Evidence map›Paper›PMID 39689714›Full record

ArticleAmerican journal of human genetics2025

TEMR: Trans-ethnic mendelian randomization method using large-scale GWAS summary datasets.

Lei Hou, Sijia Wu, Zhongshang Yuan, Fuzhong Xue, Hongkai Li

Abstract read
In one paragraph

Article in American journal of human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

5 authors.

Lei HouDepartment of Medical Data, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250000, P.R. China; Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250000, P.R. China.
Sijia WuDepartment of Medical Data, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250000, P.R. China; Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250000, P.R. China.
Zhongshang YuanInstitute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250000, P.R. China; Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250000, P.R. China.
Fuzhong XueDepartment of Medical Data, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250000, P.R. China; Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250000, P.R. China; Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250000, P.R. China. Electronic address: xuefzh@sdu.edu.cn.
Hongkai LiInstitute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250000, P.R. China; Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250000, P.R. China. Electronic address: lihongkaiyouxiang@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Available large-scale genome-wide association study (GWAS) summary datasets predominantly stem from European populations, while sample sizes for other ethnicities, notably Central/South Asian, East Asian, African, Hispanic, etc., remain comparatively limited, resulting in low precision of causal effect estimations within these ethnicities when using Mendelian randomization (MR). In this paper, we propose a trans-ethnic MR method, TEMR, to improve the statistical power and estimation precision of MR in a target population that is underrepresented, using trans-ethnic large-scale GWAS summary datasets. TEMR incorporates trans-ethnic genetic correlation coefficients through a conditional likelihood-based inference framework, producing calibrated p values with substantially improved MR power. In the simulation study, compared with other existing MR methods, TEMR exhibited superior precision and statistical power in causal effect estimation within the target populations. Finally, we applied TEMR to infer causal relationships between concentrations of 16 blood biomarkers and the risk of developing five diseases (hypertension, ischemic stroke, type 2 diabetes, schizophrenia, and major depression disorder) in East Asian, African, and Hispanic/Latino populations, leveraging biobank-scale GWAS summary data obtained from individuals of European descent. We found that the causal biomarkers were mostly validated by previous MR methods, and we also discovered 17 causal relationships that were not identified using previously published MR methods.

Indexed as

EthnicityGenome-Wide Association StudyMendelian Randomization AnalysisBiomarkersDiabetes Mellitus, Type 2HumansHypertensionMajor Depressive DisorderPolymorphism, Single NucleotideSchizophreniaBiomarkersgenetic correlationGWAS summary datastatistical powertrans-ethnic Mendelian randomization

Identifiers

PMID39689714
PMCPMC11739928

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.