P −8 −5 −7 −10 −6 −7 −7 −8 −7 −8 −6 −7 −5 −7 Estimate Braak score 3.60 0.96 2.76 3.45 1.52 5.13 3.58 2.47 1.62 3.40 1.51 2.02 4.09 3.02 Replication study 0.011 0.0067 7.93 7.93 × 10 6.48 × 10 6.05 × 10 2.79 2.79 × 10 2.30 × 10 2.08 × 10 8.27 × 10 5.44 × 10 5.61 5.61 × 10 4.95 × 10 4.19 × 10 2.99 × 10 Table P 1 and and −4 −4 −4 −4 −4 −4 −6 −6 −4 −4 −4 −4 ). This threshold of signifi of threshold This ). CNN2 BIN1 UBE2O AGPAT6 ANKRD11 PRPF8 PCNT FOXK1 UBE2O HOXA1 COQ7 SLC2A1 CDH23 RNF34, KDM2B Supplementary Table 2 Supplementary POLR2E AX747521 ANK1 RPL13 HOXA4 AK311383 AK291164 SERPINF2 AF070569 RADIL AX747521 HOXA7 HOXA5 P < 0.05. Given that the the that Given 0.05. < associated CpG Genes within50kbof , , , , , , , , , , DIP2A ITPRIPL2 , , , , ABCA7 , , , , , , , , TLCD2 AP5Z1 HOTAIRM1 C10orf105 AANAT AANAT P , , NKX6-3 , , , , FLJ32224 t r a <1.12×10 , , CPNE7 , , HOXA6 HOXA9 LOC100133311 SPG7 GPX4 , P , SERPINF1 , , , , 16 , , , < 1.20 × 10 × 1.20 < MIR22 , , CYGB CYGB BC035889 HOXA3 , , ( , , , , , HMHA1 1 MIR22HG KIAA0415

RHBDF2 RHBDF2 quantitative quantitative 7 , , , , , , , , , , . 137 CpGs CpGs 137 . JA429246 SNORD68 C I SBNO2 DQ655986 HOXA10 , , , , , , , , C10orf54 HOXA2 , PRCD PRCD , −7 WDR81 , , e l SMYD4 , which

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© 2014 Nature America, Inc. All rights reserved. les: cg22883290 in the the in cg22883290 les: Table 2 strongly correlated with a key measure of AD neuropathology. that DNAcortical methylation of a large number of discrete regions is and correlated are regions genomic certain of levels methylation that model, they explained 28.7% of the variance in NP burden, suggesting one comprehensive in CpGs 71 all If consider burden. we NP in ance and all known AD variants and CR1 the exception of tion of variance explained by genetic variants associated with AD, with of the variance in NP burden. However, this is greater than the propor den: on average, each the 71 CpGs explained 5.0% (range = 3.7–9.7%) and effect. same the analysis captured our in probably and significant being as emerged methylation of levels correlated with CpGs neighboring three to up regions, 60 7 these of ( genome the differentially throughout discrete distributed regions 60 in methylated found were CpGs 71 the Altogether, is it that suggest analyses RNA our and genes, two to proximity close in found was CpG associated An significance. of threshold our met which cg13076843, on centered ( regions. transcribed actively of periphery the on found a conformation in largely was which 3 just a region in located be to appeared CpG associated the and cortex, prefrontal dorsolateral older healthy, in conformation transcribed the Overall, bins. 200-bp in derived was state chromatin The pathology. AD-related minimal with individuals older unimpaired healthy, in assessed as shown, is region the of state chromatin the segment, DNA this in found genes the of diagram the above Finally, | the using reported is (cg22883290) CpG best the of methylation DNA of level the with correlates CpG a given at level methylation DNA which to extent The point. a given at probes CpG of density the reports line blue vertical The analysis. this for significance of threshold the highlights line blue dotted horizontal The region. this in tested CpG one represents diamond Each studies. association genome-wide in susceptibility AD with the in cg22883290 around ( modest. was distributions two the between difference absolute the and overlapped, distributions two the However, subjects. control the of that from different significantly statistically was subjects AD of distribution the that We found samples. these in methylated completely is CpG the that indicates 1.0 of value green, light (control, criteria diagnostic red, (case, AD of diagnosis a neuropathologic having as classified were that subjects for CpG that at values methylation DNA of distribution the presenting histogram a smoothed is Shown significance. of of AD in 82% of the CpGs threshold that met our on with observed, a average, we diagnosis that levels methylation ( DMRs the from probes, AD-associated most ( associations. of distribution regional and CpGs 2 Figure t r a The susceptibility variant rs744373 was moderately associated with with associated moderately was rs744373 variant susceptibility The ( region to AD in this association genetic the captures best from the index single nucleotide polymorphism (SNP), rs744373, that cg22883290 is located 5 kb from the 5 the in cg02308560 a

Notably, two of the 71 significantly associated CpGs ( ( CpGs associated significantly Individually,the of anyone Density 10 Supplementary Table 2 0 2 4 6 8 susceptibility allele explains just 1% of variance in NP burden NP in ofvariance 1% just explains allele susceptibility 0.55 P Table ) ) were found in that loci alle harbor known AD susceptibility

