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.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 亲和SNP阵列已广泛用于单核苷酸多态性（SNP）基因型调用和DNA拷贝数变异推断。虽然许多方法在这些领域已经取得了很高的准确性，大多数研究很少关注的探针杂交到脱靶等位基因序列的建模，这会极大地影响准确性。在这项研究中，我们解决了这个问题，并证明与错配核苷酸（HWMMN）的杂交发生在所有SNP探针集中，并且对等位基因浓度（AC）的估计具有关键影响。我们通过结合自由能和结合亲和力来研究序列结合，建立了探针强度复合表示（PICR）模型。PICR模型允许通过统计回归估计给定SNP处的AC。此外，我们证明与已知的真实拷贝数的细胞系数据的PICR模型可以实现合理的准确性，在一个单一的SNP位点的拷贝数估计，通过使用每个样品的AC估计的参考样品的比率，并可以揭示在异常位点的SNP的微妙的基因型结构。我们还用HapMap数据证明，PICR模型在样本、实验室甚至阵列平台上一致地产生准确的SNP基因型调用。