Intellectual disability (ID) is a clinically and genetically heterogeneous disorder, affecting 1–3% of the general population. Although research into the genetic causes of ID has recently gained momentum, identification of pathogenic mutations that cause autosomal recessive ID (ARID) has lagged behind, predominantly due to non-availability of sizeable families. Here we present the results of exome sequencing in 121 large consanguineous Pakistani ID families. In 60 families, we identified homozygous or compound heterozygous DNA variants in a single gene, 30 affecting reported ID genes and 30 affecting novel candidate ID genes. Potential pathogenicity of these alleles was supported by co-segregation with the phenotype, low frequency in control populations and the application of stringent bioinformatics analyses. In another eight families segregation of multiple pathogenic variants was observed, affecting 19 genes that were either known or are novel candidates for ID. Transcriptome profiles of normal human brain tissues showed that the novel candidate ID genes formed a network significantly enriched for transcriptional co-expression (P<0.0001) in the frontal cortex during fetal development and in the temporal–parietal and sub-cortex during infancy through adulthood. In addition, proteins encoded by 12 novel ID genes directly interact with previously reported ID proteins in six known pathways essential for cognitive function (P<0.0001). These results suggest that disruptions of temporal parietal and sub-cortical neurogenesis during infancy are critical to the pathophysiology of ID. These findings further expand the existing repertoire of genes involved in ARID, and provide new insights into the molecular mechanisms and the transcriptome map of ID.

Intellectual disability (ID) is a genetically and clinically heterogeneous disorder, characterized by limited cognitive abilities and impaired adaptive behaviors. In recent years, exome sequencing (ES) has been instrumental in deciphering the genetic etiology of ID. Here, through ES of a large cohort of individuals with ID, we identified two bi-allelic frameshift variants in METTL5, c.344_345delGA (p.Arg115Asnfs*19) and c.571_572delAA (p.Lys191Valfs*10), in families of Pakistani and Yemenite origin. Both of these variants were segregating with moderate to severe ID, microcephaly, and various facial dysmorphisms, in an autosomal-recessive fashion. METTL5 is a member of the methyltransferase-like protein family, which encompasses proteins with a seven-beta-strand methyltransferase domain. We found METTL5 expression in various substructures of rodent and human brains and METTL5 protein to be enriched in the nucleus and synapses of the hippocampal neurons. Functional studies of these truncating variants in transiently transfected orthologous cells and cultured hippocampal rat neurons revealed no effect on the localization of METTL5 but alter its level of expression. Our in silico analysis and 3D modeling simulation predict disruption of METTL5 function by both variants. Finally, mettl5 knockdown in zebrafish resulted in microcephaly, recapitulating the human phenotype. This study provides evidence that biallelic variants in METTL5 cause ID and microcephaly in humans and highlights the essential role of METTL5 in brain development and neuronal function.

Inherited deafness is clinically and genetically heterogeneous. We recently mapped DFNB86, a locus associated with nonsyndromic deafness, to chromosome 16p. In this study, whole-exome sequencing was performed with genomic DNA from affected individuals from three large consanguineous families in which markers linked to DFNB86 segregate with profound deafness. Analyses of these data revealed homozygous mutation c.208G>T (p.Asp70Tyr) or c.878G>C (p.Arg293Pro) in TBC1D24 as the underlying cause of deafness in the three families. Sanger sequence analysis of TBC1D24 in an additional large family in which deafness segregates with DFNB86 identified the c.208G>T (p.Asp70Tyr) substitution. These mutations affect TBC1D24 amino acid residues that are conserved in orthologs ranging from fruit fly to human. Neither variant was observed in databases of single-nucleotide variants or in 634 chromosomes from ethnically matched control subjects. TBC1D24 in the mouse inner ear was immunolocalized predominantly to spiral ganglion neurons, indicating that DFNB86 deafness might be an auditory neuropathy spectrum disorder. Previously, six recessive mutations in TBC1D24 were reported to cause seizures (hearing loss was not reported) ranging in severity from epilepsy with otherwise normal development to epileptic encephalopathy resulting in childhood death. Two of our four families in which deafness segregates with mutant alleles of TBC1D24 were available for neurological examination. Cosegregation of epilepsy and deafness was not observed in these two families. Although the causal relationship between genotype and phenotype is not presently understood, our findings, combined with published data, indicate that recessive alleles of TBC1D24 can cause either epilepsy or nonsyndromic deafness.

Students' perceptions of the education environment influence their learning. Ever since the major medical curriculum reform, anatomy education has undergone several changes in terms of its curriculum, teaching modalities, learning resources, and assessment methods. By measuring students' perceptions concerning anatomy education environment, valuable information can be obtained to facilitate improvements in teaching and learning. Hence, it is important to use a valid inventory that specifically measures attributes of the anatomy education environment. In this study, a new 11-factor, 132-items Anatomy Education Environment Measurement Inventory (AEEMI) was developed using Delphi technique and was validated in a Malaysian public medical school. The inventory was found to have satisfactory content evidence (scale-level content validity index [total] = 0.646); good response process evidence (scale-level face validity index [total] = 0.867); and acceptable to high internal consistency, with the Raykov composite reliability estimates of the six factors are in the range of 0.604-0.876. The best fit model of the AEEMI is achieved with six domains and 25 items (X = 415.67, P < 0.001, ChiSq/df = 1.63, RMSEA = 0.045, GFI = 0.905, CFI = 0.937, NFI = 0.854, TLI = 0.926). Hence, AEEMI was proven to have good psychometric properties, and thus could be used to measure the anatomy education environment in Malaysia. A concerted collaboration should be initiated toward developing a valid universal tool that, using the methods outlined in this study, measures the anatomy education environment across different institutions and countries. Anat Sci Educ 10: 423-432. © 2017 American Association of Anatomists.