Statistical Methods for Relating Sequence Data to Phenotype

将序列数据与表型相关的统计方法

基本信息

批准号：
8064560
负责人：
Paul Marjoram
金额：
$ 37.45万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
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

项目摘要

Project Summary 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. Project Narrative 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.

项目摘要当今生物学面临的最重要的挑战之一是理解遗传变化。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，并且具有巨大的实际意义用于人类健康以及农业和保护。见证大量目前正在进行全基因组关联研究的研究。这个长期目标项目是开发用于利用功能的关联映射方法的方法序列级数据。该项目有3个主要目标：首先，开发理论方法以有效地分析序列水平遗传。我们建议研究不同实验设计的效果，有关研究力量的数据插补方法，旨在寻找优化的设计检测与表型变异相关的遗传变异的能力。我们还建议开发允许带来独特挑战和机遇的方法由序列级数据呈现。第二，人口遗传学模型的开发用于副本的演变数字变化[CNV]数据。我们的建议将开发模型，使我们能够评估提议的机制变化拷贝数的效用，在附近序列中多态性模式的拷贝数变化模式，以及还将为将来的基于模型的方法提供关键的理论折叠。例如，单倍型推断。第三，理论方法的发展以有效地分析序列在关注特征分布与全局相关的情况下，级别数据数据的特征（例如遗传血统或位置）。我们的重点是混合模型和基于群集的方法的集成。项目叙述当今生物学面临的最重要的挑战之一是了解如何个体之间的遗传变异转化为我们可以看到或测量，例如人类的血压或大米的干旱耐受性。我们的提案旨在开发可以帮助我们使用DNA序列级的方法了解人类表型（例如疾病）的遗传原因敏感性。