Multi-ancestry Mendelian randomization of omics traits revealing drug targets of COVID-19 severity

Zheng, Jie; Zhang, Yuemiao; Zhao, Huiling; Liu, Yi; Baird, Denis; Karim, Mohd Anisul; Ghoussaini, Maya; Schwartzentruber, Jeremy; Dunham, Ian; Elsworth, Benjamin; Roberts, Katherine; Compton, Hannah; Miller-Molloy, Felix; Liu, Xingzi; Wang, Lin; Zhang, Hong; Smith, George Davey; Gaunt, Tom R.

doi:10.1016/j.ebiom.2022.104112

Cited by 19 publications

(18 citation statements)

References 45 publications

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“…Multi-ancestry element has recently been increasingly incorporated in population genetic studies, such as GWAS, fine-mapping and polygenic risk score studies, but rarely applied in MR studies. 55,56 In our study, we identified 5 biomarkers exclusively detectable in the East Asian population, highlighting the values of leveraging multi-ancestorial populations in identifying candidate causal signals. We benefited from the relatively large sample size of the Biobank Japan, one of the largest non-European population cohorts with genome-wide genetic and medical records data available, which provided adequate statistical power in constructing IVs of disease endpoints and thereby detecting significant signals in the reverse MR, while hindered from the relatively small sample size of the pQTL study in this population, limiting us from identifying more potential target signals in the forward MR.…”

Section: Discussionmentioning

confidence: 68%

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Proteome-wide Mendelian randomization identifies candidate causal proteins for cardiovascular diseases

Li,

Jay,

Sharma

et al. 2023

Preprint

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Background: Cardiovascular diseases (CVD) remain the leading cause of death worldwide, while a lack of clarity on underlying mechanisms has hindered development of novel therapies. Integration of human genetics and proteomics across different ancestries can provide novel, affordable, and systematic approach for target identification and prioritization. Methods: Mendelian randomization approach was applied to unravel causal associations between 2,940 circulating proteins and 21 CVD. Genome-wide summary statistics from the largest genetic mapping of human plasma proteome and meta-analyses on CVD across FinnGen, UK Biobank and Biobank Japan were used. Forward and reverse causation were studied to distinguish respective targets and biomarkers. Genetic instruments for Europeans and East Asians were derived separately and applied to the cardiovascular outcomes in cohorts from corresponding ancestries. We further prioritized drug targets by integrating biological, clinical and population study evidence from cross-database annotations and literature review. Single-cell enrichment analysis and phenome-wide causality scan were performed to further understand target mechanism of action. Results: We found 221 novel candidate causal proteins that impacted risk of one or more CVD through forward MR, and 16 biomarkers whose expression levels were affected by CVD through reverse MR (Bonferroni-adjusted P-value <= 0.05). Forward and reverse MR found largely non-overlapping proteins among CVD (only 2 overlapped: LGALS4 and MMP12), suggesting distinct proteomic causes and consequences of CVD. Many of the candidate causal proteins (73.4%) identified are supported by strong literature evidence for a role in immune response and atherosclerotic lesion formation, angiogenesis and vascular remodeling, myogenesis and cardiac progenitor cell differentiation, and energy metabolism. Single cell integration further prioritized ADAM23 for cardiomegaly, PAM for stable angina pectoris and ventricular arrythmia and LPL for peripheral artery disease, whose transcript expression were enriched in cardiomyocytes. Three protein functional groups were highlighted in the phenome-wide scan for their specific enrichment for CVD, including blood coagulation and fibrinolysis, angiogenesis and vascular remodeling, and cell proliferation and myogenesis. Conclusions: Our study identified potential therapeutic targets for CVD and distinguished them from biomarkers due to reverse causation. This study provides human genetics-based evidence of novel candidate genes, a foundational step towards full-scale causal human biology-based drug discovery for CVD.

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Section: Discussionmentioning

confidence: 68%

“…MulN-ancestry element has recently been increasingly incorporated in populaNon geneNc studies, such as GWAS, finemapping and polygenic risk score studies, but rarely applied in MR studies. 55,56 In our study, we…”

Section: Benefits and Limitanons In Incorporanng Muln-ancestry Elemen...mentioning

confidence: 98%

Proteome-wide Mendelian randomization identifies candidate causal proteins for cardiovascular diseases

Li,

Jay,

Sharma

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…The results of coloc can be found in Table S2 and coloc-SuSiE in Table S3. In the four analyzed tissues, we identified a total of 28 unique genes (Figure S2), among these genes, 50% (14/28) have been reported to be associated with COVID-19 ( DPP9 30,31 , WNT3 32,33 , ABO 34,35 , NAPSA , 36 GSDMB 37,38 , RAB2A 29 , OAS1 39 , OAS3 7 , LRRC37A2 40 , MAPT 32 , FUT2 41,42 , IKZF3 43 , ICAM5 41,44 , CCR9 34,45 ).…”

