Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors.
Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors.
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DOI：
10.1186/1756-0381-7-10
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发表时间：
2014
期刊：
影响因子：
4.5
通讯作者：
Williams SM
中科院分区：
文献类型：
--
作者：
Ciesielski TH;Pendergrass SA;White MJ;Kodaman N;Sobota RS;Huang M;Bartlett J;Li J;Pan Q;Gui J;Selleck SB;Amos CI;Ritchie MD;Moore JH;Williams SM
在组学研究中，例如全基因组关联研究，研究人员试图在其他数据集中重复他们的结果，以减少假阳性结果，从而为真实关联的存在提供证据。不幸的是，这种标准的验证方法不能完全消除假阳性结论，而且它还可能掩盖许多真正的关联，否则可能会促进我们对病理学的理解。这些问题提出了一个问题：我们如何才能增加从高通量遗传数据中获得的知识量？为了应对这一挑战，我们提出了一种方法，通过提请注意潜在的假阴性和假阳性结论，以及为指导未来的研究提供广泛的信息，补充标准的统计验证方法。我们提出的多样收敛证据方法（DiCE）整合了来自多个来源（组学，信息学和实验室实验）的信息，以估计支持给定关联的可用确证证据的强度。这个过程的目的是产生一个证据指标，具有实用性时，病因异质性，可变的风险因素的频率，和各种观察数据的不完善可能会导致错误的结论。我们提供了原则的例子证明，其中DiCE确定了强有力的证据，已经建立了生物学的重要性，当标准验证方法本身没有提供支持的协会。如果作为标准验证方法的辅助手段，这种方法可以利用多种不同的数据类型来改善遗传风险因素的发现/验证，促进有效的科学交流，并指导未来的研究方向。
In omic research, such as genome wide association studies, researchers seek to repeat their results in other datasets to reduce false positive findings and thus provide evidence for the existence of true associations. Unfortunately this standard validation approach cannot completely eliminate false positive conclusions, and it can also mask many true associations that might otherwise advance our understanding of pathology. These issues beg the question: How can we increase the amount of knowledge gained from high throughput genetic data? To address this challenge, we present an approach that complements standard statistical validation methods by drawing attention to both potential false negative and false positive conclusions, as well as providing broad information for directing future research. The Diverse Convergent Evidence approach (DiCE) we propose integrates information from multiple sources (omics, informatics, and laboratory experiments) to estimate the strength of the available corroborating evidence supporting a given association. This process is designed to yield an evidence metric that has utility when etiologic heterogeneity, variable risk factor frequencies, and a variety of observational data imperfections might lead to false conclusions. We provide proof of principle examples in which DiCE identified strong evidence for associations that have established biological importance, when standard validation methods alone did not provide support. If used as an adjunct to standard validation methods this approach can leverage multiple distinct data types to improve genetic risk factor discovery/validation, promote effective science communication, and guide future research directions.
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影响因子：
15.8
作者：
Ioannidis, JPA
通讯作者：
Ioannidis, JPA
影响因子：
3.7
作者：
Lian X;Selekman J;Bao X;Hsiao C;Zhu K;Palecek SP
通讯作者：
Palecek SP
影响因子：
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Kim JH;Cheong HS;Park JS;Jang AS;Uh ST;Kim YH;Kim MK;Choi IS;Cho SH;Choi BW;Bae JS;Park CS;Shin HD
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Shin HD
影响因子：
30.8
作者：
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通讯作者：
Kwiatkowski, Dominic P.
影响因子：
3.7
作者：
Jia P;Ewers JM;Zhao Z
通讯作者：
Zhao Z