Detection and Characterization of a De Novo Alu Retrotransposition Event Causing NKX2-1-Related Disorder

Heterozygous NKX2-1 loss-of-function variants cause combinations of hyperkinetic movement disorders (MDs, particularly childhood-onset chorea), pulmonary dysfunction, and hypothyroidism. Mobile element insertions (MEIs) are potential disease-causing structural variants whose detection in routine diagnostics remains challenging

Exome-wide analysis of copy number variation shows association of the human leukocyte antigen region with asthma in UK Biobank

BackgroundThe role of copy number variants (CNVs) in susceptibility to asthma is not well understood. This is, in part, due to the difficulty of accurately measuring CNVs in large enough sample sizes to detect associations. The recent availability of whole-exome sequencing (WES) in large biobank studies provides an unprecedented opportunity to study the role of CNVs in asthma.MethodsWe called common CNVs in 49,953 individuals in the first release of UK Biobank WES using ClinCNV software. CNVs were tested for association with asthma in a stage 1 analysis comprising 7098 asthma cases and 36,578 controls from the first release of sequencing data. Nominally-associated CNVs were then meta-analysed in stage 2 with an additional 17,280 asthma cases and 115,562 controls from the second release of UK Biobank exome sequencing, followed by validation and fine-mapping.ResultsFive of 189 CNVs were associated with asthma in stage 2, including a deletion overlapping the HLA-DQA1 and HLA-DQB1 genes, a duplication of CHROMR/PRKRA, deletions within MUC22 and TAP2, and a duplication in FBRSL1. The HLA-DQA1, HLA-DQB1, MUC22 and TAP2 genes all reside within the human leukocyte antigen (HLA) region on chromosome 6. In silico analyses demonstrated that the deletion overlapping HLA-DQA1 and HLA-DQB1 is likely to be an artefact arising from under-mapping of reads from non-reference HLA haplotypes, and that the CHROMR/PRKRA and FBRSL1 duplications represent presence/absence of pseudogenes within the HLA region. Bayesian fine-mapping of the HLA region suggested that there are two independent asthma association signals. The variants with the largest posterior inclusion probability in the two credible sets were an amino acid change in HLA-DQB1 (glutamine to histidine at residue 253) and a multi-allelic amino acid change in HLA-DRB1 (presence/absence of serine, glycine or leucine at residue 11).ConclusionsAt least two independent loci characterised by amino acid changes in the HLA-DQA1, HLA-DQB1 and HLA-DRB1 genes are likely to account for association of SNPs and CNVs in this region with asthma. The high divergence of haplotypes in the HLA can give rise to spurious CNVs, providing an important, cautionary tale for future large-scale analyses of sequencing data

Solve-RD: systematic pan-European data sharing and collaborative analysis to solve rare diseases.

For the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient's data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together >300 clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs; ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of >19,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be shared with controlled access. The first preliminary data re-analysis has already diagnosed 255 cases form 8393 exomes/genome datasets. This unprecedented degree of collaboration focused on sharing of data and expertise shall identify many new disease genes and enable diagnosis of many so far undiagnosed patients from all over Europe

Solving unsolved rare neurological diseases-a Solve-RD viewpoint.

Funder: Durch Princess Beatrix Muscle Fund Durch Speeren voor Spieren Muscle FundFunder: University of Tübingen Medical Faculty PATE programFunder: European Reference Network for Rare Neurological Diseases | 739510Funder: European Joint Program on Rare Diseases (EJP-RD COFUND-EJP) | 44140962

Twist exome capture allows for lower average sequence coverage in clinical exome sequencing

Background Exome and genome sequencing are the predominant techniques in the diagnosis and research of genetic disorders. Sufficient, uniform and reproducible/consistent sequence coverage is a main determinant for the sensitivity to detect single-nucleotide (SNVs) and copy number variants (CNVs). Here we compared the ability to obtain comprehensive exome coverage for recent exome capture kits and genome sequencing techniques. Results We compared three different widely used enrichment kits (Agilent SureSelect Human All Exon V5, Agilent SureSelect Human All Exon V7 and Twist Bioscience) as well as short-read and long-read WGS. We show that the Twist exome capture significantly improves complete coverage and coverage uniformity across coding regions compared to other exome capture kits. Twist performance is comparable to that of both short- and long-read whole genome sequencing. Additionally, we show that even at a reduced average coverage of 70× there is only minimal loss in sensitivity for SNV and CNV detection. Conclusion We conclude that exome sequencing with Twist represents a significant improvement and could be performed at lower sequence coverage compared to other exome capture techniques

Solving patients with rare diseases through programmatic reanalysis of genome-phenome data.

