Award Title: Artificial intelligence to create equitable multi-ethnic polygenic risk scores that improve clinical care
Funder: Economic and Social Research Council

Abstract: Overall Objective: To develop state-of-the-art artificial intelligence (AI) methods which address the ethnic inequities inherent in genomic medicine and rapidly emerging polygenic risk scores (PRSs). We will subsequently apply these approaches to demonstrate their potential for improved risk prediction and screening of atherosclerotic coronary heart disease (CHD) and hyperlipidemia.
 
Major advances in genomics, combined with machine learning methods in large cohort resources, have enabled the prediction of disease risk through PRSs. The training data for PRSs has been heavily biased toward European ancestries, therefore PRS performance in people of non-European descent is significantly worse. Since a large proportion of individuals in Canada and the UK are of non-European descent, there is a clinical and ethical obligation to work towards the equitable incorporation of PRSs into clinical care to benefit all ancestries. This will require improved PRSs tailored to the ancestries of non-Europeans. Here we propose to develop AI methods to improve PRSs for individuals of non-European descent and begin to test their clinical utility in CHD and hyperlipidemia.

This program will therefore provide innovative and cutting-edge interdisciplinary AI research, including new methodologies, software, and translatable tools for the health and biomedical sciences.
 
In order to achieve this overall objective, we have developed specific Aims, each led by a world-class investigator in Canada or the UK.
 
Aim 1: To develop machine learning methods and open source software packages to improve the accuracy of PRSs for CHD and hyperlipidemia for individuals of non-European ancestry (Leaders: van der Shaar, Durand, Inouye, Greenwood).

Aim 2: To compare the performance of these ancestry-adapted PRSs in individuals in a diverse set of cohorts, including the East London Genes and Health Study (N = 50,000), the Canadian Longitudinal Study of Aging (N =30,000), the Kadoorie Study (N = 110,000) and the BRAVE study (N = 16,000). (Leaders: Richards, Inouye, Martin)
 
Responsible AI Objective: This proposal is rooted in the development of medical AI tools that respect differences in ancestry and race by improving clinical care in people of non-European ancestry. Therefore, this proposal, if funded, would be an example of research motivated by the Responsible Use of AI.
 
UK/Canada Partnership Objectives: Building on prior collaborations, this program will expand the partnerships between the scientists involved, the students they are training and our end-users, which are the health care systems we aim to improve. Embedded in our program is bilateral training opportunities between the UK and Canada, as well research visits for the investigators.
 
Knowledge Translation Objectives: Collaborating with our four end-users (a large Canadian health care network, Genomics England and Health Data Research UK) and knowledge translation partners (Think Research), we will develop a set of tools to enable future uptake of knowledge within the electronic health record environment of our health care systems.

Team: The assembled team has experience leading large-scale, international research programs to completion. We have assembled top expertise in machine learning, genomics, non-European ancestry cohorts and clinical medicine to achieve our project objectives.
 
Training and Data Objectives: Trainees will be embedded in this program from its inception and will partake in bilateral exchanges between our research labs. As we have done previously, we will ensure equitable hiring policies, ensuring gender and minority group balance.
 
Summary: Given the above objectives, our program is well-positioned to realize the mandates of the Canada-UK AI Initiative by implementing Responsible AI to improve the health care of our populations.

Lay Abstract: Overall Objective: To develop state-of-the-art artificial intelligence (AI) methods which address the ethnic inequities inherent in genomic medicine and rapidly emerging polygenic risk scores (PRSs). We will subsequently apply these approaches to demonstrate improved screening, diagnosis and treatment of common disease.
 
Background: Recent breakthroughs in genomics and machine learning have generated personalized medicine tools that have the potential to improve patient care and health maintenance through PRSs. PRSs are risk predictors, which combine information across the entire genome to predict a person's risk of disease, or whether they have unfavorable disease risk factors, such as high cholesterol levels. While still evolving rapidly, these PRSs are more predictive of susceptibility to disease than many traditional disease risk factors. Since PRSs can predict risk of disease they could be helpful in screening programs, by identifying a group of individuals at such low risk of disease, that screening would be unlikely to be helpful-thereby allowing screening programs to focus on individuals at highest risk. They could also refine diagnosis, by identifying people in the population most likely to have a disease and then targeting required diagnostic testing in those at highest risk. Last, they can also improve disease treatment by identifying individuals most likely to benefit from treatment.
 
While PRSs could help to improve clinical care, they have been largely developed in individuals of European-only ancestry. This is because the large cohorts from which PRSs have been developed consist predominantly of European-ancestry participants. Since genetic make-up varies by ancestry, the performance of these PRSs in non-European ancestries is considerably worse; it is known that not only differences in in genetic risk factors lead to attenuation of performance of PRSs, but also that changes in correlation patterns in our genomes due to distinct population histories decrease predictive accuracy even if the actual genetic risk factors remain the same.  
 
This creates several important problems for the roll-out of such tests in the Canadian and UK healthcare environments. This is because  approximately 22% of Canada's population and 12% of the UK are visible minorities. Further, these populations can have increased rates of health care utilization. Therefore, use of European-only PRSs could serve to worsen existing health disparities.
 
Canada's Chief Information Officer stated that, "Using Artificial Intelligence in government means balancing innovation with the ethical and responsible use of emerging technologies." Using PRSs developed to benefit only the majority group in our societies would not respect these directives and therefore our program will serve to ensure the responsible use of AI. 
 
What are our Specific Goals?
1) To develop AI methods and open source software packages to improve the accuracy of PRSs for CHD and hyperlipidemia for individuals of non-European ancestry.
2) To compare the performance of these ancestry-adapted PRSs in individuals in a diverse set of cohorts, representing multiple ancestries in the UK and Canada. 
 
The Team: Recruiting leaders across the UK and Canada, we have a depth of expertise in AI methods development, human genetics, clinical medicine, cohort development for minority groups, and statistical genetics. This gender-balanced team involves an appropriate mix of senior and junior investigators.
 
Relevance: Success in this AI-enabled program will allow for the transfer of recent advances in genomic medicine to the citizens of Canada and the UK, regardless of ancestry. The AI methods developed will be widely applicable to other PRSs in development by other groups. As genomics takes root in regular clinician-patient interactions, our set of AI-based tools will ensure that the benefit derived from these advances will be shared by all citizens of our countries.

Research Activity Codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health Categories: CARDIOVASCULAR;METABOLIC AND ENDOCRINE