Diagnostic yield of microarrays in individuals with non‐syndromic developmental delay and intellectual disability
8
Citation
42
Reference
10
Related Paper
Citation Trend
Abstract:
Intellectual disability (ID), or developmental delay (DD) when the individual is yet under 5 years of age, is evident before 18 years of age and is characterised by significant limitations in both intellectual functioning and adaptive behaviour. ID/DD may be clinically classified as syndromic or non-syndromic. Genomic copy number variations (CNVs) constitute a well-established aetiological subgroup of ID/DD. Overall diagnostic yield of microarrays is estimated at 10-25% for ID/DD, especially higher when particular clinical features that render the condition syndromic accompany.In this study, we aimed to investigate the diagnostic yield of microarrays in the subgroup of individuals with non-syndromic ID/DD (NSID/NSDD). A total of 302 NSID/NSDD individuals who have undergone microarray analysis between October 2013 and April 2020 were included. Accompanying clinical data, including head circumference, delayed developmental areas, seizures and behavioural problems were collected and analysed separately in NSID and NSDD subgroups.The diagnostic yield of microarray analyses in NSID/NSDD was determined as 10.9% in NSID (10.7%) and in NSDD (11.1%). Presence of behavioural and epileptic problems did not contribute to the diagnostic yield. However, in the presence of macrocephaly, the contribution to diagnostic yield was statistically significant particularly in NSDD group. The most common pathogenic CNVs involved chromosomes 16, 15 and X. Lastly, we propose a Xq21.32q22.1 deletion as likely pathogenic in a child with isolated language delay and accompanying seizures.Particularly in neurodevelopmental diseases, microarrays are useful for establishing the diagnosis and detecting novel susceptibility regions. Future studies would accurately classify the herein presented variants of uncertain significance CNVs as pathogenic or benign.Keywords:
Macrocephaly
Etiology
Microarray databases
Gene chip analysis
Cite
Citations (2)
Macrocephaly
Megalencephaly
Progressive myoclonus epilepsy
Cite
Citations (14)
This study aimed to use gene chips to investigate differential gene expression profiles in the occurrence and development of acute myocardial infarction (AMI). The study included 12 AMI patients and 12 healthy individuals. Total mRNA of peripheral bloodwas extracted and reversed-transcribed to cDNA for microarray analysis. After establishing two pools with three subjects each (3 AMI patients and 3 healthy individuals), the remaining samples were used for RT-qPCR to confirm the microarray data. From the microarray results, seven genes were randomly selected for RT-qPCR. RT-qPCR results were analyzed by the 2-ΔΔCt method. Microarray analysis showed that 228 genes were up- regulated and 271 were down-regulated (p ≤ 0.05, |logFC| > 1). Gene ontology showed that these genes belong to 128 cellular components, 521 biological processes, and 151 molecular functions. KEGG pathway analysis showed that these genes are involved in 107 gene pathways. RT-qPCR results for the seven genes showed expression levels consistent with those obtained by microarray. Thus, microarray data could be used to select the pathogenic genes for AMI. Investigating the abnormal expression of these differentially expressed genes might suggest efficient strategies for the prevention, diagnosis, and treatment of AMI.
Microarray databases
KEGG
Gene chip analysis
Cite
Citations (18)
Pathogenic variants of HIST1H1Egene have recently been associated with a condition known as Rahman syndrome, characterized by overgrowth, intellectual disability and nonspecific dysmorphic features (high hairline, full cheeks, wide nasal bridge). Wide clinical variability is reported, especially regarding the level of neurodevelopment delay and intellectual disability. We report a 10-year-old girl with macrocephaly and global developmental delay, in whom a novel heterozygous variant in the HIST1H1Egene [c.392_395dup (p.Gly133fs)] was discovered, but involving the same C-terminal domain-protein domain reported previously. Comparing the clinical data of our patient with those previously described, a 'core phenotype' with macrocephaly, psychomotor delay/intellectual disability and mild facial dysmorphisms seems evident.
Macrocephaly
Girl
Facial dysmorphism
Cite
Citations (2)
Gene chip analysis
Microarray databases
Cite
Citations (0)
Gene chip analysis
Identification
Cite
Citations (4)
Microarray databases
Gene chip analysis
Cite
Citations (0)
Macrocephaly
Hypertelorism
Cite
Citations (21)
Large genomes contain families of highly similar genes that cannot be individually identified by microarray probes. This limitation is due to thermodynamic restrictions and cannot be resolved by any computational method. Since gene annotations are updated more frequently than microarrays, another common issue facing microarray users is that existing microarrays must be routinely reanalyzed to determine probes that are still useful with respect to the updated annotations. PICKY 2.0 can design shared probes for sets of genes that cannot be individually identified using unique probes. PICKY 2.0 uses novel algorithms to track sharable regions among genes and to strictly distinguish them from other highly similar but nontarget regions during thermodynamic comparisons. Therefore, PICKY does not sacrifice the quality of shared probes when choosing them. The latest PICKY 2.1 includes the new capability to reanalyze existing microarray probes against updated gene sets to determine probes that are still valid to use. In addition, more precise nonlinear salt effect estimates and other improvements are added, making PICKY 2.1 more versatile to microarray users. Shared probes allow expressed gene family members to be detected; this capability is generally more desirable than not knowing anything about these genes. Shared probes also enable the design of cross-genome microarrays, which facilitate multiple species identification in environmental samples. The new nonlinear salt effect calculation significantly increases the precision of probes at a lower buffer salt concentration, and the probe reanalysis function improves existing microarray result interpretations.
Cite
Citations (19)
Tissue microarray
Microarray databases
Cite
Citations (39)