Section: Resultsmentioning

confidence: 99%

Integrating population-level and cell-based signatures for drug repositioning

He,

Xu,

Zhou

et al. 2023

Preprint

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Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Furthermore, drugs with genetic evidence are more likely to progress successfully through clinical trials towards FDA approval. Exploiting these developments, single gene-based drug repositioning methods have been implemented, but approaches leveraging the entire spectrum of molecular signatures are critically underexplored. Most multi-gene-based approaches rely on differential gene expression (DGE) analysis, which is prone to identify the molecular consequence of disease and renders causal inference challenging. We propose a framework TReD (Transcriptome-informed Reversal Distance) that integrates population-level disease signatures robust to reverse causality and cell-based drug-induced transcriptome response profiles. TReD embeds the disease signature and drug profile in a high-dimensional normed space, quantifying the reversal potential of candidate drugs in a disease-related cell screen assay. The robustness is ensured by evaluation in additional cell screens. For an application, we implement the framework to identify potential drugs against COVID-19. Taking transcriptome-wide association study (TWAS) results from four relevant tissues and three DGE results as disease features, we identify 37 drugs showing potential reversal roles in at least four of the seven disease signatures. Notably, over 70% (27/37) of the drugs have been linked to COVID-19 from other studies, and among them, eight drugs are supported by ongoing/completed clinical trials. For example, TReD identifies the well-studied JAK1/JAK2 inhibitor baricitinib, the first FDA-approved immunomodulatory treatment for COVID-19. Novel potential candidates, including enzastaurin, a selective inhibitor of PKC-beta which can be activated by SARS-CoV-2, are also identified. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screens that can accelerate the search for new therapeutic strategies.

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“…Despite the success of GWASs, nearly 90% of disease-associated variants are identified to be located in the non-coding regions, which are enriched in cell-type-specific transcriptional regulatory elements relevant to disease risk. [102][103][104] Integration of GWAS summary data and eQTL data has been extensively used to discern novel candidate genes and yield functional insights into disease-relevant pharmacological effects 4,15,16 ; however, few of these insights has considered the cell-type-specific effects of drug targets. Thus, in this study, we repositioned drugs and their interacting targets for treating severe COVID-19 in a cell-type-specific context.…”

Section: Discussionmentioning

confidence: 99%

“…[13][14][15] Integrating GWAS summary statistics and expression quantitative trait loci (eQTL) data, recent studies have distinguished several candidates as putative drug targets for treating COVID-19. 4,16,17 Moreover, linking genome-wide polygenic signals with single-cell expression measurements from scRNA-seq data has considerable potential to unveil critical cell types or subpopulations relevant to complex diseases. 18 Our and other recent studies 19,20 have identified numerous immune and lung cell types that are impacted by genetic variants associated with COVID-19; for example, alveolar type 2 cells and CD8+ T cells in lung, 20 and CD16+ monocytes, megakaryocytes and memory CD8+ T cells in peripheral blood.…”

Section: Introductionmentioning

confidence: 99%

Integration of human organoids single‐cell transcriptomic profiles and human genetics repurposes critical cell type‐specific drug targets for severe COVID‐19

Ma,

Zhou,

Jiang

et al. 2023

Cell Proliferation

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Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single‐cell RNA sequencing (scRNA‐seq) technology and genome‐wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait‐relevant cell types or states. Here, we constructed a computational framework to integrate atlas‐level organoid scRNA‐seq data, GWAS summary statistics, expression quantitative trait loci, and gene–drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID‐19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID‐19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID‐19‐damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID‐19, and this cell subset showed a notable increase in cell‐to‐cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID‐19 in a cell‐type‐specific manner. Overall, our results showcase that host genetic determinants have cellular‐specific contribution to COVID‐19 severity, and identification of cell type‐specific drug targets may facilitate to develop effective therapeutics for treating severe COVID‐19 and its complications.

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Multi-ancestry Mendelian randomization of omics traits revealing drug targets of COVID-19 severity

Cited by 19 publications

References 45 publications

Proteome-wide Mendelian randomization identifies candidate causal proteins for cardiovascular diseases

Proteome-wide Mendelian randomization identifies candidate causal proteins for cardiovascular diseases

Integrating population-level and cell-based signatures for drug repositioning

Integration of human organoids single‐cell transcriptomic profiles and human genetics repurposes critical cell type‐specific drug targets for severe COVID‐19

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