NIHRDataInsights/HRCSData huggingface dataset:
Title: Statistical epidemiology in ageing research
Funder: Medical Research Council

Abstract: A programme of work that covers both the methodology and application of measures for changes associated with ageing. Modelling the ageing process to correctly investigate individual differences, missing data and the correct formulation of the time process requires both good data and improved methodology. The programme covers the improvement in methodology and the application within applied scientific papers. 1. Healthy life expectancy and multi-state modelling A programme of research that involves improving measures of healthy life expectancy modelled using longitudinal data, including the modelling process, measuring model fit, relaxing some of the assumptions and the investigation of missing data, and then undertaking analyses of health life in a variety of settings. 2. Cognitive modelling and modelling through the life course A programme of research involved in the investigation of cognition through time, over age and near death. The modelling involves a detailed investigation of the patterns of change, the relationship with covariates, the impact of missing data and the relationship with mortality. In addition the programme collaborates with other MRC units on a cross collaboration project of ageing through the lifecourse. This is investigating the methods of combining longitudinal investigations to enable understanding the entire age range. 3. MRC Collaborative Group for the Cognitive Function and Ageing Study I and II (CFAS) Overall strategic control (planning, co-ordination, and development) of statistical analysis within this large, UK, multi-centre, epidemiological, cohort study of ageing, cognitive decline and dementia. A new cohort CFAS II is currently underway, our role includes sampling, data managament and statistical oversight. Sub-studies of blood and brain tissue elucidate the role of genetic factors within ageing, and of the neuropathological processes which characterise dementia in the community. Other sub-studies investigate the costs of continuing care and the burden on carers. 3. Applied research A varied programme of investigations of application of the above methodology including mild cognitive impairment (the state between normal cognitive ageing and dementia), the relationship between ageing in life and pathology seen at death, autism in the UK and Netherlands, the investigation of disability it’s causes, incidence and recovery, and various aspects of healthy life expectancy (such as disability free life expectancy, cognitive impairment free life expectancy) and its causes and investigations of cognitive health. This all links to a focus on public health research and policy implications of the ageing population.


Research activity codes: 2.4 SURVEILLANCE AND DISTRIBUTION;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: Statistics for Stratified Medicine and Analysis of Complex Phenotypes
Funder: Medical Research Council

Abstract: Stratified Medicine is a strategic priority for the MRC (and other organisations), and a substantial programme of investment by the MRC has begun and will continue for the coming years. The stratified medicine paradigm acknowledges the enormous biotechnological advances in generating and collecting detailed biological genotypic and phenotypic information across the various levels of organisation of the (human) biological system with the capacity to match the complexity of diseases and unmask patterns which can identify new and more subtle strata of disease. The unprecedented availability and progress of “omics” technologies for characterizing individual DNA mutations, metabolic profiles, protein expression and localisation, mRNA expression and epigenetic signatures in specific cell types, coupled with imaging and more traditional clinical, lifestyle and environmental data, have presented opportunities for further understanding of mechanisms, outcomes and molecular heterogeneity of diseases. This paves the way for individual-level models aimed at risk assessment and stratified prevention, projection of disease course, treatment response, safety and the likelihood of adverse events. We will undertake a programme of statistical methodological research that encompasses risk stratification, prediction and validation, integrative and joint modelling of molecular and clinical data of various kinds and complexities (and their conversion into meaningful outputs that can inform health care decisions), mechanistic understanding and causality, treatment strategies and the design of innovative/purposeful clinical trials for biomarkers, rare diseases and dynamic treatment regimes. Both Bayesian and non-Bayesian methodology will be developed. We are research partners in a number of MRC and other funded consortia in stratified medicine, and have close links with teams in North America, Europe and the UK. Our collaborations cover many diseases, medical conditions and areas. For example, we are involved in projects in rheumatology, dementia, rare diseases, HIV, antibiotic resistance and blood donation. Some of the goals of these projects are shared. We therefore envisage that methodology developed may be transferrable across diseases and medical areas, although tailoring may be required.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Adaptive Clinical Trials
Funder: Medical Research Council

