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Novel Insight Into the Etiology of Autism Spectrum Disorder Gained by Integrating Expression Data With Genome-wide Association Statistics.

Authors
  • Pain, Oliver1
  • Pocklington, Andrew J2
  • Holmans, Peter A2
  • Bray, Nicholas J2
  • O'Brien, Heath E2
  • Hall, Lynsey S2
  • Pardiñas, Antonio F2
  • O'Donovan, Michael C2
  • Owen, Michael J2
  • Anney, Richard3
  • 1 Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom. , (United Kingdom)
  • 2 Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom. , (United Kingdom)
  • 3 Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom. Electronic address: [email protected] , (United Kingdom)
Type
Published Article
Publication Date
Aug 15, 2019
Volume
86
Issue
4
Pages
265–273
Identifiers
DOI: 10.1016/j.biopsych.2019.04.034
PMID: 31230729
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

A recent genome-wide association study (GWAS) of autism spectrum disorder (ASD) (ncases = 18,381, ncontrols = 27,969) has provided novel opportunities for investigating the etiology 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). Using FUSION software, ASD GWAS summary statistics were integrated with predictors of gene expression from 16 human datasets, including adult and fetal brains. A novel adaptation of established statistical methods was then used to test for enrichment within candidate pathways and 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 disequilibrium score regression. This study identified 14 genes as significantly differentially expressed in ASD, 13 of which were outside of known genome-wide significant loci (±500 kb). XRN2, a gene proximal to an ASD GWAS locus, was inferred to be significantly upregulated in ASD, providing insight into the 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 single nucleotide polymorphism heritability. This study has implicated several genes as significantly up/downregulated 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. Copyright © 2019 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

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