Phase I Application: Cleaning of Single Cell DNA Measurements In-Silico

第一阶段应用：单细胞 DNA 测量的计算机清洗

• 批准号: 7222074
• 负责人: Matthew Rabinowitz
• 金额: $ 19.57万
• 依托单位: NATERA, INC.
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-15 至 2007-10-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7222074
• 关键词: Adult; Affect; Algorithms; Alleles; Alzheimer' s Disease; Aneuploidy; Area; Arts; Bibliography; Binding; Biotechnology; Cardiovascular system; Cells; Child; Chromosome abnormality; Chromosomes; Clinical; Clinical Trials; Collaborations; Computer Simulation; DNA; Data; Data Aggregation; Data Collection; Data Sources; Databases; Decision Support Model; Detection; Development; Diagnosis; Disease; Disease Association; Dropout; Drops; Drug Kinetics; Elements; Embryo; Equilibrium; Extensible Markup Language; Family; Fathers; Fertilization in Vitro; Fluorescent in Situ Hybridization; Foundations; Gene Chips; Gene Expression; Genes; Genetic; Genetic Materials; Genetic Recombination; Genetic Variation; Genome; Genomics; Genotype; Goals; Healthcare; Individual; Internet; Knowledge; Laboratory Research; Link; Location; Malignant neoplasm of lung; Measurement; Measures; Meiosis; Meta-Analysis; Methods; Modeling; Molecular; Molecular Probes; Mothers; Online Mendelian Inheritance In Man; Ontology; Operative Surgical Procedures; Outcome; Output; Parents; Patients; Pharmacogenetics; Pharmacogenomics; Pharmacologic Substance; Phase; Phenotype; Predisposition; Probability; Process; Protocols documentation; Public Domains; Rate; Reaction; Records; Research; Research Personnel; Sampling; Security; Silicon; Single Nucleotide Polymorphism; Source; Staging; Standards of Weights and Measures; Statistical Models; System; Techniques; Technology; Tissue Sample; Today; Training; Translating; Translations; Uncertainty; United States National Institutes of Health; Universities; Validation; Work; anticancer research; base; concept; data modeling; desire; disease phenotype; embryo stage 2; gene function; genotyping technology; implantation; interest; member; predictive modeling; repository; research study; software development

DESCRIPTION (provided by applicant): In the process of pre-implantation genetic diagnoses (PGD), a single blastomere is removed from the early stage embryo for analysis. Currently, most PGD techniques focus on detection of chromosomal abnormalities such as aneuploidies and balanced translocations.1 However, in order to understand the inheritance of the majority of disease phenotypes, it will be necessary to measure multiple single nucleotide polymorphisms (SNPs) on the embryonic DNA. Techniques are available in research laboratories today, with estimated availability within two years, to measure SNPs from the DNA of a single cell. However, since only a single copy of the DNA is available from one cell, the SNP measurements will be highly error-prone or noisy. Gene Security Network has developed a proprietary technique, termed Parental Support TM, for cleaning the noisy measurements of embryonic DNA. In essence, the algorithm makes use of genetic data of the mother and the father, together with the knowledge of the mechanism of meiosis and the noisy measurements of the embryonic DNA, in order to reconstruct in-silicon the embryonic DNA at the location of key SNPS with a high degree of confidence. This project extends GSN's recent work in developing a translation engine for the efficient integration of multiple sets of pharamacogenomic data into a standardized ontology. The translation engine is used to create a cartridge for each local source of data. The cartridge translates the genetic, phenotypic and meta-data from the local source into the format of the standardized ontology, where it can be analyzed by expert rules and statistical models for data validation and outcome prediction. This work is being performed in collaboration with the PharmGKB Project at Stanford University. PharmGKB manages an openly-shared Internet repository for clinical trial data with the intent to uncover how individual genetic variation contributes to distinctive reactions to pharmaceuticals. As a member of the NIH Pharmacogenetics Research Network (PGRN), PharmGKB's database includes extensive pharmacokinetic and genomic records from cardiovascular, pulmonary, and cancer research. In aim 1, we will extend GSN's work with PharmGKB by working with pharmGKB to create a standardized, computable ontology for genotyping array data together with a cartridge for integrating Affymetrix genotyping array data into that format. This will enable PharmGKB to efficiently make high-throughput genotyping data publicly available for pharmacogenomic research. The computable genotyping data standard will also establish the foundation for aims 2 and 3 of this project. In aim 2, we will demonstrate the utility of the computable data format by inputting high-throughput genotyping array data from an Affymetrix 500k Gene chip Array into that standard and predicting the susceptibility to key disease phenotypes, based on data aggregated from the public domain. In aim 3, we will refine and implement the Parental Support TM technique for cleaning the embryonic DNA, measured using either PCR-based techniques, or molecular inversion-probe (MIPS) based techniques. Relevance to Healthcare Aim 1 provides a standardized ontology for genotyping array data, and a cartridge for easily submitting genotyping array data into the public domain. Having this data in the public domain will considerably benefit research in understanding gene-disease association and gene functions. In addition, the availability of the genotyping data in standardized computable format will ultimately enhance the ability of doctors to use that information for clinical decisions. Aim 2 will enable the knowledge of gene-disease associations to enhance pre-implantation genetic diagnosis. Aim 3 will refine the Parental Support method to enable genotyping technologies, operating on a single cell, to produce reliable genotyping data in the IVF setting. This reliable genotyping data is absolutely critical for the task of predicting susceptibilities to various disease phenotypes.