Abstract: Clinical trials are considered the gold-standard study design when testing for a causal effect of a new treatment or intervention. There are, however, many challenges to be faced in the future. The high, and increasing, cost of clinical trials has caused the drug development process to become more and more expensive (DiMasi, Grabowski, and Hansen 2016), meaning new design and analysis methods to increase efficiency are greatly needed. The barriers to using adaptive clinical trials are lifting, which is leading to an increasing uptake (Hatfield et al. 2016). Members of my team are working on several current and future trials that are implementing novel methods. This portfolio of trials forms a strong basis for our future methodology research plans. I have the capacity, skills and track-record to push the methodology forward to tackle even more ambitious problems. These include dealing with patient heterogeneity, exploiting multivariate outcomes in dose-finding/ranging trials, using historical data, applying bandit methodology to improve patient benefit in trials, and conducting trials within large cohorts. I have been successful in building links with industry with several ongoing collaborations and two postdoctoral positions jointly funded by GlaxoSmithKline (GSK) and AstraZeneca (AZ). My proposed future work includes five main objectives: 1. proposing novel trial design and analysis approaches for patient stratification to predict treatment benefit; 2. developing multi-arm multi-stage methodology that can be applied to a broader range of trial designs and types of endpoint; 3. extending and further enhancing model-free and model-robust methodology for dose- finding and ranging trial designs; 4. improving the efficiency of trials within small populations; 5. using the richness of complex outcomes to give more informative inference.


Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;6.9 RESOURCES AND INFRASTRUCTURE (TREATMENT EVALUATION);8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Statistical methods for next-generation clinical trials
Funder: Medical Research Council

Abstract: The process of developing and testing a new treatment or intervention takes a long time, a lot of money, and often ends in failure. The high cost is primarily due to expensive clinical trials, which are required to show that the treatment is safe and effective. Improving the efficiency of clinical trials, to make maximum use of limited resources, is a priority research area. This research programme will develop statistical methodology to ensure that clinical trials have the tools necessary to cope with current and future challenges. The first area of focus is statistical methodology for best using the increasing amount of high-dimensional biomarker data in clinical trials. With increasing availability of routinely collected biomarker data, modern clinical trials urgently require suitable methods to incorporate them prospectively. Current approaches rely on less efficient statistical methods such as testing each variant one-by-one in a logistic regression, and classifying patients as high-risk if they are positive for a certain number of biomarker. By combining novel adaptive designs and state-of-the-art high-dimensional statistical methods such as Bayesian sparse regression we can improve on this. Through working with clinicians, this novel methodology will be available for use in real trials for areas such as cardiovascular diseases and oncology. A second area of focus is methodology for ongoing trials that test multiple treatments and biomarkers. In an ongoing trial new treatments and biomarkers are added in continually as treatments are found sufficiently promising to move to phase III trials, or are dropped due to lack of effectiveness. Some real trials are already doing this due to the considerable logistical and administrative advantages. This programme will focus on statistical issues in ongoing trials. This includes how to optimally choose decision criteria for dropping or progressing a drug to phase III and how to optimally plan phase III trials that result. Collaborations with a multidisciplinary set of statisticians and clinicians will allow us to implement developed methods in practice. A third area of focus is to improve the analysis of trials using composite endpoints. In this case it is possible to gain considerable power by fitting a suitable model to the data. These methods will be applied to a variety of disease areas including oncology trials, rheumatoid arthritis and lupus.


Research activity codes: 4.5 RESOURCES AND INFRASTRUCTURE (DETECTION);5.9 RESOURCES AND INFRASTRUCTURE (TREATMENT DEVELOPMENT)
Health categories: GENERIC HEALTH RELEVANCE
Title: Statistical Modelling: Theory and Applications
Funder: Medical Research Council

Abstract: The aim of this research is to develop methods of statistical inference for the analysis of longitudinal data and other challenging data structures. Much of the work will derive from specific areas of medical research but, generally, will have broader applicability. Methodological work on methods of analysis in the presence of informative observation are considered, motivated by various longitudinal studies. Other methodological investigations address the analysis of two part models for data which are “zero-heavy” and extensions of the analysis of plaid-square designs to generalised linear models. Major collaborations in rheumatology are ongoing. Based on a cohort of patients with psoriatic arthritis, we consider the modelling of individual joint damage and quality of life, the definition of outcome variables for clinical trials and genetic determinants of disease and disease course. For systemic lupus erythematosus patients, we undertake studies of neuropsychiatric events and the development of permanent damage and continue our investigations of measures of disease activity. We also collaborate on a clinical trial to evaluate pulmonary metastectomy for colo-rectal cancer patients.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS
Health categories: GENERIC HEALTH RELEVANCE
Title: Statistical methods for the analysis of medical genomic data
Funder: Medical Research Council

