Worrall, Bradford B.

Background: The risk for intracranial (IA) and abdominal aortic aneurysms (AAA) is partially heritable, with evidence mounting in favor of shared genetic risk for both. Methods: The Brain and Aortic Aneurysm Study (BAAS, NCT#17341) at University of Virginia seeks to determine the coprevalence of IA and AAA recruiting patients presenting with an aneurysm at one site and screening for aneurysm at the other. Participants undergo genetic screening and an interview where clinical, demographic, and radiographic data are collected. Recruitment and screening are ongoing, so preliminary demographic data regarding between group differences (IA vs. AAA) are presented. All clinical and demographic data were collected and recorded in REDCap and exported to Excel. We compared a total of 256 participants (IA=164, AAA=92) for demographic factors: age, sex, smoking and past medical history. Results: The AAA cohort was older (70.98 vs. 58.51, p<.001) and more male (79.34% vs. 23.78% p<.001). The AAA cohort had a higher proportion with diabetes mellitus (28.26% vs. 15.24%, p=.012), with hyperlipidemia (67.39% vs. 44.51%, p<.001) and higher smoking pack years (44.07 vs. 17.85, p<.001). The two groups did not differ in proportion with hypertension, coronary artery disease, or peripheral vascular disease (all p>.05). Conclusions: These data suggest that there may be subtle differences in the clinical profile of patients presenting with IA and AAA, especially when considering known aneurysmal risk factors like hypertension, diabetes, and smoking history. Further analyses will consider whether these factors modulate the coprevalence of the two types of aneurysms.

Introduction: White matter hyperintensity volume (WMHv) is an important and highly heritable cerebrovascular phenotype; however, manual or semi-automated approaches to clinically acquired MRI analysis hinder large-scale studies in acute ischemic stroke (AIS). In this work, we develop a high-throughput, fully automated WMHv analysis pipeline for clinical fluid-attenuated inversion recovery (FLAIR) images to facilitate rapid genetic discovery in AIS. Methods: Automated WMHv extraction from multiple subjects relies on significant pre-processing of medical scans, including co-registration of the images. To reduce the effects of anisotropic voxel sizes, each FLAIR image is upsampled using bi-cubic interpolation. Brain extraction is performed using RObust Brain EXtraction (ROBEX). Images are then registered to an in-house FLAIR template using Advanced Normalization Tools (ANTs). The spatial covariation of WMH is learned through principal component analysis (PCA) of manual outlines from 100 subjects. Areas of leukoaraiosis are identified and separated from other lesions using the PCA modes. Volumes are then computed using non-interpolated slices for each subject. Standard deviation (SD) in WMHv (9 subjects; 6 raters each) is calculated as a measure of variability. Good agreement between automated and manual outlines is assessed in 358 subjects (automated WMHv within 3SD of manual WMHv). Results: As part of the MRI - Gen etics I nterface E xploration (MRI-GENIE) study, WMHv were calculated on a set of 2703 FLAIR images of patients from 12 independent AIS cohorts (sites). Results are shown in Figure 1. Comparing manual and automated WMHv shows that 88% of the automated WMHv fall within 3 SD from the manual WMHv, suggesting good agreement. Conclusion: WMHv segmentation using a fully-automated pipeline for analysis of clinical MRIs is both feasible and accurate. Ongoing analysis of the extracted WMHv is expected to advance current knowledge of risks and outcomes in AIS.

