Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.(Extended Data Fig. 5). These results show the value of large sample sizes in blood to detect trans-mQTLs regardless of the tissue. Trans-mQTL SNPs and DNAm exhibit patterned TF binding.Recent studies have uncovered multiple types of transcription factor (TF)-DNA interactions influenced by DNAm, including the binding of DNAm-sensitive TFs [26][27][28] and cooperativity between TFs 27,29 . To gain insights into how SNPs induce long-range DNAm changes, we mapped enrichments for DNAm sites and SNPs across binding sites for 171 TFs in 27 cell types 30,31 . We found strong enrichments for most TFs and cell types among DNAm sites with a trans association (cis + trans: 55%; trans only: 80%; cis only: 18%) and among cis-acting SNPs (cis only: 96%, cis + trans: 91%, trans only: 1%; Fig. 2b, Supplementary Tables 7 and 8, and Supplementary Figs. 22 and 23). Consistent with the observation that trans-only DNAm sites are enriched for CpG islands (Supplementary Fig. 13), DNAm sites that overlap TF-binding sites (TFBSs) were relatively hypomethylated (weighted mean DNAm levels = 21% versus 52%, P < 2.2 × 10 −16 ; Supplementary Fig. 24).Next, we hypothesized that, if a trans-mQTL is driven by TF activity 8,10 , then particular TF-TF pairs may exhibit preferential enrichment 32 . An mQTL has a pair of TFBS annotations 31 , one for the SNP and one for the DNAm site. We evaluated whether the annotation pairs among 18,584 interchromosomal trans-mQTLs were associated with TF binding in a nonrandom pattern (Supplementary Note and Extended Data Fig. 6a,b). We found that 6.1% (22,962 of 378,225) of possible pairwise combinations of SNP-DNAm site annotations were more over-or underrepresented than expected by chance after strict multiple testing correction (Supplementary Note, Supplementary Table 9 and Extended Data Fig. 6c).After accounting for abundance and other characteristics, the strongest pairwise enrichments involved sites close to TFBSs for proteins in the cohesin complex, ...

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. Here we describe results of DNA methylation-quantitative trait loci (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTL of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We reveal that the genetic architecture of DNAm levels is highly polygenic and DNAm exhibits signatures of negative and positive natural selection. Using shared genetic control between distal DNAm sites we construct networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic factors are associated with both blood DNAm levels and complex diseases but in most cases these associations do not reflect causal relationships from DNAm to trait or vice versa indicating a more complex genotype-phenotype map than has previously been hypothesised.

Rationale: Ischemic stroke (IS) is among the leading causes of adult disability. Part of the variability in functional outcome after stroke has been attributed to genetic factors but no locus has been consistently associated with stroke outcome. Objective: Our aim was to identify genetic loci influencing the recovery process using accurate phenotyping to produce the largest genome-wide association study (GWAS) in IS recovery to date. Methods and Results: A 12-cohort, two-phase (discovery-replication and joint) meta-analysis of GWAS included anterior-territory and previously independent IS cases. Functional outcome was recorded using 3-month modified Rankin Scale (mRS). Analyses were adjusted for confounders such as discharge NIHSS. A gene-based burden test was performed. The discovery phase (n=1,225) was followed by open (n=2,482) and stringent joint-analyses (n=1,791). Those cohorts with mRS recorded at timepoints other than 3-month or incomplete data on previous functional status were excluded in the stringent analyses. Novel variants in Pals1-Associated Tight Junction (PATJ) gene were associated with worse functional outcome at 3-month after stroke. The top variant was rs76221407 (G allele, beta=0•40, p=1•70×10 −9). Conclusions: Our results identify a set of common variants in PATJ gene associated with 3month functional outcome at genome-wide significance level. Future studies should examine the role of PATJ in stroke recovery and consider stringent phenotyping to enrich the information captured to unveil additional stroke outcome loci.

