Challenges in molecular diagnosis of X-linked Intellectual disability.

Background: Intellectual disability (ID) affects 1-3% of the Western population and is heterogeneous in origin. Mutations in X-linked genes represent 5-10% of ID in males. Fragile X syndrome, due to the silencing of the FMR1 gene, is the most common form of ID, with a prevalence of around 1:5000 males. Females are usually non- or mildly affected carriers, and in a few rare cases, the only gender affected. Array comparative genome hybridization (aCGH) and next-generation sequencing (NGS) have dramatically changed the nature of human genome analysis leading to the identification of new X-linked intellectual disability syndromes and disease-causing genes.
Sources of Data: Original papers, reviews, guidelines and experiences of the diagnostic laboratories.
Areas of Agreement: Family history and clinical examination still are essential to choose the appropriate diagnostic tests, including, a disease-specific genetic test, aCGH or FMR1 molecular analysis. If negative, NGS approaches like well-defined gene panels, whole exome, or even whole genome sequencing, are increasingly being used, improving diagnostics and leading to the identification of novel disease mechanisms.
Areas of Controversy: The main challenge in the era of NGS is filtering and interpretation of the data generated by the analysis of a single individual. In X-linked cases, assessing pathogenicity is particularly challenging, even more when the variant is found to be inherited from a healthy carrier mother or when a heterozygous X-linked mutation is found in an impaired female.
Growing Points: At present, variant interpretation remains a challenging task, especially in X-linked disorders. We review the main difficulties and propose a comprehensive overview that might aid in variant interpretation. Establishing a genetic diagnosis facilitates counseling and allows better delineation of clinical phenotypes.
Areas Timely for Developing Research: To improve variant interpretation, there is need to refine in silico predictions with specific criteria for each gene, and to develop cost-effective functional tools, which can be easily transferred to diagnostics.
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