A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts

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 (GWASs). 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 (SCZ)
and major depressive disorder (MDD) bring together many independently collected case control cohorts. We used these resources (31K SCZ cases, 41K controls; 248K MDD cases,
563K 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 nine methods that model genetic architecture more formally: SBLUP,
LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR,
MegaPRS) are compared.
Results: Compared to PC+T, the other nine methods give higher prediction statistics,
MegaPRS, LDPred2 and SBayesR significantly so, up to 9.2% variance in liability for SCZ
across 30 target cohorts, an increase of 44%. For MDD 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 comparison
and are recommended in applications to psychiatric disorders.