Transcriptome‐wide summary data‐based Mendelian randomization analysis reveals 38 novel genes associated with severe COVID‐19

Krishnamoorthy, Suhas; Li, Gloria Hoi‐Yee; Cheung, Ching‐Lung

doi:10.1002/jmv.28162

Cited by 28 publications

(15 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…In the four analyzed tissues, we identified a total of 28 unique genes post coloc/coloc-SuSiE (Figure S2), among these genes, 50% (14/28) have been reported to be associated with COVID-19 ( DPP9 31,32 , WNT3 33,34 , ABO 35,36 , NAPSA , 37 GSDMB 38,39 , RAB2A 30 , OAS1 40 , OAS3 5 , LRRC37A2 41 , MAPT 33 , FUT2 42,43 , IKZF3 44 , ICAM5 42,45 , CCR9 35,46 ). CCR9 and MAPT are druggable genes.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

He,

Xu,

Zhou

et al. 2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

He,

Xu,

Zhou

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, to further reduce the potential bias of horizontal pleiotropy and also increase the statistical power, the multi-SNPs-based SMR, which extended the SMR method by including multiple SNPs within a 1Mb cis region of a probe after removing SNPs exhibited very high LD with the top associated SNP (LD r 2 >0.9) was conducted as the sensitivity analysis. 17, 18 False discovery rate (FDR) multiple testing correction under the Benjamini-Hochberg method was applied, and corrected P values less than 0.05 was deemed significant.…”

Section: Methodsmentioning

confidence: 99%

Prioritization of therapeutic targets for amyotrophic lateral sclerosis using protein-wide Mendelian randomization analysis

Geng,

Chen,

Yang

et al. 2023

Preprint

View full text Add to dashboard Cite

Background and ObjectivesAmyotrophic lateral sclerosis (ALS) is a rapidly progressing neurodegenerative disease with an increasing global burden. Available treatments for ALS present marginal efficacy. To identify novel candidate therapeutic targets for ALS, we conducted a proteome-wide Mendelian randomization (MR) study.MethodsWe leveraged data from the largest summary statistics for ALS to date (27,205 patients with ALS and 110,881 controls). Genetic instruments of more than 4,000 proteins defined bycis-protein quantitative loci (pQTL) genetic instruments on plasma and cerebrospinal fluid (CSF) were obtained from Fenland (discovery, n=10,709), deCODE (replication, n=35,559), and a recently published dataset (replication, n=971). To investigate the causal ALS-associated proteins, proteome-wide Mendelian randomization based on summary-data-based MR (SMR and multi-SNP-based SMR) were performed. Then, two-sample MR analyses using five additional methods were conducted as sensitivity analyses. To further address the linkage disequilibrium bias, heterogeneity in dependent instruments test and colocalization analyses were performed. Steiger filtering and bi-directional MR analyses were conducted to address the potential reverse causality. Four drug target datasets were searched to extract druggability profiles for candidate target proteins. In addition, we carried out a case-control study involving up to 21 patients with ALS and 21 matched controls to assess the protein levels difference in CSF for evidence triangulation.ResultsGenetically predicted levels of six circulating proteins were associated with incident ALS in primary SMR analysis. After removing proteins with any linkage disequilibrium bias, SHBG,SIGLEC7, and SIGLEC9presented consistent associations with ALS risk, supported by medium-to-high colocalization across both plasma pQTL datasets. In CSF, higher level ofSHBGwas also causally associated with the risk of ALS. There was no reverse causality detected. The case-control study using CSF proteomics conducted in our center observed consistent alteration in the levels ofSHBGandSIGLEC7with MR prediction, further suggesting their functionally relevant to ALS as potential druggable targets.DiscussionCombined with the findings from MR and our observational study, we prioritizeSHBG,SIGLEC7,andSIGLEC9as drug candidate proteins for ALS, and further studies are needed to verify our findings and elucidate the underlying mechanism.

show abstract

“…We used Summary-data-based Mendelian randomization (SMR) to identify putative functional genes underlying statistical associations for SCZ and OB. In brief, SMR is a method that integrates summary statistics from GWAS and eQTL studies 29 under the MR framework to test for an association between gene expression and a target phenotype. We conducted SMR in different tissues which had signi cant SNP-heritability enrichment of SCZ and OB.…”

Section: Statistical Analysesmentioning

confidence: 99%

Investigating the shared genetic architecture between schizophrenia and obesity

Yu²,

Fu³

et al. 2023

Preprint

View full text Add to dashboard Cite

Evidence for reciprocal comorbidity of schizophrenia (SCZ) and obesity (OB) has grown in recent years. However, little is known regarding the shared genetic architecture or causality underlying the phenotypic association between SCZ and OB. Leveraging summary statistics from the hitherto largest genome-wide association study (GWAS) on each trait, we investigated the genetic overlap and causal associations of SCZ with OB. Our study demonstrated a genetic correlation between SCZ and OB, and the correlation was more evident in local genomic regions. The cross-trait meta-analysis identified 27 significant SNPs shared between SCZ and OB, most of which had the same direction of influence on both diseases. Mendelian randomization analysis showed a causal association of SCZ with OB, but not vice versa. Combining the gene expression information, we found that the genetic correlation between SCZ and OB is enriched in six regions of brain, led by the brain frontal cortex, and within these regions 34 functional genes were found to have an impact on both SCZ and OB. Taken together, our comprehensive genome-wide cross-trait analysis suggests a shared genetic basis including pleiotropic loci, tissue enrichment, and shared function genes between SCZ and OB. This work provides novel insights into the intrinsic genetic overlap of SCZ and OB, and highlights new opportunities and avenues for future investigation.

show abstract

Transcriptome‐wide summary data‐based Mendelian randomization analysis reveals 38 novel genes associated with severe COVID‐19

Cited by 28 publications

References 54 publications

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

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

Prioritization of therapeutic targets for amyotrophic lateral sclerosis using protein-wide Mendelian randomization analysis

Investigating the shared genetic architecture between schizophrenia and obesity

Contact Info

Product

Resources

About