{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:45:52Z","timestamp":1777502752084,"version":"3.51.4"},"reference-count":47,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Cancer Inform"],"published-print":{"date-parts":[[2015,1]]},"abstract":"<jats:p>Genome-wide association studies (GWASs) have identified thousands of single nucleotide polymorphisms (SNPs) robustly associated with hundreds of complex human diseases including cancers. However, the large number of G WAS-identified genetic loci only explains a small proportion of the disease heritability. This \u201cmissing heritability\u201d problem has been partly attributed to the yet-to-be-identified gene-gene (G \u00d7 G) and gene-environment (G \u00d7 E) interactions. In spite of the important roles of G \u00d7 G and G \u00d7 E interactions in understanding disease mechanisms and filling in the missing heritability, straightforward GWAS scanning for such interactions has very limited statistical power, leading to few successes. Here we propose a two-step statistical approach to test G \u00d7 G\/G \u00d7 E interactions: the first step is to perform principal component analysis (PCA) on the multiple SNPs within a gene region, and the second step is to perform Tukey's one degree-of-freedom (1-df) test on the leading PCs. We derive a score test that is computationally fast and numerically stable for the proposed Tukey's 1-df interaction test. Using extensive simulations we show that the proposed approach, which combines the two parsimonious models, namely, the PCA and Tukey's 1-df form of interaction, outperforms other state-of-the-art methods. We also demonstrate the utility and efficiency gains of the proposed method with applications to testing G \u00d7 G interactions for Crohn's disease using the Wellcome Trust Case Control Consortium (WTCCC) GWAS data and testing G \u00d7 E interaction using data from a case-control study of pancreatic cancer.<\/jats:p>","DOI":"10.4137\/cin.s17305","type":"journal-article","created":{"date-parts":[[2015,6,4]],"date-time":"2015-06-04T18:32:07Z","timestamp":1433442727000},"page":"CIN.S17305","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Powerful Tukey's One Degree-of-Freedom Test for Detecting Gene-Gene and Gene-Environment Interactions"],"prefix":"10.4137","volume":"14s2","author":[{"given":"Yaping","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Biostatistics, School of Public Health, University of Texas Health Science Center, University of Texas Health Science Center, Houston, TX, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghui","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, University of Texas Health Science Center, Houston, TX, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, University of Texas Health Science Center, University of Texas Health Science Center, Houston, TX, USA."},{"name":"Human Genetics Center, School of Public Health, University of Texas Health Science Center, Houston, TX, 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