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(申请人提供)：在植入前遗传诊断(PGD)过程中，从早期胚胎中取出单个卵裂球进行分析。目前，大多数PGD技术侧重于检测染色体异常，如非整倍体和平衡易位。1然而，为了了解大多数疾病表型的遗传，有必要测量胚胎DNA上的多个单核苷酸多态(SNPs)。今天，研究实验室已经有了从单个细胞的DNA中测量SNPs的技术，估计在两年内就可以实现。然而，由于一个细胞只有一个DNA拷贝，SNP测量将非常容易出错或有噪音。基因安全网络开发了一种名为Parental Support TM的专利技术，用于清理胚胎DNA的噪声测量。本质上，该算法利用母亲和父亲的遗传数据，结合减数分裂机制的知识和胚胎DNA的噪声测量，以高度可信的方式在关键SNPs位置重建硅内胚胎DNA。该项目扩展了GSN最近在开发翻译引擎方面的工作，该引擎用于将多组基因组数据有效地集成到标准化本体中。转换引擎用于为每个本地数据源创建一个盒式磁带。墨盒将来自本地来源的遗传、表型和元数据转换成标准化本体的格式，在其中可以通过专家规则和统计模型进行分析，以进行数据验证和结果预测。这项工作是与斯坦福大学的PharmGKB项目合作进行的。PharmGKB管理着一个开放共享的临床试验数据的互联网存储库，目的是揭示个人基因变异是如何导致对药物的不同反应的。作为美国国立卫生研究院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。在目标1中，我们将通过与PharmGKB合作来扩展GSN与PharmGKB的工作，以创建用于基因分型阵列数据的标准化、可计算的本体，以及用于将Affymetrix基因分型阵列数据集成到该格式中的卡盒。这将使PharmGKB能够有效地将高通量的基因分型数据公开用于药物基因组研究。可计算的基因分型数据标准也将为该项目的目标2和目标3奠定基础。在目标2中，我们将演示可计算数据格式的实用性，方法是将来自Affymetrix 500k基因芯片阵列的高通量基因分型阵列数据输入该标准，并基于从公共领域收集的数据预测关键疾病表型的易感性。在目标3中，我们将改进和实施Parental Support TM技术，用于清洁胚胎DNA，使用基于PCR的技术或基于分子反转探针(MIPS)的技术进行测量。与医疗保健目标1相关提供了用于基因分型阵列数据的标准化本体，以及用于将基因分型阵列数据轻松提交到公共领域的卡盒。将这些数据公之于众将大大有助于研究了解基因与疾病的关联和基因功能。此外，标准化可计算格式的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。目标2将使人们能够了解基因与疾病的关联，以加强植入前的遗传诊断。AIM 3将改进父母支持方法，使在单个细胞上操作的基因分型技术能够在试管受精环境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易感性是绝对关键的。