Statistical Methods for Relating Sequence Data to Phenotype

将序列数据与表型相关的统计方法

• 批准号: 7893074
• 负责人: Paul Marjoram
• 金额: $ 40.5万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-25 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7893074
• 关键词: Affect; Agriculture; Area; Biology; Blood Pressure; Collaborations; Copy Number Polymorphism; DNA Resequencing; DNA Sequence; Data; Development; Disease susceptibility; Droughts; Evolution; Exhibits; Experimental Designs; Future; Genetic; Genetic Models; Genetic Polymorphism; Genetic Variation; Goals; Haplotypes; Health; Human; Individual; Large-Scale Sequencing; Lead; Location; Maps; Marjoram (Spice); Measures; Methods; Mind; Modeling; Pattern; Performance; Phenotype; Population; Population Genetics; Research; Rice; Sequence Analysis; Statistical Methods; Stratification; Testing; Translating; Variant; base; design; genome wide association study; interest; meetings; simulation; trait

DESCRIPTION (provided by applicant): One of the most important challenges facing biology today is to make sense of genetic variation. Understanding how genotypic variation translates into phenotypic variation is fundamental to our understanding of evolution, and has enormous practical implications for human health as well as for agriculture and conservation. Witness the large number of genome-wide association studies now underway. The long-term objective of this project is to develop methods for association mapping methods that exploit the power of sequence-level data. The project has 3 main aims: First, the development of theoretical methods to allow efficient analysis of sequence- level genetic. We propose to investigate the effect of different experimental designs and data imputation methods on the power of the study, aiming to find designs that optimize the ability to detect genetic variation that is associated with phenotypic variation. We also propose to develop methods that allow for the unique challenges and opportunities presented by sequence-level data. Second, the development of population genetics models for the evolution of copy number variation [CNV] data. Our proposal will develop models that will allow us to assess the utility of proposed mechanisms for change in copy number, the effects of patterns of copy number variation on patterns of polymorphism in nearby sequence, and will also provide key theoretical under-pinnings for future model-based methods for haplotype inference, for example. Third, the development of theoretical methods to allow efficient analysis of sequence- level data in situations where the distribution of traits of interest is correlated with global features of the data (such as genetic ancestry or location). Our focus is on the integration of mixed-models and cluster-based methods. One of the most important challenges facing biology today is to understand how genetic variation between individuals translates into variation we can see or measure, like blood pressure in humans, or drought tolerance in rice. Our proposal seeks to develop methods that will help us use DNA sequence-level data to understand the genetic causes of human phenotypes such as disease susceptibility.

描述（由申请人提供）：当今生物学面临的最重要的挑战之一是理解遗传变异。了解基因型变异如何转化为表型变异是我们对进化的理解至关重要的，并且对人类健康以及农业和保护具有巨大的实际影响。目睹目前正在进行的全基因组协会研究正在进行中。该项目的长期目的是开发利用序列级数据功能的关联映射方法的方法。该项目具有3个主要目的：首先，开发理论方法，允许对序列水平遗传的有效分析。我们建议研究不同的实验设计和数据归合方法对研究功能的影响，旨在找到优化检测与表型变异相关的遗传变异能力的设计。我们还建议开发允许序列级别数据带来的独特挑战和机会的方法。其次，人口遗传学模型的发展拷贝数变化[CNV]数据的演变。我们的建议将开发模型，使我们能够评估提出的拷贝数变化机制的实用性，拷贝数变化模式对附近序列中多态性模式的影响，例如，还将为未来基于模型的单倍型推断的方法提供关键的理论不足。第三，在感兴趣特征分布与数据的全局特征（例如遗传血统或位置）相关的情况下，理论方法的开发允许对序列水平数据有效分析。我们的重点是集成混合模型和基于群集的方法。当今生物学面临的最重要的挑战之一是了解个体之间的遗传变异如何转化为我们可以看到或测量的变异，例如人类的血压或水稻中的干旱耐受性。我们的建议旨在开发将帮助我们使用DNA序列级数据来了解人类表型（例如疾病易感性）的遗传原因的方法。