Design and Analysis of Sequencing-based Studies for Complex Human Traits

复杂人类特征的基于测序的研究的设计和分析

• 批准号: 8666560
• 负责人: Yun Li
• 金额: $ 35.96万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-23 至 2016-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8666560
• 关键词: Accounting; Achievement; Area; Base Sequence; Complex; Computer Simulation; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Detection; Development; Disease; Environmental Risk Factor; Gene Frequency; Genetic; Genomics; Genotype; Haplotypes; Heritability; Human; Individual; Lead; Left; Massive Parallel Sequencing; Measures; Methods; Minor; Modeling; Performance; Pharmacotherapy; Phase; Publications; Publishing; Research Design; Research Personnel; Sample Size; Scientific Advances and Accomplishments; Sequence Analysis; Staging; Statistical Methods; Testing; Uncertainty; Variant; Work; base; cohort; cost; design; falls; flexibility; gene discovery; gene function; genetic variant; genome wide association study; human disease; improved; new technology; rare variant; trait

DESCRIPTION (provided by applicant): Massively parallel sequencing has transformed the field of genomic studies. These new technologies have resulted in the successful identification of causal variants for several rare Mendelian disorders. They also hold the promise to help explain some of the missing heritability from genomewide association studies of complex traits. However, the development of robust statistical and computational methods has fallen seriously behind the technological advances particularly for application to the study of complex human traits. The methodological work lags in at least three major areas. First, there are few, if any, publications on the optimal design of sequencing-based studies for complex traits that take into account the complex dynamic of sequencing cost to allow for exploration of the full range sample size and sequencing depth. Second, there are no published methods for the analysis of low coverage (in the range of 2-4X) sequencing data. Low coverage sequencing is being used to study complex diseases and traits because it can lead to substantial gains in power by increasing the effective sample size, critical for the detection of moderate genetic effects for typical complex human traits. Third, the field needs statistical methods that can efficiently analyze rare variants derived from various designs of sequencing-based studies. In this application, we will establish a comprehensive statistical framework for the design and analysis of sequencing-based studies for complex human traits. To do so, we propose the following four specific aims: 1) Develop a unified statistical framework for SNP calling, genotyping, and haplotyping from sequencing and genotyping data. 2) Provide alternative design options for sequencing-based genetic studies. 3) Develop statistical methods for the analysis of rare variants. 4) Develop, distribute and support freely available software packages for the methods proposed in this application. The proposed methods will be evaluated through analytical approaches, computer simulations and applications to multiple real datasets.

描述（由申请人提供）：大规模平行测序已经改变了基因组研究领域。这些新技术成功地鉴定了几种罕见的孟德尔疾病的致病变异。他们还有望帮助解释复杂性状的全基因组关联研究中缺失的一些遗传性。然而，强大的统计和计算方法的发展已经严重落后于技术进步，特别是应用于复杂的人类特征的研究。方法工作至少在三个主要领域滞后。首先，很少有（如果有的话）关于复杂性状的基于测序的研究的最佳设计的出版物，其考虑到测序成本的复杂动态，以允许探索全范围样本大小和测序深度。其次，没有公开的方法用于分析低覆盖度（在2-4X范围内）测序数据。低覆盖率测序正被用于研究复杂的疾病和性状，因为它可以通过增加有效样本量来大幅提高功效，这对于检测典型复杂人类性状的中度遗传效应至关重要。第三，该领域需要统计方法，可以有效地分析来自各种基于测序的研究设计的罕见变异。在本申请中，我们将建立一个全面的统计框架，用于设计和分析复杂人类特征的基于测序的研究。为此，我们提出了以下四个具体目标：1）从测序和基因分型数据中开发用于SNP调用、基因分型和单体型分型的统一统计框架。2)为基于测序的遗传学研究提供替代设计方案。3)开发用于分析罕见变异的统计方法。4)开发、分发和支持本申请中提出的方法的免费软件包。将通过分析方法、计算机模拟和应用于多个真实的数据集来评估所提出的方法。