An Efficient Lasso Framework for Admixture-Aware Polygenic Scores.

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Polygenic scores (PGS) are valuable for personalized medicine but often perform poorly in non-European populations due to training biases.
• Existing cross-population PGS methods struggle with individuals of recent admixture, limiting their clinical utility.
• The GAUDI method addresses local ancestry but is computationally intensive and limited to two-way admixture.

Purpose of the Study:

• Introduce HAUDI, an efficient LASSO-based framework for constructing polygenic scores in admixed populations.
• Improve upon the computational efficiency and multi-way admixture handling of the GAUDI method.
• Enhance the generalizability and accuracy of polygenic scores across diverse ancestral backgrounds.

Main Methods:

• Re-parameterized the GAUDI model into a standard LASSO problem for computational efficiency.
• Developed HAUDI to accommodate multi-way admixture settings.
• Validated HAUDI through extensive simulations and real-world clinical data.

Main Results:

• HAUDI demonstrated superior performance compared to GAUDI in simulations, with significantly reduced computation time.
• HAUDI outperformed GAUDI across 18 clinical phenotypes in real data analyses.
• HAUDI provided substantial benefits over ancestry-agnostic PGS for traits like white blood cell count and chronic kidney disease.

Conclusions:

• HAUDI offers an efficient and effective solution for polygenic score construction in admixed populations.
• The method enhances the portability and accuracy of PGS, advancing personalized medicine.
• HAUDI's improved performance and computational speed make it a valuable tool for genetic risk prediction in diverse groups.