A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts

Research output: Contribution to journalJournal articleResearchpeer-review

  • Guiyan Ni
  • Jian Zeng
  • Joana A. Revez
  • Ying Wang
  • Zhili Zheng
  • Tian Ge
  • Restuadi Restuadi
  • Jacqueline Kiewa
  • Dale R. Nyholt
  • Jonathan R.I. Coleman
  • Jordan W. Smoller
  • Stephan Ripke
  • Benjamin M. Neale
  • Aiden Corvin
  • James T.R. Walters
  • Kai How Farh
  • Peter A. Holmans
  • Phil Lee
  • Brendan Bulik-Sullivan
  • David A. Collier
  • Hailiang Huang
  • Pers, Tune H
  • Ingrid Agartz
  • Esben Agerbo
  • Margot Albus
  • Madeline Alexander
  • Farooq Amin
  • Silviu A. Bacanu
  • Martin Begemann
  • Richard A. Belliveau
  • Judit Bene
  • Sarah E. Bergen
  • Elizabeth Bevilacqua
  • Tim B. Bigdeli
  • Donald W. Black
  • Richard Bruggeman
  • Nancy G. Buccola
  • Mark Hansen
  • Hansen, Thomas Folkmann
  • Schizophrenia Working Group of the Psychiatric Genomics Consortium
  • Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

Background: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors. Methods: The Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared. Results: Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively. Conclusions: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparisons and are recommended in applications to psychiatric disorders.

Original languageEnglish
JournalBiological Psychiatry
ISSN0006-3223
DOIs
Publication statusAccepted/In press - 2021

Bibliographical note

Publisher Copyright:
© 2021 Society of Biological Psychiatry

    Research areas

  • Lassosum, LDpred2, Major depressive disorder, MegaPRS, Polygenic scores, PRS-CS, Psychiatric disorders, Risk prediction, SBayesR, Schizophrenia

ID: 280176854