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Genome-Wide ASD Phenotype-Genotype Association Study in Two Large Data Sets
Autism spectrum disorders (ASD) are a group of genetically and phenotypically heterogeneous neurodevelopmental disorders. Approximately 49% of ASD heritability can be attributed to common variants (Gaugler et al.,2014) which are also suggested to contribute to phenotypic variability (Devlin et al., 2011, Anney et al.,2011, Davis et al.,2012). Only few studies have reported association of specific genetic variants with quantitative ASD phenotypes. Latest research shows that a system-wide analysis of genetics of ASD phenotypes has an important role in understanding the heterogeneity of this disorder.
Objectives:
Here, we aimed at discovering associations of common variants with Autism Diagnostic Interview-Revised (ADI-R) items. We thus performed a whole-genome analysis on ADI-R scores of ASD patients. To interpret the findings in their biological context we mapped significant variants to their genes and tested for enriched pathways. Our approach aims at identifying potential pathomechanisms that modulate ASD symptoms.
Methods:
Using the “1000 Genomes” data containing ~79 million variants as reference, we imputed whole genome data of two large Datasets, the Autism Genome Project (AGP) and a newly genotyped German data set (Illumina 770K OmniExpress). We defined independent ASD phenotypes based on factors extracted from ADI-R scores following the approach of Liu et al. (2011) implementing exploratory and confirmatory factor analysis. SNPs overlapping in both cohorts with a minor allele frequency above 0.05 were included and tested for association with each of the 5 factors extracted using fixed effect linear regression models. Models were corrected for ethnicity, collecting center and gender. Nominally significant SNPs identified in both, the AGP and the German data set, were subjected to downstream analyses including gene mapping, GO-term enrichment as well as identification of co-regulatory gene modules of brain-development significantly associated with each phenotype.
Results:
We generated five Eigen-phenotypes (EP) from the ADI-R factor analysis labeled as Joint Attention, Social Interaction and Communication, Non-verbal Communication, Repetitive Sensory-motor Behavior and Compulsion/Restricted Interests. Ancestry analysis showed that distribution of ethnicities is similar between AGP and German data. In a preliminary approach using 163,136 tagging SNPs only, we identified for each EP more than 400 SNPs that were nominally associated in both cohorts. GO-term analysis revealed that genes associated with EPs were enriched for phenotype specific biological processes including “Neuron projection and development” for Joint Attention or “Locomotor behavior” for Repetitive Sensory-Motor Behavior.
Conclusions:
Our preliminary study showed that the genetic architecture of the individual EPs is specific, suggesting, that the different phenotypic aspects of ASD might have a distinct underlying genetic basis. We expect that our additional analysis including all imputed SNPs will refine our findings and increase our understanding of mechanisms modulating ASD phenotypes.