Genetic testing including targeted gene panel in a diverse clinical population of children with autism spectrum disorder: Findings and implications

Abstract

BackgroundGenetic testing of children with autism spectrum disorder (ASD) is now standard in the clinical setting, with American College of Medical Genetics and Genomics (ACMGG) guidelines recommending microarray for all children, fragile X testing for boys and additional gene sequencing, including PTEN and MECP2, in appropriate patients. Increasingly, testing utilizing high throughput sequencing, including gene panels and whole exome sequencing, are offered as well.MethodsWe performed genetic testing including microarray, fragile X testing and targeted gene panel, consistently sequencing 161 genes associated with ASD risk, in a clinical population of 100 well characterized children with ASD. Frequency of rare variants identified in individual genes was compared with that reported in the Exome Aggregation Consortium (ExAC) database.ResultsWe did not diagnose any conditions with complete penetrance for ASD; however, copy number variants believed to contribute to ASD risk were identified in 12%. Eleven children were found to have likely pathogenic variants on gene panel, yet, after careful analysis, none was considered likely causative of disease. KIRREL3 variants were identified in 6.7% of children compared to 2% in ExAC, suggesting a potential role for KIRREL3 variants in ASD risk. Children with KIRREL3 variants more often had minor facial dysmorphism and intellectual disability. We also observed an increase in rare variants in TSC2. However, analysis of variant data from the Simons Simplex Collection indicated that rare variants in TSC2 occur more commonly in specific racial/ethnic groups, which are more prevalent in our population than in the ExAC database.ConclusionThe yield of genetic testing including microarray, fragile X (boys) and targeted gene panel was 12%. Gene panel did not increase diagnostic yield; however, we found an increase in rare variants in KIRREL3. Our findings reinforce the need for racial/ethnic diversity in large‐scale genomic databases used to identify variants that contribute to disease risk.

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

“…Moreover, they found 38 proteins related to cognitive trajectory independently of β-amyloid plaques and neurofibrillary tangles [ 52 ]. Studies conducted on non-neurological tissues highlighted changes in quantities of proteins associated with inflammation or regulation of the immune system, including some interleukins [ 50 , 51 , 105 , 106 , 107 , 108 ]. Interestingly, most proteomic studies investigating ASD identified proteins involved in lipid metabolism and differentially expressed in ASD [ 51 , 55 , 57 , 105 , 106 , 109 , 110 ].…”

“…Studies conducted on non-neurological tissues highlighted changes in quantities of proteins associated with inflammation or regulation of the immune system, including some interleukins [ 50 , 51 , 105 , 106 , 107 , 108 ]. Interestingly, most proteomic studies investigating ASD identified proteins involved in lipid metabolism and differentially expressed in ASD [ 51 , 55 , 57 , 105 , 106 , 109 , 110 ]. As an example, Corbett et al identified differentially expressed peptides using LC-MS/MS, which allowed for the recognition of complement factor H-related protein (FHR1), apolipoprotein (APO) B-100, fibronectin 1 (FN1), and complement C1q as dysregulated proteins in children with ASD compared to control subjects [ 53 ].…”

The High-Throughput Analyses Era: Are We Ready for the Data Struggle?

“…These techniques have impacted every field of molecular research, escalating previously used sequencing technologies [ 11 ], and opening the way to the -omic sciences foundation [ 1 , 2 ]. Indeed, NGS methods allow the sequencing of entire genomes [ 12 , 13 , 14 , 15 ], of exomes [ 16 , 17 , 18 ], of panels of genes related to a disease of interest [ 19 , 20 , 21 ], or of a single gene [ 22 , 23 , 24 , 25 , 26 ], but can also be used to explore the entire transcriptome [ 27 , 28 , 29 ], small RNAs [ 30 , 31 , 32 ], the epigenome [ 33 , 34 ], and the microbiome [ 35 , 36 , 37 , 38 ].…”

“…In this view, it is not surprising that NGS is also becoming a reference method for molecular diagnostics [ 10 ]. In particular, NGS allows not only the analysis, in more patients simultaneously, of disease-related genes in less time and at lower costs than traditional approaches, but also the sequencing of panels of genes up to the complete exome [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. In this way, it is possible to increase the diagnostic sensitivity, to discover novel disease-related genes and also obtain data regarding other genes that were potentially acting as disease-phenotype modifiers [ 19 , 40 , 41 , 42 ].…”

Increased Burden of Common Risk Alleles in Children With a Significant Fracture History

“…Typical examples include extreme presentations of obesity, short or tall stature, and autism spectrum disorders . However, in most cases, children with such phenotypic presentations investigated with targeted gene panels are found to be negative for mutations in known disease‐related genes . Although such cases could still be caused by Mendelian mutations in unknown genes, it is also possible that such patients have an increased burden of common risk alleles, which individually confer a small risk of disease but taken together can have large effects on disease risk .…”