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

描述（由申请人提供）：广泛的全基因组单核苷酸多态性（SNP）现在可用。从理论上讲，我们可以系统地考虑基因组的所有区域，以确定与疾病易感性或不利风险因素相关的区域。然而，在我们能够实际使用全基因组关联研究中的所有遗传信息之前，需要解决重要的问题。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的分析策略，以解释多重比较。 许多大型的以家族为基础的研究，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有较大影响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500，000个SNPs对900名成年人在旧秩序阿米什人家系。我们的家庭非常适合全基因组关联研究，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。 这个应用程序解决了两个具体的问题，有关全基因组关联研究的家庭。 一个涉及开发，实施，测试和传播有效的方法来清洁和处理全基因组SNP数据，然后创建单倍型。 另一个涉及开发分析策略，结合联合收割机信息，从人口和传播为基础的关联测试，以提高功率，最大限度地减少假阳性率，并提高效率，检测SNP性状协会。

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.