'Unlocking therapeutic innovation in heart failure through genomic data science
Lead Research Organisation:
University College London
Department Name: Institute of Health Informatics
Abstract
Existing classification systems do not adequately describe the diversity of human disease, nor do they account for common mechanisms across diseases. Since underlying mechanism often predicts therapeutic efficacy, a mechanism-based taxonomy of disease is required to unlock therapeutic progress. A data-science approach provides the framework to address this unmet need by integrating diverse patient data from genotype to disease outcomes. Heart failure (HF) is a complex clinical syndrome for which rising prevalence stands in contrast to a lack of effective therapeutic options. This project seeks to reclassify HF patients according to underlying causal mechanisms through analysis of harmonised patient data from HDR London, UK Biobank and the HERMES Consortium (a major global collaboration in HF genomics founded by the applicant) using emerging genome bioinformatics and unsupervised learning methods. The overall aim is to identify and validate molecular pathways as candidate targets for novel therapeutic intervention and to provide an exemplar within HDR UK for novel approaches to the study of complex disease.
Objective 1. Define the contribution of common genetic variation to risk of HF and clinically established sub-phenotypes.
A genome-wide association meta-analysis (GWAMA) of >32,000 HF cases from 25 studies has been completed by the HERMES Consortium, yielding variants at a number of loci. To investigate sub-significant association signals, I will perform an extended analysis with >60,000 HF cases (HERMES, HDR London and UK Biobank) and investigate HF subgroups defined by comorbidity (atrial fibrillation, coronary heart disease) and left ventricular phenotypes by stratified analysis. The results of these analyses may enable the identification of novel causal genes and will form the starting point for investigating clinical and genetic heterogeneity (Objective 2). In addition, they will provide the HF outcome dataset for target validation Mendelian randomisation analyses (Objective 3).
Objective 2. Identify and characterise new HF sub-phenotypes using clinical, EHR and genomic data.
Preliminary analyses indicate that HF risk variants exhibit allelic heterogeneity among HF subtypes defined by comorbid disease status. The observed clinical and genetic complexity suggest an opportunity to define a new, data-driven taxonomy. As well as studying established HF subtypes, I will use unsupervised learning methods (machine learning) to uncover new HF disease clusters based on phenotypic similarity. To explore genetic heterogeneity of HF subgroups, I will test for differences in HF-associated allele frequencies from Objective 1 using locally available, individual participant level data. To validate these findings, I have established a collaboration with investigators from Harvard/ MIT and Boston University. Using these approaches, I will seek to define a data-driven taxonomy of HF subtypes and to provide insights into mechanisms through genomic analysis.
Objective 3. Discover and validate therapeutic targets for heart failure subgroups through genetic causal inference analysis.
Genetic variants that alter the abundance or function of gene transcripts or their cognate protein can serve as instruments to explore the causal role of a protein in a disease outcome using Mendelian randomisation analysis. The genetic determinants of a many circulating proteins are known and can be used to investigate potential therapeutic targets in HF and/or HF subtypes (from Objective 2). I will prioritise proteins for study based on the presence of prior observational associations or other evidence of a causal role in disease. For proteins with evidence of a causal role, I will seek to characterise the mediators of effect by exploring the association of the genetic tool variants with related imaging and EHR phenotypes, to inform the conduct of clinical trials.
Objective 1. Define the contribution of common genetic variation to risk of HF and clinically established sub-phenotypes.
A genome-wide association meta-analysis (GWAMA) of >32,000 HF cases from 25 studies has been completed by the HERMES Consortium, yielding variants at a number of loci. To investigate sub-significant association signals, I will perform an extended analysis with >60,000 HF cases (HERMES, HDR London and UK Biobank) and investigate HF subgroups defined by comorbidity (atrial fibrillation, coronary heart disease) and left ventricular phenotypes by stratified analysis. The results of these analyses may enable the identification of novel causal genes and will form the starting point for investigating clinical and genetic heterogeneity (Objective 2). In addition, they will provide the HF outcome dataset for target validation Mendelian randomisation analyses (Objective 3).
Objective 2. Identify and characterise new HF sub-phenotypes using clinical, EHR and genomic data.
Preliminary analyses indicate that HF risk variants exhibit allelic heterogeneity among HF subtypes defined by comorbid disease status. The observed clinical and genetic complexity suggest an opportunity to define a new, data-driven taxonomy. As well as studying established HF subtypes, I will use unsupervised learning methods (machine learning) to uncover new HF disease clusters based on phenotypic similarity. To explore genetic heterogeneity of HF subgroups, I will test for differences in HF-associated allele frequencies from Objective 1 using locally available, individual participant level data. To validate these findings, I have established a collaboration with investigators from Harvard/ MIT and Boston University. Using these approaches, I will seek to define a data-driven taxonomy of HF subtypes and to provide insights into mechanisms through genomic analysis.
