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

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。我们的第一次 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 多项独立的全基因组DNA测序研究。我们的第二个目标是使用这些工具 整合2500名青光眼患者的300多个外显子发现与青光眼相关的基因 来自美国国立卫生研究院资助的其他测序研究的外显子和基因组。 DNA替换、插入、缺失和重排的全基因组分析范围很广 从测量已知的单核苷酸多态(SNP)，到对编码外显子的蛋白质进行测序，再到 对整个基因组进行测序。NEI和NIH资助的一些研究已经在全基因组范围内产生了不同的结果 通过对数百名或数千名患者的研究收集数据。这些研究产生了初级和 二次(派生)数据。主要数据是来自患者DNA的高质量、未映射的读取。次要的 数据是将原始数据映射到参考人类基因组并调用 替换、插入、删除和重新排列。应用于辅助数据的查询仅限于 通过用于生成主要数据和派生次要数据的方法来确定范围和准确性。 当存在多个数据集时，查询辅助数据的限制会变得更加明显 加在一起。在原始派生方法不同的程度上，跨多个次要查询 数据集存在不完整或不准确的风险，并可能返回错误的答案。 我们将开发新的工具，以弥补查询辅助数据的限制，使其 可以使用以下工具计算关于基因疾病关联的查询的准确和有意义的答案 多个全基因组DNA测序数据集。这些工具将创建一个框架，其中每个查询驱动 仅根据准确回答查询所需的原始数据重新推导变体。 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。 这些工具将应用于与青光眼相关的四个数据集：来自300个POAG的外显子序列数据 患者，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，以及外显子组 来自两个非眼病控制队列的序列数据，每个队列有超过1000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。目标1将建立一个连贯的、有质量控制的参考 来自2800个外显子的数据集。AIM 2将构建工具来将外显子组(或基因组)数据集与 AIM 1中内置的参考，通过罕见的变异来发现和检查与POAG相关的基因。

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 difered, queries acros multiple secondary datasets risk being incomplete or inaccurate and can return false answers. We will develop new tools that will addres 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 wil 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.