Article

Genome-wide Transcriptome Profiling Reveals the Functional Impact of Rare De Novo and Recurrent CNVs in Autism Spectrum Disorders

Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, 90095, USA.
The American Journal of Human Genetics (Impact Factor: 10.99). 06/2012; 91(1):38-55. DOI: 10.1016/j.ajhg.2012.05.011
Source: PubMed

ABSTRACT Copy-number variants (CNVs) are a major contributor to the pathophysiology of autism spectrum disorders (ASDs), but the functional impact of CNVs remains largely unexplored. Because brain tissue is not available from most samples, we interrogated gene expression in lymphoblasts from 244 families with discordant siblings in the Simons Simplex Collection in order to identify potentially pathogenic variation. Our results reveal that the overall frequency of significantly misexpressed genes (which we refer to here as outliers) identified in probands and unaffected siblings does not differ. However, in probands, but not their unaffected siblings, the group of outlier genes is significantly enriched in neural-related pathways, including neuropeptide signaling, synaptogenesis, and cell adhesion. We demonstrate that outlier genes cluster within the most pathogenic CNVs (rare de novo CNVs) and can be used for the prioritization of rare CNVs of potentially unknown significance. Several nonrecurrent CNVs with significant gene-expression alterations are identified (these include deletions in chromosomal regions 3q27, 3p13, and 3p26 and duplications at 2p15), suggesting that these are potential candidate ASD loci. In addition, we identify distinct expression changes in 16p11.2 microdeletions, 16p11.2 microduplications, and 7q11.23 duplications, and we show that specific genes within the 16p CNV interval correlate with differences in head circumference, an ASD-relevant phenotype. This study provides evidence that pathogenic structural variants have a functional impact via transcriptome alterations in ASDs at a genome-wide level and demonstrates the utility of integrating gene expression with mutation data for the prioritization of genes disrupted by potentially pathogenic mutations.

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Available from: Yuan Tian, Jul 02, 2015
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