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From common to rare variant functional architectures of human diseases

从人类疾病的常见到罕见变异功能结构

基本信息

批准号：
10237415
负责人：
Steven Gazal
金额：
$ 24.06万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-12 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10237415
关键词：
Architecture Area Autoimmune Diseases Binding Sites Biological Code Communities Complex Computer software Data Data Set Deoxyribonuclease I Disease Distal Elements Enhancers Frequencies Genes Goals Heritability Hi-C Human Hypersensitivity Immune Individual Institutes Joints Knowledge Link Mentorship Methods Modeling Nucleic Acid Regulatory Sequences Output Pathway interactions Phase Price Public Health Schools Research Research Proposals Role Sample Size Sampling Signal Transduction Site Source Statistical Methods Technology Testing Tissues Training Untranslated RNA Variant Weight annotation system base biobank cell type computerized tools exome sequencing functional genomics genetic architecture genome sequencing genome wide association study genome-wide genomic data human disease improved insight interest model development promoter rare variant simulation trait transcription factor whole genome

项目摘要

Project Summary/Abstract Large-scale genome-wide association studies (GWAS) have highlighted that heritability explained by common variants is concentrated into non-coding functional annotations that are often cell-type or tissue specific. However, the leveraging of non-coding regulatory variants to detect new disease genes or gene sets is largely unknown. In this proposal, I will investigate the effects of non-coding variants in lower frequency architecture by developing a new statistical method partitioning heritability of low-frequency variants. Then, I will use this method to connect functional heritability to genes, in order to increase the statistical power to detect genes and gene sets enriched in coding and non-coding disease variants. My K99 training will be conducted at the Harvard T.H. Chan School of Public Health, as well as the Broad Institute, under the mentorship of Dr. Alkes Price. The key areas of my training will be: development of models for partitioning heritability explained by low-frequency variants across functional annotations (including gene set annotations); analyses of large-scale GWAS and whole genome sequencing datasets; and joint analyses of multiple large functional genomics datasets. The long-term goal of this research is to produce functional annotations and software that will enable geneticists to analyze large GWAS and whole genome sequencing datasets, in order to make discoveries that will improve our biological knowledge of human diseases. The first aim of this proposal is to develop a method for partitioning the heritability of common and low- frequency variants across functional annotations. I will apply this method on large GWAS data sets, and will use the results to fit an evolutionary model that will predict the distribution of rare variant effect sizes for each annotation. The second aim is to determine the best strategy to connect functional heritability to genes. I will compare different strategies using Hi-C data, conserved annotations, and other functional data to connect functional elements to genes and determine which strategy is maximally informative for trait heritability. Then, I will use this strategy to identify gene sets enriched for heritability. The third aim will leverage insights from common and low-frequency variant enrichments estimated from large GWAS data sets (Aim 1) as well as insights on how to connect functional elements to a gene (Aim 2) to improve the statistical power of gene- based rare variant association tests. The new annotations and computational tools developed in this research proposal will be distributed to the scientific community.

项目总结/文摘