Statistical methods for assessing copy number variation in SNP arrays

描述（由申请人提供）： 两种形式的遗传变异是常见的，并且可以使用最近的高通量基因分型平台在基因组规模上测量：单核苷酸多态性（SNP）和拷贝数变异（CNV）。不像高通量基因分型算法是高度准确的，拷贝数估计是非常不精确的，用于估计拷贝数和推断CNV区域的工具仍在开发中。我的直接科学目标是为以下每一层估计问题提供第一代算法：（i）按基因座：估计阵列上每个基因座的原始拷贝数并量化不确定性，（ii）按样本：推断CNV区域，和（iii）样本之间：评估CNV对疾病易感性的贡献。我的长期目标是建立一个生物统计学和人类遗传学的跨学科研究实验室，为高通量基因组数据提供创造性的计算和统计解决方案。该奖项将促进必要的培训和技能，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中的领导机会，如GWAs@JohnsHopkins工作组，来自多个研究机构的新合作，以及在国家会议上的演讲，包括流行病学（美国心脏协会）、方法学（联合统计会议）和专题（例如，拷贝数变体车间）。一个由国际知名专家组成的科学顾问小组将监督我的研究。在这个奖项的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，以及我将积极追求的R 01资助机会。 公共卫生-个体之间的遗传变异很常见，并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，以识别遗传变异并描述其对疾病易感性的贡献。

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.