Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis

Abstract

Autism spectrum disorder (ASD) is characterized by substantial phenotypic and genetic heterogeneity, which greatly complicates the identification of genetic factors that contribute to the disease. Study designs have mainly focused on group differences between cases and controls. The problem is that, by their nature, group difference-based methods (e.g., differential expression analysis) blur or collapse the heterogeneity within groups. By ignoring genes with variable within-group expression, an important axis of genetic heterogeneity contributing to expression variability among affected individuals has been overlooked. To this end, we develop a new gene expression analysis method-aberrant gene expression analysis, based on the multivariate distance commonly used for outlier detection. Our method detects the discrepancies in gene expression dispersion between groups and identifies genes with significantly different expression variability. Using this new method, we re-visited RNA sequencing data generated from post-mortem brain tissues of 47 ASD and 57 control samples. We identified 54 functional gene sets whose expression dispersion in ASD samples is more pronounced than that in controls, as well as 76 co-expression modules present in controls but absent in ASD samples due to ASD-specific aberrant gene expression. We also exploited aberrantly expressed genes as biomarkers for ASD diagnosis. With a whole blood expression data set, we identified three aberrantly expressed gene sets whose expression levels serve as discriminating variables achieving >70 % classification accuracy. In summary, our method represents a novel discovery and diagnostic strategy for ASD. Our findings may help open an expression variability-centered research avenue for other genetically heterogeneous disorders.

MicroRNA-mRNA Co-sequencing Identifies Transcriptional and Post-transcriptional Regulatory Networks Underlying Muscle Wasting in Cancer Cachexia

“…This variability in the expression profile of these set of genes may help to explain why protein catabolism genes in human studies have failed to recapitulate the findings from murine models [14,43]. This variability in gene expression, also known as noise, has been described as an essential feature of any biological system [44], and previous gene expression studies have been able to classify different states of diseases based on this variability [45][46][47][48].…”

OUTRIDER: A statistical method for detecting aberrantly expressed genes in RNA sequencing data

“…Some of these global changes may have a technical basis, such as the two discarded samples from the GTEx data set with aberrantly low coverage, whereas others may reflect the phenotype of the disease. Alternative methods have been developed to detect such samples; these exploit global measures such as the Mahalanobis distance to measure the dissimilarity of a sample from the population and to identify aberrantly expressed gene sets 27,28 . Analysis of these genome-wide regulatory defects could be interesting.…”

Transcriptional Analysis of Blood Lymphocytes and Skin Fibroblasts, Keratinocytes, and Endothelial Cells as a Potential Biomarker for Alzheimer’s Disease

“…Indeed, such expression may go unnoticed when looking at average changes between cohorts, potentially missing important biological factors. In a very recent report, Guan and colleagues using a similar strategy of data analysis in a much larger cohort, identified potential biomarkers in autism spectrum disorder [28]. …”