Severe neurocognitive and growth disorders due to variation in THOC2, an essential component of nuclear mRNA export machinery

Highly conserved TREX-mediated mRNA export is emerging as a key pathway in neuronal development and differentiation. TREX subunit variants cause neurodevelopmental disorders (NDDs) by interfering with mRNA export from the cell nucleus to the cytoplasm. Previously we implicated four missense variants in the X-linked THOC2 gene in intellectual disability (ID). We now report an additional six affected individuals from five unrelated families with two de novo and threematernally inherited pathogenic or likely pathogenic variants in THOC2 extending the genotypic and phenotypic spectrum. These comprise three rare missense THOC2 variants that affect evolutionarily conserved amino acid residues and reduce protein stability and two with canonical splice-site THOC2 variants that result in C-terminally truncated THOC2 proteins.We present detailed clinical assessment and functional studies on a de novo variant in a female with an epileptic encephalopathy and discuss an additional four families with rare variants in THOC2 with supportive evidence for pathogenicity. Severe neurocognitive features, including movement and seizure disorders, were observed in this cohort. Taken together our data show that even subtle alterations to the canonical molecular pathways such asmRNAexport, otherwise essential for cellular life, can be compatible with life, but lead to NDDs in human

eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics

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.This project has received funding from the “la Caixa” Foundation, the CRG emergent translational research award and the European Union's H2020 Research and Innovation Programme under the grant agreement No 635290 (PanCanRisk)

eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics

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.This project has received funding from the “la Caixa” Foundation, the CRG emergent translational research award and the European Union's H2020 Research and Innovation Programme under the grant agreement No 635290 (PanCanRisk)

Exon-focused genome-wide association study of obsessive-compulsive disorder and shared polygenic risk with schizophrenia.

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.The study was supported by Fundación María José Jove, Fondo Europeo de Desarrollo Regional (FEDER), Xunta de Galicia; and by grants from the Instituto de Salud Carlos III FIS PI13/01136 to AC, CP11/00163 to JC, PI13/00918 to JMM, PI14/00413 to PA; the Generalitat de Catalunya AGAUR 2014 SGR-1138; the Spanish MINIECO SAF2013-49108- R Plan Estatal; and the European Commission 7th Framework Program, Project N. 262055 (ESGI) to XE. LD is supported by a Severo Ochoa fellowship of the Spanish MINIECO