High dimensional statistical data modeling and integration for studying regulatory variation

用于研究监管变化的高维统计数据建模和集成

• 批准号: 10610872
• 负责人: Sunduz Keles
• 金额: $ 37.88万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-26 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10610872
• 关键词: 3-Dimensional; Address; Bioconductor; Biodiversity; Biologic Characteristic; Biological; Cells; Chromatin; Clinical; Communities; Computer Analysis; Computer software; Data; Data Analytics; Data Set; Dedications; Development; Dimensions; Disease; Environmental Risk Factor; Gene Expression Regulation; Genes; Genetic; Genome; Genomic Segment; Genomics; Heterogeneity; Hi-C; Human; Human Genome; Individual; Intervention; Laboratory Organism; Link; Mammalian Cell; Maps; Measurement; Mediating; Methodology; Methods; Modality; Modeling; Molecular; Molecular Conformation; Multiomic Data; Mus; Nature; Noise; Non-Insulin-Dependent Diabetes Mellitus; Nucleotides; Phenotype; Predisposition; Property; Quantitative Trait Loci; Regulator Genes; Research; Resolution; Resources; Risk; Role; Signal Transduction; Source; Statistical Methods; Technology; Training; Translations; Untranslated RNA; Validation; Variant; autoencoder; cell type; chromosome conformation capture; data integration; data modeling; denoising; epigenome; epigenomics; experimental study; flexibility; follow-up; genome wide association study; high dimensionality; high throughput technology; improved; innovation; interest; model organism; multiple omics; novel; open source; programs; risk variant; scale up; simulation; single cell sequencing; trait; transcriptomics; translational genetics

Project Summary Gene regulatory programs of mammalian cells are largely influenced by long-range chromatin interactions. We propose to develop robust and scalable statistical methods for two critical genomic inference problems hinging upon long-range chromatin interactions. First, the study of long-range interactions at the single cell-level with 3C- based method scHi-C is fundamental to fully understanding cell type-specific gene regulation. scHi-C measurements harbor unexplored biological diversity. However, these measurements are prone to extreme sparsity, technological bias, and noise. While initial inference methods simply focused on lower dimensional representations of scHi-C data, lack of a scalable framework that can exploit nonlinearities in de-noising of the data impedes key inference tasks from these experiments. We will address these critical shortcomings by developing a novel deep generative model for scHi-C data. By de- noising the data, these methods will improve the power with which signals of interest can be studied. Second, while advances in sequencing and large-scale availability of epigenome data improved the power and interpretation of genome-wide association studies (GWAS), shortcomings in identifying which genes noncoding SNPs might be impacting through long-range chromatin interactions hinder the translation of GWAS findings into clinical interventions. Leveraging existing large-scale studies of diversity outbred mice, we will develop a rigorous framework that integrates multi-omics functional data modalities to fine-map model organism molecular quantitative trait loci and transfer the results to humans for linking noncoding GWAS SNPs to their effector, i.e., susceptibility, genes. Large-scale application with type 2 diabetes (T2D) traits will deliver candidate T2D effector genes and their regulatory loci that are amenable for experimental follow-up. Both aims will be accomplished through a combination of methodological development, theoretical analysis, data-driven simulation, computational analysis, and experimental validation. Statistical resources generated from this project will be disseminated as open-source software. Successful completion of the project will help to ensure that maximal information is obtained from powerful scHi-C experiments and model organism multi-omics data.

项目总结