Statistical methods for assessing copy number variation in SNP arrays

评估 SNP 阵列中拷贝数变异的统计方法

• 批准号: 8258323
• 负责人: Robert B. Scharpf
• 金额: $ 24.27万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-11 至 2013-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8258323
• 关键词: Algorithms; Area; Award; Biologic Characteristic; Chromosome abnormality; Chromosomes; Collaborations; Computer software; Copy Number Polymorphism; DNA; Development; Disease; Educational workshop; Environment; Faculty; Family; Funding Opportunities; Generations; Genome; Genomics; Genotype; Goals; Individual; Link; Measures; Mentors; Methods; Parents; Phase; Phenotype; Positioning Attribute; Principal Component Analysis; Research; Research Design; Sampling; Single Nucleotide Polymorphism; Somatic Cell; Statistical Methods; Statistical Models; Uncertainty; Variant; Weight; cancer genome; density; dosage; improved; markov model; method development; new technology; novel; novel strategies; oncology; open source; professor; symposium; tool; trait

Copy number variants (CNV) are common and can be measured on a genomic scale using high throughput genotyping platforms. However, copy number estimates from existing algorithms are often inaccurate and imprecise. During the mentored phase of this Award, first generation algorithms for the locus-level estimation of copy number and hidden Markov models to identify regions of copy number gain and loss were developed. During the ROO phase these algorithms will be further improved by generalizations that broaden the scope of the problems and final versions of open source software. Specific methodologic areas areas that will be targeted during the ROO phase of this Award include improved estimates of uncertainty that distinguish between outliers and CNV and statistical models that 'borrowstrength' across loci and across samples. In parallel with the development of these methods, we will explore new approaches for CNV-phenotype inference that accommodate features of the study design (e.g., unrelated subjects versus case-parent trios) and biological characteristics of the disease. For instance, we expect that extensions of segmentation methods and principal component analysis will benefit the study of cancer genomes. Activities during the K99 phase of this Award will be helpful for achieving these goals. First, 1 successfully identified a tenure-track faculty position at the rank of Assistant Professor. Faculty in the Department of Oncology are supportive of the development of these methods. Secondly, I developed new collaborations during the K99 phase that will spur the development of statistical methods in the above areas. Finally, I participated in workshops, statistical conferences, and completed coursework in subject-relevant areas that will be helpful as I transition to the independent phase. I look forward to competing for R01 funding opportunities as new technologies and applications for genomic research emerge.

拷贝数变异（CNV）是常见的，可以使用高通量在基因组规模上测量 基因分型平台。然而，来自现有算法的拷贝数估计通常是不准确的， 不精确。在该奖项的指导阶段，第一代定位级估计算法 的拷贝数和隐马尔可夫模型，以确定区域的拷贝数的增益和损失， 开发在ROO阶段，这些算法将通过扩展 问题的范围和开源软件的最终版本。 在本奖项的ROO阶段将针对的具体方法领域包括改进的 区分离群值和CNV的不确定性估计以及“增强”的统计模型 跨基因座和跨样本。在开发这些方法的同时，我们将探索 适应研究设计特征的CNV-表型推断的新方法（例如， 无关受试者与病例-父母三人组）和疾病的生物学特征。例如我们 期望分割方法和主成分分析的扩展将有益于 癌症基因组 K99阶段的活动将有助于实现这些目标。首先，1成功 确定了一个助理教授级别的终身教职职位。教育系教师 肿瘤学支持这些方法的发展。其次，我开发了新的合作项目， 在K99阶段，这将刺激上述领域统计方法的发展。最后我 参加了讲习班、统计会议，并完成了与主题有关的领域的课程， 会对我过渡到独立阶段有帮助我期待着竞争R 01资金 随着基因组研究的新技术和应用的出现，