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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Polygenic height prediction for the Han Chinese in Taiwan.

Chih-Hao Chang1,2, Che-Yu Chou1, Timothy G Raben3

  • 1Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.

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|February 5, 2025
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Incorporating non-genetic factors like birth year and age improves human height prediction accuracy in the Han Chinese population. This combined approach enhances correlation and reduces discrepancies between predicted and actual height.

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Area of Science:

  • Human genetics
  • Population health
  • Biostatistics

Background:

  • Genetic factors alone show limited accuracy for human height prediction across diverse ancestries.
  • Existing height prediction models perform poorly when applied to different ethnic groups.

Purpose of the Study:

  • To evaluate the effectiveness of incorporating non-genetic factors into height prediction models for the Han Chinese population in Taiwan.
  • To improve the accuracy of human height prediction by integrating genetic and non-genetic data.

Main Methods:

  • Analysis of data from 78,719 Taiwan Biobank (TWB) and 40,641 Taiwan Precision Medicine Initiative (TPMI) participants.
  • Utilized genome-wide association study and multivariable linear regression with least absolute shrinkage and selection operator (LASSO) methods.
  • Incorporated genetic profile data along with non-genetic factors such as birth year and age at measurement.

Main Results:

  • Combining birth year (nutritional status proxy), age at measurement, and genetic data significantly improves height prediction accuracy.
  • The enhanced prediction model shows increased correlation between predicted and actual height.
  • Discrepancies between predicted and actual height were substantially reduced for both males and females.

Conclusions:

  • Integrating non-genetic factors alongside genetic data offers a more accurate approach to human height prediction.
  • This study provides an improved height prediction model tailored for the Han Chinese population in Taiwan.
  • The findings highlight the importance of considering environmental and demographic factors in genetic prediction models.