Imputation-based statistical methods for genetic studies of human complex disease

一段时间以来，人们已经知道，患冠心病、II型糖尿病和肺癌等疾病的风险会受到个体的影响。的遗传密码。过去一年中，发现的疾病基因数量激增，这些基因被发现用于这些疾病，并且通过一种称为全基因组关联研究的新型研究设计成为可能。这些研究收集了数千名个体的人类基因组中多达100万个位置的数据，无论是否患有特定疾病。我最近开发了一种方法，可以利用这些数据来预测基因组中数百万个尚未采样的其他位置的遗传数据。这些新数据使得疾病基因更有可能被发现，并促进了来自世界各地不同研究小组的数据集的组合。我现在建议扩展和发展该方法，以允许分析正在变得可用的新型遗传数据。这种分析将有助于发现新的疾病基因，并随后为许多常见的复杂疾病提供更好的治疗和预测测试。

It has been known for some time that the risk of developing diseases such as Coronary Heart Disease, Type II Diabetes and Lung Cancer are influenced by an individual?s genetic code. The last year has a seen an explosion in the number of disease genes that have been discovered for diseases such as these, and have been made possible by a new type of study design known as a genome-wide association study. These studies collect data at up to 1 million positions across the human genome from thousands of individuals, with and without a specific disease. I recently developed a method that takes this data and uses it to predict the genetic data at millions of other positions across the genome that have not been sampled. This new data makes it more likely that a disease gene will be uncovered, and facilitates the combination of datasets from different research groups around the world. I now propose to extend and develop the method to allow the analysis of new types of genetic data that are becoming available. This analysis will allow the discovery of new disease genes and subsequently lead to better treatments and predictive tests for many common complex diseases.