scholarly journals Identification of Pathogenic Structural Variants in Rare Disease Patients through Genome Sequencing

2019 ◽  
Author(s):  
James M. Holt ◽  
Camille L. Birch ◽  
Donna M. Brown ◽  
Manavalan Gajapathy ◽  
Nadiya Sosonkina ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Rare Disease ◽  
Genome Sequencing ◽  
Genomic Analysis ◽  
Whole Genome ◽  
Structural Variants ◽  
Single Nucleotide Variants ◽  
Standard Clinical Practice ◽  
Wide Range ◽  
Genetic Features

AbstractPurposeClinical whole genome sequencing is becoming more common for determining the molecular diagnosis of rare disease. However, standard clinical practice often focuses on small variants such as single nucleotide variants and small insertions/deletions. This leaves a wide range of larger “structural variants” that are not commonly analyzed in patients.MethodsWe developed a pipeline for processing structural variants for patients who received whole genome sequencing through the Undiagnosed Diseases Network (UDN). This pipeline called structural variants, stored them in an internal database, and filtered the variants based on internal frequencies and external annotations. The remaining variants were manually inspected and then interesting findings were reported as research variants to clinical sites in the UDN.ResultsOf 477 analyzed UDN cases, 286 cases (≈ 60%) received at least one structural variant as a research finding. The variants in 16 cases (≈ 4%) are considered “Certain” or “Highly likely” molecularly diagnosed and another 4 cases are currently in review. Of those 20 cases, at least 13 were identified originally through our pipeline with one finding leading to identification of a new disease. As part of this paper, we have also released the collection of variant calls identified in our cohort along with heterozygous and homozygous call counts. This data is available at https://github.com/HudsonAlpha/UDN_SV_export.ConclusionStructural variants are key genetic features that should be analyzed during routine clinical genomic analysis. For our UDN patients, structural variants helped solve ≈ 4% of the total number of cases (≈ 13% of all genome sequencing solves), a success rate we expect to improve with better tools and greater understanding of the human genome.

scholarly journals Sarek: A portable workflow for whole-genome sequencing analysis of germline and somatic variants

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 63 ◽  
Author(s):  
Maxime Garcia ◽  
Szilveszter Juhos ◽  
Malin Larsson ◽  
Pall I. Olason ◽  
Marcel Martin ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Scientific Progress ◽  
Whole Genome ◽  
Sequencing Analysis ◽  
Research Groups ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Link Type ◽  
Wide Range

Whole-genome sequencing (WGS) is a fundamental technology for research to advance precision medicine, but the limited availability of portable and user-friendly workflows for WGS analyses poses a major challenge for many research groups and hampers scientific progress. Here we present Sarek, an open-source workflow to detect germline variants and somatic mutations based on sequencing data from WGS, whole-exome sequencing (WES), or gene panels. Sarek features (i) easy installation, (ii) robust portability across different computer environments, (iii) comprehensive documentation, (iv) transparent and easy-to-read code, and (v) extensive quality metrics reporting. Sarek is implemented in the Nextflow workflow language and supports both Docker and Singularity containers as well as Conda environments, making it ideal for easy deployment on any POSIX-compatible computers and cloud compute environments. Sarek follows the GATK best-practice recommendations for read alignment and pre-processing, and includes a wide range of software for the identification and annotation of germline and somatic single-nucleotide variants, insertion and deletion variants, structural variants, tumour sample purity, and variations in ploidy and copy number. Sarek offers easy, efficient, and reproducible WGS analyses, and can readily be used both as a production workflow at sequencing facilities and as a powerful stand-alone tool for individual research groups. The Sarek source code, documentation and installation instructions are freely available at https://github.com/nf-core/sarek and at https://nf-co.re/sarek/.

scholarly journals Sarek: A portable workflow for whole-genome sequencing analysis of germline and somatic variants

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 63 ◽  
Author(s):  
Maxime Garcia ◽  
Szilveszter Juhos ◽  
Malin Larsson ◽  
Pall I. Olason ◽  
Marcel Martin ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Scientific Progress ◽  
Whole Genome ◽  
Sequencing Analysis ◽  
Research Groups ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Link Type ◽  
Wide Range

Whole-genome sequencing (WGS) is a fundamental technology for research to advance precision medicine, but the limited availability of portable and user-friendly workflows for WGS analyses poses a major challenge for many research groups and hampers scientific progress. Here we present Sarek, an open-source workflow to detect germline variants and somatic mutations based on sequencing data from WGS, whole-exome sequencing (WES), or gene panels. Sarek features (i) easy installation, (ii) robust portability across different computer environments, (iii) comprehensive documentation, (iv) transparent and easy-to-read code, and (v) extensive quality metrics reporting. Sarek is implemented in the Nextflow workflow language and supports both Docker and Singularity containers as well as Conda environments, making it ideal for easy deployment on any POSIX-compatible computers and cloud compute environments. Sarek follows the GATK best-practice recommendations for read alignment and pre-processing, and includes a wide range of software for the identification and annotation of germline and somatic single-nucleotide variants, insertion and deletion variants, structural variants, tumour sample purity, and variations in ploidy and copy number. Sarek offers easy, efficient, and reproducible WGS analyses, and can readily be used both as a production workflow at sequencing facilities and as a powerful stand-alone tool for individual research groups. The Sarek source code, documentation and installation instructions are freely available at https://github.com/nf-core/sarek and at https://nf-co.re/sarek/.