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74,330 AD AD at any one associated probe was modest ( of AD. As noted above, the increased level of methylation in relation to methylated regions were more methylated in subjects with a diagnosis differentially the of 82% associations: these of direction the in zation is one criterion for a neuropathologic AD diagnosis. We noted a polari of association ( the CpGs associated with NP burden displayed at least some evidence ( significance of level genome-wide a at AD of diagnosis a with associated also were CpGs post-mortem, neuropathologic diagnosis of AD. 22 of the NP ary analysis correlating the level of methylation at these 71 CpGs with a process. disease the on and effects independent have that (genetic epigenetic) variation genomic of sources different by affected be therefore may AD of risk Overall, susceptibility. AD on effects ent SNPs in of regions, both and these to CpGs appeared have independ kb away, 25 is so, which at cg02308560, of methylation level the with burden NP with associated was of case In the methylation. CpG by could to disease SNP’sof the for mediation association test not formally therefore we and susceptibility, AD with associated not was = 4.37, beta covariate: a as rs744373 with (model burden NP to association the CpG of size effect the change meaningfully not did rs744373 for ing by adjust was not driven variant: the AD with pathology association the level of methylation at cg22883290 ( PRPSAP 127,660 Strongly transcribedregion Weakly transcribedregion Weak enhancer Active enhancer Strong promotor Inactive/poised promotor Active promotor Repetitive Heterochromatin Low signal PC/repressed To facilitate the interpretation of our results, we performed a second Strongly transcribedregion Weakly transcribedregion Weak enhancer Active enhancer Strong promotor Inactive/poised promotor Active promotor Repetitive Heterochromatin Low signal PC/repressed S 1 advance online publication online advance PHK1 74,390 127,720 P d = 4.91 × = 10 4.91 ) Regional association plot around the the around plot association ) Regional P < 0.001) with AD. This is not surprising, as NP burden UBE2O cg0624533 Chromosome 17position(kb) Chromosome 2position(kb) Table Table 74,450 127,780 AANA RHBDF −7 cg05810363 BIN1 2 ). In our data set of modest size, rs744373 rs744373 size, ). of In set modest our data 1 8 cg2288329 ). and BIN1 T RHBDF region 2 region 2 cg1307684 ABCA7 3 Supplementary Table Supplementary 2 gene appeared to be in an open, open, an in be to appeared gene , but had no association ( association no had but , 2 74,510 127,840 BIN1 0 P C = 0.0003). However, the CpG YG , the index SNP (rs3764650) (rs3764650) SNP index the , 3 PRCD

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C Illumina450 probes per kb per probes Illumina450 kb per probes Illumina450 E - - - - © 2014 Nature America, Inc. All rights reserved. diminished given the reduced sample size ( the subjects, of non-impaired In subset the asymptomatic. was that of pathology amyloid mulation of large accu demonstrated fraction non-impaired, older individuals studies imaging and neuropathological in well-documented time of death (no AD and no mild cognitive impairment). As has been those subjects who were deemed to be cognitively non-impaired at the to analysis NP the limited we AD, of process neurodegenerative the of effect an or cause a is regions of associated level the in methylation DNA increased the whether of question the explore to begin To methylation in alterations same the display subjects non-impaired Cognitively ( tion ABCA7 the Both profiled. are sizes sample larger 4 Fig. of most of size ( sets effect data two the across the consistent was CpGs that these found we CpG, 71 of set entire the evaluating when of association: evidence suggestive displayed CpGs other many much CpGs, significant a the to addition In screen’s and results. discov ery our of replication pathology robust AD observed we of size, sample measure smaller related, but different, a ( validated were study discovery the near from regions methylated region differentially the of 11 methylated differentially this same in significant were CpGs ( 12 analysis that found and analysis this in ( platform HumanMethylation450 Illumina same the using cortex tal staging) (Braak of pathology AD of measure collection quantitative different a with independent subjects 117 an in the CpGs evaluated associated we results, significantly our 71 of robustness the assess further To set sample CpG in an independent of Validation the associated nature nature not were effect, association’s the of magnitude the capture which Supplementary Table 1b Table Supplementary Table ), suggesting that most of these CpGs will be validated as as validated be will CpGs these of most that suggesting ), b a (

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); given that two of these CpGs were found in the the in found were CpGs these of two that given ); Heterochromatin . These subjects were profiled in a sample of fron of sample a in profiled were subjects These . Weakly transcribedregion Inactive/poised promoter 0.038 EN C Heterochromatin E Strong promoter Active enhancer Weak enhancer Active promoter

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of whether altered DNA methylation contributes to the pathologic pathologic the to contributes methylation DNA altered whether of question The process. dementing of the stages later the to secondary stage of the disease. These DNA methylation changes are therefore not presymptomatic the in occurs and pathology AD of feature early an is study in our discovery that we DNAidentified altered methylation ( collection sample from the entire calculated values from the beta different significantly analyzed. were that dinucleotides CpG 415,848 all in found states chromatin of distribution ( plaques neuritic with associated was level methylation whose dinucleotides CpG associated 71 those at found states chromatin of distribution the compares histogram The found. were dinucleotides CpG interrogated the of each which in state chromatin ( marks. histone of complement a unique had state chromatin Each mark. histone particular a for immunoprecipitation after DNA of a segment in found sequences of abundance relative the displays graphically high) blue, low; (white, heatmap The examination. neuropathological on pathology minimal had and death of time the at non-impaired cognitively were that used were subjects MAP states. chromatin discrete 11 1 of to segment chromosomal each assigned we effort, Roadmap Epigenomic Health’s of Institute National the by generated data Using cortex. prefrontal dorsolateral the ( cortex. mid-frontal in found states chromatin 11 3 Figure available probes for each gene. was diagnosis, withnon-ADbeingthereference subjectgroup.Thesignificancethreshold Beta referstoeffectsizeinthelinearregression associatinggeneexpressionandAD AGPAT6 CYGB SPG7 WDR81 RNF34 UBE2O FOXK1 C10orf54 ANKRD11 COQ7 PCNT AP5Z1 SLC2A1 PRPF8 SMYD4 ANK1 SERPINF2 SERPINF1 RHBDF2 RPL13 DIP2A CDH23 Gene amyloid burden Table 2 It is derived from two MAP subjects who were cognitively non-impairedEpigenomics Roadmap team ( the dorsolateral prefrontal cortex, generated in collaboration with the of map chromatin a to relation in results our interpreted we CpGs, associated the of consequences functional the understand Tobetter Distribution of associated CpGs among different chromatin states neurodegenerative the of open. remains process epiphenomenon early an is or process P <0.0023given22 tests. Resultsareshownformeanexpression levelacross

