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Lookup NU author(s): Professor Raj KalariaORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2024. Background: African ancestry populations have the highest burden of stroke worldwide, yet the genetic basis of stroke in these populations is obscure. The Stroke Investigative Research and Educational Network (SIREN) is a multicenter study involving 16 sites in West Africa. We conducted the first-ever genome-wide association study (GWAS) of stroke in indigenous Africans. Methods: Cases were consecutively recruited consenting adults (aged > 18 years) with neuroimaging-confirmed ischemic stroke. Stroke-free controls were ascertained using a locally validated Questionnaire for Verifying Stroke-Free Status. DNA genotyping with the H3Africa array was performed, and following initial quality control, GWAS datasets were imputed into the NIH Trans-Omics for Precision Medicine (TOPMed) release2 from BioData Catalyst. Furthermore, we performed fine-mapping, trans-ethnic meta-analysis, and in silico functional characterization to identify likely causal variants with a functional interpretation. Results: We observed genome-wide significant (P-value < 5.0E−8) SNPs associations near AADACL2 and miRNA (MIR5186) genes in chromosome 3 after adjusting for hypertension, diabetes, dyslipidemia, and cardiac status in the base model as covariates. SNPs near the miRNA (MIR4458) gene in chromosome 5 were also associated with stroke (P-value < 1.0E−6). The putative genes near AADACL2, MIR5186, and MIR4458 genes were protective and novel. SNPs associations with stroke in chromosome 2 were more than 77 kb from the closest gene LINC01854 and SNPs in chromosome 7 were more than 116 kb to the closest gene LINC01446 (P-value < 1.0E−6). In addition, we observed SNPs in genes STXBP5-AS1 (chromosome 6), GALTN9 (chromosome 12), FANCA (chromosome 16), and DLGAP1 (chromosome 18) (P-value < 1.0E−6). Both genomic regions near genes AADACL2 and MIR4458 remained significant following fine mapping. Conclusions: Our findings identify potential roles of regulatory miRNA, intergenic non-coding DNA, and intronic non-coding RNA in the biology of ischemic stroke. These findings reveal new molecular targets that promise to help close the current gaps in accurate African ancestry-based genetic stroke’s risk prediction and development of new targeted interventions to prevent or treat stroke.
Author(s): Akinyemi RO, Tiwari HK, Srinivasasainagendra V, Akpa O, Sarfo FS, Akpalu A, Wahab K, Obiako R, Komolafe M, Owolabi L, Osaigbovo GO, Mamaeva OA, Halloran BA, Akinyemi J, Lackland D, Obiabo OY, Sunmonu T, Chukwuonye II, Arulogun O, Jenkins C, Adeoye A, Agunloye A, Ogah OS, Ogbole G, Fakunle A, Uvere E, Coker MM, Okekunle A, Asowata O, Diala S, Ogunronbi M, Adeleye O, Laryea R, Tagge R, Adeniyi S, Adusei N, Oguike W, Olowoyo P, Adebajo O, Olalere A, Oladele O, Yaria J, Fawale B, Ibinaye P, Oyinloye O, Mensah Y, Oladimeji O, Akpalu J, Calys-Tagoe B, Dambatta HA, Ogunniyi A, Kalaria R, Arnett D, Rotimi C, Ovbiagele B, Owolabi MO
Publication type: Article
Publication status: Published
Journal: Genome Medicine
Year: 2024
Volume: 16
Online publication date: 05/02/2024
Acceptance date: 12/12/2023
Date deposited: 29/02/2024
ISSN (electronic): 1756-994X
Publisher: BioMed Central Ltd
URL: https://doi.org/10.1186/s13073-023-01273-5
DOI: 10.1186/s13073-023-01273-5
Data Access Statement: Requests for resources and information should be directed to and will be fulfilled by the lead contact, M. O (mayowaowolabi@yahoo.com). Phenotype and genotype data are available under managed access to researchers. Requests for access will be granted for all research consistent with the consent provided by participants. This would include any research in the context of health and disease that does not involve identifying the participants in any way. The array data have been deposited at the H3Africa Bionet for the European Genome-phenome Archive (accession number: the SIREN study page is: https://ega-archive.org/studies/EGAS00001007331 which contains 2 datasets; phenotype data: EGAD00001011075 https://ega-archive.org/datasets/EGAD00001011075; genotype data: EGAD00010002551 https://ega-archive.org/datasets/EGAD00010002551).
PubMed id: 38317187
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