A benchmarking framework for the accurate and cost-effective detection of clinically-relevant structural variants for cancer target identification and diagnosis

BACKGROUND

Accurate clinical structural variant (SV) calling is essential for cancer target identification and diagnosis but has been historically challenging due to the lack of ground truth for clinical specimens. Meanwhile, reduced clinical-testing cost is the key to the widespread clinical utility.

METHODS

We analyzed massive data from tumor samples of 476 patients and developed a computational framework for accurate and cost-effective detection of clinically-relevant SVs. In addition, standard materials and classical experiments including immunohistochemistry and/or fluorescence in situ hybridization were used to validate the developed computational framework.

RESULTS

We systematically evaluated the common algorithms for SV detection and established an expert-reviewed SV call set of 1,303 tumor-specific SVs with high-evidence levels. Moreover, we developed a random-forest-based decision model to improve the true positive of SVs. To independently validate the tailored 'two-step' strategy, we utilized standard materials and classical experiments. The accuracy of the model was over 90% (92-99.78%) for all types of data.

CONCLUSION

Our study provides a valuable resource and an actionable guide to improve cancer-specific SV detection accuracy and clinical applicability.

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases

BACKGROUND

Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome.

METHODS

We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants.

RESULTS

Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving.

CONCLUSIONS

Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing.

Genomic variant benchmark: if you cannot measure it, you cannot improve it

Genomic benchmark datasets are essential to driving the field of genomics and bioinformatics. They provide a snapshot of the performances of sequencing technologies and analytical methods and highlight future challenges. However, they depend on sequencing technology, reference genome, and available benchmarking methods. Thus, creating a genomic benchmark dataset is laborious and highly challenging, often involving multiple sequencing technologies, different variant calling tools, and laborious manual curation. In this review, we discuss the available benchmark datasets and their utility. Additionally, we focus on the most recent benchmark of genes with medical relevance and challenging genomic complexity.

Reply: Correspondence on NanoVar's performance outlined by Jiang T. et al. in 'Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation'

We published a paper in BMC Bioinformatics comprehensively evaluating the performance of structural variation (SV) calling with long-read SV detection methods based on simulated error-prone long-read data under various sequencing settings. Recently, C.Y.T. et al. wrote a correspondence claiming that the performance of NanoVar was underestimated in our benchmarking and listed some errors in our previous manuscripts. To clarify these matters, we reproduced our previous benchmarking results and carried out a series of parallel experiments on both the newly generated simulated datasets and the ones provided by C.Y.T. et al. The robust benchmark results indicate that NanoVar has unstable performance on simulated data produced from different versions of VISOR, while other tools do not exhibit this phenomenon. Furthermore, the errors proposed by C.Y.T. et al. were due to them using another version of VISOR and Sniffles, which caused many changes in usage and results compared to the versions applied in our previous work. We hope that this commentary proves the validity of our previous publication, clarifies and eliminates the misunderstanding about the commands and results in our benchmarking. Furthermore, we welcome more experts and scholars in the scientific community to pay attention to our research and help us better optimize these valuable works.

Long-read sequencing identifies a common transposition haplotype predisposing for CLCNKB deletions

BACKGROUND

Long-read sequencing is increasingly used to uncover structural variants in the human genome, both functionally neutral and deleterious. Structural variants occur more frequently in regions with a high homology or repetitive segments, and one rearrangement may predispose to additional events. Bartter syndrome type 3 (BS 3) is a monogenic tubulopathy caused by deleterious variants in the chloride channel gene CLCNKB, a high proportion of these being large gene deletions. Multiplex ligation-dependent probe amplification, the current diagnostic gold standard for this type of mutation, will indicate a simple homozygous gene deletion in biallelic deletion carriers. However, since the phenotypic spectrum of BS 3 is broad even among biallelic deletion carriers, we undertook a more detailed analysis of precise breakpoint regions and genomic structure.

METHODS

Structural variants in 32 BS 3 patients from 29 families and one BS4b patient with CLCNKB deletions were investigated using long-read and synthetic long-read sequencing, as well as targeted long-read sequencing approaches.

RESULTS

We report a ~3 kb duplication of 3'-UTR CLCNKB material transposed to the corresponding locus of the neighbouring CLCNKA gene, also found on ~50 % of alleles in healthy control individuals. This previously unknown common haplotype is significantly enriched in our cohort of patients with CLCNKB deletions (45 of 51 alleles with haplotype information, 2.2 kb and 3.0 kb transposition taken together, p=9.16×10-9). Breakpoint coordinates for the CLCNKB deletion were identifiable in 28 patients, with three being compound heterozygous. In total, eight different alleles were found, one of them a complex rearrangement with three breakpoint regions. Two patients had different CLCNKA/CLCNKB hybrid genes encoding a predicted CLCNKA/CLCNKB hybrid protein with likely residual function.

CONCLUSIONS

The presence of multiple different deletion alleles in our cohort suggests that large CLCNKB gene deletions originated from many independently recurring genomic events clustered in a few hot spots. The uncovered associated sequence transposition haplotype apparently predisposes to these additional events. The spectrum of CLCNKB deletion alleles is broader than expected and likely still incomplete, but represents an obvious candidate for future genotype/phenotype association studies. We suggest a sensitive and cost-efficient approach, consisting of indirect sequence capture and long-read sequencing, to analyse disease-relevant structural variant hotspots in general.

Applications of advanced technologies for detecting genomic structural variation

Chromosomal structural variation (SV) encompasses a heterogenous class of genetic variants that exerts strong influences on human health and disease. Despite their importance, many structural variants (SVs) have remained poorly characterized at even a basic level, a discrepancy predicated upon the technical limitations of prior genomic assays. However, recent advances in genomic technology can identify and localize SVs accurately, opening new questions regarding SV risk factors and their impacts in humans. Here, we first define and classify human SVs and their generative mechanisms, highlighting characteristics leveraged by various SV assays. We next examine the first-ever gapless assembly of the human genome and the technical process of assembling it, which required third-generation sequencing technologies to resolve structurally complex loci. The new portions of that "telomere-to-telomere" and subsequent pangenome assemblies highlight aspects of SV biology likely to develop in the near-term. We consider the strengths and limitations of the most promising new SV technologies and when they or longstanding approaches are best suited to meeting salient goals in the study of human SV in population-scale genomics research, clinical, and public health contexts. It is a watershed time in our understanding of human SV when new approaches are expected to fundamentally change genomic applications.

Intercross population study reveals that co-mutation of mitfa genes in two subgenomes induces red skin color in common carp ( Cyprinus carpio wuyuanensis)

Common carp are among the oldest domesticated fish in the world. As such, there are many food and ornamental carp strains with abundant phenotypic variations due to natural and artificial selection. Hebao red carp (HB, Cyprinus carpio wuyuanensis), an indigenous strain in China, is renowned for its unique body morphology and reddish skin. To reveal the genetic basis underlying the distinct skin color of HB, we constructed an improved high-fidelity (HiFi) HB genome with good contiguity, completeness, and correctness. Genome structure comparison was conducted between HB and a representative wild strain, Yellow River carp (YR, C. carpio haematopterus), to identify structural variants and genes under positive selection. Signatures of artificial selection during domestication were identified in HB and YR populations, while phenotype mapping was performed in a segregating population generated by HB×YR crosses. Body color in HB was associated with regions with fixed mutations. The simultaneous mutation and superposition of a pair of homologous genes ( mitfa) in chromosomes A06 and B06 conferred the reddish color in domesticated HB. Transcriptome analysis of common carp with different alleles of the mitfa mutation confirmed that gene duplication can buffer the deleterious effects of mutation in allotetraploids. This study provides new insights into genotype-phenotype associations in allotetraploid species and lays a foundation for future breeding of common carp.

