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

Output Details

Objective This study aims to address disparities in risk prediction by evaluating the performance of polygenic risk score (PRS) models using the 90 risk variants across 78 independent loci previously linked to Parkinson’s disease (PD) risk across seven diverse ancestry populations.

Meet the Authors

  • User avatar fallback logo

    Paula Saffie Awad

  • User avatar fallback logo

    Inas Elasyed

  • User avatar fallback logo

    Arinola O. Sanyaolu

  • User avatar fallback logo

    Peter Wild Crea

  • User avatar fallback logo

    Artur Francisco Shumacher-Schuh

  • User avatar fallback logo

    Kristin S Levine

  • User avatar fallback logo

    Dan Vitale

  • User avatar fallback logo

    Mathew J Koretsky

  • User avatar fallback logo

    Jeffrey Kim

  • User avatar fallback logo

    Thiago Peixoto Leal

  • User avatar fallback logo

    Sumit Dey

  • User avatar fallback logo

    Alastair J Noyce

  • User avatar fallback logo

    Armando Palomares Reyes

  • User avatar fallback logo

    Noela Rodriguez-Losada

  • User avatar fallback logo

    Jia Nee Foo

  • User avatar fallback logo

    Wael Mohamed

  • User avatar fallback logo

    Karl Heilbron

  • User avatar fallback logo

    Lucy Norcliffe-Kaufmann

  • User avatar fallback logo

    23andMe Research Team

  • User avatar fallback logo

    Mie Rizig

  • User avatar fallback logo

    Njideka Okubadejo

  • User avatar fallback logo

    Mike Nalls

  • Cornelis Blauwendraat, PhD

    Coalition for Aligning Science

  • Andrew Singleton, PhD

    Global Parkinson's Genetics Program

  • User avatar fallback logo

    Hampton Leonard

  • User avatar fallback logo

    Mary B Makarious

  • User avatar fallback logo

    Ignacio Mata

  • User avatar fallback logo

    Sara Bandres-Ciga