How tall will you be? This question has fascinated scientists for years. While genetics play a crucial role, height is also shaped by environmental factors. A new study published in npj Genomic Medicine has developed a more accurate height prediction model by integrating genetics, birth year, and age at measurement in the Han Chinese population in Taiwan.

The study, based on data from over 119,000 individuals from the Taiwan Biobank (TWB) and the Taiwan Precision Medicine Initiative (TPMI), significantly improves height prediction accuracy. The findings not only refine genetic models but also offer insights into how genetics and environment interact to shape human stature.

Beyond DNA: The Missing Puzzle Pieces in Height Prediction

Traditionally, height predictions rely on polygenic scores (PGS), which aggregate thousands of genetic variants linked to stature. While these models work well for European populations, they tend to be less accurate for non-European groups due to genetic diversity.

This study enhances existing models by incorporating two additional variables:

• Birth year – A proxy for improvements in nutrition and healthcare over time, which were significant in Taiwan during the lifetime of participants.
• Age at measurement – Adjusting for age-related height loss, especially in older populations.

By combining these factors, the new model significantly outperforms traditional genetic models, reducing prediction errors in both men and women. However, nutritional and environmental improvements differ across populations, even among Han Chinese in other countries. This means that model parameters should be adjusted accordingly rather than applied universally.

Stronger Predictions, Better Insights

Using genome-wide association studies (GWAS) and machine learning techniques, the researchers optimized a model that achieved:

• Pearson correlation of 0.7759 in males and 0.6084 in females, indicating strong predictive power.
• 40% improvement in accuracy in the TPMI validation dataset compared to PGS-only models.
• Identification of thousands of height-associated genetic variants, some linked to diseases like cardiovascular disease, diabetes, and cancer.

Why Does This Matter?

Height isn’t just about appearance—it has been linked to several health conditions:

• Heart disease – Some studies suggest shorter individuals have a higher risk.
• Osteoporosis – Age-related height loss is linked to bone density issues.
• Reproductive success – In Taiwan, taller men tend to have more children.

By refining height prediction, this research paves the way for better genetic risk assessments, ultimately contributing to personalized medicine.

What’s Next?

This study is a major step in population-specific genetic research. Future research aims to:

• Expand the model to other non-European populations.
• Investigate how height-related genes influence other traits and diseases.
• Integrate more lifestyle and environmental factors to enhance prediction accuracy.

Final Thoughts

Height prediction has moved beyond basic genetics. This study highlights how a holistic approach—combining genetics with environmental factors—can improve predictions and advance personalized medicine. However, variations in nutritional history across different populations mean that prediction models should always be adjusted for local contexts. As genomic research progresses, the future of precision health is closer than ever.