A comprehensive approach to haplotype-specific analysis by penalized likelihood.
A comprehensive approach to haplotype-specific analysis by penalized likelihood.
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DOI：
10.1038/ejhg.2009.118
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发表时间：
2010-01
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单倍型可以保存关键信息，以了解候选基因在疾病病因学中的作用。然而，标准的单倍型分析尚未能够完全揭示单倍型保留的信息。在大多数分析中，单倍型推断侧重于与任意选择的基线单倍型相比的相对效应。它不能描述效应结构，除非在二次事后分析中使用额外的推理程序，并且这种分析往往缺乏功效。在这项工作中，我们提出了一个惩罚回归的方法来系统地评估单倍型效应的模式和结构。通过对单倍型效应的成对差异指定L1罚分，我们提出了一种基于模型的单倍型分析来检测和表征单倍型关联信号。该方法避免了选择基线单倍型的需要，同时对所有单倍型进行效应估计和效应比较，并根据效应大小输出单倍型组结构。最后，我们的惩罚权重在理论上被设计成以适当的方式平衡似然和惩罚项。所提出的方法可以被用作一种工具，以理解从基因组或染色体扫描识别的候选区域。仿真实验表明，与传统的单倍型关联方法相比，该方法具有更好的单倍型效应结构识别能力，证明了该方法的信息量和有效性.
Haplotypes can hold key information to understand the role of candidate genes in disease etiology. However, standard haplotype analysis has yet been able to fully reveal the information retained by haplotypes. In most analysis, haplotype inference focuses on relative effects compared to an arbitrarily-chosen baseline haplotype. It does not depict the effect structure unless an additional inference procedure is used in a secondary post-hoc analysis, and such analysis tends to be lack of power. In this work, we propose a penalized regression approach to systematically evaluate the pattern and structure of the haplotype effects. By specifying an L1 penalty on the pairwise difference of the haplotype effects, we present a model-based haplotype analysis to detect and to characterize the haplotypic association signals. The proposed method avoids the need to choose a baseline haplotype; it simultaneously carries out the effect estimation and effect comparison of all haplotypes, and outputs the haplotype group structure based on their effect size. Finally, our penalty weights are theoretically designed to balance the likelihood and the penalty term in an appropriate manner. The proposed method can be used as a tool to comprehend candidate regions identified from a genome or chromosomal scan. Simulation studies reveal the better abilities of the proposed method to identify the haplotype effect structure compared to the traditional haplotype association methods, demonstrating the informativeness and powerfulness of the proposed method.
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影响因子：
2.7
作者：
Wang, Hansheng;Li, Runze;Tsai, Chih-Ling
通讯作者：
Tsai, Chih-Ling
影响因子：
2.1
作者：
Clayton, D;Chapman, J;Cooper, J
通讯作者：
Cooper, J
影响因子：
2.1
作者：
Seltman, H;Roeder, K;Devlin, B
通讯作者：
Devlin, B
影响因子：
2.9
作者：
Souverein, Olga W.;Zwinderman, Aeilko H.;Tanck, Michael W. T.
通讯作者：
Tanck, Michael W. T.
影响因子：
2.1
作者：
Tzeng, JY
通讯作者：
Tzeng, JY