GENOTYPE AND COPY NUMBER CALLING

基因型和拷贝数检测

• 批准号: 8171736
• 负责人: Robert C. Elston
• 金额: $ 0.49万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8171736
• 关键词: Address; Affect; Affinity; Alleles; Attention; Binding; Cell Line; Computer Retrieval of Information on Scientific Projects Database; Copy Number Polymorphism; DNA copy number; Data; Free Energy; Funding; Genotype; Grant; Institution; Laboratories; Methods; Modeling; Nucleotides; Regression Analysis; Research; Research Personnel; Resources; Sampling; Single Nucleotide Polymorphism; Source; Structure; United States National Institutes of Health

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Affymetrix SNP arrays have been widely used for single-nucleotide polymorphism (SNP) genotype calling and DNA copy number variation inference. Although numerous methods have achieved high accuracy in these fields, most studies have paid little attention to the modeling of hybridization of probes to off-target allele sequences, which can affect the accuracy greatly. In this study, we address this issue and demonstrate that hybridization with mismatch nucleotides (HWMMN) occurs in all SNP probe-sets and has a critical effect on the estimation of allelic concentrations (ACs). We study sequence binding through binding free energy and then binding affinity, and develop a probe intensity composite representation (PICR) model. The PICR model allows the estimation of ACs at a given SNP through statistical regression. Furthermore, we demonstrate with cell-line data of known true copy numbers that the PICR model can achieve reasonable accuracy in copy number estimation at a single SNP locus, by using the ratio of the estimated AC of each sample to that of the reference sample, and can reveal subtle genotype structure of SNPs at abnormal loci. We also demonstrate with HapMap data that the PICR model yields accurate SNP genotype calls consistently across samples, laboratories and even across array platforms.

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