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

眼病基因组：跨多个全基因组数据查询变异的方法

基本信息

批准号：
8451284
负责人：
THERESA GAASTERLAND
金额：
$ 32.82万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-04-01 至 2015-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8451284
关键词：
Affect Age Alleles American Biological Assay Blindness Code Collaborations Complex Cost Savings DNA DNA Sequence DNA Sequence Rearrangement Data Data Analyses Data Set Databases Deposition Derivation procedure Disease Disease Association Early Diagnosis Exons Eye diseases Funding Genes Genetic Genome Genotype Glaucoma Goals Guidelines Health Benefit Heart Human Genome Individual Institutes Lead Learning Letters Maps Measures Methods National Heart, Lung, and Blood Institute National Human Genome Research Institute Pathogenesis Patients Phenotype Prevention Primary Open Angle Glaucoma Proteins Public Health Quality Control Reading Research Research Personnel Risk Secondary to Series Single Nucleotide Polymorphism United States United States National Institutes of Health Universities Variant Visual impairment Washington Work cohort computerized tools cost data sharing database of Genotypes and Phenotypes design disorder control exome exome sequencing gene discovery genome sequencing genome wide association study genome-wide improved insertion/deletion mutation insight response tool

项目摘要

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.

眼病中的基因组：跨多个基因组数据集查询变体的方法该应用程序响应NEI在集成数据分析上的RFA，具有两个目标。我们的第一个目标是提供计算工具来支持借助来自数据的集成基因比较比较查询多个独立的全基因组DNA测序研究。我们的第二个目标是使用这些工具通过整合2500名青光眼患者的300多种外观，发现与青光眼相关的基因来自其他NIH资助的测序研究的例外和基因组。全基因组取代，插入，删除和重排范围范围的全基因组测定法从测量已知的单核苷酸多态性（SNP）到测序AL蛋白质编码外显子到测序整个基因组。许多由NIH资助的研究产生了不同的全基因组通过研究数百或数千名患者的研究集。这些研究同时产生主要和次级（派生）数据。主要数据是从患者DNA中获得的高质量，未映射的读数。次要数据是将主要数据映射到参考人类基因组并调用后发现的变体取代，插入，删除和重排。应用于辅助数据的查询有限通过用于生成主要数据并得出辅助数据的方法的范围和准确性。当多个数据集为时，查询辅助数据的限制变得更加明显合并。在原始派生方法差异的范围内，查询Acros多次次级数据集有可能不完整或不准确的风险，并且可能会返回错误的答案。我们将开发新工具，以增加查询辅助数据的限制，从而使其可以使用多个全基因组DNA测序数据集。这些工具将创建一个框架，每个查询驱动器重新启用变体从仅准确回答查询所需的主要数据。我们将使用这些工具来询问与主要开放角度青光眼（POAG）研究有关的数据。这些工具将应用于与青光眼相关的四个数据集：300 POAG的外显子序列数据患者，来自约5,000名POAG患者的珠子阵列基因型数据，包括300名外来受试者和外显子组来自两个非眼疾病控制队列的序列数据，每个人都有1,000多名受试者。一个控制队列将来自NIH壁内Clinseq项目；另一个将来自NHLBI资助的心脏研究。这项工作将以两个目标完成。 AIM 1 WIR会建立一个连贯的，质量控制的参考来自2,800多个EXOMES的数据集。 AIM 2将构建工具以将外显子组（或基因组）数据集与参考目标1通过稀有变体发现和检查与POAG相关的基因。