scholarly journals Analysis of Structural Variants Reveal Novel Selective Regions in the Genome of Meishan Pigs by Whole Genome Sequencing

2021 ◽  
Vol 12 ◽  
Author(s):  
Heng Du ◽  
Xianrui Zheng ◽  
Qiqi Zhao ◽  
Zhengzheng Hu ◽  
Haifei Wang ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome Sequencing Data ◽  
Phenotypic Traits ◽  
Whole Genome ◽  
Structural Variants ◽  
Domestic Pig ◽  
Pig Breeds ◽  
Wide Range ◽  
Meishan Pig

Structural variants (SVs) represent essential forms of genetic variation, and they are associated with various phenotypic traits in a wide range of important livestock species. However, the distribution of SVs in the pig genome has not been fully characterized, and the function of SVs in the economic traits of pig has rarely been studied, especially for most domestic pig breeds. Meishan pig is one of the most famous Chinese domestic pig breeds, with excellent reproductive performance. Here, to explore the genome characters of Meishan pig, we construct an SV map of porcine using whole-genome sequencing data and report 33,698 SVs in 305 individuals of 55 globally distributed pig breeds. We perform selective signature analysis using these SVs, and a number of candidate variants are successfully identified. Especially for the Meishan pig, 64 novel significant selection regions are detected in its genome. A 140-bp deletion in the Indoleamine 2,3-Dioxygenase 2 (IDO2) gene, is shown to be associated with reproduction traits in Meishan pig. In addition, we detect two duplications only existing in Meishan pig. Moreover, the two duplications are separately located in cytochrome P450 family 2 subfamily J member 2 (CYP2J2) gene and phospholipase A2 group IVA (PLA2G4A) gene, which are related to the reproduction trait. Our study provides new insights into the role of selection in SVs' evolution and how SVs contribute to phenotypic variation in pigs.

scholarly journals Effective variant filtering and expected candidate variant yield in studies of rare human disease

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Brent S. Pedersen ◽  
Joe M. Brown ◽  
Harriet Dashnow ◽  
Amelia D. Wallace ◽  
Matt Velinder ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Rare Disease ◽  
Genome Sequencing ◽  
Autosomal Dominant ◽  
De Novo ◽  
Autosomal Dominant Inheritance ◽  
Compound Heterozygous ◽  
Whole Genome ◽  
Dominant Inheritance ◽  
Family Based

AbstractIn studies of families with rare disease, it is common to screen for de novo mutations, as well as recessive or dominant variants that explain the phenotype. However, the filtering strategies and software used to prioritize high-confidence variants vary from study to study. In an effort to establish recommendations for rare disease research, we explore effective guidelines for variant (SNP and INDEL) filtering and report the expected number of candidates for de novo dominant, recessive, and autosomal dominant modes of inheritance. We derived these guidelines using two large family-based cohorts that underwent whole-genome sequencing, as well as two family cohorts with whole-exome sequencing. The filters are applied to common attributes, including genotype-quality, sequencing depth, allele balance, and population allele frequency. The resulting guidelines yield ~10 candidate SNP and INDEL variants per exome, and 18 per genome for recessive and de novo dominant modes of inheritance, with substantially more candidates for autosomal dominant inheritance. For family-based, whole-genome sequencing studies, this number includes an average of three de novo, ten compound heterozygous, one autosomal recessive, four X-linked variants, and roughly 100 candidate variants following autosomal dominant inheritance. The slivar software we developed to establish and rapidly apply these filters to VCF files is available at https://github.com/brentp/slivar under an MIT license, and includes documentation and recommendations for best practices for rare disease analysis.

Abstract 343: Bayesian Selection of Modifier Genes in Hypertrophic Cardiomyopathy Through Whole Genome Sequencing

2015 ◽  
Vol 117 (suppl_1) ◽  
Author(s):  
Matthew Wheeler ◽  
Daryl Waggott ◽  
Megan Grove ◽  
Frederick Dewey ◽  
Cuiping Pan ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
A Priori ◽  
Copy Number Variants ◽  
Whole Genome Sequence ◽  
Monogenic Disease ◽  
Whole Genome ◽  
Genetic Modifiers ◽  
Structural Variants

