Annotations capturing cell type-specific TF binding explain a large fraction of disease heritability.
Annotations capturing cell type-specific TF binding explain a large fraction of disease heritability.
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捕获细胞类型特异性 TF 结合的注释解释了大部分疾病遗传性。
DOI：
10.1093/hmg/ddz226
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发表时间：
2020
影响因子：
3.5
通讯作者：
Price,AlkesL
中科院分区：
文献类型：
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作者：
vandeGeijn,Bryce;Finucane,Hilary;Gazal,Steven;Hormozdiari,Farhad;Amariuta,Tiffany;Liu,Xuanyao;Gusev,Alexander;Loh,Po-Ru;Reshef,Yakir;Kichaev,Gleb;Raychauduri,Soumya;Price,AlkesL
调控变异在复杂疾病中起着重要作用，转录因子（TF）的细胞类型特异性结合对基因调控至关重要。然而，评估TF结合位点的遗传变异对疾病遗传性的贡献是具有挑战性的，因为结合通常是细胞类型特异性的，并且直接测量的TF结合的注释目前对于大多数细胞类型-TF对不可用。我们调查的方法来注释TF结合，包括直接测量的染色质数据和基于序列的预测。我们发现，通过将基于序列的TF结合预测与细胞类型特异性染色质数据交叉构建的TF结合注释解释了广泛疾病和相应细胞类型的大部分遗传性;这种构建注释的策略既解决了相同序列可以根据周围染色质环境结合或不结合的限制，又解决了序列-基于的预测通常不是小区类型特定的。我们使用分层连锁不平衡（LD）评分回归与基线LD模型（这不是细胞类型特异性）加上新的注释，划分了49种疾病和复杂性状的遗传力。我们确定，基于MotifMap测序的TF结合预测周围的100 bp窗口与六种细胞类型特异性染色质标记的结合一致（使用ChromImpute进行插补）表现最好，与单独的染色质标记相比，（11.6× vs. 7.3×，差异P= 9 × 10− 14），根据基线LD模型的注释，细胞类型特异性信号增加20%（差异P= 8 × 10− 11）。我们的研究结果表明，TF结合注释解释了大量的疾病遗传性，并有助于改善全基因组关联信号。
Regulatory variation plays a major role in complex disease and that cell type-specific binding of transcription factors (TF) is critical to gene regulation. However, assessing the contribution of genetic variation in TF-binding sites to disease heritability is challenging, as binding is often cell type-specific and annotations from directly measured TF binding are not currently available for most cell type-TF pairs. We investigate approaches to annotate TF binding, including directly measured chromatin data and sequence-based predictions. We find that TF-binding annotations constructed by intersecting sequence-based TF-binding predictions with cell type-specific chromatin data explain a large fraction of heritability across a broad set of diseases and corresponding cell types; this strategy of constructing annotations addresses both the limitation that identical sequences may be bound or unbound depending on surrounding chromatin context and the limitation that sequence-based predictions are generally not cell type-specific. We partitioned the heritability of 49 diseases and complex traits using stratified linkage disequilibrium (LD) score regression with the baseline-LD model (which is not cell type-specific) plus the new annotations. We determined that 100 bp windows around MotifMap sequenced-based TF-binding predictions intersected with a union of six cell type-specific chromatin marks (imputed using ChromImpute) performed best, with an 58% increase in heritability enrichment compared to the chromatin marks alone (11.6× vs. 7.3×,P= 9 × 10−14for difference) and a 20% increase in cell type-specific signal conditional on annotations from the baseline-LD model (P= 8 × 10−11for difference). Our results show that TF-binding annotations explain substantial disease heritability and can help refine genome-wide association signals.