Evidence map›Paper›PMID 36788230›Full record

ArticleNature communications2023

Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics.

Jiacheng Miao, Hanmin Guo, Gefei Song, Zijie Zhao, Lin Hou, Qiongshi Lu

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing 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

50 citing papers in PubMed.

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  17. Polygenic prediction of treatment efficacy with causal transfer learning.medRxiv : the preprint server for health sciences · 2025
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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

6 authors.

Jiacheng Miao *Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Hanmin Guo *Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China.ORCID 0000-0001-9022-5307
Gefei SongDepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Zijie ZhaoDepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Lin HouCenter for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China. houl@tsinghua.edu.cn.ORCID 0000-0002-4283-8501
Qiongshi LuDepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA. qlu@biostat.wisc.edu.ORCID 0000-0002-4514-0969

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of Europeans are known to have substantially reduced predictive accuracy in non-European populations, limiting their clinical utility and raising concerns about health disparities across ancestral populations. Here, we introduce a statistical framework named X-Wing to improve predictive performance in ancestrally diverse populations. X-Wing quantifies local genetic correlations for complex traits between populations, employs an annotation-dependent estimation procedure to amplify correlated genetic effects between populations, and combines multiple population-specific PRS into a unified score with GWAS summary statistics alone as input. Through extensive benchmarking, we demonstrate that X-Wing pinpoints portable genetic effects and substantially improves PRS performance in non-European populations, showing 14.1%-119.1% relative gain in predictive R

Indexed as

Genome-Wide Association StudyPolymorphism, Single NucleotideGenetic Predisposition to DiseaseHumansMultifactorial InheritanceRisk Factors

Identifiers

PMID36788230
PMCPMC9929290

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Registered trials

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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.