Inherited and Multiple De Novo Mutations in Autism Risk Genes Suggests a Multifactorial Model

Poster Presentation
Friday, May 11, 2018: 5:30 PM-7:00 PM
Hall Grote Zaal (de Doelen ICC Rotterdam)
H. Guo1,2, T. Wang2, H. Wu1, M. Long1, B. P. Coe2, R. Bernier3, E. E. Eichler2 and K. Xia1, (1)Central South University, CHANGSHA, China, (2)Department of Genome Science, University of Washington, Seattle, WA, (3)Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA
Background: Large-scale, genome-wide and targeted sequencing analyses are dramatically accelerating the discovery of candidate genes associated with autism spectrum disorders (ASD), establishing dozens of novel high-risk genes. Despite these advances, only a small fraction of the genetic risk has been defined, the penetrance of most mutated genes is unknown, and genotype–phenotype correlations are only beginning to be understood.

Objectives: To better understand the genetic architecture, penetrance, and genotype–phenotype relationships of ASD mutations.

Methods: We targeted 188 autism candidate genes for sequencing in 2,926 families from the Autism Clinical and Genetic Resources in China (ACGC) cohort using a modified molecular inversion probe (MIP) method, which enables ultra-low-cost candidate gene resequencing in very large cohorts.

Results: We validated recurrent de novo likely gene-disruptive (LGD) mutations in 13 genes and identified three potential novel risk genes (ZNF292, GRIA2 and RALGAPB) as well as genes associated with macrocephaly (GIGYF2 and WDFY3). During this analysis, we identified transmission of private gene-disruptive mutations in genes predominantly associated with de novo mutations (DNMs) (e.g., CHD8 and KMT5B) and showed that clinical reevaluation of carrier parents revealed mild neurodevelopmental or related endophenotypes. We also identified families with DNMs in two or more candidate genes. Combining available exome sequence data, we identified 10 such “double-hit” families involving well-known autism risk genes (SCN2A, CHD8, etc.). We show that such oligogenic cases occur more frequently in probands associated with more severe phenotypes, including social impairments and seizure burden.

Conclusions: Our data suggest a multifactorial model of multiple high-impact mutations in some ASD patients indicating that monogenic models may be too simplistic. Disease risk of genes associated with DNMs requires a much more comprehensive understanding of the full spectrum of mutation as well as patient follow-up in larger number of affected families in order to accurately determine penetrance and the role of additional rare mutations in different genes in modifying the ASD phenotype.

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See more of: Genetics