Evidence map›Paper›PMID 37851288›Full record

ArticleBreast cancer research and treatment2024

The largest genome-wide association study for breast cancer in Taiwanese Han population.

Yu-Ching Hsu, Hung-Lin Chen, Chi-Fung Cheng, Amrita Chattopadhyay, Pei-Shan Chen, Che-Chen Lin, Hsiu-Yin Chiang, Ting-Yuan Liu, Chi-Hao Huang, Chin-Chi Kuo and 3 more

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Article in Breast cancer research and treatment, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 23% of its field
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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
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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

13 authors at 2 institutions in 1 country.

Yu-Ching Hsu *Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei, Taiwan.
Hung-Lin Chen *Big Data Center, China Medical University Hospital, Taichung, Taiwan.
Chi-Fung ChengBig Data Center, China Medical University Hospital, Taichung, Taiwan.
Amrita ChattopadhyayCenter for Translational Genomic Research, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
Pei-Shan ChenBig Data Center, China Medical University Hospital, Taichung, Taiwan.
Che-Chen LinBig Data Center, China Medical University Hospital, Taichung, Taiwan.
Hsiu-Yin ChiangBig Data Center, China Medical University Hospital, Taichung, Taiwan.
Ting-Yuan LiuMillion-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
Chi-Hao HuangDivision of Breast Surgery, Department of Surgery, China Medical University Hospital, Taichung, Taiwan.
Chin-Chi KuoBig Data Center, China Medical University Hospital, Taichung, Taiwan.
Eric Y ChuangBioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan.
Tzu-Pin LuInstitute of Health Data Analytics and Statistics, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan. tplu@ntu.edu.tw.ORCID http://orcid.org/0000-0003-3697-0386
Fuu-Jen TsaiGenetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan. d0704@mail.cmuh.org.tw.
China Medical University · TWNational Taiwan University · TW

Funding

National Science and Technology Council MOST-106-2314-B-002-134-MY2National Science and Technology Council MOST-108-2314-B-002-103-MY2National Science and Technology Council MOST-109-2314-B-002-151-MY3
6 · The paper itself

Abstract

purposeBreast cancer is a molecularly heterogeneous disease, and multiple genetic variants contribute to its development and prognosis. Most of previous genome-wide association studies (GWASs) and polygenic risk scores (PRSs) analyses focused on studying breast cancers of Caucasian populations, which may not be applicable to other population. Therefore, we conducted the largest breast cancer cohort of Taiwanese population to fill in the knowledge gap.

methodsA total of 152,534 Participants recruited by China Medical University Hospital between 2003 and 2019 were filtered by several patient selection criteria and GWAS quality control steps, resulting in the inclusion of 2496 cases and 9984 controls for this study. We then conducted GWAS for all breast cancers and PRS analyses for all breast cancers and the four breast cancer subtypes, including luminal A, luminal B, basal-like, and HER2-enriched.

resultsThe GWAS analyses identified 113 SNPs, 50 of which were novel. The PRS models for all breast cancers and the luminal A subtype showed positively correlated trends between the PRS and the risk of developing breast cancer. The odds ratios (95% confidence intervals) for the groups with the highest PRS in all breast cancers and the luminal A subtype were 5.33 (3.79-7.66) and 3.55 (2.13-6.14), respectively.

conclusionIn summary, we explored the association of genetic variants with breast cancer in the largest Taiwanese cohort and developed two PRS models that can predict the risk of developing any breast cancer and the luminal A subtype in Taiwanese women.

Indexed as

Breast NeoplasmsGenome-Wide Association StudyEast Asian PeopleFemaleGenetic Predisposition to DiseaseHumansPolymorphism, Single NucleotidePrognosisRisk FactorsBreast cancerGenome-wide association studyPolygenic risk scoreTaiwanese population

Identifiers

PMID37851288
OpenAlexW4387729538

What OpenQuestion holds

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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.