A spectral approach integrating functional genomic annotations for coding and noncoding variants.
A spectral approach integrating functional genomic annotations for coding and noncoding variants.
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DOI：
10.1038/ng.3477
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发表时间：
2016-02
期刊：
影响因子：
30.8
通讯作者：
Buxbaum JD
中科院分区：
文献类型：
--
作者：
Ionita-Laza I;McCallum K;Xu B;Buxbaum JD
在过去的几年里，人们在人类基因组序列变异的功能注释方面投入了大量的努力。这样的注释可以在识别发生在感兴趣位置的丰富自然变异中的推定因果变异方面发挥关键作用。使用这些不同的注释的主要挑战包括它们的数量和多样性。在这里，我们开发了一种无监督的方法来将这些不同的注释集成到一个功能重要性度量(Eigen)中，与大多数现有方法不同，该方法不基于任何标记的训练数据。我们表明，与最近提出的CADD评分相比，使用已发表研究(在编码和非编码区域)中与疾病相关的和假定为良性的变体，所得到的元分数具有更好的区分能力。在不同的场景中，Eigen评分通常比任何单一的注释表现得更好，代表了一个强大的单一功能评分，可以纳入精细映射研究中。
Over the past few years, substantial effort has been put into the functional annotation of variation in human genome sequence. Such annotations can play a critical role in identifying putatively causal variants among the abundant natural variation that occurs at a locus of interest. The main challenges in using these various annotations include their large numbers, and their diversity. Here we develop an unsupervised approach to integrate these different annotations into one measure of functional importance (Eigen), that, unlike most existing methods, is not based on any labeled training data. We show that the resulting meta-score has better discriminatory ability using disease associated and putatively benign variants from published studies (in both coding and noncoding regions) compared with the recently proposed CADD score. Across varied scenarios, the Eigen score performs generally better than any single individual annotation, representing a powerful single functional score that can be incorporated in fine-mapping studies.