Genetic studies of hippocampal granule neuron development have been used to elucidate cellular functions of Pten and Fmr1. While mutations in each gene cause neurodevelopmental disorders such as autism and fragile X syndrome, how Pten and Fmr1 function alone or together during normal development is not known. Moreover, Pten mRNA is bound by the fragile X mental retardation protein (FMRP) RNA binding protein, but how this physical interaction impinges on phosphatase and tensin homolog protein (PTEN) expression is not known. To understand the interaction of PTEN and FMRP, we investigated the dentate gyrus granule neuron development in Pten and Fmr1 knockout (KO) mice. Interestingly, heterozygosity of Pten restored Fmr1 KO cellular phenotypes, including dendritic arborization, and spine density, while PTEN protein expression was significantly increased in Fmr1 KO animals. However, complete deletion of both Pten and Fmr1 resulted in a dramatic increase in dendritic length, spine density, and spine length. In addition, overexpression of PTEN in Fmr1 KO Pten heterozygous background reduced dendritic length, arborization, spine density, and spine length including pS6 levels. Our findings suggest that PTEN levels are negatively regulated by FMRP, and some Fmr1 KO phenotypes are caused by dysregulation of PTEN protein. These observations provide evidence for the genetic interaction of PTEN and FMRP and a possible mechanistic basis for the pathogenesis of Fmr1-related fragile X neurodevelopmental disorders.

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits.

Neurons derived from human induced pluripotent stem cells (hiPSCs) have been used to model basic cellular aspects of neuropsychiatric disorders, but the relationship between the emergent phenotypes and the clinical characteristics of donor individuals has been unclear. We analyzed RNA expression and indices of cellular function in hiPSC-derived neural progenitors and cortical neurons generated from 13 individuals with high polygenic risk scores (PRSs) for schizophrenia (SCZ) and a clinical diagnosis of SCZ, along with 15 neurotypical individuals with low PRS. We identified electrophysiological measures in the patient-derived neurons that implicated altered Na+ channel function, action potential interspike interval, and gamma-aminobutyric acid-ergic neurotransmission. Importantly, electrophysiological measures predicted cardinal clinical and cognitive features found in these SCZ patients. The identification of basic neuronal physiological properties related to core clinical characteristics of illness is a potentially critical step in generating leads for novel therapeutics.