Rosand, Jonathan M.

Intracerebral haemorrhage (ICH) and small vessel ischaemic stroke (SVS) are the most severe manifestations of cerebral small vessel disease. In a cross-phenotype genome-wide association analysis, Chung et al. identify two novel associations at 2q33 and 13q34 plus a previously identified locus at 1q22 for non-lobar ICH and SVS risk.

Background and Purpose— We evaluated deep learning algorithms’ segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods— Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms’ performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results— The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P <0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm 3 (0.9–16.6 cm 3 ). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P <0.0001) and different topography compared with other stroke subtypes. Conclusions— Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.

Introduction: Risk of lobar and non-lobar intracerebral hemorrhage (ICH) varies among blacks, whites and Hispanics. We sought to determine whether these differences could be due to variability in the effects of Apolipoprotein E (APOE) epsilon (ε) alleles, the most potent genetic risk factor for ICH. Methods: Primary ICH cases and controls were collected from US and European sites contributing to the International Stroke Genetic Consortium (ISGC). We meta-analyzed the effects of APOE allele status on ICH risk applying a two-stage clustering approach based on race/ethnicity and the contributing study. Models were adjusted for age, sex, history of hypertension, hypercholesterolemia, warfarin, statin and antiplatelet use, smoking and alcohol use. A propensity score analysis was used to model the influence of APOE against the burden of hypertension across races/ethnicities. Results: 13,124 subjects (54.5% male, median age 66 years) were included. In whites, APOE ε2 (odds ratio (OR)=1.85, 95% confidence interval (CI)=1.27-2.69, p<0.001) and APOE ε4 (OR=1.94, 95% CI=1.58-2.38, p<0.001) were independently associated with lobar ICH risk, however within self-identified Hispanics and blacks, no associations were found (Figure). After propensity score-matching for hypertension burden, APOE ε4 was associated with lobar ICH risk among whites (OR=1.12, 95% CI=1.08-1.17) and Hispanics (OR=1.07, 95% CI=1.01-1.15, p=0.01), but not blacks (OR=1.02 95% CI=0.98-1.07, p=0.251). APOE ε2 and ε4 did not show an effect on non-lobar ICH risk in any race/ethnicity. Conclusion: APOE ε4 and ε2 alleles affect lobar ICH risk variably by race and ethnicity. Associations are confirmed in whites but can be shown in Hispanics only when the excess burden of hypertension is propensity score-matched. Further studies are needed to explore interactions between APOE alleles and environmental exposures that vary by race and ethnicity in representative populations at risk for ICH.

Introduction: Genome-wide association (GWA) studies have contributed substantially to the understanding of complex vascular traits including ischemic stroke; however, genetic determinants of acute cerebral ischemia remain to be elucidated. Objective: To investigate the genetic architecture of cerebral infarct lesion burden in patients with acute ischemic stroke (AIS). Methods: Twelve international sites contributed 3,301 AIS subjects with acute MRI and genome-wide genotyping to the MRI-GENIE study. In a preliminary analysis of 657 subjects of European ancestry, acute cerebral infarct lesions were outlined on diffusion-weighted images (DWI) using a semi-automated volumetric method. Standard quality control measures were performed per single nucleotide polymorphism (SNP) and per subject. SNPs were imputed to the Haplotype Reference Consortium v1.1 panel. Natural log-transformed DWI volume was used for GWA analysis with allelic dosage per SNP, age, sex and principal components 1-5 of genetic ancestry as covariates. Results: GWA testing for DWI volume in 7.7 million SNPs yielded no signal crossing the Bonferroni-corrected genome-wide significance threshold of p <5*10 -8 . However, several loci passed the nominal significance threshold of p <1*10 -6 (Figure 1), including a locus on chromosome 2 in the SPATA3-AS1 (spermatogenesis associated 3 antisense RNA 1) gene (lead SNP: rs2368999, MAF=36%, p =8.4*10 -7 ) and an intronic locus on chromosome 10 in the ATRNL1 (Attractin like 1) gene (lead SNP: rs592284, MAF=10%, p =9.1*10 -7 ). Conclusion: In this first-to-date, preliminary GWAS of acute cerebral infarct volume in AIS patients, we identified several new, nominally significant loci including a locus in ATRNL1, a gene previously linked to carotid plaque burden. Further analyses of the MRI-GENIE cohort to replicate these findings, to include stroke subtype specific analyses, and to increase the overall statistical power of the study are ongoing.

