{"doi":"10.1093/bib/bbae456","title":"A high-dimensional omnibus test for set-based association analysis","abstract":"Set-based association analysis is a valuable tool in studying the etiology of complex diseases in genome-wide association studies, as it allows for the joint testing of variants in a region or group. Two common types of single nucleotide polymorphism (SNP)-disease functional models are recognized when evaluating the joint function of a set of SNP: the cumulative weak signal model, in which multiple functional variants with small effects contribute to disease risk, and the dominating strong signal model, in which a few functional variants with large effects contribute to disease risk. However, existing methods have two main limitations that reduce their power. Firstly, they typically only consider one disease-SNP association model, which can result in significant power loss if the model is misspecified. Secondly, they do not account for the high-dimensional nature of SNPs, leading to low power or high false positives. In this study, we propose a solution to these challenges by using a high-dimensional inference procedure that involves simultaneously fitting many SNPs in a regression model. We also propose an omnibus testing procedure that employs a robust and powerful P-value combination method to enhance the power of SNP-set association. Our results from extensive simulation studies and a real data analysis demonstrate that our set-based high-dimensional inference strategy is both flexible and computationally efficient and can substantially improve the power of SNP-set association analysis. Application to a real dataset further demonstrates the utility of the testing strategy.","journal":"Briefings in Bioinformatics","year":2024,"id":503319,"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":0.9626,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1203161,"name":"Xin Wang","orcid":"0000-0002-6229-1945","position":1,"is_corresponding":false},{"id":1353228,"name":"Zechen Zhang","orcid":"0009-0003-8164-558X","position":2,"is_corresponding":false},{"id":1353587,"name":"Fuzhao Chen","orcid":null,"position":3,"is_corresponding":false},{"id":1353229,"name":"Cao Hong-yan","orcid":"0000-0002-0315-7156","position":4,"is_corresponding":false},{"id":1353588,"name":"Lina Yan","orcid":null,"position":5,"is_corresponding":false},{"id":267567,"name":"Xia Gao","orcid":"0000-0003-0979-9990","position":6,"is_corresponding":false},{"id":1353230,"name":"Hui Dong","orcid":"0009-0009-9193-0169","position":7,"is_corresponding":false},{"id":478783,"name":"Yuehua Cui","orcid":"0000-0001-8099-1753","position":8,"is_corresponding":false},{"id":478781,"name":"Haitao Yang","orcid":"0009-0006-4493-0690","position":0,"is_corresponding":true}],"reference_count":66,"raw_metadata":null,"created_at":"2026-07-19T02:10:31.826282Z","pmid":"39288231","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":[]}