NIHRDataInsights/HRCSData huggingface dataset:
Title: Machine Learning for Biomedicine
Funder: Medical Research Council

Abstract: Advances in machine learning (ML) and statistics have opened up new possibilities in the analysis of large and complex data. At the same time, advances in data acquisition technologies are driving major changes in the biomedical disciplines. These developments span many areas (including, among others, multi-omics, single-cell assays, scalable perturbations, deep phenotyping, and more) and pose new methodological challenges in machine learning and statistics. Motivated by this broad background of parallel developments in the data and biomedical sciences, the focus of this programme is on methodology in ML and high-dimensional statistics, with a specific emphasis on reliable prediction, heterogeneous and high-dimensional data and causality. A general theme is the need to combine ideas from ML and high-dimensional statistics, leveraging and building upon powerful tools and concepts developed in recent years. Specific aims include: (1) Efficient methods for heterogeneous and high-dimensional data. Large-scale data are often heterogeneous, spanning multiple subgroups (e.g. molecular subtypes or disease strata) and data types. If unknown at the outset, the identification of subgroups is typically of interest in itself and furthermore, if not properly accounted for, may lead to potentially severe issues in high-dimensional estimation. Here, the focus is on methodology that scales to very high-dimensional settings and deals with heterogeneity across the samples. (2) High-dimensional causal learning. Predictive machine learning approaches are well developed, but biomedical questions often have a strongly causal flavour, going beyond prediction to ask what would happen under intervention. Yet underlying molecular processes tend to be complex, with specific features (such as latent variables and nonlinearities) that pose analytical challenges. Building upon recent work in this area, we propose to combine ideas from machine learning and causality to allow learning of causal relationships in high-dimensional biology. (3) Robust and effective ML for precision medicine. Existing ML and statistical approaches provide a good base for prediction in precision medicine, but further work is needed to understand crucial issues such as robustness to distributional shifts, interpretability and transfer learning. We will build on the already powerful machine learning and high-dimensional toolkit, including deep learning approaches, to address these questions in the context of combining high-dimensional omics and multi-omics data with disease phenotypes.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Statistical and computational methods with application in genomics, causality and drug discovery
Funder: Medical Research Council

Abstract: Parallel rapid advances in our ability to generate large scale biomedical data and to integrate and analyse these data could transform our understanding of disease aetiology and allow us to build in-silico models of the consequences of therapeutic interventions on individuals. Notably, causal approaches using genetic variants as instrumental variables have already become central to drug discovery. However, current computational methods fail to fully realise these opportunities, so there is great value in further methods development to enable better understanding of the causal mechanisms underlying human disease. For example, we seek to improve our ability to: 1) Identify the causal genes underlying genetic association signals and understand the consequences of such variants for downstream function, eg on the encoded causal protein or proteins. 2) Integrate genetic data across related rare and common disease with available functional data exploiting both natural and engineered (eg by gene/epigenetic editing) variation to determine causal pathways and mechanisms of disease. 3) Exploit the understanding generated to determine the consequences of intervention at given points in these pathways and hence estimate the effects of therapeutic intervention at potential drug targets. 4) Predict effects of therapy conditional on measured characteristics, ie to enable stratified medicine. This endeavour will require integration of data across multiple modalities and scales. Specifically, we will integrate population scale data connecting genetic variation with disease endpoints and intermediate phenotypes (eg gene expression, proteomics, metabolomics, biomarkers) with lab scale data informing on gene regulation and function (eg DNase-seq, ATAC-seq, epigenetic marks, SCAFE). By first intent we will develop Bayesian probabilistic models to address these problems: this facilitates inference, naturally incorporates assessment of model uncertainty, and enables a modular approach to model building. However, given the scale of the data we aspire to use, we will move to computationally simply approaches where necessary. Software tools implementing the methods developed will be made freely available. This work has synergies with existing BSU work in machine learning, statistical ‘omics, Mendelian randomisation and precision medicine and we will work closely with those programmes to jointly maximise impact.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: Adaptive Designs For Clinical Trials, Their Evaluation, and Implementation
Funder: Medical Research Council

