Genetic Evaluation of Multimorbidity towards INdividualisation of Interventions - GEMINI

1.使用遗传学和观察学相结合的方法确定疾病聚集性；并检验以下假设：a)使用多个数据来源和遗传相关性将提供比迄今可能的更可靠的聚集性估计，以及b)聚集性的模式在性别和少数民族之间有所不同。2.为提高多病研究的重现性，并在相关情况下与其他合作投标合作，开发STATA和R包以标准化MRC参考：MR/V005359/1第2页，共16个SPF解决多病：跨GP数据库的疾病编码支持过程案例，并使这些工具公开可用。3.开发和应用遗传和非遗传“工具”和因果推断方法，以确定疾病聚集性的共同生物和非生物决定因素。这些工具将包括特定于潜在共同疾病过程的遗传变异簇，并根据疾病发生率和不良后果进行随机分组。4.确定新出现的疾病集群的子集，以便进一步深入研究；在具有不同确定标准的附加数据集中验证这些集群；并使用纵向数据检验假设，即不同疾病成分到已识别集群的时间序列不同于机会(生命过程)。5.根据频率、严重程度和我们的PPI顾问的信息，确定影响最大的集群相关结果；估计每个集群中产生最大影响的患者的比例，并确定潜在的可修改的风险因素。6.确定与仅患有其中一种构成疾病的患者相比，导致临床上有意义的预后差异的疾病群。

1. To identify disease clusters using a combined genetic and observational approach; and to test the hypotheses that: a) using multiple data sources and genetic correlations will provide more robust estimates of clustering than has been so far possible, and that b) patterns of clustering differ between the sexes and ethnic minorities. 2. To improve reproducibility of multimorbidity research, and working with other collaborative bids where relevant, to develop STATA and R packages to standardize the MRC Reference: MR/V005359/1 Page 2 of 16 SPF Tackling Multimorbidity at Scale: Case for Support process of disease coding across GP databases, and to make these tools publicly accessible. 3. To develop and apply genetic and non-genetic "instruments" and causal inference methods to identify shared biological and non-biological determinants of disease clustering. These instruments will include clusters of genetic variants specific to potential shared disease processes and be randomized to disease incidence and adverse outcomes. 4. To identify a subset of emerging disease clusters for further, in depth study; to validate these clusters in additional datasets with different ascertainment criteria; and to test the hypothesis, using longitudinal data, that the timing sequences of the different disease components to the identified clusters differ from chance (life course). 5. To identify the cluster-associated outcomes of most impact, as informed by frequency, severity and our PPI advisors; to estimate the proportion of patients in each cluster who develop the most impactful outcomes and identify potential modifiable risk factors. 6. To identify the disease clusters that result in clinically meaningful differences in outcomes compared to patients with only one of the constituent diseases.