AUTHOR=Alonso-Gonzalez Aitana , Calaza Manuel , Rodriguez-Fontenla Cristina , Carracedo Angel 

TITLE=Novel Gene-Based Analysis of ASD GWAS: Insight Into the Biological Role of Associated Genes

JOURNAL=Frontiers in Genetics

VOLUME=Volume 10 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.00733

DOI=10.3389/fgene.2019.00733

ISSN=1664-8021

ABSTRACT=Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by its significant social impact and high heritability. The latest meta-analysis of ASD GWAS (Genome-Wide Association Studies) has revealed the association of several SNPs that were replicated in additional sets of independent samples. However, summary statistics from these GWAS can be used to identify new ASD susceptibiliy loci. To achieve this, gene-based analysis (GBA) methods should be employed. GBA combine genetic information for all SNPs in a gene. Thus, PASCAL (Pathway scoring algorithm), a novel GBA tool, has been applied to the summary statistics from the latest meta-analysis of ASD.  In addition, a gene network analysis and an enrichment analysis for KEGG and GO terms were carried out for the ASD associated genes and their predicted interactors. Finally, GENE2FUN was used to create gene expression heatmaps and to carry out differential expression analysis (DEA) employing GTEx v7 and Brainspan data.
Results: PASCAL has identified other loci associated with ASD even thought most of them were previously reported by MAGMA. Moreover, PASCAL has been useful to highlight the association of genes that share the same LD (linkage disequilibrium) block than other previous ASD associated loci.  Different GO and KEGG terms have been associated for the ASD associated genes and their predicted interactors. Moreover, GENE2FUNC has revealed several up- and down- regulated clusters that could be relevant in ASD etiology.
Conclusions: This study identifies new associations at a gene-level that were not previously reported as associated when MAGMA was applied. Thus, PASCAL has been revealed as an efficient GBA tool to extract additional information from previous GWAS summary statistics due to its consideration of the LD structure. Moreover, a biological insight into the biological function and role of the associated genes across brain regions and neurodevelopmental stages is provided.