Abstract: A conventional clinical trial does not allow for adaptation or early stopping (unless it is unsafe) during the trial itself and would continue until the planned finish. Adaptive Designs for Clinical Trials can offer more efficiency on how clinical trials are conducted in practice due to opportunities to make decisions in the trials as the data come in. Therefore, these designs can offer many benefits to patients by delivering better treatment earlier and by treating more patients in the trials themselves on more promising treatment. However, the statistical methods for adaptive designs are more complex and require a bespoke approach. This project has three main objective: (i) Develop innovative adaptive designs for treatment individualisation. Conventionally, in earlier phases of drug development, it is assumed that many patients will response similarly to a treatment. However, the response, in many instances, can differ given their personal characteristics (or previous medical history). To draw solid and valid conclusions on which characteristics do influence the response and which do not, and how to optimality identify group-specific treatments, specialised statistical approaches are needed. (ii) Develop framework for fairer and comprehensive evaluation of adaptive designs via simulations It is increasingly common to assess designs via simulations as the closed form solutions might not be available, or simulations are needed as a check. Conducting a comprehensive, meaningful and reproduceable simulation study is a challenging task. Furthermore, the choice of simulation settings adds subjectivity into the assessment of a method. Currently, there is a little guidance on how to conduct simulation studies of clinical trials designs. (iii) Support implementation of efficient clinical trial designs in practice Adaptive designs are more challenging to implement in practice and will require a close collaboration between the clinical and statistical teams. The common implementation obstacle is a lack of training and accessible software. The objective of this workstream is to bridge the existing gap between advanced methods and practice.


Research activity codes: 6.9 RESOURCES AND INFRASTRUCTURE (TREATMENT EVALUATION)
Health categories: GENERIC HEALTH RELEVANCE
Title: Method - Efficient and effective conduct of clinical trials
Funder: Medical Research Council

Abstract: Efficiently and effectively conducted trials produce reliable results in a timely manner. Much current practice for the conduct of trials it is not based on high-quality or sometimes any evidence. This programme focuses on important evidence gaps that we are strategically well-placed to address. During this QQ the Programme has undergone considerable change with new leadership providing oversight and resetting the agenda. Our research includes the following: • Timely and reliable access to electronic health records has the potential to transform the conduct of many national clinical trials, making them considerably more cost-effective. Our work will involve developing primary evidence on data utility, and collation of experiences and consensus to drive guideline development for the use of electronic health records in trials. The research goals need to be achieved in partnership; we have a strategic partnership with NHS Digital, are an integral part of Heath Data Research UK and are leading the NIHR-MRC Trials Methodology Research Partnership Health Informatics Working Group. • Clearer communication of trial results will increase impact, improve research transparency, and reduce waste. We are studying how to better provide findings and results to participants and clinicians. • Novel trial designs are more likely be taken up by others when there are accessible case studies demonstrating successful implementation and documented solutions to challenges. We aim to support successful implementation, particularly adaptive trials testing more than one primary research hypothesis. • High-quality monitoring of trial data are essential for patient safety and trial integrity. This is a key issue in trial delivery but has been under-researched. We aim to develop a monitoring toolkit to provide resources and a more structured approach to monitoring. We have established a national UKCRN Task and Finish Group in this area. • Academic interventional phase III clinical trials always aim to impact clinical practice and increasingly impact needs to be achieved through review by regulators and payers. The pace of product development and potential number of potential indications which new therapies might be effective requires that industry and academia build partnerships and to find ways to make academically sponsored and run trials “acceptable” to regulators and payers – a form of regulatory “future-proofing”’ for trials. These regulatory “future-proofed” approaches must be less resource intensive than is standard for industry but must retain confidence of a reliable route to license extension.


Research activity codes: 6.9 RESOURCES AND INFRASTRUCTURE (TREATMENT EVALUATION);8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Interrogation of nucleic acid mediated inflammation, and of the role of the snoRNA U8 in human disease
Funder: Medical Research Council