Background and Purpose— Hematoma volume is an important determinant of clinical outcome in spontaneous intracerebral hemorrhage (ICH). We performed a genome-wide association study (GWAS) of hematoma volume with the aim of identifying novel biological pathways involved in the pathophysiology of primary brain injury in ICH. Methods— We conducted a 2-stage (discovery and replication) case-only genome-wide association study in patients with ICH of European ancestry. We utilized the admission head computed tomography to calculate hematoma volume via semiautomated computer-assisted technique. After quality control and imputation, 7 million genetic variants were available for association testing with ICH volume, which was performed separately in lobar and nonlobar ICH cases using linear regression. Signals with P <5×10 − 8 were pursued in replication and tested for association with admission Glasgow coma scale and 3-month post-ICH dichotomized (0–2 versus 3–6) modified Rankin Scale using ordinal and logistic regression, respectively. Results— The discovery phase included 394 ICH cases (228 lobar and 166 nonlobar) and identified 2 susceptibility loci: a genomic region on 22q13 encompassing PARVB (top single-nucleotide polymorphism rs9614326: β, 1.84; SE, 0.32; P =4.4×10 −8 ) for lobar ICH volume and an intergenic region overlying numerous copy number variants on 17p12 (top single-nucleotide polymorphism rs11655160: β, 0.95; SE, 0.17; P =4.3×10 −8 ) for nonlobar ICH volume. The replication included 240 ICH cases (71 lobar and 169 nonlobar) and corroborated the association for 17p12 ( P =0.04; meta-analysis P =2.5×10 −9 ; heterogeneity, P =0.16) but not for 22q13 ( P =0.49). In multivariable analysis, rs11655160 was also associated with lower admission Glasgow coma scale (odds ratio, 0.17; P =0.004) and increased risk of poor 3-month modified Rankin Scale (odds ratio, 1.94; P =0.045). Conclusions— We identified 17p12 as a novel susceptibility risk locus for hematoma volume, clinical severity, and functional outcome in nonlobar ICH. Replication in other ethnicities and follow-up translational studies are needed to elucidate the mechanism mediating the observed association.

Objective Genome‐wide association studies (GWAS) have been successful at identifying associations with stroke and stroke subtypes, but have not yet identified any associations solely with small vessel stroke (SVS). SVS comprises one quarter of all ischemic stroke and is a major manifestation of cerebral small vessel disease, the primary cause of vascular cognitive impairment. Studies across neurological traits have shown that younger‐onset cases have an increased genetic burden. We leveraged this increased genetic burden by performing an age‐at‐onset informed GWAS meta‐analysis, including a large younger‐onset SVS population, to identify novel associations with stroke. Methods We used a three‐stage age‐at‐onset informed GWAS to identify novel genetic variants associated with stroke. On identifying a novel locus associated with SVS, we assessed its influence on other small vessel disease phenotypes, as well as on messenger RNA (mRNA) expression of nearby genes, and on DNA methylation of nearby CpG sites in whole blood and in the fetal brain. Results We identified an association with SVS in 4,203 cases and 50,728 controls on chromosome 16q24.2 (odds ratio [OR; 95% confidence interval {CI}] = 1.16 [1.10–1.22]; p = 3.2 × 10 −9 ). The lead single‐nucleotide polymorphism (rs12445022) was also associated with cerebral white matter hyperintensities (OR [95% CI] = 1.10 [1.05–1.16]; p = 5.3 × 10 −5 ; N = 3,670), but not intracerebral hemorrhage (OR [95% CI] = 0.97 [0.84–1.12]; p = 0.71; 1,545 cases, 1,481 controls). rs12445022 is associated with mRNA expression of ZCCHC14 in arterial tissues ( p = 9.4 × 10 −7 ) and DNA methylation at probe cg16596957 in whole blood ( p = 5.3 × 10 −6 ). Interpretation 16q24.2 is associated with SVS. Associations of the locus with expression of ZCCHC14 and DNA methylation suggest the locus acts through changes to regulatory elements. Ann Neurol 2017;81:383–394

