Dataset Information

Clinical interpretation of variants identified in RNU4ATAC, a non-coding spliceosomal gene.

ABSTRACT: Biallelic variants in RNU4ATAC, a non-coding gene transcribed into the minor spliceosome component U4atac snRNA, are responsible for three rare recessive developmental diseases, namely Taybi-Linder/MOPD1, Roifman and Lowry-Wood syndromes. Next-generation sequencing of clinically heterogeneous cohorts (children with either a suspected genetic disorder or a congenital microcephaly) recently identified mutations in this gene, illustrating how profoundly these technologies are modifying genetic testing and assessment. As RNU4ATAC has a single non-coding exon, the bioinformatic prediction algorithms assessing the effect of sequence variants on splicing or protein function are irrelevant, which makes variant interpretation challenging to molecular diagnostic laboratories. In order to facilitate and improve clinical diagnostic assessment and genetic counseling, we present i) an update of the previously reported RNU4ATAC mutations and an analysis of the genetic variations affecting this gene using the Genome Aggregation Database (gnomAD) resource; ii) the pathogenicity prediction performances of scores computed based on an RNA structure prediction tool and of those produced by the Combined Annotation Dependent Depletion tool for the 285 RNU4ATAC variants identified in patients or in large-scale sequencing projects; iii) a method, based on a cellular assay, that allows to measure the effect of RNU4ATAC variants on splicing efficiency of a minor (U12-type) reporter intron. Lastly, the concordance of bioinformatic predictions and cellular assay results was investigated.

SUBMITTER: Benoit-Pilven C

PROVIDER: S-EPMC7337319 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Clinical interpretation of variants identified in RNU4ATAC, a non-coding spliceosomal gene.

Benoit-Pilven Clara C Besson Alicia A Putoux Audrey A Benetollo Claire C Saccaro Clément C Guguin Justine J Sala Gabriel G Cologne Audric A Delous Marion M Lesca Gaetan G Padgett Richard A RA Leutenegger Anne-Louise AL Lacroix Vincent V Edery Patrick P Mazoyer Sylvie S

PloS one 20200706 7

Biallelic variants in RNU4ATAC, a non-coding gene transcribed into the minor spliceosome component U4atac snRNA, are responsible for three rare recessive developmental diseases, namely Taybi-Linder/MOPD1, Roifman and Lowry-Wood syndromes. Next-generation sequencing of clinically heterogeneous cohorts (children with either a suspected genetic disorder or a congenital microcephaly) recently identified mutations in this gene, illustrating how profoundly these technologies are modifying genetic test ...[more]

PMID: 32628740

Similar Datasets

Project description:BackgroundIdentification of functional non-coding variants and their mechanistic interpretation is a major challenge of modern genomics, especially for precision medicine. Transcription factor (TF) binding profiles and epigenomic landscapes in reference samples allow functional annotation of the genome, but do not provide ready answers regarding the effects of non-coding variants on phenotypes. A promising computational approach is to build models that predict TF-DNA binding from sequence, and use such models to score a variant's impact on TF binding strength. Here, we asked if this mechanistic approach to variant interpretation can be combined with information on genotype-phenotype associations to discover transcription factors regulating phenotypic variation among individuals.ResultsWe developed a statistical approach that integrates phenotype, genotype, gene expression, TF ChIP-seq, and Hi-C chromatin interaction data to answer this question. Using drug sensitivity of lymphoblastoid cell lines as the phenotype of interest, we tested if non-coding variants statistically linked to the phenotype are enriched for strong predicted impact on DNA binding strength of a TF and thus identified TFs regulating individual differences in the phenotype. Our approach relies on a new method for predicting variant impact on TF-DNA binding that uses a combination of biophysical modeling and machine learning. We report statistical and literature-based support for many of the TFs discovered here as regulators of drug response variation. We show that the use of mechanistically driven variant impact predictors can identify TF-drug associations that would otherwise be missed. We examined in depth one reported association-that of the transcription factor ELF1 with the drug doxorubicin-and identified several genes that may mediate this regulatory relationship.ConclusionOur work represents initial steps in utilizing predictions of variant impact on TF binding sites for discovery of regulatory mechanisms underlying phenotypic variation. Future advances on this topic will be greatly beneficial to the reconstruction of phenotype-associated gene regulatory networks.

Project description:Clinical interpretation of DNA sequence variants is a critical step in reporting clinical genetic testing results. Application of next-generation sequencing technology in molecular genetic testing has facilitated diagnoses of genetic disorders in clinical practice. However, the large number of DNA sequence variants detected in clinical specimens, many of which have never been seen before, make clinical interpretation challenging. Recommendations by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) have been widely adopted by clinical laboratories around the world to guide clinical interpretation of sequence variants. The ClinGen Sequence Variant Interpretation Working Group and various disease-specific variant curation expert panels have also developed specifications for the ACMG/AMP recommendations. Despite these efforts to standardize variant interpretation in clinical practice, different laboratories may subjectively use professional judgment to determine which criteria are applicable when classifying a variant. In addition, clinicians and researchers who are not familiar with the variant interpretation process may have difficulty understanding clinical genetic reports and communicating the clinical significance of genetic testing results. Here we provide a step-by-step protocol for clinical interpretation of sequence variants, including practical examples. By following this protocol, clinical laboratory geneticists can interpret the clinical significance of sequence variants according to the ACMG/AMP recommendations and ClinGen framework. Furthermore, this article will help clinicians and researchers to understand variant classification in clinical genetic testing reports and evaluate the quality of the reports. © 2020 by John Wiley & Sons, Inc. Basic Protocol: Interpreting the clinical significance of sequence variants Support Protocol: Reevaluating the clinical significance of sequence variants.

Dataset Information

Clinical interpretation of variants identified in RNU4ATAC, a non-coding spliceosomal gene.

Publications

Clinical interpretation of variants identified in RNU4ATAC, a non-coding spliceosomal gene.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets