Integrative analysis of whole genomes and transcriptomes from multiple cell types in rare disease patients

罕见病患者多种细胞类型的全基因组和转录组的综合分析

• 批准号: 10587683
• 负责人: Ernest Turro
• 金额: $ 66.89万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10587683
• 关键词: Address; Affect; Allelic Imbalance; Bayesian Method; Biological Assay; Biological Models; Blood; Blood Cells; Blood Platelet Disorders; Blood Platelets; CD4 Positive T Lymphocytes; Catalogs; Cell Line; Cell model; Cells; Classification; Clinical Data; Collection; DNA Binding; Data; Data Set; Defect; Diagnosis; Disease; Enhancers; Ensure; Etiology; Event; Exhibits; Exons; Frequencies; General Population; Genes; Genetic; Genetic Predisposition to Disease; Genomics; Genotype-Tissue Expression Project; Hematological Disease; Heterozygote; Individual; Joints; Knowledge; Logistics; Measures; Mediator; Megakaryocytes; Methodology; Methods; Methylation; Modeling; Molecular; Molecular Conformation; Mutation; Nature; Nonsense-Mediated Decay; Output; Participant; Pathogenicity; Pathologic; Pathology; Pathway interactions; Patients; Pattern; Persons; Phenotype; Plasmids; Population; Population Distributions; Probability; RNA Sequences; RNA Splicing; RNA Stability; RNA Transport; Rare Diseases; Regulatory Element; Reporter; Reporting; Research; Reverse Transcriptase Polymerase Chain Reaction; Sampling; Site; Statistical Methods; Tissues; Transcript; Untranslated RNA; Validation; Variant; Work; causal variant; cell type; clinical phenotype; cohort; detection method; diagnostic value; disorder risk; experience; genetic disorder diagnosis; genetic testing; genome editing; genome sequencing; high dimensionality; machine learning prediction; monocyte; neutrophil; novel; participant enrollment; phenotypic data; precision medicine; progenitor; programs; rare genetic disorder; rare variant; response; transcriptome; transcriptome sequencing; transcriptomic profiling; whole genome

Whole-genome sequencing (WGS) is revolutionizing the diagnosis of rare diseases. However, at present, even the most powerful approaches to etiological discovery typically fail to find a genetic cause in a majority of partici- pants (Turro et al., Nature 2020). There are a number of reasons for this. Firstly, rare disease studies are typically composed of small sets of unresolved cases, each sharing a different genetic etiology, which constrains statistical power when only WGS and clinical phenotype data are available on participants. Secondly, the unknown causal variants may have molecular consequences that are challenging to predict computationally, such as disruptions to the regulatory elements (REs) of a gene or the introduction of a cryptic splice site. Thirdly, some types of causal mutations, such as structural variants, are prone to being missed by WGS. Systematic, transcriptomic profiling of homogeneous cell populations taken from rare disease patients has the potential to overcome these limitations. We have access to a collection of ⇠1,000 comprehensively phenotyped rare disease study participants with WGS and RNA-seq of platelets, neutrophils, monocytes and CD4+ T-cells. Here, we present a research program of statistical, computational and experimental approaches to uncover novel etiologies of rare diseases that exploits the high dimensionality and the hierarchical nature of these data. We will concentrate on the etiologies under- lying ⇠300 cases with a rare platelet disorder (RPD), exploiting our expertise in blood genomics. In Aim 1, we will develop a Bayesian method for identifying rare disease-causing rare variants in REs, treating expression as a molecular mediator of genetic etiology. Our approach models the causal path between rare variants that overlap cell type-specific REs, the corresponding cell type-specific changes in expression, and the consequent alteration in rare disease risk. To include a recently discovered class of enhancer marked by H3K122ac but not H3K27ac in our hypothesis search space, we will generate H3K122ac data on the relevant cell types from healthy donors. In Aim 2, we will apply several approaches for identifying pathogenic changes in transcript sequences. For ex- ample, we will apply recently developed methodology for identifying splicing outliers within the cohort. To ensure these outliers are extreme in the wider population, we will compute splicing frequency spectra in large RNA-seq datasets such as GTEx. These spectra will capture the population distribution of the within-individual proportion of RNA-seq reads for a gene that include a given splice junction. We will also exploit the joint availability of WGS and RNA-seq in patients to identify extreme allelic imbalances at WGS-called heterozygote sites. The candidate variants that we identify will be validated in cell lines and primary samples. Rare diseases collectively affect one in 20 people but current etiological knowledge cannot resolve half of patients by WGS alone. The modeling and analysis of large-scale, patient-derived RNA-seq data on multiple cell types as molecular mediators of disease risk can fill this gap. The methodological and etiological output of our research program will ultimately boost the diagnostic power of WGS and broaden the scope of precision medicine.

全基因组测序(WGS)正在给罕见疾病的诊断带来革命性的变化。不过，目前甚至连 最有效的病因学发现方法通常不能在大多数情况下fi和遗传原因。 裤子(Turro等人，《自然》2020)。造成这种情况的原因有很多。首先，罕见疾病的研究通常是 由一小组未解决的病例组成，每个病例都有不同的遗传病因，这限制了统计 只有在参与者的WGS和临床表型数据可用时才有效。第二，原因不明 变异体可能会产生难以在计算上预测的分子后果，例如干扰 基因的调控元件(RE)或隐秘剪接位点的引入。第三，一些类型的因果关系 突变，如结构变异，很容易被WGS遗漏。系统的，转录切割的ProfiLing 从罕见疾病患者身上提取的同质细胞群体有可能克服这些限制。 我们可以访问⇠1,000名患有WGS的综合表型罕见疾病研究参与者的集合 血小板、中性粒细胞、单核细胞和CD4+T细胞的RNA序列。在这里，我们提出了一个研究计划， 利用统计、计算和实验方法揭示罕见疾病的新病因 这些数据的高维性和层次性。我们会集中研究以下各项的病因： 利用我们在血液基因组学方面的专业知识，躺着⇠300例患有罕见的血小板紊乱症的患者。在目标1中，我们 将开发一种贝叶斯方法来识别RES中罕见的致病罕见变异，将表达视为 遗传病因学的分子介体。我们的方法模拟了重叠的罕见变异之间的因果路径 细胞类型--特定于fic-res，相应的细胞类型--特定于fic的表达变化，以及由此产生的改变。 有罕见疾病的风险。包括最近发现的一类由H3K122ac标记而不是H3K27ac标记的增强剂 在我们的假设搜索空间中，我们将生成来自健康捐赠者的相关细胞类型的H3K122ac数据。 在目标2中，我们将应用几种方法来识别转录本序列中的致病变化。对于前- 例如，我们将应用最近开发的方法来识别队列中的剪接异常值。为了确保 这些离群值在更广泛的人群中是极端的，我们将在大的RNA-SEQ中计算剪接频谱 数据集，如GTEx。这些光谱将捕捉到个体内比例的种群分布 RNA-seq的读数为包含给定剪接连接的基因。我们还将利用WGS的联合可用性 和患者的RNA-seq，以确定WGS杂合子位置的极端等位基因不平衡。候选人 我们确定的变异体将在细胞系和初级样本中得到验证。罕见疾病共同影响一个人 但目前的病因学知识不能仅通过WGS解决一半的患者。建模和 关于多种细胞类型作为疾病分子介质的大规模、患者来源的RNA-SEQ数据的分析 风险可以弥补这一差距。fi。我们研究项目的方法论和病因学成果最终将促进 WGS的诊断能力，拓宽了精准医学的范围。