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.

每 20 人中就有 1 人患有罕见病，但在 10,000 种列入目录的罕见病中，只有不到一半的人患有罕见病。 解决了来自大型、全基因组测序（WGS）数据的遗传关联分析。 相比之下，表型多样化的罕见病患者集合增强了新病因的发现 进行研究内分析，通过增加多个病例共享遗传病因的概率以及通过增强 控制的数量（Turro 等人，Nature 2020）。 Gabriella Miller Kids First (KF) 计划具有种系。 来自 20 项研究的全基因组测序 (WGS) 数据，涉及 18,547 名先证者或患有出生缺陷或儿科癌症的先证者亲属。 然而，由于分析如此庞大且复杂的 WGS 数据集面临生物信息学和统计挑战， 我们从未进行过全面的跨领域遗传关联分析。 计算和统计方法程序，用于发现 KF 罕见疾病的新种系病因 并在我们可以访问的其他队列中复制它们 在目标 1 中，我们将构建一个紧凑且便携式的。 关系数据库，包含 KF WGS 数据中所有罕见变异基因型的稀疏表示。 根据自然选择，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，我们将用反映其预测有害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，我们将加载样本特异性。 信息进入数据库，包括谱系成员资格、最大一组不相关的成员的成员资格 用于病例/对照关联分析的 ipants (MSUP) 和小组成员资格 在目标 2 中，我们将开发一个网络。 应用程序允许经过身份验证的用户按基因或样本浏览变体。网络界面将允许用户。 直接单击基因型表中的样本 ID 可查看杂合个体的表型 侧图中稀有变异的给定结果类别的合子、纯合子或复合杂合子。 该应用程序还将托管并显示推理结果，例如关联的后验概率 （PPA），遗传模式的后验概率，后果类的后验概率 致病变异和变异致病性的后验概率将是可访问的。 在目标 3 中，我们将获得每个基因与跨学科授权合作专家之间的 PPA。 我们将根据每个研究的数据限制（如果有）确定 KF 中每个病例集的集合。 我们将使用 Mondo 疾病本体论和人类表型本体论术语分配给病例的病例集。 使用谱系信息选择给定病例组中的先证者，并将其与不在该病例组中的参与者进行比较 我们将尝试在我们的谱系中复制 PPA >0.95 的结果。 之前使用 GeneMatcher 部署的数据库超过 100,000 人。 跨不同患者集合的强大、轻量级和便携式分析框架，有望推进 先天性疾病的其余未知原因的病因学发现和复制。