Abstract
SummaryAccumulation of diverse types of omics data on schizophrenia (SCZ) requires a systems approach to model the interplay between genome, transcriptome, and proteome. We introduce Markov affinity-based proteogenomic signal diffusion (MAPSD), a method to model intra-cellular protein trafficking paradigms and tissue-wise single-cell protein abundances. MAPSD integrates multi-omics data to amplify the signals at SCZ risk loci with small effect sizes, and reveal convergent disease-associated gene modules in the brain. We predicted a set of high-confidence SCZ risk loci followed by characterizing the subcellular localization of proteins encoded by candidate SCZ risk genes, and illustrated that most are enriched in neuronal cells in the cerebral cortex as well as Purkinje cells in the cerebellum. We demonstrated how the identified genes may be involved in neurodevelopment, how they may alter SCZ-related biological pathways, and how they facilitate drug repurposing. MAPSD is applicable in other polygenic diseases and can facilitate our understanding of disease mechanisms.
Highlights
The emergence of omics technologies has revolutionized neuropsychiatric research[1] by generating high-throughput genomic data, bridging genome and transcriptome to phenome.[2]
Markov affinity-based proteogenomic signal diffusion (MAPSD) starts with a large-scale protein-protein interactions (PPIs) network which is assembled from multiple sources[31,32,33,34]
Looking at the newly identified gene set by MAPSD, we found several genes to be implicated in other brain disorders
Summary
The emergence of omics technologies has revolutionized neuropsychiatric research[1] by generating high-throughput genomic data, bridging genome and transcriptome to phenome.[2] For example, genome-wide association studies (GWAS), such as the Psychiatric Genomics Consortium (PGC)[3] and the CLOZUK consortium[4] have created a repertoire of tens of thousands of samples worldwide, leading to the discovery of many common variants associated with schizophrenia (SCZ). While such studies mark important milestones in SCZ research, they face critical challenges with regard to extracting novel biological. It is not trivial to accurately pinpoint the corresponding risk genes in each GWAS risk locus, as such loci may cover a myriad of genes while the genuine causal variants may be away from the top-ranking single nucleotide polymorphisms (SNPs).[6]
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