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: 7641202
• 负责人: Robert B. Scharpf
• 金额: $ 9万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-24 至 2010-06-10
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7641202
• 关键词: Algorithms; American Heart Association; Atherosclerosis; Award; Biometry; Collaborations; Communities; Computational Biology; Computer software; Copy Number Polymorphism; Data; Development; Diabetes Mellitus; Disease; Disease susceptibility; Educational workshop; Epidemiology; Fathers; Foundations; Funding; Funding Opportunities; Future; Gene Expression; Generations; Genetic; Genetic Variation; Genome; Genomics; Genotype; Goals; Human Genetics; Individual; Inherited; Interdisciplinary Study; Joints; Lead; Leadership; Link; Malignant Neoplasms; Measures; Medicine; Methods; Mothers; Participant; Phase; Phenotype; Public Health; Research; Research Institute; Research Personnel; Risk; Sampling; Secure; Single Nucleotide Polymorphism; Solid; Solutions; Somatic Cell; Statistical Methods; Structure; Technology; Testing; Uncertainty; Variant; Work; career development; genetic epidemiology; genetic variant; markov model; meetings; men; new technology; proband; programs; skills training; statistics; symposium; tool; trait; working group

DESCRIPTION (provided by applicant): Two forms of genetic variation are common and can be measured on a genomic scale using recent high throughput genotyping platforms: single nucleotide polymorphisms (SNPs) and copy number variants (CNVs). Unlike high throughput genotyping algorithms that are highly accurate, copy number estimates are very imprecise and tools for estimating copy number and inferring regions of CNV are still under development. My immediate scientific goals are to provide first generation algorithms for each of the following tiers of estimation problems: (i) By locus: estimate the raw copy number at each locus on the array and quantify the uncertainty, (ii) By sample: infer regions of CNV, and (iii) Between samples: assess the contribution of CNV to disease susceptibility. My long term goal is to establish an interdisciplinary research lab in biostatistics and human genetics that supports creative computational and statistical solutions to high throughput genomic data. This Award will facilitate the necessary training and skills to transition to independent research through formal coursework in statistical genetics and computational biology, leadership opportunities in structured career development activities, such as the GWAs@JohnsHopkins working group, new collaborations from multiple research institutes, and presentations at national conferences, including epidemiological (American Heart Association), methodological (Joint Statistical Meetings), and topical (e.g., a copy number variant workshop). A scientific advisory panel of internationally recognized experts will oversee my research. New technologies and applications for genomic research developed during the course of this Award will lead to exciting new opportunities for biostatistical research, as well as R01 funding opportunities that I will actively pursue. PUBLIC HEALTH REVELANCE - Genetic variation between individuals is common and has been linked to common diseases such as diabetes and cancer. I propose to develop statistical methods for new genome-scale technologies to identify genetic variants and to characterize their contribution to disease susceptibility.

描述（由申请人提供）：两种形式的遗传变异是常见的，可以使用最近的高吞吐量基因分型平台在基因组尺度上测量：单核苷酸多态性（SNP）和拷贝数变体（CNV）。与高度准确的高通量基因分型算法不同，拷贝数估计值非常不精确，并且用于估算CNV的拷贝数和推断区域的工具仍在开发中。我的直接科学目标是为以下估计问题的每一个层提供第一代算法：（i）基因座：估算阵列上每个基因座的原始拷贝数，并量化不确定性，（ii）通过样本：CNEV的CNV区域：（III）在样本之间：评估CNV对疾病敏感性的贡献。我的长期目标是在生物统计学和人类遗传学中建立一个跨学科研究实验室，该实验室支持高吞吐量基因组数据的创新计算和统计解决方案。该奖项将促进通过统计遗传学和计算生物学的正式课程，结构化职业发展活动的领导机会，例如GWAS@Johnshopkins工作组，多个研究机构的新合作以及国家会议上的新合作，包括流行病学（美国心脏协会）的统计学（包括统计学），以及统计学杂志（包括统计学），以及一定的统计信息（包括统计学），以及一定的统计学杂志（包括统计学），以及统计学杂志，统计学杂志（包括统计学），将有助于通过统计遗传学和计算生物学的正式课程过渡到独立研究的必要培训。国际认可的专家的科学咨询小组将监督我的研究。在本奖项过程中开发的基因组研究的新技术和应用将为生物统计研究带来令人兴奋的新机会，以及我将积极寻求的R01资金机会。 公共卫生的启示 - 个体之间的遗传变异很常见，并且与糖尿病和癌症等常见疾病有关。我建议为新的基因组规模技术开发统计方法，以鉴定遗传变异并表征其对疾病易感性的贡献。