Deep embedded clustering by relevant scales and genome-wide association study in autism

Abstract

The etiology of autism spectrum disorders (ASD) remains unclear. Stratifying patients with ASD may help to identify genetically homogeneous subgroups. Using a deep embedded clustering algorithm, we conducted cluster analyses of Simons Foundation Powering Autism Research for Knowledge (SPARK) datasets and performed genome-wide association studies (GWAS) of the clusters. We observed no significant associations in the conventional GWAS comparing all patients to all controls. However, in the GWAS, comparing patients divided into clusters with similar phenotypes to controls (cluster-based GWAS), we identified 90 chromosomal loci that satisfied the P < 5.0 x 10-8, several of which were located within or near previously reported candidate genes for ASD. Our findings suggest that clustering may successfully identify subgroups with relatively homogeneous disease etiologies.