Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies.
Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies.
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DOI：
10.3389/fgene.2021.801261
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发表时间：
2021
影响因子：
3.7
通讯作者：
Du D
中科院分区：
文献类型：
--
作者：
Guo Y;Cheng H;Yuan Z;Liang Z;Wang Y;Du D
导致复杂疾病的原因不明的遗传变异通常由基因-基因相互作用（GGIs）引起。基于基因的方法是目前在病例对照全基因组关联研究中发现GGIs的统计方法之一，不仅在统计学上很强大，而且在生物学上也可解释。然而，大多数方法都包括对全球治理指标形式的假设，这导致统计业绩不佳。因此，我们提出了基于最大邻域系数（MNC）的基因测试，称为基于基因的基因-基因交互通过最大邻域系数（GBMNC）。MNC是一种度量，用于捕获具有任意但不一定相等维度的两个随机向量之间的广泛关系。我们建立了一个统计，利用MNC的情况下，并在控制样品作为GGIs存在的指示，基于假设的联合分布的两个基因的情况下，控制不应该有很大的不同，如果它们之间没有相互作用。然后，我们使用基于置换的统计检验来评估该统计量，并计算统计p值以表示相互作用的显著性。使用模拟和真实的数据的实验结果表明，我们的方法优于早期的方法检测GGIs。
Unexplained genetic variation that causes complex diseases is often induced by gene-gene interactions (GGIs). Gene-based methods are one of the current statistical methodologies for discovering GGIs in case-control genome-wide association studies that are not only powerful statistically, but also interpretable biologically. However, most approaches include assumptions about the form of GGIs, which results in poor statistical performance. As a result, we propose gene-based testing based on the maximal neighborhood coefficient (MNC) called gene-based gene-gene interaction through a maximal neighborhood coefficient (GBMNC). MNC is a metric for capturing a wide range of relationships between two random vectors with arbitrary, but not necessarily equal, dimensions. We established a statistic that leverages the difference in MNC in case and in control samples as an indication of the existence of GGIs, based on the assumption that the joint distribution of two genes in cases and controls should not be substantially different if there is no interaction between them. We then used a permutation-based statistical test to evaluate this statistic and calculate a statistical p-value to represent the significance of the interaction. Experimental results using both simulation and real data showed that our approach outperformed earlier methods for detecting GGIs.
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DOI：
10.1038/nrg2579
发表时间：
2009-06
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Cordell HJ
通讯作者：
Cordell HJ
影响因子：
14.9
作者：
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者：
Parkinson, Helen
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影响因子：
3
作者：
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通讯作者：
Liu, Jianxiao
影响因子：
16.6
作者：
Fang, Gang;Wang, Wen;Myers, Chad L.
通讯作者：
Myers, Chad L.
影响因子：
7.5
作者：
Cen, Shuizhong;Wang, Peng;Wu, Yanfeng
通讯作者：
Wu, Yanfeng