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One common feature of these data sets is their plethora of interval-censored outcomes. However, very few tools are available for the analysis of genetic data sets with interval-censored outcomes, and in particular, there is a lack of methodology available for set-based inference. Set-based inference is used to associate a gene, biological pathway, or other genetic construct with outcomes and is one of the most popular strategies in genetics research. This work develops three such tests for interval-censored settings beginning with a variance components test for interval-censored outcomes, the interval-censored sequence kernel association test (ICSKAT). We also provide the interval-censored version of the Burden test, and then we integrate ICSKAT and Burden to construct the interval censored sequence kernel association test\u2014optimal (ICSKATO) combination. These tests unlock set-based analysis of interval-censored data sets with analogs of three highly popular set-based tools commonly applied to continuous and binary outcomes. Simulation studies illustrate the advantages of the developed methods over ad hoc alternatives, including protection of the type I error rate at very low levels and increased power. The proposed approaches are applied to the investigation that motivated this study, an examination of the genes associated with bone mineral density deficiency and fracture risk.<\/jats:p>","DOI":"10.1111\/biom.13636","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T02:12:41Z","timestamp":1644891161000},"page":"1573-1585","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Inference for Set-Based Effects in Genetic Association Studies with Interval-Censored Outcomes"],"prefix":"10.1093","volume":"79","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1176-1561","authenticated-orcid":false,"given":"Ryan","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of Texas MD Anderson Cancer Center , Houston, Texas ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Division of Clinical and Translational Sciences, Department of Internal Medicine, University of Texas Health Science Center at Houston , Houston, Texas ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yimei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, St. Jude Children's Research Hospital , Memphis, Tennessee ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yutaka","family":"Yasui","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital , Memphis, Tennessee ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leslie","family":"Robison","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital , Memphis, Tennessee ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,2,14]]},"reference":[{"key":"2026041420273483700_biom13636-bib-0001","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1056\/NEJMsr1809937","article-title":"The \u201cAll of Us\u201d research program","volume":"381","author":"All of Us Research Program Investigators.","year":"2019","journal-title":"New England Journal of Medicine"},{"key":"2026041420273483700_biom13636-bib-0002","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.hlpt.2012.07.003","article-title":"UK Biobank: current status and what it means for epidemiology","volume":"1","author":"Allen","year":"2012","journal-title":"Health Policy and Technology"},{"key":"2026041420273483700_biom13636-bib-0003","doi-asserted-by":"crossref","first-page":"2569","DOI":"10.1016\/S0140-6736(17)31610-0","article-title":"The cumulative burden of surviving childhood cancer: an initial report from the St. Jude Lifetime Cohort Study (SJLIFE)","volume":"390","author":"Bhakta","year":"2017","journal-title":"Lancet"},{"key":"2026041420273483700_biom13636-bib-0004","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.ajhg.2020.06.003","article-title":"A fast and accurate method for genome-wide time-to-event data analysis and its application to UK Biobank","volume":"107","author":"Bi","year":"2020","journal-title":"American Journal of Human Genetics"},{"key":"2026041420273483700_biom13636-bib-0005","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","article-title":"The UK Biobank resource with deep phenotyping and genomic data","volume":"562","author":"Bycroft","year":"2018","journal-title":"Nature"},{"key":"2026041420273483700_biom13636-bib-0006","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1002\/gepi.21791","article-title":"Sequence kernel association test for survival traits","volume":"38","author":"Chen","year":"2014","journal-title":"Genetic Epidemiology"},{"key":"2026041420273483700_biom13636-bib-0007","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1093\/biomet\/82.4.835","article-title":"Analysis of transformation models with censored data","volume":"82","author":"Cheng","year":"1995","journal-title":"Biometrika"},{"key":"2026041420273483700_biom13636-bib-0008","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1086\/522036","article-title":"So many correlated tests, so little time! 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