Epigenetic Combinatorial Patterns Predict Disease Variants.
Epigenetic Combinatorial Patterns Predict Disease Variants.
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DOI：
10.3389/fgene.2017.00071
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发表时间：
2017
影响因子：
3.7
通讯作者：
Zhang Y
中科院分区：
文献类型：
--
作者：
Zhang Y
在全基因组关联研究中发现的大多数遗传变异都是非编码的，很可能标记了附近的因果变异。准确定位致病变异的准确位置并了解它们在疾病中的作用是一项具有挑战性的任务。改善精细图谱的一个有前途的方法是整合目前在数百种人体组织和细胞类型上可用的功能数据。虽然有几种方法使用功能数据来确定疾病变量的优先顺序，但它们主要使用线性模型或等效的基于朴素似然的模型进行预测。在这里，我们调查研究跨细胞类型的功能数据的组合模式是否可以提高对疾病变异的预测准确性。使用127种人类细胞类型的功能注释，我们首先介绍了一种贝叶斯方法，以在基因组规模上识别重复出现的细胞类型特异性分区。我们表明，我们的表观基因组分割模式的从头识别与已知的细胞类型起源很好地一致，并且相关的功能元件在疾病变体中强烈富含。利用表观遗传的细胞类型特异性以及功能成分的丰富，我们进一步证明了预测疾病变异的能力可以比使用线性模型实现的能力大大提高。因此，我们的方法提供了一种新的方法来优先进行疾病功能变异的测试。
Most genetic variants identified in genome-wide association studies are noncoding and are likely tagging nearby causal variants. It is a challenging task to pinpoint the precise locations of disease-causal variants and understand their functions in disease. A promising approach to improve fine mapping is to integrate the functional data currently available on hundreds of human tissues and cell types. Although there are several methods that use functional data to prioritize disease variants, they mainly use linear models, or equivalent naive likelihood-based models for prediction. Here, we investigate whether study of the combinatorial patterns of functional data across cell types can improve prediction accuracy for disease variants. Using functional annotation in 127 human cell types, we first introduce a Bayesian method to identify recurring cell-type-specificity partitions on the scale of the genome. We show that our de novo identification of epigenome partition patterns agrees well with known cell-type origins and that the associated functional elements are strongly enriched in disease variants. Using epigenetic cell-type specificity in addition to enrichment of functional elements, we further demonstrate that the power to predict disease variants can be greatly improved over that achievable with linear models. Our approach thus provides a new way to prioritize disease functional variants for testing.