A toolbox for the promotion of healthy ageing: Phenotypic prediction from genes and environment

促进健康老龄化的工具箱：基因和环境的表型预测

• 批准号: BB/I014144/1
• 负责人: Christopher Haley
• 金额: $ 64.76万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI014144%2F1
• 关键词: toolbox; promotion; healthy; ageing; Phenotypic

Many different factors influence the health of individuals, be they domestic animals or humans. These factors can broadly be categorised as either genetic or environmental. Thus the genes inherited from parents and the environments encountered during life are paramount in determining health status as one ages. These factors may also interact, such that individuals with one genetic make-up may react well to a particular environment, whereas a different genetic make-up may react badly. Where a substantial proportion of the genetic and environmental factors can be identified it is possible to provide accurate predictions of individuals' health as they age. Using such genetic information in prediction has great potential as it can be measured early in life and is unchanging throughout life. So there is the potential to be aware in advance of the environmental conditions that will optimise the future health of individuals. Such prediction is potentially a powerful tool to promote healthy ageing and wellbeing in both humans and companion animals, as it allows increasing efficiency of interventions, such as recommended diets or even drug treatments, and the targeting interventions towards those individuals who will most benefit. Combining genetic and environmental information is therefore the natural way to proceed when predicting how animals or humans will age and this project is concerned with developing accurate mathematical and statistical models to do this. Research in animals and humans has started the process of identifying genes affecting the traits associated with healthy ageing such as obesity or bone strength. However it has become clear that traits associated with healthy ageing are generally controlled by large numbers of genes with small effects. To unequivocally find such genes and accurately estimate their effects requires very large studies and relatively few genes have as yet been identified. Thus the amount of variation explained jointly by all the genes found in studies so far is usually much less than 10%, even though genetic variation in total may explain as much as 80% of the overall variation. Alongside genetic information, factors such as age, gender, diet and other lifestyle characteristics are often major contributors to how individuals develop. In addition, it is often known that metabolic or predisposing traits like glucose or lipid concentration in blood may correlate with health. Such traits may be more amenable to measurement or may be measured earlier than overall health status and may be used as indicators or predictors of future health. Thus information can also be combined across traits to improve the accuracy of prediction, and to allow prediction of (unmeasured) correlated traits. With this background we propose to develop mathematical methods which make best use of available genomic information and to combine this information with environmental data and across multiple traits. We will use several different approaches and compare them in their ability to accurately predict performance and how they may be extended to account for data from many traits and environments. We plan to apply and extend methods currently used in animal breeding for the related task of identifying genetically superior animals for breeding. These will be compared with machine learning methods from computer science. We plan to demonstrate the effectiveness of these methods applied to the analysis of data from human populations on body mass index - a proxy for obesity - and blood glucose levels, and will also include in the analyses environmental variables like smoking, diet and exercise. The data are currently available from human studies and methods and results will be relevant to this species. In due course, the methods developed will be directly applicable to companion animals as data become available.

许多不同的因素影响着个体的健康，无论是家畜还是人。这些因素大致可分为遗传因素和环境因素。因此，从父母那里遗传的基因和一生中遇到的环境在决定一个人年龄的健康状况方面是至关重要的。这些因素也可能相互作用，例如，具有一种基因构成的个体可能对特定环境反应良好，而具有不同基因构成的个体可能反应不良。在能够确定很大一部分遗传和环境因素的情况下，就有可能准确预测个人随着年龄增长的健康状况。利用这种遗传信息进行预测具有巨大的潜力，因为它可以在生命早期进行测量，并且在整个生命过程中保持不变。因此，有可能提前意识到环境条件，这将优化个人未来的健康。这种预测可能是促进人类和伴侣动物健康老龄化和福祉的有力工具，因为它可以提高干预措施的效率，例如推荐饮食甚至药物治疗，并针对那些最受益的个体进行有针对性的干预。因此，在预测动物或人类将如何衰老时，结合遗传和环境信息是一种自然的方式，而这个项目涉及开发准确的数学和统计模型来做到这一点。对动物和人类的研究已经开始识别影响与健康衰老相关的特征（如肥胖或骨骼强度）的基因。然而，很明显，与健康衰老相关的特征通常是由大量基因控制的，影响很小。要明确地找到这些基因并准确地估计它们的影响，需要进行非常大规模的研究，而迄今为止已确定的基因相对较少。因此，迄今为止研究中发现的所有基因共同解释的变异量通常远低于10%，尽管总的遗传变异可以解释多达80%的总体变异。除了遗传信息，年龄、性别、饮食和其他生活方式特征等因素往往是影响个体发育的主要因素。此外，人们通常知道代谢或易感特征，如血液中的葡萄糖或脂质浓度可能与健康有关。这些特征可能更易于测量，或者可以比总体健康状况更早测量，并且可以用作未来健康的指标或预测因素。因此，信息也可以跨性状组合，以提高预测的准确性，并允许预测（未测量的）相关性状。在此背景下，我们建议发展数学方法，以充分利用现有的基因组信息，并将这些信息与环境数据和多个性状相结合。我们将使用几种不同的方法，并比较它们准确预测性能的能力，以及它们如何扩展到解释来自许多特征和环境的数据。我们计划将目前在动物育种中使用的方法应用和扩展到鉴定遗传优良动物用于育种的相关任务。这些将与计算机科学中的机器学习方法进行比较。我们计划证明这些方法在分析人类体质指数（肥胖的代表）和血糖水平数据方面的有效性，并将吸烟、饮食和锻炼等环境变量纳入分析。目前从人类研究中获得的数据、方法和结果将与该物种相关。在适当的时候，当数据可用时，所开发的方法将直接适用于伴侣动物。

项目成果

Bayes U: A genomic prediction method based on the Horseshoe prior

Bayes U：一种基于马蹄先验的基因组预测方法

• 发表时间: 2014
• 期刊: Proceedings, 10th World Congress of Genetics Applied to Livestock Production
• 作者: Pong-Wong, R

A comprehensive catalogue of regulatory variants in the cattle transcriptome

• DOI: 10.1101/2020.12.01.406280
• 发表时间: 2020-12
• 期刊: bioRxiv
• 作者: Shuli Liu;Yahui Gao;O. Canela‐Xandri;Sheng Wang;Ying Yu;Wentao Cai;Bingjie Li;E. Pairo-Castineira;K. D'Mellow;K. Rawlik;Charley Xia;Yuelin Yao;Xiujin Li;Ze Yan;Congjun Li;B. Rosen;C. V. Van Tassell;P. Vanraden;Shengli Zhang;Li Ma;J. Cole;G. Liu;A. Tenesa;L. Fang

Application of high-dimensional feature selection: evaluation for genomic prediction in man.

• DOI: 10.1038/srep10312
• 发表时间: 2015-05-19
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Bermingham ML;Pong-Wong R;Spiliopoulou A;Hayward C;Rudan I;Campbell H;Wright AF;Wilson JF;Agakov F;Navarro P;Haley CS
• 通讯作者: Haley CS

A comparison of body mass index and waist-hip ratio in the genomic prediction of obesity associated health risk.

体重指数和腰臀比在肥胖相关健康风险基因组预测中的比较。

• 发表时间: 2014
• 作者: Alcántara, M

Genomic prediction of health traits in humans: demonstrating the value of marker selection.

人类健康特征的基因组预测：证明标记选择的价值。

• 发表时间: 2014
• 期刊: 10th World Congress of Genetics Applied to Livestock Production
• 作者: Bermingham, M