Funder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health); doi: https://doi.org/10.13039/100011272; Grant(s): 305444, 305444Funder: Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness); doi: https://doi.org/10.13039/501100003329Funder: Generalitat de Catalunya (Government of Catalonia); doi: https://doi.org/10.13039/501100002809Funder: EC | European Regional Development Fund (Europski Fond za Regionalni Razvoj); doi: https://doi.org/10.13039/501100008530Funder: Instituto Nacional de Bioinformática ELIXIR Implementation Studies Centro de Excelencia Severo OchoaFunder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health)Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP's Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Methods for detection of germline and somatic copy-number variants in next generation sequencing data

Germline copy-number variants (CNVs), as well as somatic copy-number alterations (CNAs), play an important role in many phenotypic traits, including genetic diseases and cancer. Next Generation Sequencing (NGS) allows accurate detection of short variants, but reliable detection of large-scale CNVs in NGS data remains challenging. In this work, I address this issue and describe a novel statistical method for detection of CNVs and CNAs implemented in the tool called ClinCNV. I present analytical performance measures of “ClinCNV” in different datasets, compare it with the performance of other existing methods, and show the advantages of ClinCNV. ClinCNV is already implemented as a part of the diagnostics pipeline at the Institute of Medical Genetics and Applied Genomics (IMGAG), Tuebingen, Germany. ClinCNV has the potential to facilitate molecular diagnostic of genetic-based diseases as well as cancer through accurate detection of copy-number variants.Las variantes en el número de copias genéticas, tanto en estado germinal (CNV) como en somático (CNA), juegan un papel muy importante en muchos rasgos fenotípicos y están frecuentemente relacionadas con una gran variedad enfermedades genéticas y cáncer. Aunque la secuenciación de próxima generación (NGS) permite detectar variantes cortas con una gran precisión, la correcta detección de CNVs a gran escala con datos de secuenciación sigue siendo un gran desafío. En esta tesis, me centro en abordar este problema y describo un nuevo método estadístico para la detección de CNV y CNA englobado en una nueva herramienta llamada ClinCNV. Para el análisis del rendimiento de ClinCNV y demostrar las ventajas de este nuevo algoritmo, comparamos nuestra herramienta con otras existentes en distintos conjuntos de datos. Por otra parte, ClinCNV ya está implementado como parte del sistema de trabajo de diagnóstico en el Instituto de Genética Médica y Genómica Aplicada (IMGAG) en Tuebingen (Alemania). En resumen, ClinCNV tiene el potencial de facilitar el diagnóstico molecular de enfermedades genéticas y cáncer mediante la precisa detección de variantes en el número de copias genéticas

Methods for detection of germline and somatic copy-number variants in next generation sequencing data

Germline copy-number variants (CNVs), as well as somatic copy-number alterations (CNAs), play an important role in many phenotypic traits, including genetic diseases and cancer. Next Generation Sequencing (NGS) allows accurate detection of short variants, but reliable detection of large-scale CNVs in NGS data remains challenging. In this work, I address this issue and describe a novel statistical method for detection of CNVs and CNAs implemented in the tool called ClinCNV. I present analytical performance measures of “ClinCNV” in different datasets, compare it with the performance of other existing methods, and show the advantages of ClinCNV. ClinCNV is already implemented as a part of the diagnostics pipeline at the Institute of Medical Genetics and Applied Genomics (IMGAG), Tuebingen, Germany. ClinCNV has the potential to facilitate molecular diagnostic of genetic-based diseases as well as cancer through accurate detection of copy-number variants.Las variantes en el número de copias genéticas, tanto en estado germinal (CNV) como en somático (CNA), juegan un papel muy importante en muchos rasgos fenotípicos y están frecuentemente relacionadas con una gran variedad enfermedades genéticas y cáncer. Aunque la secuenciación de próxima generación (NGS) permite detectar variantes cortas con una gran precisión, la correcta detección de CNVs a gran escala con datos de secuenciación sigue siendo un gran desafío. En esta tesis, me centro en abordar este problema y describo un nuevo método estadístico para la detección de CNV y CNA englobado en una nueva herramienta llamada ClinCNV. Para el análisis del rendimiento de ClinCNV y demostrar las ventajas de este nuevo algoritmo, comparamos nuestra herramienta con otras existentes en distintos conjuntos de datos. Por otra parte, ClinCNV ya está implementado como parte del sistema de trabajo de diagnóstico en el Instituto de Genética Médica y Genómica Aplicada (IMGAG) en Tuebingen (Alemania). En resumen, ClinCNV tiene el potencial de facilitar el diagnóstico molecular de enfermedades genéticas y cáncer mediante la precisa detección de variantes en el número de copias genéticas