Mapping gene and gene pathways associated with coronary artery disease: a CARDIoGRAM exome and multi-ancestry UK biobank analysis

Hariharan, Praveen; Dupuis, Josée

doi:10.1038/s41598-021-95637-9

Cited by 9 publications

(4 citation statements)

References 69 publications

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“…DUSP13 was associated with the regulation of the MAPK signaling pathway after stress stimulation in cardiomyocytes [ 35 ]. The up-regulation of DUSP13 to cardiac stress contributed to coronary artery disease, a common cardiovascular comorbidity of COVID-19 [ 36 , 37 ]. UNC5A retrained virus replication through autophagy induction instead of apoptosis [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

A Novel 3-Gene Signature for Identifying COVID-19 Patients Based on Bioinformatics and Machine Learning

Lai

Liu

Deng

et al. 2022

Genes

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Although many biomarkers associated with coronavirus disease 2019 (COVID-19) were found, a novel signature relevant to immune cells has not been developed. In this work, the “CIBERSORT” algorithm was used to assess the fraction of immune infiltrating cells in GSE152641 and GSE171110. Key modules associated with important immune cells were selected by the “WGCNA” package. The “GO” enrichment analysis was used to reveal the biological function associated with COVID-19. The “Boruta” algorithm was used to screen candidate genes, and the “LASSO” algorithm was used for collinearity reduction. A novel gene signature was developed based on multivariate logistic regression analysis. Subsequently, M0 macrophages (PRAUC = 0.948 in GSE152641 and PRAUC = 0.981 in GSE171110) and neutrophils (PRAUC = 0.892 in GSE152641 and PRAUC = 0.960 in GSE171110) were considered as important immune cells. Forty-three intersected genes from two modules were selected, which mainly participated in some immune-related activities. Finally, a three-gene signature comprising CLEC4D, DUSP13, and UNC5A that can accurately distinguish COVID-19 patients and healthy controls in three datasets was constructed. The ROCAUC was 0.974 in the training set, 0.946 in the internal test set, and 0.709 in the external test set. In conclusion, we constructed a three-gene signature to identify COVID-19, and CLEC4D, DUSP13, and UNC5A may be potential biomarkers for COVID-19 patients.

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Section: Discussionmentioning

confidence: 99%

A Novel 3-Gene Signature for Identifying COVID-19 Patients Based on Bioinformatics and Machine Learning

Lai

Liu

Deng

et al. 2022

Genes

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“…In FAM177B , we identified a variant (I3S) that corresponded with increased risk (OR, 1.18) and has recently been associated with CAD in a gene‐based meta‐analysis along with the substitution Q523R in MST1R . 76 These data illustrate that the 79 candidate genes are clinically important by increasing or decreasing the relative risk of EOMI and reveal variants of potentially novel mechanistic interest.…”

Section: Resultsmentioning

confidence: 88%

“… 109 , 110 Our analyses showed that MST1R is connected in STRING to EPOR , a regulator of JAK2 (Figure 2E ), and contains a single nucleotide polymorphism that is directly associated with increased EOMI risk (R1335G; OR, 1.15; EA=43.52) (Figure 3B ). MST1R has recently been identified by gene‐based association tests for CAD, 76 and MST1R null mice have impaired nitric oxide levels and increased tissue damage in response to acute stress. 111 Third, DHX58 represents a previously reported yet understudied association in the context of CAD.…”

Section: Discussionmentioning

confidence: 99%

Evolutionary Action–Machine Learning Model Identifies Candidate Genes Associated With Early‐Onset Coronary Artery Disease

Shapiro,

Lee,

Asmussen

et al. 2023

JAHA

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Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome‐wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble‐based supervised learning, the Evolutionary Action–Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them, INPP5F and MST1R are examples of potentially novel coronary artery disease risk genes that modulate immune signaling in response to cardiac stress. Conclusions We concluded that machine learning on the functional impact of coding variants, based on a massive amount of evolutionary information, has the power to suggest novel coronary artery disease risk genes for mechanistic and therapeutic discoveries in cardiovascular biology, and should also apply in other complex polygenic diseases.

