Structural Variation and Hematological Traits

结构变异和血液学特征

基本信息

批准号：
10657020
负责人：
Daniel Evan Bauer
金额：
$ 76.56万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10657020
关键词：
3-Dimensional Algorithms Area Atlases Biological Biological Models Biology Blood Blood Cells Blood Platelets Blood coagulation Bone Marrow Cells Cardiovascular Diseases Cardiovascular system Cells Cellular biology Chromatin Circulation Clinical Clinical Trials Design Collaborations Collection Complex Computer software DNA Sequence Data Data Set Disease Disease Outcome Ensure Erythrocytes Experimental Designs Genes Genetic Genetic study Genome Genomic Segment Genomics Genotype Heart Diseases Hematological Disease Hematology Hematopoiesis Hematopoietic Hemostatic function Heritability Human Human Biology Immune response Individual Differences Inflammation Leadership Leukocytes Lung Lung diseases Measures Methodology Methods Modeling Molecular Multiomic Data National Heart, Lung, and Blood Institute National Human Genome Research Institute Outcome Participant Pathogenesis Phase Phenotype Play Population Regulation Repetitive Sequence Research Research Personnel Resources Role Sample Size Sampling Sleep Source Structure Technology Testing Thrombosis Time Trans-Omics for Precision Medicine Variant alpha Globin biobank candidate validation clinical diagnosis clinically relevant cohort database of Genotypes and Phenotypes epidemiology study epigenome editing functional genomics genetic architecture genetic association genome editing genome sequencing genome wide association study genome-wide genome-wide analysis genomic variation improved insight interdisciplinary approach multi-ethnic novel oxygen transport precision medicine programs stem cells trait transcriptome sequencing translational genetics treatment response venous thromboembolism whole genome working group

项目摘要

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.

红细胞、白细胞和血小板对于内在血细胞和血小板的临床诊断很重要。造血系统疾病，也可作为各种心脏、肺和血液疾病结果的预测因子。血液数量性状具有高度遗传性，可作为研究遗传性状的模型系统。复杂性状的结构，同时在理解血液学性状的遗传基础方面取得了重大进展。在过去的十年里，新兴的全基因组测序（WGS）数据的财富 NHLBI 精准医学跨组学 (TOPMed) 计划等资源提供了前所未有的机会，可以进一步了解几个关键领域，包括结构变异的作用 (SV) 虽然已知一些常见的 SV（例如 α-珠蛋白）与血细胞特征相关，但更多的 SV 与血细胞特征相关。需要对大样本中的 SV 进行系统且不可知的全基因组搜索，以识别新的生物学特性。从 NHLBI TOPMed 和 NHGRI 共同中心集中获取深度测序的 DNA 疾病基因组学 (CCDG) 计划以及来自英国生物银行和其他队列的全基因组数据使得通过提高全基因组 SV 的准确性，在群体范围内全面表征全基因组 SV。调用我们新的 Genvisis 软件包中实施的 WGS 数据并验证候选因果关系 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将遗传关联发现转化为机制见解，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在目标 1 中，我们将使用 TOPMed 和 CCG 参与者的 WGS 数据应用新的方法。与当前适用于两者的 SV 调用算法相比，可以生成高质量且更准确的 SV 调用在目标 2 中，我们将使用新生成的 SV 调用进行 WGS 和现有阵列数据。基于基因的 SV 与血细胞特征和相关临床结果的分段关联分析 570,319 名参与者的协会调查结果将被复制到多达 760,000 名人群/研究参与者中。与血细胞特征显着相关的 SV 将随后被用于发现阶段。测试与其他血液疾病的关联，包括不确定潜力的克隆造血 (CHIP) 在目标 3 中，将在选定的样本中进行靶向长程测序以精确定位。我们还将在复杂的基因组区域中进行新发现的与血液性状相关的 SV。对复制的血细胞特征-SV 关联进行注释，然后采用最先进的基因编辑方法了解造血遗传调控的新机制。该模型综合方法。推进心脏、肺和血液疾病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，有助于更好地理解造血并为血液疾病精准医学的新研究铺平道路。