Intellectual disability (ID) is an early childhood neurodevelopmental disorder that is characterized by impaired intellectual functioning and adaptive behavior. It is one of the major concerns in the field of neurodevelopmental disorders across the globe. Diversified approaches have been put forward to overcome this problem. Among all these approaches, high throughput transcriptomic analysis has taken an important dimension. The identification of genes causing ID rapidly increased over the past 3 to 5 years owing to the use of sophisticated high throughput sequencing platforms. Early monitoring and preventions are much important for such disorder as their progression occurs during fetal development. This study is an attempt to identify differentially expressed genes (DEGs) and upregulated biological processes involved in development of ID patients through comparative analysis of available transcriptomics data. A total of 7 transcriptomic studies were retrieved from National Center for Biotechnology Information (NCBI) and were subjected to quality check and trimming prior to alignment. The normalization and differential expression analysis were carried out using DESeq2 and EdgeR packages of Rstudio to identify DEGs in ID. In progression of the study, functional enrichment analysis of the results obtained from both DESeq2 and EdgeR was done using gene set enrichment analysis (GSEA) tool to identify major upregulated biological processes involved in ID. Our findings concluded that monitoring the level of E2F targets, estrogen, and genes related to oxidative phosphorylation, DNA repair, and glycolysis during the developmental stage of an individual can help in the early detection of ID disorder.
Keyphrases
- intellectual disability
- single cell
- genome wide identification
- autism spectrum disorder
- dna repair
- genome wide
- high throughput
- bioinformatics analysis
- end stage renal disease
- newly diagnosed
- dna damage
- ejection fraction
- electronic health record
- high throughput sequencing
- rna seq
- big data
- dna methylation
- chronic kidney disease
- peritoneal dialysis
- healthcare
- genome wide analysis
- patient reported