Abstract: The broad focus of my group is methodology development for the analysis of medical genomic data. A particular area of focus is methods for genetic fine-mapping, which aim to identify causal variants in a disease-associated region. Recent “meta-GWAS”, comprising tens of thousands of people, have boosted the power of genetic association studies. Typically, however, only simple summary results are published, which considerably complicates the ability of fine-mapping studies to identify a small set of variants for further functional follow-up owing to strong correlations between genotypes at neighbouring genetic variants. My group is working on efficient variable selection methods, which can explore complex joint models in summary data by accounting genetic correlations from a “reference” population. In collaboration with researchers at the Institute of Cancer Research Sutton and the PRACTICAL meta-GWAS consortium, our methods are being applied to explore genetic drivers of Prostate Cancer risk. Relatedly, my group is exploring the use of “meta-GWAS” summaries in Mendelian Randomisation and other frameworks that allow causal inference from observational datasets by using genetic variants as proxies, or “instruments”, to assess the impact of intervening on the exposure of interest. This latter work is motivated by collaborations with researchers at the MRC Epidemiology Unit as well as the Cardiovascular Epidemiology Unit where, where we will use these methods to explore drivers of cardiovascular risk, cancer and diabetes. Another focus of my research is predictive inference. Realising the potential of genetic risk prediction for most complex traits will require huge training datasets. In collaboration with Frank Dudbridge (Leicester), we are exploring methodology for building high-dimensional predictive models from meta-GWAS summaries. I am also interested in the use of loss functions in Bayesian variable selection and predictive inference; in particular their use in prognostic model building, whereby loss functions could be used to target metrics of real world clinical utility tailored to a particular treatment decision. This work is motivated by a collaboration with hepatologists Mike Allison (Addenbrookes) and Mark Thursz (Imperial) to explore prognostic signatures of steroid efficacy in alcoholic hepatitis using data from the STOPAH trial, as well as a collaboration with Paul Pharoah (Cambridge) exploring risk thresholds for treatment decisions in breast cancer. In collaboration with James Wason (Newcastle/BSU), my group is also exploring improved approaches for biomarker-based clinical trial design, which involves predicting treatment effects from the complex biomarker data that is increasingly collected in modern clinical trials. This project is also motivated by our collaboration with the STOPAH trial working group.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS
Health categories: GENERIC HEALTH RELEVANCE
Title: The Rise And Fall Of Executive Functions: Modelling neurodevelopmental changes in higher cognitive abilities
Funder: Medical Research Council

Abstract: Executive functions, comprising higher cognitive functions such as reasoning and goal management, have important implications for the individual and for society. Individual differences in executive functions are associated with a variety of socioeconomic, neurological and health outcomes, and preserved executive functions are essential to living and working autonomously in old age. They are among the last to develop during adolescence and often show steep decline in old age. Although the prefrontal cortex is known to be important for executive functions, the precise relationship between prefrontal change and the development of these abilities is poorly understood. In this research programme, I use psychometric models and longitudinal, multimodal neuroimaging in large cohorts to determine the effect of prefrontal changes on executive functions across the lifespan. These include Growth Curve Modelling of neurodevelopmental trajectories in adolescents, hierarchical MIMIC models to capture the cascade of effects of white matter changes on processing speed and reasoning, task-based functional connectivity following prefrontal atrophy to model evidence for adaptive reorganization and equality-constrained multifactor MIMIC models to compare brain-behavior relationships between healthy and non-healthy cognitive agers. To develop these analyses in appropriate datasets, I have established (inter)national collaborations with several world-leading figures specializing in large-sample neurocognitive development: Professor Dolan, PI of the NPSN adolescent ageing project at UCL and Professor Lindenberger, PI of the Berlin Ageing Study and director of the Max Planck Institute of Lifespan Psychology and Professor Nyberg, director of the the Umeå Center for Functional Brain Imaging (UFBI). These collaborations will enable me to study three of the largest, most innovative neurocognitive ageing projects in the world, and allow me to benefit from the expertise associated with each project. Together, the unique datasets and novel statistical approaches in this research programme promise to greatly increase our understanding of neurocognitive development of executive functions, a key step towards early detection of developmental challenges and the design of interventions to facilitate developing executive functions in adolescence and sustained cognitive abilities in old age.


