Empirical-Bayesian Testing for Family Genome-wide Association Data

家族全基因组关联数据的经验贝叶斯测试

• 批准号: 8252112
• 负责人: Tao Wang
• 金额: $ 16.66万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-06 至 2013-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8252112
• 关键词: Address; Admixture; Affect; Alleles; Attention deficit hyperactivity disorder; Case-Control Studies; Child; Chromosome Mapping; Comb animal structure; Complex; Data; Disadvantaged; Disease; Ethnic group; Family; Family Study; Genotype; Individual; Lead; Location; Natural Selections; Parents; Performance; Phenotype; Population; Predisposition; Research; Resolution; Sample Size; Sampling; Software Tools; Source; Stratification; Testing; Triad Acrylic Resin; Variant; base; case control; cost; database of Genotypes and Phenotypes; design; genetic association; genome wide association study; genotyping technology; human disease; improved; next generation; novel; offspring; population based; public health relevance; simulation; statistics; tool; trait; transmission process

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.

描述（由申请人提供）：随着新的基因分型技术的可用性，特别是SNP阵列以及即将到来的下一代测序，人类疾病/性状基因定位的努力一直集中在遗传关联研究上。基于家系和基于群体的研究是遗传关联研究的两种常用设计。与基于人群的研究相比，基于家庭的研究对由于人群分层引起的偏倚具有稳健性。然而，基于家庭的研究往往不如基于人口的研究，因为家庭之间的关联信息是不使用的，由于其对人口分层的偏见的敏感性。该应用程序的目的是开发一种新的分析工具，以充分利用数据，并保持人口分层的鲁棒性。具体地说，我们提出了一种新的统计方法下的经验贝叶斯框架，在其中估计的个别基因座的人口分层的偏差，使家庭之间的信息量有助于测试统计量收缩的基础上的偏差。为了证明所提出的方法的有效性和上级性能相比，现有的方法，我们计划通过广泛的模拟评估它，并将其应用到家庭全基因组关联数据dbGaP。 公共卫生相关性：随着新的基因分型技术的出现，全基因组关联研究（genome-wide association study，GWAS）已成为人类疾病/性状基因定位的研究热点。基于家庭和基于人群的研究是GWAS中常用的两种研究设计，每种研究设计都有其独特的优点和缺点。本申请的目的是开发一种新的分析工具，以利用两种设计的优势-保持对人群分层的稳健性，并实现更高的功效。该研究为复杂疾病相关基因位点的研究提供了新的思路。