Common disease analysis using multivariate adaptive regression splines (MARS): Genetic analysis workshop 12 simulated sequence data
Common disease analysis using multivariate adaptive regression splines (MARS): Genetic analysis workshop 12 simulated sequence data
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DOI：
10.1002/gepi.2001.21.s1.s649
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发表时间：
2001-01-01
影响因子：
2.1
通讯作者：
Eaves, LJ
中科院分区：
文献类型：
--
作者：
York, TP;Eaves, LJ
多变量自适应回归样条（MARS）是一种新发展起来的现代分析方法，用于确定一种常见疾病的病因学中涉及的遗传和非遗传因素。我们测试了这种方法的模拟数据提供的遗传分析研讨会（GAW）12在问题2的隔离人口。MARS同时分析所有输入，在这种情况下是DNA序列变异和非遗传数据，并选择性地修剪掉对内部交叉验证拟合贡献不显著的变量，以获得可推广的响应预测模型。通过M-ARS计算的重要性值确定的相关因素被认为与疾病风险相关。随后的一系列模型的应用确定了数量性状和一个主要的基因直接贡献的风险责任使用五组7,000个人。(C)2001 Wiley-Liss，Inc.
A newly developed modem analytic approach, Multivariate Adaptive Regression Splines (MARS), was used to identify both genetic and non-genetic factors involved in the etiology of a common disease. We tested this method on the simulated data provided by the Genetic Analysis Workshop (GAW) 12 in problem 2 for the isolated population. MARS simultaneously analyzes all inputs, in this case DNA sequence variants and non-genetic data, and selectively prunes away variables contributing insignificantly to fit by internal cross-validation to arrive at a generalizable predictive model of the response. The relevant factors identified, by means of an importance value computed by M-ARS, were assumed to be associated with risk to the disease. The application of a series of subsequent models identified the quantitative traits and a single major gene contributing directly to risk liability using five sets of 7,000 individuals. (C) 2001 Wiley-Liss, Inc.