{"doi":"10.1101/2025.10.02.25337098","title":"Comparative Evaluation of Cross-Ancestry Polygenic Risk Scoring of Type 1 Diabetes in the All of Us Cohort","abstract":"Type 1 diabetes is a highly heritable autoimmune condition characterized by the destruction of pancreatic beta cells, resulting in insulin deficiency. Here, we developed a novel polygenic score we call the HLA-Augmented SBayesRC Framework (HLA-ARC). HLA-ARC integrates direct modeling of HLA haplotypes, with a Bayesian regression approach for the non-HLA component. SBayesRC leverages extensive functional genomic annotations and linkage disequilibrium patterns across approximately 7.4 million variants, substantially enhancing predictive accuracy. We systematically compared HLA-ARC to three existing T1D polygenic scores (Polygenic Risk Score extension for Diabetes Mellitus [PRSedm], Trans-Ancestry Polygenic Score for Diabetes [TA-PS], and Type 1 Diabetes Multi-Ancestry Polygenic Score [T1D-MAPS]) using data from the ancestrally-diverse All of Us cohort. Among the three existing methods, T1D-MAPS showed superior performance in all ancestry groups. However, HLA-ARC consistently outperformed the existing methods, achieving AUROC values exceeding 0.91 in European individuals and 0.89 in non-European groups. Our results demonstrate that integrating HLA haplotype modeling with genomic annotation and ancestry-informed linkage disequilibrium methods significantly improves polygenic risk prediction for autoimmune diseases characterized by major genetic risk loci.","journal":"medRxiv","year":2025,"id":559228,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9483,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":336020,"name":"Spencer Moore","orcid":"0000-0002-1229-9578","position":1,"is_corresponding":false},{"id":1461035,"name":"Ivan Davidson","orcid":null,"position":2,"is_corresponding":false},{"id":1460456,"name":"Jonathan Anomaly","orcid":"0000-0001-5485-0121","position":3,"is_corresponding":false},{"id":5381,"name":"Robert Maier","orcid":"0000-0002-3044-090X","position":4,"is_corresponding":false},{"id":555434,"name":"Jeremiah H. Li","orcid":"0000-0002-9344-0237","position":5,"is_corresponding":false},{"id":1460457,"name":"Michael Cronquist Christensen","orcid":"0000-0002-3605-7223","position":6,"is_corresponding":false},{"id":1460458,"name":"David Stern","orcid":"0000-0001-9280-189X","position":7,"is_corresponding":false},{"id":1177114,"name":"Tobias Wolfram","orcid":"0000-0002-0280-2512","position":8,"is_corresponding":false},{"id":1460455,"name":"Mohammad Ahangari","orcid":"0000-0002-3509-3652","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:55:34.849815Z","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":[]}