8. We found 116 genes associated with Crohn’s disease show differential expression in equivalent colon tissue cell types sampled from healthy and diseased individuals in an external dataset. This includes ZBTB38, a candidate susceptibility gene implicated in recent GWAS.

7. We highlight an interesting example of cell type-specific causal effect of NCF4 gene in Crohn’s disease, which shows a protective (-) effect in B naive and risk-increasing (+) effect in B memory, implicating context-specific regulation that can be resolved using sc-eQTL MR.

6. Drugs targeting gene-trait associations identified through sc-eQTL MR in this study are 3.3 times more likely to have secured regulatory approval. Among these were major targets such as JAK2 & TNF for Crohn’s disease, APP for Alzheimer’s disease, and GLP1R for type 2 diabetes.

5. We found different polygenic enrichment patterns amongst dendritic cell (DC) subtypes: Crohn’s disease enrichment were found in cDC1, cDC2, and ASDC subtypes, and COVID-19 found in pDC only - consistent with its function for rapid interferon signalling in viral infection.

4. Through single-cell Disease Relevance Score (scDRS) analysis, we found that peripheral immune cells are enriched for polygenic signature of most complex traits, implicating widespread pleiotropy beyond immune function and peripheral blood composition.

3. We identified 190,449 gene-trait associations, including 34% not found by gene-level analysis of GWAS data, and 61% not found by MR using whole blood eQTL. Associations found only by sc-eQTL MR are often restricted to fewer cell types, implicating cell type specificity.

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!

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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 positive rg without causal effect. Interestingly, T2D shows this pattern on all HF subtypes.

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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.

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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.

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Next, we characterised the downstream effect of lead variants in HF susceptibility loci 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.

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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.

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Through heritability enrichment analysis, we found differential involvement of tissues across HF subtypes. Notably, whilst other HF subtypes were mostly enriched for genes that are more specifically expressed in cardiac tissues, ni-HFpEF was distinctly enriched for kidney and pancreas.

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Integrating multiple gene prioritisation strategies, we shortlisted 142 candidate effector genes across 66 genetic loci for HF, and nominated the most likely effector gene for each locus. This includes IGFBP7 for HFpEF, which is linked to cardiomyocyte senescence and cardiac remodelling.

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We found 66 independent genetic loci associated with at least 1 HF phenotype, including 37 not previously linked to HF. Of note, 10 / 66 loci were identified in GWAS of ni-HF subtypes despite smaller N compared to HF-all; showing the importance of phenotype definition in a case-control GWAS

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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

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