The association between DNA methylation and human height and a prospective model of DNA methylation-based height prediction
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature..
As a vital anthropometric characteristic, human height information not only helps to understand overall developmental status and genetic risk factors, but is also important for forensic DNA phenotyping. We utilized linear regression analysis to test the association between each CpG probe and the height phenotype. Next, we designed a methylation sequencing panel targeting 959 CpGs and subsequent height inference models were constructed for the Chinese population. A total of 11,730 height-associated sites were identified. By employing KPCA and deep neural networks, a prediction model was developed, of which the cross-validation RMSE, MAE and R2 were 5.62 cm, 4.45 cm and 0.64, respectively. Genetic factors could explain 39.4% of the methylation level variance of sites used in the height inference models. Collectively, we demonstrated an association between height and DNA methylation status through an EWAS analysis. Targeted methylation sequencing of only 959 CpGs combined with deep learning techniques could provide a model to estimate human height with higher accuracy than SNP-based prediction models.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:143 |
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| Enthalten in: |
Human genetics - 143(2024), 3 vom: 08. März, Seite 401-421 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Zhonghua [VerfasserIn] |
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Date Completed 24.04.2024 Date Revised 25.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s00439-024-02659-0 |
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| Förderinstitution / Projekttitel: |
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| 520 | |a As a vital anthropometric characteristic, human height information not only helps to understand overall developmental status and genetic risk factors, but is also important for forensic DNA phenotyping. We utilized linear regression analysis to test the association between each CpG probe and the height phenotype. Next, we designed a methylation sequencing panel targeting 959 CpGs and subsequent height inference models were constructed for the Chinese population. A total of 11,730 height-associated sites were identified. By employing KPCA and deep neural networks, a prediction model was developed, of which the cross-validation RMSE, MAE and R2 were 5.62 cm, 4.45 cm and 0.64, respectively. Genetic factors could explain 39.4% of the methylation level variance of sites used in the height inference models. Collectively, we demonstrated an association between height and DNA methylation status through an EWAS analysis. Targeted methylation sequencing of only 959 CpGs combined with deep learning techniques could provide a model to estimate human height with higher accuracy than SNP-based prediction models | ||
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| 700 | 1 | |a Ma, Guanju |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Chunyan |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Qian |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Chaolong |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Lihong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xiaojing |e verfasserin |4 aut | |
| 700 | 1 | |a Cong, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Shujin |e verfasserin |4 aut | |
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