Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia

Background: Over the past decade genome-wide association studies (GWAS) have been applied to aid in the understanding of the biology of traits. The success of this approach is governed by the underlying effect sizes carried by the true risk variants and the corresponding statistical power to observe such effects given the study design and sample size under investigation. Previous ASD GWAS have identified genome-wide significant (GWS) risk loci; however, these studies were of only of low statistical power to identify GWS loci at the lower effect sizes (odds ratio (OR) <1.15). Methods: We conducted a large-scale coordinated international collaboration to combine independent genotyping data to improve the statistical power and aid in robust discovery of GWS loci. This study uses genome-wide genotyping data from a discovery sample (7387 ASD cases and 8567 controls) followed by meta-analysis of summary statistics from two replication sets (7783 ASD cases and 11359 controls; and 1369 ASD cases and 137308 controls). Results: We observe a GWS locus at 10q24.32 that overlaps several genes including PITX3, which encodes a transcription factor identified as playing a role in neuronal differentiation and CUEDC2 previously reported to be associated with social skills in an independent population cohort. We also observe overlap with regions previously implicated in schizophrenia which was further supported by a strong genetic correlation between these disorders (Rg = 0.23; P = 9 × 10−6). We further combined these Psychiatric Genomics Consortium (PGC) ASD GWAS data with the recent PGC schizophrenia GWAS to identify additional regions which may be important in a common neurodevelopmental phenotype and identified 12 novel GWS loci. These include loci previously implicated in ASD such as FOXP1 at 3p13, ATP2B2 at 3p25.3, and a ‘neurodevelopmental hub’ on chromosome 8p11.23. Conclusions: This study is an important step in the ongoing endeavour to identify the loci which underpin the common variant signal in ASD. In addition to novel GWS loci, we have identified a significant genetic correlation with schizophrenia and association of ASD with several neurodevelopmental-related genes such as EXT1, ASTN2, MACROD2, and HDAC4

Gene-ontology enrichment analysis in two independent family-based samples highlights biologically plausible processes for autism spectrum disorders

Recent genome-wide association studies (GWAS) have implicated a range of genes from discrete biological pathways in the aetiology of autism. However, despite the strong influence of genetic factors, association studies have yet to identify statistically robust, replicated major effect genes or SNPs. We apply the principle of the SNP ratio test methodology described by O'Dushlaine et al to over 2100 families from the Autism Genome Project (AGP). Using a two-stage design we examine association enrichment in 5955 unique gene-ontology classifications across four groupings based on two phenotypic and two ancestral classifications. Based on estimates from simulation we identify excess of association enrichment across all analyses. We observe enrichment in association for sets of genes involved in diverse biological processes, including pyruvate metabolism, transcription factor activation, cell-signalling and cell-cycle regulation. Both genes and processes that show enrichment have previously been examined in autistic disorders and offer biologically plausibility to these findings. © 2011 Macmillan Publishers Limited All rights reserved

Converging evidence does not support GIT1 as an ADHD risk gene

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neuropsychiatric disorder with a complex genetic background. The G protein-coupled receptor kinase interacting ArfGAP 1 (GIT1) gene was previously associated with ADHD. We aimed at replicating the association of GIT1 with ADHD and investigated its role in cognitive and brain phenotypes. Gene-wide and single variant association analyses for GIT1 were performed for three cohorts: (1) the ADHD meta-analysis data set of the Psychiatric Genomics Consortium (PGC, N=19,210), (2) the Dutch cohort of the International Multicentre persistent ADHD CollaboraTion (IMpACT-NL, N=225), and (3) the Brain Imaging Genetics cohort (BIG, N=1,300). Furthermore, functionality of the rs550818 variant as an expression quantitative trait locus (eQTL) for GIT1 was assessed in human blood samples. By using Drosophila melanogaster as a biological model system, we manipulated Git expression according to the outcome of the expression result and studied the effect of Git knockdown on neuronal morphology and locomotor activity. Association of rs550818 with ADHD was not confirmed, nor did a combination of variants in GIT1 show association with ADHD or any related measures in either of the investigated cohorts. However, the rs550818 risk-genotype did reduce GIT1 expression level. Git knockdown in Drosophila caused abnormal synapse and dendrite morphology, but did not affect locomotor activity. In summary, we could not confirm GIT1 as an ADHD candidate gene, while rs550818 was found to be an eQTL for GIT1. Despite GIT1's regulation of neuronal morphology, alterations in gene expression do not appear to have ADHD-related behavioral consequences