Common single-nucleotide polymorphisms (SNPs) account for a large proportion of the heritability of obsessive-compulsive disorder (OCD). Co-ocurrence of OCD and schizophrenia is commoner than expected based on their respective prevalences, complicating the clinical management of patients. This study addresses two main objectives: to identify particular genes associated with OCD by SNP-based and gene-based tests; and to test the existence of a polygenic risk shared with schizophrenia. The primary analysis was an exon-focused genome-wide association study of 370 OCD cases and 443 controls from Spain. A polygenic risk model based on the Psychiatric Genetics Consortium schizophrenia data set (PGC-SCZ2) was tested in our OCD data. A polygenic risk model based on our OCD data was tested on previous data of schizophrenia from our group. The most significant association at the gene-based test was found at DNM3 (P=7.9 × 10−5), a gene involved in synaptic vesicle endocytosis. The polygenic risk model from PGC-SCZ2 data was strongly associated with disease status in our OCD sample, reaching its most significant value after removal of the major histocompatibility complex region (lowest P=2.3 × 10−6, explaining 3.7% of the variance). The shared polygenic risk was confirmed in our schizophrenia data. In conclusion, DNM3 may be involved in risk to OCD. The shared polygenic risk between schizophrenia and OCD may be partially responsible for the frequent comorbidity of both disorders, explaining epidemiological data on cross-disorder risk. This common etiology may have clinical implications.

Although the etiology of obsessive–compulsive disorder (OCD) is largely unknown, it is accepted that OCD is a complex disorder. There is a known bi-directional interaction between the gut microbiome and brain activity. Several authors have reported associations between changes in gut microbiota and neuropsychiatric disorders, including depression or autism. Furthermore, a pediatric-onset neuropsychiatric OCD-related syndrome occurs after streptococcal infection, which might indicate that exposure to certain microbes could be involved in OCD susceptibility. However, only one study has investigated the microbiome of OCD patients to date. We performed 16S ribosomal RNA gene-based metagenomic sequencing to analyze the stool and oropharyngeal microbiome composition of 32 OCD cases and 32 age and gender matched controls. We estimated different α- and β-diversity measures and performed LEfSe and Wilcoxon tests to assess differences in bacterial distribution. OCD stool samples showed a trend towards lower bacterial α-diversity, as well as an increase of the relative abundance of Rikenellaceae, particularly of the genus Alistipes, and lower relative abundance of Prevotellaceae, and two genera within the Lachnospiraceae: Agathobacer and Coprococcus. However, we did not observe a different Bacteroidetes to Firmicutes ratio between OCD cases and controls. Analysis of the oropharyngeal microbiome composition showed a lower Fusobacteria to Actinobacteria ratio in OCD cases. In conclusion, we observed an imbalance in the gut and oropharyngeal microbiomes of OCD cases, including, in stool, an increase of bacteria from the Rikenellaceae family, associated with gut inflammation, and a decrease of bacteria from the Coprococcus genus, associated with DOPAC synthesis.

In recent years, next‐generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors, and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the AB distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state‐of‐the‐art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies. Availability: https://github.com/Francesc-Muyas/ABB.

The rate of response to pharmacological treatment in Obsessive-compulsive disorder (OCD) oscillates between 40 and 70%. Genetic and environmental factors have been associated with treatment response in OCD. This study analyzes the predictive ability of a polygenic risk score (PRS) built from OCD-risk variants, for treatment response in OCD, and the modulation role of stressful life events (SLEs) at the onset of the disorder. PRSs were calculated for a sample of 103 patients. Yale–Brown Obsessive Compulsive Scale (YBOCS) scores were obtained before and after a 12-week treatment. Regression analyses were performed to analyze the influence of the PRS and SLEs at onset on treatment response. PRS did not predict treatment response. The best predictive model for post-treatment YBOCS (post YBOCS) included basal YBOCS and age. PRS appeared as a predictor for basal and post YBOCS. SLEs at onset were not a predictor for treatment response when included in the regression model. No evidence for PRS predictive ability for treatment response was found. The best predictor for treatment response was age, agreeing with previous literature specific for SRI treatment. Suggestions are made on the possible role of neuroplasticity as a mediator on this association. PRS significantly predicted OCD severity independent on pharmacological treatment. SLE at onset modulation role was not evidenced. Further research is needed to elucidate the genetic and environmental bases of treatment response in OCD.

Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole‐exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20–30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods. Here, we present the exome Disease Variant Analysis (eDiVA; http://ediva.crg.eu), an automated computational framework for identification of causal genetic variants (coding/splicing single‐nucleotide variants and small insertions and deletions) for rare diseases using WES of families or parent–child trios. eDiVA combines next‐generation sequencing data analysis, comprehensive functional annotation, and causal variant prioritization optimized for familial genetic disease studies. eDiVA features a machine learning‐based variant pathogenicity predictor combining various genomic and evolutionary signatures. Clinical information, such as disease phenotype or mode of inheritance, is incorporated to improve the precision of the prioritization algorithm. Benchmarking against state‐of‐the‐art competitors demonstrates that eDiVA consistently performed as a good or better than existing approach in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.

Although the etiology of obsessive-compulsive disorder (OCD) is largely unknown, it is accepted that OCD is a complex disorder. There is a known bi-directional interaction between the gut microbiome and brain activity. Several authors have reported associations between changes in gut microbiota and neuropsychiatric disorders, including depression or autism. Furthermore, a pediatric-onset neuropsychiatric OCD-related syndrome occurs after streptococcal infection, which might indicate that exposure to certain microbes could be involved in OCD susceptibility. However, only one study has investigated the microbiome of OCD patients to date. We performed 16S ribosomal RNA gene-based metagenomic sequencing to analyze the stool and oropharyngeal microbiome composition of 32 OCD cases and 32 age and gender matched controls. We estimated different α- and β-diversity measures and performed LEfSe and Wilcoxon tests to assess differences in bacterial distribution. OCD stool samples showed a trend towards lower bacterial α-diversity, as well as an increase of the relative abundance of Rikenellaceae, particularly of the genus Alistipes, and lower relative abundance of Prevotellaceae, and two genera within the Lachnospiraceae: Agathobacer and Coprococcus. However, we did not observe a different Bacteroidetes to Firmicutes ratio between OCD cases and controls. Analysis of the oropharyngeal microbiome composition showed a lower Fusobacteria to Actinobacteria ratio in OCD cases. In conclusion, we observed an imbalance in the gut and oropharyngeal microbiomes of OCD cases, including, in stool, an increase of bacteria from the Rikenellaceae family, associated with gut inflammation, and a decrease of bacteria from the Coprococcus genus, associated with DOPAC synthesis.

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.

Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the ‘Rare Variant Genome Wide Association Study’ (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.