Long-Read Whole-Genome Sequencing Using a Nanopore Sequencer and Detection of Structural Variants in Cancer Genomes

Long-read sequencing technologies enable us to precisely identify structural variants (SVs), which would be occasionally associated with various types of diseases, including cancers. In this section, we introduce experimental and computational procedures for conducting long-read whole-genome sequencing (WGS) of cancer genomes from fresh frozen tissues/cells. We also demonstrate the analysis of SVs in cancer genomes using long-read WGS data from lung cancer cell lines by several representative computational tools, such as cuteSV and Sniffles2, as examples.

SvAnna: efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing

Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to sequencing technology limitations. Long-read sequencing enables comprehensive detection of SVs, but approaches for prioritization of candidate SVs are needed. Structural variant Annotation and analysis (SvAnna) assesses all classes of SVs and their intersection with transcripts and regulatory sequences, relating predicted effects on gene function with clinical phenotype data. SvAnna places 87% of deleterious SVs in the top ten ranks. The interpretable prioritizations offered by SvAnna will facilitate the widespread adoption of long-read sequencing in diagnostic genomics. SvAnna is available at https://github.com/TheJacksonLaboratory/SvAnn a .

Short structural variants as informative genetic markers for ALS disease risk and progression

There is considerable variability in disease progression for patients with amyotrophic lateral sclerosis (ALS) including the age of disease onset, site of disease onset, and survival time. There is growing evidence that short structural variations (SSVs) residing in frequently overlooked genomic regions can contribute to complex disease mechanisms and can explain, in part, the phenotypic variability in ALS patients. Here, we discuss SSVs recently characterized by our laboratory and how these discoveries integrate into the current literature on ALS, particularly in the context of application to future clinical trials. These markers may help to identify and differentiate patients for clinical trials that have a similar ALS disease mechanism(s), thereby reducing the impact of participant heterogeneity. As evidence accumulates for the genetic markers discovered in SQSTM1, SCAF4, and STMN2, we hope to improve the outcomes of future ALS clinical trials.

PRINCESS: comprehensive detection of haplotype resolved SNVs, SVs, and methylation

Long-read sequencing has been shown to have advantages in structural variation (SV) detection and methylation calling. Many studies focus either on SV, methylation, or phasing of SNV; however, only the combination of variants provides a comprehensive insight into the sample and thus enables novel findings in biology or medicine. PRINCESS is a structured workflow that takes raw sequence reads and generates a fully phased SNV, SV, and methylation call set within a few hours. PRINCESS achieves high accuracy and long phasing even on low coverage datasets and can resolve repetitive, complex medical relevant genes that often escape detection. PRINCESS is publicly available at https://github.com/MeHelmy/princess under the MIT license.

Detecting copy number variation in next generation sequencing data from diagnostic gene panels

Background

Detection of copy number variation (CNV) in genes associated with disease is important in genetic diagnostics, and next generation sequencing (NGS) technology provides data that can be used for CNV detection. However, CNV detection based on NGS data is in general not often used in diagnostic labs as the data analysis is challenging, especially with data from targeted gene panels. Wet lab methods like MLPA (MRC Holland) are widely used, but are expensive, time consuming and have gene-specific limitations. Our aim has been to develop a bioinformatic tool for CNV detection from NGS data in medical genetic diagnostic samples.

Results

Our computational pipeline for detection of CNVs in NGS data from targeted gene panels utilizes coverage depth of the captured regions and calculates a copy number ratio score for each region. This is computed by comparing the mean coverage of the sample with the mean coverage of the same region in other samples, defined as a pool. The pipeline selects pools for comparison dynamically from previously sequenced samples, using the pool with an average coverage depth that is nearest to the one of the samples. A sliding window-based approach is used to analyze each region, where length of sliding window and sliding distance can be chosen dynamically to increase or decrease the resolution. This helps in detecting CNVs in small or partial exons. With this pipeline we have correctly identified the CNVs in 36 positive control samples, with sensitivity of 100% and specificity of 91%. We have detected whole gene level deletion/duplication, single/multi exonic level deletion/duplication, partial exonic deletion and mosaic deletion. Since its implementation in mid-2018 it has proven its diagnostic value with more than 45 CNV findings in routine tests.

