Transfer learning for genomic prediction in underrepresented populations
General Science
Researchers studied how to improve disease risk predictions from genetic data in underrepresented populations by using transfer learning from larger European genetic studies. Polygenic risk scores, which predict disease risk from hundreds of millions of genetic variants, have historically been much less accurate for non-European populations because most genetic research has focused on Europeans. The study found that while borrowing European genetic data helps when target population sample sizes are very small, adding European data actually worsens prediction accuracy once a population has enough of its own genetic data, particularly for traits with population-specific genetic patterns. The findings suggest that the optimal approach depends on both the size of available target population data and how genetically similar a trait is across different populations.
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