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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：
10297562
负责人：
Anshul Kundaje
金额：
$ 35.22万
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/10297562
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 Machine Learning 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 de novo mutation deep learning 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 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.

摘要