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“…Because there is no standardization on MAGMA window size beyond the notion that a larger window size incorporates SNPs falling in cis-regulatory elements, we evaluated the impact of the most used window sizes on results (details in Methods) (M. J. Zhang et al 2022; Jia et al 2022; Watanabe et al 2017; Sobrin et al 2022; Hariharan and Dupuis 2021; Watanabe et al 2019; Skene et al 2018; Zhu and Stephens 2018; Finucane et al 2018). Different window sizes for RA analyses only changed the significance calls for 16 of the 77 cell clusters in at least one of the window-sizes, half of which are only different in one window size (Figure 5).…”

Section: Resultsmentioning

confidence: 99%

Single-cell based integrative analysis of transcriptomics and genetics reveals robust associations and complexities for inflammatory diseases

Townsend,

Rosenberger,

Vanderlinden

et al. 2024

Preprint

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BackgroundUnderstanding genetic underpinnings of immune-mediated inflammatory diseases is crucial to improve treatments. Single-cell RNA sequencing (scRNA-seq) identifies cell states expanded in disease, but often overlooks genetic causality due to cost and small genotyping cohorts. Conversely, large genome-wide association studies (GWAS) are commonly accessible.MethodsWe present a 3-step robust benchmarking analysis of integrating GWAS and scRNA-seq to identify genetically relevant cell states and genes in inflammatory diseases.First, we applied and compared the results of two recent algorithms, based on networks (scGWAS) or single-cell disease scores (scDRS), according to accuracy/sensitivity and interpretability (M. J. Zhang et al. 2022; Jia et al. 2022). While previous studies focused on coarse cell types, we used disease-specific, fine-grained single-cell atlases (183,742 and 228,211 cells) and GWAS data (Ns of 97,1,73 and 45,975) for rheumatoid arthritis (RA) and ulcerative colitis (UC) (F. Zhang et al. 2023; Ishigaki et al. 2022; Smillie et al. 2019; de Lange et al. 2017).Second, given the lack of scRNA-seq for many diseases with GWAS, we further tested the tools’ resolution limits by differentiating between similar diseases with only one fine-grained scRNA-seq atlas.Lastly, we provide a novel evaluation of noncoding SNP incorporation methods by testing which enabled the highest sensitivity/accuracy of known cell-state calls.ResultsWe first found that single-cell based tool scDRS called superior numbers of supported cell states, like MERTK+ myeloid cells in RA, which were overlooked by network-based scGWAS. While scGWAS was advantageous for gene exploration, scDRS captured cellular heterogeneity of disease-relevance without single-cell genotyping. For noncoding SNP integration, we found a key trade-off between statistical power and confidence with positional (e.g. MAGMA) and non-positional approaches (e.g. chromatin-interaction, eQTL). Even when directly incorporating noncoding SNPs through 5’ scRNA-seq measures of regulatory elements, non disease-specific atlases gave misleading results by not containing disease-tissue specific transcriptomic patterns. Despite this criticality of tissue-specific scRNA-seq, we showed that scDRS enabled deconvolution of two similar diseases with a single fine-grained scRNA-seq atlas and separate GWAS. Indeed, we identified supported and novel genetic-phenotype linkages separating RA and ankylosing spondylitis, and UC and crohn’s disease. Overall, while noting evolving single-cell technologies, our study provides key findings for integrating expanding fine-grained scRNA-seq, GWAS, and noncoding SNP resources to unravel the complexities of inflammatory diseases.

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Mapping gene and gene pathways associated with coronary artery disease: a CARDIoGRAM exome and multi-ancestry UK biobank analysis

Cited by 9 publications

References 69 publications

A Novel 3-Gene Signature for Identifying COVID-19 Patients Based on Bioinformatics and Machine Learning

A Novel 3-Gene Signature for Identifying COVID-19 Patients Based on Bioinformatics and Machine Learning

Evolutionary Action–Machine Learning Model Identifies Candidate Genes Associated With Early‐Onset Coronary Artery Disease

Single-cell based integrative analysis of transcriptomics and genetics reveals robust associations and complexities for inflammatory diseases

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