Distinguishing different psychiatric disorders using DDx-PRS

AI Summary

DDx-PRS jointly estimates posterior probabilities across diagnostic categories by modelling variance-covariance, leveraging case-control PRS and clinical prior probabilities.
Applied to large Psychiatric Genomics Consortium datasets (total n = 1,048,683; held-out n = 11,460), DDx-PRS was well calibrated and well powered.
Top deciles of predicted diagnosis probabilities showed substantially higher true diagnosis probabilities than baseline, indicating appreciable potential clinical utility in some settings.

TL;DR (AI Generated)

We introduce differential diagnosis-polygenic risk score (DDx-PRS), which jointly estimates the posterior probabilities of each diagnostic category (e.g., SCZ = 50%, BIP = 25%, MDD = 15%, control = 10%) by modeling variance-covariance structure across disorders, leveraging case-control polygenic risk scores and prior clinical probabilities for each diagnostic category.

We applied DDx-PRS to Psychiatric Genomics Consortium SCZ, BIP, MDD and control data, including summary-level training data from three case-control genome-wide association studies (n = 41,917-173,140 cases; total n = 1,048,683) and held-out test data from different cohorts with equal numbers for each diagnostic category (total n = 11,460).

Basic summary

Nat Genet. 2026 Aug 20. doi: 10.1038/s41588-026-02684-x. Online ahead of print.

ABSTRACT

Despite great progress on case-control polygenic prediction, an unmet need remains for a method that genetically distinguishes clinically related disorders (e.g., schizophrenia (SCZ) versus bipolar disorder (BIP) versus major depressive disorder (MDD) versus controls). We introduce differential diagnosis-polygenic risk score (DDx-PRS), which jointly estimates the posterior probabilities of each diagnostic category (e.g., SCZ = 50%, BIP = 25%, MDD = 15%, control = 10%) by modeling variance-covariance structure across disorders, leveraging case-control polygenic risk scores and prior clinical probabilities for each diagnostic category. We applied DDx-PRS to Psychiatric Genomics Consortium SCZ, BIP, MDD and control data, including summary-level training data from three case-control genome-wide association studies (n = 41,917-173,140 cases; total n = 1,048,683) and held-out test data from different cohorts with equal numbers for each diagnostic category (total n = 11,460). DDx-PRS was well calibrated and well powered (consistent with simulations) and produced comparable results to methods that require tuning data. True diagnosis probabilities in the top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, implying appreciable potential for clinical utility in certain settings.

PMID:42625058 | DOI:10.1038/s41588-026-02684-x

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