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Three open questions in polygenic score portability.

Joyce Y Wang1, Neeka Lin1, Michael Zietz2

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Summary
This summary is machine-generated.

Polygenic scores (PGS) show limited portability across genetic ancestries. Factors beyond genetic distance, including socioeconomic status and trait evolution, significantly impact prediction accuracy, necessitating a broader understanding for equitable applications.

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Area of Science:

  • Genetics
  • Population Genetics
  • Bioinformatics

Background:

  • Polygenic scores (PGS) are increasingly used in genetic research but face challenges with portability across diverse populations.
  • Limited portability stems from differences in genetic ancestry between individuals and the samples used to develop PGS.

Purpose of the Study:

  • To investigate the factors influencing the prediction accuracy and portability of polygenic scores (PGS).
  • To move beyond global ancestry groupings and explore other determinants of PGS performance.

Main Methods:

  • Assessed PGS prediction accuracy as a function of genome-wide genetic dissimilarity (genetic distance) to the Genome-Wide Association Study (GWAS) sample.
  • Analyzed portability trends across different traits, including immunity-related traits and type 2 diabetes.

Main Results:

  • Individual-level PGS prediction accuracy is weakly correlated with genetic distance, with socioeconomic measures explaining comparable variation.
  • Portability varies by trait; immunity-related traits show sharp accuracy declines with genetic distance.
  • Performance metrics like precision and recall exhibit complex relationships with genetic distance, as seen in type 2 diabetes.

Conclusions:

  • PGS portability is influenced by a complex interplay of factors including genetic distance, trait evolution, genetic architecture, social context, and PGS construction methods.
  • Global ancestry alone is insufficient to understand PGS portability.
  • Addressing these factors is crucial for developing more accurate and equitable polygenic scores.