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

• 批准号: 10408102
• 负责人: Steven Gazal
• 金额: $ 23.71万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 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/10408102
• 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

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/摘要 大规模的全基因组关联研究（GWAS）强调，遗传性解释的共同 变体集中在非编码功能注释中，这些注释通常是细胞类型或组织特异性的。 然而，利用非编码调节变体来检测新的疾病基因或基因集在很大程度上是不可能的。 未知在这个提议中，我将研究非编码变体在较低频率下的影响 通过开发一种新的统计方法划分低频变异的遗传性来构建。 然后，我将使用这种方法将功能遗传力与基因联系起来，以增加功能遗传力。 检测编码和非编码疾病变体中富集的基因和基因集的统计能力。 我的K99培训将在哈佛T.H.进行。陈冯富珍公共卫生学院以及 在Alkes Price博士的指导下，研究所。我的培训的关键领域将是：模型开发 用于划分由低频变异在功能注释（包括基因）中解释的遗传性， 集注释）;大规模GWAS和全基因组测序数据集的分析;以及 多个大型功能基因组学数据集。这项研究的长期目标是产生功能性的 注释和软件，使遗传学家能够分析大型GWAS和全基因组测序 数据集，以便发现将改善我们对人类疾病的生物学知识。 本建议的第一个目的是发展一种划分普通和低- 跨功能注释的频率变体。我将在大型GWAS数据集上应用此方法， 使用结果来拟合进化模型，该模型将预测每个基因的罕见变异效应量的分布。 注释。第二个目标是确定将功能遗传力与基因联系起来的最佳策略。我会 使用Hi-C数据、保存的注释和其他功能数据比较不同的策略， 功能元件的基因，并确定哪些策略是最大的信息性状遗传力。那我 将使用这一策略来识别基因集丰富的遗传性。第三个目标将利用来自 从大型GWAS数据集（目标1）估计的常见和低频变异富集，以及 关于如何将功能元件连接到基因的见解（目标2），以提高基因的统计能力- 基于罕见变异关联测试。本研究开发的新注释和计算工具 该提案将分发给科学界。