Research activity codes: 1.2 PSYCHOLOGICAL AND SOCIOECONOMIC PROCESSES;2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS
Health categories: GENERIC HEALTH RELEVANCE
Title: Mutagenesis and its Biomedical Impact
Funder: Medical Research Council

Abstract: Discriminating mutations of medical and functional importance from the many more that are of negligible biological consequence is a major unmet challenge for genomic medicine. This problem is particularly acute for sequence changes in the non-protein-coding majority of the genome. Solving this is a key component of realizing the MRC’s objective “to use genetics, imaging and biological indicators to understand predispositions to disease”. We will help address this problem by disentangling the mutually confounding patterns of mutation and selection, both of which if separately resolved can give insight into the biology of the genome and the phenotypic consequences of a DNA sequence change. Our three main aims are to: 1. Reveal the fine-scale mutation landscape of the genome and identify the processes that shape it. 2. Understand the molecular consequences of mutation at regulatory sites in the non-protein-coding genome. 3. Relate molecular phenotypes to organism biology through improved measures of selection that are applicable to the non-protein-coding genome. Our approach is predominately computational, applying statistical analyses to somatic (cancer), population and between species variation obtained from genome sequencing studies. These data are intersected with transcription and chromatin state measurements and key missing data generated by collaborators such as the international FANTOM consortium, or ourselves in which case may involve obtaining and processing primary human tissue samples. Specific hypotheses generated from our initial analyses, such as mutagenic processes are explored experimentally using yeast or in vitro systems where tractable.


Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: GENERIC HEALTH RELEVANCE
Title: RNA synthetic biology
Funder: Medical Research Council

Abstract: As large-scale sequencing projects uncover new variation in the human genome, understanding the consequences of this variation becomes increasingly important. In the next five years, we will use synthetic biology, next-generation sequencing, and computational modelling to study the relationships between gene sequence, expression, structure, and function. We will focus on studies of noncoding RNAs and synonymous mutations in protein-coding genes. We believe that a detailed understanding of genotype-phenotype relations for selected model transcripts will uncover principles applicable to many RNA and protein molecules. Specifically, we aim: 1) To understand the molecular mechanisms underlying an RNA fitness landscape. 2) To understand the effects of codon usage on various stages of gene expression. 3) To develop new applications of genotype-phenotype mapping. In aim 1, we will use yeast U3 snoRNA as a model system to study genotype-phenotype relations. We previously constructed a library of 60,000 mutants of U3 to study the effects of mutations on fitness in wild-type yeast. To study the role of gene-gene interactions, we will express the library in a collection of strains depleted of U3-interacting proteins, and to study gene-environment interactions, we will express the mutants in a range of environmental conditions. We will also develop high-throughput assays to measure the effects of mutations on U3 RNA abundance and RNA-protein interactions. In aim 2, we will use human cell culture models to measure the effects of synonymous mutations on transcription, RNA stability, RNA export and translation. We will then use machine learning to uncover the sequence determinants of expression, and to understand which stages of expression are most strongly influenced by mutations. In collaboration with partners in industry (ThermoFisher) and academia (Laurence Hurst, University of Bath), we will then use our data to develop new strategies for codon optimization. In aim 3, we will collaborate with University of Edinburgh researchers to apply our genotype-phenotype mapping methods to understand human disease mutations. We will focus on mutations in genes relevant to the local research community, encoding a range of transcription factors, hormone receptors, and metabolic enzymes.


Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: GENERIC HEALTH RELEVANCE
Title: Chromatin architecture and regulation
Funder: Medical Research Council

Abstract: Characterising and investigating the 3D architecture of regulatory features (promoters, and enhancers) is fundamental to understanding how these components function. Based on our previous studies and evidence in the literature we hypothesise that chromatin architecture of local genomic features form discrete structural states that regulate activity. These states are further modulated by genetic variants and mutations, which influence gene expression and phenotypic variation. This idea moves away from the notion that local chromatin is merely a structural phenomenon but is instead a gatekeeper for determining regulation. To test our hypothesis we will investigate local chromatin architecture of genomic features (promoters and enhancers) and define their structural states. We will then modulate local chromatin architecture to understand how this affects transcription. Finally we will investigate how sequence variants and mutations can alter local chromatin architecture and transcription. This study will provide new insight into the structural organisation of the human genome and enable us to understand the molecular basis for critical aspects of gene regulation. Importantly it will also provide a framework for generating the first high-resolution chromatin fibre map of the human genome. To achieve these goals we will study three inter-related aims: 1. Develop chromatin interaction analysis (CIA) to investigate the 3D architecture of the higher-order chromatin fibre. 2. Investigate distinct classes of promoter chromatin architecture. 3. Analyse the 3D chromatin architecture of specific regulatory elements (at Pax6 and Pou5F1).


Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: GENERIC HEALTH RELEVANCE