SUMMARY
We previously established the contribution of
de novo
damaging sequence variants to Tourette disorder (TD) through whole-exome sequencing of 511 trios. Here, we sequence an additional 291 TD trios and analyze the combined set of 802 trios. We observe an overrepresentation of
de novo
damaging variants in simplex, but not multiplex, families; we identify a high-confidence TD risk gene,
CELSR3
(
cadherin EGF LAG seven-pass G-type receptor 3
); we find that the genes mutated in TD patients are enriched for those related to cell polarity, suggesting a common pathway underlying pathobiology; and we confirm a statistically significant excess of
de novo
copy number variants in TD. Finally, we identify significant overlap of
de novo
sequence variants between TD and obsessive-compulsive disorder and
de novo
copy number variants between TD and autism spectrum disorder, consistent with shared genetic risk.
Significance
The question of colonization of Europe by Neolithic people of the Near East and their contribution to the farming economy of Europe has been addressed with extensive archaeological studies and many genetic investigations of extant European and Near Eastern populations. Here, we use DNA polymorphisms of extant populations to investigate the patterns of gene flow from the Near East to Europe. Our data support the hypothesis that Near Eastern migrants reached Europe from Anatolia. A maritime route and island hopping was mainly used by these Near Eastern migrants to reach Southern Europe.
Tic disorders are moderately heritable common psychiatric disorders that can be highly troubling, both in childhood and in adulthood. In this study, we report results obtained in the first epigenome-wide association study (EWAS) of tic disorders. The subjects are participants in surveys at the Netherlands Twin Register (NTR) and the NTR biobank project. Tic disorders were measured with a self-report version of the Yale Global Tic Severity Scale Abbreviated version (YGTSS-ABBR), included in the 8th wave NTR data collection (2008). DNA methylation data consisted of 411,169 autosomal methylation sites assessed by the Illumina Infinium HumanMethylation450 BeadChip Kit (HM450k array). Phenotype and DNA methylation data were available in 1,678 subjects (mean age = 41.5). No probes reached genome-wide significance (p < 1.2 × 10−7). The strongest associated probe was cg15583738, located in an intergenic region on chromosome 8 (p = 1.98 × 10−6). Several of the top ranking probes (p < 1 × 10−4) were in or nearby genes previously associated with neurological disorders (e.g., GABBRI, BLM, and ADAM10), warranting their further investigation in relation to tic disorders. The top significantly enriched gene ontology (GO) terms among higher ranking methylation sites included anatomical structure morphogenesis (GO:0009653, p = 4.6 × 10−15) developmental process (GO:0032502, p = 2.96 × 10−12), and cellular developmental process (GO:0048869, p = 1.96 × 10−12). Overall, these results provide a first insight into the epigenetic mechanisms of tic disorders. This first study assesses the role of DNA methylation in tic disorders, and it lays the foundations for future work aiming to unravel the biological mechanisms underlying the architecture of this disorder.
The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional pilot project to create an atlas of genetic mutations responsible for cancer. One of the aims of this project is to develop an infrastructure for making the cancer related data publicly accessible, to enable cancer researchers anywhere around the world to make and validate important discoveries. However, data in the cancer genome atlas 1 are organized as text archives in a set of directories. Devising bioinformatics applications to analyse such data is still challenging, as it requires downloading very large archives and parsing the relevant text files in order to collect the critical co-variates necessary for analysis. Furthermore, the various types of experimental results are not connected biologically, i.e. in order to truly exploit the data in the genome-wide context in which the TCGA project was devised, the data needs to be converted into a structured representation and made publicly available for remote querying and virtual inte-
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