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Emerging Epidemiological Data on Rare Intellectual Disability Syndromes from Analyzing the Data of a Large Iranian
Farzane Zare Ashrafi1, Tara Akhtarkhavari1, Zohreh Fattahi1
1Genetics Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
Background:
Intellectual disability (ID) is a genetically heterogeneous condition, and so far, 1679 human genes have been identified for this phenotype. Countries with a high rate of parental consanguinity, such as Iran, provide an excellent opportunity to identify the remaining novel ID genes, especially those with an autosomal recessive (AR) mode of inheritance. This study aimed to investigate the most prevalent ID genes identified via next-generation sequencing (NGS) in a large ID cohort at the Genetics Research Center (GRC) of the University of Social Welfare and Rehabilitation Sciences.
Methods:
First, we surveyed the epidemiological data of 619 of 1295 families in our ID cohort, who referred to the Genetics Research Center from all over the country between 2004 and 2021 for genetic investigation via the NGS pipeline. We then compared our data with those of several prominent studies conducted in consanguineous countries. Data analysis, including cohort data extraction, categorization, and comparison, was performed using the R program version 4.1.2.
Results:
We categorized the most common ID genes that were mutated in more than two families into 17 categories. The most common syndromic ID in our cohort was AP4 deficiency syndrome, and the most common non-syndromic autosomal recessive intellectual disability (ARID) gene was ASPM. We identified two unrelated families for the 36 ID genes. We found 14 genes in common between our cohort and the Arab and Pakistani groups, of which three genes (AP4M1, AP4S1, and ADGRG1) were repeated more than once.
Conclusion:
To date, there has been no comprehensive targeted NGS platform for the detection of ID genes in our country. Due to the large sample size of our study, our data may provide the initial step toward designing an indigenously targeted NGS platform for the diagnosis of ID, especially common ARID in our population.
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