Integrative genomic analyses for identification and prioritization of long non-coding RNAs associated with autism

Abstract

Genetic studies have identified many risk loci for autism spectrum disorder (ASD) although causal factors in the majority of cases are still unknown. Currently, known ASD risk genes are all protein-coding genes; however, the vast majority of transcripts in humans are non-coding RNAs (ncRNAs) which do not encode proteins. Recently, long non-coding RNAs (lncRNAs) were shown to be highly expressed in the human brain and crucial for normal brain development. We have constructed a computational pipeline for the integration of various genomic datasets to identify lncRNAs associated with ASD. This pipeline utilizes differential gene expression patterns in affected tissues in conjunction with gene co-expression networks in tissue-matched non-affected samples. We analyzed RNA-seq data from the cortical brain tissues from ASD cases and controls to identify lncRNAs differentially expressed in ASD. We derived a gene co-expression network from an independent human brain developmental transcriptome and detected a convergence of the differentially expressed lncRNAs and known ASD risk genes into specific co-expression modules. Co-expression network analysis facilitates the discovery of associations between previously uncharacterized lncRNAs with known ASD risk genes, affected molecular pathways and at-risk developmental time points. In addition, we show that some of these lncRNAs have a high degree of overlap with major CNVs detected in ASD genetic studies. By utilizing this integrative approach comprised of differential expression analysis in affected tissues and connectivity metrics from a developmental co-expression network, we have prioritized a set of candidate ASD-associated lncRNAs. The identification of lncRNAs as novel ASD susceptibility genes could help explain the genetic pathogenesis of ASD.

Risk factors diagnosis prognosis and treatment of autism

“…LncRNA were found to be more likely involved in the molecular function and/or regulation of the specific ASD risk genes (172,(187)(188)(189)(190)(191)(192)(193)(194)(195)(196)(197)(198) Figure 1. Concurrence of ASD in various syndromes and disorders.…”

Identification and functional analysis of long non-coding RNAs in autism spectrum disorders

“…These connected protein-coding genes were considered as the ceRNA neighbors of ASD-related lncRNAs. We speculated that the human cortex was more relevant to the pathophysiology of ASD compared to the other brain regions, based on much more ASD researches using the cortex samples [6,17,35]. So we obtained gene co-expression neighbors of ASD-related lncRNAs from the weighted gene co-expression network based on gene expression profiles of the cortex samples.…”

“…Although many studies addressing the genetic architecture of ASD have mainly focused on illustrating the roles of protein-coding genes, increasing number of researchers are exploring the association between lncRNAs and the pathogenesis of ASD [6,10,15]. But most evidence, which characterized lncRNA dysregulation as an integral component of the transcriptomic signature of ASD, was derived from gene expression profiles of individuals with ASD [16][17][18]. Only several lncRNAs associated with ASD have been identified by genome-derived evidence, and further explored in action mechanisms by loss-of-function and gain-of-function experiments, such as SHANK2-AS, MSNP1-AS and BDNF-AS etc [19,20].…”

Genomic data mining for functional annotation of human long noncoding RNAs

“…With the RNA transcript as the functional unit, the biological function of a lncRNA may be investigated by examining the expression differences between various groups of samples, such as diseased versus control tissues or fetal brains versus adult brains. The two most common expressionbased approaches are differential expression analysis and co-expression network analysis, both of which have been used to investigate lncRNAs (Liao et al, 2011;Necsulea et al, 2014;Chaudhary et al, 2017;Gudenas et al, 2017;Cogill et al, 2018).…”

“…We have used differential expression analysis and co-expression network analysis for identification and functional annotation of candidate lncRNAs associated with ASD (Gudenas et al, 2017;Cogill et al, 2018). Various genomic datasets were integrated and then used to identify and prioritize a list of highconfidence candidate lncRNAs that were differentially expressed in the ASD brain, co-expressed with known ASD risk genes during neurodevelopment, and co-located with ASD-associated CNVs (Gudenas et al, 2017). Gene co-expression network analysis also identified two distinct groups of lncRNA modules showing elevated prenatal and postnatal expression patterns, respectively (Cogill et al, 2018).…”