MICA: STELAR (Study Team for Early Life Asthma Research) consortium - Asthma e-lab and identification of novel endotypes of childhood asthma

哮喘是儿童最常见的慢性疾病，导致许多住院。在过去的30年里，患有哮喘的儿童人数急剧增加。目前还不清楚为什么有些人会得哮喘，而有些人不会。哮喘在很大程度上是可遗传的，但尽管我们做了很多努力，但在确定哪些基因是重要的方面取得的成功有限，而且哮喘的遗传研究尚未对患者护理产生积极影响。环境中的许多因素可能会导致哮喘的发展（例如饮食，免疫接种，抗生素，宠物和烟草烟雾），但我们不知道如何改变环境以降低风险。哮喘的病因难以理解的一个原因是，哮喘可能是引起类似症状的不同疾病的集合。由于哮喘通常在生命早期开始，研究它的最佳方法是招募新生儿并随着他们的成长而跟踪他们（所谓的出生队列）。在生命早期，可以测量许多事情，例如暴露于过敏原（通常是猫，狗和尘螨）的饮食成分和抗生素的使用。测试仪用于从父母那里收集有关症状的信息，随着儿童年龄的增长，他们可以参加肺功能和过敏测试的测量。大多数人愿意提供基因检测样本。在英国，有5个这样的出生队列，旨在促进哮喘和过敏风险因素的研究。曼彻斯特研究有1,000多名儿童接受积极随访;临床随访完成年龄为1岁，3岁，5岁，8岁和11岁。这项来自阿伯丁的研究包括1,924名儿童，他们被随访了6个月、1年、2年、5年和10年。阿什福德出生队列研究跟踪了642名儿童，直到他们12岁。怀特岛的研究招募了1，456名儿童，在1岁，2岁，4岁，10岁和18岁时完成随访。ALSPAC通过前埃文县的产前诊所招募，招募了14 062名婴儿。随访至16岁。所有人都使用问卷收集数据，并在整个早期生活中定期进行肺功能和皮肤测试。领导这些研究的研究人员在过去7年中作为一个网络一起工作-早期哮喘研究研究小组（STELAR联盟）。近年来，我们采用了相同的研究方法，认识到虽然每个队列都是独特的，但我们可以在许多方面进行合作，以增加我们发现哮喘主要原因的机会。我们现在提议创建一个重要的新联盟，该联盟将联合收割机将我们在出生队列方面的世界领先专业知识（STELAR财团）与健康信息学研究方面的专业知识（NW生物健康信息学研究所）和尖端计算统计方法（所谓的统计机器学习，与我们的工业合作伙伴微软研究院的专家一起在剑桥）。我们的联盟包括临床医生，公共卫生和流行病学研究人员，统计学家，信息学家和软件工程师。我们将构建一个基于网络的资源（哮喘电子实验室），在其中安全地存储收集的所有队列数据和分析数据的方法，以便更多的科学家可以重复这项工作。这将使所有合作伙伴能够一致地记录、描述和分享数据和新的调查结果。必要时，我们还将完成队列参与者的临床随访。我们将共同努力，应用最新开发的最先进的数据分析技术，建立复杂的模型来描述不同类型的“哮喘”，并调查每种哮喘亚型的风险因素。通过这样做，我们希望了解不同形式哮喘的基本生物学机制，我们的研究结果可能会支持哮喘预防的新试验，并可能有助于确定与特定患者相匹配的新疗法的发现目标。

Asthma is the most common chronic disease of childhood and causes many hospital admissions. The number of children suffering from asthma has increased dramatically over the past 30 years. It is unclear why some people get asthma and others do not. Asthma is largely heritable, but despite lots of effort we have had limited success identifying which genes are important, and genetic studies of asthma have not yet had a positive impact on patient care. Many factors in the environment may contribute to the development of asthma (for example diet, immunizations, antibiotics, pets and tobacco smoke) but we don't know how to modify the environment to reduce the risks. One reason for the difficulty in understanding causes of asthma is that asthma may be a collection of different diseases which cause similar symptoms. As asthma generally starts early in life, the best way to study it is to recruit new born babies and follow them as they grow (so-called birth cohort). During early life it is possible to measure many things, such as exposure to allergens, (generally cats, dogs and dust mite) constituents of the diet and antibiotic usage. Questionnaires are used to collect information from parents about symptoms, and as the children get older they can take part in measurements of lung function and allergy testing. Most are willing to give sample for genetic testing. In the UK there are 5 such birth cohorts that have been designed to facilitate the study of risk factors for asthma and allergies. The Manchester Study has more than 1,000 children under active follow up; clinical follow up is complete for age 1, 3, 5, 8 and 11 years. The study from Aberdeen included 1,924 children who were followed up 6 months, 1, 2, 5 and 10 years. The Ashford birth cohort followed 642 children until age 12 years. The Isle of Wight study recruited 1,456 children, completing follow up at ages 1, 2, 4, 10 and 18 years. ALSPAC recruited through antenatal clinics in the former County of Avon, and enrolled 14,062 infants. Follow up is complete to age 16 years. All have collected data using questionnaire and performed measures of lung function and skin tests at intervals throughout early life. The researchers who lead these studies have worked together as a network over the last 7 years - the Study Team for Early Life Asthma Research (STELAR consortium). Over recent years we have adopted identical research methodologies, recognising that although each cohort is unique, there are many aspects in which we can work together, to increase our chances of detecting the main causes of asthma. We now propose to create a major new alliance which will combine our world-leading expertise in birth cohorts (STELAR consortium) with expertise in health informatics research (NW Institute for Bio-Health Informatics) and cutting-edge computational statistical methods (so-called statistical machine learning, with experts at our Industrial partner at Microsoft Research Cambridge). Our alliance includes clinicians, public health and epidemiology researchers, statisticians, informaticians and software engineers. We will construct a web based resource (Asthma e-Lab) in which to securely store all the data collected on the cohorts and recipes for analysing the data so that a larger group of scientists can repeat the work. This will enable consistent recording, description and sharing of data and emerging findings across all partners. We will also complete clinical follow-up of cohort participants where necessary. We will work together to apply newly developed state-of-the-art data analysis techniques to build complex models to describe different types of 'asthma' and investigate risk factors for each asthma subtype. In doing so we hope to understand the basic biological mechanisms that underlie the different forms of asthma, Our findings may underpin new trials of asthma prevention and may help identify targets for the discovery of novel therapies which are matched to specific patients.