ObjectiveIn observational epidemiologic studies, higher plasma high‐density lipoprotein cholesterol (HDL‐C) has been associated with increased risk of intracerebral hemorrhage (ICH). DNA sequence variants that decrease cholesteryl ester transfer protein (CETP) gene activity increase plasma HDL‐C; as such, medicines that inhibit CETP and raise HDL‐C are in clinical development. Here, we test the hypothesis that CETP DNA sequence variants associated with higher HDL‐C also increase risk for ICH.MethodsWe performed 2 candidate‐gene analyses of CETP. First, we tested individual CETP variants in a discovery cohort of 1,149 ICH cases and 1,238 controls from 3 studies, followed by replication in 1,625 cases and 1,845 controls from 5 studies. Second, we constructed a genetic risk score comprised of 7 independent variants at the CETP locus and tested this score for association with HDL‐C as well as ICH risk.ResultsTwelve variants within CETP demonstrated nominal association with ICH, with the strongest association at the rs173539 locus (odds ratio [OR] = 1.25, standard error [SE] = 0.06, p = 6.0 × 10−4) with no heterogeneity across studies (I2 = 0%). This association was replicated in patients of European ancestry (p = 0.03). A genetic score of CETP variants found to increase HDL‐C by ∼2.85mg/dl in the Global Lipids Genetics Consortium was strongly associated with ICH risk (OR = 1.86, SE = 0.13, p = 1.39 × 10−6).InterpretationGenetic variants in CETP associated with increased HDL‐C raise the risk of ICH. Given ongoing therapeutic development in CETP inhibition and other HDL‐raising strategies, further exploration of potential adverse cerebrovascular outcomes may be warranted. Ann Neurol 2016;80:730–740

Background and purposeAlthough the genetic contribution to stroke risk is well known, it remains unclear if young‐onset stroke has a stronger genetic contribution than old‐onset stroke. This study aims to compare the heritability of ischaemic stroke risk between young and old, using common genetic variants from whole‐genome array data in population‐based samples.MethodsThis analysis included 4050 ischaemic stroke cases and 5765 controls from six study populations of European ancestry; 47% of cases were young‐onset stroke (age < 55 years). To quantify the heritability for stroke risk in these unrelated individuals, the pairwise genetic relatedness was estimated between individuals based on their whole‐genome array data using a mixed linear model. Heritability was estimated separately for young‐onset stroke and old‐onset stroke (age ≥ 55 years).ResultsHeritabilities for young‐onset stroke and old‐onset stroke were estimated at 42% (±8%, P < 0.001) and 34% (±10%, P < 0.001), respectively.ConclusionsOur data suggest that the genetic contribution to the risk of stroke may be higher in young‐onset ischaemic stroke, although the difference was not statistically significant.

Background and Purpose— NINDS (National Institute of Neurological Disorders and Stroke)-SiGN (Stroke Genetics Network) is an international consortium of ischemic stroke studies that aims to generate high-quality phenotype data to identify the genetic basis of pathogenic stroke subtypes. This analysis characterizes the etiopathogenetic basis of ischemic stroke and reliability of stroke classification in the consortium. Methods— Fifty-two trained and certified adjudicators determined both phenotypic (abnormal test findings categorized in major pathogenic groups without weighting toward the most likely cause) and causative ischemic stroke subtypes in 16 954 subjects with imaging-confirmed ischemic stroke from 12 US studies and 11 studies from 8 European countries using the web-based Causative Classification of Stroke System. Classification reliability was assessed with blinded readjudication of 1509 randomly selected cases. Results— The distribution of pathogenic categories varied by study, age, sex, and race ( P <0.001 for each). Overall, only 40% to 54% of cases with a given major ischemic stroke pathogenesis (phenotypic subtype) were classified into the same final causative category with high confidence. There was good agreement for both causative (κ 0.72; 95% confidence interval, 0.69–0.75) and phenotypic classifications (κ 0.73; 95% confidence interval, 0.70–0.75). Conclusions— This study demonstrates that pathogenic subtypes can be determined with good reliability in studies that include investigators with different expertise and background, institutions with different stroke evaluation protocols and geographic location, and patient populations with different epidemiological characteristics. The discordance between phenotypic and causative stroke subtypes highlights the fact that the presence of an abnormality in a patient with stroke does not necessarily mean that it is the cause of stroke.