Regularized regression method for genome-wide association studies

Jin Liu; Kai Wang; Shuangge Ma; Jian Huang

doi:10.1186/1753-6561-5-S9-S67

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Regularized regression method for genome-wide association studies

Journal article

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Regularized regression method for genome-wide association studies

Jin Liu, Kai Wang, Shuangge Ma and Jian Huang

BMC proceedings, Vol.5(Suppl 9), pp.S67-S67

2011

DOI: 10.1186/1753-6561-5-S9-S67

PMCID: PMC3287906

PMID: 22373491

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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).

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Title: Subtitle: Regularized regression method for genome-wide association studies
Creators: Jin Liu - Division of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, CT 06520, USA
Kai Wang - Division of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, CT 06520, USA
Shuangge Ma - Division of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, CT 06520, USA
Jian Huang - Division of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, CT 06520, USA
Resource Type: Journal article
Publication Details: BMC proceedings, Vol.5(Suppl 9), pp.S67-S67
DOI: 10.1186/1753-6561-5-S9-S67
PMID: 22373491
PMCID: PMC3287906
NLM abbreviation: BMC Proc
ISSN: 1753-6561
eISSN: 1753-6561
Publisher: BioMed Central
Language: English
Date published: 2011
Academic Unit: Statistics and Actuarial Science; Biostatistics
Record Identifier: 9983997350802771

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