{"doi":"10.1101/2025.10.02.25337130","title":"Formal Statistical Replication Analysis in Lung Cancer Genome-Wide Association Studies","abstract":"Abstract Dozens of genome-wide association studies (GWAS) have identified thousands of single nucleotide polymor-phisms (SNPs) associated with lung cancer risk. However, it remains challenging to translate these findings to clinical insights. One well-known obstacle is the large amount of type I error attached to GWAS; attempted solutions such as setting a p -value threshold across multiple cohorts or looking for small meta-analysis p -values have only somewhat reduced false positive findings. In contrast, here we advocate for a statistical model-based replication analysis. We first demonstrate that a formal statistical test for the replication com-posite null hypothesis - i.e. that the regression coefficient of a SNP falls in the same direction in multiple cohorts simultaneously - can curate a smaller, higher-quality list of significant SNPs than common alterna-tives. In two-way simulations, the false discovery rate (FDR) of model-based replication analysis is 6.4 times lower than that of meta-analysis with a p < 10 −8 threshold. In three-way replication analysis, 9.8% of the International Lung Cancer Consortium GWAS significant SNPs are replicated for squamous cell lung cancer while 33.8% are replicated for lung adenocarcinoma. Finally, we construct polygenic risk scores (PRSs) and find the replication-based PRS achieves virtually identical performance to a GWAS-significant PRS while us-ing 87.3% fewer variants. Thus, formal model-based replication analysis can greatly reduce spurious findings while still identifying important variants, allowing for more robust and more efficient translation of GWAS results.","journal":"medRxiv","year":2025,"id":576797,"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":0.9507,"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":353894,"name":"Jinyoung Byun","orcid":"0000-0001-8579-1435","position":1,"is_corresponding":false},{"id":608053,"name":"Bryan R. Gorman","orcid":"0000-0002-4239-4672","position":2,"is_corresponding":false},{"id":261929,"name":"Rayjean J. Hung","orcid":"0000-0002-4486-7496","position":3,"is_corresponding":false},{"id":334596,"name":"James McKay","orcid":"0000-0002-1787-3874","position":4,"is_corresponding":false},{"id":5679,"name":"Christopher I. Amos","orcid":"0000-0002-8540-7023","position":5,"is_corresponding":false},{"id":11352,"name":"Saiju Pyarajan","orcid":"0000-0002-9047-3762","position":6,"is_corresponding":false},{"id":21368,"name":"Arjun Bhattacharya","orcid":"0000-0003-1196-4385","position":7,"is_corresponding":false},{"id":235843,"name":"Ryan Sun","orcid":"0000-0003-1176-1561","position":8,"is_corresponding":false},{"id":1338095,"name":"Yung-Han Chang","orcid":"0009-0004-3041-0390","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:58:00.620755Z","pmid":"41256160","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":[]}