Objectives: The primary objectives of this study were: 1) to assess the potential of quantitative trait association analyses coupled with subphenotype analyses to uncover novel single nucleotide polymorphisms (SNPs) associated with ASD; 2) to examine the genetic heterogeneity of the ASD population with regard to the identified SNPs.
Methods: Genome-wide association data from the study by Wang et al. (Nature 459: 528-533, 2009) was downloaded from the Autism Speaks website at ftp://ftp.autismspeaks.org/Data/CHOP_PLINK/AGRERELEASE.ped. The file named CHOP.clean100121 where the data was “cleaned” by Jennifer K. Lowe in the laboratory of Daniel H. Geschwind (UCLA) was used for this analysis. Raw ADI-R scores for subjects in this study were obtained through Dr. Vlad Kustanovich of AGRE. Quantitative traits were determined for each individual by summing relevant ADI-R item scores, and ASD subtypes were identified through cluster analyses, both as described by Hu and Steinberg (Autism Res. 2:67-77, 2009). PLINK software (Purcell et al., Am. J. Hum Genet. 81: 559-575, 2007) was used for all association analyses.
Results: We demonstrate that quantitative trait analyses of ASD symptoms combined with case-control association analyses using distinct ASD subphenotypes identified on the basis of symptomatic profiles results in the identification of statistically significant associations with 18 novel single nucleotide polymorphisms (SNPs). The symptom categories included deficits in language usage, non-verbal communication, social development, and play skills, as well as insistence on sameness or ritualistic behaviors. Ten of the trait-associated SNPs, or quantitative trait loci (QTL), were associated with more than one subtype, providing replication of the identified QTL. Several of the QTL reside within chromosomal regions associated with rare copy number variants that have been previously reported in autistic samples (Pinto et al., Nature 466: 368-372, 2010). Pathway analyses of the genes associated with the QTL identified in this study implicate neurological functions and disorders associated with autism pathophysiology.
Conclusions: While quantitative trait association analyses help to filter and prioritize functionally relevant SNPs, subphenotype genetic association analyses based on the identified QTL reveal the genetic heterogeneity of the ASD population. This study thus underscores the advantage of incorporating both quantitative traits as well as subphenotypes into large-scale genome-wide genetic analyses of complex disorders.
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