The contribution of alternative splicing to genetic risk for psychiatric disorders

Abstract

A genetic contribution to psychiatric disorders has clearly been established and genome-wide association studies now provide the location of risk genes and genetic variants associated with risk. However, the mechanism by which these genes and variants contribute to psychiatric disorders is mostly undetermined. This is in part because non-synonymous protein coding changes cannot explain the majority of variants associated with complex genetic traits. Based on this, it is predicted that these variants are causing gene expression changes, including changes to alternative splicing. Genetic changes influencing alternative splicing have been identified as risk factors in Mendelian disorders; however, currently there is a paucity of research on the role of alternative splicing in complex traits. This stems partly from the difficulty of predicting the role of genetic variation in splicing. Alterations to canonical splice site sequences, nucleotides adjacent to splice junctions, and exonic and intronic splicing regulatory sequences can influence splice site choice. Recent studies have identified global changes in alternatively spliced transcripts in brain tissues, some of which correlate with altered levels of splicing trans factors.Disease-associated variants have also been found to affect cis-acting splicing regulatory sequences and alter the ratio of alternatively spliced transcripts. These findings are reviewed here, as well as the current datasets and resources available to study alternative splicing in psychiatric disorders. Identifying and understanding risk variants that cause alternative splicing is critical to understanding the mechanisms of risk as well as to pave the way for new therapeutic options.

Accelerated evolution of oligodendrocytes in human brain

“…The human-specific oligodendrocyte genes are enriched for functional categories such as RNA metabolism and RNA processing. While such molecular functions are underexplored with respect to oligodendrocytes, there is increasing evidence that these functions are altered in cognitive diseases (Glatt et al, 2011;Quesnel-Vallieres et al, 2018;Reble et al, 2018). Moreover, the brain GWAS and PsychENCODE enrichments for cell-type expression modules suggest that human-specific cell typespecific evolutionary trajectories of gene expression are implicated in disease pathophysiology in multiple cognitive disorders.…”

Long non-coding RNA Neat1 regulates adaptive behavioural response to stress in mice

“…Alternative splicing occurs at high frequency in the CNS and is crucial for normal functioning of the brain (Yeo et al, 2004). Indeed, global changes in alternative splicing pattern have been identified in the brain of patients with psychiatric diseases such as schizophrenia (Reble et al, 2018). In our RNA-Seq dataset, we also detected a number of genes differentially spliced in the cortex of Neat1 -/mice.…”

G2B reviews: Epigenetics, epitranscriptomics, microRNAs and more: Emerging approaches to the study of genes, brain and behavior

“…Their article succeeds to that end in providing a valuable and accessible resource. As does an article here by Reble et al, the focus of which is also QTLs, but specifically splicing QTL (sQTLs). Their piece underscores the importance of considering alternative splicing as a key source of variation in gene regulation and expression (ie sQTLs) that, in turn, could account for a notable proportion of inherited risk in psychiatric disorders.…”

“…Their piece underscores the importance of considering alternative splicing as a key source of variation in gene regulation and expression (ie sQTLs) that, in turn, could account for a notable proportion of inherited risk in psychiatric disorders. Illustrating their case, Reble et al review recent work pointing to various differentially spliced genes in Autism Spectrum Disorders, Bipolar Disorder and Schizophrenia. Looking ahead, they discuss the prospect of leveraging novel bioinformatics and computational methods to predicting splicing patterns from genomic data, and raise the exciting possibility of targeting splicing events for therapeutic ends.…”