Objectives: I will describe an approach to ‘pathway’ definition using gene expression data that can unite a number of autism-implicated common polymorphisms and rare variants into a network with functional implications.
Methods: In a previous genome wide association study (GWAS), significant association with autism was detected near the SEMA5A gene, which has also shown evidence for reduced expression in autism (Weiss et al, 2009). Here we have used public expression and genetic data in controls to define eQTLs and master regulators for SEMA5A expression in lymphoblast cell lines. We have gone on to test for SNP association and CNV association in autism datasets within these putative expression networks using set-based approaches. Further, we have used RNAi knock-down techniques in lymphoblast cell lines to functionally validate the genetic-expression networks we have identified.
Results: We have identified SNP association in one large autism GWAS dataset. We have identified CNV association in 4 autism datasets. We also show cellular expression data for human lymphoblast cell lines.
Conclusions: Our approach of defining an expression regulatory pathway for a SNP-associated candidate gene has revealed additional common, and now rare, variants associated with autism and may provide a framework for identifying which rare CNVs are likely to contribute to autism risk.