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.
Organisations
- University of Edinburgh (Lead Research Organisation, Project Partner)
- UNIVERSITY OF ABERDEEN (Collaboration)
- UNIVERSITY OF GLASGOW (Collaboration)
- GlaxoSmithKline (GSK) (Collaboration)
- PHARMATICS LIMITED (Collaboration)
- UNIVERSITY OF DUNDEE (Collaboration)
- GlaxoSmithKline (United Kingdom) (Project Partner)
- Central Queensland University (Project Partner)
- MRC Institute of Genetics and Molecular Medicine (Project Partner)
People |
ORCID iD |
Christopher Haley (Principal Investigator) |
Publications
Alcántara, M
(2014)
Predicting body mass index and waist-hip ratio from genome-wide single nucleotide polymorphism data.
in Quantitative Genomics 2014
Bermingham ML
(2015)
Application of high-dimensional feature selection: evaluation for genomic prediction in man.
in Scientific reports
Bermingham, M
(2014)
Genomic prediction for complex traits following feature selection: results from Bayes C and genomic best linear unbiased prediction (G-BLUP).
in 42nd European Mathematical Genetics Meeting (EMGM)
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
Bermingham, M
(2013)
Genomic Prediction for Complex Traits Following Feature Selection: Results from Bayes C and Genomic Best Linear Unbiased Prediction (G-BLUP)
in Human heredity
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
Spiliopoulou A
(2015)
Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models.
in Human molecular genetics
Spiliopoulou, A
(2014)
Combining different sources of information to optimise genomic prediction of complex traits
in The European Human Genetics Conference, ESHG 2014
Spiliopoulou, A
(2013)
Multiple Kernel Learning for Genomic Predictions of Complex Traits
in European Mathematical Genetics Meeting, EMGM 2013, Leiden, The Netherlands
Zeng Y
(2021)
Lifestyle and Genetic Factors Modify Parent-of-Origin Effects on the Human Methylome.
in EBioMedicine
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 | 08/2016 |
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 | 08/2019 |
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 |