Abstract: The Crow laboratory leverages the power of Mendelian disease to dissect complex biology in the human context. The overarching aims of the group are to: • Shed new light on fundamental principles of cellular homeostasis • Ensure a meaningful translational focus, through diagnostics, therapeutics and public engagement The group has two particular areas of focus. In the first, we are interested in nucleic acid mediated induction of the innate immune system, and definition of the cellular contexts in which self-derived nucleic acid ligands are generated/sensed. The use of our unique pipeline, involving the ‘agnostic’ screening of diverse clinical disease phenotypes to identify human mutant states predisposing to upregulated type I interferon signalling (so-called type I interferonopathies), enables us to define novel genes/proteins and cellular processes which, when dysfunctional/disturbed, lead to innate immune system engagement. Recent examples relate to our work demonstrating that mitochondria and intranuclear genomic DNA represent sources of immunostimulatory nucleic acid ligand. Through such studies we gain insights into mechanisms of self/non-self discrimination, immunological homeostasis and anti-viral immunity. In a second area of study, we wish to determine the link between mutations in the non-protein-encoding SNORD118 and the tissue-discrete cerebral microangiopathy referred to as leukoencephalopathy with calcifications and cysts (LCC), or Labrune syndrome. How dysfunction of the non-protein encoding snoRNA U8, encoded by SNORD118, results in disease remains unclear, and might relate to ribosomal and/or non-ribosomal activities, both of which possibilities we are exploring. Here, we draw on knowledge of LCC molecular pathology to better understand LCC and cerebrovascular homeostasis, at the same time deriving basic principles relating to snoRNA biology. Each of the workstreams outlined above draw on expertise in clinical phenotyping, Mendelian genetics, ex vivo and in vitro assays and animal modelling. Importantly, both of our research themes have strong translational potential, informing molecular diagnostics and the future development of targeted therapies.


Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING;2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: GENERIC HEALTH RELEVANCE
Title: China Kadoorie Biobank (CKB)
Funder: Medical Research Council

Abstract: Despite recent advances, our ability to prevent and treat many non-communicable chronic diseases (eg, stroke, heart disease, diabetes, cancer) is still limited. Understanding what causes these diseases in diverse populations can lead to improvements in risk prediction, treatment and disease prevention, and the development of “precision” medicines. CKB is a blood-based prospective cohort study of >512,000 adults, recruited from 10 diverse areas across China (during 2004-08), with extensive data collection at baseline and subsequent resurveys and long-term storage of biological samples. By 1.1.2017, ~45,000 deaths and ~1M coded episodes of hospitalisation of ~1500 major disease types have been recorded. These exposure and outcome data are complemented by cohort-wide genotyping (currently for 102,000 participants), large-scale whole genome sequencing, and conventional and multi-omics assays for nested case-control studies of specific diseases. Over the next 5 years, the CKB will continue to develop, maintain, and share the resource through: (i) continued follow-up for disease events through linkage to mortality/morbidity registries and health insurance systems; (ii) ascertainment, adjudication and sub-phenotyping of major disease outcomes (e.g. stroke, IHD, cancer); (iii) resurvey of ~25,000 survivors with additional enhancements (e.g. accelerometers) and new sample collection (e.g. faecal/saliva samples); (iv) maintenance, enhancement and development of IT systems underpinning the infrastructure and coordination of the study; (v) processing, managing and sharing large and complex data with the wider research community; (vi) secure bio-repositories with DNA extraction and sample reformatting. Based on the data collected or to be generated, a wide range of research will be undertaken, both for hypothesis testing and generating. These will include (i) prospective analyses of major risk factors with major diseases and their subtypes; (ii) Genome-wide association studies of traits, lifestyle factors and diseases; (iii) Mendelian randomisation studies of causal relevance of traits for major diseases; (iv) Phenome-wide association studies of functional variants to aid drug development; (v) Comprehensive assessment of the health effects of environmental exposures (eg, ambient and household air pollution, ambient temperature, and built environment) on major CVD and non-CVD. The research priority will be given to those that are considered to be novel and to have major public health impact. CKB is a global resource with huge potential to improve our understanding of disease aetiology, risk prediction and development of new therapies. It will also complement other large prospective studies (eg, UK Biobank) enabling investigations into a different range of genetic and other exposures that may be rare or absent in Western populations.


Research activity codes: 2.4 SURVEILLANCE AND DISTRIBUTION;2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: The Mexico City Prospective Study
Funder: Medical Research Council

