Evidence map›Paper›PMID 42230773›Full record

ArticleNature genetics2026

Integrative analyses elucidate transcriptional regulatory functions of risk alleles for metabolic liver disease.

Biying Zhu, Na He, Yang Xiao, Bin Chen, Chen Li, Ravi Mandla, Yifan Liu, Jiayu Zhang, Xiao Chang, Fulong Yu and 8 more

Abstract read
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In one paragraph

Article in Nature genetics, 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
–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.

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

5 · Who and what money

Authors and funding

18 authors.

Biying Zhu *GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China.
Na He *GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China.
Yang Xiao *Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Yang.Xiao@pennmedicine.upenn.edu.ORCID http://orcid.org/0000-0002-9297-0973
Bin Chen *Guangzhou National Laboratory, Guangzhou, China.
Chen Li *GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China.
Ravi MandlaDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-0782-0138
Yifan LiuInstitute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Jiayu ZhangInstitute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Xiao ChangCollege of Medical Information and Artificial Intelligence for Medical Sciences, Shandong First Medical University, Shandong, China.
Fulong YuGMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China.ORCID http://orcid.org/0000-0002-6100-8300
Marijana VujkovicDepartment of Medicine, Division of Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0003-4924-5714
Julie A LynchSalt Lake City Veterans Affairs Medical Center, Salt Lake City, UT, USA.ORCID http://orcid.org/0000-0003-0108-2127
Kyong-Mi ChangMedical Research, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0001-6811-9364
VA Million Veteran Program
Bogdan PasaniucDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-0227-2056
Daniel J RaderDepartment of Medicine, Division of Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-9245-9876
Mitchell A LazarInstitute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. lazar@pennmedicine.upenn.edu.ORCID http://orcid.org/0000-0001-8653-1280
Wenxiang HuGMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China. hu_wenxiang@gzlab.ac.cn.ORCID http://orcid.org/0000-0002-6754-5625

Funding

BLRD VA I01 BX003362National Natural Science Foundation of China (National Science Foundation of China) 82270868
6 · The paper itself

Abstract

Genome-wide association studies have identified >100 loci associated with metabolic dysfunction-associated steatotic liver disease (MASLD), yet the mechanisms by which noncoding variants alter disease risk remain unclear. Here we map chromatin accessibility in human MASLD liver nuclei, revealing enrichment of risk variants within cell-type-specific regulatory elements bound by lineage-determining transcription factors. Using a massively parallel reporter assay, we identified hundreds of differential activity variants (DAVs) that act in a cell-type-dependent and stimulus-dependent manner and perturb transcriptional regulatory networks linked to liver pathology. Integration of liver expression quantitative trait loci, chromatin looping and single-cell CRISPR interference screening assigns target genes to these DAVs. Importantly, DAVs at numerous loci, including SLC22A3 and key triglyceride metabolism regulators (APOA5, ANGPTL3 and LPL), modulate gene expression, lipid metabolism and hepatic stellate cell activation. Moreover, these DAVs allow improved prediction of MASLD risk. These results define a regulatory framework linking noncoding genetic variation to MASLD pathogenesis.

Indexed as

Fatty LiverTranscription, GeneticAllelesChromatinGene Expression RegulationGene Regulatory NetworksGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLipid MetabolismLiverQuantitative Trait LociChromatin

Identifiers

What OpenQuestion holds

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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.