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

开发从常规收集的健康数据中识别临床表型的方法，并将其应用于中风遗传学。

• 批准号: MR/S004130/1
• 负责人: Kristiina Rannikmae
• 金额: $ 59.8万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS004130%2F1
• 关键词: Developing; methods; identifying; clinical; phenotypes

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.

已知与中风相关的遗传和其他危险因素在很大程度上是特定类型和亚型的。系统评价数据和英国生物银行试点工作数据表明，约40%的中风病例未指定中风类型（缺血性与出血性），约70%的中风病例未指定进一步的中风亚型（TOAST、OCSP、出血部位等）。通过专家裁决，可以为大约80%的病例分配类型和亚型，但这不是一种适合大型研究的可扩展方法（例如，UK Biobank）。我建议开发可扩展的自动化方法，通过调查各种算法代码组合和自然语言处理方法的使用，可以从常规收集的健康数据中进一步进行中风分类和亚分类，这些方法可以应用于免费文本医疗记录和成像报告。然后，我将建议直接验证这些方法，以及通过将它们与遗传研究中的其他表型方法进行比较来间接验证这些方法。现象关联研究可用于系统地检查一个或多个遗传变异在广泛的人类表型中的影响，并有可能揭示潜在疾病机制的新见解，以及在识别新的药物靶点和药物再利用机会方面具有巨大的潜力。英国生物银行拥有大量多样的表型数据，是一个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确定感兴趣的结果。我建议应用现有的全现象关联研究方法来研究基于假设的关联与中风作为一种模型疾病，并进一步发展这些方法以供更广泛的使用。在过去的十年中，全基因组关联研究的发现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联系。然而，这是一种过于简化的方法来解决潜在生物机制的复杂性。下一步将考虑遗传变异之间的相互作用，或上位性。上位性检测带来了新的分析挑战，因为目前在全基因组范围内分析每一个核苷酸多态性组合是不切实际的。我建议应用现有的方法，并进一步发展这些方法，以更广泛的应用，从假设驱动的方法开始，研究选定的中风基因之间的上位性关联。

项目成果

Rare Missense Functional Variants at COL4A1 and COL4A2 in Sporadic Intracerebral Hemorrhage.

• DOI: 10.1212/wnl.0000000000012227
• 发表时间: 2021-07-19
• 期刊: Neurology
• 影响因子: 9.9
• 作者: Chung J;Hamilton G;Kim M;Marini S;Montgomery B;Henry J;Cho AE;Brown DL;Worrall BB;Meschia JF;Silliman SL;Selim M;Tirschwell DL;Kidwell CS;Kissela B;Greenberg SM;Viswanathan A;Goldstein JN;Langefeld CD;Rannikmae K;Sudlow CLM;Samarasekera N;Rodrigues M;Al-Shahi Salman R;Prendergast JGD;Harris SE;Deary I;Woo D;Rosand J;Van Agtmael T;Anderson CD
• 通讯作者: Anderson CD

Frequency and phenotype associations of rare variants in five monogenic cerebral small vessel disease genes in 200,000 UK Biobank participants with whole exome sequencing data

200,000 名英国生物银行参与者中 5 个单基因脑小血管疾病基因的罕见变异的频率和表型关联以及全外显子组测序数据

• DOI: 10.1101/2021.11.17.21266447
• 发表时间: 2021
• 作者: Ferguson A

Contribution of Common Genetic Variants to Risk of Early-Onset Ischemic Stroke.

• DOI: 10.1212/wnl.0000000000201006
• 发表时间: 2022-10-17
• 期刊: Neurology
• 影响因子: 9.9

Global Assessment of Mendelian Stroke Genetic Prevalence

孟德尔中风遗传患病率的全球评估

• 发表时间: 2020
• 期刊: Stroke
• 影响因子: 8.3
• 作者: Grami N

Genetically Determined Levels of Circulating Cytokines and Risk of Stroke: Role of Monocyte Chemoattractant Protein-1

• DOI: 10.1161/circulationaha.118.035905
• 发表时间: 2019-01-08
• 期刊: CIRCULATION
• 影响因子: 37.8
• 作者: Georgakis, Marios K.;Gill, Dipender;Dichgans, Martin
• 通讯作者: Dichgans, Martin