Genetic Evaluation of Multimorbidity towards INdividualisation of Interventions - GEMINI

对干预措施个体化的多发病的遗传评估 - GEMINI

• 批准号: MR/V005359/1
• 负责人: Timothy Frayling
• 金额: $ 12.56万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV005359%2F1
• 关键词: Genetic; Evaluation; Multimorbidity; towards; INdividualisation

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.

1.使用遗传和观察相结合的方法识别疾病集群;并检验以下假设：a）使用多个数据源和遗传相关性将提供比迄今为止可能的更可靠的集群估计，以及B）集群模式在性别和少数民族之间存在差异。2.为了提高多发病研究的可重复性，并在相关情况下与其他合作投标合作，开发STATA和R软件包以标准化MRC参考：MR/V005359/1第2页，共16页SPF大规模处理多发病：支持GP数据库中疾病编码过程的案例，并使这些工具可公开访问。3.开发和应用遗传和非遗传“工具”和因果推理方法来识别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群，并随机分配到疾病发病率和不良结局。4.识别新出现疾病集群的子集，以供进一步深入研究;在具有不同确定标准的其他数据集中验证这些集群;并使用纵向数据来测试假设，即不同疾病成分到已识别集群的时间序列与机会（生命过程）不同。5.根据频率、严重程度和我们的PPI顾问，确定最具影响力的群集相关结局;估计每个群集中产生最具影响力结局的患者比例，并确定潜在的可改变风险因素。6.确定与仅患有其中一种组成疾病的患者相比，导致结局具有临床意义差异的疾病群。

项目成果

188-LB: Type 2 Diabetes Is Genetically Correlated with Multiple Long-Term Conditions but These Correlations Are Only Partly Explained by BMI

188-LB：2 型糖尿病在基因上与多种长期病症相关，但这些相关性只能通过 BMI 来部分解释

• DOI: 10.2337/db21-188-lb
• 发表时间: 2021
• 期刊: Diabetes
• 影响因子: 7.7
• 作者: PILLING L