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

基本信息

批准号：
10474459
负责人：
Anshul Kundaje
金额：
$ 72.74万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
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/10474459
关键词：
Algorithmic Software 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 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 innovation 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

项目摘要

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.

抽象的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，并在这个项目中，我们计划利用最新的进展来识别和解释具有监管功能的变体。实验功能基因组学（包括单细胞方法和高通量微扰方法）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我们组建了一支具有多元化和互补专业知识的研究团队，深入研究。学习、单细胞基因组学、细胞 QTL 和 GWAS 以及高通量验证 – 构建、测试和具体来说，我们的目标是 (1) 开发预测模型来解释疾病关联。 (2) 预测并验证细胞类型- （三）与IGVF合作建立核苷酸级监管图谱的联盟我们在该项目中的最终目标是创建一个核苷酸级监管图谱。人类基因组的核苷酸分辨率顺式调控图谱，将疾病变异与功能联系起来表型，在不同的细胞类型、状态和空间背景下。