Asymmetric independence modeling identifies novel gene-environment interactions

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dc.contributor.authorYu, Guoqiangen
dc.contributor.authorMiller, David J.en
dc.contributor.authorWu, Chiung-Tingen
dc.contributor.authorHoffman, Eric P.en
dc.contributor.authorLiu, Chunyuen
dc.contributor.authorHerrington, David M.en
dc.contributor.authorWang, Yueen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2019-07-23T17:51:51Zen
dc.date.available2019-07-23T17:51:51Zen
dc.date.issued2019-02-21en
dc.description.abstractMost genetic or environmental factors work together in determining complex disease risk. Detecting gene-environment interactions may allow us to elucidate novel and targetable molecular mechanisms on how environmental exposures modify genetic effects. Unfortunately, standard logistic regression (LR) assumes a convenient mathematical structure for the null hypothesis that however results in both poor detection power and type 1 error, and is also susceptible to missing factor, imperfect surrogate, and disease heterogeneity confounding effects. Here we describe a new baseline framework, the asymmetric independence model (AIM) in case-control studies, and provide mathematical proofs and simulation studies verifying its validity across a wide range of conditions. We show that AIM mathematically preserves the asymmetric nature of maintaining health versus acquiring a disease, unlike LR, and thus is more powerful and robust to detect synergistic interactions. We present examples from four clinically discrete domains where AIM identified interactions that were previously either inconsistent or recognized with less statistical certainty.en
dc.description.notesThis work was supported by the National Institutes of Health under Grants HL111362, HL133932, BC171885P1, U24CA160036-05S1, and MH110504.en
dc.description.sponsorshipNational Institutes of Health [HL111362, HL133932, BC171885P1, U24CA160036-05S1, MH110504]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1038/s41598-019-38983-zen
dc.identifier.issn2045-2322en
dc.identifier.other2455en
dc.identifier.pmid30792419en
dc.identifier.urihttp://hdl.handle.net/10919/91929en
dc.identifier.volume9en
dc.language.isoenen
dc.publisherSpringer Natureen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectesophageal canceren
dc.subjecttobacco smokingen
dc.subjectalcohol intakeen
dc.subjectrisken
dc.subjectepistasisen
dc.titleAsymmetric independence modeling identifies novel gene-environment interactionsen
dc.title.serialScientific Reportsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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