Introduction: Even though neuroinflammation is increasingly recognized as an essential contributor to ischemic brain injury, the exact underlying mechanisms remain unclear. Higher expression of the vascular cell adhesion molecule 1 (VCAM-1) increases leukocyte-brain endothelium interaction and has been associated with larger infarct size following acute ischemic stroke (AIS). The activation of the TGF-β signaling pathways can down-regulate VCAM-1 expression and ameliorate deleterious tissue outcome during neuroinflammation. Hypothesis: We sought to investigate whether genetic variation in the TGF-beta pathway and adhesion molecule genes is associated with acute stroke lesion size. Methods: We completed genome-wide association (GWA) testing and diffusion-weighted imaging lesion volume (DWIv) analysis in a discovery cohort of 532 AIS patients of European ancestry enrolled within 48 hours of symptom onset. An independent European ancestry cohort of 724 AIS patients with GWA data and automated DWIv served as replication cohort. GWA testing per SNP was performed using linear regression modeling of natural log-transformed DWIv adjusted for age, sex and relevant principal components. We selected 42 inflammatory genes in the TGF-beta pathway for a gene-based analysis using VEGAS (Versatile Gene-based -Association Study) software. A pre-specified discovery phase Bonferroni-corrected threshold was set at p<0.001. In the replication phase, 14 SNPs overlapping were tested at the Bonferroni-corrected p-value threshold of p<0.004. Results: Of all genes in the TGF-beta pathway, VCAM1 (p=0.0006) was significantly associated with DWIv in the discovery AIS cohort (age: 66 ± 14.9 years, sex: 63.4% male, DWIv: 2.2cm 3 (IQR: 0.6-11.7cm 3 )). A single SNP within the VCAM1 gene boundaries (rs3176876 (BETA=0.2341, p=0.003)) was significantly associated with DWIv in the replication AIS cohort (age: 65 ± 14.1 years, sex: 68.6% male, DWIv: 4cm 3 (IQR: 1.3-17.2cm 3 )). Conclusion: The genetic variant rs3176876 in the VCAM1 gene is associated with larger infarct lesion size measured on brain MRI of AIS patients. These findings suggest genetic contribution to the pro-inflammatory mechanisms in acute cerebral ischemia and warrant further investigation.

Stroke subtype classification, applying standard criteria to clinical data, can reduce the heterogeneity of ischemic stroke for genetics studies. To date, most of the replicated genetic loci for ischemic stroke appear subtype-specific. Increasing homogeneity of phenotype comes with a trade-off in sample size, which influences the potential for successful identification of new loci. We estimated genetic associations using the union and intersection of two widely used stroke subtyping systems to assess the influence of sample size and homogeneity on test statistics. Methods: The Stroke Genetics Network (SiGN) study used both the Causative Classification of Stroke (CCS) and the Trial of ORG10172 Acute Stroke Trial (TOAST) classification systems. The CCS generates both a causal and a phenotypic subtype. Using all available data from case-control strata from the a previously performed GWAS, we focused on three main stroke subtypes: cardioembolic (CE), large artery atherosclerosis (LAA), and small artery occlusion (SAO). We estimated genetic associations with the union of the two CCS outputs and TOAST ( eg called CE by at least one method) and the intersection ( eg called CE by all three methods). Our modelling approach included a fixed-effects meta-analysis, i.e. pooling stratum specific estimates from logistic regression models using 10 principle components to account for genetic ancestry responsible for population stratification. Results: The majority of confirmatory findings from SiGN ( PITX2 and ZFHX3 for CE and HDAC9 for LAA) were evident in both models and in both the union and intersection of the subtyping systems. Some findings ( TSPAN2 in LAA) showed up best in the intersection with stronger corresponding p-values and odds ratios whereas other findings (12q24 for SAO) appear stronger in all of the union analyses, but were also evident in intersection analyses. We identify 16 potential novel loci, 6 of which appear in numerous analyses. Conclusion: Both expanding and refining stroke subtypes may help in the identification of additional stroke genetic risk variants and should be considered as complementary to a single classification system. We are pursuing replication of the novel findings.

