Reviewed and updated Algorithm for Genetic Characterization of Syndromic Obesity Phenotypes

Abstract

Background: Individuals with a phenotype of early-onset severe obesity associated with intellectual disability can have molecular diagnoses ranging from monogenic to complex genetic traits. Severe overweight is the major sign of a syndromic physical appearance and predicting the influence of a single gene and/or polygenic risk profile is extremely complicated among the majority of the cases. At present, considering rare monogenic bases as the principal etiology for the majority of obesity cases associated with intellectual disability is scientifically poor. The diversity of the molecular bases responsible for the two entities makes the appliance of the current routinely powerful genomics diagnostic tools essential. Objective: Clinical investigation of these difficult-to-diagnose patients requires pediatricians and neurologists to use optimized descriptions of signs and symptoms to improve genotype correlations. Methods: The use of modern integrated bioinformatics strategies which are conducted by experienced multidisciplinary clinical teams. Evaluation of the phenotype of the patient's family is also of importance. Results: The next step involves discarding the monogenic canonical obesity syndromes and considering infrequent unique molecular cases, and/or then polygenic bases. Adequate management of the application of the new technique and its diagnostic phases is essential for achieving good cost/efficiency balances. Conclusion: With the current clinical management, it is necessary to consider the potential coincidence of risk mutations for obesity in patients with genetic alterations that induce intellectual disability. In this review, we describe an updated algorithm for the molecular characterization and diagnosis of patients with a syndromic obesity phenotype.

Keywords: Syndromic obesity; classical obesity syndrome; exome sequencing; non-canonical obesity syndrome; nonsyndromic monogenic obesity; whole-genome array.

Fig. (1)

An example of the Face2Gene software consultation related to Prader-willy Syndrome. An example of the Face2Gene software consultation related to Prader-Willi Syndrome. The main use of this computer-assisted facial recognition is the prediction of the etiology of patients with global developmental delay and dysmorphic features and to determine a diagnosis. This is achieved through the comparison of photographs of the patients with photographs of the syndrome model (left upper panel). The clinician is able to indicate the type of diagnosis made; a differential diagnosis, clinical diagnosis or molecular diagnosis (right upper panel). The software provides information on the chromosomal location involved, the inheritance mode and the OMIM reference (left lower panel). The Face2Gene search can be refined by selecting the phenotypic traits that the patient has, enabling a perfected diagnostic approach (right lower panel).

Fig. (2)

Variant assessment workflow. Variant assessment workflow. Genetic variants identified by laboratory testing are annotated with information from various sources including publications, computational prediction algorithms, and public, collaborative and internal databases. After evaluation of all pertinent information, and in conjunction with patient-specific clinical and familial information, a professionally trained individual will classify the variant into one of the five clinical categories and combine all the findings into a clinical report [55]. “Adapted from Duzkale 2013”.