Background and Purpose-The risk of stroke recurrence in patients with Embolic Stroke of Undetermined Source (ESUS)is high, and the optimal antithrombotic strategy for secondary prevention is unclear. We investigated whether congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, and stroke or transient ischemic attack (TIA; CHADS 2 ) and CHA 2 DS 2 -VASc scores can stratify the long-term risk of ischemic stroke/TIA recurrence and death in ESUS. Methods-We pooled data sets of 11 stroke registries from Europe and America. ESUS was defined according to the Cryptogenic Stroke/ESUS International Working Group. Cox regression analyses were performed to investigate if prestroke CHADS 2 and congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke or TIA, vascular disease, age 65-74 years, sex category (CHA 2 DS 2 -VASc) scores were independently associated with the risk of ischemic stroke/TIA recurrence or death. The Kaplan-Meier product limit method was used to estimate the cumulative probability of ischemic stroke/TIA recurrence and death in different strata of the CHADS 2 and CHA 2 DS 2 -VASc scores. Results-One hundred fifty-nine (5.6% per year) ischemic stroke/TIA recurrences and 148 (5.2% per year) deaths occurred in 1095 patients (median age, 68 years) followed-up for a median of 31 months. Compared with CHADS 2 score 0, patients with CHADS 2 score 1 and CHADS 2 score >1 had higher risk of ischemic stroke/TIA recurrence (hazard ratio [HR], 2.38; 95% confidence interval [CI], 1.41-4.00 and HR, 2.72; 95% CI, 1.68-4.40, respectively) and death (HR, 3.58; 95% CI, and HR, 5.45; 95% CI, respectively). Compared with low-risk CHA 2 DS 2 -VASc score, patients with high-risk CHA 2 DS 2 -VASc score had higher risk of ischemic stroke/TIA recurrence (HR, 3.35; 95% CI,) and death (HR, 13.0; 95% CI, Ntaios et al Risk Stratification in ESUS 2279T he term Embolic Stroke of Undetermined Source (ESUS) has been introduced by the Cryptogenic Stroke/ESUS International Working Group to include patients with ischemic stroke for whom the source of embolism remains unidentified despite recommended investigation; potential embolic sources include covert atrial fibrillation (AF), the mitral and aortic valves, the left cardiac chambers, the proximal cerebral arteries of the aortic arch, and the venous system via paradoxical embolism.1,2 The risk of stroke recurrence is higher in ESUS patients than in patients with noncardioembolic strokes, which could be a sign that current antithrombotic strategy of treating ESUS patients with antiplatelet agents may be suboptimal. 3,4 In this context, 3 randomized trials are currently under way to assess whether antiplatelet or oral anticoagulation is the optimal treatment in ESUS patients. 5-7The congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, and stroke or transient ischemic attack (TIA) (CHADS 2 ) and congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke or TIA, vascular disease, age 65-74 years, sex catego...

Background and Purpose: Large-scale observational studies of acute ischemic stroke (AIS) promise to reveal mechanisms underlying cerebral ischemia. However, meaningful quantitative phenotypes attainable in large patient populations are needed. We characterize a dynamic metric of AIS instability, defined by change in National Institutes of Health Stroke Scale score (NIHSS) from baseline to 24 hours baseline to 24 hours (NIHSS baseline – NIHSS 24hours = ΔNIHSS 6-24h ), to examine its relevance to AIS mechanisms and long-term outcomes. Methods: Patients with NIHSS prospectively recorded within 6 hours after onset and then 24 hours later were enrolled in the GENISIS study (Genetics of Early Neurological Instability After Ischemic Stroke). Stepwise linear regression determined variables that independently influenced ΔNIHSS 6 –24h . In a subcohort of tPA (alteplase)-treated patients with large vessel occlusion, the influence of early sustained recanalization and hemorrhagic transformation on ΔNIHSS 6–24h was examined. Finally, the association of ΔNIHSS 6 –24h with 90-day favorable outcomes (modified Rankin Scale score 0–2) was assessed. Independent analysis was performed using data from the 2 NINDS-tPA stroke trials (National Institute of Neurological Disorders and Stroke rt-PA). Results: For 2555 patients with AIS, median baseline NIHSS was 9 (interquartile range, 4–16), and median ΔNIHSS 6 –24h was 2 (interquartile range, 0–5). In a multivariable model, baseline NIHSS, tPA-treatment, age, glucose, site, and systolic blood pressure independently predicted ΔNIHSS 6 –24h (R 2 =0.15). In the large vessel occlusion subcohort, early sustained recanalization and hemorrhagic transformation increased the explained variance (R 2 =0.27), but much of the variance remained unexplained. ΔNIHSS 6 –24h had a significant and independent association with 90-day favorable outcome. For the subjects in the 2 NINDS-tPA trials, ΔNIHSS 3 –24h was similarly associated with 90-day outcomes. Conclusions: The dynamic phenotype, ΔNIHSS 6–24h , captures both explained and unexplained mechanisms involved in AIS and is significantly and independently associated with long-term outcomes. Thus, ΔNIHSS 6 –24h promises to be an easily obtainable and meaningful quantitative phenotype for large-scale genomic studies of AIS.

