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Using LASSO regression to detect predictive aggregate effects in genetic studies

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Title: Using LASSO regression to detect predictive aggregate effects in genetic studies
Author(s): Fontanarosa, Joel B; Dai, Yang
Abstract: We use least absolute shrinkage and selection operator (LASSO) regression to select genetic markers and phenotypic features that are most informative with respect to a trait of interest. We compare several strategies for applying LASSO methods in risk prediction models, using the Genetic Analysis Workshop 17 exome simulation data consisting of 697 individuals with information on genotypic and phenotypic features (smoking, age, sex) in 5-fold cross-validated fashion. The cross-validated averages of the area under the receiver operating curve range from 0.45 to 0.63 for different strategies using only genotypic markers. The same values are improved to 0.69–0.87 when both genotypic and phenotypic information are used. The ability of the LASSO method to find true causal markers is limited, but the method was able to discover several common variants (e.g., FLT1) under certain conditions.
Issue Date: 2010-10
Publisher: BioMed Central
Citation Info: Fontanarosa, J. B. & Dai, Y. 2011. Using LASSO regression to detect predictive aggregate effects in genetic studies. BMC Proceedings, 5 Suppl 9: S69. DOI: 10.1186/1753-6561-5-S9-S69
Type: Article
Description: © 2011 Fontanarosa and Dai; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. DOI: 10.1186/1753-6561-5-S9-S69
URI: http://hdl.handle.net/10027/8301
ISSN: 1753-6561
Sponsor: The Genetic Analysis Workshop is supported by National Institutes of Health grant R01 GM031575.
Date Available in INDIGO: 2012-04-30
 

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