Developing methods for identifying clinical phenotypes from routinely collected health data with applications to stroke genetics.

已知与中风相关的遗传和其他危险因素在很大程度上是类型和亚型特异性的。系统性综述数据和英国生物库试点工作数据显示，约40%的卒中类型（缺血性与出血性）未明确，约70%的卒中病例（从常规收集的编码健康数据中确定）未明确进一步的卒中亚型（吐司、OCSP、出血部位等）。通过专家裁定，可以为大约80%的这些病例分配类型和亚型，但这不是适合于非常大的研究的可扩展方法（例如，UK Biobank）。我建议开发可扩展的自动化方法，通过研究各种算法代码组合和自然语言处理方法（可应用于自由文本医疗记录和成像报告）的使用，从常规收集的健康数据中进一步进行中风分型和子分型。然后，我建议直接验证这些方法，以及通过将它们与遗传学研究中的其他表型分析方法进行比较来间接验证这些方法。表型关联研究可用于系统地检查一个或多个遗传变异对广泛的人类表型的影响，并有可能揭示潜在疾病机制的新见解，并且在新的药物靶点的鉴定和药物再利用机会方面具有巨大的潜力。英国生物库拥有大量多样的表型数据，是非常适合这些研究的数据集。然而，迄今为止，相对缺乏复杂的表型方法来选择和鉴定感兴趣的结果。我建议应用现有的全表型关联研究方法来研究基于假设的与中风作为模型疾病的关联，并进一步发展这些方法以获得更广泛的应用。在过去的十年中，全基因组关联研究的结果提高了我们对复杂疾病遗传学的认识和理解。统计分析通常通过单变异测试来寻找表型和单个遗传变异之间的关联。然而，这是一种过于简单的方法来解决潜在的生物机制的复杂性。下一步将是考虑遗传变异之间的相互作用，或上位性。上位性检测产生了新的分析挑战，因为分析每个单核苷酸多态性组合目前在全基因组范围内是不切实际的。我建议应用现有的方法，并进一步发展这些更广泛的使用，从一个假设驱动的方法来调查选定的中风基因之间的上位性关联。

Genetic, and other risk factor associations with stroke are known to be to a large extent type and subtype specific. Systematic review data and UK Biobank pilot work data has shown that stroke type (ischaemic versus haemorrhagic) is not specified for around 40%, and further stroke subtype (TOAST, OCSP, haemorrhage location etc) is not specified for around 70% of stroke cases ascertained from routinely collected coded health data. With expert adjudication it is possible to assign a type and subtype for around 80% of these cases, but this is not a scalable method suitable for very large studies (e.g., UK Biobank). I propose to develop scalable, automated methods that will allow further stroke typing and subtyping from routinely collected health data by investigating the use of various algorithmic code combinations and of natural language processing methods that could be applied to free text medical records and imaging reports. I would then propose to validate these methods directly, as well as indirectly by comparing them with other phenotyping approaches in genetic studies.Phenome-association studies can be used to systematically examine the impact of one or many genetic variants across a broad range of human phenotypes, and have the potential to reveal novel insights to underlying disease mechanisms, as well as hold great potential for the identification of novel drug targets and drug repurposing opportunities. UK Biobank with its vast and varied phenotypic data is a dataset that is highly suitable for these studies. However, to date there is a relative lack of sophisticated phenotypic methods to select and identify outcomes of interest. I propose to apply existing phenome-wide association study methods to investigate hypothesis-based associations with stroke as a model disease, and to develop these methods further for wider use. During the past decade, findings of genome-wide association studies have improved our knowledge and understanding of complex disease genetics. Statistical analysis typically looks for association between a phenotype and single genetic variants taken individually via single-variant tests. However, this is an oversimplified approach to tackle the complexity of underlying biological mechanisms. The next steps would be to also consider the interactions between genetic variants, or epistasis. Epistasis detection gives rise to new analytic challenges since analysing every single nucleotide polymorphism combination is at present impractical at a genome-wide scale. I propose to apply existing methods and develop these further for wider use, starting with a hypothesis-driven approach to investigate epistatic associations between selected stroke genes.