ChIP_seq https://www.dropbox.com/s/jz8jrhdzq33rmxr/Year1Report.pdf?dl=0 Figure1##################################################### Fig. 1b lower-dimension embeding a.clusterEpimapAndeGTExSample_V1.3.1.GCrm.CCA.R Fig. S1 correlation a.clusterEpimapAndeGTExSample_V1.3.2.GCrm.CCA.R Figure2##################################################### #preprocess merge, allowing peaks with 2 individuals or ovlp with ref a.mergePeak2ReadsCounts_V1.2_addRefPeak.R => a_mergePeak2HeightsMean_V1.2_addRefPeak tissue peaks to individua presence: a2.mergePeak2IndivPeakPresence_V1.1_allSamps.R => a2_mergePeak2IndPeak compare all peaks to CS: b.cmp2RoadmapChromStates_V1.2_peakLen.R => b_cmpPeak2RoadmapChromStates_V1.2_pkLen normalization: a.2.normalizePeakCounts_V1.3_filterByCS.R => a_2_peakHeightsNormalized_hg19.narrowPeak.RSC0.8.RdsTot1e7.logQ2_V1.3_CSFilt merge across tissue: c.mergePeaksAcrossTissues.R #peak sharing, partitioned by cell type specific promoters Peak.sharing.R #modules a.2.extractRefSignalForMergedPeaksFromEpimap.R a.3.identifyAREModulesAcrossEpimap_V1.6_addMultiTiss.R a.4.getAnnotationForModule_4tissMergedPks_onEpiMap_V1.2.3_CSandeGTExActivity.R #submodules a.4.identifysubAREModulesAcrossEpimap_mergedPk4Tiss.R a.3.EpiMapAREModulesActivtyAcrosseGTEx_merged4Tiss.R a.4.2.getARESubModuleActivityForIndividual.R4TissMergedPeaks.inEpimap.H3K27acSignal.RData #annotations c.2.annotatemARE.R c.2.annotatemARE_PromotAndGene.R #GO a.4.annotateModuleByrGREAT_4tissMergedPk_Epimap.R #motif a.4.getTFMotifsEnrichedForAREModule.byHomer_V1.1_EpiMapMotif.R a.4.2.visTFMotifsEnrichedInAREModule.byHomer_EpimapMotif.R a.4.3.getTFMotifsInstanceForAREModule.byHomer_EpimapMotif.R a.4.3.getMotifInstanceForAREModlue_byHomer.shs a.4.4.getTFMotifsInstance2Bed.R a.4.5.collectTiss2TF.ARE.target.R #saturation c.saturationCurveForPeaksV1.1_grp14vsrest.R c.saturationCurveForPeaksV1.1_grp14vsrest_summary.R #deconvolution a.4.CellTypeARESignal.Epimap_filtSamp.NMF.R a.5.testTissSampPCsPeersByCovariates_EpimapCellFract.R #sexbiased peaks b.varianceVSSexAgeDis_withCovar_V1.1_byLimma.R b.2.cmpAndAnnotPeaksSexAgeDis_byrGREAT.R b.2.visPeaksSex_byheatmap_V1.1_limma.R b.3.cmpSexPeaks.vs.sexGene_V1.3_rmSexbiasGenesdAcrossTiss.R Figure3##################################################### #identify haQTL a.identifyCovariatesForhaQTL_V1.2.1_filtPeak_geneINT.Peer_Lung.R => a_haQTLCalling_covariate_V1.2.1_filtPeaks.gINT.peer a.identifyCovariatesForhaQTL_V1.2.1_filtPeak_geneINT.Peer.R a.2.callTisshaQTL.paral.R a.2.callTisshaQTL_V1.2.2_filtPeaks_gINT.Peer_rmAgeBatch.R a.2.callTisshaQTL_V1.2.2.2_filtPeaks_gINT.Peer_rmAgeBatch.RSC0.8.R a.3.visHaQTLNumVSCovNum.R a.4.callTisshaQTL_fixedCov_V1.2.2_filtPk_gINT.Peer_rmAgeBatch.para.R a.4.callTisshaQTL_fixedCov_V1.2.2_filtPk_gINT.Peer_rmAgeBatch.R a.5.multipleTestOnQTL_forPeer.R a.6.summaryHaQTLandgARENum.R #visualization Manhattan.plot.R parse_snp_for_manhattan.pl #sharing 2.Cross.tissue.compare.R 3.ARE_type.explain.eQTL_specificity.R Figure4##################################################### #LDSC a.prepAnnotForLDSC_mAREGrp_byTiss.R a2.LDSCpartitionHeritab_mAREGrpsByTiss_V1.1_updatedGWAS.R a3.visLDSC_mAREGrpsByTiss_V1.1.updatedGWAS.R #GWAS-haQTL.coloc a.colocalizeTest.haQTLVSGWAS.coloc_V1.1_para.R a.colocalizeTest.haQTLVSGWAS.coloc_V1.1_per.GTEx.GWAS.R a.colocalizeTest.haQTLVSGWAS.coloc_V1.1_per.Lung.GWAS.R a.colocalizeTest.haQTLVSGWAS.coloc_V1.1_per.PGC.GWAS.R