Abstract: Between 1998 and 2004, the Mexican Ministry of Health, in collaboration with epidemiologists at the University of Oxford’s Clinical Trial Service Unit & Epidemiological Studies Unit (in which the PHRU is embedded), established in Mexico City a prospective study in which over 150k middle-aged adults were enrolled (the ‘Mexico City Prospective Study’ [MCPS]). Participants completed a questionnaire, had a physical assessment (weight, height, waist and hip circumference, blood pressure) and gave a blood sample. Stored plasma and buffy coat samples (for DNA) of good quality are available for 98% of participants and are in long-term storage in Oxford in liquid nitrogen storage facilities. Participants are tracked for cause-specific mortality using the national mortality register, with nearly 20k dead by 2016. A resurvey of 10k survivors, including additional questions, measurements, and samples (urine) was completed in February 2019. Cohort-wide fieldwork to collect information on major non-fatal diseases will start in mid-2019. Funding to genotype and exome sequence the whole cohort has recently been obtained. The MCPS is by far the largest study of its type in a Latin-American population. What sets it apart from other large blood-based cohorts is the extremely high prevalence of obesity and diabetes. By age 60, around half of women and one third of men had a body-mass index of at least 30 kg/m2 at recruitment, while more than one in five had received a diagnosis of diabetes. Those with diabetes had poor glycaemic control (mean HbA1c was 9%) and had an all-cause mortality rate at ages 35-74 that was four times greater than those without diabetes (much more extreme than has been documented in other populations studied to date). We plan to continue to follow-up as well as enhance the cohort (eg, with the collection of information on non-fatal diseases) with the primary aim of continuing to study the major known environmental causal risk factors. However, the availability of genetic data in addition to a wide range of other phenotypic data that already exists – from a single study with >15 years follow-up covering a wide range of diseases, and in an admixed population in which adiposity and diabetes are much more common than in most previously-studied populations – creates substantial new research opportunities. These include: discovery of novel genetic variants for diabetes, adiposity and related traits; replication and fine-mapping studies, and trans-ethnic meta-analyses; causal evaluation of environmental risk factors and blood biomarkers (through Mendelian randomisation studies); phenome-wide association studies; creation of risk prediction tools; and genome wide association studies of other traits. The potential subsequent translational opportunities (eg, with respect to the development of novel therapeutic approaches) are substantial.


Research activity codes: 2.4 SURVEILLANCE AND DISTRIBUTION;2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: Cohorts and data collection
Funder: Medical Research Council

Abstract: Aim This theme serves the underpinning cohort platforms, providing essential data for all programmes. Research Objectives We plan core data collection on key health and sociodemographic variables as identified in individual programmes. We build on the strengths of the well-established core funded MRC National Survey of Health and Development (NSHD) and will capitalise on linkages with the WT funded Delirium and Population Health Informatics Cohort (DELPHIC) cohort. Investigators within the Unit also lead the Southall and Brent REvisited (SABRE) tri-ethnic older age cohort which has complementary data of relevance to some programmes. Methods The Cohorts and Data Collection theme has two sub-themes; the first is Data Collection, concerned with de novo clinical, home visit and questionnaire data capture for the current QQ, and the second is Cohort Maintenance, which is concerned with data and sample curation, linkages, and overall Unit management. Translational aspects, and public health exploitation are detailed in individual programmes.


Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING);2.4 SURVEILLANCE AND DISTRIBUTION;2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY);4.5 RESOURCES AND INFRASTRUCTURE (DETECTION)
Health categories: GENERIC HEALTH RELEVANCE
Title: Physiological resilience: mechanisms and consequences
Funder: Medical Research Council

Abstract: Aim We will use dynamic phenotyping to investigate mechanisms underlying impaired physiological resilience with ageing, and quantify its association with functional change and clinical events. Importance of research Resilience – the ability to resist or recover from stressors – declines with age, and determines functional status, loss of independence and mortality. While resilience is multifaceted, underlying pathways of impaired resilience are likely to be common across systems, but are poorly understood. Objectives Quantify the impact of controlled dynamic stressors on resilience, and determine the extent to which responses to individual stressors are correlated. Understand the life course environmental exposures, and midlife molecular and physiological determinants of resilience in NSHD. Explore the consequences of the spectrum of resilience in terms of hospital admission and events Methodology Physiological responses to everyday dynamic stressors delivered in an experimental clinic setting, such as mobility, cognitive tasks, orthostatic challenge and food ingestion, will be used to quantify resilience across multiple systems (cardiovascular, metabolic, skeletal muscle, neurological). Participants from NSHD (with rich data on determinants of resilience) will be studied. Potential translational impact Identification of predictive resilience measures and underlying pathways would highlight novel biomarkers to categorise older people at high risk of adverse outcomes, and novel targets for intervention.


Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING;2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS
Health categories: GENERIC HEALTH RELEVANCE