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© 2014 Nature America, Inc. All rights reserved. Supplementary Table 6 Supplementary ( associations cant signifi had genes selected 21 the of 7 of expression of level the that 5 Table ( Mayo by Clinic the assembled individuals non-AD methylated regions (DMRs) in an set of independent 202 AD and 197 ( vicinity the in found genes of sion expres of level the assessing by AD in role their evaluated we stage, replication the in validated been have that CpGs 12 the on Focusing associations CpG the of validation Functional to CpG islands ( in different genic features or in different structures in defined relation or inactive in the healthy older brain. There were no enrichments noted appear to primarily affect genomic regions that are weakly transcribed brain may not be strongly altered by AD. Rather, methylation changes drive fundamental cellular processes of neurons and glia in the healthy chromatin that the architecturesuggest data of strong promoters that promoter profile in the reference chromatin map ( we found a strong under-representation in regions displaying a strong state ( ( enhancers was an enrichment of associated CpGs in regions predicted to be weak the 71 associated CpGs were found in chromatinevery state, but there this tissue. Using this reference map, we that observed at least some of in found elements regulatory putative and states transcriptional the 1 of 11 chromatin states ( ment of the genome was annotated as being in methods established and profiles histone-modification Using ination. exam post-mortem on pathology associated AD- minimal had and death of time the at gray.in shown are AD with associated be to known not proteins connecting The meaning. no have colors the associations); epigenomic and (genetic AD in implicated genes by encoded 6 Fig. also (see process same the in DIP2A implicate and biology amyloid on SORL1 and PLD3 both of effects reported the with consistent are interconnections These (APP). protein precursor amyloid the to connects that allele, susceptibility AD a common with a gene to and allele AD susceptibility a has rare described recently the connects DIP2A Furthermore, left). (bottom network the of component amyloid the of elements different to connect SERPINF2 and SERPINF1 Notably, network. this in role a central has that susceptibility AD with associated genetically kinase tyrosine a protein PTK2B, to connects and green in figure the of top the at displayed is RHBDF2 example, For AD. with associated be to known not is that protein interactor common one for allowing analysis an of results the are Shown AD. to relative expressed differentially also were that DMRs in found genes seven the and genes) Mendelian and (susceptibility genes AD known among connectivity of extent the evaluated algorithm DAPPLE the data, interaction protein protein- Using genes. susceptibility AD known of a to network connect screen methylation 4 Figure t r a SERPINF1

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© 2014 Nature America, Inc. All rights reserved. molecule as molecule well. Although little is known regarding the potential role as such genes, AD other Several in macrophages. and of infiltrating the microglia activation involved modulating cascade signaling the of element key a is that gene AD gene. this of outgrowth neurite to reduced leads ( AD in reduced Notably, amyloid machinery. amyloid on the of elements to effect connect also direct SERPINF2 a and to SERPINF1 of processing. related burden be the well to may relation pathology its NP processing, amyloid in SORL1 and sur cell a protein as receptor face function may DIP2A network. susceptibility AD the to connected not otherwise is that gene susceptibility AD reported known resolve to question. this conducted be to needs work experimental Further ogy. pathol AD of consequence early an are changes these that possible is it epigenome, the of plasticity the given causal: are changes methylation in observed the that state cannot we Thus, trait. a with tions associa reports only scan epigenome-wide our studies, genetic with as However, AD. of onset the in involved are changes methylation ( network susceptibility AD existing an ( genes methylated differentially the of six of connection ( subjects in non-impaired cognitively associations CpG the of presence the with along pathology, AD associations. by SNP driven not are tions associations genetic significant no found and subjects same the in trait same the with study association SNP a completed we genome-wide recently also AD tions with pathology: associa genetic by driven not are observed we that associations the variation can drive differences in DNA methylation for certain CpGs genetic although that, point the illustrate loci two These vicinity. its in found allele susceptibility of the independent was association this in the cg02308560 of expression the on integrate that effects independent have can CpG) and (SNP tion the of understanding our refine therefore results Our subjects. additional ( consistent was effect the of direction the analysis, replication small ( suggestive were results the although and, study, The bility locus and a recent report of enhanced BIN1 expression in AD AD suscepti our to study a links well-validated set data Mayo Clinic ( expression mRNA ( healthy brain older the in conformation chromatin transcribed poorly a healthy in be to likely the more were regions in associated the Instead, brain. older transcribed actively were that genes in found be to regions were in targeted methylated AD and ferentially were unlikely dif specific process: appear to genome-wide be of part a generalized, the and functionally methylation presymptomatic accumulation DNA of replicated, AD pathology. altered These changes do not between several associations found validated nonetheless we ylation), closely related chromatin marks (DNA two methylation and DNA hydroxymeth distinguish to inability array’s the and CpGs genome’s nature nature with is consistent PTK2B with of RHBDF2 connection the of ANK1, Table On the other hand, hand, other the On the to directly connects DIP2A map, network AD the at Looking The colocation of genetic susceptibility with CpG associations to to associations CpG with susceptibility genetic of colocation The BIN1 BIN1

Fig. 3 Fig. 1 SORL1 ), suggesting that the association is likely to validate with with validate to likely is association the that suggesting ), NEUR cg22883290 association was significant in our discovery discovery our in significant was association cg22883290 locus and suggest that different types of genomic varia genomic of types different that suggest and locus b OSCI susceptibility gene susceptibility ). Altered DNA methylation ( methylation DNA Altered ). Table Table ABCA7 BIN1 EN P 3 = 1.09 × 10 × 1.09 = 0 C and, given the putative role of PLD3 (ref. (ref. of PLD3 role putative the given and, 2 ANK1 E and influence AD susceptibility. Similarly, Similarly, susceptibility. AD influence and

