Modeling the dynamicimpact of rare and common genetic variation on gene expression anddisease

要了解人类疾病的遗传基础，就需要深入了解基因对人类健康的影响。 基因表达。绝大多数疾病风险基因座是非编码的，因此为了将它们与靶点联系起来， 基因，细胞通路和细胞类型，我们试图确定哪些基因的表达，他们破坏和下 什么条件。对基因表达的群体研究现在已经提供了成千上万的"表达"结果。 数量性状基因座"（eQTL），其中个体遗传变异与靶基因的表达相关 基因虽然跨组织和群体的eQTL研究已经成为查询的宝贵资源， 疾病位点的可能基因靶点，关键障碍仍然存在。首先，eQTL研究根本不解决罕见的 遗传变异，因此排除了每个个体全基因组数万种变异的评估 序列和许多已知的致病基因座。其次，即使在常见的变体中，据估计， 超过一半的疾病基因座与任何已知的eQTL不一致，即使基于当前的多组织数据。的 其余的疾病位点和罕见变异需要新的数据和统计方法，以表征 他们的机制。在这里，我们提出了一个研究议程，以破译监管的复杂影响， 频率范围内的遗传变异1)首先，我们将继续分析罕见的遗传变异， 以及个人全基因组解读的统计学方法。目前的方法根本不提供 对全基因组测序中的大多数变异的可靠预测，以及 对人类疾病的罕见调控变异是未知的。我们将研究个人RNA-seq的使用， 和其他功能数据，以补充全基因组序列（WGS）， 影响，罕见疾病患者的个人基因组解释，以及将罕见变异纳入 人口研究和遗传风险评分。2)其次，我们将考虑常见的疾病变异， 目前的eQTL研究几乎都使用静态的成体组织样品和批量样品， RNA-seq数据基因对基因表达的影响不是静态的，而是随着时间、细胞类型和细胞周期的变化而变化。 环境，使疾病机制的鉴定复杂化。一些疾病位点可能只有 例如，在发育过程中对近端基因表达的瞬时影响。我们将暂时学习 动态和特定环境的遗传效应。在一项新的研究中，我们将评估基因对个体的影响， 细胞分化期间的细胞类型和状态，使用跨个体的时间序列单细胞RNA-seq。 我们还将根据患者的纵向特征评估疾病进展期间的动态遗传效应。 数据结合新型统计方法，这些将提供细胞类型的遗传效应图， 时间和背景可能更好地解释疾病位点。所有的数据、方法和软件都将公开 available.我们的工作将提供一个更好的了解调控基因的影响， 罕见的变异，从而能够更好地识别遗传性疾病的潜在机制。

Understanding the genetic basis of human disease will require a deep understanding of genetic effects on gene expression. The vast majority of disease-risk loci are non-coding, so in order to link them to target genes, cellular pathways, and cell types, we seek to identify which genes’ expression they disrupt and under what conditions. Population studies of gene expression have now provided thousands of “expression quantitative trait loci” (eQTLs) where individual genetic variants are associated with expression of a target gene. While eQTL studies across tissues and populations have served as a valuable resource for querying the likely gene targets of disease loci, key obstacles remain. First, eQTL studies simply do not address rare genetic variation, thus excluding evaluation of tens of thousands of variants per individual whole genome sequence, and many known pathogenic loci. Second, even among common variants, it is estimated that over half of disease loci do not coincide with any known eQTL, even based on current multi-tissue data. The remainder of disease loci and rare variants require new data and statistical methods in order to characterize their mechanisms. Here, we propose a research agenda to decipher the complex impact of regulatory genetic variation across the frequency spectrum. 1) First, we will pursue analysis of rare genetic variation and statistical methods for personal whole genome interpretation. Current methods simply do not provide confident predictions for the majority of the variants from whole genome sequencing, and the overall impact of rare regulatory variation on human disease is unknown. We will investigate the use of personal RNA-seq and other functional data to complement whole genome sequence (WGS) in the evaluation of rare variant impact, personal genome interpretation for rare disease patients, and incorporation of rare variants into population studies and genetic risk scores. 2) Second, we will consider common disease variants that are not characterized by current eQTL studies, which almost all use static, adult tissue samples and bulk RNA-seq data. Genetic effects on gene expression are not static, but rather vary over time, cell type, and environment, complicating the identification of disease mechanism. Some disease loci may have only transient effects on a proximal gene’s expression during development, for example. We will study temporally dynamic and context-specific genetic effects. In a novel study, we will evaluate genetic effects on individual cell types and states during cellular differentiation using time-series single-cell RNA-seq across individuals. We will also evaluate dynamic genetic effects during disease progression based on patient longitudinal data. Combined with novel statistical methods, these will provide a map of genetic effects over cell-type, time, and context that may better explain disease loci. All data, methods, and software will be made publicly available. Our work will provide a greater understanding of regulatory genetic effects for both common and rare variants, enabling improved identification of the mechanisms underlying heritable disease.