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 多态性（SNP）由于基因组学的快速发展而变得越来越普遍。 生物技术在这些研究产生的许多统计挑战中，典型的有限样本 由于高基因分型成本对限制基因型的数量施加了压力， 个体基因分型;增加统计学显著性水平，因为担心太多假阳性， 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度风险。 该应用程序考虑了两种策略来解决这个问题：（1）通过合并数据来增加样本量 从几个来源获得;（2）设计更好的统计和计算工具， 数据的使用。相应地，第一个目标是开发遗传的估计和推理程序， 使用从基于人群的病例对照和基于家庭的研究中获得的数据， 适应病例和对照的不同确定方案，而第二个目标是 开发分析和规范化方法，提高疾病相关的可能性， 变体和它们的相互作用实际上可以被识别。第二个目标也与 从这些SNP构建风险预测模型。 高度密集的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标记连锁的情况下开发的 平衡，这是一个可能被SNP密度所违背的假设。第三个目标是发展 并评估在连锁不平衡的存在下多点连锁分析的估计程序 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.