描述（由申请人提供）：在植入前遗传诊断（PGD）的过程中，从早期胚胎中除去单个胚胎以进行分析。目前，大多数PGD技术都集中在检测诸如非整倍体和平衡易位等染色体异常的检测。1然而，为了了解大多数疾病表型的遗传，必须测量多个单核苷酸多态性（SNP）在胚胎DNA上。当今的研究实验室可以使用技术，并在两年内估计可用性，可从单个细胞的DNA中测量SNP。但是，由于只有一个单元格可用DNA的单个副本，因此SNP测量值将非常容易出错或嘈杂。 Gene Security网络已开发出一种专有技术，称为父母支持TM，用于清洁胚胎DNA的嘈杂测量。从本质上讲，算法利用母亲和父亲的遗传数据，以及了解减数分裂机制和胚胎DNA的嘈杂测量的知识，以高度置信度重建胚胎中的胚胎DNA。该项目将GSN在开发翻译引擎方面的最新工作，以有效地集成多组PharamAcogenomic数据到标准化的本体论。翻译引擎用于为每个本地数据源创建墨盒。墨盒将遗传，表型和元数据从局部来源转化为标准化本体论的格式，可以通过专家规则和数据验证和结果预测的统计模型对其进行分析。这项工作正在与斯坦福大学的PharmGKB项目合作进行。 PharmGKB管理一个公开共享的Internet存储库，以供临床试验数据，以发现单个遗传变异如何对药物的独特反应有何影响。作为NIH药物遗传学研究网络（PGRN）的成员，PharmGKB的数据库包括来自心血管，肺部和癌症研究的广泛的药代动力学和基因组记录。在AIM 1中，我们将通过与PharmGKB合作来扩展GSN与PharmGKB的合作，以创建标准化的，可计算的基因分型阵列数据的本体，以及用于将Affymetrix基因分型阵列数据集成到该格式中的墨盒。这将使PharmGKB能够有效地使高通量基因分型数据公开用于药物基因组学研究。可计算的基因分型数据标准还将为该项目的目标2和3建立基础。在AIM 2中，我们将通过输入来自Affymetrix 500K基因芯片阵列的高通量基因分型阵列数据的高通量基因分型阵列数据的实用性，并根据与公共领域汇总的数据进行预测对关键疾病表型的易感性。在AIM 3中，我们将完善并实施父母支持TM技术，以清洁使用基于PCR的技术或基于分子反转探针（MIPS）技术测量的胚胎DNA。与Healthcare AIM 1的相关性提供了用于基因分型阵列数据的标准化本体，以及用于轻松将基因分型阵列数据提交到公共领域的墨盒。在公共领域中拥有这些数据将大大受益于理解基因疾病关联和基因功能的研究。此外，标准化计算格式中基因分型数据的可用性最终将增强医生将这些信息用于临床决策的能力。 AIM 2将使基因疾病关联的了解能够增强植入前的遗传诊断。 AIM 3将完善父母支持方法，以使基因分型技术在单个细胞上运行，以在IVF设置中产生可靠的基因分型数据。这种可靠的基因分型数据对于预测各种疾病表型的敏感性的任务至关重要。