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）：在植入前遗传学诊断（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，例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，有必要测量胚胎上的多个单核苷酸多态性 (SNP)。脱氧核糖核酸。如今，研究实验室已有技术可用于测量单细胞 DNA 中的 SNP，预计两年内即可实现。然而，由于一个细胞只能获得 DNA 的单个拷贝，因此 SNP 测量将非常容易出错或存在噪音。 Gene Security Network 开发了一种名为 Parental Support TM 的专有技术，用于清除胚胎 DNA 的噪音测量结果。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，以便在关键位置在硅中重建胚胎 DNA SNPS 具有高度置信度。该项目扩展了 GSN 最近在开发翻译引擎方面的工作，该引擎可将多组药物基因组数据有效集成到标准化本体中。翻译引擎用于为每个本地数据源创建盒式磁带。该盒将本地来源的遗传、表型和元数据转换为标准化本体的格式，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 PharmGKB 项目合作进行的。 PharmGKB 管理着一个公开共享的临床试验数据互联网存储库，旨在揭示个体遗传变异如何导致对药物的独特反应。作为 NIH 药物遗传学研究网络 (PGRN) 的成员，PharmGKB 的数据库包含来自心血管、肺部和癌症研究的广泛药代动力学和基因组记录。在目标 1 中，我们将与 pharmGKB 合作，扩展 GSN 与 PharmGKB 的合作，创建用于基因分型阵列数据的标准化、可计算本体，以及用于将 Affymetrix 基因分型阵列数据集成到该格式的盒。这将使 PharmGKB 能够有效地公开高通量基因分型数据，用于药物基因组学研究。可计算的基因分型数据标准也将为该项目的目标 2 和 3 奠定基础。在目标 2 中，我们将通过将来自 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，并根据从公共领域汇总的数据预测对关键疾病表型的易感性，来展示可计算数据格式的实用性。在目标 3 中，我们将完善和实施用于清洁胚胎 DNA 的 Parental Support TM 技术，该技术使用基于 PCR 的技术或基于分子倒置探针 (MIPS) 的技术进行测量。与医疗保健目标的相关性 1 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盒式磁带。将这些数据放在公共领域将大大有利于了解基因与疾病关联和基因功能的研究。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使用该信息进行临床决策的能力。目标 2 将使基因与疾病关联的知识能够增强植入前遗传学诊断。目标 3 将完善家长支持方法，使基因分型技术能够在单细胞上运行，从而在 IVF 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任务绝对至关重要。