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Sparse estimation of gene-gene interactions in prediction models
Univ Texas Southwestern Med Ctr Dallas, Quantitat Biomed Res Ctr, Dallas, TX 75390 USA.
Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden.
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular epidemiology.ORCID iD: 0000-0003-2256-6972
Seoul Natl Univ, Dept Stat, San56-1 Shin Lim Dong, Seoul 151747, South Korea.
2017 (English)In: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334, Vol. 26, no 5, p. 2319-2332Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
2017. Vol. 26, no 5, p. 2319-2332
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-274548DOI: 10.1177/0962280215597261ISI: 000412931500021PubMedID: 26265764OAI: oai:DiVA.org:uu-274548DiVA, id: diva2:896898
Funder
Swedish Research CouncilAvailable from: 2016-01-22 Created: 2016-01-22 Last updated: 2018-01-10Bibliographically approved

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Ingelsson, Erik

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