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This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

Using LASSO regression to detect predictive aggregate effects in genetic studies

Joel B Fontanarosa* and Yang Dai

Author Affiliations

Bioinformatics Program, Department of Bioengineering (MC 063), University of Illinois at Chicago, 851 S. Morgan Street, 218 SEO, Chicago, IL 60607-7052, USA

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BMC Proceedings 2011, 5(Suppl 9):S69  doi:10.1186/1753-6561-5-S9-S69

Published: 29 November 2011

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.