Objective 3. Discover and validate therapeutic targets for heart failure subgroups through genetic causal inference analysis.
Genetic variants that alter the abundance or function of gene transcripts or their cognate protein can serve as instruments to explore the causal role of a protein in a disease outcome using Mendelian randomisation analysis. The genetic determinants of a many circulating proteins are known and can be used to investigate potential therapeutic targets in HF and/or HF subtypes (from Objective 2). I will prioritise proteins for study based on the presence of prior observational associations or other evidence of a causal role in disease. For proteins with evidence of a causal role, I will seek to characterise the mediators of effect by exploring the association of the genetic tool variants with related imaging and EHR phenotypes, to inform the conduct of clinical trials.
Technical Summary
Objective 1. Define the contribution of common genetic variation to risk of HF and clinically established sub-phenotypes We will update the existing HERMES analysis to incorporate imputation to the Haplotype Reference Consortium panel. Analysis will be limited to European ancestry participants. For case-control analysis, we will compare all HF (prevalent + incident) with non-HF and perform stratified analysis of subtypes defined by comorbidity. For ejection fraction stratified analysis, we will use randomly selected proportional controls. We will include sex and age as covariates and results will be combined using METAL.
Objective 2. Identify and characterise new HF sub-phenotypes using clinical, EHR and genomic data. We will select clinical characteristics a priori based on past publications and expert judgment. We will test a range of methods for feature selection and dimensionality reduction to identify the most predictive and will use principal components and factor analysis to further reduce dimensionality. To cluster participants, we will use machine learning algorithms including connectivity models, centroid models, and expectation-maximisation. To investigate the genetic heterogeneity of disease clusters we will use BUHMBOX and other approaches and, where powered, GWA.
Objective 3. Discover and validate therapeutic targets for heart failure subgroups through genetic causal inference analysis. To investigate a potential causal role of serum proteins and other candidate drug targets, we will derive tool variants from expression quantitative trait loci (QTL), protein QTL from multiplexed aptamer- and affinity-based assays and downstream intermediate phenotypes. We will select variants in cis- with the cognate protein for each LD block using cluster analysis. We will perform Mendelian randomisation analysis using a range of regression models and will characterise effect mediators using multi-omic datasets enriched for imaging and EHR.
Objective 2. Identify and characterise new HF sub-phenotypes using clinical, EHR and genomic data. We will select clinical characteristics a priori based on past publications and expert judgment. We will test a range of methods for feature selection and dimensionality reduction to identify the most predictive and will use principal components and factor analysis to further reduce dimensionality. To cluster participants, we will use machine learning algorithms including connectivity models, centroid models, and expectation-maximisation. To investigate the genetic heterogeneity of disease clusters we will use BUHMBOX and other approaches and, where powered, GWA.
Objective 3. Discover and validate therapeutic targets for heart failure subgroups through genetic causal inference analysis. To investigate a potential causal role of serum proteins and other candidate drug targets, we will derive tool variants from expression quantitative trait loci (QTL), protein QTL from multiplexed aptamer- and affinity-based assays and downstream intermediate phenotypes. We will select variants in cis- with the cognate protein for each LD block using cluster analysis. We will perform Mendelian randomisation analysis using a range of regression models and will characterise effect mediators using multi-omic datasets enriched for imaging and EHR.
People |
ORCID iD |
Richard Lumbers (Principal Investigator / Fellow) |
Publications
Banerjee A
(2023)
Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study.
in The Lancet. Digital health
Banerjee A
(2022)
A population-based study of 92 clinically recognized risk factors for heart failure: co-occurrence, prognosis and preventive potential
in European Journal of Heart Failure
Borges MC
(2022)
The impact of fatty acids biosynthesis on the risk of cardiovascular diseases in Europeans and East Asians: a Mendelian randomization study.
in Human molecular genetics
DeBenedittis P
(2021)
Coupled myovascular expansion directs cardiac growth and regeneration
DeBenedittis P
(2022)
Coupled myovascular expansion directs cardiac growth and regeneration
in Development
Denaxas S
(2019)
UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER.
in Journal of the American Medical Informatics Association : JAMIA
Denaxas S
(2021)
Mapping the Read2/CTV3 controlled clinical terminologies to Phecodes in UK Biobank primary care electronic health records: implementation and evaluation.
in AMIA ... Annual Symposium proceedings. AMIA Symposium
Henry A
(2022)
Therapeutic Targets for Heart Failure Identified Using Proteomics and Mendelian Randomization
in Circulation
Huang QQ
(2022)
Transferability of genetic loci and polygenic scores for cardiometabolic traits in British Pakistani and Bangladeshi individuals.
in Nature communications
Jordan E
(2021)
Evidence-Based Assessment of Genes in Dilated Cardiomyopathy.
in Circulation
Jordan E
(2020)
An Evidence-based Assessment of Genes in Dilated Cardiomyopathy
Katsoulis M
(2021)
Estimating the Effect of Reduced Attendance at Emergency Departments for Suspected Cardiac Conditions on Cardiac Mortality During the COVID-19 Pandemic.
in Circulation. Cardiovascular quality and outcomes
Katsoulis M
(2021)
Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records.
in The lancet. Diabetes & endocrinology
Katsoulis M
(2021)
Weight Change and the Onset of Cardiovascular Diseases: Emulating Trials Using Electronic Health Records.
in Epidemiology (Cambridge, Mass.)
