Methodology for the identification of shared genetic aetiology between epidemiologically linked disorders

许多常见的健康障碍经常被观察到在个体中同时发生。数以百计的遗传疾病关联分析已经完成，结果确定了许多与特定疾病相关的遗传变异，如2型糖尿病、癌症和骨关节炎。拟议研究的主要目的是开发一种方法来检测对两种疾病的易感性有影响的遗传变异，这两种疾病被怀疑具有共同的遗传原因。现有的大多数方法都集中在识别单个性状的遗传偶然因素，只有少数方法联合分析相关的疾病。有许多对健康障碍已被确定为要么经常同时存在，要么呈反比关系，其中一种障碍的存在往往会降低另一种疾病的风险。疾病共发配对包括2型糖尿病伴克罗恩病、癌症和精神疾病，以及骨关节炎伴身体质量指数（BMI）和身高。已经确定前列腺癌和2型糖尿病之间存在反比关系。此外，一种治疗前列腺癌的方法被发现会增加患糖尿病和心血管疾病的风险。这可能是由于特定的共享遗传变异的作用。因此，对这两种疾病中的任何一种的非不良治疗的发展可能通过鉴定对这两种疾病具有这种反向作用的遗传变异而得到协助。除其他外，这突出了确定相关疾病之间共同遗传原因的重要性。通过对每种疾病进行简单的单独分析，已经确定了大多数这些疾病配对的共同遗传原因。一种方法是比较每个分析的个别结果，并选择确定遗传疾病关联的共同标准。然后检查两组结果的重叠部分。这种方法的一个警告是，每个分析在检测关联的程度上都有不同的水平，因此，尽管两个特征之间可能存在关联，但它可能只在一个研究中被检测到，因此在重叠分析中没有发现。此外，与许多其他方法一样，它不利用任何已知的遗传信息。或者，可以在已确认与另一种疾病相关的基因中搜索与一种疾病的关联。然而，这大大减少了搜索区域。在开发联合分析性状的方法时，考虑到已知的遗传和生物信息片段，例如先前确定的遗传变异，以及归因于它们的生物学功能，可以提高检测关联程度的水平。将开发统计分析方法，并通过在各种假设下生成各种似是而非的数据集来测试其性能。然后将性能最佳的方法应用于实际数据集，如2型糖尿病合并精神分裂症、腰臀比合并BMI、骨关节炎合并BMI、骨关节炎合并偏头痛。

Many common health disorders are frequently observed to co-occur in individuals. Hundreds of genetic disease association analyses have been completed, resulting in the identification of numerous genetic variants that are associated with a specific disease, such as type 2 diabetes, cancer, and osteoarthritis. The primary aim of the proposed research is to develop methodology to detect genetic variants that have an influence on susceptibility to two disorders, which are suspected of having shared genetic causes. The majority of existing approaches have focused on identifying genetic casual elements for a single trait, and only few of them jointly analyse linked disorders. There are many pairs of health disorders that have been identified as either frequently existing together, or having an inverse relationship, where the presence of one disorder tends to reduce the risk of another disease. Disease co-occurrence parings include type 2 diabetes with Crohn's disease, cancer, and psychiatric disorders, as well as osteoarthritis with body mass index (BMI) and height. It has been established that there is an inverse relationship between prostate cancer and type 2 diabetes. Moreover, a treatment for prostate cancer was found to increase the risk of diabetes and cardiovascular disease. This may be due to the roles of particular shared genetic variants. Thus, the development of non-adverse treatments for either of the two diseases may be assisted by the identification of gentic variants with such inverse effects on two diseases. This highlights, among others, the importance of identifying common genetic causes between linked diseases.Shared genetic causes for most of these disease pairings have been identified using simple separate analyses of each disease. One method is to compare the individual results from each analysis, and to choose common criteria for the identification of genetic disease associations. The overlap of the two sets of results is then examined. A caveat of this approach is that each analysis has a different level of how well it can detect associations, so that although an association may exist with both traits, it may only be detectable within one of the studies, and thus not found in the overlap analysis. Also, as with many other approaches, it does not take advantage of any known genetic information. Alternatively, associations with one disease may be searched for within the genes that have been recognized as associated with the other disease. However, this greatly reduces the search area.In developing methods to jointly analyse traits, the level of how well associations could be detected may be increased by taking into account known pieces of genetic and biological information, such as previously identified genetic variants, and the biological functions ascribed to them. Statistical analysis methods will be developed and tested for performance by generating various plausible datasets under an assortment of assumptions. The method with the best performance will then be applied to real datasets, such as type 2 diabetes with schizophrenia, waist-hip ratio with BMI, osteoarthritis with BMI, and osteoarthritis with migraine.