Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions.
Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions.
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DOI：
10.1186/1756-0381-2-5
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发表时间：
2009-09-22
期刊：
影响因子：
4.5
通讯作者：
Moore JH
中科院分区：
文献类型：
--
作者：
Greene CS;Penrod NM;Kiralis J;Moore JH
全基因组关联研究正在成为人类常见疾病遗传分析的事实上的标准。鉴于生物网络的复杂性和健壮性，这种疾病不太可能是单一故障点的结果，而很可能是由两个或更多相互作用的组成部分的联合故障引起的。全基因组筛查的希望是，这些故障点可以与单核苷酸多态(SNPs)联系在一起，单核苷酸多态(SNPs)赋予疾病易感性。然而，在没有单基因效应的情况下检测导致疾病的相互作用变异是困难的，而且为了相互作用而详尽分析这些变异集合的方法本质上是组合的，因此在计算上是不可行的。需要高效的算法来检测相互作用的SNPs。ReliefF算法就是这样一种很有前景的算法，尽管它在处理有噪声的数据集时，当交互影响较小时成功率较低。ReliefF已经与迭代方法Tuned ReliefF(TURF)配对，该方法改进了噪声数据中的权重估计，但不会从根本上改变潜在的ReliefF算法。为了提高使用这些方法检测微小影响的研究的灵敏度，我们引入了空间均匀ReliefF(SURF)。SURF检测这一领域的相互作用的能力明显强于ReliefF。同样，在上位性模型下，SURF与TURF策略的结合显著优于单独使用TURF进行SNP选择。重要的是要注意，这种成功率的增加不需要增加算法的复杂性，并且允许提高成功率，即使从算法中去除了讨厌的参数。进行遗传关联研究并旨在发现与疾病易感性增加相关的基因-基因相互作用的研究人员应该使用SURF来代替ReliefF。例如，在详尽的MDR分析之前，应该使用SURF而不是ReliefF来筛选数据集。这一变化提高了研究检测基因-基因相互作用的能力。SURF算法是在开源多因素降维(MDR)软件包中实现的，可从获得。
Genome-wide association studies are becoming the de facto standard in the genetic analysis of common human diseases. Given the complexity and robustness of biological networks such diseases are unlikely to be the result of single points of failure but instead likely arise from the joint failure of two or more interacting components. The hope in genome-wide screens is that these points of failure can be linked to single nucleotide polymorphisms (SNPs) which confer disease susceptibility. Detecting interacting variants that lead to disease in the absence of single-gene effects is difficult however, and methods to exhaustively analyze sets of these variants for interactions are combinatorial in nature thus making them computationally infeasible. Efficient algorithms which can detect interacting SNPs are needed. ReliefF is one such promising algorithm, although it has low success rate for noisy datasets when the interaction effect is small. ReliefF has been paired with an iterative approach, Tuned ReliefF (TuRF), which improves the estimation of weights in noisy data but does not fundamentally change the underlying ReliefF algorithm. To improve the sensitivity of studies using these methods to detect small effects we introduce Spatially Uniform ReliefF (SURF). SURF's ability to detect interactions in this domain is significantly greater than that of ReliefF. Similarly SURF, in combination with the TuRF strategy significantly outperforms TuRF alone for SNP selection under an epistasis model. It is important to note that this success rate increase does not require an increase in algorithmic complexity and allows for increased success rate, even with the removal of a nuisance parameter from the algorithm. Researchers performing genetic association studies and aiming to discover gene-gene interactions associated with increased disease susceptibility should use SURF in place of ReliefF. For instance, SURF should be used instead of ReliefF to filter a dataset before an exhaustive MDR analysis. This change increases the ability of a study to detect gene-gene interactions. The SURF algorithm is implemented in the open source Multifactor Dimensionality Reduction (MDR) software package available from .
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DOI：
10.1056/nejmra0808700
发表时间：
2009-04-23
期刊：
The New England journal of medicine
影响因子：
--
作者：
Hardy J;Singleton A
通讯作者：
Singleton A
影响因子：
1.8
作者：
Moore, JH
通讯作者：
Moore, JH
影响因子：
120.7
作者：
Moore, JH;Ritchie, MD
通讯作者：
Ritchie, MD
影响因子：
4.5
作者：
McKinney BA;Crowe JE;Guo J;Tian D
通讯作者：
Tian D
影响因子：
7.5
作者：
Robnik-Sikonja, M;Kononenko, I
通讯作者：
Kononenko, I