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

通过顺式调控密码的视角预测遗传变异的特定背景分子和表型效应

• 批准号: 10659170
• 负责人: Anshul Kundaje
• 金额: $ 72.74万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659170
• 关键词: Algorithms; Atlases; Beds; Binding; Biological Assay; Brain; Cardiac Myocytes; Catalogs; Cells; Chromatin; Chromatin Modeling; Code; Collaborations; Communities; Complex; Computer software; Computing Methodologies; DNA Sequence; Data Collection; Disease; Elements; Enhancers; Ethnic Origin; Fetal Heart; Gene Expression; Gene Expression Regulation; Gene Frequency; Genes; Genetic; Genetic Models; Genetic Variation; Genome; Genomics; Goals; Human; Human Genetics; Human Genome; Individual; Maps; Methods; Modeling; Molecular; Molecular Disease; Nucleotides; Output; Performance; Phenotype; Quantitative Trait Loci; Rare Diseases; Regulator Genes; Regulatory Element; Reproducibility; Research; Research Personnel; Resolution; Testing; Time; Untranslated RNA; Validation; Variant; Vision; base; causal variant; cell type; cohort; combinatorial; data-driven model; de novo mutation; deep learning; deep learning model; design; disease phenotype; disorder risk; diverse data; experimental study; functional genomics; genetic variant; genome wide association study; genome-wide; histone modification; human genomics; improved; in silico; lens; machine learning method; machine learning model; molecular phenotype; neural network; next generation; open source; outreach; polygenic risk score; portability; predictive modeling; programs; promoter; single cell technology; spatiotemporal; syntax; transcription factor; working group

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.

摘要 人类基因组学的一个核心挑战是解释非编码基因组的调控功能， 鉴定和解释具有调节功能的变体。在这个项目中，我们计划利用最新的进展， 实验功能基因组学（包括单细胞方法和高通量微扰方法） 除了最近在基因调控的深度学习模型方面取得的进展， 问题我们已经组建了一个具有不同和互补专业知识的调查团队-深入了解 学习，单细胞基因组学，细胞QTL和GWAS，以及高通量验证-构建，测试， 实现解释疾病关联的预测模型。具体而言，我们的目标是（1）发展 用于调控序列的可解释的基础分辨率深度学习模型;（2）预测和验证细胞类型- 调节变体对分子表型和疾病的特异性影响;（3）与IGVF合作 构建核苷酸水平调控图谱的联盟。我们在这个项目中的最终目标是创造一个 人类基因组的核苷酸解析顺式调控图谱，将疾病变异与功能联系起来， 表型，在不同的细胞类型，状态和空间背景。