Introduction: White matter hyperintensity (WMH) is a highly heritable trait and a significant contributor to stroke risk and severity. Vascular risk factors contribute to WMH severity; however, knowledge of the determinants of WMH in acute ischemic stroke (AIS) is still limited. Hypothesis: WMH volume (WMHv) varies across AIS subtypes and is modified by vascular risk factors. Methods: We extracted WMHv from the clinical MRI scans of 2683 AIS subjects from the MRI-Genetics Interface Exploration (MRI-GENIE) study using a novel fully-automated, volumetric analysis pipeline. Demographic data, stroke risk factors and stroke subtyping for the Causative Classification of Stroke (CCS) were performed at each of the 12 international study sites. WMHv was natural log-transformed for linear regression analyses. Results: Median WMHv was 5.7cm 3 (interquartile range (IQR): 2.2-12.8cm 3 ). In univariable analysis, age (63.1 ± 14.7 years, β=0.04, SE=0.002), prior stroke (10.2%, β=0.66, SE=0.08), hypertension (65.4%, β=0.75, SE=0.05), diabetes mellitus (23.1%, β=0.35, SE=0.06), coronary artery disease (17.6%, β=0.04, SE=0.002), and atrial fibrillation (14.6%, β=0.48, SE=0.07) were significant predictors of WMHv (all p<0.0001), as well as smoking status (52.2%, β=0.15, SE=0.05, p=0.005), race (16.5% Non-Caucasian, β=0.25, SE=0.07) and ethnicity (8.2% Hispanic, β=0.30, SE=0.11) (all p<0.01). In multivariable analysis, age (β=0.04, SE=0.002), prior stroke (β=0.56, SE=0.08), hypertension (β=0.33, SE=0.05), smoking status (β=0.16, SE=0.05), race (β=0.42, SE=0.06), and ethnicity (β=0.34, SE=0.09) were independent predictors of WMHv (all p<0.0001), as well as diabetes mellitus (β=0.13, SE=0.06, p=0.02). WMHv differed significantly (p<0.0001, unadjusted) across CCS stroke subtypes: cardioembolic stroke (8.0cm 3 , IQR: 4.2-15.4cm 3 ), large-artery stroke (6.9cm 3 , IQR: 3.1-14.7cm 3 ), small-vessel stroke (5.8cm 3 , IQR: 2.5-13.5cm 3 ), stroke of undetermined (4.7cm 3 , IQR: 1.6-11.0cm 3 ) or other (2.55cm 3 , IQR: 0.9-8.8cm 3 ) causes. Conclusion: In this largest-to-date, multicenter hospital-based cohort of AIS patients with automated WMHv analysis, common vascular risk factors contribute significantly to WMH burden and WMHv varies by CCS subtype.

Introduction: In observational studies, higher plasma high-density lipoprotein cholesterol (HDL-C) has been associated with increased risk of spontaneous intracerebral hemorrhage (ICH). Common DNA sequence variants within the cholesteryl ester transfer protein ( CETP ) gene decrease CETP protein activity and increase plasma HDL-C; as such, medicines that inhibit CETP and raise HDL-C are in clinical development to combat coronary artery disease. Hypothesis: Common CETP DNA sequence variants associated with higher HDL-C also increase risk for ICH. Methods: We performed a two-stage case-control genetic association study in Caucasians. The discovery phase utilized data on 12 independent loci within CETP (+/- 50 kilobases) from 3 genome-wide association studies of ICH. Replication involved direct genotyping in 5 additional studies. We also constructed a genetic risk score with 7 independent CETP variants and tested it for association with HDL-C and ICH risk. We used principal component analysis to account for population structure and a Bonferroni-adjusted p<0.004 (12 tests) to declare statistical significance. Results: The discovery phase included 1149 ICH cases (43% lobar hemorrhages) and 1238 controls. Twelve variants were nominally associated (p<0.05) with ICH, with the strongest association at the rs173539 locus (Figure 1: OR 1.25, 95%CI 1.11-1.41; p=6.0x10 -4 ) and no heterogeneity across studies (I 2 =0%). This association was replicated in 1625 cases (43% lobar hemorrhages) and 1845 controls (OR 1.12, 95%CI 1.02-1.24; p=0.03). A genetic score of independent CETP variants known to increase HDL-C by ~2.85 mg/dL was strongly associated with ICH risk (OR 1.86, 95%CI 1.44-2.40; p=1.4x10 -6 ). Conclusion: Genetic 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 is warranted.

Introduction: The MRI-Genetics Interface Exploration (MRI-GENIE) study is the first international collaboration that aims to facilitate genetic discoveries in clinical cohorts of patients with acute ischemic stroke (AIS). We have amassed the largest-to-date collection of AIS cases with brain MRI scans and genome-wide genotyping to test the role of genetic susceptibility in MRI-based cerebrovascular traits. Objective/Hypothesis: To elucidate the genetic architecture of white matter hyperintensity (WMH) burden in AIS patients. Methods: Using a novel automated algorithm, we extracted WMH volume (WMHv) from clinical MRI scans of 2704 AIS patients (age 63.1 ± 14.7 years, 60.6% male) of European ancestry. Quality control (QC) measures were undertaken per subject and per SNP, excluding subjects with non-European ancestry and poor genotyping, as well as SNPs deviating from Hardy-Weinberg equilibrium and high levels of missingness. Imputation to the Haplotype Reference Consortium (HRC version r1.1) was conducted for 1712 remaining subjects with 2.8 million SNPs on the Michigan Imputation Server. After exclusion of poorly imputed SNPs (R 2 <0.5) and SNPs with minor allele frequency < 1%, 7.7 million SNPs remained for further analysis. Genome-wide association testing of natural log-transformed WMHv on the allelic dosage per SNP was adjusted for age, sex and principal components 1-10. Results: Genome-wide association testing has identified a novel locus on chromosome 2 (T allele at rs72856504) near the LDL Receptor related Protein 1B gene (LRP1B) that was significantly associated with WMHv burden in AIS (β=0.54, SE=0.098, p=3.65*10 -8 ). Conclusion: We have identified a novel locus (T allele rs72856504) on chromosome 2 near the LRP1B gene, which is specific for WMH in AIS and has not been previosuly described in stroke-free WMH cohorts. A replication effort involving additional independent cohorts of AIS patients with brain MRI and genome-wide genotyping is ongoing.

