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Updated: May 7, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
Rare coding variation and stroke heterogeneity in Saudi Arabia: an exome‑wide association study across severity,
Fahad Alkhamis1, Majed M Alabdali1, Abdullah S Alamri1
1Department of Neurology, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia.
Background:
Ischemic stroke in Saudi Arabia arises in a highly consanguineous population with a distinctive genetic architecture, likely enriching rare coding variants that influence stroke risk. Yet the contribution of these variants to stroke susceptibility, age at onset, and subtype patterns in this setting remains incompletely defined.
Methods:
We analyzed whole-exome sequencing data from 514 stroke patients in a case only study design. Gene-based rare variant burden analyses was performed using SAIGE-GENE+ burden and SKAT-O tests across predefined phenotype contrasts within the cohort, stroke severity (modified Rankin Scale, mild < 3 vs. severe ≥ 3 ,), age at onset (early-onset < 25 years vs. late-onset > 45 years; mid-life onset < 45 vs. > 45), etiological subtypes (TOAST classification), and vascular imaging patterns (intracranial vs. extracranial).Post-association functional annotations included GTEx expression, gnomAD constraint metrics, and draggability insights.
Results:
Gene-level associations at a suggestive threshold (p < 0.005) identified several candidates including HSP90AB1, PRR23A, and LRRC42 (severity and age-at-onset); POGZ and SMIM34 (age-at-onset); and COL9A3, DCP1B, and ADGRV1 (imaging and etiology subtypes). The highlighted genes showed varying expression across brain and vascular tissues and intolerance to loss-of-function variation. Notably, HSP90AB1, a molecular chaperone highly expressed in the brain and vasculature, has small-molecule inhibitors, supporting its potential relevance to stroke biology.
Conclusions:
Our findings identify exploratory candidate gene-level signals across clinically defined ischemic stroke phenotypes based on case-only within-cohort comparisons, in underrepresented population. These results should be considered hypothesis-generating and require replication and functional validation before biological or clinical inferences can be made.
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