Evidence map›Paper›PMID 39915470›Full record

ArticleNature communications2025

SPA

He Xu, Yuzhuo Ma, Lin-Lin Xu, Yin Li, Yufei Liu, Ying Li, Xu-Jie Zhou, Wei Zhou, Seunggeun Lee, Peipei Zhang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

12 authors.

He XuDepartment of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China.
Yuzhuo MaDepartment of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China.
Lin-Lin XuRenal Division, Peking University First Hospital; Peking University Institute of Nephrology, Beijing, China.
Yin LiDepartment of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.
Yufei LiuDepartment of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China.
Ying LiDepartment of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China.
Xu-Jie ZhouRenal Division, Peking University First Hospital; Peking University Institute of Nephrology, Beijing, China.ORCID http://orcid.org/0000-0002-7215-707X
Wei ZhouCenter for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0001-7719-0859
Seunggeun LeeGraduate School of Data Science, Seoul National University, Seoul, Republic of Korea.ORCID http://orcid.org/0000-0002-8097-3878
Peipei ZhangDepartment of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China. peipei.zhang@pku.edu.cn.ORCID http://orcid.org/0000-0003-1742-1680
Weihua YuePeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China. dryue@bjmu.edu.cn.ORCID http://orcid.org/0000-0002-1201-8465
Wenjian BiDepartment of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China. wenjianb@pku.edu.cn.ORCID http://orcid.org/0000-0002-5108-9311

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62273010
6 · The paper itself

Abstract

Sample relatedness is a major confounder in genome-wide association studies (GWAS), potentially leading to inflated type I error rates if not appropriately controlled. A common strategy is to incorporate a random effect related to genetic relatedness matrix (GRM) into regression models. However, this approach is challenging for large-scale GWAS of complex traits, such as longitudinal traits. Here we propose a scalable and accurate analysis framework, SPA

Indexed as

Genome-Wide Association StudyModels, GeneticComputer SimulationGenotypeHumansLongitudinal StudiesPhenotypePolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID39915470
PMCPMC11803118

What OpenQuestion holds

Textmetadata
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.