Solving for X: Evidence for sex-specific autism biomarkers across multiple transcriptomic studies.
Samuel C LeeThomas P QuinnJerry LaiSek Won KongIrva Hertz-PicciottoStephen J GlattTamsyn M CrowleySvetha VenkateshThin NguyenPublished in: American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics (2018)
Autism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sex-specific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sex-dependent biomarkers and characterize their function, we present an analysis of a single paired-end postmortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sex-independent dysregulation. Moreover, through pathway analysis, we find that these sex-independent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both postmortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by making a call for new transcriptomic data that comprise large female cohorts.
Keyphrases
- autism spectrum disorder
- rna seq
- intellectual disability
- attention deficit hyperactivity disorder
- gene expression
- single cell
- genome wide
- electronic health record
- white matter
- dna methylation
- systematic review
- machine learning
- resting state
- multiple sclerosis
- case control
- bioinformatics analysis
- copy number
- functional connectivity