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https://doi.org/10.1186/1753-6561-5-S9-S67
Title: | Regularized regression method for genome-wide association studies | Authors: | Liu, J Wang, K Ma, S Huang, J |
Keywords: | Bayes theorem conference paper genetic analysis genetic association genome heredity human least absolute shrinkage and selection operator priority journal regression analysis single nucleotide polymorphism |
Issue Date: | 2011 | Publisher: | BMC | Citation: | Liu, J, Wang, K, Ma, S, Huang, J (2011). Regularized regression method for genome-wide association studies. BMC Proceedings 5 (SUPPL. 9) : S67. ScholarBank@NUS Repository. https://doi.org/10.1186/1753-6561-5-S9-S67 | Rights: | Attribution 4.0 International | 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). © 2011 Liu et al; licensee BioMed Central Ltd. | Source Title: | BMC Proceedings | URI: | https://scholarbank.nus.edu.sg/handle/10635/178163 | ISSN: | 1753-6561 | DOI: | 10.1186/1753-6561-5-S9-S67 | Rights: | Attribution 4.0 International |
Appears in Collections: | Staff Publications Elements |
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