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

全基因组测序（WGS）正在彻底改变罕见疾病的诊断。不过，目前，即使 最有力的病因学发现方法通常无法在大多数患者中找到遗传原因， 裤子（Turro等人，Nature 2020）。这有几个原因。首先，罕见病研究通常是 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，这限制了统计学 当只有WGS和临床表型数据可用于受试者时的功效。第二，不明原因 变异体可能具有难以通过计算预测的分子后果，例如破坏 基因的调控元件（RE）或隐蔽剪接位点的引入。第三，一些因果关系 突变，例如结构变体，易于被WGS遗漏。系统性转录组学分析 从罕见疾病患者中获得的同质细胞群具有克服这些限制的潜力。 我们收集了1,000名患有WGS的综合表型罕见疾病研究参与者 和血小板、嗜中性粒细胞、单核细胞和CD 4 + T细胞的RNA-seq。在这里，我们提出了一个研究计划， 统计、计算和实验方法来揭示罕见疾病的新病因， 这些数据的高维性和层次性。我们将集中在病因下- 利用我们在血液基因组学方面的专业知识，对1300例罕见血小板疾病（RPD）患者进行了研究。目标1： 将开发一种贝叶斯方法，用于识别RE中罕见的致病罕见变异，将表达视为 遗传病因学的分子介质。我们的方法模拟了重叠的罕见变异之间的因果路径 细胞类型特异性RE，相应的细胞类型特异性表达变化，以及随之而来的改变 罕见疾病风险。包括最近发现的一类由H3 K122 ac而不是H3 K27 ac标记的增强子 在我们的假设搜索空间中，我们将生成来自健康供体的相关细胞类型的H3 K122 ac数据。 在目标2中，我们将应用几种方法来识别转录物序列中的致病性变化。用于 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。确保 这些异常值在更广泛的群体中是极端的，我们将计算大RNA序列中的剪接频谱。 例如GTEx。这些光谱将捕获个体内比例的群体分布 包括给定剪接点的基因的RNA-seq读段。我们还将利用WGS的联合可用性 和RNA-seq在患者中鉴定在WGS称为杂合子位点的极端等位基因不平衡。候选 我们鉴定的变体将在细胞系和原始样品中进行验证。罕见病集体影响一个 但目前的病因学知识不能单独通过WGS解决一半的患者。建模与 分析作为疾病分子介质的多种细胞类型的大规模患者来源的RNA-seq数据 风险可以填补这一空白。我们研究项目的方法学和病因学成果将最终促进 提高WGS的诊断能力，拓宽精准医疗的范围。

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.