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

Open Access Proceedings

Regularized regression method for genome-wide association studies

Jin Liu1*, Kai Wang2, Shuangge Ma3 and Jian Huang12

Author Affiliations

1 Department of Statistics and Actuarial Science, University of Iowa, 241 Schaeffer Hall, Iowa City, IA 52242, USA

2 Department of Biostatistics, University of Iowa, C22 General Hospital, Iowa City, IA 52242, USA

3 Division of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, CT 06520, USA

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BMC Proceedings 2011, 5(Suppl 9):S67  doi:10.1186/1753-6561-5-S9-S67

Published: 29 November 2011

Abstract

We use a novel penalized approach for genome-wide association study that accounts for the linkage disequilibrium between adjacent markers. This method uses a penalty on the difference of the genetic effect at adjacent single-nucleotide polymorphisms and combines it with the minimax concave penalty, which has been shown to be superior to the least absolute shrinkage and selection operator (LASSO) in terms of estimator bias and selection consistency. Our method is implemented using a coordinate descent algorithm. The value of the tuning parameters is determined by extended Bayesian information criteria. The leave-one-out method is used to compute p-values of selected single-nucleotide polymorphisms. Its applicability to a simulated data from Genetic Analysis Workshop 17 replication one is illustrated. Our method selects three SNPs (C13S522, C13S523, and C13S524), whereas the LASSO method selects two SNPs (C13S522 and C13S523).