Systematic characterization of tandem repeat variants contributing to complex traits

摘要摘要 全基因组关联研究(GWAS)已经确定了数千个与 复杂的性状，但确定因果变异、目标基因和生物机制负责 事实证明，每一个信号都具有挑战性。此外，基于单核苷酸多态的标准GWA (SNPs)由于无法解释所研究的大多数性状的大部分遗传力以及无法解释 捕获多等位基因变异，如拷贝数变异(CNV)和未被SNP标记的重复。 我们专注于基因组重复区域的遗传变异所起的作用。具体来说，我们考虑 两种重复类型：短串联重复序列(STR)，由1-6bp的重复基序组成；以及可变数量 串联重复序列(VNTRs)，基序为7+bp。我们将STR和VNR统称为串联重复 (TRS)。TRS大约包含200万个基因座，占基因组的3%以上。它们表现出快速的 突变率高，是遗传变异的最大来源之一。越来越多的证据表明，TRs 这很可能是GWAs“遗漏遗传力”的一部分原因。然而，由于生物信息学和实验性的 在研究重复序列的挑战中，TRs在人类特征中的全基因组作用大多仍未被探索。 我们假设，tr变异是复杂性状的关键驱动因素。我们最近确认了数千人 被预测为因果调节基因表达的STR(称为表达STR，或eSTR)，并揭示 ESTR可能通过多种机制发挥作用，包括调节核小体定位和DNA 或RNA二级结构。此外，我们还确定了特定的eSTR，可能是已发表的Gwas信号的基础 治疗身高和精神分裂症。此外，其他研究小组最近也发现，TRs是导致 包括疟疾抗药性、癌症风险和双相情感障碍在内的复杂特征。 虽然这些发现提供了耐人寻味的证据，表明数以千计的TR对人类表型有贡献， 它们有几个限制。这些包括：从下一步可以准确地进行基因分型的RR的范围- 世代测序(NGS)；缺乏足够大的NGS数据集来执行关联 分析；对受体参与基因调控的潜在机制的了解有限。 在这里，我们利用(I)我们最近开发的TR基因分型工具和(Ii)我们发布的单倍型面板 将TRs注入到现有的SNP阵列数据集中，系统地评估TRs对基因的贡献 人类的规则和复杂的特征。我们将首先生成与以下内容相关联的RR的全面目录 基因调控(目标1)，并建立使用大规模平行报告验证TR效应的框架 分析和基因组编辑(目标2)。然后，我们将向现有的大型GWA投入200多万个RR 数据集并执行精细映射，以识别与一系列复杂性状相关联的TRs，并深入 描述几个被预测为GWAS信号的因果驱动因素的TRs(目标3)。这个项目将填补一个 在我们对复杂性状的遗传结构的知识方面存在着重要的差距。

SUMMARY ABSTRACT Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with complex traits, but determining the causal variants, target genes, and biological mechanisms responsible for each signal has proven challenging. Furthermore, standard GWAS based on single nucleotide polymorphisms (SNPs) have been limited by failure to explain the majority of heritability for most traits studied and an inability to capture multi-allelic variants such as copy number variants (CNVs) and repeats not tagged by SNPs. We focus on the role of genetic variation at repetitive regions of the genome. Specifically, we consider two repeat types: short tandem repeats (STRs), consisting of repeated motifs of 1-6bp; and variable number tandem repeats (VNTRs), with motifs of 7+bp. We collectively refer to STRs and VNTRs as tandem repeats (TRs). TRs encompass approximately 2 million loci comprising over 3% of the genome. They exhibit rapid mutation rates and are one of the largest sources of genetic variation. Growing evidence suggests that TRs are likely to account for part of the “missing heritability” of GWAS. However, due to bioinformatic and experimental challenges in studying repeats, the genome-wide role of TRs in human traits remains mostly unexplored. We hypothesize that TR variants are key drivers of complex traits. We recently identified thousands of STRs predicted to causally regulate gene expression (termed expression STRs, or eSTRs) and revealed that eSTRs potentially act through a variety of mechanisms including modulating nucleosome positioning and DNA or RNA secondary structure. We additionally identified specific eSTRs likely underlying published GWAS signals for height and schizophrenia. Furthermore, other groups have recently discovered TRs as causal drivers of complex traits including malaria resistance, cancer risk, and bipolar disorder. While these findings offer intriguing evidence that thousands of TRs contribute to human phenotypes, they have several limitations. These include: the range of TRs that can be accurately genotyped from next- generation sequencing (NGS); a lack of sufficiently large NGS datasets for most traits for performing association analyses; and limited understanding of the potential mechanisms by which TRs participate in gene regulation. Here, we leverage (i) our recently developed TR genotyping tools and (ii) our published haplotype panel allowing imputation of TRs into available SNP-array datasets, to systematically evaluate the contribution of TRs to gene regulation and complex traits in humans. We will first generate a comprehensive catalog of TRs associated with gene regulation (Aim 1) and establish a framework for validating TR effects using massively parallel reporter assays and genome editing (Aim 2). We will then impute more than 2 million TRs into large existing GWAS datasets and perform fine-mapping to identify TRs associated with a range of complex traits and deeply characterize several TRs predicted to be causal drivers of GWAS signals (Aim 3). This project will fill an important gap in our knowledge of the genetic architecture of complex traits.