Given the increasingly large number of loci discovered by psychiatric GWAS, specification of the key biological pathways underlying these loci has become a priority for the field. We have previously leveraged the pleiotropic genetic relationships between schizophrenia and two cognitive phenotypes (educational attainment and cognitive task performance) to differentiate two subsets of illness-relevant SNPs: (1) those with 'concordant' alleles, which are associated with reduced cognitive ability/education and increased schizophrenia risk; and (2) those with 'discordant' alleles linked to reduced educational and/or cognitive levels but lower schizophrenia susceptibility. In the present study, we extend our prior work, utilizing larger input GWAS datasets and a more powerful statistical approach to pleiotropic meta-analysis, the Pleiotropic Locus Exploration and Interpretation using Optimal test (PLEIO). Our pleiotropic meta-analysis of schizophrenia and the two cognitive phenotypes revealed 768 significant loci (159 novel). Among these, 347 loci harbored concordant SNPs, 270 encompassed discordant SNPs, and 151 'dual' loci contained concordant and discordant SNPs. Competitive gene-set analysis using MAGMA related concordant SNP loci with neurodevelopmental pathways (e.g., neurogenesis), whereas discordant loci were associated with mature neuronal synaptic functions. These distinctions were also observed in BrainSpan analysis of temporal enrichment patterns across developmental periods, with concordant loci containing more prenatally expressed genes than discordant loci. Dual loci were enriched for genes related to mRNA translation initiation, representing a novel finding in the schizophrenia literature.

The authors have declared no competing interest.

This work was supported by the National Institute of Mental Health of the National Institutes of Health (NIH) under award no. R01MH117646 (T.L., principal investigator). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Data and Code Availability 1. GWAS summary statistics for pleiotropy analysis and gene set analysis a. GWAS: gs://pleiotropy-proj-1/02-interim-testing/MTAG_PLEIO/PLEIO_EDU_Cog_SCZ_ldsc_vcf/PLEIO_Output b. FUMA: gs://pleiotropy-proj-1/02-interim-testing/Post_GWAS_Anlalysis/FUMA_results/PLEIO.PLEIO_FUMA_job240552 c. MAGMA: gs://pleiotropy-proj-1/NoMHC_Extended_25Mb_35Mb d. Summary statistics and other auxiliary files to be made available upon acceptance of the manuscript 2. Github links a. https://github.com/mlamcogent/cogent-data-curation/tree/main 3. Publicly available GWAS summary statistics a. SSGAC https://thessgac.com b. Cognitive Task Performance (Lam et al., 2022) https://storage.googleapis.com/broad_institute_mlam/brainstorm-v2-local-gencor-1/03_quality_control_sumstatsqc/07_Data_Release_GWAS_Catalog_01/Lam_et_al_2021_CognitiveTaskPerformance.tsv.gz c. PGC3 https://pgc.unc.edu/for-researchers/download-results/ 4. Bioinformatic Tools & Resources a. Bedtools https://github.com/arq5x/bedtools2?tab=readme-ov-file b. Mungesumstats https://github.com/neurogenomics/MungeSumstats c. PLEIO https://github.com/cuelee/pleio d. MSigDB https://www.gsea-msigdb.org/gsea/msigdb/ e. BrainSpan https://www.brainspan.org/