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次要等位基因频率（MAF）低于1%，其中多个基因突变的频率低于1%。 仅观察一次（即，“singletons”）。虽然孟德尔疾病主要由罕见的等位基因引起， 罕见变异体对复杂表型的累积作用仍在激烈辩论中。在我们最近 工作中，我们证明了超稀有变体（MAF<0.01%）对遗传做出了重大贡献 人类转录调控的结构（遗传变异和复杂的 疾病）1，低频变异构成了几个复杂性状的遗传力的近一半（在 平均值）2.在这项研究中，我们将从功能上验证UltraAre变异体在人类基因中的作用， 使用大规模平行报告基因测定（MPRAs）进行表达。MPRA彻底改变了 可以测定增强子的活性。我们将利用MPRA功能验证我们的发现， 超稀有变体支配人类基因表达的遗传结构。我们将利用来自 这项技术将推动遗传变异数据的统计和生物信息学改进， 分析了然后，我们将扩大我们的分析，以量化基因表达的遗传结构， 组织中人体内的所有组织都来自基本相同的DNA，但表现出明显不同的 基因表达模式。我们将扩展我们的Haseman-Elston（HE）回归建模方法 多性状基因表达的遗传结构，以揭示跨组织和组织特异性 使用来自GTEx项目5的WGS和多组织RNA测序数据的遗传效应。最后我们将 通过描述特定人群的遗传特征，改善基于基因组的精准医学工作， 复杂特征的结构。每一个人类群体都经历了不同的进化历史， 最近的过去（不同的病原体，对生殖生长的不同限制等）。因此，每个人口 有不同的遗传变异分布。因此，不同的人群可能有不同的 复杂性状的遗传结构。此外，许多未充分研究的人群是混合的（与祖先 来自多个群体）。我们将把我们的HE回归方法扩展到共享模型， 群体特异性遗传效应使用来自多个群体的> 14万个样本， 基因组测序数据和来自TOPMed项目6的复杂性状数据。

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.