The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.
Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel.
Genetics. 2021 Apr 15;217(4). doi: 10.1093/genetics/iyab016.
Recent computational methods have enabled the inference of the cell-type-specificity of eQTLs based on bulk transcriptomes from highly heterogeneous tissues. However, these methods are limited in their scalability to highly heterogeneous tissues and limited in their broad applicability to any cell-type specificity of eQTLs. Here we present and demonstrate Cell Lineage Genetics (CeL-Gen), a novel computational approach that allows inference of eQTLs together with the subsets of cell types in which they have an effect, from bulk transcriptome data. To obtain improved scalability and broader applicability, CeL-Gen takes as input the known cell lineage tree and relies on the observation that dynamic changes in genetic effects occur relatively infrequently during cell differentiation. CeL-Gen can therefore be used not only to tease apart genetic effects derived from different cell types but also to infer the particular differentiation steps in which genetic effects are altered.
最近的计算方法使我们能够根据来自高度异质组织的大量转录组数据推断出 eQTL 的细胞类型特异性。然而，这些方法在高度异质组织中的扩展性有限，并且在广泛适用于任何 eQTL 的细胞类型特异性方面也受到限制。在这里，我们提出并展示了 Cell Lineage Genetics（CeL-Gen），这是一种新颖的计算方法，允许从大量转录组数据中推断 eQTL 及其影响的细胞类型子集。为了获得更好的可扩展性和更广泛的适用性，CeL-Gen 将已知的细胞谱系树作为输入，并依赖于这样一个观察结果，即遗传效应的动态变化在细胞分化过程中相对较少发生。因此，CeL-Gen 不仅可用于分离来自不同细胞类型的遗传效应，还可用于推断遗传效应发生改变的特定分化步骤。
