Bayesian genetic association analysis of all rare diseases in the Kids First cohort

Kids First 队列中所有罕见疾病的贝叶斯遗传关联分析

• 批准号: 10643463
• 负责人: Ernest Turro
• 金额: $ 16.9万
• 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: 2023-03-21 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10643463
• 关键词: Affect; Authorization documentation; Bioinformatics; Bite; Child; Clinical Data; Collaborations; Collection; Complex; Computing Methodologies; Congenital Abnormality; Congenital Disorders; Credentialing; Data; Data Set; Databases; Discipline; Disease; Etiology; Foundations; Gene Frequency; Genes; Genetic; Genetic Predisposition to Disease; Genome; Genotype; Hereditary Disease; Heterozygote; Human; Individual; Malignant Childhood Neoplasm; Methods; Minor; Molecular; Natural Selections; Nature; Ontology; Participant; Pathogenicity; Patients; Pediatric Cardiac Genomics Consortium; Persons; Phenotype; Probability; Rare Diseases; Research; Resources; Sampling; Side; Statistical Data Interpretation; Statistical Methods; Transcript; Validation; Variant; Work; authority; clinical phenotype; cohort; data access; database schema; experience; forging; genetic analysis; genetic association; genetic pedigree; genetic variant; genome sequencing; genome wide association study; indexing; insertion/deletion mutation; light weight; novel; portability; proband; programs; rare variant; relational database; web app; web interface; whole genome

Rare diseases affect 1 in 20 people, but fewer than half of the ⇠10,000 catalogued rare diseases have a re- solved genetic etiology. Genetic association analyses of whole-genome sequencing (WGS) data from large, phenotypically diverse collections of rare disease patients enhance the discovery of novel etiologies, compared to within-study analyses, by increasing the probability of multiple cases sharing a genetic etiology and by boost- ing the number of controls (Turro et al., Nature 2020). The Gabriella Miller Kids First (KF) program has germline WGS data from 20 studies on 18,547 probands or relatives of probands with a birth defect or pediatric cancer. However, due to the bioinformatic and statistical challenges of analyzing such large and complex WGS datasets, a comprehensive cross-cutting genetic association analysis has never been performed. We present a research program of computational and statistical approaches to uncover novel germline etiologies of rare diseases in KF and replicate them in other cohorts to which we have access. In Aim 1, we will build a compact and portable relational database containing a sparse representation of all the rare variant genotypes in the KF WGS data. Due to natural selection, almost all pathogenic variants responsible for rare congenital or hereditary disorders are rare and will thus be included. We will annotate the variants with scores reflecting their predicted deleteriousness and their minor allele frequencies, and with their predicted molecular consequences. We will load sample-specific information into the database, including pedigree membership, membership of a maximal set of unrelated partic- ipants (MSUP) and group memberships for case/control association analyses. In Aim 2, we will develop a web application allowing authenticated users to browse variants by gene or sample. The web interface will allow users to click on sample IDs directly in a table of genotypes to view the phenotypes of individuals who are heterozy- gous, homozygous or compound heterozygous for a given consequence class of rare variants in a side panel. The application will also host and display the results of inference, such as posterior probabilities of association (PPAs), posterior probabilities over the mode of inheritance, posterior probabilities over the consequence class of pathogenic variants and posterior probabilities of the pathogenicities of variants. The application will be accessi- ble by authorized collaborating experts across disciplines. In Aim 3, we will obtain a PPA between each gene and each of a collection of case sets in KF in accordance with each study's data restrictions, if any. We will determine the case sets using Mondo Disease Ontology and Human Phenotype Ontology terms assigned to cases. We will select probands in a given case set using pedigree information and compare them to participants not in the case set who are in other pedigrees and in the MSUP. We will attempt to replicate findings with a PPA >0.95 in our previously deployed databases encompassing >100,000 individuals and using GeneMatcher. The deployment of powerful, lightweight and portable analytical frameworks across different patient collections, promises to advance etiological discovery and replication of the remaining unknown causes of congenital disorders.

罕见病影响20人中的1人，但在10,000种罕见病中，只有不到一半的人有重新诊断的机会。 解决了遗传病因。对来自大规模， 表型多样的罕见病患者集合促进了新病因的发现， 研究内分析，通过增加多个病例共享遗传病因的可能性和加强- 计算对照的数量（Turro等人，Nature 2020）。加布里埃拉米勒儿童第一（KF）计划有生殖细胞 WGS数据来自20项研究，涉及18，547名患有出生缺陷或儿科癌症的先证者或先证者亲属。 然而，由于分析如此庞大而复杂的WGS数据集的生物信息学和统计学挑战， 从未进行过全面的交叉遗传关联分析。我们提出了一项研究 在KF中发现罕见疾病的新生殖系病因的计算和统计方法计划 并在我们能接触到的其他群体中复制它们。在目标1中，我们将建立一个紧凑和便携式 包含KF WGS数据中所有罕见变异基因型的稀疏表示的关系数据库。由于 由于自然选择，几乎所有导致罕见先天性或遗传性疾病的致病变异都是罕见的。 因此将被包括在内。我们将用反映其预测准确性的分数来注释变体， 他们的次要等位基因频率，以及他们预测的分子后果。我们将加载特定样本 数据库中的信息，包括系谱成员，成员的最大一组无关的部分， 用于病例/对照关联分析的受试者（MPEG4）和组成员。在目标2中，我们将开发一个Web 应用程序允许认证用户浏览基因或样本的变异。Web界面将允许用户 直接点击基因型表中的样本ID，查看杂合子个体的表型- 对于侧图中的罕见变体的给定结果类别，纯合子或复合杂合子。 该应用程序还将托管和显示推理结果，例如关联的后验概率 （PPA），遗传模式的后验概率， 致病性变体和变体致病性的后验概率。该应用程序将访问- 由跨学科的授权合作专家完成。在目标3中，我们将获得每个基因之间的PPA， 根据每个研究的数据限制（如有），在KF中收集每个病例集。我们将确定 使用分配给病例的Mondo疾病本体和人类表型本体术语的病例集。我们将 使用系谱信息选择给定病例集中的先证者，并将其与不在该病例中的参与者进行比较 在其他地方，在其他地方，在其他地方。我们将尝试复制PPA >0.95的结果， 以前部署的数据库包含> 100，000个个体并使用GeneMatcher。部署 强大、轻便、可移植的分析框架，可用于不同的患者集合， 发现和复制先天性疾病的其余未知原因的病因。