Structural Variation and Hematological Traits

红细胞、白色细胞和血小板对于内在血细胞和血小板的临床诊断是重要的。 造血系统疾病，并且还作为各种心脏、肺和血液疾病结果的预测因子。此外，委员会认为， 血液数量性状是高度遗传的，并作为研究遗传的模型系统。 复杂特征的结构。虽然在理解血液学特征的遗传基础方面取得了重大进展， 在过去的十年中，全基因组测序（WGS）数据的财富不断涌现， 诸如NHLBI精准医学跨组学（TOPMed）计划等资源提供了一个 前所未有的机会，可以在几个关键领域获得进一步的见解，包括结构变体的作用 （SV）。而一些常见的SV（例如，α-珠蛋白）与血细胞性状有关， 需要在大样本中对SV进行系统的和不可知的全基因组搜索以鉴定新的生物学。的 从NHLBI TOPMed和NHGRI共同中心集中获得深度测序的DNA 疾病基因组学（CCDG）项目，沿着来自英国生物库和其他队列的全基因组数据， 在群体规模上对SV全基因组进行全面表征。通过提高全基因组SV的准确性 调用我们新的Genvisis软件包中实现的WGS数据，并通过验证候选因果关系， SV在造血细胞中使用最先进的基因编辑技术，我们的跨学科方法将 促进遗传关联发现转化为机械见解，发现新的生物学基础 造血，并最终确定导致病理生物学或反应个体差异的因素 到治疗。在目标1中，使用来自TOPMed和CCDG参与者的WGS数据，我们将采用新的方法 产生高质量和更准确的SV调用比SV调用算法目前可用于两者 WGS和现有阵列数据。在目标2中，我们将使用新生成的SV调用来进行单变量， 基于基因的SV与血细胞性状和相关临床结局的节段性关联分析， 570 319人参加。将在人群/研究中的多达760，000名参与者中复制关联结果 在发现阶段不使用。与血细胞性状显著相关的SV随后将被 检测与其他血液疾病的相关性，包括不确定潜能的克隆性造血（CHIP） 和VTE。在目标3中，将在选定的样本中进行靶向远程测序，以精确定位 新发现的复杂基因组区域中的血液性状相关SV。我们还将进行功能基因组学研究， 复制的血细胞性状-SV关联的注释，随后是最先进的基因编辑方法， 了解造血遗传调控的新机制。这种模式的综合方法 推进心脏、肺和血液疾病的精准医学研究将首次展示 SV在血液学性状遗传结构中的作用，并有助于更好地理解 造血，并为血液疾病的精准医学新研究铺平道路。

Red blood cells, white blood cells, and platelets are important for the clinical diagnosis of intrinsic blood cell and hematopoietic disorders, and also as predictors of various heart, lung, and blood disease outcomes. Moreover, hematologic quantitative traits are highly heritable and serve as a model system for studying the genetic architecture of complex traits. While significant strides in understanding the genetic basis of hematological traits have been made over the past decade, the wealth of whole genome sequencing (WGS) data from emerging resources such as the NHLBI Trans-Omics for Precision Medicine (TOPMed) program provides an unprecedented opportunity to gain further insight in several key areas, including the role of structural variants (SVs). While a few common SVs (e.g., α-globin) are known to be associated with blood cell traits, a more systematic and agnostic genome-wide search for SVs in large samples is required to identify new biology. The centralized availability of deeply sequenced DNA from the NHLBI TOPMed and the NHGRI Centers for Common Disease Genomics (CCDG) programs, along with genome-wide data from UK Biobank and other cohorts, allows for full characterization of SVs genome-wide at population-scale. By improving the accuracy of genome-wide SV calling for WGS data as implemented in our new Genvisis software package and by validating candidate causal SVs using state-of-the-art gene-editing technologies in hematopoietic cells, our interdisciplinary approach will facilitate the translation of genetic association findings into mechanistic insights, discover new biology underlying hematopoiesis, and ultimately identify factors that account for individual differences in pathobiology or response to treatments. In Aim 1, using WGS data from TOPMed and CCDG participants, we will apply novel methodology to generate high-quality and more accurate SV calls than the SV calling algorithms currently available for both WGS and existing array data. In Aim 2, we will use the newly generated SV calls to conduct single-variant and gene-based segmental association analyses of SVs with blood cell traits and related clinical outcomes in up to 570,319 participants. Association findings will be replicated in up to 760,000 participants in populations/studies not used in the discovery phase. SVs that are significantly associated with blood cell traits will subsequently be tested for association with other blood disorders including clonal hematopoiesis of indeterminate potential (CHIP) and VTE. In Aim 3, targeted long-range sequencing will be performed in selected samples to precisely localize newly identified blood trait-associated SVs in complex genomic regions. We will also perform functional genomic annotation of replicated blood cell trait-SV associations followed by state-of-the art gene-editing approaches to understand novel mechanisms underlying genetic regulation of hematopoiesis. This model integrative approach to advancing precision medicine research in heart, lung, and blood diseases will demonstrate for the first time the role of SVs in the genetic architecture of hematologic traits and contribute to a better understanding of hematopoiesis and pave the way for new research into Precision Medicine for blood diseases.