Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

Tania Islam; Md Rezanur Rahman; Asaduzzaman Khan; Mohammad Ali Moni

doi:10.1016/j.jbi.2023.104345

Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

J Biomed Inform. 2023 May:141:104345. doi: 10.1016/j.jbi.2023.104345. Epub 2023 Mar 21.

Authors

Tania Islam¹, Md Rezanur Rahman², Asaduzzaman Khan¹, Mohammad Ali Moni³

Affiliations

¹ School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4067, Australia.
² Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia.
³ School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4067, Australia. Electronic address: m.moni@uq.edu.au.

PMID: 36958462
DOI: 10.1016/j.jbi.2023.104345

Abstract

Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; P_HEIDI > 0.01) that were pleiotropically or potentially causally associated with stroke risk. Of those candidate biomarker genes, 10 genes (HTRA1, PMF1, FBN2, C9orf84, COL4A1, BAG4, NEK6, SH2B3, SH3PXD2A, ACAD10) were differentially expressed in genome-wide blood transcriptomics data from stroke and healthy individuals (FDR < 0.05). Functional enrichment analysis of the identified candidate biomarker genes revealed gene ontologies and pathways involved in stroke, including "cell aging", "metal ion binding" and "oxidative damage". Based on the evidence of genetically regulated expression of genes through SMR and directly measured expression of genes in blood, our integrative analysis suggests ten genes as blood biomarkers of stroke risk. Furthermore, our study provides a better understanding of the influence of DNA methylation on the expression of genes linked to stroke risk.

Keywords: DNA methylation; DNA methylation quantitative trait loci; Expression quantitative trait loci; Gene expression; Genome-wide association studies; Stroke; Summary data-based Mendelian randomisation.

MeSH terms

Acyl-CoA Dehydrogenase / genetics
Genetic Markers
Genetic Predisposition to Disease
Genome-Wide Association Study
High-Temperature Requirement A Serine Peptidase 1 / genetics
Humans
NIMA-Related Kinases / genetics
Phenotype
Polymorphism, Single Nucleotide
Stroke* / diagnosis
Stroke* / genetics
Systems Biology*

Substances

Genetic Markers
NEK6 protein, human
NIMA-Related Kinases
HTRA1 protein, human
High-Temperature Requirement A Serine Peptidase 1
ACAD10 protein, human
Acyl-CoA Dehydrogenase