Discovering pathways to autism spectrum disorder by using functional and integrative genomics approaches to assess monozygotic twin differences

Saffari, Ayden (2017) Discovering pathways to autism spectrum disorder by using functional and integrative genomics approaches to assess monozygotic twin differences. Doctoral thesis, Birkbeck, University of London.

Abstract

Autism spectrum disorder (ASD) is a common developmental disorder typified by deficits in social communication and stereotyped behaviours. Despite evidence of a strong genetic basis to the disorder, molecular studies have thus far had little success in identifying risk variants or other biomarkers, and presently there is no unified pathomechanistic explanation. Monozygotic (MZ) twins show incomplete concordance in autistic traits, which suggests that alternative risk pathways involving non-shared environmental (NSE) factors could also have an important role to play in ASD. In this thesis, we describe microarray and RNA-seq studies characterising gene expression in a sample of 53 ASD MZ twin pairs from TEDS. The overall aims were to: 1) establish convergent evidence for genes and pathways involved in the etiology of ASD comparing affected and unaffected subjects across the sample 2) to identify those responsive to the environment by examining differences within the discordant pairs. We found a number of genes were differentially expressed including DEPDC1B - the most significant finding in cases vs controls, which also showed consistent down regulation within pairs. We further identified IGHG4, IGHG3, IGHV3-66, HSPA8P14, HSPA13, SLC15A2, and found that these results were enriched for transcriptional control, immune, and PI3K/AKT signalling pathways. We suggest that as these were found to be perturbed in the discordant twins, they could represent ASD risk pathways sensitive to the NSE. Next, we investigated integrative genomics methods for performing meta-dimensional analysis using the expression data along with methylation data on the same cohort. After applying regression-based joint analysis methods, and meta-analysis p-value combination methods to our datasets, a number of genes obtained nominal significance across the datasets, including potential genes of interest: NLGN2, UBE3A, OXTR. We suggest these represent genes with evidence for being functionally relevant to ASD.

Metadata

Statistics

DownloadsShow export options

Export as XMLJSONCSV

Activity Overview

6 month trend

504Downloads

6 month trend

352Hits

Additional statistics are available via IRStats2.

Archive Staff Only (login required)

Edit/View Item