{"doi":"10.1101/2020.07.30.228122","title":"Epigenetic age-predictions in mice using pyrosequencing, droplet digital PCR or barcoded bisulfite amplicon sequencing","abstract":"Abstract Age-associated DNA methylation reflects aspects of biological aging - therefore epigenetic clocks for mice can help to elucidate the impact of treatments or genetic background on the aging process in this model organism. Initially, age-predictors for mice were trained on genome-wide DNA methylation profiles, whereas we have recently described a targeted assay based on pyrosequencing of DNA methylation at only three CG dinucleotides (CpGs). Here, we have re-evaluated pyrosequencing approaches in comparison to droplet digital PCR (ddPCR) and barcoded bisulfite amplicon sequencing (BBA-seq). At individual CpGs the correlation of DNA methylation with chronological age was slightly higher for pyrosequencing and ddPCR as compared to BBA-seq. On the other hand, BBA-seq revealed that neighboring CpGs tend to be stochastically modified in murine age-associated regions. Furthermore, the binary sequel of methylated and non-methylated CpGs in individual reads can be used for single-read predictions, which may reflect heterogeneity in epigenetic aging. In comparison to C57BL/6 mice the epigenetic age-predictions using BBA-seq were also accelerated in the shorter-lived DBA/2 mice, and in C57BL/6 mice with a lifespan quantitative trait locus of DBA/2 mice. Taken together, we describe further optimized and alternative targeted methods to determine epigenetic clocks in mice.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":130517,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.959,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":313668,"name":"Miloš Nikolić","orcid":"0000-0003-0029-7601","position":1,"is_corresponding":false},{"id":315785,"name":"Michael Gobs","orcid":null,"position":2,"is_corresponding":false},{"id":313669,"name":"Julia Franzen","orcid":"0000-0002-2459-5841","position":3,"is_corresponding":false},{"id":313670,"name":"Gerald de Haan","orcid":"0000-0001-9706-0138","position":4,"is_corresponding":false},{"id":313671,"name":"Hartmut Geiger","orcid":"0000-0002-5794-5430","position":5,"is_corresponding":false},{"id":10959,"name":"Wolfgang Wagner","orcid":"0000-0002-1971-3217","position":6,"is_corresponding":false},{"id":313667,"name":"Yang Han","orcid":"0000-0001-6249-370X","position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-18T23:15:56.698770Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}