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

Lead Research Organisation: University of Edinburgh
Department Name: MRC Human Genetics Unit

Abstract

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.

Technical Summary

The most prevalent non-infectious causes of poor health in companion animals and humans are related to aging and are multifactorial in origin: partly genetic and partly environmental, with these factors acting independently or through complex interactions. Where a substantial proportion of the systematic factors can be identified and modelled it is possible to provide accurate predictions of future life events including specific risks of developing these age-related syndromes or diseases in as yet unaffected individuals or beneficial or adverse drug response in those already affected. Such prediction is potentially a powerful tool to promote healthy ageing in both humans and animals, as it allows increasing effectiveness of interventions, and cost-effectiveness, by stratifying the population into subgroups, e.g. at risk/not at risk, responders and non-responders to particular environmental interventions and then targeting interventions. Key to accurate prediction is our ability to explain as much as possible of the trait variance, both genetic and non-genetic and we propose to develop methods that allow us to make best use of the information recorded in a clinical context or in population studies. This information includes records of environmental exposures, phenotypes of interest and genomic information. We propose improving predictive performance by better using information through: 1) Taking appropriate account of all genetic marker information, not just that from few highly selected markers 2) Using models that account for environment and genomic factors and their interactions 3) Utilising information from correlated traits in a multitrait framework 4) Identifying optimum prediction methods from those specific for problems where the set of predictive variables is much larger than the number of observations We will combine and refine these different methods and demonstrate their efficacy using simulated data and crossvalidation on large-scale GWAS data.

Planned Impact

Impact to the academic community Our findings will be disseminated to the academic community by publishing in peer reviewed journals and by presenting them at national and international meetings. Publication in high-impact, peer-reviewed journals is necessary to give our finding authority. These journals will include general scientific journals where possible, and also leading specialist journals in the areas of genetics, animal breeding, veterinary medicine and medicine, and applications of machine learning methods (see Track Record and CVs). The range of stakeholders who will benefit from our research is very broad, and includes academics in the area of veterinary medicine, both for companion animals and livestock and medicine. Impact to the general public The MRC press office is the link between MRC scientists and the media, and is in charge of publicising research activities from MRC scientists. It also provides corporate information to the media and reports on recent research findings on the MRC news page. The press office represents our first step towards the communication of the results of our investigations to the lay public. In addition, the MRC participates annually in events like the Edinburgh Science Festival, and these are very excellent platforms for the dissemination of science, in which we would be very happy to participate. A specific and important issue to take into account when dealing with obtaining/using genetic information from humans is the fact that a proportion of the population is concerned about 'genetic discrimination'. An important task would be to emphasise the benefits that will come from using genetic information, and that, with a strong NHS, the risks of discrimination are somehow lower. Impact to potential users We will provide excellent tools for the prediction of phenotypic risk and hope to provide the foundation its application in veterinary and clinical medicine, as a means to promote lifelong health and wellbeing. We will engage directly with potential users of the approach and with policy makers to discuss aspects of the implementation. Potential users and groups involved in these discussions should include veterinarians, clinicians, clinical geneticists, epidemiologists, and the animal breeding and pharmaceutical industries. We have links with these user groups through ongoing collaborations (see Track Record section for examples). Careful dissemination of our findings to these groups and policy makers is of utmost importance and our work could be the basis for a larger collaborative project that could be more focussed on implementation and cost-effectiveness of the methods we will develop in the different fields of application. In addition to other dissemination already proposed, we propose to set up a workshop in the last semester of our project, to disseminate and discuss our findings amongst potential user groups and policy makers, and discuss potential applications. Impact to Health We believe the methodology proposed could significantly increase the accuracy of prediction of phenotypic value of health-related traits both in managed animals and in humans compared to currently used methods. This would represent an important step towards the possibility of using high-throughput genotyping at a population-wide level as a powerful tool to prevent poor health in old age. With escalating treatment costs, good screening strategies become more cost effective. To allow the full implementation of the methodology SNP array genotyping on a population basis is needed. This will become possible in the near future as genotyping costs decrease. Economic Impact Once each individual is typed, its genetic information could be used to inform on his or her risk to suffer a wide range of disorders later in life, and that will make the strategy very cost effective and will have an enormous impact on the way health services work, and on health.

Publications

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Bermingham, M (2014) Genomic prediction of health traits in humans: demonstrating the value of marker selection. in 10th World Congress of Genetics Applied to Livestock Production

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Pong-Wong, R (2014) Bayes U: A genomic prediction method based on the Horseshoe prior in Proceedings, 10th World Congress of Genetics Applied to Livestock Production

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Spiliopoulou, A (2014) Combining different sources of information to optimise genomic prediction of complex traits in The European Human Genetics Conference, ESHG 2014

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Spiliopoulou, A (2013) Multiple Kernel Learning for Genomic Predictions of Complex Traits in European Mathematical Genetics Meeting, EMGM 2013, Leiden, The Netherlands