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RNA expression with AD ( AD with expression RNA CDH23 of assessment our in GFAP expression for term a adding that found we astrocytes, for marker surrogate a as GFAP expression using ple, some of alterations the to transcriptional cell Forcertain types. exam types cell ( expression mRNA model seems to account for the AD-associated differences in for an of using estimate an cells the RNA-based number of microglial in process. Consistent with adjusting the this, AD pathophysiological may be involved in the role of and macrophages microglia infiltrating text of AD ( but our data that suggest (EGF) factor growth TNF of release the in functions which protease, TACE/ADAM17 the of specificity that suggests also pathogens to responses immune systemic impairs in mice affects the normal release of TNF which releases TNF of transport TNF the known role of this in molecule myeloid cells: it is for necessary the goal for the development of AD therapies. AD of development the for goal the epigenome is remodeling a whether using systems, model fruitful and that contribute to to matin structure AD assess, pathophysiology aging chro of alterations the the map precisely more to need in clearly we brain, forward Going pathology. amyloid accumulated had but impairment, cognitive no displayed subjects while process, logic patho the in important early occur changes the epigenomic these that made observation also We organs. other or brain the in studies of human future design the to calibrate used be can our results hand, in sizes effect clear With studies. epidemiology epigenomic relevant other to broadly more epigenome brain’s the investigating for egy study at work. be may processes other, or unsuspected these of one than more that likely also Itis populations. cell cortical that alters circulation the systemic relative of abundance the different neurons are or lost; of is from there the cells immune a influx modest as such populations some as changes cortex the of populations cell overexpress that in change, of the such plaques vicinity as astrocytes neuritic activated in relation to a AD of fraction pathology: the constituent cells cortical occur that methylation in changes robust, but modest, these explain too is It time. given a early at to confidently differentiate between three possibilities affected that could are cells of number small a only and decades several over accumulates pathology AD as sample, cal represent the altered methylation state of a subset of cells in likely our corti most observed we that methylation DNA in changes The ies. capillar cortical microglia, from cells as endothelial and cells such immune peripheral cells other and glia, neurons, of types different mechanistically. dissected now be can that methylation in alteration a robust uncovered screen of case the in of mechanism, Regardless exact the effects. both of combination a or astrocytes these of an activation the by plaques, neuritic near caused astrocytes of number was increased methylation) DNA altered its presumably (and of alteration the whether distinguish not activation astrocyte with enhanced is GFAP expression Such cell-type adjustments using surrogate markers for different different for markers surrogate using adjustments cell-type Such Overall, the replication of our study’s results by an independent independent an by results study’s our of replication the Overall, many includes that architecture complex a has cortex human The 2 4 and its functional validation with RNA our strat with makes data validation and its functional RNA expression largely abrogated the association of association the abrogated largely RNA expression 37– Table 3 9 are crude analyses, but are helpful to begin to assign assign to begin to helpful are but analyses, crude are α , as well as that of other proteins such as epidermal epidermal as such proteins other of that as well as , α CDH23 2 RHBDF2 converting enzyme (TACE, also called ). Its connection to PTK2B further suggests that it suggests ). Itsto further PTK2B connection α Supplementary Table 7a Supplementary 3 from the cell surface 6 . Its exact role in AD is not clear at this point, point, this at clear not is AD in role exact Its . RHBDF2 ; the relative proportion of the constituent constituent the of proportion relative the ; may function in regulating the substrate substrate the regulating in function may Supplementary Table 7b Table Supplementary expression is increased in is expression the con increased CDH23 α 3 from the cell surface 3 CDH23 . . The absence of , our DNA methylation methylation DNA our , , b ). t r a RNA expression expression RNA 3 5 . . In vitro In ). Given that that Given ). 40 , ADAM17 4 C I 1 RHBDF2 RHBDF2 , we , can we CDH23 e l 3 work work 4 and s ), ), ------

© 2014 Nature America, Inc. All rights reserved. 13. 12. 11. 10. 9. 8. 7. 6. 5. 4. 3. 2. 1. reprints/index.htm at online available is information permissions and Reprints The authors declare no competing interests.financial reviewed the critically manuscript. the study. P.L.D., D.A.B. and L.B.C. wrote the manuscript. All of the authors and analyzed RNA data from AD and non-AD brains. P.L.D. and D.A.B. designed analyzed the replication data. N.E.-T., J.B., H.S.C., C.Y., F.Z. and S.G.Y. provided L.Y. performed analyses on the resulting data. K.L., L.C.S. and J.M. generated and generated data from the samples. G.S., L.B.C., J.E., B.T.K., M.K., T.R., J.R. and C.M., A.T., W.B., S.G., C.B.E., B.E.B., A.M. and J.A.S. preparedcollected, and Equipment Grant from Alzheimer’s Research UK. was funded by US National Institutes of Health grant AG036039 to J.M. and an Buren Alzheimer’s Research Disease Program to N.E.-T., S.G.Y. and F.Z. This work Siragusa Foundation to N.E.-T., and the and Robert Clarice Smith and Abigail Van ES017155, KL2 RR024151, K25 AG041906-01). Support was also provided by the AG032990, R01 AG18023, RC2 AG036547, P30 AG10161, P50 AG016574, U01 of Health (R01 AG036042, R01AG036836, R01 AG17917,R01AG15819, R01 Support for this research was provided by grants from the US National Institutes participants of the ROS and MAP studies for their participation in studies.these and families who made this research possible. We also would like to thank the Brains for Dementia Research (Alzheimer Brain Bank UK) and the donors Dementia in the South London and Maudsley NHS Foundation Trust (SLaM), We thank the National Institute for Health (NIHR) Biomedical Research Unit in onlin Note: Any Supplementary Information and Source Data files are available in the the in versio available are references associated any and Methods M t r a COM AUTH Acknowledgmen

