Rarely Common: Uncovering the dominant role of rare variants in the genetic architecture of complex human traits.

摘要： 绝大多数人类突变的微小等位基因频率(MAF)低于1%，而大多数 只观察到一次(即“单例”)。虽然孟德尔病主要是由罕见的等位基因引起的， 稀有变异对复杂表型的累积贡献仍然存在激烈的争论。在我们最近的 工作中，我们证明了超稀有变种(MAF&lt；0.01%)对遗传有很大贡献。 人类转录调控的结构(介于基因变异和复杂性之间的中间体 疾病)1，低频变异构成了几个复杂性状遗传力的近一半(ON 在这项研究中，我们将从功能上验证超强变异在人类基因中所起的作用 使用大规模平行报告分析(MPRA)进行表达。MPRA已经彻底改变了这种方式 可以检测增强剂的活性。我们将利用MPRA从功能上验证我们的发现 超强变异主导着人类基因表达的遗传结构。我们将利用来自 这项技术旨在推动统计和生物信息学方面的改进，使遗传变异数据 分析过了。然后，我们将扩展我们的分析，以量化基因表达的遗传结构 纸巾。人体内的所有组织基本上都来自相同的DNA，但表现出显著的不同 基因表达模式。我们将扩展我们的Haseman-Elston(HE)回归方法进行建模 揭示跨组织和组织特异性的多个性状的基因表达的遗传结构 使用WGS和来自GTEx项目的多组织RNA测序数据的遗传效应5。最后，我们会 通过表征人群特有的遗传特征，改善基于基因组的精准医疗工作 具有复杂特征的建筑。每个人类群体都经历了不同的进化史 最近的过去(不同的病原体，生殖生长的不同限制，等等)。因此，每个人口 有不同的遗传变异分布。因此，不同的人群可能会有不同的 复杂性状的遗传结构。此外，许多未被研究的种群(与祖先)混杂在一起 来自多个种群)。我们将扩展我们的HE回归方法来模拟共享和 使用来自多个种群的14万个样本的群体特异性遗传效应 来自TOPMed项目的基因组测序数据和复杂性状数据6。

ABSTRACT: The vast majority of human mutations have minor allele frequencies (MAF) under 1%, with the plurality observed only once (i.e., “singletons”). While Mendelian diseases are predominantly caused by rare alleles, the cumulative contribution of rare variants to complex phenotypes remains hotly debated. In our recent work, we demonstrated that ultrarare variants (MAF<0.01%) make a substantial contribution to the genetic architecture of human transcriptional regulation (an intermediate between genetic variation and complex disease)1, and low frequency variants constitute nearly half the heritability of several complex traits (on average)2. In this study, we will functionally validate the role that ultrarare variants play in human gene expression using massively parallel reporter assays (MPRAs). MPRA have revolutionized the way enhancers can be assayed for activity. We will utilize MPRAs to functionally validate our finding that ultrarare variants dominate the genetic architecture of human gene expression. We will use insights from this technology to drive statistical and bioinformatic improvements in the way genetic variation data are analyzed. We will then expand our analysis to quantify the genetic architecture of gene expression across tissues. All tissues in the human body derive from essentially the same DNA but exhibit remarkably different patterns of gene expression. We will extend our Haseman-Elston (HE) regression approach for modeling the genetic architecture of gene expression to multiple traits to uncover cross-tissue and tissue-specific genetic effects using WGS and multi-tissue RNA-sequencing data from the GTEx project5. Finally, we will improve genomic-based precision medicine efforts for all by characterizing the population-specific genetic architecture of complex traits. Every human population has experienced a different evolutionary history in the recent past (different pathogens, different limits on reproductive growth, etc). Each population therefore has a different distribution of genetic variation. As a consequence, different populations likely have different genetic architectures for complex traits. Further, many understudied populations are admixed (with ancestry deriving from multiple populations). We will extend our HE regression approach to model shared and population-specific genetic effects using >140 thousand samples from multiple populations with whole genome sequencing data and complex trait data from the TOPMed Project6.