Conclusions

With this pipeline as part of our diagnostic practices it is now possible to detect partial, single or multi-exonic, and intragenic CNVs in all genes in our target panel. This has helped our diagnostic lab to expand the portfolio of genes where we offer CNV detection, which previously was limited by the availability of MLPA kits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01059-x.

Application of long-read sequencing to the detection of structural variants in human cancer genomes

In recent years, the so-called long-read sequencing technology has had a substantial impact on various aspects of genome sciences. Here, we introduce recent studies of cancerous structural variants (SVs) using long-read sequencing technologies, namely Pacific Biosciences (PacBio) sequencers, Oxford Nanopore Technologies (ONT) sequencers, and linked-read methods. By taking advantage of long-read lengths, these technologies have enabled the precise detection of SVs, including long insertions by transposable elements, such as LINE-1. In addition to SV detection, the epigenome status (including DNA methylation and haplotype information) surrounding SV loci has also been unveiled by long-read sequencing technologies, to identify the effects of SVs. Among the various research fields in which long-read sequencing has been applied, cancer genomics has shown the most remarkable advances. In fact, many studies are beginning to shed light on the detection of SVs and the elucidation of their complex structures in various types of cancer. In the particular case of cancers, we summarize the technical limitations of the application of this technology to the analysis of clinical samples. We will introduce recent achievements from this viewpoint. However, a similar approach will be started for other applications in the near future. Therefore, by complementing the current short-read sequencing analysis, long-read sequencing should reveal the complex nature of human genomes in their healthy and disease states, which will open a new opportunity for a better understanding of disease development and for a novel strategy for drug development.

Insertion of an endogenous Jaagsiekte sheep retrovirus element into the BCO2 - gene abolishes its function and leads to yellow discoloration of adipose tissue in Norwegian Spælsau (Ovis aries)

Background

The accumulation of carotenoids in adipose tissue leading to yellow fat is, in sheep, a heritable recessive trait that can be attributed to a nonsense mutation in the beta-carotene oxygenase 2 (BCO2) gene. However, not all sheep breeds suffering from yellow fat have this nonsense mutation, meaning that other functional mechanisms must exist. We investigated one such breed, the Norwegian spælsau.

Results

In spælsau we detected an aberration in BCO2 mRNA. Nanopore sequencing of genomic DNA revealed the insertion of a 7.9 kb endogenous Jaagsiekte Sheep Retrovirus (enJSRV) sequence in the first intron of the BCO2 gene. Close examination of its cDNA revealed that the BCO2 genes first exon was spliced together with enJSRV-sequence immediately downstream of a potential -AG splice acceptor site at enJSRV position 415. The hybrid protein product consists of 29 amino acids coded by the BCO2 exon 1, one amino acid coded by the junction sequence, followed by 28 amino acids arbitrary coded for by the enJSRV-sequence, before a translation stop codon is reached.

Conclusions

Considering that the functional BCO2 protein consists of 575 amino acids, it is unlikely that the 58 amino acid BCO2/enJSRV hybrid protein can display any enzymatic function. The existence of this novel BCO2 allele represents an alternative functional mechanism accounting for BCO2 inactivation and is a perfect example of the potential benefits for searching for structural variants using long-read sequencing data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07826-5.

Whole-genome sequencing of phenotypically distinct inflammatory breast cancers reveals similar genomic alterations to non-inflammatory breast cancers

BACKGROUND

Inflammatory breast cancer (IBC) has a highly invasive and metastatic phenotype. However, little is known about its genetic drivers. To address this, we report the largest cohort of whole-genome sequencing (WGS) of IBC cases.

