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.

📣 Out today: nature.com/articles/s4158… 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 Sean Zheng , James Ware , Tom Lumbers

See Sean Zheng thread on our preprint for key summary x.com/SeanLZheng/sta…

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

Exciting news! Our latest research on dilated cardiomyopathy has just been published in Nature Genetics: rdcu.be/d0ZzH A huge thank you to our incredible HERMES collaborators for making this possible. hermesconsortium.org James Ware Sean Zheng Albert Henry

🚨 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. HERMES Consortium Tom Lumbers 🧵 highlighting key findings 1/

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/

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/

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/

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/

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/

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/

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/

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/

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/

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/

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/

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.htm… * European ancestry meta-analysis: cvd.hugeamp.org/dinspector.htm… 13/

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.app 14/

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, Tom Lumbers who led this project, friends and collaborators within the HERMES Consortium, and all study participants. END/