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© 2014 Nature America, Inc. All rights reserved. criteria. The NIA-Reagan criteria, whichonNationallikelihood theof based high AD Institute on Aging (NIA)-Reaganintegrates both the Consortium to were classifiedEstablish as having a pathologic diagnosis AD if they had intermediate or subjects Specifically,examination. post-mortem on determined is which AD, diagnosis, we also assessed for associations with a neuropathologic diagnosis of skewed, we used the square-root–transformed values in our statistical analyses. the five regions. Because the distribution of these average standardized counts is that same region, and then taking the average of the standardized counts across subject’sthe populationingregionsin thefives.d. by the of eachcount rawin score of neuritic plaque burden was then computed for each individual by divid the hippocampal CA1 sector. As in prior publications midfrontal,the temporal,middle inferiorandentorhinalparietal andcortices, was used to visualize neuritic plaques in tissue sections from five brain regions: performed across the US, as described previously thology in the brain at the time of death. Brain autopsies in ROS and MAP were neuriticofquantitativeplaques,den a measureamounttheof neuropa ADof Phenotypes. Preprocessed probe transcript levels were used in the BioConductordownstream ofanalysis. package lumi the using filtering probe and malization withbackground correction, variance stabilizing transformation, quantile nor sion data exported from Genome Studio software (Illumina) were preprocessed to diagnosis, age, sex and RNA Integrity Numbers (RIN). Rawacross probe-levelchips and platesexpres using a stratified approach to ensure balance with respect used for the transcriptome measurements of RNA samples that were lishedrandomized methods. Whole-genome DASL expression microarrays (Illumina) were Bioanalyzer,pubrespectively,2100 Agilentto according andRNAqueous kit (non-AD, ( pathologicAD withsubjects autopsiedfrombrainsof cortex temporal the frommeasured weretranscripts detail in described is methodology the obtained as part of a recently published brain expression GWAS (eGWAS), where before analysis. purity and degradation for tested and standard method extraction phenol-chloroform a using region brain dissected each of mg ~100 from isolated was by trainedsected specialists, snap-frozen and stored atGenomic °C.−80 DNA London-Neurodegenera re ( Bank Brain London Disease MRC Neurodegenerative the in archived individuals 117 from obtained (PFC) cortex HapMap reference, as previously described the usingimputation and genotypes Omni1-Quad Illumina or 6.0 GeneChip Genotypes were available from prior studies and were derived from Affymetrix previouslyfoundin published be literaturecohortscan regarding two the University Medical Center and Partners Healthcare. More detailed information aspects of these studies were approved by the Institutional Reviewteamlargeandofaphenotypes set were collected studies. identicalboth inAll Boards of Rush as they were designed to be combined, were maintained by a single investigative previousstudies in As 85%. Act donating their brains at the time of death. The overall autopsy rate exceeds exceeds 90%. Similarly, participants in both cohorts signed an Anatomical Gift annual detailed clinical evaluations and cognitive tests, and the rate of follow-up ment. All participants in ROS and MAP signed an informed consent agreeing to enrollof time dementiatheknownat of free andageyearsof 53 wereleastat who area Chicago the in retirementfromfacilitiesprimarily women and men enrollment.cohort,MAPTheestablished consists1997,in ofmore than1,600 states who were at least 55 years of age 12 andin groupsfree 40 offrom brothersknown and nuns dementiapriests, Catholic at 1,100 the than moretime of of Memory and Aging Project the Center: UniversityRushMedical at jects from two large, prospectively followed cohorts maintained by investigators Subjects and genotypes. ONLINE MET doi: a Registry for Alzheimer’s Disease (CERAD) estimates of neuritic plaque density search/MRC-London-Ne Toput associationsthe withneuritic plaque burden contextthein of anAD Temporal cortex expression levels for the autopsied Mayo Clinic subjects were prefrontal of samples uses analysis methylation DNA replication The 10.1038/nn.3786 n = 197). Total RNA extraction and QC were done using the Ambion Our primary phenotype of interest in this manuscript was the bur H ODS The analyses in this manuscript included deceased sub tive-Diseases-Brain-Bank.asp (MAP). The ROS cohort, established in 1994, consists 1 7 urodegenerative-Dise , we analyzed the ROS and MAP cohorts jointly,cohorts MAP and ROS the analyzed we , n = 202) and those with other brainpathologiesotherwith those and 202) = 44 Religious Orders StudyOrdersReligious , 4 5 2 . Briefly,. expression24,526 of levels http://www.kcl.a 3 . 42 , 4 1 3 7 . Bielschowsky silver stain ases-Brain-Bank/MRC- , a quantitative composite x ). All sampleswere All dis ). c.uk/iop/depts/cn/ (ROS) and the and (ROS) 46 42 , , 4 4 7 3 ------. .

