Genome-wide Association in Families: Data Integrity, Design and Methods Issue

家庭全基因组关联：数据完整性、设计和方法问题

• 批准号: 7104529
• 负责人: JEFFREY R O'CONNELL
• 金额: $ 30.59万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-15 至 2009-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7104529
• 关键词: Mennonite; automated data processing; cardiovascular disorder risk; clinical research; cooperative study; data quality /integrity; family genetics; gene mutation; genotype; high throughput technology; human subject; linkage mapping; mathematical model; model design /development; patient oriented research; single nucleotide polymorphism

DESCRIPTION (provided by applicant): Extensive genome-wide single nucleotide polymorphisms (SNPs) are now available. Theoretically, we can systematically consider all the regions of the genome to identify those regions associated with disease susceptibility or unfavorable risk factors. Important issues need to be resolved however, before we can practically use all the genetic information in genome-wide association studies. Methods need to be developed to detect and resolve genotyping errors and we need new, analytical strategies designed specifically for family data that will account for multiple comparisons. Many large family-based studies, including our own, were initiated with the goal of first detecting linkage to identify chromosomal regions likely to harbor mutations having relatively large effects on important risk factors for heart disease. One of our studies, the Heritability and Phenotype Intervention (HAPI) Heart Study, includes extensive coronary heart disease risk factor data and 500,000 SNPs on 900 adults in Old Order Amish pedigrees. Our families are very suitable for genome-wide association studies and they offer special opportunities, compared to population-based samples, because families provide a direct test of allelic transmissions. This application addresses two specific issues pertaining to genome-wide association studies in families. One relates to development, implementation, testing, and dissemination of efficient ways to clean and process genome-wide SNP data and then create haplotypes. The other relates to developing analytic strategies that combine information from population- and transmission-based association tests to improve power, minimize false positive rates, and enhance efficiency for detecting SNP-trait associations.

描述（由申请人提供）：现在可以获得广泛的全基因组单核苷酸多态性（SNP）。理论上，我们可以系统地考虑基因组的所有区域，以确定那些与疾病易感性或不利危险因素相关的区域。然而，在我们能够在全基因组关联研究中实际使用所有遗传信息之前，需要解决一些重要问题。需要开发方法来检测和解决基因分型错误，我们需要专门为家庭数据设计的新分析策略，以进行多重比较。 许多以家庭为基础的大型研究，包括我们自己的研究，都是为了首先检测连锁，以确定可能含有对心脏病重要危险因素影响相对较大的突变的染色体区域。我们的一项研究，遗传性和表型干预 (HAPI) 心脏研究，包括广泛的冠心病危险因素数据和 900 名旧秩序阿米什人谱系成年人的 500,000 个 SNP。我们的家庭非常适合进行全基因组关联研究，与基于人群的样本相比，它们提供了特殊的机会，因为家庭提供了等位基因传播的直接测试。 该应用程序解决了与家庭全基因组关联研究有关的两个具体问题。 其中之一涉及开发、实施、测试和传播有效方法来清理和处理全基因组 SNP 数据，然后创建单倍型。 另一个涉及开发分析策略，将基于人群和基于传播的关联测试的信息结合起来，以提高功效、最大限度地减少假阳性率并提高检测 SNP 性状关联的效率。