Katsoulis Michail
(2021)
Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records
in LANCET DIABETES & ENDOCRINOLOGY
Kotecha D
(2022)
CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research
in European Heart Journal
Kuan V
(2021)
Data-driven identification of ageing-related diseases from electronic health records.
in Scientific reports
Kuan V
(2019)
A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service.
in The Lancet. Digital health
Kuan Valerie
(2019)
A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service
in LANCET DIGITAL HEALTH
Legault MA
(2020)
A genetic model of ivabradine recapitulates results from randomized clinical trials.
in PloS one
Lind L
(2021)
Life-Time Covariation of Major Cardiovascular Diseases: A 40-Year Longitudinal Study and Genetic Studies.
in Circulation. Genomic and precision medicine
Lumbers RT
(2021)
The genomics of heart failure: design and rationale of the HERMES consortium.
in ESC heart failure
Lumbers RT
(2019)
Do beta-blockers and inhibitors of the renin-angiotensin aldosterone system improve outcomes in patients with heart failure and left ventricular ejection fraction >40%?
in Heart (British Cardiac Society)
Martin N
(2021)
Beta-blockers and inhibitors of the renin-angiotensin aldosterone system for chronic heart failure with preserved ejection fraction.
in The Cochrane database of systematic reviews
Martin N
(2018)
Beta-blockers and inhibitors of the renin-angiotensin aldosterone system for chronic heart failure with preserved ejection fraction
in Cochrane Database of Systematic Reviews
McGurk KA
(2022)
Correspondence on "ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG)" by Miller et al.
in Genetics in medicine : official journal of the American College of Medical Genetics
Meyer HV
(2020)
Genetic and functional insights into the fractal structure of the heart.
in Nature
Mordi IR
(2021)
Type 2 Diabetes, Metabolic Traits, and Risk of Heart Failure: A Mendelian Randomization Study.
in Diabetes care
Pei J
(2020)
H3K27ac acetylome signatures reveal the epigenomic reorganization in remodeled non-failing human hearts.
in Clinical epigenetics
Shah S
(2020)
Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure.
in Nature communications
Tadros R
(2021)
Shared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect.
in Nature genetics
Description | Completion of Cochrane Review as senior author |
Geographic Reach | Multiple continents/international |
Policy Influence Type | Influenced training of practitioners or researchers |
Impact | The Cochrane review has been widely cited in clinical cardiology journals. We demonstrated limited efficacy of conventional HF medications in patients with preserved ejection fraction and this finding has been used to justify efforts to address the unmet clinical need that we identified. |
Description | NIHR ULCH BRC Small Grants Award |
Amount | £51,176 (GBP) |
Organisation | National Institute for Health Research |
Sector | Public |
Country | United Kingdom |
Start | 03/2018 |
End | 06/2019 |
Title | HERMES Consortium: Release of summary statistics for genome-wide association study |
Description | We have published the largest GWAS meta-analysis of heart failure and made the results freely available on two online resources: GWAS catalog and Broad CVDI Knowledge Portal. Genome-wide association study provides new insights into the genetic architecture and pathogenesis of heart failure. Sonia Shah*, Albert Henry*, >140 others, Lumbers RT Nature Communications 2020. doi: 10.1038/s41467-019-13690-5 |
Type Of Material | Database/Collection of data |
Year Produced | 2019 |
Provided To Others? | Yes |
Impact | A large number of secondary analysis project, using data from the HERMES analysis, are underway or completed. |
URL | http://www.broadcvdi.org |
Description | Pfizer Innovative Target Exploration Network Partnership |
Organisation | Pfizer Inc |
Country | United States |
Sector | Private |
PI Contribution | PI of academic-industry partnership for drug discovery in heart failure. |
Collaborator Contribution | Scientific and data and funding contribution under pre-competitive model. |
Impact | None yet. |
Start Year | 2021 |
Description | SCALLOP consortium |
Organisation | SCALLOP Consortium |
Sector | Private |
PI Contribution | Analysis of protein quantitative trait associations with heart failure and measures of cardiac structure and function. |
Collaborator Contribution | Provision of meta-analysis GWAS summary association data for OLink plasma biomarkers |
Impact | Manuscript pending submission |
Start Year | 2019 |