Subject and probe quality control. ropathological phenotypes of each subject conducted by laboratory personnel who were blinded as to the clinical and neu color channel normalization and background removal. All data generation was procedures of the proprietary Illumina GenomeStudio software, recommended which the includesimplementing by file data a produces platform assay.The fordata generation by Illuminathe InfiniumHumanMethylation450 chip bead as measured by PicoGreen was used by the Broad Institute’s Genomics Platform (ICA). However, when applying these approaches to our data and comparingand data our approachesto However, applyingthese (ICA). when normalize our data, including confirmedwas also by our PCAanalysis. We evaluated different approaches to the use of two different thermocyclers during data generation. This batch effect N downstream analyses.for selected were samples 708 of total a check, quality subject’ ‘per our after Hence, efficiency.conversion bisulfite poor having for samples 40 additional an Weexcludedattempted. was assay Illumina the which for subjects 761 of total a from subjects 13 removed we criteria, these Using samples. the of all forautosomeprobes 50,000 of selection random a using performed was PCA ± we used principal component analysis (PCA) to select subjects that were within remove subjects with poor quality data from further consideration. Specifically, cleaning. The first step in this component of our quality control pipeline was to 415,848 autosomal CpGs for downstream analysis. try, such as our subjects. These CpGs were removed from consideration, leaving polymorphic CpGs with 0.01 analysisprevious a on Based occurs. base pairs upstream or downstream of the CpG site, where single base extension(MAF) frequency allele minor a with SNP a which in methylationthesample onourgivenremovedThus,datasize. we effect CpGs genotype. subject’s a majorhavenot shouldfrequencies by allele minor lowHowever, very SNPs with affected be could CpGs these of level Methylation probes. Using these criteria, we selected 470,913 out of a total of 485,577 tested probes that had detection represent the quality of the probes compared to background noise. We selected detection the used was to used quantitate the DNA. 16 Qiagen (cat. #51306) QIAamp DNA mini protocol. The Qubit 2.0 Fluorometer fully dissected from the white matter. DNA extraction was performed using the Disease Center. These sections were thawed on ice, and the gray matter was deceased caresubjects from the ROS and MAP studies based at the Rush Alzheimer’s sections of frozen dorsolateral prefrontal cortex were obtained from each of 761 E the neuropathology staff. by diagnosis) clinical (relativeto fashion blinded a in collected was data logic study.neuropathothe during All collected data clinical the to accesswithout as reported implemented was pathology, tangle neurofibrillary of staging Braak and also overlap with known SNP sites based on the 1000 Genomes database Genomes 1000 the on based sites SNP known with overlap also methylation levels. on sex of influence well-described the given model analytic our in gender for below,adjustednotedwe Furthermore,as downstreamanalysis. for list probe We implemented this recommendation and removed the 29,233 probes HumanMethylation450from our bead chip assay that meet this sequence match criterion. authorsInfiniumtheIllumina recommendprobesthediscarding from 29,233 chromosomeduringalignmentsequencesequenceusingBLAT sex the match nucleotides 50 of 47 which in probes that discarding recommends report recent a in gender to association strong showed probes these of chromosomes sex the with hybridize of the autosomes. onearefoundprobes, in 460,045470,913 these analysis.Of probes forfurther 3 s.d. from the mean of a principal component (PC) for PC1, PC2 and PC3. and PC2 PC1, forcomponent principal(PC) a of mean the from s.d. 3 xperimental protocol for ormalization of the data. We also evaluated the ‘per subject’ quality of the data after the initial CpG data In addition to cross-reactive probes, a substantial fraction of CpG probes CpG of fraction substantial a probes, cross-reactive to addition In cross- to predicted are probes some unique: are probes all not However, 4 8 Tee igoe ae ae y or-etfe neuropathologists board-certified by made are diagnoses These . P value criteria recommended by Illumina. These Illumina. byrecommended criteria value P < 0.01 for all samples to ensure the use of good quality We observed a strong batch effect in our data due to DN ≤ COMBAT MAF (EU) A extraction from post-mortem brain. For the initial quality check of the data, we µ 4 l of DNA at a concentration of 50 ng 9 4 , we found a total of 14,964 autosomal,14,964 of total a found we , 9 5 based on sequence alignment. Many alignment. sequence on based 0 and independent component analysis ≤ 0.99 in subjects of European ances nature nature ≥ 0.01 existed within 10 within existed 0.01 NEUR 4 9 . Specifically,. OSCI P 100-mg values EN µ C l 4 −1 9 E - - - - .

© 2014 Nature America, Inc. All rights reserved. mental batch and bisulfite conversion efficiency. A logistic regression was used used a linear model, adjusting for age at death, sex, study (ROS or MAP), experi differentiallymethylated regions associated with neuritic plaque pathology, we k missing Any details). for below (see approaches estimates of DNA methylation derived from whole genome bisulfite sequencing from 0 (no methylation) to 1 (100% methylation) and show good correlation to interpretablethan tain favorable statistical properties relative to valuesof(logisticallyM transformed methylation level measurement for the targeted CG site in a given sample ses, insteadwe used the D to identify the most conservatively associated subset of 71 CpG. analysis. However, we added it as a covariate in a secondary analysis that led us AD ( resulting measure was not significantly associated with a pathologic diagnosis of estimate of the proportion of NeuN combinations of purified profiles using nonlinear least-squares. This yielded an We used the data from the NeuN data. methylationDNA using sample brain each in neurons) (primarily cells packageR an mainused text,wethe in difference in the proportion of neuronal cells in our brain samples. As discussed cortex can change with AD, we evaluated a technique to account for the possible that the proportion of neurons and other cell types found in the human cerebral Accounting for differences in the proportion of cell types in our tissue. of validation. roundstwo with positives false minimize andnegativeresults false of risk the ciated with technical or demographic confounders. Thus, we prefer to minimize avoid over-correcting our data for variables that are not demonstrated in theto primary analysisbe to directly assoaddress the source of technical variation and to minimize false positives ( given our strategy of validating the CpG analysis with two rounds of validation tion to the neuritic amyloid trait (95% remain at a threshold of significance, they retained strongly suggestive evidence of associa other 67 CpGs that were significant in our primary analysis fluctuated below our significant regardless of the approach used and that, although the normalization), we found that the top 71 CpG in the primary analysis remained (including known technical confounders such as batch versus COMBAT and ICA used in our evaluation of normalization methods. appreciable percentage of the variance. These seven surrogate variables determined werethat 7 was a conservativethen number of components that explained an the average variance explained in the methylation matrix by each value of refined components as the surrogate variables. To set an optimal the independent component most correlated with component ated with component beta values populated only by methylation probes that were significantly associ componentindependent each for in used that to similar refinementstep a performed then assigned pendent components to the matrix of methylation beta values to infer the number of statistically inde at cally independent, non-Gaussian factors using an R package ments of disease pathophysiology that is not yet understood. knownconfounders. Thus,such ICA-derived relatedvariables be could eleto variablesthat capture structure withinof set adata and are not associated with normalizing our data. In particular, ICA includes adjustmentswithin forour data,many these unknownapproaches ( as batch number and age) that we find to be associatedthem withwith anprincipal approach componentsin which we adjust for given confounding variables (such nature nature foranalysis,ADthe using samethe covariates. As noted above, we considered, -nearest neighbor algorithm for ata analysis and statistical modeling. http://cran. ICA is a matrix factorizationintomatrixstatistimatrixseparatestechnique that a a is ICA To perform our methylome-wide association study (MWAS) and discover (MWAS)and study association methylome-wide our Toperform In our comparison of the results obtained using the two different approaches P = 0.08), and we therefore did not include it as a covariate in our primary k NEUR surrogatevariables representing r-project.org/web/pa β OSCI values reported by the Illumina platform for each probe as the β values i k . We then performed ICA on this sub-matrix and selected EN iteratively from 1 to 40. In each instance, all samples are C 5 E Fig. 3 . We. thereforeuse optedto 1 + k ), we opted to use the batch variable approach nuclei found in this package to create convex COMBAT = 100. ckages/fastICA/index.htm i + ( nuclei in each of our samples. However, the 1 β

