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

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.