Title: Leveraging the impact of diversity in neurodevelopmental disability by integrating machine learning in personalized interventions.
Funder: Economic and Social Research Council

Abstract: The general objective of this project is to show that the use of machine learning (ML) approach(es) will allow to combine clinical diagnosis and personalized medicine, to further treat and improve health outcomes of patients with neurodevelopmental disability (NDD), which is known to affect about 13% of the population1. The UK is a leader in the use of population genetic data allowing them to prioritize specific drugs use, which has already borne fruit in common diseases such as diabetes, cancer and cardiovascular disorders. Importantly, correlating disease severity with in-depth genetic analysis, will allow to identify genes causing more or less severe clinical presentation for targeted treatment. All in all, prioritizing already known genetic targets leads to more than twofold increased success of drug in clinical trials2,3. Complexity resulting from the diversity observed in NDD has been a challenge to developing treatment. But the advent of ML will allow our teams to synergize and develop better treatment. The UK and Canada have been leaders in the use of genomic testing for the discovery of genetic causes of NDD. Canada is also a pionner in ML and we aim to crystalize the efforts in genomics by implementing novel ML approaches developed recently. We will take advantage of the established large datasets of genomic and phenotypic data on individuals with NDD (UK: Deciphering Developmental Disorder [DDD] which includes over 12000 individuals; CANADA: Care4Rare and MSSNG) and combine them with multi-omics data structured through Open Targets platform. Up until now, the focus in NDD has been identifying disease-causing genes. In spite of the difficulties involving the use of traditional data-mining approaches, ML will allow the discovery of signatures genes associated with overall variation in phenotype and inform better choice of a candidate treatment. (Obj.1) For this project, we will develop an integrated multi-featured database for ML analysis of genotype-phenotype association in NDD, incorporating both open source data and restricted genetic biomedical information. Next, (Obj.2), using various cutting-edge ML computation models, we will develop a ML-based pipeline for identification of features associated with optimized phenotypes in NDD. First, we will assess genetic modifiers with impact on the severity of intellectual disability, which affects the ability of individuals to live independently. We will also assess the variants associated with sleep difficulties as this is a significant marker of worse health familial economic outcome. Those assessments will serve as cases to develop methodologies that could later be applied to any other phenotype of interest. Second, we will use multi-objective approaches to assess if optimization of some phenotypes can penalize others. This will involve deep learning methods. (Obj. 3) For our third aim, we will develop an open source portal providing gene-by-gene modifier information. This portal will be integrated with the Open Targets Platform which is already widely used by academics and industry for better drug design. Our project will result in information exchange between scientists and families of individuals with NDD, patient engagement and international discussion about privacy, ethics and legal guidance for ML. Through the established partnership between industry and academia with Open Targets, we will also be able to accelerate drug development in NDD. Our findings will be applicable to other treatment development and accelerate further research in NDD.

Lay abstract: Neurodevelopmental disability (NDD), which is an umbrella term for autism, attention deficit, and intellectual and learning disability, affects 13% of the population. It has major economic and quality-of-life impacts on NDD individuals and families, and substantial economic burden on the healthcare system. So far, treatment is aimed only at general symptoms, which often leads to low efficacy and frequent side effects. The advent of novel genetic testing methods has provided plenty of evidence of the major impact that genes and their regulation have on clinical presentation in NDD. Nonetheless, there is a large diversity among individuals with NDD, even with the same genetic mutation. This is not unique to NDD as it is seen widely in many other medical conditions. The complexity derived from the genetic heterogeneity and the clinical (neuro) diversity has proven challenging to traditional approaches for treatment. Recent research in the UK and Canada has led to the development of large databases recording detailed information about individuals with NDD. Artificial intelligence (AI) now provides us with the tools to quickly analyze the information in those datasets. In particular, we will use machine learning (ML) to manage complex information, leading to the acceleration and better prioritization of interventions. Also, our project takes a novel view on the understanding of genomic information in NDD. Instead of directing our focus only on exploring data from a single individual or small group of individuals carrying the same gene mutation, our team will apply ML to large databases to identify features (from genes and their biology) correlated with improved clinical outcomes. In addition, we will use ML to better understand the interdependence between different symptoms to develop treatments that have a globally positive impact. In other words, we would find solutions that improve cognitive skills without impacting sleep negatively or generating more anxiety, as has been seen in previous clinical trials. We will finish by providing the entire scientific community with an open access portal, including our research findings, which will be integrated with the current Open Targets platform, a partnership between academia and industry in the UK that allows researchers to access linked data on diseases, genes and drugs in a single site. Researchers will be able to provide further information, which will improve the ML model. To ensure that we accomplish our objectives, we have assembled a team of experts in clinical and genetics of NDD: Dr. Bolduc (Canada); in computer science of genomics, molecular and pharmacological data: Dr. Dunham (UK); bioinformatics: Dr. Droit; machine learning: Dr. Greiner; social sciences, patient engagement and health economic: Dr. Zwicker. Our team has also developed strong links with NDD patient and research organizations in Canada and the UK, which will provide insight throughout the project. We are supported by collaborators involved in family and government engagement, ethics and data management in the UK and Canada. The project will also be a unique opportunity for multidisciplinary international training. Our project will show how ML can disassemble the complexity and diversity seen in NDD to develop more successful interventions. It will allow us to develop new ML approaches that will be readily applicable to other disorders where personalized interventions have been lagging behind diagnosis. More importantly, it will bring together families, society and scientists into a shared space where more and better information is exchanged. Finally, our project will embrace responsible implementation of data privacy and confidentiality while recognizing the need for data sharing to develop better interventions.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: MENTAL HEALTH