Intellectual disability (ID) is a group of disorders associated with abnormal function of the nervous system or brain. Genetic factors are known to play a significant role in the development of these disorders, which represent the majority of genetic cases attending medical genetics clinics. Oman has a high rate of consanguinity; reported in some studies up to 50%. Consequently, the risk of autosomal recessive conditions and the prevalence of such disorders increase. With the advancement in Next Generation Sequencing (NGS) as well as the improvement of bioinformatics tools, Whole Exome Sequencing (WES) becomes the most efficient diagnostic test to identify disease causing variants in monogenic disorders. Identification of these variants is beneficial in understanding the genetic mechanism underlying this group of disorders. This will lead to the development of early diagnostic tests, new treatments and proper counseling of affected families. The aim of this study is to estimate the diagnostic yield of WES after data re-annotation and reanalysis. Further, to establish and standardize pipelines for filtering and prioritization of variants at our Institute. This study will also allow us to select novel candidate genes for future functional research projects. Total of 51 data files were provided by Radboud Diagnostic laboratory where the original exome analysis took place. These files were pre-processed and re-annotated using ANNOVAR tool through the in-house bioinformatics unit. A general pipeline was used in filtration and prioritization of variants. This pipeline was verified by the analysis of an additional 15 solved cases, the disease-causing variants in these cases were identified in initial exome analysis at Radboud laboratory. Prioritized variants in the 51 cases studied were classified into three groups, disease-causative variants, possible disease-causing variants and variants in novel genes. In this study, around 10% diagnostic yield was obtained by identification of pathogenic/likely pathogenic variants in five cases. Possible disease-causing variants were detected in 16 cases (31%) and variants in novel genes were detected in 10 cases (19 %). Recent publications were very helpful in identification of new variants that were not reported in initial exome reports either because they are new disease-causing genes or due to improvement in bioinformatics tools. In conclusion, reanalysis of negative exome data can increase the diagnostic yield of WES of ID cases. Improvement in bioinformatics tools as well as update in bioinformatics databases and literature were the main two reasons for identification of confirmed and possible disease-causative variants in this study.