Background and Purpose: Stroke is a complex disease with multiple genetic and environmental risk factors. Blacks endure a nearly 2-fold greater risk of stroke and are 2× to 3× more likely to die from stroke than European Americans. Methods: The COMPASS (Consortium of Minority Population Genome-Wide Association Studies of Stroke) has conducted a genome-wide association meta-analysis of stroke in >22 000 individuals of African ancestry (3734 cases, 18 317 controls) from 13 cohorts. Results: In meta-analyses, we identified one single nucleotide polymorphism (rs55931441) near the HNF1A gene that reached genome-wide significance ( P =4.62×10 −8 ) and an additional 29 variants with suggestive evidence of association ( P <1×10 −6 ), representing 24 unique loci. For validation, a look-up analysis for a 100 kb region flanking the COMPASS single nucleotide polymorphism was performed in SiGN (Stroke Genetics Network) Europeans, SiGN Hispanics, and METASTROKE (Europeans). Using a stringent Bonferroni correction P value of 2.08×10 −3 (0.05/24 unique loci), we were able to validate associations at the HNF1A locus in both SiGN ( P =8.18×10 −4 ) and METASTROKE ( P =1.72×10 −3 ) European populations. Overall, 16 of 24 loci showed evidence for validation across multiple populations. Previous studies have reported associations between variants in the HNF1A gene and lipids, C-reactive protein, and risk of coronary artery disease and stroke. Suggestive associations with variants in the SFXN4 and TMEM108 genes represent potential novel ischemic stroke loci. Conclusions: These findings represent the most thorough investigation of genetic determinants of stroke in individuals of African descent, to date.

ObjectiveObservational studies point to an inverse correlation between low‐density lipoprotein (LDL) cholesterol levels and risk of intracerebral hemorrhage (ICH), but it remains unclear whether this association is causal. We tested the hypothesis that genetically elevated LDL is associated with reduced risk of ICH.MethodsWe constructed one polygenic risk score (PRS) per lipid trait (total cholesterol, LDL, high‐density lipoprotein [HDL], and triglycerides) using independent genomewide significant single nucleotide polymorphisms (SNPs) for each trait. We used data from 316,428 individuals enrolled in the UK Biobank to estimate the effect of each PRS on its corresponding trait, and data from 1,286 ICH cases and 1,261 matched controls to estimate the effect of each PRS on ICH risk. We used these estimates to conduct Mendelian Randomization (MR) analyses.ResultsWe identified 410, 339, 393, and 317 lipid‐related SNPs for total cholesterol, LDL, HDL, and triglycerides, respectively. All four PRSs were strongly associated with their corresponding trait (all p < 1.00 × 10‐100). While one SD increase in the PRSs for total cholesterol (odds ratio [OR] = 0.92; 95% confidence interval [CI] = 0.85–0.99; p = 0.03) and LDL cholesterol (OR = 0.88; 95% CI = 0.81–0.95; p = 0.002) were inversely associated with ICH risk, no significant associations were found for HDL and triglycerides (both p > 0.05). MR analyses indicated that 1mmol/L (38.67mg/dL) increase of genetically instrumented total and LDL cholesterol were associated with 23% (OR = 0.77; 95% CI = 0.65–0.98; p = 0.03) and 41% lower risks of ICH (OR = 0.59; 95% CI = 0.42–0.82; p = 0.002), respectively.InterpretationGenetically elevated LDL levels were associated with lower risk of ICH, providing support for a potential causal role of LDL cholesterol in ICH. ANN NEUROL 2020 ANN NEUROL 2020;88:56–66

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.