Open AccessGenetically meaningful phenotypic subgroups in autism spectrum disorders
Author(s) -
Veatch O. J.,
VeenstraVanderWeele J.,
Potter M.,
PericakVance M. A.,
Haines J. L.
Publication year - 2014
Publication title -
genes, brain and behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.315
H-Index - 91
eISSN - 1601-183X
pISSN - 1601-1848
DOI - 10.1111/gbb.12117
Subject(s) - autism , autism spectrum disorder , heritability of autism , autism diagnostic observation schedule , heritability , genetics , genetic heterogeneity , neurodevelopmental disorder , biology , phenotype , psychology , developmental psychology , gene
Autism spectrum disorder ( ASD ) is a complex neurodevelopmental disorder with strong evidence for genetic susceptibility. However, the effect sizes for implicated chromosomal loci are small, hard to replicate and current evidence does not explain the majority of the estimated heritability. Phenotypic heterogeneity could be one phenomenon complicating identification of genetic factors. We used data from the Autism Diagnostic Interview‐Revised, Autism Diagnostic Observation Schedule, Vineland Adaptive Behavior Scales, head circumferences, and ages at exams as classifying variables to identify more clinically similar subgroups of individuals with ASD . We identified two distinct subgroups of cases within the Autism Genetic Resource Exchange dataset, primarily defined by the overall severity of evaluated traits. In addition, there was significant familial clustering within subgroups (odds ratio, OR ≈ 1.38–1.42, P < 0.00001), and genotypes were more similar within subgroups compared to the unsubgrouped dataset (Fst = 0.17 ± 0.0.0009). These results suggest that the subgroups recapitulate genetic etiology. Using the same approach in an independent dataset from the Autism Genome Project, we similarly identified two distinct subgroups of cases and confirmed this severity‐based dichotomy. We also observed evidence for genetic contributions to subgroups identified in the replication dataset. Our results provide more effective methods of phenotype definition that should increase power to detect genetic factors influencing risk for ASD .