values) ≤ 5 For our primary and secondary analy 2

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≤ and ICA) are overly conservative in k

k entries of the mixing matrix. Wematrix.mixing the ofentries β ) we created a sub-matrix of the the of sub-matrix a created we ) 5 values, they are less biologically 1 . Althoughvalues. haveM cer β P value was imputed using a using imputed was value < 5 × 10 β iSVA values, which rangewhichvalues, −5 fastICA l . We applied ICA ). As a result, and i , and used those (ref. k P , we explored value of the described 5 1) where 1) Given k , and + ------

measures obtained from the entire population (that is, the total cohort of 708 of cohort total the is, (thatpopulationentire the fromobtained measures estimate beta takinginto account standardthe error, were different from those testand the impaired subjects are a subset of the entire cohort and tested, using a one-samplethe beta estimate derived from the entire cohort. We used the fact that the non- CpG, whether the beta estimate from the non-impaired subset was differentcognitively non-impairedfrom at the time of death ( ( subjects of set full the fromobtained analysis association our ofresults comparedthe we conversion efficiency. probesIlluminaprovidesthatestimatethe of tobisulfitevalue median the ing tak by calculated is term conversionbisulfite The sites. control ten the from efficiency term used in the primary analysis is the median methylation estimate to used control for non-complete bisulfite conversion. The bisulfite conversion geted by type II probes), where we expect each CpG to be 100% methylated, are CpGstarcontrolprobesIlluminaby4and as I CpGs targeted sites type (6by version control probes, based on Illumina guidelines. Ten CpG sites designated reference genome. human hg19 the used we probes, CpG the of annotationFor analysis. our in autosomal415,848probes CpGtestedforgenome-widethe givensignificance wea Bonferroni used correction that yielded a hypotheses, multiple of testing the for Toaccount variables. demographic or or(2)surrogate variables that displayed nocorrelation withavailable technical but elected not to include, terms for (1) the proportion of neurons in each sample or learned from the sequence data (available at ingwere previouslyperformedas reported extracted from each of the cortical samples. Library construction and sequenc frontal cortex that is also sampled in our DNA methylation scan. predorsolateralChromatinthe from obtainedwere samples cases, was both examination.In neuropathologicalon pathology body lewy and vascular AD,of evidence mal subjects who were cognitively non-impaired at the time of death and had mini H3K4me3, H3K9ac and H3K9me3) was performed independently for two MAP ies targeting six different chromatin marks (H3K36me3, H3K27me3, H3K4me1, EpigenomicMapping Center, chromatinimmunoprecipitation antibod using C four subjects, the mean subject #MAP50403446, #ROS20963578, subject follows: as are WGBS) versus (Illumina replicates technical the of comparison the of sured by the two technologies was compared using a Student’s individual. In each subject, the estimated level of methylation at each CpG mea a random 50,000 autosomal CpG sites that are present in both data sets for each fromIllumina450k as well as bisulfite-sequencing, were compared by selecting sidered for further analysis. mismatches.coveredCpGs10%more byOnlythan with using custom software assembly.referencemade wereGRCh37 Subsequently, calls methylation CpG 36 base protocol Libraries were then purified and run on the Illumina HiSeq2000 using(Qiagen). EpitectkitBisulfite the treatments bisulfite ausing 5-h roundsof standardtwo before size-selected adaptor-ligatedwere fragmentsthe and fragments, the to previouslydescribed from ATDBio). Whole-genome bisulfite sequencing (WGBS) was performed as repaired, A-tailed and ligated with methylated paired-end adaptors (purchased mented to 100–500 bp using a Covaris S2 sonicator. DNA fragments were end- Array to perform whole genome bisulfite sequencing. Genomic DNA was frag we used the same DNA sample profiled usingwoman), the Illumina HumanMethylationa and man a of consists pair each non-impaired, two and AD with Bisulfite-sequencingdatageneration analysis.and subjects) from which the subset was drawn. hromatin state map. state hromatin g To assess whether changes in DNA methylation are an early feature of AD,featureof early anDNA methylationare in changes To whether assess The bisulfite conversion efficiency term is calculated using the bisulfite con Methylation profile from four samples (two with AD; two non-AD) generated (ref. 2.7 BSMap using aligned were libraries WGBS The ). The H3K36me3 and H3K27me3 data were from donor id 149 while the while 149 id donorfrom were data H3K27me3 and H3K36me3 The ). t n distribution, whetherresultsthe from subset,thisthe specifically = 708) to those obtained from the subset of subjects who were who subjects of subset the from obtained those to 708) = 5 4 . 5 4 5 . In short, Illumina genomicIlluminaDNAshort,In adaptors . wereadded 6 In collaboration with the Broad Institute’sRoadmap Broad the with collaboration In r , excluding duplicate, low-quality reads as well as reads = 0.972. r = 0.971; subject #ROS20214850, r = 0.975; subject #MAP5797875, subject 0.975; = 5 7 http://www.roadmapepigenomics. . Chromatin. state maps were then n P = 217) by assessing, for a given < 1.20 × 10 For fourindividuals (two ≥ doi: r 5× reads wereconreads 5× = 0.972. Across the −7 5 t 10.1038/nn.3786 as the threshold test. The results 5) to the hg19/ the to 5) r = 0.969; = ------© 2014 Nature America, Inc. All rights reserved. 0.037). Once the seven genes that emerge from the RNAvalidationthatemergegenesthefunctionalfrom seven the Once0.037). ( Count Edge genetically defined AD network was significantly connected based on its Direct Our network. original the to comparisonfor distributions empirical generate tonetworks random these parametersonconnectivity networkfour evaluates to build random networks that mimic the structure of the original network and PPI networks, DAPPLE applies a within-degree node-label permutation strategy interactionsinvolving 12,793 proteins pair-wise high-confidence 169,810 contains which InWebdatabase, the from indirectnetworks ofconnected proteins usingofevidence physical interaction network with a cutoff of 2 interacting binding degrees. and DAPPLE creates direct and AD, including 25 late-onset AD GWAS genes, 3 early onset AD associated genes proteins discovered in genetic studies. We associated compiledAD known with a interact significantlylist study methylation DNA of our genes associated with DAPPLEtool online the using Pathway analysis. regions, where indicated. brain assessed the in tissue vascular and/or gliosis loss, neuronal for account to includedwere expression levels five these of all or Some (ILMN_1732799). OLIG2 GFAP following probes were used as covariates: the CNS, the in present types cell five main the for specific are that genes of levels expression the for corrected that analyses those For (RIN-RINmean)2. and RIN plate, PCR sex, death, at age including variables, biological or nical tech for correct to covariates adjusting implemented, was model regression explored the relation of mRNA levels to an AD diagnosis, a multivariable linear elsewhere reported been has samples Clinic R chromatin states to that of all 415,848 probes tested using a chi-square test. analysis,we compared thedistribution of the71associated CpGs across the11 optimal model for our tissue sample. ChromHMM the applying by trained the default settings of the otherdatawere sets from donor 112.Thedataid were dichotomized using the doi: N To assess whether certain chromatin states are enriched for associations in our A data and analysis. 10.1038/nn.3786 -center TREM1 for astrocytes (ILMN_1697176), astrocytes for for oligodendrocytes (ILMN_1727567) and (ILMN_1727567) oligodendrocytes for option to the already signal-extended bed files. The models were models The files. bed signal-extended already the to option (J. Replogle, D.A.B. & P.L.D., unpublished data). We produced a PPI . We selected a model with 11 distinct chromatin states as the the as states chromatin distinct 11 with model a selected We . P = 0.0072) and Seed Common Interactors Degrees Mean ( Mean Degrees InteractorsCommon Seed and 0.0072) = We constructed a protein-protein interaction (PPI) network BinarizeBed A detailed description of the RNA data from the Mayo LearnModel 59 to determine whether the genes identified in identified genes the whether determine to command of 6 omn wt dfut etns from settings default with command 0 .To assess the statistical significance of CD68 ENO2 44 for microglia (ILMN_2267914), microgliafor , 4 for neurons (ILMN_1765796), ChromHMM 5 . Given that the analysis here analysis the that Given . CD34 for endothelial cells endothelial for 5 8 except applying P = -

