Predicting context-specific molecular and phenotypic effects of genetic variation through the lens of the cis-regulatory code

摘要 人类基因组学的一个中心挑战是解释非编码基因组的调节功能，并 识别和解释具有监管功能的变体。在这个项目中，我们计划利用最近在 实验功能基因组学(包括单细胞法和高通量微扰法) 伴随着基因调控深度学习模型的最新进展，在这些方面取得根本性进展 有问题。我们已经组建了一支拥有多样化和互补性专业知识的调查团队-深入 学习、单细胞基因组学、细胞QTL和GWA，以及高通量验证-用于构建、测试和 实施用于解释疾病关联的预测性模型。具体来说，我们的目标是(1)发展 用于调控序列的可解释的基本分辨率深度学习模型；(2)预测和验证细胞类型- 调控变异体对分子表型和疾病的特异性影响；(3)与IGVF合作 该联盟将建立核苷酸水平的调控图。我们在这个项目中的最终目标将是创建一个 人类基因组的核苷酸分辨顺式调控图，将疾病变体与功能和 表型，在不同的细胞类型、状态和空间背景下。

ABSTRACT A central challenge in human genomics is to interpret the regulatory functions of the noncoding genome, and to identify and interpret variants with regulatory functions. In this project we plan to leverage recent advances in experimental functional genomics (including single cell methods and high throughput perturbation methods) alongside recent progress in deep learning models of gene regulation, to make fundamental progress on these problems. We have assembled a team of investigators with diverse and complementary expertise – in deep learning, single-cell genomics, cellular QTLs and GWAS, and high throughput validations – to build, test, and implement predictive models for interpreting disease associations. Specifically, we aim to (1) Develop interpretable base-resolution deep-learning models for regulatory sequences; (2) Predict and validate cell type- specific effects of regulatory variants on molecular phenotypes and disease; (3) Collaborate with the IGVF Consortium to build nucleotide-level regulatory maps. Our ultimate goal in this project will be to create a nucleotide-resolution cis-regulatory map of the human genome to connect disease variants to functions and phenotypes, in diverse cell types, states, and spatial contexts.