Multi-locus nonparametric linkage analysis of complex trait loci with neural networks
Multi-locus nonparametric linkage analysis of complex trait loci with neural networks
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DOI：
10.1159/000022816
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发表时间：
1998-09-01
期刊：
影响因子：
1.8
通讯作者：
Ott, J
中科院分区：
文献类型：
--
作者：
Lucek, P;Hanke, J;Ott, J
复杂性状通常被认为受到多个基因的影响，这些基因可能相互作用而导致对疾病的易感性。当前用于定位此类基因的统计方法基本上在单基因模型下工作，无论是隐式的还是显式的。在复杂疾病基因的基因组筛选中，一些标记位点必须与疾病易感基因紧密连锁。我们开发了一种通用的多基因座方法来识别此类标记基因座的集合，我们的方法侧重于受影响的同胞对数据，并采用使用人工神经网络的非参数模式识别技术。该技术同时分析所有标记，以检测基因座相互作用的模式。当应用于之前发布的 I 型糖尿病同胞对数据时，我们的方法发现了与已发布的报告中相同的基因以及一些新的基因座。对于特定的双基因座遗传模型，我们的方法的能力高于当前使用的分析标准。
Complex traits are generally taken to be under the influence of multiple genes, which may interact with each other to confer susceptibility to disease. Statistical methods in current use for localizing such genes essentially work under single-gene models, either implicitly or explicitly. In genomic screens for complex disease genes, some of the marker loci must be in tight linkage with disease susceptibility genes. We developed a general multi-locus approach to identify sets of such marker loci, Our approach focuses on affected sib pair data and employs a nonparametric pattern recognition technique using artificial neural networks. This technique analyzes all markers simultaneously in order to detect patterns of locus interactions. When applied to previously published sib pair data on type I diabetes, our approach finds the same genes as in the published report in addition to some new loci. For a specific two-locus model of inheritance, the power of our approach is higher than that of the currently used analysis standard.