60. 59. 58. 57. 56. 55. 54. 52. 51. 50. 49. 48. 47. 46. 45. 44. 43. 42. Mean ( sis, both the Direct Edeg Count ( network is expanded and includes six of these genes. PPI theanalysis, Inthe consideredin be thisto genes of list the iterationto added areanalysis of the analy 53.

ae K. Lage, and discovery E.J. Rossin, chromatin-state automating ChromHMM: M. Kellis, & J. Ernst, Zhu, J. program. MAPping sequence M.J. Ziller, bisulfite genome whole BSMAP: W. Li, & Y. Xi, C.A. Gifford, Guintivano, J., Aryee, M.J. & Kaminsky, Z.A. A cell epigenotype specific model for model specific epigenotype cell A Kaminsky,Z.A. & M.J. Aryee, J., Guintivano, P. microarray Du, in effects batch Adjusting A. Rabinovic, & C. Li, W.E., Johnson, Y.A. Chen, variance-stabilizing Model-based D.A. Bennett, W.A. Kibbe, & W. Huber, P., Du, S.M., Lin, microarray.Illumina P.,processing Du, for pipeline a W.A.lumi: Kibbe, S.M. Lin, & F.Zou, M. Allen, Schneider,D.A., Bennett, findings Wilson,and & Overview Z. R.S. Arvanitakis, J.A., Bennett, D.A. ok C Aayig n itrrtn DA ehlto data. methylation DNA interpreting and Analysing C. Bock, methylation levels by microarray analysis. microarray by levels methylation methods. Bayes empirical (2007). using data expression (2013). implicated in genetic disorders. genetic in implicated 7 biology. underlying suggest and interact physically disease mediated characterization. cues. environmental and genome. Bioinformatics BMC cells. stem embryonic human (2012). 705–719 depression. major and region brain age, to application its and bias heterogeneity cellular brain of correction the Illumina Infinium HumanMethylation450 microarray. HumanMethylation450 Infinium Illumina studies. community-based two from data. microarray Illumina for transformation Bioinformatics variants. disease-associated human expression. gene study. orders religious the from Res. Alzheimer Curr. , e1001273 (2011). e1001273 , P = 0.042) measures remain significant. et al. et al. et t al. et Nature et al. et t al. et et al. et Genome-wide chromatin state transitions associated with developmental t al. et et al. et Brain expression genome-wide association study (eGWAS) identifies (eGWAS) study association genome-wide expression Brain et al. et oprsn f eavle n Mvle ehd fr quantifying for methods M-value and Beta-value of Comparison et al.

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