Article in Communications biology, 2022. 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.
Ghislain RocheleauThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA.ORCID 0000-0002-9989-7553
Iain S ForrestThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA.ORCID 0000-0002-5642-422X
Áine DuffyThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA.
Shantanu BafnaThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA.ORCID 0000-0002-5299-9325
Amanda DobbynThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA.
Marie VerbanckUR 7537 - BioSTM, Biostatistique, Traitement et Modélisation des données biologiques, Faculté de Pharmacie de Paris, Université de Paris, 4 avenue de l'Observatoire, 75270, Paris, France.
Hong-Hee WonSamsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.ORCID 0000-0001-5719-0552
Daniel M JordanThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA.ORCID 0000-0002-5318-8225
Ron DoThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA. ron.do@mssm.edu.ORCID 0000-0002-3144-3627
Funding
MOUNT SINAI MEDICAL SCIENTIST TRAINING PROGRAMT32GM007280 · NIGMS · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI BARON, MARGARET H · 1985 to 2021
$22.8M
TRAINING PROGRAM IN MOLECULAR AND CELLULAR CARDIOLOGYT32HL007824 · NHLBI · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI GELB, BRUCE D · 1995 to 2022
$8.6M
Towards an integrated map of causal connections for common, complex diseasesR35GM124836 · NIGMS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Ron Do · 2017 to 2026
$3.9M
Elucidating hereditary transthyretin-mediated heart failure risk using machine learning, polygenic risk and recall by genotype approaches in African ancestry individualsR01HL155915 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DO, RON, NADKARNI, GIRISH NITIN · 2021 to 2024
$3.4M
Resolving Causal Influences Among Correlated Risk Biomarkers for Coronary Artery DiseaseR01HL139865 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DO, RON · 2018 to 2021
$1.7M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHLBI NIH HHS R01 HL139865NHLBI NIH HHS R01 HL155915NHLBI NIH HHS T32 HL007824NIGMS NIH HHS R35 GM124836NIGMS NIH HHS T32 GM007280U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI) R01HL139865
6 · The paper itself
Abstract
Phenome-wide association studies identified numerous loci associated with traits and diseases. To help interpret these associations, we constructed a phenome-wide network map of colocalized genes and phenotypes. We generated colocalized signals using the Genotype-Tissue Expression data and genome-wide association results in UK Biobank. We identified 9151 colocalized genes for 1411 phenotypes across 48 tissues. Then, we constructed bipartite networks using the colocalized signals in each tissue, and showed that the majority of links were observed in a single tissue. We applied the biLouvain clustering algorithm in each tissue-specific network to identify co-clusters of genes and phenotypes. We observed significant enrichments of these co-clusters with known biological and functional gene classes. Overall, the phenome-wide map provides links between genes, phenotypes and tissues, and can yield biological and clinical discoveries.
Indexed as
Genome-Wide Association StudyPolymorphism, Single NucleotideBiological Specimen BanksPhenotypeUnited Kingdom
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 tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank. · full record | OpenQuestion