Pleiotropic Meta-Analysis of Cognition, Education, and Schizophrenia Differentiates Roles of Early Neurodevelopmental and Adult Synaptic Pathways

Abstract

Liability to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published GWAS in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results.Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected ("Concordant") direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants demonstrating the counterintuitive ("Discordant") relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education and/or schizophrenia at p<5x10 −8 . Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs, and many of these have been validated by larger, more recent single-phenotype GWAS.Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms: early neurodevelopmental pathways that characterize concordant allelic variation, and adulthood synaptic pruning pathways that were linked to the paradoxical positive genetic association between education and schizophrenia. Further, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia, but also to the broader biological dimensions that are implicated in both general health outcomes and psychiatric illness.

Genetic analysis identifies molecular systems and biological pathways associated with household income

“…Of note are the variables of autism and schizophrenia. As found in previous studies 8,49,51,65,66 schizophrenia showed a small positive genetic correlation with EA (rg=0.06, SE=0.02, P=1.15 × 10 −3 ) whereas, in the present study, income showed a negative genetic correlation with schizophrenia (rg=−0.14, SE=0.02, P=6.49×10 −9 ); the difference between these two genetic correlations was significant (P=6.57×10 −11 ). Autism also showed a positive genetic correlation with EA (rg=0.27, SE = 0.03, P=1.10×10 −15 ) as found previously, 8,51,67 whereas income showed no detectable genetic correlation with autism (rg=0.04, SE=0.05, P=0.37), and this difference was again significant (P=1.17×10 −11 ).…”

Identifying Nootropic Drug Targets via Large-Scale Cognitive GWAS and Transcriptomics

“…A total of 39 of these were novel and not found in the input GWAS. We then looked up reports that have used multi-trait strategies to enhance power for GCA 17,20 and found that of the 39 loci, 8 loci were also reported by Hill et al 17 , 1 locus was reported by Lam et al 20 and 1 locus was reported by both of these studies.…”

“…Dissecting the pleiotropic genetic architectures underlying GCA, educational attainment, and schizophrenia. We have recently shown that neurodevelopmental pathways and adulthood synaptic processes are dissociable etiologic mechanisms relating to genetic liability to psychosis 20 .…”

Transcriptome-wide association analysis of 211 neuroimaging traits identifies new genes for brain structures and yields insights into the gene-level pleiotropy with other complex traits

“…The left ten genes, which were novel findings of TWAS, also had known associations with many cognitive and mental health traits. For example, previous GWAS reported that HCK was associated with chronotype 82 , LGALS3 with schizophrenia 83 , UBE2C with reaction time 75 , KLRD1 with adolescent idiopathic scoliosis 84 , OSER1 with cognitive performance 77 and Alzheimer’s disease 76 , and PRPF3 with chronotype 76,85 and neuropsychiatric disorders 86 . In summary, TWAS novel and validated genes expand the overview of gene-level pleiotropy across these traits, suggesting that neuroimaging-derived biomarkers could be useful in studying a wide range of complex traits.…”