Genome-wide Association and linkage Studies with Diverse Resources

涉及数百或数千个单核苷酸的全基因组关联和连锁研究 由于基因的快速发展，多态现象(SNP)变得越来越普遍 生物技术。在这些研究带来的许多统计挑战中，典型的有限样本 大小是特别令人担忧的，因为高昂的基因分型成本迫使限制数量 个体进行基因分型；提高了统计显著水平，因为担心由于以下原因而出现太多假阳性 多重比较；以及复杂疾病中每个疾病相关变异等位基因的中等风险。 此应用程序考虑两种策略来解决此问题：(1)通过汇集数据来增加样本大小 (2)设计更好的统计和计算工具，以提高效率 数据的使用情况。相应地，第一个目标是开发基因的估计和推断程序 使用从基于人群的病例对照研究和基于家庭的研究中获得的数据进行关联， 适应不同的病例和控制查明方案，而第二个目标是 开发分析和正规化方法，以提高与疾病相关的可能性 变种及其相互作用实际上是可以识别的。第二个目标也与 根据这些SNP构建风险预测模型。 高密度的SNP标记也给更传统的基于模型的连锁分析带来了问题 基因发现，因为这种分析的方法是在假设连锁标记的情况下开发出来的 平衡，这一假设很可能随着SNPs的密度而被违反。第三个目标是发展 并对存在连锁不平衡的多点连锁分析的估计方法进行了评价 在SNP标记中。

Genome-wide association and linkage studies involving hundreds or thousands of Single Nucleotide Polymorphisms (SNPs) are becoming increasingly common due to the rapid development of biotechnologies. Among many statistical challenges arising from these studies, the typical limited sample size is of particular concern because of high genotyping cost putting pressure to limit the number of individuals genotyped; increased statistical significant level for fear of too many false positives due to multiple comparisons; and moderate risk from each disease-associated variant allele in complex diseases. This application considers two strategies to address this issue: (1) to increase sample size by pooling data obtained from several sources; (2) to devise better statistical and computational tools for more efficient usage of the data. Correspondly, the first aim is to develop estimation and inference procedures for genetic association using data obtained from both population-based case-control and family-based studies, accommodating diverse ascertainment schemes of cases and controls, whereas the second aim is to develop analysis and regularization methods that enhance the possibility that the disease-associated variants and their interactions can actually be identified. The second aim is also concerned with the construction of risk predictive models from these SNPs. The highly dense SNP markers also pose problems to a more traditional model-based linkage analysis for gene discovery, because the methods for this analysis were developed assuming markers in linkage equilibrium, an assumption that is likely violated with the density of the SNPs. The third aim is to develop and evaluate estimating procedures for multipoint linkage analysis in the presence of linkage disequilibrium among SNP markers.