A novel support vector machine-based approach for rare variant detection.
A novel support vector machine-based approach for rare variant detection.
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DOI：
10.1371/journal.pone.0071114
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Chiu YF
中科院分区：
文献类型：
--
作者：
Fang YH;Chiu YF
下一代测序技术的进步使得能够鉴定涉及疾病或性状的多个罕见单核苷酸多态性。最近提出了几种用于鉴定导致疾病易感性的罕见变异的策略。许多这些统计方法的一个重要特征是汇集或分解多个罕见的单核苷酸变体，以实现合理的高频率和效果。然而，如果汇集的罕见变体在不同方向上与性状相关联，则汇集可能会削弱信号，从而降低其统计功效。在本文中，我们提出了一种基于后向支持向量机（BSVM）的变异选择程序，以识别信息丰富的疾病相关的罕见变异。在选择过程中，根据罕见变异与疾病的正相关或负相关性对罕见变异进行加权和折叠，这些变异可能与具有保护性、有害性或中性作用的常见变异和罕见变异相关。这种非参数变量选择程序能够解释混杂因素，也可以在其他回归框架中采用。仿真研究和数据示例的结果表明，在所考虑的场景下，所提出的BSVM方法比其他四种方法更强大，同时保持了有效的第一类错误。
Advances in next-generation sequencing technologies have enabled the identification of multiple rare single nucleotide polymorphisms involved in diseases or traits. Several strategies for identifying rare variants that contribute to disease susceptibility have recently been proposed. An important feature of many of these statistical methods is the pooling or collapsing of multiple rare single nucleotide variants to achieve a reasonably high frequency and effect. However, if the pooled rare variants are associated with the trait in different directions, then the pooling may weaken the signal, thereby reducing its statistical power. In the present paper, we propose a backward support vector machine (BSVM)-based variant selection procedure to identify informative disease-associated rare variants. In the selection procedure, the rare variants are weighted and collapsed according to their positive or negative associations with the disease, which may be associated with common variants and rare variants with protective, deleterious, or neutral effects. This nonparametric variant selection procedure is able to account for confounding factors and can also be adopted in other regression frameworks. The results of a simulation study and a data example show that the proposed BSVM approach is more powerful than four other approaches under the considered scenarios, while maintaining valid type I errors.
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DOI：
10.1186/1471-2210-10-6
发表时间：
2010-06-07
期刊：
BMC pharmacology
影响因子：
--
作者：
Spiess AN;Neumeyer N
通讯作者：
Neumeyer N
影响因子：
7.5
作者：
Tax, DMJ;Duin, RPW
通讯作者：
Duin, RPW
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影响因子：
4.4
作者：
Li, LB;Jiang, W;Rao, S
通讯作者：
Rao, S
影响因子：
9.8
作者：
Li, Bingshan;Leal, Suzanne M.
通讯作者：
Leal, Suzanne M.
影响因子：
4.5
作者：
Neale BM;Rivas MA;Voight BF;Altshuler D;Devlin B;Orho-Melander M;Kathiresan S;Purcell SM;Roeder K;Daly MJ
通讯作者：
Daly MJ