Genetic architecture of Tourette syndrome: our current understanding.

Tourette syndrome (TS) is a severe neuropsychiatric disorder characterized by recurrent, involuntary physical and verbal tics. With a prevalence as high as 1% in children, a deeper understanding of the etiology of the disorder and contributions to risk is critical. Here, we cover the current body of knowledge in scientific literature regarding the genetics of TS. We first review the history and diagnostic criteria for TS cases. We then cover the prevalence, and begin to address the etiology of the disorder. We highlight long-standing evidence for a genetic contribution to TS risk from epidemiology studies focused on twins, families, and population-scale data. Finally, we summarize current large-scale genetic studies of TS along specific classes of genetic variation, including common variation, rare copy number variation, and de novo variation that impact protein-coding sequence. Although these variants do not account for the entirety of TS genetic risk, current evidence is clear that each class of variation is a factor in the overall risk architecture across TS cases.

Whole-exome sequencing identifies rare variants in STAB2 associated with venous thromboembolic disease.

Deep vein thrombosis and pulmonary embolism, collectively defined as venous thromboembolism (VTE), are the third leading cause of cardiovascular death in the United States. Common genetic variants conferring increased varying degrees of VTE risk have been identified by genome-wide association studies (GWAS). Rare mutations in the anticoagulant genes PROC, PROS1 and SERPINC1 result in perinatal lethal thrombosis in homozygotes and markedly increased VTE risk in heterozygotes. However, currently described VTE variants account for an insufficient portion of risk to be routinely used for clinical decision making. To identify new rare VTE risk variants, we performed a whole-exome study of 393 individuals with unprovoked VTE and 6114 controls. This study identified 4 genes harboring an excess number of rare damaging variants in patients with VTE: PROS1, STAB2, PROC, and SERPINC1. At STAB2, 7.8% of VTE cases and 2.4% of controls had a qualifying rare variant. In cell culture, VTE-associated variants of STAB2 had a reduced surface expression compared with reference STAB2. Common variants in STAB2 have been previously associated with plasma von Willebrand factor and coagulation factor VIII levels in GWAS, suggesting that haploinsufficiency of stabilin-2 may increase VTE risk through elevated levels of these procoagulants. In an independent cohort, we found higher von Willebrand factor levels and equivalent propeptide levels in individuals with rare STAB2 variants compared with controls. Taken together, this study demonstrates the utility of gene-based collapsing analyses to identify loci harboring an excess of rare variants with functional connections to a complex thrombotic disease.

meaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays.

Here we present an open-source R package 'meaRtools' that provides a platform for analyzing neuronal networks recorded on Microelectrode Arrays (MEAs). Cultured neuronal networks monitored with MEAs are now being widely used to characterize in vitro models of neurological disorders and to evaluate pharmaceutical compounds. meaRtools provides core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments. meaRtools functionality covers novel solutions for spike train analysis, including algorithms to assess electrode cross-correlation using the spike train tiling coefficient (STTC), mutual information, synchronized bursts and entropy within cultured wells. Also integrated is a solution to account for bursts variability originating from mixed-cell neuronal cultures. The package provides a statistical platform built specifically for MEA data that can combine multiple MEA recordings and compare extracted features between different genetic models or treatments. We demonstrate the utilization of meaRtools to successfully identify epilepsy-like phenotypes in neuronal networks from Celf4 knockout mice. The package is freely available under the GPL license (GPL> = 3) and is updated frequently on the CRAN web-server repository. The package, along with full documentation can be downloaded from: https://cran.r-project.org/web/packages/meaRtools/.

Annotating pathogenic non-coding variants in genic regions.

Identifying the underlying causes of disease requires accurate interpretation of genetic variants. Current methods ineffectively capture pathogenic non-coding variants in genic regions, resulting in overlooking synonymous and intronic variants when searching for disease risk. Here we present the Transcript-inferred Pathogenicity (TraP) score, which uses sequence context alterations to reliably identify non-coding variation that causes disease. High TraP scores single out extremely rare variants with lower minor allele frequencies than missense variants. TraP accurately distinguishes known pathogenic and benign variants in synonymous (AUC = 0.88) and intronic (AUC = 0.83) public datasets, dismissing benign variants with exceptionally high specificity. TraP analysis of 843 exomes from epilepsy family trios identifies synonymous variants in known epilepsy genes, thus pinpointing risk factors of disease from non-coding sequence data. TraP outperforms leading methods in identifying non-coding variants that are pathogenic and is therefore a valuable tool for use in gene discovery and the interpretation of personal genomes.While non-coding synonymous and intronic variants are often not under strong selective constraint, they can be pathogenic through affecting splicing or transcription. Here, the authors develop a score that uses sequence context alterations to predict pathogenicity of synonymous and non-coding genetic variants, and provide a web server of pre-computed scores.

