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Sparse estimation of gene-gene interactions in prediction models
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular epidemiology. (Molekylär epidemiologi, molecular epidemiology)ORCID iD: 0000-0003-2256-6972
2015 (English)In: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334Article in journal (Refereed) Epub ahead of print
Abstract [en]

Current assessment of gene-gene interactions is typically based on separate parallel analysis, where each interaction term is tested separately, while less attention has been paid on simultaneous estimation of interaction terms in a prediction model. As the number of interaction terms grows fast, sparse estimation is desirable from statistical and interpretability reasons. There is a large literature on sparse estimation, but there is a natural hierarchy between the interaction and its corresponding main effects that requires special considerations. We describe random-effect models that impose sparse estimation of interactions under both strong and weak-hierarchy constraints. We develop an estimation procedure based on the hierarchical-likelihood argument and show that the modelling approach is equivalent to a penalty-based method, with the advantage of the models being more transparent and flexible. We compare the procedure with some standard methods in a simulation study and illustrate its application in an analysis of gene-gene interaction model to predict body-mass index.

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Medical and Health Sciences
URN: urn:nbn:se:uu:diva-274548DOI: 10.1177/0962280215597261PubMedID: 26265764OAI: oai:DiVA.org:uu-274548DiVA: diva2:896898
Available from: 2016-01-22 Created: 2016-01-22 Last updated: 2016-01-25Bibliographically approved

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Ingelsson, Erik
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Science for Life Laboratory, SciLifeLabMolecular epidemiology
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