Empirical-Bayesian Testing for Family Genome-wide Association Data

描述(申请人提供)：随着新的基因分型技术的出现，特别是SNP阵列以及即将到来的下一代测序，绘制人类疾病/特征基因图谱的努力一直集中在遗传关联研究上。基于家族的研究和基于群体的研究是两种常用的遗传关联研究设计。与基于人口的研究相比，基于家庭的研究对人口分层造成的偏差具有较强的稳健性。然而，基于家庭的研究往往不如基于人口的研究那么强大，因为家庭之间的关联信息由于容易受到人口分层的偏见而没有被利用。这一应用的目的是开发一种新的分析工具，以充分利用数据，并保持对人口分层的稳健性。具体地说，我们在经验贝叶斯框架下提出了一种新的统计方法，该方法估计了个体基因座群体分层的偏差，从而使家庭之间对检验统计量贡献的信息量基于偏差而减少。为了证明提出的方法的有效性和优于现有方法的性能，我们计划通过广泛的模拟对其进行评估，并将其应用于DBGaP中的家庭全基因组关联数据。 公共卫生相关性：随着新的基因分型技术的出现，绘制人类疾病/特征基因图谱的努力一直集中在全基因组关联研究(Gwas)上。以家庭为基础的研究和以人群为基础的研究是GWAS中常用的两种设计，每种设计都有各自的优缺点。此应用程序的目的是开发一种新的分析工具，以利用这两种设计的优势-保持对人口分层的稳健性，并实现更高的功率。建议的研究有可能促进识别与复杂疾病相关的新基因座的研究。

DESCRIPTION (provided by applicant): With availability of new genotyping technology, in particular SNP arrays as well as the coming next generation sequencing, efforts of mapping genes of human diseases/traits have been focusing on genetic association study. Family-based and population-based studies are two commonly-used designs of genetic association study. In contrast to population-based study, family based study is robust to bias due to population stratification. However, family-based study is often less powerful than population-based study because association information between families is not used due to its susceptibility to the bias of population stratification. The aim of this application is to develop a new analytic tool to fully utilize data as well as maintain the robustness to population stratification. Specifically, we propose a new statistic approach under the Empirical Bayesian framework, in which the bias of population stratification of individual loci is estimated so that the amount of information between families contributed to the testing statistic shrinks based on the bias. To demonstrate the validity and superior performance of the proposed approach compared to approaches available, we plan to evaluate it by extensive simulations and apply it to the family genome-wide association data in dbGaP. PUBLIC HEALTH RELEVANCE: With availability of new genotyping technologies, efforts of mapping genes of human diseases/traits have been focusing on genome-wide association study (GWAS). Family-based and population-based studies are two commonly-used designs used in GWAS, and each of them has unique advantages and disadvantages. The aim of this application is to develop a new analytic tool to make use of advantages of both designs - to maintain the robustness against population stratification and to achieve higher power. The proposed study has the potential to facilitate the research for identifying novel loci related to complex diseases.