Whole Exome Sequencing Reveals Severe Thrombophilia in Acute Unprovoked Idiopathic Fatal Pulmonary Embolism.

BACKGROUND: Acute unprovoked idiopathic fatal pulmonary embolism (IFPE) causes sudden death without an identifiable thrombogenic risk. We aimed to investigate the underlying genomic risks of IFPE through whole exome sequencing (WES). METHODS: We reviewed 14years of consecutive out-of-hospital fatal pulmonary embolism records (n=1478) from the ethnically diverse population of New York City. We selected 68 qualifying IFPE cases for WES. We compared the WES data of IFPE cases to those of 9332 controls to determine if there is an excess of rare damaging variants in the genome using ethnicity-matched controls in collapsing analyses. FINDINGS: We found nine of the 68 decedents (13·2%) who died of IFPE had at least one pathogenic or likely pathogenic variant in one of the three anti-coagulant genes: SERPINC1 (Antithrombin III), PROC, and PROS1. The odds ratio of developing IFPE as a variant carrier for SERPINC1 is 144·2 (95% CI, 26·3-779·4; P=1·7×10-7), for PROC is 85·6 (95% CI, 13·0-448·9; P=2.0×10-5), and for PROS1 is 56·4 (95% CI, 5·3-351·1; P=0·001). The average age-at-death of anti-coagulant gene variant carriers is significantly younger than that of non-carriers (28·56years versus 38·02years; P=0·01). INTERPRETATION: This study showed the important role of severe thrombophilia due to natural anti-coagulant deficiency in IFPE. Evaluating severe thrombophilia in out-of-hospital fatal PE beyond IFPE is warranted.

Differential gene expression in dentate granule cells in mesial temporal lobe epilepsy with and without hippocampal sclerosis.

OBJECTIVE: Hippocampal sclerosis is the most common neuropathologic finding in cases of medically intractable mesial temporal lobe epilepsy. In this study, we analyzed the gene expression profiles of dentate granule cells of patients with mesial temporal lobe epilepsy with and without hippocampal sclerosis to show that next-generation sequencing methods can produce interpretable genomic data from RNA collected from small homogenous cell populations, and to shed light on the transcriptional changes associated with hippocampal sclerosis. METHODS: RNA was extracted, and complementary DNA (cDNA) was prepared and amplified from dentate granule cells that had been harvested by laser capture microdissection from surgically resected hippocampi from patients with mesial temporal lobe epilepsy with and without hippocampal sclerosis. Sequencing libraries were sequenced, and the resulting sequencing reads were aligned to the reference genome. Differential expression analysis was used to ascertain expression differences between patients with and without hippocampal sclerosis. RESULTS: Greater than 90% of the RNA-Seq reads aligned to the reference. There was high concordance between transcriptional profiles obtained for duplicate samples. Principal component analysis revealed that the presence or absence of hippocampal sclerosis was the main determinant of the variance within the data. Among the genes up-regulated in the hippocampal sclerosis samples, there was significant enrichment for genes involved in oxidative phosphorylation. SIGNIFICANCE: By analyzing the gene expression profiles of dentate granule cells from surgically resected hippocampal specimens from patients with mesial temporal lobe epilepsy with and without hippocampal sclerosis, we have demonstrated the utility of next-generation sequencing methods for producing biologically relevant results from small populations of homogeneous cells, and have provided insight on the transcriptional changes associated with this pathology.

Mosaic mutations in early-onset genetic diseases.