 
Description We have explored the use of genetic information from DNA sequences and markers to predict future health outcomes. In particular we have explored alternative machine learning approaches to do this using genomic information from genome-wide molecular markers and the value of alternative data structures. One finding indicates the value of data from close relatives in prediction for example. This suggests that as genomic information become ubiquitous and gathered on whole populations including from families, its value will only increase. In future this might enable early prediction of those in need of additional support or treatment or sensitive or insentive to particular drugs (this is applicable to both humans and other species such as livestock).
Exploitation Route Pharmaceutical companies might be able to identify differential response to drugs. Healthcare professional may be able to identify those who need treatment or advice on lifestyle factors in advance of problems arising. The results indicate the value of extending genomic technology to whole populations as the cost of generating these data reduces. For example genotyping all at birth, using genomic arrays or in future whole genome sequencing is likely to become a viable healthcare option in future to contribute to optimising the human healthspan. The same approaches are also being used by livestock breeding companies in selecting the best performing and healthy parents to optimise future livestock sustainability.
Sectors Agriculture, Food and Drink,Healthcare,Pharmaceuticals and Medical Biotechnology

 
Description There is an increasing recognition as we have shown here genomic prediction may be effective within populations but are generally ineffective between populations. Improvement of prediction particularly between populations will be very valuable to commercial animal breeding programmes and tools to improve these predictions base on this study are being explored in collaboration with commercial partners. One CASE studentship with a breeding company partner has shown how leveraging information from related populations can increase genetic improvement whilst reducing risks of inbreeding.
First Year Of Impact 2019
Sector Agriculture, Food and Drink
 
Description BBSRC KTN CASE Studentship
Amount £80,000 (GBP)
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 09/2016 
End 08/2021
 
Description Exploiting large-scale exome sequence data to determine the genetic control of healthy aging
Amount £72,000 (GBP)
Funding ID 2274606 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 08/2019 
End 08/2023
 
Description Exploiting large-scale exome sequence data to determine the genetic control of healthy aging-BBSRC NATIONAL PRODUCTIVITY INVESTMENT FUND (NPIF) STUDENTSHIPS
Amount £72,000 (GBP)
Funding ID BB/S508032/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 09/2019 
End 08/2023
 
Description Investigating the genetic architecture of complex traits in Soay sheep
Amount £72,000 (GBP)
Funding ID 2278106 
Organisation Natural Environment Research Council 
Sector Public
Country United Kingdom
Start 08/2019 
End 08/2023
 
Description Investigating the mechanisms underlying disease using multiOmics data
Amount £72,000 (GBP)
Funding ID 2259226 
Organisation Medical Research Council (MRC) 
Sector Public
Country United Kingdom
Start 08/2019 
End 02/2023
 
Description MRC response mode
Amount £17,672 (GBP)
Funding ID MR/N003179/1 
Organisation Medical Research Council (MRC) 
Sector Public
Country United Kingdom
Start 11/2015 
End 10/2018
 
Description BBSRC Toolbox prediction project 
Organisation GlaxoSmithKline (GSK)
Country Global 
Sector Private 
PI Contribution We are performing research to develop computational prediction of health and disease outcomes utlising genomics and life style data funded by the BBSRC
Collaborator Contribution Provide access to large population data and advice
Impact Publications
Start Year 2012
 
Description BBSRC Toolbox prediction project 
Organisation Pharmatics Limited
Country United Kingdom 
Sector Private 
PI Contribution We are performing research to develop computational prediction of health and disease outcomes utlising genomics and life style data funded by the BBSRC
Collaborator Contribution Provide access to large population data and advice
Impact Publications
Start Year 2012
 
Description Stratifying Anxiety and Depression Longitudinally (STRADL) 
Organisation University of Aberdeen
Department Institute of Biological and Environmental Sciences
Country United Kingdom 
Sector Academic/University 
PI Contribution Contribution to design and performance of genetic analyses of data
Collaborator Contribution Contribution of data and trait domain expertise
Impact Publications are listed separately: Zeng et al (2017); Zeng et al. (2016 a, b); McIntosh et al. (2016); Fernandez-Pujals et al. (2016)
Start Year 2015
 
Description Stratifying Anxiety and Depression Longitudinally (STRADL) 
Organisation University of Dundee
Department College of Life Sciences
Country United Kingdom 
Sector Academic/University 
PI Contribution Contribution to design and performance of genetic analyses of data
Collaborator Contribution Contribution of data and trait domain expertise
Impact Publications are listed separately: Zeng et al (2017); Zeng et al. (2016 a, b); McIntosh et al. (2016); Fernandez-Pujals et al. (2016)
Start Year 2015
 
Description Stratifying Anxiety and Depression Longitudinally (STRADL) 
Organisation University of Glasgow
Department Institute of Health and Wellbeing
Country United Kingdom 
Sector Academic/University 
PI Contribution Contribution to design and performance of genetic analyses of data
Collaborator Contribution Contribution of data and trait domain expertise
Impact Publications are listed separately: Zeng et al (2017); Zeng et al. (2016 a, b); McIntosh et al. (2016); Fernandez-Pujals et al. (2016)
Start Year 2015
 
Description Doors open day 
Form Of Engagement Activity Participation in an open day or visit at my research institution
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Public/other audiences
Results and Impact Participation in open day at IGMM
Year(s) Of Engagement Activity 2015
 
