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

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。该项目中产生的软件将作为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。

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.