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Elucidating prostate cancer risk mechanisms through large-scale cistrome wide association studies

通过大规模顺反组广泛关联研究阐明前列腺癌风险机制

基本信息

批准号：
10686418
负责人：
MATTHEW L FREEDMAN
金额：
$ 66.05万
依托单位：
DANA-FARBER CANCER INST
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10686418
关键词：
ATAC-seq Acetylation Affect Algorithms Alleles Allelic Imbalance Automobile Driving Benign Binding Sites Biological Assay Biology CRISPR/Cas technology Cell Line ChIP-seq Chromatin Chromosomes Clustered Regularly Interspaced Short Palindromic Repeats Code Communities Complex Data Data Set Disease Elements Enhancers Epigenetic Process Evaluation Gene Expression Gene Expression Profiling Generations Genes Genetic Genetic Code Genetic Transcription Genome Genotype Goals Heritability Heterozygote High-Throughput Nucleotide Sequencing Human Human Genetics Hybrids Individual Libraries Link Location Malignant neoplasm of prostate Maps Measurement Measures Mediating Mendelian disorder Mendelian randomization Methods Modeling Molecular Open Reading Frames Pathogenesis Pathogenicity Population Prevention Prostate Proteins Quantitative Trait Loci RNA Regulatory Element Risk Sampling Signal Transduction Site Specimen Structure Susceptibility Gene Testing Transcript Transposase Untranslated RNA Validation Variant cancer risk candidate identification candidate validation causal variant chromatin immunoprecipitation disorder risk epigenome epigenome editing epigenomics genetic risk factor genetic variant genome editing genome wide association study genome-wide genome-wide analysis histone modification in vivo innovation large scale data novel prostate cancer risk risk variant scale up tool trait transcription factor transcriptome sequencing transcriptomics tumor

项目摘要

PROJECT SUMMARY/ABSTRACT In stark contrast to Mendelian disorders, the majority of complex trait-associated common variants map to non-protein coding regions. Since there is a less well-developed genetic code for the much larger non- protein coding portion of the genome, identifying the gene(s) and causal alleles underlying non- Mendelian/complex traits presents a challenge. Given the rapidity with which genome wide association studies (GWAS) are discovering regions associated with complex traits, causal allele and susceptibility gene identification have become severe bottlenecks. The overall goal of this proposal is to outline a rigorous and comprehensive strategy to discover functionally causal variants and their target genes. While the proposal focuses on prostate cancer, the strategies can be applied to any non-protein coding locus. The central hypothesis is that cancer risk loci are regulatory elements. Recent data convincingly demonstrate that GWAS loci are enriched for regulatory elements. Regulatory elements control the level of expression of genes. Causal variants are difficult to discover because the scientific community is less adept at annotating the non-protein coding portion of the genome. This proposal seeks to develop a novel computational and statistical framework to prioritize candidate causal variants and then to experimentally validate these predictions. The proposal will jointly model quantitative trait loci (QTL) and allelic imbalance (AI) signals in epigenetic data (ChIP-seq and ATAC-seq) and transcripts (RNA-seq) in a novel framework that we term cistrome wide association studies (CWAS). The most significant CWAS loci will be subjected to epigenome and genome editing to functionally characterize and identify causal variants. Aim 1 will utilize novel experimental methods to create the large-scale datasets that will inform Aim 2. The ultimate goal of Aim 1 is to perform H3K27 acetylation, and AR chromatin immunoprecipitation and high- throughput sequencing (ChIP-seq) to annotate active enhancers, Assay for Transposase-Accessible Chromatin (ATAC-seq) to identify open chromatin, and RNA-seq. All of these data will be subjected to target enrichment at a predefined set of variants, which will enable the rigorous and systematic measurement of AI at heterozygote sites. Aim 2 will utilize these data in a structured framework to computationally identify statistically significant CWAS prostate risk loci that. These loci will be experimentally tested in Aim 3 where Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) evaluation of the candidate causal variants will be performed. At the completion of this project, we fully anticipate that we will have begun to unravel the causal (i.e., pathogenic) variants that initiate human prostate cancer. Discovering the mechanisms underlying human traits will not only inform the biology of disease, but may also reveal opportunities to more rationally intervene in treatment and prevention.

项目总结/文摘