Genomes in Eye Disease: Methods to Query Variants Across Multiple Genome-wide Dat

描述(由申请人提供)：眼病中的基因组：跨多个基因组范围数据集查询变异的方法。该应用程序响应NEI关于综合数据分析的RFA，有两个目标。我们的第一个目标是提供计算工具来支持集成的基因比较查询，这些查询利用来自多个独立的全基因组DNA测序研究的数据。我们的第二个目标是使用这些工具通过整合来自青光眼患者的300多个外显子和2500个外显子以及来自NIH资助的其他测序研究的基因组来发现与青光眼相关的基因。DNA替换、插入、缺失和重排的全基因组分析范围从测量已知的单核苷酸多态(SNP)到测序蛋白质编码外显子，再到测序整个基因组。许多由NEI和NIH资助的研究已经通过对数百或数千名患者的研究产生了不同的全基因组数据集。这些研究产生了原始数据和次要(衍生)数据。主要数据是来自患者DNA的高质量、未映射的读取。二级数据是将原始数据映射到参考人类基因组并进行替换、插入、缺失和重排后识别的变体。应用于辅助数据的查询在范围和准确性上受到用于生成主数据和派生辅助数据的方法的限制。当组合多个数据集时，查询辅助数据的限制变得更加明显。在原始派生方法不同的程度上，跨多个辅助数据集的查询有可能不完整或不准确，并可能返回错误的答案。我们将开发新的工具，解决查询二级数据的限制，使使用多个基因组范围的DNA测序数据集来计算关于基因疾病关联的查询的准确和有意义的答案成为可能。这些工具将创建一个框架，在该框架中，每个查询仅根据准确回答查询所需的主要数据来驱动变体的重新推导。我们将使用这些工具来询问与原发性开角型青光眼(POAG)研究相关的数据。这些工具将被应用于四个与青光眼相关的数据集：来自300名POAG患者的外显子组序列数据，来自大约5,000名POAG患者的珠阵列型数据，包括300名外显子组受试者，以及来自两个非眼病对照队列的外显子组序列数据，每个队列有超过1,000名受试者。一个对照队列将来自NIH Intral ClinSeq项目；另一个将来自NHLBI资助的心脏研究。这项工作将通过两个目标来完成。AIM 1将从2800多个外显子中建立一个连贯的、质量受控的参考数据集。AIM 2将建立工具，将外显子组(或基因组)数据集与AIM 1中内置的参考数据进行比较，以通过稀有变异发现和检查与POAG相关的基因。 公共卫生相关性：该项目寻求开发工具，使使用多个全基因组数据集来计算有关基因的查询的有意义、正确和准确的答案成为可能，重点关注与原发性开角型青光眼(POAG)研究相关的数据。POAG是美国最常见的青光眼形式，也是全球不可逆转失明和视力障碍的主要原因，影响40岁以上的美国人超过225万，每年导致约10万美国人和全球300万人失明。NEI和NIH资助的一些研究已经通过将这些分析应用于数百或数千名患有和没有眼病的患者而产生了不同的数据集；现在有机会一起使用不同的数据集来了解更多关于眼病的基因贡献。这项研究通过识别与疾病发病机制相关的基因及其变体，可能会对公众健康产生相当大的好处，从而为早期诊断、治疗和预防带来新的战略。

DESCRIPTION (provided by applicant): Genomes in Eye Disease: Methods to Query Variants across Multiple Genome-wide Datasets. This application, in response to the NEI's RFA on Integrative Data Analysis, has two goals. Our first goal is to provide computational tools to support integrated gene-comparison queries that draw upon data from multiple, independent genome-wide DNA sequencing studies. Our second goal is to use these tools to discover genes associated with glaucoma by integrating over 300 exomes from glaucoma patients with 2500 exomes and genomes from other NIH-funded sequencing studies. Genome-wide assays for DNA substitutions, insertions, deletions, and rearrangements range in scope from measuring known single nucleotide polymorphisms (SNP), to sequencing al protein coding exons, to sequencing entire genomes. A number of NEI- and NIH-funded studies have generated distinct genome-wide datasets through studies of hundreds, or thousands, of patients. These studies generate both primary and secondary (derived) data. Primary data are the high quality, unmapped reads from patient DNA. Secondary data are the variants identified after mapping primary data to a reference human genome and calling substitutions, insertions, deletions, and rearrangements. Queries applied to the secondary data are limited in scope and accuracy by the methods used to generate the primary data and to derive the secondary data. Limitations on querying secondary data become more pronounced when multiple datasets are combined. To the extent that the original derivation methods differed, queries across multiple secondary datasets risk being incomplete or inaccurate and can return false answers. We will develop new tools that will address the limitations on querying secondary data, making it possible to compute accurate and meaningful answers to queries about gene-disease associations using multiple genome-wide DNA sequencing datasets. These tools will create a framework where each query drives re-derivation of variants from just the primary data necessary to answer the query accurately. We will use the tools to interrogate data relevant to the study of primary open angle glaucoma (POAG). These tools will be applied to four datasets relevant to glaucoma: exome sequence data from 300 POAG patients, bead-array genotype data from ~5,000 POAG patients, including the 300 exome subjects, and exome sequence data from two non-eye disease control cohorts, each with over 1,000 subjects. One control cohort will be from the NIH Intramural ClinSeq project; the other will be from an NHLBI funded heart study. The work will be accomplished in two aims. Aim 1 will build a coherent, quality-controlled reference dataset from the 2,800+ exomes. Aim 2 will build tools to compare an exome (or genome) dataset against the reference built in Aim 1 to discover and examine genes associated with POAG through rare variants. PUBLIC HEALTH RELEVANCE: This project seeks to develop tools that will make it possible to compute meaningful, correct, and accurate answers to queries about genes using multiple genome-wide datasets, with a focus on data relevant to the study of primary open angle glaucoma (POAG), the most common form of glaucoma in the United States and a leading cause of irreversible blindness and visual impairment worldwide, affecting more than 2.25 million Americans over age 40, and causing blindness in ~100,000 Americans and 3 million people worldwide each year. A number of NEI- and NIH-funded studies have already generated distinct datasets by applying these assays to hundreds or thousands of patients with and without eye-disease; an opportunity exists to use the different datasets together to learn more about genetic contributions to eye-disease. This research could have considerable public health benefit by identifying genes and their variants associated with pathogenesis of disease, leading to new strategies for early diagnosis, treatment and prevention.