Genetics of atherothrombotic and lacunar stroke

Dissecting Polygenic Etiology of Ischemic Stroke in the Era of Precision Medicine

Ischemic stroke (IS), the leading cause of death and disability worldwide, is caused by many modifiable and non-modifiable risk factors. This complex disease is also known for its multiple etiologies with moderate heritability. Polygenic risk scores (PRSs), which have been used to establish a common genetic basis for IS, may contribute to IS risk stratification for disease/outcome prediction and personalized management. Statistical modeling and machine learning algorithms have contributed significantly to this field. For instance, multiple algorithms have been successfully applied to PRS construction and integration of genetic and non-genetic features for outcome prediction to aid in risk stratification for personalized management and prevention measures. PRS derived from variants with effect size estimated based on the summary statistics of a specific subtype shows a stronger association with the matched subtype. The disruption of the extracellular matrix and amyloidosis account for the pathogenesis of cerebral small vessel disease (CSVD). Pathway-specific PRS analyses confirm known and identify novel etiologies related to IS. Some of these specific PRSs (e.g., derived from endothelial cell apoptosis pathway) individually contribute to post-IS mortality and, together with clinical risk factors, better predict post-IS mortality. In this review, we summarize the genetic basis of IS, emphasizing the application of methodologies and algorithms used to construct PRSs and integrate genetics into risk models.

Increased risk of stroke in oral contraceptive users carried replicated genetic variants: a population-based case-control study in China

Combined oral contraceptives (COC) use is a unique risk factor for stroke in women, and may modify the associations between genetic polymorphisms and stroke. To investigate whether the genetic variants identified in a recent genome-wide association study (GWAS) could be replicated in Chinese women, as well as, whether related risk was different in COC users, 451 stroke cases and 831 age- and region-matched controls were recruited from our cohort. Genotyping of 3 SNPs (rs700651, rs10958409, and rs1333040) was performed by the polymerase chain reaction assay with TaqMan probes. The history of contraceptive use and relevant information were obtained from a face-to-face interview. Odds ratios (OR) with 95 % confidence interval (CI) were estimated under conditional logistic regression model after adjustment for cardiovascular covariates. Our study replicated the associations of rs10958409 and rs1333040, with the risk of stroke, especially hemorrhagic subtype, but failed to confirm association of rs700651. COC use was associated with a 1.56-fold (OR 1.56, 95 % CI 1.21-2.01) increased risk of stroke. COC users with rs10958409 GA/AA or rs1333040 CT/TT genotypes had an increased risk of overall stroke by 1.59-fold (OR 2.59, 95 % CI 1.59-4.19) and 3.24-fold (OR 4.24, 95 % CI 1.71-10.49), respectively, compared with the non-users with wild-type genotypes. Moreover, the risk of hemorrhagic stroke increased by 4.81- and 15.06-fold when risk allele carriers of rs10958409 or rs1333040 who took COC. Our results confirmed the associations of two GWAS SNPs, also suggested combination effects of these genetic variants and COC use on stroke risk.

Heterogeneity of the phenotypic definition of coronary artery disease and its impact on genetic association studies

BACKGROUND

Variability in phenotypic characterization of coronary artery disease (CAD) may contribute to the heterogeneity of genetic association studies, and more consistency in phenotype definitions might improve replication of genetic associations. We assessed the extent of phenotypic heterogeneity and quantified its impact in a large literature sample of association studies.

METHODS AND RESULTS

We searched for large (≥15 studies) meta-analyses of genetic associations and reviewed all studies included therein. From each primary study, we extracted phenotypic definitions, demographics, study design characteristics, and genotypic data. For each association, we assessed the magnitude and heterogeneity of genetic effects within and across CAD phenotypes, using meta-analytic methodologies. A total of 965 individual studies investigating 32 distinct variants in 22 genes were included, from which we grouped CAD phenotypes into 3 categories: acute coronary syndromes (ACS) (426 [44%] studies); angiographically documented disease (323 [34%] studies); and broad, not otherwise specified CAD (216 [22%] studies). These clinical phenotypes were overlapping. Subgroup meta-analyses by phenotype showed discordant results, but phenotypic classification generally explained small proportions of between-study heterogeneity. Differences between phenotypic groups were minimized for associations with robust statistical support. No CAD phenotype was consistently associated with larger or more homogeneous genetic effects in meta-analyses.

CONCLUSIONS

Substantial phenotypic heterogeneity exists in CAD genetic associations, but differences in phenotype definition make a small contribution to between-study heterogeneity. We did not find a consistent effect in terms of the magnitude or homogeneity of summary effects for a specific phenotype to support its preferential use in genetic studies or meta-analyses for CAD.