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[Preprint]. 2024 May 6:2024.05.05.24306908.
doi: 10.1101/2024.05.05.24306908.

Noncoding de novo mutations in SCN2A are associated with autism spectrum disorders

Yuan Zhang  1 , Mian Umair Ahsan  1 , Kai Wang  1   2
Affiliations

Noncoding de novo mutations in SCN2A are associated with autism spectrum disorders

Yuan Zhang et al. medRxiv. .

Abstract

Coding de novo mutations (DNMs) contribute to the risk for autism spectrum disorders (ASD), but the contribution of noncoding DNMs remains relatively unexplored. Here we use whole genome sequencing (WGS) data of 12,411 individuals (including 3,508 probands and 2,218 unaffected siblings) from 3,357 families collected in Simons Foundation Powering Autism Research for Knowledge (SPARK) to detect DNMs associated with ASD, while examining Simons Simplex Collection (SSC) with 6383 individuals from 2274 families to replicate the results. For coding DNMs, SCN2A reached exome-wide significance (p=2.06×10-11) in SPARK. The 618 known dominant ASD genes as a group are strongly enriched for coding DNMs in cases than sibling controls (fold change=1.51, p =1.13×10-5 for SPARK; fold change=1.86, p =2.06×10-9 for SSC). For noncoding DNMs, we used two methods to assess statistical significance: a point-based test that analyzes sites with a Combined Annotation Dependent Depletion (CADD) score ≥15, and a segment-based test that analyzes 1kb genomic segments with segment-specific background mutation rates (inferred from expected rare mutations in Gnocchi genome constraint scores). The point-based test identified SCN2A as marginally significant (p=6.12×10-4) in SPARK, yet segment-based test identified CSMD1, RBFOX1 and CHD13 as exome-wide significant. We did not identify significant enrichment of noncoding DNMs (in all 1kb segments or those with Gnocchi>4) in the 618 known ASD genes as a group in cases than sibling controls. When combining evidence from both coding and noncoding DNMs, we found that SCN2A with 11 coding and 5 noncoding DNMs exhibited the strongest significance (p=4.15×10-13). In summary, we identified both coding and noncoding DNMs in SCN2A associated with ASD, while nominating additional candidates for further examination in future studies.

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Figures

Fig 1.
Fig 1.. Analysis workflow.
(a) DNMs analytic pipeline. For noncoding DNMs, this study used two methods to assess statistical significance (b) point-based test that analyzes sites with a Combined Annotation Dependent Depletion (CADD) score ≥15, and (c) segment-based test that uses Gnocchi genome constraint scores in 1kb genomic segments to infer background mutation rates.
Fig 2.
Fig 2.. Distribution of DNMs in SPARK.
(a) distribution of coding DNMs. (b) distribution of missense DNMs. (c) distribution of loss of function DNMs. (d) distribution of noncoding DNMs. (e) distribution of gene-relate noncoding DNMs. (f) distribution of intergenic noncoding DNMs. (g) distribution of noncoding DNMs have a CADD score ≥15. (h) distribution of gene-relate noncoding DNMs have a CADD score ≥15. (i) distribution of intergenic noncoding DNMs have a CADD score ≥15.
Fig 3.
Fig 3.. Point variation of the noncoding DNMs for the SCN2A gene.
(a) point variation of chr2:165275094 (T>G). (b) point variation of chr2:165294336 (T>C). (c) point variation of chr2:165296095 (G>C). (d) point variation of chr2:165297139 (T>C). (e) point variation of chr2:165370303 (A>T). (f) probability of the mutations situated less than 20 base pairs (bp) distant from the exon being splice-altering.
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