Genotype-Phenotype Relationships: Exploration of Shared and Distinct Genetic Risk for ASD and ID

Oral Presentation
Thursday, May 10, 2018: 10:30 AM
Jurriaanse Zaal (de Doelen ICC Rotterdam)
L. Klei1, S. De Rubeis2, S. Sanders3, X. Xu4, B. K. Sheppard5, C. Betancur6, M. State3, E. Cook7, J. Buxbaum8, K. Roeder9 and B. Devlin10, (1)University of Pittsburgh, Pittsburgh, PA, (2)Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, (3)Psychiatry, University of California San Francisco, San Francisco, CA, (4)Icahn School of Medicine at Mount Sinai, New York, NY, (5)Johns Hopkins School of Public Health, Baltimore, MD, (6)Sorbonne Universite, Paris, France, (7)University of Illinos at Chicago, Chicago, IL, (8)Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, (9)Carnegie Mellon University, Pittsburgh, PA, (10)Univ of Pittsburgh School of Medicine, Pittburgh, PA
Background: Whole-exome sequencing has identified dozens of genes affecting risk for autism spectrum disorder (ASD) and even more for severe developmental disorder (SDD). SDD typically involves intellectual disability (ID), which is often comorbid with ASD.

Objectives: Because these discoveries intersect for some genes and not others, we hypothesized that the joint study of data from ASD and SDD subjects could reveal the patterns of genetic overlap and etiology of these disorders.

Methods: We assessed rare de novo variants identified via exome sequencing of 4,109 ASD trios from the Autism Sequencing Consortium (ASC) and 4,293 SDD trios from the Deciphering Developmental Disorders (DDD) Study. We applied the same statistical algorithm, TADA (Transmission And De novo Association), to both datasets to identify likely risk genes in each disorder based on identical criteria.

Results: We find substantially greater clustering of probably damaging mutations in specific genes in SDD than ASD subjects, consistent with there being a greater role for polygenic risk in ASD than SDD. We identify 33 and 149 genes associated with risk for ASD and SDD, respectively (FDR < 0.05), with 17 falling in both gene sets. Meta-analysis of the two datasets yields additional significant genes, while also producing evidence for genetic heterogeneity: disruption of some genes confers risk predominantly for one disorder whereas disruption of others affects risk for both. Genes implicated in SDD alone or in risk for SDD and ASD have an impact on the IQ of ASD subjects, whereas mutations in genes conferring predominantly ASD risk tend to have smaller impact.

Conclusions: Our analyses identify genes that appear to have a more direct role in ASD traits and others with greater impact on general neurodevelopment. The gene sets we identify should be useful for understanding early developmental similarities and differences that lead to ASD with and without ID.