Induced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation on gene regulation across different cell types and as models for studies of complex disease. To do so, we established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression levels, chromatin accessibility, and DNA methylation. Our analysis focused on a comparison of inter-individual regulatory variation across cell types. While most cell-type–specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell-type–specific regulatory QTLs are in shared open chromatin. This observation motivated us to develop a deep neural network to predict open chromatin regions from DNA sequence alone. Using this approach, we were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell-type–specific chromatin accessibility.

诱导多能干细胞 (iPSC) 是研究细胞分化和细胞类型的重要工具，否则难以获得。我们调查了使用 iPSC 和 iPSC 衍生细胞来研究遗传变异对不同细胞类型基因调控的影响，以及作为复杂疾病研究模型的用途。为此，我们从 58 个经过深入研究的约鲁巴类淋巴母细胞系 (LCL) 中建立了一组 iPSC；这些细胞系中的 14 个进一步分化为心肌细胞。我们通过测量基因表达水平、染色质可及性和 DNA 甲基化来描述个体和细胞类型之间的调控差异。我们的分析侧重于比较跨细胞类型的个体间监管差异。虽然大多数细胞类型特异性调控数量性状位点 (QTL) 位于仅在受影响细胞类型中开放的染色质中，但我们发现 20% 的细胞类型特异性调控 QTL 位于共享开放染色质中。这一观察结果促使我们开发了一个深度神经网络来仅从 DNA 序列预测开放的染色质区域。使用这种方法，我们能够使用分离单倍型的序列来预测常见 SNP 对细胞类型特异性染色质可及性的影响。