Genetic Dissection of a QTL Influencing the Development of Type 2 Diabetes and the Metabolic Syndrome.

糖尿病通常与肥胖有关，并且通常是影响个体代谢的更广泛干扰的一部分。这些其他因素包括高血压和控制脂肪（如胆固醇），以及血液中的糖（葡萄糖）。医生们注意到，这些异常通常在个体中聚集在一起，并将其称为代谢综合征，其主要长期影响是增加一个人患心脏病和血液循环障碍的风险。自1981年以来，澳大利亚患有代谢综合征主要成分之一的2型糖尿病的人数增加了两倍，并且仍在增加。除了个人痛苦之外，这也给国家带来了重大的公共卫生负担（每年约30亿美元）。目前可用的基于生活方式的药物治疗似乎不足以控制越来越多的受影响的个人。不幸的是，疾病的原因知之甚少，虽然遗传因素是已知的重要，换句话说，它在家庭中运行。该项目旨在确定其中一些因素（基因）以及它们如何导致疾病。使用DNA上的分子标记（如DNA指纹），我们以前发现3号染色体上的一个小区域可能携带一个或多个这些疾病基因。但是这个区域有100多个基因，所以为了帮助选择最有可能的候选基因进行测试，我们开发了一个自动计算机数据库搜索程序，将列表缩小到六个最有可能的基因。该项目将确定这些基因中的任何一个是否参与其中。一个成功的发现意味着我们将更多地了解疾病发展的机制，并能够更好地开发新的治疗和预防方法。如果这些基因都不是罪魁祸首，我们将继续检查下一组可能涉及的基因，直到我们成功。

Diabetes is usually associated with obesity and is often part of a wider disturbance affecting an individuals metabolism. These other factors include high blood pressure and the control of fats (eg cholesterol), and sugars (glucose) in the blood stream. Physicians have noted that these abnormalities often cluster together in individuals and have called it the metabolic syndrome, the main long term implication of which is to increase a persons risk of developing heart disease and disorders in blood circulation. The number of affected people with one the major components of the metabolic syndrome, type 2 diabetes, has trebled since 1981 in Australia and is still increasing. Apart from individual suffering, this presents a major public health burden for the country (approx $3 billion annually). Currently available lifestyle based and pharmaceutical therapies appear inadequate to control the increasing numbers of affected individuals. Unfortunately the cause of disease is poorly understood, although genetic factors are known to be important, in other words it runs in the family. This project proposes to identify some of these factors (genes) and how they contribute to the disease. Using molecular flags on the DNA (like DNA fingerprinting) we have previously found that a small region on chromosome 3 is likely to carry one or more of these disease genes. But there are over 100 genes in the region, so to help choose the most likely candidates first for testing, we have developed an automated computer database searching program that narrowed the list to the six most likely genes. This project will determine which if any of these genes are involved. A successful finding means we will know more about the mechanism of disease development and be able to better develop new therapies for treatment and prevention. If none of these genes are the culprit, we would continue examination of the next set of genes likely to be involved and so on until we are successful.