METHODS

We performed WGS of 20 IBC samples and paired normal blood DNA to identify genomic alterations. For comparison, we used 23 matched non-IBC samples from the Cancer Genome Atlas Program (TCGA). We also validated our findings using WGS data from the International Cancer Genome Consortium (ICGC) and the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We examined a wide selection of genomic features to search for differences between IBC and conventional breast cancer. These include (i) somatic and germline single-nucleotide variants (SNVs), in both coding and non-coding regions; (ii) the mutational signature and the clonal architecture derived from these SNVs; (iii) copy number and structural variants (CNVs and SVs); and (iv) non-human sequence in the tumors (i.e., exogenous sequences of bacterial origin).

RESULTS

Overall, IBC has similar genomic characteristics to non-IBC, including specific alterations, overall mutational load and signature, and tumor heterogeneity. In particular, we observed similar mutation frequencies between IBC and non-IBC, for each gene and most cancer-related pathways. Moreover, we found no exogenous sequences of infectious agents specific to IBC samples. Even though we could not find any strongly statistically distinguishing genomic features between the two groups, we did find some suggestive differences in IBC: (i) The MAST2 gene was more frequently mutated (20% IBC vs. 0% non-IBC). (ii) The TGF β pathway was more frequently disrupted by germline SNVs (50% vs. 13%). (iii) Different copy number profiles were observed in several genomic regions harboring cancer genes. (iv) Complex SVs were more frequent. (v) The clonal architecture was simpler, suggesting more homogenous tumor-evolutionary lineages.

CONCLUSIONS

Whole-genome sequencing of IBC manifests a similar genomic architecture to non-IBC. We found no unique genomic alterations shared in just IBCs; however, subtle genomic differences were observed including germline alterations in TGFβ pathway genes and somatic mutations in the MAST2 kinase that could represent potential therapeutic targets.

An international virtual hackathon to build tools for the analysis of structural variants within species ranging from coronaviruses to vertebrates

In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research.   The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at  https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.

An international virtual hackathon to build tools for the analysis of structural variants within species ranging from coronaviruses to vertebrates

In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research.   The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at  https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.

Elucidation of molecular pathogenesis and drug development for psychiatric disorders from rare disease-susceptibility variants

Recent rapid progress in genome analysis and large-scale consortia has made it possible to discover variants with a variety of allele frequencies and effect sizes associated with psychiatric disorders. Among psychiatric disorder-susceptibility variants, rare variants with large effect sizes detected by sequencing analysis or array comparative genomic hybridization would be particularly useful for elucidating pathophysiology by developing disease models, such as genome-edited mouse or induced pluripotent stem cells. In the last decade, investigations of rare variants with large effect size have revealed an important role of neurodevelopment in the pathogenesis of psychiatric disorders. In future research, integration of recent evidence concerning the contribution of the immune system or gut microbiota will enhance our understanding of psychiatric disorders and facilitate novel drug development.

22q11.2 microdeletion and increased risk for type 2 diabetes

BACKGROUND

The 22q11.2 microdeletion is the pathogenic copy number variation (CNV) associated with 22q11.2 deletion syndrome (22q11.2DS, formerly known as DiGeorge syndrome). Familiar endocrinological manifestations include hypoparathyroidism and hypothyroidism, with recent elucidation of elevated risk for obesity in adults. In this study, we aimed to determine whether adults with 22q11.2DS have an increased risk of developing type 2 diabetes (T2D).

METHODS

We studied the effect of the 22q11.2 microdeletion on risk for T2D, defined by history and glycosylated hemoglobin (HbA1c), using weighted survey data from the adult Canadian population (based on n = 11,874) and from a clinical cohort of adults with 22q11.2DS (n = 314), aged 17-69 years. Binomial logistic regression models accounted for age, sex, non-European ethnicity, family history of T2D, obesity, and antipsychotic medication use.

FINDINGS

The 22q11.2 microdeletion was a significant independent risk factor for T2D (OR 2·44, 95% CI 1·39-4·31), accounting for other factors (p < 0·0001). All factors except sex were also significant within 22q11.2DS. The median age at diagnosis of T2D was significantly younger in 22q11.2DS than in the Canadian population sample (32 vs 50 years, p < 0·0001). In adults without T2D, HbA1c was significantly higher in 22q11.2DS than the population (p = 0·042), after accounting for younger age of the 22q11.2DS group.

