{"doi":"10.1016/j.xpro.2025.104299","title":"Protocol: Estimating cross-ancestry local genetic correlation using Logica","abstract":"Here, we present a reproducible protocol for estimating cross-ancestry local genetic correlation using Logica, a likelihood-based framework that employs summary statistics from genome-wide association studies (GWASs) and ancestry-specific linkage disequilibrium (LD). We describe steps for estimating locus-level heritability and cross-ancestry genetic correlation and outlining required inputs. We then detail analytical procedures to enable accurate and scalable inference of shared genetic architecture. For complete details on the use and execution of this protocol, please refer to Gao et al. 1 • Estimate ancestry-specific confounding parameters using modified LDER • Compute local heritability and cross-ancestry genetic correlation with Logica • Identify genomic regions with non-zero cross-ancestry local genetic correlation • Integrate QC, LD alignment, and region-wise computation into a unified workflow Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Here, we present a reproducible protocol for estimating cross-ancestry local genetic correlation using Logica, a likelihood-based framework that employs summary statistics from genome-wide association studies (GWASs) and ancestry-specific linkage disequilibrium (LD). We describe steps for estimating locus-level heritability and cross-ancestry genetic correlation and outlining required inputs. We then detail analytical procedures to enable accurate and scalable inference of shared genetic architecture.","journal":"STAR Protocols","year":2025,"id":587036,"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":0.9468,"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":287828,"name":"Zheng Li","orcid":"0000-0002-2978-2531","position":1,"is_corresponding":false},{"id":1502578,"name":"Xiang Ryan Zhou","orcid":null,"position":2,"is_corresponding":false},{"id":712949,"name":"Boran Gao","orcid":"0000-0002-8620-0534","position":0,"is_corresponding":true}],"reference_count":6,"raw_metadata":null,"created_at":"2026-07-19T02:59:36.020030Z","pmid":"41447520","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":[]}