Mendelian Randomisation

孟德尔随机化（MR）是一种统计技术，它使用遗传特征研究来了解复杂性状和疾病之间的因果关系。它的广泛使用反映了遗传数据的日益可用性以及分析工具和方法的进步。基因数据在研究设计、样本量、基因组分析的组织和表型的广度方面的令人兴奋的发展正在扩大因果推理问题的范围，但这些新的数据和问题必须由统计方法和工具来服务，以实现可靠的因果推理。我们将研究遗传研究快速增长到数百万MR样本所带来的复杂性，并确定如何使用这些资源，包括基于家庭的数据，以最有效地改善因果推理。我们将通过领导一项新的大型国际合作来扩大遗传研究，以系统地确定在整个生命过程中发生变化的遗传影响，并利用这些因素来确定时间关键的疾病发展过程。我们将开发新的方法来更好地模拟疾病的分子原因，这将为我们的药物开发制药合作提供信息。我们将继续开发方法和数据资源，以扩大疾病的流行病学分析，从优先考虑疾病发作的因果因素，到确定影响预后和生存的因素，创造识别新疗法的潜力。

Mendelian randomization (MR) is a statistical technique that uses genetically characterised studies to understand cause-and-effect relationships among complex traits and diseases. Its wide use reflects the growing availability of genetic data and advancements in analytical tools and methods. Exciting developments in genetic data in terms of study designs, sample sizes, tissues genomically analysed and breadth of phenotyping are expanding the scope of causal inference questions, but these new data and questions must be served by statistical methods and tools that enable reliable causal inference.We will examine the complexities arising from rapid growth of genetic studies into millions of samples for MR, and determine how to use such resources, including family-based data, to most effectively improve causal inference. We will expand genetic studies by leading a new large international collaboration to systematically identify genetic influences that change across the lifecourse, and use such factors to identify time-critical disease development processes. We will develop new approaches to better model the molecular causes of disease, which will feed into our pharma collaborations for drug development. We will continue developing methods and data resources that expand epidemiological analysis of disease from prioritising causal factors of disease onset, to identify factors that influence prognosis and survival, creating the potential to identify novel therapies.