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Separating the wheat from the chaff: systematic identification of functionally relevant noncoding variants in ADHD.
Mol Psychiatry. 2016 Nov;21(11):1589-1598
Authors: Tong JH, Hawi Z, Dark C, Cummins TD, Johnson BP, Newman DP, Lau R, Vance A, Heussler HS, Matthews N, Bellgrove MA, Pang KC
Abstract
Attention deficit hyperactivity disorder (ADHD) is a highly heritable psychiatric condition with negative lifetime outcomes. Uncovering its genetic architecture should yield important insights into the neurobiology of ADHD and assist development of novel treatment strategies. Twenty years of candidate gene investigations and more recently genome-wide association studies have identified an array of potential association signals. In this context, separating the likely true from false associations (’the wheat’ from ’the chaff’) will be crucial for uncovering the functional biology of ADHD. Here, we defined a set of 2070 DNA variants that showed evidence of association with ADHD (or were in linkage disequilibrium). More than 97% of these variants were noncoding, and were prioritised for further exploration using two tools-genome-wide annotation of variants (GWAVA) and Combined Annotation-Dependent Depletion (CADD)-that were recently developed to rank variants based upon their likely pathogenicity. Capitalising on recent efforts such as the Encyclopaedia of DNA Elements and US National Institutes of Health Roadmap Epigenomics Projects to improve understanding of the noncoding genome, we subsequently identified 65 variants to which we assigned functional annotations, based upon their likely impact on alternative splicing, transcription factor binding and translational regulation. We propose that these 65 variants, which possess not only a high likelihood of pathogenicity but also readily testable functional hypotheses, represent a tractable shortlist for future experimental validation in ADHD. Taken together, this study brings into sharp focus the likely relevance of noncoding variants for the genetic risk associated with ADHD, and more broadly suggests a bioinformatics approach that should be relevant to other psychiatric disorders.
PMID: 27113999 [PubMed – in process]
via https://www.ncbi.nlm.nih.gov/pubmed/27113999?dopt=Abstract