@tomlumbers

Cardiologist (heart failure) @BartsHospital, Scientist (molecular epidemiology) @UCL_IHI and @HDR_UK

 London, England
hdruk.ac.uk/people/dr-tom-lumbers/
Joined October 2009
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19Media
Lastly, it goes without saying that it takes a village to publish this study. I'd like to thank my previous PhD and postdoc advisor at UCL, @tomlumbers who led this project, friends and collaborators within the HERMES Consortium, and all study participants. END/
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BONUS for scrolling: We also have an online supplementary information with more details on: 1. GWAS QC pipeline 2. Locus zoom, gene prioritisation, cross-trait association, and study-level association for each heart failure locus hermes2-supp-note.netlify.ap… 14/
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We have also released the GWAS summary statistics for browsing and download via the CVD Knowledge Portal: * Mixed-ancestry meta-analysis: cvd.hugeamp.org/dinspector.h… * European ancestry meta-analysis: cvd.hugeamp.org/dinspector.h… 13/
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We described some more analyses in the paper that are not covered here; including genetic architecture, heritability, polygenic risk score, finemapping and pathway enrichment. Do have a read if you find our paper interesting, and let us know if you have any feedback! 12/
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We performed genetic correlation (rg) and Mendelian randomisation analyses to distinguish between shared genetics and causal relationships. This is most apparent in CAD and ni-HF, which shows + rg without causal effect. Notably, T2D shows this pattern on all HF subtypes. 11/
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We further explored the extent of pleiotropic effects in HF loci on risk factors and diseases associated with HF. Through colocalisation analysis, we found that HF shared causal genetic variants with at least one of 22 other traits at 42 loci. 10/
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The identified genotype-phenotype clusters provide insights into etiological modules underlying HF pathology, e.g. cluster 1: ischaemic & major cardiovascular disorders, cluster 2: arrythmia & cardiomyopathies, cluster 4: hypertension, cluster 5: metabolic disorders. 9/
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Next, we characterised the downstream effect of the lead genetic variants on 294 human diseases in UK Biobank. We then used network analysis and community detection technique to identify 18 distinct genotype-phenotype clusters from these phenome-wide association results. 8/
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Using sn-RNAseq from 16 healthy & 28 failing heart donors, we found enrichment of cardiomyocyte genes. We also identified 53 GWAS genes that were differentially expressed in cardiac cell types, notably cardiomyocytes and fibroblasts. 7/
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Using heritability enrichment analysis, we found differential involvement of tissues across subtypes. Notably, whilst other subtypes were enriched for genes that are more specifically expressed in cardiac tissues, ni-HFpEF was distinctly enriched for kidney and pancreas. 6/
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Integrating multiple gene prioritisation methods, we shortlisted 142 candidate effector genes across the 66 loci, and nominated the most likely effector gene in each locus. This includes IGFBP7 for HFpEF, which is linked to cardiomyocyte senescence and cardiac remodelling. 5/
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We found 66 independent genetic loci associated with ≥1 HF phenotype, including 37 not previously reported. Of note, 10 loci were identified in GWAS of ni-HF subtypes despite smaller N compared to HF-all; showing the importance of phenotype definition in GWAS 4/
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First, we performed GWAS meta-analyses on 4 subsets of HF phenotypes comprising >150k cases: 1. Overall HF (HF-all) 2. non-ischaemic HF (ni-HF) 3. ni-HF with reduced (<50%) ejection fraction (ni-HFrEF) 4. ni-HF with preserved (≥50%) ejection fraction (ni-HFpEF) 3/
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Heart failure (HF) is a complex disease associated with many etiologies and risk factors. Here, we study how genetic variation influence risk of different HF subtypes and integrate these results with other genomic information to uncover disease etiology 2/
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🚨 Our latest GWAS of heart failure subtypes is now out in Nature Genetics! rdcu.be/eb9O0 A massive global collaboration from HERMES Consortium involving >40 studies with >150,000 heart failure cases. @HERMESgenetics @tomlumbers 🧵 highlighting key findings 1/
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Bonus (in case buried in the paper): Here's an online dashboard showing regional association plots of the identified 80 genetic susceptibility loci for DCM (including Broad & Strict definition and multitrait GWAS) integrated with gene prioritisation score: hermes-dcm-locus.netlify.app…
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See @SeanLZheng thread on our preprint for key summary xitter.cf/SeanLZheng/status/1707…
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📣 Out today: nature.com/articles/s41588-0… We probe into the molecular aetiology of dilated cardiomyopathy using GWAS, RVAS, cellular transcriptomics, PGS (and more!) Massive collaborative effort from the HERMES Consortium jointly led by @SeanLZheng , @drjamesware , @tomlumbers

Genome-wide association analysis provides insights into the molecular etiology of dilated cardiom...

Nature Genetics - Genome-wide association analyses comprising 14,256 cases and 1,199,156 controls and incorporating correlated cardiac magnetic resonance imaging traits provide insights into the...

nature.com
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9/ We took a deeper dive to investigate the extent of pleiotropic effects in HF loci on selected risk factor and endophenotypes of HF. Through colocalisation analysis, we found that HF shared causal variants with at least one of 22 other traits at 42 loci.
a. Colocalisations between HF and 22 GWAS phenotypes (out of 24 tested) across 42 (out of 66) loci with posterior probability of shared causal variants (PPcoloc H4) > 0.8. Each band connects a pair of locus - phenotype nodes, and represents sharing of causal variants between the phenotype and HF at the locus. Annotated number represents total number of colocalisations by locus and phenotype, which is displayed in panel b. & c.

ALT a. Colocalisations between HF and 22 GWAS phenotypes (out of 24 tested) across 42 (out of 66) loci with posterior probability of shared causal variants (PPcoloc H4) > 0.8. Each band connects a pair of locus - phenotype nodes, and represents sharing of causal variants between the phenotype and HF at the locus. Annotated number represents total number of colocalisations by locus and phenotype, which is displayed in panel b. & c.

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8/ The identified genotype-phenotype clusters provide insights into aetiological modules underlying HF pathology, e.g. cluster 1: ischaemic & major cardiovascular disorders, cluster 2: arrythmia & cardiomyopathies, cluster 4: hypertension, cluster 5: metabolic disorders.
Aetiologic clusters identified through locus-phenotype pleiotropy network analysis of the phenome-wide association estimates. Nodes represent loci (solid background) and phenotype (translucent background), with size representing centrality measure. Edges represent locus-phenotype association, with thickness representing strength of association measured by absolute Z score

ALT Aetiologic clusters identified through locus-phenotype pleiotropy network analysis of the phenome-wide association estimates. Nodes represent loci (solid background) and phenotype (translucent background), with size representing centrality measure. Edges represent locus-phenotype association, with thickness representing strength of association measured by absolute Z score

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