Evidence map›Paper›PMID 42277437›Full record

ArticleCommunications medicine2026

Optimized post-GWAS analysis identifies therapeutic targets for essential thrombocythemia and polycythemia vera.

Xiaoli Li, Wenbin An, Xin Wang, Zixi Yin, Yangyang Gao, Yang Lan, Luyang Zhang, Beibei Zhao, Lixian Chang, Min Ruan and 4 more

Abstract read
In one paragraph

Article in Communications medicine, 2026. 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
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

14 authors.

Xiaoli Li *State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.ORCID http://orcid.org/0009-0005-3271-0896
Wenbin An *State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Xin WangState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Zixi YinState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Yangyang GaoState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Yang LanState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Luyang ZhangState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Beibei ZhaoState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Lixian ChangState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Min RuanState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Li ZhangState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Yao ZouState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Wenyu YangState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Xiaofan ZhuState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China. xfzhu@ihcams.ac.cn.ORCID http://orcid.org/0000-0002-2572-6495

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLimited insight into the biology of essential thrombocythemia (ET) and polycythemia vera (PV) has constrained therapeutic development. We therefore aimed to prioritize genetically supported molecular determinants underlying disease susceptibility and hematologic phenotypes, and to distinguish shared and subtype-specific regulation.

methodsWe implemented an optimized post-GWAS framework combining omics-wide Bayesian colocalization with Mendelian randomization (MR) to prioritize genetically supported molecular determinants of ET and PV. Colocalization-guided MR analyses were used to infer causal relationships, with additional analyses incorporating other phenotypic traits to support biological interpretation.

resultsHere, we show that higher genetically predicted levels of PARP1, BRAP, ERP29, PPP1CC, and RPN1 are consistently associated with risk of both ET and PV, whereas MYB is specifically associated with ET. Several biomarkers influenced blood cell counts, with BRAP, ERP29, and PPP1CC increasing platelet, red blood cell, and white blood cell counts, while MYB increased platelet production but reduced red and white blood cells. Additionally, MYB promoted BRAP, ERP29, PPP1CC, and EPO signaling while suppressing THPO, whereas MYB gene effects showed opposite patterns on these pathways, blood cell counts, and ET risk. BRAP, PPP1CC, and ERP29 exerted concordant regulatory effects on MPL, RPN1, THPO, and PARP1, except for EPO, which they negatively regulated. Finally, hypermethylation at cg17916418 and cg23511909 suppressed RPN1 expression and increased ET and PV risk, with RPN1 acting as a key mediator.

conclusionsThis study provides a genetics-guided map of shared and subtype-specific molecular features in ET and PV, highlighting potential therapeutic pathways and molecular signals distinguishing the two diseases.

Identifiers

PMID42277437
PMCPMC13272951

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