» Articles » PMID: 40019509

Genetic Variants and Phenotypic Data Curated for the CAGI6 Intellectual Disability Panel Challenge

Abstract

Neurodevelopmental disorders (NDDs) are common conditions including clinically diverse and genetically heterogeneous diseases, such as intellectual disability, autism spectrum disorders, and epilepsy. The intricate genetic underpinnings of NDDs pose a formidable challenge, given their multifaceted genetic architecture and heterogeneous clinical presentations. This work delves into the intricate interplay between genetic variants and phenotypic manifestations in neurodevelopmental disorders, presenting a dataset curated for the Critical Assessment of Genome Interpretation (CAGI6) ID Panel Challenge. The CAGI6 competition serves as a platform for evaluating the efficacy of computational methods in predicting phenotypic outcomes from genetic data. In this study, a targeted gene panel sequencing has been used to investigate the genetic causes of NDDs in a cohort of 415 paediatric patients. We identified 60 pathogenic and 49 likely pathogenic variants in 102 individuals that accounted for 25% of NDD cases in the cohort. The most mutated genes were ANKRD11, MECP2, ARID1B, ASH1L, CHD8, KDM5C, MED12 and PTCHD1 The majority of pathogenic variants were de novo, with some inherited from mildly affected parents. Loss-of-function variants were the most common type of pathogenic variant. In silico analysis tools were used to assess the potential impact of variants on splicing and structural/functional effects of missense variants. The study highlights the challenges in variant interpretation especially in cases with atypical phenotypic manifestations. Overall, this study provides valuable insights into the genetic causes of NDDs and emphasises the importance of understanding the underlying genetic factors for accurate diagnosis, and intervention development in neurodevelopmental conditions.

References
1.
Amberger J, Bocchini C, Scott A, Hamosh A . OMIM.org: leveraging knowledge across phenotype-gene relationships. Nucleic Acids Res. 2018; 47(D1):D1038-D1043. PMC: 6323937. DOI: 10.1093/nar/gky1151. View

2.
Aspromonte M, Bellini M, Gasparini A, Carraro M, Bettella E, Polli R . Characterization of intellectual disability and autism comorbidity through gene panel sequencing. Hum Mutat. 2019; 40(9):1346-1363. PMC: 7428836. DOI: 10.1002/humu.23822. View

3.
Berman H, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H . The Protein Data Bank. Nucleic Acids Res. 1999; 28(1):235-42. PMC: 102472. DOI: 10.1093/nar/28.1.235. View

4.
Burley S, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L . RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Res. 2022; 51(D1):D488-D508. PMC: 9825554. DOI: 10.1093/nar/gkac1077. View

5.
Carraro M, Monzon A, Chiricosta L, Reggiani F, Aspromonte M, Bellini M . Assessment of patient clinical descriptions and pathogenic variants from gene panel sequences in the CAGI-5 intellectual disability challenge. Hum Mutat. 2019; 40(9):1330-1345. PMC: 7341177. DOI: 10.1002/humu.23823. View