Genetic aetiologies for childhood speech disorder: novel pathways co-expressed during brain development

Abstract

Childhood apraxia of speech (CAS), the prototypic severe childhood speech disorder, is characterized by motor programming and planning deficits. Genetic factors make substantive contributions to CAS aetiology, with a monogenic pathogenic variant identified in a third of cases, implicating around 20 single genes to date. Here we aimed to identify molecular causation in 70 unrelated probands ascertained with CAS. We performed trio genome sequencing. Our bioinformatic analysis examined single nucleotide, indel, copy number, structural and short tandem repeat variants. We prioritised appropriate variants arising de novo or inherited that were expected to be damaging based on in silico predictions. We identified high confidence variants in 18/70 (26%) probands, almost doubling the current number of candidate genes for CAS. Three of the 18 variants affected SETBP1, SETD1A and DDX3X, thus confirming their roles in CAS, while the remaining 15 occurred in genes not previously associated with this disorder. Fifteen variants arose de novo and three were inherited. We provide further novel insights into the biology of child speech disorder, highlighting the roles of chromatin organization and gene regulation in CAS, and confirm that genes involved in CAS are co-expressed during brain development. Our findings confirm a diagnostic yield comparable to, or even higher, than other neurodevelopmental disorders with substantial de novo variant burden. Data also support the increasingly recognised overlaps between genes conferring risk for a range of neurodevelopmental disorders. Understanding the aetiological basis of CAS is critical to end the diagnostic odyssey and ensure affected individuals are poised for precision medicine trials.

Fig. 1. Genetic variant identification and variant filtering pipeline for individuals with CAS.

Workflow covers recruitment of patients (CAS in red, affected relative in blue, unaffected in black, not sequenced in white), DNA sequencing, analysis and filtering of genomic data, identification of potential causative variants, geneticist review, molecular validation, segregation and integration of all findings. Please note that affectedness status refers to a parent having speech therapy as a child but not necessarily for a diagnosis of CAS which is not historically well reported for that generation. *Only the damaging effects of small intragenic variants are predicted bioinformatically.

Fig. 2. Phenotypic overlap in childhood apraxia of speech (CAS) cohort.

Phenotypic features of CAS cohort with (blue, n = 18) and without (orange, n = 52) pathogenic variants. Data based on children with psychometric assessments by health professionals (i.e., cognition, language, motor, formal ASD diagnoses). Data from Tables 1, 2; Supplementary Tables 1a, b. Dots indicate percent of children with (blue) and without (orange) pathogenic variants who had psychometric test results confirming diagnoses.

Fig. 3. Families with high confidence variants analysed by genome sequencing.

Families analysed by Genome Sequencing. Pedigrees (A–F, M–R, Y-D1) from 18 families with 18 different high confidence variants. Sequence chromatograms (G, I, J, L, S, V, W, X, E1) showing de novo or inherited variants. Sanger sequencing was not performed for the variants in eight of the families (H, K, T, U, F1, G1, H1, I1) because they had variants in known genes with sufficient coverage in the genome sequencing to be confident they were real, heterozygous variants. The large duplication in Family 17 (J1) could not be validated by Sanger sequencing.

Fig. 3. Families with high confidence variants analysed by genome sequencing.

Families analysed by Genome Sequencing. Pedigrees (A–F, M–R, Y-D1) from 18 families with 18 different high confidence variants. Sequence chromatograms (G, I, J, L, S, V, W, X, E1) showing de novo or inherited variants. Sanger sequencing was not performed for the variants in eight of the families (H, K, T, U, F1, G1, H1, I1) because they had variants in known genes with sufficient coverage in the genome sequencing to be confident they were real, heterozygous variants. The large duplication in Family 17 (J1) could not be validated by Sanger sequencing.

Fig. 3. Families with high confidence variants analysed by genome sequencing.

Families analysed by Genome Sequencing. Pedigrees (A–F, M–R, Y-D1) from 18 families with 18 different high confidence variants. Sequence chromatograms (G, I, J, L, S, V, W, X, E1) showing de novo or inherited variants. Sanger sequencing was not performed for the variants in eight of the families (H, K, T, U, F1, G1, H1, I1) because they had variants in known genes with sufficient coverage in the genome sequencing to be confident they were real, heterozygous variants. The large duplication in Family 17 (J1) could not be validated by Sanger sequencing.

Fig. 4. Previously identified neurodevelopmental conditions in candidate genes for CAS.

A Candidate Genes for CAS identified in this study and Hildebrand et. al. (*) also have been shown to cause other neurodevelopmental disorder traits. B Venn diagram showing the overlap of these genes and multiple neurodevelopmental disorder traits.

Fig. 5. CAS candidate gene co-expression.

A Gene co-expression matrix for the 18 high-confidence candidate genes with pairwise Spearman’s rank correlation coefficients between genes shown, based on 280 samples from 24 individuals (8 weeks post conception to 10 months after birth) from the BrainSpan resource. Genes ordered by hierarchical clustering, using the median linkage method. B Network of gene co-expression. Nodes represent genes; edges represent gene–pair correlations that exceed the threshold for the top 5% most highly correlated gene pairs genome-wide (|ρ | > 0.64) (blue—positive correlation, red—negative correlation). C Gene co-expression matrix for the 18 high-confidence candidate genes (black) as well as the genes from [4] (green) and [5] (blue). D Network of gene co-expression. Nodes represent genes; edges represent gene–pair correlations that exceed the threshold for the top 5% most highly correlated gene pairs genome-wide (|ρ | > 0.64) (blue—positive correlation, red – negative correlation). Black nodes—novel genes from this work, green nodes genes from [4] and blue nodes from [5]. E Network of gene co-expression. Nodes represent genes; edges represent gene–pair correlations that exceed the threshold for the top 5% most highly correlated gene pairs genome-wide (|ρ | > 0.64) (blue—positive correlation, red—negative correlation). Black nodes are a set of co-expressed genes including genes from the present study and from previous studies [4, 5]. Blue nodes—the top prioritized genes from cytogenic variants described in Table 4.