Molecular PsychiatryGeneticsPeer-reviewed journal article
Exons as units of phenotypic impact for truncating mutations in autism
Andrew H. Chiang, Jonathan Chang, Jiayao WangiD, Dennis VitkupiD
Abstract
Autism spectrum disorders (ASD) are a group of related neurodevelopmental diseases displaying significant genetic and phenotypic heterogeneity1-4. Despite recent progress in understanding ASD genetics, the nature of phenotypic heterogeneity across probands remains unclear5, 6. Notably, likely gene-disrupting (LGD) de novo mutations affecting the same gene often result in substantially different ASD phenotypes. Nevertheless, we find that truncating mutations that affect the same exon frequently lead to strikingly similar intellectual phenotypes in unrelated ASD probands. Analogous patterns are observed for two independent proband cohorts and several other important ASD-associated phenotypes. We find that exons biased towards prenatal and postnatal expression preferentially contribute to ASD cases with lower and higher IQ phenotypes, respectively. These results suggest that exons, rather than genes, often represent a unit of effective phenotypic impact for truncating mutations in autism. The observed phenotypic effects are likely mediated by nonsense-mediated decay (NMD) of splicing isoforms, with autism phenotypes usually triggered by relatively mild (15-30%) decreases in overall gene dosage. We find that each gene with recurrent ASD mutations can be described by a parameter, phenotype dosage sensitivity (PDS), which characterizes the quantitative relationship between changes in a genes dosage and changes in a given disease phenotype. We further demonstrate analogous relationships between LGD mutations and changes in gene expression across human tissues. Therefore, similar phenotypic patterns may be also observed in multiple other systems and genetic disorders.
- organism: human
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- research focus: polygenic risk
- approach: statistical model
- Citations
- 11
- FWCI
- 0.94
- Field percentile
- top 28%
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