Evidence map›Paper›PMID 39180788›Full record

ArticleEBioMedicine2024

A multi-modal framework improves prediction of tissue-specific gene expression from a surrogate tissue.

Yue Xu, Chunfeng He, Jiayao Fan, Yuan Zhou, Chunxiao Cheng, Ran Meng, Ya Cui, Wei Li, Eric R Gamazon, Dan Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 2024. 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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

10 authors.

Yue XuSchool of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China.
Chunfeng HeSchool of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China.
Jiayao FanSchool of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China.
Yuan ZhouSchool of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China.
Chunxiao ChengSchool of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China.
Ran MengSchool of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Ya CuiDivision of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, CA, 92697, USA.
Wei LiDivision of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, CA, 92697, USA.
Eric R GamazonVanderbit Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Data Science Institute, Vanderbilt University Medical Center, Nashville, TN, USA. Electronic address: eric.gamazon@vumc.org.
Dan ZhouSchool of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China. Electronic address: danzhou@zju.edu.cn.

Funding

ECHO Laboratory Core at Vanderbilt for Integrated Sample Biobanking and ProcessingU24OD035523 · OD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Suman Ranjan Das, NATASHA Bassam HALASA · 2023 to 2026
$32.8M
Haplotype-aware models of gene and isoform expression with application to genetic studies of disease in diverse populationsR01GM140287 · NIGMS · SEATTLE CHILDREN'S HOSPITAL · PI GAMAZON, ERIC R, MOHAMMADI, PEJMAN · 2021 to 2024
$2.8M
Functional Genomics: A Phenome-wide SurveyR35HG010718 · NHGRI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GAMAZON, ERIC R · 2019 to 2023
$2.2M
Advancing Multi-Omics and Electronic Health Records Computational MethodologiesR01HG011138 · NHGRI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GAMAZON, ERIC R · 2020 to 2024
$1.6M
Advancing drug repositioning and development for Alzheimer's Disease using functional genomics and computational phenomicsR56AG068026 · NIA · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GAMAZON, ERIC R · 2021 to 2022
$1.5M
NHGRI NIH HHS R01 HG011138NHGRI NIH HHS R35 HG010718NIA NIH HHS R56 AG068026NIGMS NIH HHS R01 GM140287NIH HHS U24 OD035523
6 · The paper itself

Abstract

backgroundTissue-specific analysis of the transcriptome is critical to elucidating the molecular basis of complex traits, but central tissues are often not accessible. We propose a methodology, Multi-mOdal-based framework to bridge the Transcriptome between PEripheral and Central tissues (MOTPEC).

methodsMulti-modal regulatory elements in peripheral blood are incorporated as features for gene expression prediction in 48 central tissues. To demonstrate the utility, we apply it to the identification of BMI-associated genes and compare the tissue-specific results with those derived directly from surrogate blood.

findingsMOTPEC models demonstrate superior performance compared with both baseline models in blood and existing models across the 48 central tissues. We identify a set of BMI-associated genes using the central tissue MOTPEC-predicted transcriptome data. The MOTPEC-based differential gene expression (DGE) analysis of BMI in the central tissues (including brain caudate basal ganglia and visceral omentum adipose tissue) identifies 378 genes overlapping the results from a TWAS of BMI, while only 162 overlapping genes are identified using gene expression in blood. Cellular perturbation analysis further supports the utility of MOTPEC for identifying trait-associated gene sets and narrowing the effect size divergence between peripheral blood and central tissues.

interpretationThe MOTPEC framework improves the gene expression prediction accuracy for central tissues and enhances the identification of tissue-specific trait-associated genes.

fundingThis research is supported by the National Natural Science Foundation of China 82204118 (D.Z.), the seed funding of the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province (2020E10004), the National Institutes of Health (NIH) Genomic Innovator Award R35HG010718 (E.R.G.), NIH/NHGRI R01HG011138 (E.R.G.), NIH/NIA R56AG068026 (E.R.G.), NIH Office of the Director U24OD035523 (E.R.G.), and NIH/NIGMS R01GM140287 (E.R.G.).

Indexed as

Gene Expression ProfilingOrgan SpecificityTranscriptomeAlgorithmsBody Mass IndexComputational BiologyGene Expression RegulationHumansBMICross-tissue predictionGene expressionMulti-omicsTWAS

Identifiers

PMID39180788
PMCPMC11388271

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.