Genotype-phenotype correlation for congenital heart disease in Down syndrome through analysis of partial trisomy 21 cases

Pelleri, Maria Chiara; Gennari, Elena; Locatelli, Chiara; Piovesan, Allison; Caracausi, Maria; Antonaros, Francesca; Rocca, Alessandro; Donati, Costanza Maria; Conti, Letizia; Strippoli, Pierluigi; Seri, Marco; Vitale, Lorenza; Cocchi, Guido

doi:10.1016/j.ygeno.2017.06.004

Cited by 31 publications

(24 citation statements)

References 48 publications

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“…In 2013, a specific study on children with DS implied that copy number alteration of DSCR4 participate in the pathogenesis of DS patients with AVSD, functionally interacting with CRELD1 [ 83 ]. In 2017, another study [ 84 ] also confirmed that in partial trisomy 21 cases, a specific gene region of DSCR4 and its neighbor gene KCNJ6 have been duplicated in patients with AVSD, compared with other patients without AVSD, validating our prediction.…”

Section: Discussionsupporting

confidence: 70%

Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection

Pan

Zhang

et al. 2018

Genes

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Atrioventricular septal defect (AVSD) is a clinically significant subtype of congenital heart disease (CHD) that severely influences the health of babies during birth and is associated with Down syndrome (DS). Thus, exploring the differences in functional genes in DS samples with and without AVSD is a critical way to investigate the complex association between AVSD and DS. In this study, we present a computational method to distinguish DS patients with AVSD from those without AVSD using the newly proposed self-normalizing neural network (SNN). First, each patient was encoded by using the copy number of probes on chromosome 21. The encoded features were ranked by the reliable Monte Carlo feature selection (MCFS) method to obtain a ranked feature list. Based on this feature list, we used a two-stage incremental feature selection to construct two series of feature subsets and applied SNNs to build classifiers to identify optimal features. Results show that 2737 optimal features were obtained, and the corresponding optimal SNN classifier constructed on optimal features yielded a Matthew’s correlation coefficient (MCC) value of 0.748. For comparison, random forest was also used to build classifiers and uncover optimal features. This method received an optimal MCC value of 0.582 when top 132 features were utilized. Finally, we analyzed some key features derived from the optimal features in SNNs found in literature support to further reveal their essential roles.

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Section: Discussionsupporting

confidence: 70%

Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection

Pan

Zhang

et al. 2018

Genes

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“…Down syndrome (DS) is the most frequent human chromosomal disorder with a frequency of 1/400 conceptions and 1/1,000 births worldwide (1,2). Since the initial discovery of Lejeune et al (3), it is known that the presence of full or partial chromosome 21 (Hsa21) in three copies (trisomy 21) in the cells of the affected subjects is responsible for the typical features of DS, in particular intellectual disability (ID), cardiovascular defects (4,5) and craniofacial dysmorphism. Importantly, a highly restricted 'Down syndrome critical region' of 34 kb on distal 21q22.13 appears to be specifically duplicated in all individuals with DS (6,7).…”

Section: Introductionmentioning

confidence: 99%

Analysis of a nanoparticle‑enriched fraction of plasma reveals miRNA candidates for Down syndrome pathogenesis

et al. 2019

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Down syndrome (DS) is caused by the presence of part or all of a third copy of chromosome 21. DS is associated with several phenotypes, including intellectual disability, congenital heart disease, childhood leukemia and immune defects. Specific microRNAs (miRNAs/miR) have been described to be associated with DS, although none of them so far have been unequivocally linked to the pathology. The present study focuses to the best of our knowledge for the first time on the miRNAs contained in nanosized RNA carriers circulating in the blood. Fractions enriched in nanosized RNA-carriers were separated from the plasma of young participants with DS and their non-trisomic siblings and miRNAs were extracted. A microarray-based analysis on a small cohort of samples led to the identification of the three most abundant miRNAs, namely miR-16-5p, miR-99b-5p and miR-144-3p. These miRNAs were then profiled for 15 pairs of DS and non-trisomic sibling couples by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results identified a clear differential expression trend of these miRNAs in DS with respect to their non-trisomic siblings and gene ontology analysis pointed to their potential role in a number of typical DS features, including 'nervous system development', 'neuronal cell body' and certain forms of 'leukemia'. Finally, these expression levels were associated with certain typical quantitative and qualitative clinical features of DS. These results contribute to the efforts in defining the DS-associated pathogenic mechanisms and emphasize the importance of properly stratifying the miRNA fluid vehicles in order to probe biomolecules that are otherwise hidden and/or not accessible to (standard) analysis.

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“…Further, children with DS show marked motor deficits [ 4 ] related to specific syndrome characteristics such as hypotonia [ 5 ]. Other more constant and typical features of DS are craniofacial dysmorphisms, together with other variable signs and symptoms such as cardiac malformations and delayed growth [ 6 , 7 , 8 , 9 , 10 , 11 ]. Considering intellectual functioning, the great majority of individuals with DS show intellectual disability ranging from mild to severe, with a mean Intellectual Quotient (IQ) of 50 and a mental age that rarely exceeds 8 years of age [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Is the Age of Developmental Milestones a Predictor for Future Development in Down Syndrome?

et al. 2021

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Down Syndrome (DS) is the most common genetic alteration responsible for intellectual disability, which refers to deficits in both intellectual and adaptive functioning. According to this, individuals with Down Syndrome (DS) reach developmental milestones (e.g., sitting, walking, and babbling) in the same order as their typically developing peers, but later in life. Since developmental milestones are the first blocks on which development builds, the aims of the current study are to: (i) expand the knowledge of developmental milestone acquisition; and (ii) explore the relationship between developmental milestone acquisition and later development. For this purpose 105 children/adolescents with DS were involved in this study, divided in two groups, Preschoolers (n = 39) and School-age participants (n = 66). Information on the age of acquisition of Sitting, Walking, Babbling, and Sphincter Control was collected, together with cognitive, motor, and adaptive functioning. Sitting predicted later motor development, but, with age, it became less important in predicting motor development in everyday life. Babbling predicted later language development in older children. Finally, Sphincter Control emerged as the strongest predictor of motor, cognitive, language, and adaptive skills, with its role being more evident with increasing age. Our data suggest that the age of reaching the milestones considered in the study has an influence on successive development, a role that can be due to common neural substrates, the environment, and the developmental cascade effect.

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Genotype-phenotype correlation for congenital heart disease in Down syndrome through analysis of partial trisomy 21 cases

Cited by 31 publications

References 48 publications

Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection

Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection

Analysis of a nanoparticle‑enriched fraction of plasma reveals miRNA candidates for Down syndrome pathogenesis

Is the Age of Developmental Milestones a Predictor for Future Development in Down Syndrome?

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