Novel Insight into the Aetiology of Autism Spectrum Disorder Gained by Integrating Expression Data with Genome-wide Association Statistics

Abstract

Background:A recent genome-wide association study (GWAS) of autism spectrum disorders (ASD) (N cases =18,381, N controls =27,969) has provided novel opportunities for investigating the aetiology of ASD. Here, we integrate the ASD GWAS summary statistics with summary-level gene expression data to infer differential gene expression in ASD, an approach called transcriptome-wide association study (TWAS). Methods:Using FUSION software, ASD GWAS summary statistics were integrated with predictors of gene expression from 16 human datasets, including adult and fetal brain. A novel adaptation of established statistical methods was then used to test for enrichment within candidate pathways, specific tissues, and at different stages of brain development. The proportion of ASD heritability explained by predicted expression of genes in the TWAS was estimated using stratified linkage disequilibriumscore regression. Results:This study identified 14 genes as significantly differentially expressed in ASD, 13 of which were outside of known genome-wide significant loci (±500kb). XRN2, a gene proximal to an ASD GWAS locus, was inferred to be significantly upregulated in ASD, providing insight into functional consequence of this associated locus. One novel transcriptome-wide significant association from this study is the downregulation of PDIA6, which showed minimal evidence of association in the GWAS, and in gene-based analysis using MAGMA. Predicted gene expression in this study accounted for 13.0% of the total ASD SNP-heritability. Conclusion:This study has implicated several genes as significantly up-/down-regulated in ASD providing novel and useful information for subsequent functional studies. This study also explores the utility of TWAS-based enrichment analysis and compares TWAS results with a functionally agnostic approach.

Cross-disorder GWAS meta-analysis for Attention Deficit/Hyperactivity Disorder, Autism Spectrum Disorder, Obsessive Compulsive Disorder, and Tourette Syndrome

“…It has been previously associated with ASD (15,58). Furthermore, a recent large-scale transcriptome association study in ASD identified significant differential expression and splicing of XRN2 in ASD, further suggesting a functional role of XRN2 in this neurodevelopmental disorder (58).…”

“…This gene is widely expressed in various human tissues and encodes an essential nuclear 5'→3' exoRNase with many functions in the processing and regulation of RNA molecules (57). It has been previously associated with ASD (15,58). Furthermore, a recent large-scale transcriptome association study in ASD identified significant differential expression and splicing of XRN2 in ASD, further suggesting a functional role of XRN2 in this neurodevelopmental disorder (58).…”

Sites of active gene regulation in the prenatal frontal cortex and their role in neuropsychiatric disorders

“…Emerging evidence suggests that some of the common genetic influences on risk for major depressive disorder also operate in utero (Wray et al, 2018;Hall et al, 2020), although we note that, in the present study, the enrichment of SNP heritability for the condition in fetal brain OCRs was consistently lower than that for bipolar disorder and schizophrenia. While rare genetic risk variants for ASD are known to disrupt genes functioning in the prenatal brain (Willsey et al, 2013;Parikshak et al, 2013;Satterstrom et al, 2020), only recently have ASD GWAS yielded sufficient signal for biological insights into the condition (Grove et al, , Pain et al, 2019Forsyth et al, 2020;Hall et al, 2020).…”

Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants With Treatment Resistance in Schizophrenia

“…For our main analysis, we used samples from large genomic studies of schizophrenia. 27,28 Some of the largest cohorts in these research initiatives were recruited based on clozapine prescription (a proxy of TRS status), and forming a case-case data set from them would require avoiding confounding factors, such as GWAS batch effects 30 or population stratification, 31 which are difficult to control in a multiple-cohort design. 32 As a safeguard against these, we have used a meta-analytic procedure to assess the differences between GWAS in which individuals with TRS and non-TRS have been compared with matched sets of healthy controls, before comparing the allelic association effect sizes of these 2 GWASs on a genomewide basis to create a GWAS specific to treatment resistance.…”

“…This limitation likely reflects the previously discussed problem of individuals with treatment-resistant psychiatric symptoms being disproportionately underrepresented in research studies owing to poor health, limited capacity to consent, and other causes of attrition, including therapeutic pessimism, 25 which have precluded the execution of well-powered genome-wide association studies (GWASs). 26 In this study, we aim to characterize the contribution of common genetic variants to treatment resistance in schizophrenia by exploiting data generated by large consortium-based GWASs of schizophrenia, 27,28 in which individuals with TRS can be formally defined. Our main hypotheses are that schizophrenia risk alleles will show different genetic associations in the analysis of individuals with and without TRS and that these differences reflect the underlying genetics of treatment resistance.…”