Article in Journal of the American Heart Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
9 authors.
Yijie He *Laboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Ministry of Science and Technology, National Engineering Center of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Laboratory of Cerebral Small Vessel Disease and Osteoporosis (Abnormal Bone Metabolism) Capital Medical University Beijing China.ORCID 0009-0007-8805-3068
Tao Zhang *Department of Intensive Care Unit Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Huanhu Hospital Tianjin China.ORCID 0000-0002-9034-2295
Ping ZhuLaboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Ministry of Science and Technology, National Engineering Center of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Laboratory of Cerebral Small Vessel Disease and Osteoporosis (Abnormal Bone Metabolism) Capital Medical University Beijing China.
Shan GaoLaboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Ministry of Science and Technology, National Engineering Center of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Laboratory of Cerebral Small Vessel Disease and Osteoporosis (Abnormal Bone Metabolism) Capital Medical University Beijing China.
Shiyang WuLaboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Ministry of Science and Technology, National Engineering Center of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Laboratory of Cerebral Small Vessel Disease and Osteoporosis (Abnormal Bone Metabolism) Capital Medical University Beijing China.
Fengzhen LiuLaboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Ministry of Science and Technology, National Engineering Center of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Laboratory of Cerebral Small Vessel Disease and Osteoporosis (Abnormal Bone Metabolism) Capital Medical University Beijing China.
Yan ChenDepartment of Epidemiology and Biostatistics School of Public Health, Wannan Medical College Wuhu Anhui China.ORCID 0000-0003-4833-7676
Yingshui YaoDepartment of Epidemiology and Biostatistics School of Public Health, Wannan Medical College Wuhu Anhui China.
Guiyou LiuLaboratory of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Ministry of Science and Technology, National Engineering Center of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Laboratory of Cerebral Small Vessel Disease and Osteoporosis (Abnormal Bone Metabolism) Capital Medical University Beijing China.ORCID 0000-0002-1126-2888
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
backgroundPrevious studies have integrated genome-wide association studies with expression quantitative trait locus (eQTL) data from bulk tissues to identify stroke susceptibility genes. However, eQTL data exhibit high cell-type specificity, and genetic variants may have distinct effects across stroke subtypes.
methodsWe applied the summary-data-based Mendelian randomization (MR) method to integrate eQTL data from 7 brain cell types with genome-wide association studies data for 5 stroke phenotypes (stroke, ischemic stroke, cardioembolic stroke, large artery stroke, and small vessel stroke). Results were compared with summary-data-based MR using eQTL data from 49 tissues in the Genotype-Tissue Expression project. Robustness of significant single-cell summary-data-based MR associations was assessed via MR and colocalization analyses. Further evaluations included single-cell RNA-seq differential expression, protein-protein interaction, druggability, and phenome-wide association studies.
resultsSingle-cell summary-data-based MR identified many novel significant genes not detected using bulk tissue eQTL data. Validated associations revealed 2 stroke risk genes (
conclusionsIntegrating single-cell eQTL with stroke-subtype genome-wide association studies uncovers novel cell-type-specific causal genes and highlights promising therapeutic targets, advancing understanding of stroke pathogenesis.
Indexed as
Genetic Predisposition to DiseaseGenome-Wide Association StudyQuantitative Trait LociStrokeAnimalsHumansMendelian Randomization AnalysisMicePhenotypeSingle-Cell AnalysisSingle-Cell Gene Expression Analysisexpression quantitative trait locigenome‐wide association studiesMendelian randomizationsingle‐cell analysisstroke
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Integration of Genome-Wide Association Studies With Single-Cell and Bulk Expression Quantitative Trait Locus to Identify Stroke Susceptibility Genes. · full record | OpenQuestion