From common to rare variant functional architectures of human diseases

项目摘要/摘要 大规模全基因组关联研究(GWAS)强调了由常见的 变体集中在通常是细胞类型或组织特定的非编码功能注释中。 然而，利用非编码调控变体来检测新的疾病基因或基因集在很大程度上是 未知。在这项建议中，我将研究频率较低的非编码变体的影响 通过开发一种新的统计方法来划分低频变异的遗传力。 然后，我将使用这种方法将功能遗传性与基因联系起来，以增加 检测编码和非编码疾病变异丰富的基因和基因集的统计能力。 我的K99培训将在哈佛大学公共卫生学院和博德大学进行 研究所，在阿尔克斯·普莱斯博士的指导下。我培训的主要领域将是：开发模型 用于跨功能注释(包括基因)划分由低频变体解释的遗传性 Set Annotation)；对大规模GWAS和全基因组测序数据集的分析；以及联合分析 多个大型功能基因组数据集。这项研究的长期目标是生产功能 使遗传学家能够分析大型GWAs和全基因组测序的注释和软件 数据集，以便做出将提高我们对人类疾病的生物学知识的发现。 这项建议的第一个目标是开发一种划分普通和低基因遗传力的方法。 跨功能注释的频率变体。我将把这种方法应用于大型GWAS数据集，并将 使用结果来拟合一个进化模型，该模型将预测每种情况下罕见的不同效应大小的分布 注释。第二个目标是确定将功能遗传性与基因联系起来的最佳策略。这就做 使用Hi-C数据、保留的注释和其他功能数据连接来比较不同的策略 基因的功能元素，并确定哪种策略对性状遗传力最具信息性。然后，我 将使用这一策略来识别遗传性丰富的基因组。第三个目标将利用来自 从大型全球气候变化数据集估计的常见和低频变异富集量(目标1)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- 基于罕见变量关联测试。在这项研究中开发的新注释和计算工具 提案将分发给科学界。

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.