{"doi":"10.1101/2022.06.09.22276204","title":"Pruning and thresholding approach for methylation risk scores in multi-ancestry populations","abstract":"Abstract Recent efforts have focused on developing methylation risk scores (MRS), a weighted sum of the individual’s DNAm values of pre-selected CpG sites. Most of the current MRS approaches that utilize Epigenome-wide association studies (EWAS) summary statistics only include genome-wide significant CpG sites and do not consider co-methylation. New methods that relax the p-value threshold to include more CpG sites and account for the inter-correlation of DNAm might improve the predictive performance of MRS. We paired informed co-methylation pruning with P-value thresholding to generate pruning and thresholding (P+T) MRS and evaluated its performance among multi-ancestry populations. Through simulation studies and real data analyses, we demonstrated that pruning provides an improvement over simple thresholding methods for prediction of phenotypes. We demonstrated that European-derived summary statistics can be used to develop P+T MRS among other population such as African population. However, the prediction accuracy of P+T MRS may differ across multi-ancestry population due to environmental/cultural/social differences.","journal":"medRxiv","year":2022,"id":300202,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9495,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":991276,"name":"Evan Gatev","orcid":null,"position":1,"is_corresponding":false},{"id":354348,"name":"Todd M. Everson","orcid":"0000-0003-2732-4550","position":2,"is_corresponding":false},{"id":326599,"name":"Karen N. Conneely","orcid":"0000-0002-1994-6934","position":3,"is_corresponding":false},{"id":5181,"name":"Nastassja Koen","orcid":"0000-0002-1119-8142","position":4,"is_corresponding":false},{"id":31536,"name":"Michael P. Epstein","orcid":"0000-0001-9647-9738","position":5,"is_corresponding":false},{"id":5180,"name":"Michael S. Kobor","orcid":"0000-0003-4140-1743","position":6,"is_corresponding":false},{"id":5182,"name":"Heather J. Zar","orcid":"0000-0002-9046-759X","position":7,"is_corresponding":false},{"id":235059,"name":"Dan J. Stein","orcid":"0000-0001-7218-7810","position":8,"is_corresponding":false},{"id":817486,"name":"Anke Huels","orcid":null,"position":9,"is_corresponding":false},{"id":473343,"name":"Junyu Chen","orcid":"0000-0001-9659-0225","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:31:53.559757Z","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":[]}