PURPOSE: An emerging approach in medical genetics is to identify de novo mutations in patients with severe early-onset genetic disease that are absent in population controls and in the patient's parents. This approach, however, frequently misses post-zygotic "mosaic" mutations that are present in only a portion of the healthy parents' cells and are transmitted to offspring. METHODS: We constructed a mosaic transmission screen for variants that have an ~50% alternative allele ratio in the proband but are significantly less than 50% in the transmitting parent. We applied it to two family-based genetic disease cohorts consisting of 9 cases of sudden unexplained death in childhood (SUDC) and 338 previously published cases of epileptic encephalopathy. RESULTS: The screen identified six parental-mosaic transmissions across the two cohorts. The resultant rate of ~0.02 identified transmissions per trio is far lower than that of de novo mutations. Among these transmissions were two likely disease-causing mutations: an SCN1A mutation transmitted to an SUDC proband and her sibling with Dravet syndrome, as well as an SLC6A1 mutation in a proband with epileptic encephalopathy. CONCLUSION: These results highlight explicit screening for mosaic mutations as an important complement to the established approach of screening for de novo mutations.Genet Med 18 7, 746-749.

The Intolerance of Regulatory Sequence to Genetic Variation Predicts Gene Dosage Sensitivity.

Noncoding sequence contains pathogenic mutations. Yet, compared with mutations in protein-coding sequence, pathogenic regulatory mutations are notoriously difficult to recognize. Most fundamentally, we are not yet adept at recognizing the sequence stretches in the human genome that are most important in regulating the expression of genes. For this reason, it is difficult to apply to the regulatory regions the same kinds of analytical paradigms that are being successfully applied to identify mutations among protein-coding regions that influence risk. To determine whether dosage sensitive genes have distinct patterns among their noncoding sequence, we present two primary approaches that focus solely on a gene's proximal noncoding regulatory sequence. The first approach is a regulatory sequence analogue of the recently introduced residual variation intolerance score (RVIS), termed noncoding RVIS, or ncRVIS. The ncRVIS compares observed and predicted levels of standing variation in the regulatory sequence of human genes. The second approach, termed ncGERP, reflects the phylogenetic conservation of a gene's regulatory sequence using GERP++. We assess how well these two approaches correlate with four gene lists that use different ways to identify genes known or likely to cause disease through changes in expression: 1) genes that are known to cause disease through haploinsufficiency, 2) genes curated as dosage sensitive in ClinGen's Genome Dosage Map, 3) genes judged likely to be under purifying selection for mutations that change expression levels because they are statistically depleted of loss-of-function variants in the general population, and 4) genes judged unlikely to cause disease based on the presence of copy number variants in the general population. We find that both noncoding scores are highly predictive of dosage sensitivity using any of these criteria. In a similar way to ncGERP, we assess two ensemble-based predictors of regional noncoding importance, ncCADD and ncGWAVA, and find both scores are significantly predictive of human dosage sensitive genes and appear to carry information beyond conservation, as assessed by ncGERP. These results highlight that the intolerance of noncoding sequence stretches in the human genome can provide a critical complementary tool to other genome annotation approaches to help identify the parts of the human genome increasingly likely to harbor mutations that influence risk of disease.

A novel application of pattern recognition for accurate SNP and indel discovery from high-throughput data: targeted resequencing of the glucocorticoid receptor co-chaperone FKBP5 in a Caucasian population.

The detection of single nucleotide polymorphisms (SNPs) and insertion/deletions (indels) with precision from high-throughput data remains a significant bioinformatics challenge. Accurate detection is necessary before next-generation sequencing can routinely be used in the clinic. In research, scientific advances are inhibited by gaps in data, exemplified by the underrepresented discovery of rare variants, variants in non-coding regions and indels. The continued presence of false positives and false negatives prevents full automation and requires additional manual verification steps. Our methodology presents applications of both pattern recognition and sensitivity analysis to eliminate false positives and aid in the detection of SNP/indel loci and genotypes from high-throughput data. We chose FK506-binding protein 51(FKBP5) (6p21.31) for our clinical target because of its role in modulating pharmacological responses to physiological and synthetic glucocorticoids and because of the complexity of the genomic region. We detected genetic variation across a 160 kb region encompassing FKBP5. 613 SNPs and 57 indels, including a 3.3 kb deletion were discovered. We validated our method using three independent data sets and, with Sanger sequencing and Affymetrix and Illumina microarrays, achieved 99% concordance. Furthermore we were able to detect 267 novel rare variants and assess linkage disequilibrium. Our results showed both a sensitivity and specificity of 98%, indicating near perfect classification between true and false variants. The process is scalable and amenable to automation, with the downstream filters taking only 1.5h to analyze 96 individuals simultaneously. We provide examples of how our level of precision uncovered the interactions of multiple loci, their predicted influences on mRNA stability, perturbations of the hsp90 binding site, and individual variation in FKBP5 expression. Finally we show how our discovery of rare variants may change current conceptions of evolution at this locus.