Statistical Tools for Whole-Genome Prediction of Complex Traits and Diseases

用于复杂性状和疾病的全基因组预测的统计工具

• 批准号: 8274041
• 负责人: Gustavo de los Campos
• 金额: $ 24.11万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-01 至 2015-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8274041
• 关键词: Animals; Architecture; Bioinformatics; Complex; Computer software; Data Files; Disease; Environment; Genetic; Genetic Predisposition to Disease; Genome; Genomics; Goals; Health; Human; Linear Regressions; Malignant Neoplasms; Methodology; Methods; Modeling; Outcome; Phenotype; Plants; Procedures; Research; Research Project Grants; Testing; Variant; animal breeding; base; biomedical informatics; clinically relevant; design; genome wide association study; health application; human data; improved; software development; tool; trait; user-friendly

DESCRIPTION (provided by applicant): Genome Wide Association Studies (GWAS) have uncovered an unprecedented number of variants associated with important health-related traits and diseases. Evidence from these studies suggests that most clinically relevant traits have complex genetic architectures. Whole Genome Prediction (WGP) is a predictive approach, primarily developed and tested in the field of animal breeding, designed to confront some of the challenges emerging in the prediction of complex traits and diseases. Implementing WGP requires specialized software, which is not available in standard statistical packages. In our research projects involving plant, animal and more recently human data, we have developed, tested and used statistical software for parametric and semi-parametric WGP. In this project we propose to integrate and further develop this software in ways that will improve its value for applications with human data. We will integrate parametric and semi-parametric procedures for WGP into a unified framework and will deliver software that could be used with un-censored, censored, binary and ordinal traits. The software produced in this project will be delivered as an R-package and will be integrated into GenePattern; a bioinformatics platform where users will be able to develop analysis pipelines by combining our software with other bioinformatics tools. PUBLIC HEALTH RELEVANCE: Genome Wide Association Studies (GWAS) have uncovered an unprecedented number of variants associated with important health-related traits and diseases. Evidence from these studies suggests that most clinically relevant traits have complex genetic architectures. Whole Genome Prediction (WGP) is a predictive approach, primarily developed and tested in the field of animal breeding, designed to confront some of the challenges emerging in the prediction of complex traits and diseases. We believe that this methodology offers great opportunities to advance our ability to predict genetic predisposition to complex human traits and diseases. Implementing WGP methods requires specialized software, which is not available in standard statistical packages. In our research we have developed, tested, and used statistical software for parametric and non-parametric WGP. The proposed project will integrate these software into a unified framework, will further develop these packages by implementing additional regression methods, and will extend the software to handle traits often encountered in human applications such as censored, binary and ordinal outcomes. The software developed in this project will be integrated into R and into GenePattern, a bioinformatics workflow platform which will enable users to integrate our software with other bioinformatics tools.

描述（由申请人提供）：全基因组关联研究（GWAS）发现了与重要健康相关性状和疾病相关的前所未有的变异数量。来自这些研究的证据表明，大多数临床相关性状具有复杂的遗传结构。全基因组预测（WGP）是一种预测方法，主要在动物育种领域开发和测试，旨在应对复杂性状和疾病预测中出现的一些挑战。执行工作组需要专门的软件，而标准统计软件包中没有这种软件。在我们涉及植物，动物和最近的人类数据的研究项目中，我们已经开发，测试和使用参数和半参数WGP的统计软件。在这个项目中，我们建议整合和进一步开发这个软件的方式，将提高其价值的应用程序与人类数据。我们将把WGP的参数和半参数程序整合到一个统一的框架中，并将提供可用于未删失、删失、二元和有序性状的软件。该项目中生成的软件将作为R包交付，并将集成到GenePattern中;这是一个生物信息学平台，用户将能够通过将我们的软件与其他生物信息学工具相结合来开发分析管道。 公共卫生关系：全基因组关联研究（GWAS）发现了与重要健康相关性状和疾病相关的前所未有的变异数量。来自这些研究的证据表明，大多数临床相关性状具有复杂的遗传结构。全基因组预测（WGP）是一种预测方法，主要在动物育种领域开发和测试，旨在应对复杂性状和疾病预测中出现的一些挑战。我们相信，这种方法为提高我们预测复杂人类特征和疾病的遗传易感性的能力提供了很好的机会。实施工作组方法需要专门的软件，而标准统计软件包中没有这种软件。在我们的研究中，我们已经开发，测试和使用统计软件的参数和非参数WGP。拟议的项目将把这些软件整合到一个统一的框架中，通过实施额外的回归方法进一步开发这些软件包，并将扩展软件以处理人类应用程序中经常遇到的特征，如审查，二元和有序结果。该项目开发的软件将集成到R和GenePattern中，这是一个生物信息学工作流程平台，使用户能够将我们的软件与其他生物信息学工具集成。