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，导致许多人住院。在过去的30年里，患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遗传的，但尽管我们付出了很多努力，但我们在确定哪些基因是重要的方面取得了有限的成功，哮喘的基因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导致哮喘的发展（例如饮食、免疫接种、抗生素、宠物和吸烟），但我们不知道如何改变环境来降低风险。难以理解哮喘病因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，研究它的最好方法是招募新生婴儿，并随着他们的成长进行跟踪（所谓的出生队列）。在生命早期，有可能测量许多事情，例如接触过敏原，（通常是猫，狗和尘螨）饮食成分和抗生素使用。调查问卷用于从父母那里收集有关症状的信息，随着孩子年龄的增长，他们可以参加肺功能的测量和过敏测试。大多数人愿意为基因检测提供样本。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。曼彻斯特研究对1000多名儿童进行了积极的随访；完成了1、3、5、8和11岁的临床随访。来自阿伯丁的这项研究包括1924名儿童，他们分别随访了6个月、1年、2年、5年和10年。阿什福德的出生队列跟踪了642名儿童，直到他们12岁。怀特岛的这项研究招募了1456名儿童，他们分别在1岁、2岁、4岁、10岁和18岁时完成了随访。ALSPAC通过前埃文县的产前诊所进行招募，并招募了14062名婴儿。随访完成至16岁。所有人都使用问卷收集数据，并在生命早期进行肺功能测量和皮肤测试。在过去的7年里，领导这些研究的研究人员作为一个网络——早期生命哮喘研究小组（STELAR联盟）一起工作。近年来，我们采用了相同的研究方法，认识到尽管每个队列都是独特的，但我们可以在许多方面共同努力，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，将我们在出生队列（STELAR联盟）方面的世界领先的专业知识与健康信息学研究（NW生物健康信息学研究所）的专业知识和尖端的计算统计方法（所谓的统计机器学习，与我们的工业合作伙伴微软剑桥研究院的专家）结合起来。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。我们将建立一个基于网络的资源（哮喘e-Lab），在其中安全地存储收集到的所有数据，以及用于分析数据的配方，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、描述和共享数据和新发现。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危险因素。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。我们的发现可能会为哮喘预防的新试验提供基础，并可能有助于确定发现与特定患者相匹配的新疗法的目标。

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.