Zheng An, Shen Zeyang, Glass Christopher K, Gymrek Melissa
Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA.
Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA.
Bioinform Adv. 2023 Jan 12;3(1):vbad002. doi: 10.1093/bioadv/vbad002. eCollection 2023.
Previous studies have shown that the heritability of multiple brain-related traits and disorders is highly enriched in transcriptional enhancer regions. However, these regions often contain many individual variants, while only a subset of them are likely to causally contribute to a trait. Statistical fine-mapping techniques can identify putative causal variants, but their resolution is often limited, especially in regions with multiple variants in high linkage disequilibrium. In these cases, alternative computational methods to estimate the impact of individual variants can aid in variant prioritization.
Here, we develop a deep learning pipeline to predict cell-type-specific enhancer activity directly from genomic sequences and quantify the impact of individual genetic variants in these regions. We show that the variants highlighted by our deep learning models are targeted by purifying selection in the human population, likely indicating a functional role. We integrate our deep learning predictions with statistical fine-mapping results for 8 brain-related traits, identifying 63 distinct candidate causal variants predicted to contribute to these traits by modulating enhancer activity, representing 6% of all genome-wide association study signals analyzed. Overall, our study provides a valuable computational method that can prioritize individual variants based on their estimated regulatory impact, but also highlights the limitations of existing methods for variant prioritization and fine-mapping.
The data underlying this article, nucleotide-level importance scores, and code for running the deep learning pipeline are available at https://github.com/Pandaman-Ryan/AgentBind-brain.
Supplementary data are available at online.
先前的研究表明，多种与大脑相关的性状和疾病的遗传力在转录增强子区域高度富集。然而，这些区域通常包含许多个体变异，而其中只有一部分可能对性状有因果贡献。统计精细定位技术可以识别推定的因果变异，但其分辨率往往有限，尤其是在处于高连锁不平衡状态的具有多个变异的区域。在这些情况下，估计个体变异影响的替代计算方法有助于变异优先级排序。
在此，我们开发了一种深度学习流程，可直接从基因组序列预测细胞类型特异性增强子活性，并量化这些区域中个体遗传变异的影响。我们表明，我们的深度学习模型突出显示的变异在人类群体中受到纯化选择的靶向，这可能表明其具有功能作用。我们将深度学习预测结果与8种与大脑相关性状的统计精细定位结果相结合，识别出63个不同的候选因果变异，预计这些变异通过调节增强子活性对这些性状有贡献，占所有分析的全基因组关联研究信号的6%。总体而言，我们的研究提供了一种有价值的计算方法，该方法可以根据估计的调控影响对个体变异进行优先级排序，但同时也突出了现有变异优先级排序和精细定位方法的局限性。
本文所依据的数据、核苷酸水平的重要性得分以及运行深度学习流程的代码可在https://github.com/Pandaman-Ryan/AgentBind-brain获取。
补充数据可在网上获取。
