Shared genetic architecture of stroke and its comorbid conditions reveals latent phenotypes that differ among populations: A genomic structural equation modelling approach

Abstract

Stroke affects over 101 million people worldwide and is the second most fatal non-communicable disease with over 6.55 million deaths in 2019. Stroke has a complex etiology, which is further influenced by the genetics of ethnicity and its comorbid conditions. A genomic structure equation model was applied to identify the latent factors for stroke and its comorbid conditions in European and East Asian population. Two latent factors characteristic to the population emerged in each of the populations - one was an immunological factor and other a metabolic factor. The inflammation factor was defined by the shared variants in ischemic stroke and ischemic heart disease in the European population, and by shared variants in ischemic stroke and high systolic blood pressure in the East Asian population. The metabolic factor was defined by the shared variants in high BMI and Type 2 diabetes in both the populations. The significant variants associated with the latent factors were identified using genomic SEM and characterised using FUMA. A total of 99 new loci associated with stroke and its latent factors were identified. Expression quantitative trait loci highlight the differential effect of change in gene expression of the colocalizing variants among the two populations. The drug targets and pathways identified for these latent factors provides direction for drug repurposing and intervention points that can be leveraged to reduce the burden of stroke. Precise understanding of these genomic insights will aid in designing appropriate therapeutic and prevention strategies to reduce the burden of stroke.