The Importance of Addressing Heterogeneity in Autism Genomics Research to Inform Precision Medicine

Poster Presentation
Friday, May 11, 2018: 11:30 AM-1:30 PM
Hall Grote Zaal (de Doelen ICC Rotterdam)
V. W. Hu, Biochemistry and Molecular Medicine, The George Washington University, Washington, DC
Background: Autism spectrum disorder (ASD) encompasses a broad range of neurodevelopmental disorders marked by core deficits in social communication and interaction as well as by repetitive, stereotypic behaviors and restricted interests. Despite the shared core symptoms, there is extensive heterogeneity in the clinical symptoms and behavioral manifestations among individuals diagnosed with ASD. This heterogeneity poses a significant challenge to patient-centered studies seeking to determine how knowledge of genetic and biological underpinnings of ASD can be useful to informing treatment, especially when a combined group of cases are compared against a group of controls.

Objectives: The goals of our ongoing studies have been to reduce the clinical heterogeneity within the ASD population in order to investigate the biological pathways and functions impacted in different subphenotypes of ASD and to increase the power of genetics analyses to identify genetic variation capable of distinguishing cases from controls. We accomplish these goals by: 1) subgrouping individuals according to their severity profiles across a breadth of behavioral and clinical symptoms probed by the Autism Diagnostic Interview-Revised diagnostic instrument, and 2) performing large-scale gene expression profiling and genetics analyses of the resulting ASD subgroups in comparison to controls to identify subgroup-dependent biological deficits as well as genetic variants and loci that are associated with each subphenotype.

Methods: Individuals with ASD were divided into four phenotypic subgroups by multivariate cluster analyses of item-level severity scores from the individual’s ADI-R diagnostic assessment. Lymphoblastoid cell lines from individuals representing several clinically distinct ASD subgroups and a group of controls were used for gene expression profiling on 2-color custom human transcriptome arrays which were analyzed using the MeV microarray software suite. Ingenuity Pathway Analyses software was used to identify over-represented biological pathways and functions among the differentially expressed genes. Quantitative trait and case-control association analyses using ADI-R scoresheets of cases and the respective genotype data were performed using Plink, and linkage analyses were conducted with Merlin, again using publicly available genetic databases.

Results: Distinct but partially overlapping patterns of differentially expressed genes were identified for several subtypes of ASD in comparison to that of controls. Each expression profile suggested subgroup-dependent differences in biological pathways and functions that could be exploited for targeted therapies or for diagnosis. Enriched functions included a set of circadian rhythm-associated genes, such as AA-NAT, the rate-limiting enzyme in melatonin biosynthesis. Combined quantitative trait and case-control association analyses in which cases were divided by subphenotypes identified Bonferroni-significant SNPs that clearly distinguished cases from controls, while linkage analyses revealed subgroup-dependent genetic loci that were capable of unmasking genetic heterogeneity at both inter- and intra-family levels, with the highest subgroup-dependent LOD scores exceeding 4.0.

Conclusions: Large-scale omics and genetic analyses of ASD are greatly enhanced by reducing the heterogeneity of the cases included within the study. Heterogeneity reduction is critical for identifying subgroup-specific deficits (e.g., melatonin deficiency) for targeted therapies, thus advancing precision medicine.