a.colocalizeTest.haQTLVSGWAS.coloc_V1.1_summary.R b.annotate.GWAScoloc.gARE_V1.3.1_updatedGWAS.R (including MR result) #GWAS-haQTL.MR a.MR.haQTLVSGWAS_V1.1_para.R a.MR.haQTLVSGWAS_V1.1_per.GTEx.GWAS.R a.MR.haQTLVSGWAS_V1.1_per.Lung.GWAS.R a.MR.haQTLVSGWAS_V1.1_per.PGC.GWAS.R a.MR.haQTLVSGWAS_V1.1_summary.R #GWAS-eQTL.coloc #test for all haQTL windows b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.1_allhaQTLWind_para.R b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.1_per.GTEx.GWAS.R b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.1_per.Lung.GWAS.R b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.1_per.PGC.GWAS.R b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.1_allhaQTLWind_summary.R #test for single cell b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.2_sceQTL_para.R b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.2_sceQTL_perCellType.R b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.2_sceQTL_summary.R #test for only GWAS-haQTL colocalized loci b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.3_updatedGWAS_para.R b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.3_updatedGWAS_per.GTEx.GWAS.R b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.3_updatedGWAS_per.Lung.GWAS.R b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.3_updatedGWAS_per.PGC.GWAS.R b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.3_updatedGWAS_summary.R #test for SuSIE signal b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.1.4_perGWAS_SuSIE.R #SCZ across tissue c.colocalizeTest.VSGWAS.haQTLWindow_Vis_perGWAS.perLocus_V1.3.1_updatedGWAS.R #bulk-eQTL-missing b.2.annot.gAREColocWithGWAS.missBYeQTL_byTiss_V1.1_updateGWAS.R b.2.1.annot.gAREColocWithGWAS.missBYeQTL_summary_V1.1_updatedGWAS.R Figure5##################################################### #gARE enriched for FMeQTL-promximal elements a.eQTLvsH3K27acPeak_V1.6.4.3_FMeQTL_addeGeneAssociated a.eQTLvsH3K27acPeak_V1.6.4.4_distBin.R b.gLinkScores.FMeQTLOvlpMotif.R #gLink scores a.getgAREsAroundeGene.R #coloc a.colocalizeTest.haQTLVSeQTL.coloc_para.V1.2.2.R a.colocalizeTest.haQTLVSeQTL.coloc_perGene.V1.2.2_gARE.R a.colocalizeTest.haQTLVSeQTL.coloc_summary.V1.2.2.R #MR a.linkGenePeak_MR-Egger_para.V1.2.R a.linkGenePeak_MR-Egger_perGene.V1.2_gAREs.R a.linkGenePeak_MR-Egger_summary.V1.2.R #PGS a.estGeneticCompInExp_glmnet_V1.3_allData_eGene_paral.R a.estGeneticCompInExp_glmnet_V1.3_allData_eGene.R a.estGeneticCompInExp_glmnet_V1.3_allData_eGene_summary.R a.linkGenePeak_PGSExpPCor_V1.4_gARE.R # #compare across other metric a.2.summaryLinks.coloc.MR.PRS_V1.8_AUPRC.R b.cmp.geneticLinks.withScores_gARE.V1.5.6_unifiedScore.R a.colocalizeTest.haQTLVSeQTL.coloc_visualize.V1.2.7_FMeQTLARE.gARE.R Figure6##################################################### #gLinkedARE vs GWAS-haQTL Coloc ARE & target genes vs GWAS-eQTL coloc, other predicted disease targets b.3.cmpgLinkedAREvsGWASColocARE_V1.3.1_udpatedGWAS.R #target genes based on gLink scores across tissue c.vis.Links.perGWAS.acrossTiss_V1.2.1_updatedGWAS.R c.2.vis.Links.perGWAS.withTFPPI.R #GWAS-eQTL coloc across different tissues b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.2.1_acrossTiss_updatedGWAS_para.R b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.2.1_acrossTiss_updatedGWAS_perTiss.R b.colocalizeTest.eQTLvsGWAS.haQTLWindow_V1.2.1_acrossTiss_summary.R