Regularized regression method for genome-wide association studies.
Regularized regression method for genome-wide association studies.
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DOI：
10.1186/1753-6561-5-s9-s67
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发表时间：
2011-11-29
期刊：
影响因子：
--
通讯作者：
Huang J
中科院分区：
文献类型：
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作者：
Liu J;Wang K;Ma S;Huang J
我们使用一种新型的惩罚方法进行全基因组关联研究，该方法解释了相邻标记之间的连锁不平衡。该方法利用相邻单核苷酸多态性遗传效应差异的罚分，并将其与极大极小凹罚分相结合，在估计偏差和选择一致性方面上级最小绝对收缩和选择算子（LASSO）。我们的方法是使用坐标下降算法。调整参数的值由扩展贝叶斯信息准则确定。使用留一法计算所选单核苷酸多态性的p值。它的适用性，从遗传分析研讨会17复制一个模拟数据说明。我们的方法选择了三个SNP（C13S522，C13S523和C13S524），而LASSO方法选择了两个SNP（C13S522和C13S523）。
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).