Description Edinburgh Alliance for Complex Trait Genetics 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact Co-organise a twice-yearly meeting to coordinate complex trait genetic research focussed on Edinburgh but with national participation.
Year(s) Of Engagement Activity 2011,2012,2013,2014,2015,2016,2017,2018,2019,2020
URL https://www.wiki.ed.ac.uk/display/eactg/Edinburgh+Alliance+for+Complex+Trait+Genetics
 
Description Edinburgh International Science Festival 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? Yes
Geographic Reach Local
Primary Audience Public/other audiences
Results and Impact Several members of the group have participated for the last years in the MRC-HGU or IGMM public engagement activities at the Edinburgh Science Festival. Hundreds of families and children participated in various hand-on activities related to genetics. This sparked questions and discussions both from adults and children from different backgrounds, and we engaged with them tailoring our dialogue to the appropriate level according to our interlocutor(s).
The feedback from both the public and the organisers has consistently been very good.

unknown
Year(s) Of Engagement Activity 2011,2012,2013,2014,2015
URL http://www.sciencefestival.co.uk/
 
Description Genomic Prediction workshop 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact Organised a scientific workshop on the subject of genomic prediction / precision medicine to highlight our own and ongoing research. Around 10 presentations to a largely academic audience
Year(s) Of Engagement Activity 2015
 
Description Participation in an activity, workshop or similar - Visit to CEIP 9 D'OCTUBRE (ALCÀSSER, Spain) Pre-School and Primary School for the 2020 International Day of Women and Girls in Science 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Schools
Results and Impact Main event 30/01/2020, with engagement prior to the visit (sending introductory letter, discussing prior activities with teaching staff) and following-up (ongoing) additional science/genetics related activities.
To kick off activities related to the 2020 International Day of Women and Girls in Science, Pau Navarro visited the Pre-School and Primary School CEIP 9 D'OCTUBRE (ALCÀSSER, Spain). She engaged with 250 pupils between the ages of 3 and 7 (2 classes each of 25 pupils for 3, 4 and 5 year-olds (pre-school), and 2 classes each of 6 and 7 year-olds (primary 1 and 2 equivalent)), their teaching and support staff and parent volunteers. The activities were delivered to eight groups of 25 pupils each (3-6 year-olds) and a single group of 50 7 year-olds.
The activities were designed to explain to the pupils what being a scientist means, and let them have a go at being a hands-on budding one through observation and description of objects looked at through magnifying glasses, a traditional microscope and a small digital camera and a microscope attached to a phone that allowed recording of images.
The activity was tailored to the different age groups and discussions with the primary school groups also involved introducing the concepts of phenotypic variation, inheritance and chromosomes.
Engagement with the pupils started prior to the visit through an introductory letter sent to the pupils, and a series of tasks (i.e., collect interesting objects, prepare questions for the visiting scientist), and has continued after, with primary school children continuing activities introduced during the visit (i.e. looking at photos of cells under the microscope and drawing with detail, "colourful chromosomes activity), and preparation of further question list with questions that were sparked by the visit. We are working on preparing a web story jointly with the pupils.
Year(s) Of Engagement Activity 2020
 
Description Sciennes Science Fair 2015 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Schools
Results and Impact I co-organised the parent led Science Fair at Sciennes school in 2015. I got in touch and coordinated participation from various researchers from the University of Edinburgh, including Volcanologists, Mineralogists, Computing Scientists, Chemists, Mathematicians and Neuroscientists. Activities were set up in the school and made available to pupils, their families and the general public. The fair was very well attended, and engagement with the activities was very good, sparking lots of questions. The audience was estimated to be in excess of 1000 people.
Year(s) Of Engagement Activity 2015
URL http://sciennesnewsflash.blogspot.co.uk/2015/06/remarkable-parent-led-summer-science.html?spref=fb
 
Description Visit to IGMM from High School student 
Form Of Engagement Activity Participation in an open day or visit at my research institution
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Schools
Results and Impact An S6 pupil trying to decide on his future career visited the IGMM, in a visit that I organised and talked to a series of colleagues about their research. He reported that the visit was really useful and helped him decide on the path he wants to take to further his education.
Year(s) Of Engagement Activity 2016
 
Description Visit to Rainbows group 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Other audiences
Results and Impact Annual visit to my local Rainbow unit, that is received with lots of enthusiasm by both girls and leaders. We discuss cells and inheritance, and do related crafty activities. This year we built cells in petri dishes with airdough. The visits sparks lots of interest and questions. It is used by the unit leaders as both a science and a "girls can" activity, and to promotes the visibility of women in science in this case.
Year(s) Of Engagement Activity 2016
 
Description Visit to Sciennes Primary School 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Schools
Results and Impact We have made a series of workshops with P4 pupils talking about science, cells and genetics, that were welcome with a lot of interest by staff and students. The school reports that the visits really enrich the way curricular content is delivered, and that they are valued by staff and students. PhD students in our group (Charley Xia and Richard Oppong) also joined the workshops.
Year(s) Of Engagement Activity 2014,2016