Polygenic height prediction for the Han Chinese in Taiwan

Human height prediction based on genetic factors alone shows positive correlation, but predictors developed for one population perform less well when applied to population of different ancestries. In this study, we evaluated the utility of incorporating non-genetic factors in height predictors for the Han Chinese population in Taiwan. We analyzed data from 78,719 Taiwan Biobank (TWB) participants and 40,641 Taiwan Precision Medicine Initiative (TPMI) participants using genome-wide association study and multivariable linear regression least absolute shrinkage and selection operator (LASSO) methods to incorporate genetic and non-genetic factors for height prediction. Our findings establish that combining birth year (as a surrogate for nutritional status), age at measurement (to account for age-associated effects on height), and genetic profile data improves the accuracy of height prediction. This method enhances the correlation between predicted and actual height and significantly reduces the discrepancies between predicted and actual height in both males and females.

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Publication Details

Journal
npj Genomic Medicine
Published
2025-02-05
DOI
https://doi.org/10.1038/s41525-025-00468-6
Citations
1
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
1.82

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article

Polygenic height prediction for the Han Chinese in Taiwan

Stephen Hsu, Shun‐Fa Yang, Pui–Yan Kwok, See‐Tong Pang et al.
1 citations
npj Genomic Medicine
Genetic Associations and Epidemiology
1.82
article

Polygenic height prediction for the Han Chinese in Taiwan

Stephen Hsu, Shun‐Fa Yang, Pui–Yan Kwok, See‐Tong Pang, Chih‐Yang Huang, Yuh‐Jyh Jong, Tso‐Fu Wang, Erh‐Chan Yeh, Chien-Hsiun Chen, Gwo‐Chin Ma, I‐Ming Chen, Hsiang‐Cheng Chen, Erik Widén, Chih‐Hao Chang, Chen‐Fang Hung, Yao‐Ming Chang, Timothy G. Raben, Yen‐Lin Chen, Che-Yu Chou, Jeng–Yih Wu, Shih-Ann Chen, Chun-Yu Wei, S. Lee
article en
1 citations

Abstract

Human height prediction based on genetic factors alone shows positive correlation, but predictors developed for one population perform less well when applied to population of different ancestries. In this study, we evaluated the utility of incorporating non-genetic factors in height predictors for the Han Chinese population in Taiwan. We analyzed data from 78,719 Taiwan Biobank (TWB) participants and 40,641 Taiwan Precision Medicine Initiative (TPMI) participants using genome-wide association study and multivariable linear regression least absolute shrinkage and selection operator (LASSO) methods to incorporate genetic and non-genetic factors for height prediction. Our findings establish that combining birth year (as a surrogate for nutritional status), age at measurement (to account for age-associated effects on height), and genetic profile data improves the accuracy of height prediction. This method enhances the correlation between predicted and actual height and significantly reduces the discrepancies between predicted and actual height in both males and females.

npj Genomic MedicineVol. 10(1)
Tri-Service General Hospital (TW), Kaohsiung Medical University (TW), National Chung Hsing University (TW), Chang Gung University (TW), University of California, San Francisco (US), China Medical University (TW), Tzu Chi University (TW), Tzu Chi Foundation (TW), Kaohsiung Medical University Chung-Ho Memorial Hospital (TW), Taipei Veterans General Hospital (TW), Taichung Veterans General Hospital (TW), Institute of Biomedical Sciences, Academia Sinica (TW), Chung Shan Medical University Hospital (TW), Linkou Chang Gung Memorial Hospital (TW), Institute of Human Genetics (PL), China Medical University Hospital (TW), Genomic Prediction (United States) (US), Koo Foundation Sun Yat-Sen Cancer Center (TW), Changhua Christian Hospital (TW), National Defense Medical Center (TW), Michigan State University (US), Chung Shan Medical University (TW)
Michigan State University, Academia Sinica, College of Engineering, Michigan State University
Zero hunger
Openalex Percentile: Top 19%
Genetic Associations and Epidemiology
1.82
DOIOpen Access PDFOpenAlex
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