Genome-wide association analysis by lasso penalized logistic regression
Genome-wide association analysis by lasso penalized logistic regression
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DOI：
10.1093/bioinformatics/btp041
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发表时间：
2009-03-15
期刊：
影响因子：
5.8
通讯作者：
Lange, Kenneth
中科院分区：
文献类型：
--
作者：
Wu, Tong Tong;Chen, Yi Fang;Lange, Kenneth
动机：在普通回归中，施加套索（lasso）惩罚使连续的模型选择变得简单直接。当预测变量的数量远远超过观测值的数量时，套索惩罚回归尤其具有优势。
方法：本文评估了套索惩罚逻辑回归在具有大量单核苷酸多态性（SNP）预测变量的病例 - 对照疾病基因定位中的性能。套索惩罚的强度可以进行调整，以选择预定数量的最相关的SNP和其他预测变量。对于给定的调整常数的值，通过循环坐标上升法可以快速使惩罚似然最大化。一旦确定了最有效的边际预测变量，还可以通过套索惩罚逻辑回归检查它们的二阶及更高阶相互作用。
结果：这一策略在模拟数据和真实数据上都进行了测试。我们在乳糜泻方面的研究结果重复了之前的SNP结果，并揭示了SNP之间可能存在的相互作用。
Motivation: In ordinary regression, imposition of a lasso penalty makes continuous model selection straightforward. Lasso penalized regression is particularly advantageous when the number of predictors far exceeds the number of observations.Method: The present article evaluates the performance of lasso penalized logistic regression in case-control disease gene mapping with a large number of SNPs (single nucleotide polymorphisms) predictors. The strength of the lasso penalty can be tuned to select a predetermined number of the most relevant SNPs and other predictors. For a given value of the tuning constant, the penalized likelihood is quickly maximized by cyclic coordinate ascent. Once the most potent marginal predictors are identified, their two-way and higher order interactions can also be examined by lasso penalized logistic regression.Results: This strategy is tested on both simulated and real data. Our findings on coeliac disease replicate the previous SNP results and shed light on possible interactions among the SNPs.