Evidence map›Paper›PMID 38609356›Full record

ArticleNature communications2024

Utility of polygenic scores across diverse diseases in a hospital cohort for predictive modeling.

Ting-Hsuan Sun, Chia-Chun Wang, Ting-Yuan Liu, Shih-Chang Lo, Yi-Xuan Huang, Shang-Yu Chien, Yu-De Chu, Fuu-Jen Tsai, Kai-Cheng Hsu

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. 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

9 authors.

Ting-Hsuan SunArtificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan.ORCID http://orcid.org/0000-0002-0785-3212
Chia-Chun WangArtificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan.
Ting-Yuan LiuMillion-person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan.ORCID http://orcid.org/0000-0002-2729-0541
Shih-Chang LoArtificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan.ORCID http://orcid.org/0000-0003-2461-0498
Yi-Xuan HuangArtificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan.ORCID http://orcid.org/0009-0006-2356-6762
Shang-Yu ChienArtificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan.
Yu-De ChuArtificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan.ORCID http://orcid.org/0009-0002-4228-8092
Fuu-Jen Tsai *Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan. 000704@tool.caaumed.org.tw.ORCID http://orcid.org/0000-0002-1373-245X
Kai-Cheng Hsu *Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan. kaichenghsu66@gmail.com.ORCID http://orcid.org/0000-0003-2376-5009

Funding

Ministry of Health and Welfare, Taiwan | Health Promotion Administration, Ministry of Health and Welfare (Health Promotion Administration of the Taiwan Ministry of Health and Welfare) MOHW112-TDU-B-212-144004
6 · The paper itself

Abstract

Polygenic scores estimate genetic susceptibility to diseases. We systematically calculated polygenic scores across 457 phenotypes using genotyping array data from China Medical University Hospital. Logistic regression models assessed polygenic scores' ability to predict disease traits. The polygenic score model with the highest accuracy, based on maximal area under the receiver operating characteristic curve (AUC), is provided on the GeneAnaBase website of the hospital. Our findings indicate 49 phenotypes with AUC greater than 0.6, predominantly linked to endocrine and metabolic diseases. Notably, hyperplasia of the prostate exhibited the highest disease prediction ability (P value = 1.01 × 10

Indexed as

Health FacilitiesHospitalsChinaGenetic Predisposition to DiseaseHumansHyperplasiaMale

Identifiers

PMID38609356
PMCPMC11014845

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.