Background: Technological advances have greatly reduced the cost of whole genome sequencing. For single individuals clinical application is apparent, while exome sequencing in tens of thousands of people has allowed a more global view of genetic variation that can inform interpretation of specific variants in individuals. We hypothesized that genome sequencing of patients with monogenic cardiomyopathy would facilitate discovery of genetic modifiers of phenotype. Methods and Results: We identified 48 individuals diagnosed with cardiomyopathy and with putative mutations in MYH7, the gene encoding beta myosin heavy chain. We carried out whole genome sequencing and applied a newly developed analytical pipeline optimized for discovery of genes modifying severity of clinical presentation and outcomes. Using a combination of external priors and rare variant burden tests we scored genes as potential modifiers. There were 96 genes that reached a modifier score of 6 out of 12 or better (9=2, 8=8, 7=17, 6=69). We identified NCKAP1, a gene that regulates actin filament dynamics, and CAMSAP1, a calmodulin regulate gene that regulates microtubule dynamics, as top scoring modifiers of hypertrophic cardiomyopathy phenotypes (score=9) while LDB2, RYR2, FBN1 and ATP1A2 had modifier scores of 8. Of the top scoring genes, 21 out of 96 were identified as candidates a priori. Our candidate prioritization scheme identified the previously described modifiers of cardiomyopathy phenotype, FHOD3 and MYBPC3, as top scoring genes. We identified structural variants in 21 clinically sequenced cardiomyopathy associated genes, 13 of which were at less than 10% frequency. Copy number variants in ILK and CSRP3 were nominally associated with ejection fraction (p=0.03), while 8 genes showed copy gains (GLA, FKTN, SGCD, TTN, SOS1, ANKRD1, VCL and NEBL). Structural variants were found in CSRP3, MYL3 and TNNC1, all of which have been implicated as causative for HCM. Conclusion: Evaluation of the whole genome sequence, even in the case of putatively monogenic disease, leads to important diagnostic and scientific insights not revealed by panel-based sequencing.

scholarly journals Detection and characterization of copy number variants based on whole-genome sequencing by DNBSEQ platforms

2019 ◽  
Author(s):  
Junhua Rao ◽  
Lihua Peng ◽  
Fang Chen ◽  
Hui Jiang ◽  
Chunyu Geng ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Copy Number Variants ◽  
Copy Number Variant ◽  
Whole Genome ◽  
Genome Wide ◽  
Wide Range ◽  
Distribution Sensitivity ◽  
Cnv Detection

AbstractBackgroundNext-generation sequence (NGS) has rapidly developed in past years which makes whole-genome sequencing (WGS) becoming a more cost- and time-efficient choice in wide range of biological researches. We usually focus on some variant detection via WGS data, such as detection of single nucleotide polymorphism (SNP), insertion and deletion (Indel) and copy number variant (CNV), which playing an important role in many human diseases. However, the feasibility of CNV detection based on WGS by DNBSEQ™ platforms was unclear. We systematically analysed the genome-wide CNV detection power of DNBSEQ™ platforms and Illumina platforms on NA12878 with five commonly used tools, respectively.ResultsDNBSEQ™ platforms showed stable ability to detect slighter more CNVs on genome-wide (average 1.24-fold than Illumina platforms). Then, CNVs based on DNBSEQ™ platforms and Illumina platforms were evaluated with two public benchmarks of NA12878, respectively. DNBSEQ™ and Illumina platforms showed similar sensitivities and precisions on both two benchmarks. Further, the difference between tools for CNV detection was analyzed, and indicated the selection of tool for CNV detection could affected the CNV performance, such as count, distribution, sensitivity and precision.ConclusionThe major contribution of this paper is providing a comprehensive guide for CNV detection based on WGS by DNBSEQ™ platforms for the first time.

scholarly journals Sarek: A portable workflow for whole-genome sequencing analysis of germline and somatic variants

2018 ◽  
Author(s):  
Maxime Garcia ◽  
Szilveszter Juhos ◽  
Malin Larsson ◽  
Pall I. Olason ◽  
Marcel Martin ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Open Source ◽  
Genome Sequencing ◽  
Development Project ◽  
Whole Genome ◽  
Sequencing Analysis ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Insertion And Deletion ◽  
Sample Heterogeneity

AbstractSummaryWhole-genome sequencing (WGS) is a cornerstone of precision medicine, but portable and reproducible open-source workflows for WGS analyses of germline and somatic variants are lacking. We present Sarek, a modular, comprehensive, and easy-to-install workflow, combining a range of software for the identification and annotation of single-nucleotide variants (SNVs), insertion and deletion variants (indels), structural variants, tumor sample heterogeneity, and karyotyping from germline or paired tumor/normal samples. Sarek is implemented in a bioinformatics workflow language (Nextflow) with Docker and Singularity compatible containers, ensuring easy deployment and full reproducibility at any Linux based compute cluster or cloud computing environment. Sarek supports the human reference genomes GRCh37 and GRCh38, and can readily be used both as a core production workflow at sequencing facilities and as a powerful stand-alone tool for individual research groups.AvailabilitySource code and instructions for local installation are available at GitHub (https://github.com/SciLifeLab/Sarek) under the MIT open-source license, and we invite the research community to contribute additional functionality as a collaborative open-source development project.

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