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

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 terms of which DNA variants are included in the score and the weights assigned to them. PGSs are evaluated in independent target samples of individuals with known disease status. Evaluation of new PGS methods are made using simulated data or single target cohort, however, in real data sets there can be heterogeneity between target sample cohorts, which could reflect a number of real or artefactual factors. The Psychiatric Genomics Consortium working groups for schizophrenia (SCZ) and major depressive disorder (MDD) bring together many independently collected case-control cohorts for GWAS meta-analysis. These resources are used here in repeated application of leave-one-cohort-out GWAS analyses, generating robust conclusions for PGS prediction applied across multiple target (left-out) cohorts. Eight PGS methods (P+T, SBLUP, LDpred-Inf, LDpred-funct, LDpred, PRS-CS, PRS-CS-auto, SBayesR) are compared. We found that SBayesR had the highest prediction evaluation statistics in most comparisons. For SCZ across 30 target cohorts, the SBayesR PGS achieved a mean area under the receiver operator characteristic curve (AUC) of 0.733, and explained 9.9% of variance on the liability scale. For MDD across 26 target cohorts, the AUC and variance explained were 0.601 and 4.0%, respectively. The variance explained by the SBayesR PGS was 46% and 43% higher for SCZ and MDD, respectively, compared to the basic p-value thresholding P+T method.

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Background Distinctions between major depressive disorder (MDD) and perinatal depression (PND) reflect varying views of PND, from a unique etiological subtype of MDD to an MDD episode that happens to coincide with childbirth. This case–control study investigated genetic differences between PND and MDD outside the perinatal period (non‐perinatal depression or NPD). Methods We conducted a genome‐wide association study using PND cases (Edinburgh Postnatal Depression Scale score ≥ 13) from the Australian Genetics of Depression Study 2018 data (n = 3804) and screened controls (n = 6134). Results of gene‐set enrichment analysis were compared with those of women with non‐PND. For six psychiatric disorders/traits, genetic correlations with PND were evaluated, and logistic regression analysis reported polygenic score (PGS) association with both PND and NPD. Results Genes differentially expressed in ovarian tissue were significantly enriched (stdBeta = 0.07, p = 3.3e−04), but were not found to be associated with NPD. The genetic correlation between PND and MDD was 0.93 (SE = 0.07; p = 3.5e−38). Compared with controls, PGS for MDD are higher for PND cases (odds ratio [OR] = 1.8, confidence interval [CI] = [1.7–1.8], p = 9.5e−140) than for NPD cases (OR = 1.6, CI = [1.5–1.7], p = 1.2e−49). Highest risk is for those reporting both antenatal and postnatal depression, irrespective of prior MDD history. Conclusions PND has a high genetic overlap with MDD, but points of distinction focus on differential expression in ovarian tissue and higher MDD PGS, particularly for women experiencing both antenatal and postpartum PND.