Age and stroke severity are the main mortality predictors after ischemic stroke. However, chronological age and biological age are not exactly concordant. Age-related changes in DNA methylation in multiple CpG sites across the genome can be used to estimate biological age, which is influenced by lifestyle, environmental factors, and genetic variation. We analyzed the impact of biological age on 3-month mortality in ischemic stroke. We assessed 594 patients with acute ischemic stroke in a cohort from Hospital del Mar (Barcelona) and validated the results in an independent cohort. Demographic and clinical data, including chronological age, vascular risk factors, initial stroke severity (NIHSS score), recanalization treatment, and previous modified Rankin scale were registered. Biological age was estimated with an algorithm based on DNA methylation in 71 CpGs. Biological age was predictive of 3-month mortality (p = 0.041; OR = 1.05, 95% CI 1.00–1.10), independently of NIHSS score, chronological age, TOAST, vascular risk factors, and blood cell composition. Stratified by TOAST classification, biological age was associated with mortality only in large-artery atherosclerosis etiology (p = 0.004; OR = 1.14, 95% CI 1.04–1.25). As estimated by DNA methylation, biological age is an independent predictor of 3-month mortality in ischemic stroke regardless of chronological age, NIHSS, previous modified Rankin scale, and vascular risk factors.

Key Points Question Is history of hypertension and apolipoprotein E ( APOE ) associated with intracerebral hemorrhage risk in participants stratified by self-reported race/ethnicity? Findings In this case-control study of 13 124 adults, having a copy of APOE ε4 alleles increased the risk for lobar intracerebral hemorrhage only in white individuals, but after propensity score matching for hypertension burden, Hispanic individuals showed the same risk of APOE ε4. Meaning APOE ε4 appears to be confirmed as a risk factor for lobar intracerebral hemorrhage in nonwhite populations but is masked by differential hypertension burden in Hispanic individuals; further studies are needed to explore the interactions between APOE alleles and environmental exposures.

ObjectiveTo examine etiologic stroke subtypes and vascular risk factor profiles and their association with white matter hyperintensity (WMH) burden in patients hospitalized for acute ischemic stroke (AIS).MethodsFor the MRI Genetics Interface Exploration (MRI-GENIE) study, we systematically assembled brain imaging and phenotypic data for 3,301 patients with AIS. All cases underwent standardized web tool–based stroke subtyping with the Causative Classification of Ischemic Stroke (CCS). WMH volume (WMHv) was measured on T2 brain MRI scans of 2,529 patients with a fully automated deep-learning trained algorithm. Univariable and multivariable linear mixed-effects modeling was carried out to investigate the relationship of vascular risk factors with WMHv and CCS subtypes.ResultsPatients with AIS with large artery atherosclerosis, major cardioembolic stroke, small artery occlusion (SAO), other, and undetermined causes of AIS differed significantly in their vascular risk factor profile (all p < 0.001). Median WMHv in all patients with AIS was 5.86 cm3 (interquartile range 2.18–14.61 cm3) and differed significantly across CCS subtypes (p < 0.0001). In multivariable analysis, age, hypertension, prior stroke, smoking (all p < 0.001), and diabetes mellitus (p = 0.041) were independent predictors of WMHv. When adjusted for confounders, patients with SAO had significantly higher WMHv compared to those with all other stroke subtypes (p < 0.001).ConclusionIn this international multicenter, hospital-based cohort of patients with AIS, we demonstrate that vascular risk factor profiles and extent of WMH burden differ by CCS subtype, with the highest lesion burden detected in patients with SAO. These findings further support the small vessel hypothesis of WMH lesions detected on brain MRI of patients with ischemic stroke.