{"doi":"10.3389/fgene.2021.710055","title":"SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data","abstract":"The explosion of biobank data offers unprecedented opportunities for gene-environment interaction (GxE) studies of complex diseases because of the large sample sizes and the rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in G×E assessment, especially for set-based G×E variance component (VC) tests, which are a widely used strategy to boost overall G×E signals and to evaluate the joint G×E effect of multiple variants from a biologically meaningful unit (e.g., gene). In this work, we focus on continuous traits and present SEAGLE, a S calable E xact A l G orithm for L arge-scale set-based G× E tests, to permit G×E VC tests for biobank-scale data. SEAGLE employs modern matrix computations to calculate the test statistic and p -value of the GxE VC test in a computationally efficient fashion, without imposing additional assumptions or relying on approximations. SEAGLE can easily accommodate sample sizes in the order of 10 5 , is implementable on standard laptops, and does not require specialized computing equipment. We demonstrate the performance of SEAGLE using extensive simulations. We illustrate its utility by conducting genome-wide gene-based G×E analysis on the Taiwan Biobank data to explore the interaction of gene and physical activity status on body mass index.","journal":"Frontiers in Genetics","year":2021,"id":203863,"datarank":0.3155446410750677,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.04678072069085941,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.04678072069085941,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":4,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9528,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":785607,"name":"Ilse C. F. Ipsen","orcid":"0000-0001-5645-5854","position":1,"is_corresponding":false},{"id":785608,"name":"Tzu‐Hung Hsiao","orcid":"0000-0003-0365-9970","position":2,"is_corresponding":false},{"id":280668,"name":"Ching‐Heng Lin","orcid":"0000-0002-2450-6108","position":3,"is_corresponding":false},{"id":261852,"name":"Li‐San Wang","orcid":"0000-0002-3684-0031","position":4,"is_corresponding":false},{"id":568260,"name":"Wan‐Ping Lee","orcid":"0000-0002-5305-1181","position":5,"is_corresponding":false},{"id":395428,"name":"Tzu‐Pin Lu","orcid":"0000-0003-3697-0386","position":6,"is_corresponding":false},{"id":524049,"name":"Jung‐Ying Tzeng","orcid":"0000-0002-5505-1775","position":7,"is_corresponding":false},{"id":786164,"name":"Jocelyn T. Chi","orcid":null,"position":0,"is_corresponding":true}],"reference_count":39,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:51:22.166488Z","pmid":"34795690","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":[]}