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.

罕见疾病每20人中就有1人受到影响，但在⇠列出的10,000种罕见疾病中，只有不到一半的人 解决了遗传病因学。全基因组测序(WGS)数据的遗传关联分析 与之相比，罕见疾病患者的表型多样化集合促进了新病因的发现 为了进行研究内分析，通过增加多个病例共享同一基因病因的可能性，并通过促进- 控制措施的数量(Turro等人，《自然》，2020年)。加布里埃拉·米勒儿童优先(KF)计划有生殖系 WGS的数据来自20项研究，这些研究涉及18,547名先证者或先证者的亲属，他们患有出生缺陷或儿童癌症。 然而，由于分析如此庞大和复杂的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 计算和统计方法程序以发现KF罕见疾病的新种系病因 并将它们复制到我们可以接触到的其他队列中。在目标1中，我们将构建一个紧凑和便携的 关系数据库，包含KF WGS数据中所有稀有变异基因类型的稀疏表示。到期 对于自然选择来说，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fl分数来注释这些变体，以预测它们的危害性 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Sample-Speific 输入数据库的信息，包括谱系成员、最大无关部分集的成员- 用于病例/对照关联分析的IPANTS(MSUP)和组成员身份。在目标2中，我们将开发一个网络 允许经过身份验证的用户根据基因或样本浏览变体的应用程序。网络界面将允许用户 要直接单击遗传型表中的样本ID以查看异型个体的表型- Gous，纯合子或复合杂合子，在侧板中的稀有变异的给定结果类别。 该应用程序还将托管并显示推断结果，例如关联的后验概率 (PPAS)，遗传模式上的后验概率，结果类上的后验概率 致病变种和变异体致病的后验概率。该应用程序将被访问- 可由跨学科的授权协作专家执行。在目标3中，我们将获得每个基因和 KF中的每个病例集集合都符合每个研究的数据限制(如果有的话)。我们将决定 该案例集使用了蒙多病本体和人类表型本体术语分配给案例。我们会 使用谱系信息选择给定案例集中的先证者，并将他们与不在该案例中的参与者进行比较 设置哪些人在其他系谱和MSUP中。我们将尝试使用PPA&>0.95在我们的 以前部署的数据库包含100,000个人，并使用GeneMatcher。部署 跨不同患者集合的强大、轻量级和可移植的分析框架，有望推动 先天性疾病的其余未知原因的病因学发现和复制。