Factors Affecting Prediction Accuracy of Complex Human Traits and Diseases

描述（由申请人提供）： 人类基因组计划的完成带来了基因组医学的希望--利用基因组信息来预防、诊断和治疗疾病。然而，尽管基因分型技术取得了巨大进步，但我们预测复杂人类特征和疾病遗传易感性的能力仍然非常有限。我们之所以缺乏预测复杂人类特征的能力，部分原因可能在于全基因组关联研究中常用的统计方法的局限性。我们相信，主要来自动物育种领域的替代方法（WGP，全基因组预测）可以提高我们预测复杂人类特征和疾病的能力，从而为在个性化医疗中更密集地使用基因组信息铺平道路。 然而，WGP成功应用的群体在选择历史、等位基因频率分布、程度连锁不平衡（LD）和近交等方面与人类群体有很大差异。初步证据表明，这些因素会影响WGP的预测性能。因此，需要用人类数据对WGP进行全面评估，并且可能需要开发新的方法来科普预测复杂人类特征所带来的挑战。 我们提出了一个框架来研究影响WGP解释和预测未观察到的QTL方差的能力的因素。使用这个框架，并结合模拟和真实的数据分析，我们将产生现有的WGP与人类数据的第一次全面评估，并将量化的数据，感兴趣的特质，和回归方法对现有的WGP程序的预测精度的关键特征的影响。我们将利用这些信息来开发新的方法，以应对现有方法的局限性。 公共卫生相关性： 尽管基因分型技术取得了很大进展，但我们预测遗传风险的能力仍然非常有限。我们认为，主要从动物育种中改编的替代统计方法（WGP，全基因组预测）可能为提高我们预测重要健康结果的能力提供机会。然而，人类群体与动物和植物育种群体在可以极大地影响WGP的预测性能的方面不同。使用模拟和真实数据分析相结合，我们将：（a）产生一个全面的评估现有的WGP与人类数据，（B）量化的数据，感兴趣的特质，和回归方法的关键特征的影响WGP的预测精度，和（c）开发新的回归程序，旨在面对现有的局限性。

DESCRIPTION (provided by applicant): The completion of the human genome project brought the promise of genomic medicine-the use of genomic information for prevention, diagnosis and treatment of diseases. Yet, despite great progress in genotyping technologies, our ability to predict genetic predisposition to complex human traits and diseases remains very limited. Part of the explanation of our paradoxical lack of ability to predict complex human traits may reside in the limitations posed by the statistical methods commonly used in genome wide association studies. We believe that alternative methods, largely adapted from the field of animal breeding (WGP, whole-genome prediction), can enhance our ability to predict complex human traits and diseases, thus paving the way towards more intensive use of genomic information in personalized medicine. However, the populations to which WGP has been successfully applied differ greatly from human populations in aspects such as selection history, distribution of allele frequency, extent linkage disequilibrium (LD) and inbreeding. And preliminary evidence indicates that these factors can impact the predictive performance of WGP. Therefore, a comprehensive evaluation of WGP with human data is needed, and new methods may need to be developed to cope with the challenges posed by the prediction of complex human traits. We propose a framework to study the factors affecting the ability of WGP to account for and to predict variance at un-observed QTL. Using this framework, and a combination of simulation and real data analysis, we will produce the first comprehensive evaluation of existing WGP with human data and will quantify the effects of key features of the data, of the trait of interest, and of the regression method on the prediction accuracy of existing WGP procedures. We will use this information to develop new methods designed to confront the limitations of existing ones. PUBLIC HEALTH RELEVANCE: Project Narrative Despite great progress in genotyping technologies our ability to predict genetic risk remains very limited. We believe that alternative statistical methods (WGP, Whole Genome Prediction) largely adapted from animal breeding, may offer opportunities for advancing our ability to predict important health outcomes. However, human populations differ from animal and plant breeding populations in aspects that can greatly affect the predictive performance of WGP. Using a combination of simulations and real-data analysis we will: (a) produce a comprehensive evaluation of existing WGP with human data, (b) quantify the effects of key features of the data, of the trait of interest, and of the regression method on the prediction accuracy of WGP, and (c) develop new regression procedures designed to confront the limitations identified in existing ones.