In one paragraphArticle in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed, 3 citations in OpenAlex.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
15 authors at 3 institutions in 2 countries.
Yuka SuzukiInstitut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France.ORCID 0000-0003-0930-5591 Hervé MénagerInstitut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France.ORCID 0000-0002-7552-1009 Bryan BrancotteInstitut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France.
Raphaël VernetUniversité Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France.
Cyril NerinInstitut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France.
Christophe BoettoInstitut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France.ORCID 0009-0000-5479-3550 Antoine AuvergneInstitut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France.ORCID 0009-0004-5749-8820 Christophe LinhardUniversité Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France.
Rachel TorchetInstitut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France.ORCID 0000-0002-2306-5566 Pierre LechatInstitut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France.
Lucie TroubatInstitut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France.
Michael H ChoChanning Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA, 02115, USA.ORCID 0000-0002-4907-1657 Emmanuelle BouzigonUniversité Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France.ORCID 0000-0001-5756-4286 Hugues AschardInstitut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France.ORCID 0000-0002-7554-6783 Hanna JulienneInstitut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France.ORCID 0000-0001-8214-9412 Institut Pasteur · FRInserm · FRBrigham and Women's Hospital · US
Funding
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
$3.5MClinical Implications, Genomics, and Transcriptions of Mucus Plugging in SmokersR01HL149861 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CHO, MICHAEL H., DIAZ, ALEJANDRO · 2020 to 2023
$3.2MUncovering the genetically-driven differential susceptibility to chronic obstructive pulmonary disease and pulmonary fibrosisR01HL162813 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MICHAEL H. CHO · 2023 to 2026
$3.1MIntegrative genomic, transcriptomic and proteomic studies of pulmonary function and COPDR01HL153248 · NHLBI · UNIVERSITY OF VIRGINIA · PI CHO, MICHAEL H., MANICHAIKUL, ANI WANG · 2021 to 2024
$2.8MIdentifying Subtypes of COPD Using Metabolomic and Genomic ApproachesR01HL168199 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MICHAEL H. CHO · 2024 to 2026
$2.1MNHLBI NIH HHS R01 HL147148NHLBI NIH HHS R01 HL149861NHLBI NIH HHS R01 HL153248NHLBI NIH HHS R01 HL162813NHLBI NIH HHS R01 HL168199
6 · The paper itselfAbstract
Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with an increased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearson's
Identifiers
PMID37961722
PMCPMC10634875
OpenAlexW4388179734
What OpenQuestion holds
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