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Evaluating the performance of polygenic risk profiling across diverse ancestry populations in Parkinson's disease.

Paula Saffie-AwadInas ElsayedArinola O SanyaoluPeter Wild CreaArtur Francisco Schumacher SchuhKristin S LevineDan VitaleMathew J KoretskyJonggeol Jeffrey KimThiago Peixoto LealMaria Teresa PeriñánSumit DeyAlastair J NoyceArmando Reyes-PalomaresNoela Rodriguez-LosadaJia-Nee FooWael M Y MohamedKarl HeilbronLucy Norcliffe-Kaufmannnull nullMie RizigNjideka Ulunma OkubadejoMike NallsCornelis BlauwendraatAndrew B SingletonHampton L LeonardMary B MakariousIgnacio Fernandez MataSara Bandres-Ciga
Published in: medRxiv : the preprint server for health sciences (2023)
This study represents the first comprehensive assessment of how PRS models predict PD risk and age at onset in a multi-ancestry fashion. Given the heterogeneity and distinct genetic architecture of PD across different populations, our assessment emphasizes the need for larger and diverse study cohorts of individual-level target data and well-powered ancestry-specific summary statistics. Our current understanding of PD status unraveled through GWAS in European populations is not generally applicable to other ancestries. Future studies should integrate clinical and *omics level data to enhance the accuracy and predictive power of PRS across diverse populations.
Keyphrases
  • single cell
  • electronic health record
  • genetic diversity
  • genome wide association study
  • deep learning