Article in PLoS genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 23% of its field
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.
3 · Its place in the literature
Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
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
13 authors at 3 institutions in 2 countries.
Wei LiuProgram of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.ORCID 0000-0003-2558-1377
Wenxuan DengDepartment of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.
Ming ChenDepartment of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.
Zihan DongDepartment of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.
Biqing ZhuProgram of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.ORCID 0000-0002-7428-6297
Zhaolong YuProgram of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
Daiwei TangDepartment of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.
Maor SaulerPulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America.
Chen LinDepartment of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.ORCID 0000-0001-9821-2578
Louise V WainDepartment of Health Sciences, University of Leicester, Leicester, United Kingdom.
Michael H ChoChanning Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.ORCID 0000-0002-4907-1657
Naftali KaminskiPulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America.ORCID 0000-0001-5917-4601
Hongyu ZhaoProgram of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.ORCID 0000-0003-1195-9607
Yale University · USBrigham and Women's Hospital · USUniversity of Leicester · GB
Funding
Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
(2 of 2) Genetic Epidemiology of COPDR01HL089856 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SILVERMAN, EDWIN K · 2012 to 2016
$18.9M
Genetic and Genomic Characterization of the Occurrence and Progression of Interstitial Lung AbnormalitiesR01HL135142 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MICHAEL H. CHO, GARY MATTHEW HUNNINGHAKE · 2017 to 2026
$7.7M
Genomic Analysis of Tissue and Cellular Heterogeneity in IPFR01HL127349 · NHLBI · YALE UNIVERSITY · PI BENOS, PANAGIOTIS V, KAMINSKI, NAFTALI · 2015 to 2025
$5.9M
Genetic and Functional Dissection of a Cluster of COPD GWAS Signals on Chromosome 4qR01HL147148 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CHO, MICHAEL H., SILVERMAN, EDWIN K · 2019 to 2022
$3.5M
Identifying functional variants in COPD GWAS lociR01HL137927 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CHO, MICHAEL H., SILVERMAN, EDWIN K · 2017 to 2020
$3.5M
Role of MIF and CD74 in the pathogenesis of emphysemaR01HL155948 · NHLBI · YALE UNIVERSITY · PI BUCALA, RICHARD J, SAULER, MAOR · 2021 to 2025
$3.4M
Epithelial Protective Effects of Thyroid Hormone Signaling in FibrosisR01HL141852 · NHLBI · YALE UNIVERSITY · PI KAMINSKI, NAFTALI · 2019 to 2022
$3.4M
Normal Aging Lung Cell Atlas (NALCA)U01HL145567 · NHLBI · YALE UNIVERSITY · PI KAMINSKI, NAFTALI · 2019 to 2022
$2.8M
Mir-29 mimicry as a therapy for pulmonary fibrosisUH2HL123886 · NHLBI · YALE UNIVERSITY · PI KAMINSKI, NAFTALI · 2014 to 2015
$2.7M
Novel statistical methods and tools to integrate multiple endophenotypes and functional annotation data to study the roles of rare variants in complex human diseases using sequencing dataR01GM134005 · NIGMS · YALE UNIVERSITY · PI WU, BAOLIN, ZHAO, HONGYU · 2020 to 2023
Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. In this manuscript, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed good statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblasts in lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.
Indexed as
Breast NeoplasmsPulmonary Disease, Chronic ObstructiveFemaleGene Expression ProfilingGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLungPolymorphism, Single Nucleotide
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.
A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases. · full record | OpenQuestion