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

描述(申请人提供)：大规模并行测序已经改变了基因组研究领域。这些新技术已经成功地识别了几种罕见的孟德尔疾病的因果变异。他们还承诺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，稳健的统计和计算方法的发展严重落后于技术进步，特别是在应用于复杂人类特征研究方面。方法论工作至少在三个主要领域滞后。首先，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的复杂特征的基于测序的研究的优化设计的出版物很少(如果有的话)。其次，目前还没有公开的方法来分析低覆盖率(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特征，因为它可以通过增加有效样本量来大幅提高能力，这对于检测典型复杂人类特征的适度遗传效应至关重要。第三，该领域需要统计方法，能够有效地分析来自各种基于测序的研究设计的罕见变异。在这项应用中，我们将建立一个全面的统计框架，用于设计和分析基于测序的复杂人类特征研究。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建立SNP调用、基因分型和单倍型的统一统计框架。2)为基于测序的基因研究提供替代设计选择。3)发展分析稀有变种的统计方法。4)为本申请中提出的方法开发、分发和支持免费提供的软件包。所提出的方法将通过分析方法、计算机模拟和对多个真实数据集的应用来进行评估。 公共卫生相关性：大规模并行测序已经改变了基因组研究领域。这些新技术已经成功地识别了几种罕见的孟德尔疾病的因果变异，并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，稳健的统计和计算方法的发展严重落后于技术进步，特别是在应用于复杂人类特征研究方面。在这项应用中，我们将建立一个全面的统计框架，用于设计和分析基于测序的复杂人类特征研究。

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. PUBLIC HEALTH RELEVANCE: 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 and 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. In this application, we will establish a comprehensive statistical framework for the design and analysis of sequencing-based studies for complex human traits.