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疾病的影响。 基因表达。绝大多数疾病风险基因是非编码的，所以为了将它们与靶基因联系起来 基因、细胞途径和细胞类型，我们试图确定它们干扰和破坏了哪些基因的表达。 什么条件。对基因表达的群体研究现已提供了数千种“表达 数量性状基因座“(EQTL)，其中单个遗传变异与目标的表达有关 吉恩。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研究根本没有解决稀有问题 遗传变异，因此排除了对每个个体整个基因组数万个变异的评估 序列，以及许多已知的致病基因座。其次，即使在常见的变种中，据估计 即使基于当前的多组织数据，超过一半的疾病基因座与任何已知的eQTL都不一致。这个 其余的疾病位点和罕见的变异需要新的数据和统计方法来表征 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 频谱上的遗传变异。1)首先，我们将对罕见的遗传变异进行分析 以及用于个人全基因组解释的统计方法。目前的方法根本不能提供 对来自全基因组测序的大多数变异的信心预测以及总体影响 人类疾病的罕见调控变异是未知的。我们将调查个人RNA-seq的使用情况 和其他功能数据，以补充全基因组序列(WGS)在评估罕见变异 影响，对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，我们将考虑以下常见疾病变体 不具有当前eQTL研究的特征，这些研究几乎都使用静态的、成体的组织样本和散装的 RNA序列数据。基因表达的遗传效应不是一成不变的，而是随着时间、细胞类型和 环境，使致病机制的鉴定复杂化。一些疾病部位可能只有 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 动态的和特定于环境的遗传效应。在一项新的研究中，我们将评估基因对个体的影响 使用跨个体时间序列单细胞rna-seq研究细胞分化过程中的细胞类型和状态。 我们还将根据患者的纵向情况评估疾病进展过程中的动态遗传效应 数据。与新的统计方法相结合，这些将提供细胞类型的遗传效应图谱， 可能更好地解释疾病部位的时间和背景。所有数据、方法和软件都将公开发布 可用。我们的工作将为我们更好地理解常见的和 罕见的变异，使人们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.