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E-pub ahead of print

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

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  1. Examining Sex-Differentiated Genetic Effects Across Neuropsychiatric and Behavioral Traits

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  2. Sex-Dependent Shared and Nonshared Genetic Architecture Across Mood and Psychotic Disorders

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  3. Dysregulated Lipid Metabolism Precedes Onset of Psychosis

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  1. Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction

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  2. Genetic, Clinical, and Sociodemographic Factors Associated With Stimulant Treatment Outcomes in ADHD

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  • Schizophrenia Working Group of the Psychiatric Genomics Consortium
  • Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium
  • Thomas Mears Werge
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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.

OriginalsprogEngelsk
TidsskriftBiological Psychiatry
Vol/bind90
Udgave nummer9
Sider (fra-til)611-620
Antal sider10
ISSN0006-3223
DOI
StatusE-pub ahead of print - 4 maj 2021

ID: 67622255