INTERPRETATION

The results support the 22q11.2 microdeletion as a novel independent risk factor and potential model for early onset T2D. The findings complement emerging evidence that rare CNVs may contribute to risk for T2D. The results have implications for precision medicine and research into the underlying pathogenesis of T2D.

Long-read individual-molecule sequencing reveals CRISPR-induced genetic heterogeneity in human ESCs

Quantifying the genetic heterogeneity of a cell population is essential to understanding of biological systems. We develop a universal method to label individual DNA molecules for single-base-resolution haplotype-resolved quantitative characterization of diverse types of rare variants, with frequency as low as 4 × 10-5, using both short- or long-read sequencing platforms. It provides the first quantitative evidence of persistent nonrandom large structural variants and an increase in single-nucleotide variants at the on-target locus following repair of double-strand breaks induced by CRISPR-Cas9 in human embryonic stem cells.

Loqusdb: added value of an observations database of local genomic variation

BACKGROUND

Exome and genome sequencing is becoming the method of choice for rare disease diagnostics. One of the key challenges remaining is distinguishing the disease causing variants from the benign background variation. After analysis and annotation of the sequencing data there are typically thousands of candidate variants requiring further investigation. One of the most effective and least biased ways to reduce this number is to assess the rarity of a variant in any population. Currently, there are a number of reliable sources of information for major population frequencies when considering single nucleotide variants (SNVs) and small insertion and deletions (INDELs), with gnomAD as the most prominent public resource available. However, local variation or frequencies in sub-populations may be underrepresented in these public resources. In contrast, for structural variation (SV), the background frequency in the general population is more or less unknown mostly due to challenges in calling SVs in a consistent way. Keeping track of local variation is one way to overcome these problems and significantly reduce the number of potential disease causing variants retained for manual inspection, both for SNVs and SVs.

RESULTS

Here, we present loqusdb, a tool to solve the challenge of keeping track of any type of variant observations from genome sequencing data. Loqusdb was designed to handle a large flow of samples and unlike other solutions, samples can be added continuously to the database without rebuilding it, facilitating improvements and additions. We assessed the added value of a local observations database using 98 samples annotated with information from a background of 888 unrelated individuals.

CONCLUSIONS

We show both how powerful SV analysis can be when filtering for population frequencies and how the number of apparently rare SNVs/INDELs can be reduced by adding local population information even after annotating the data with other large frequency databases, such as gnomAD. In conclusion, we show that a local frequency database is an attractive, and a necessary addition to the publicly available databases that facilitate the analysis of exome and genome data in a clinical setting.

Clinical spectrum, genetic complexity and therapeutic approaches for retinal disease caused by ABCA4 mutations

The ABCA4 protein (then called a “rim protein”) was first identified in 1978 in the rims and incisures of rod photoreceptors. The corresponding gene, ABCA4, was cloned in 1997, and variants were identified as the cause of autosomal recessive Stargardt disease (STGD1). Over the next two decades, variation in ABCA4 has been attributed to phenotypes other than the classically defined STGD1 or fundus flavimaculatus, ranging from early onset and fast progressing cone-rod dystrophy and retinitis pigmentosa-like phenotypes to very late onset cases of mostly mild disease sometimes resembling, and confused with, age-related macular degeneration. Similarly, analysis of the ABCA4 locus uncovered a trove of genetic information, including >1200 disease-causing mutations of varying severity, and of all types – missense, nonsense, small deletions/insertions, and splicing affecting variants, of which many are located deep-intronic. Altogether, this has greatly expanded our understanding of complexity not only of the diseases caused by ABCA4 mutations, but of all Mendelian diseases in general. This review provides an in depth assessment of the cumulative knowledge of ABCA4-associated retinopathy – clinical manifestations, genetic complexity, pathophysiology as well as current and proposed therapeutic approaches.

Whole genome resequencing of the Iranian native dogs and wolves to unravel variome during dog domestication

BACKGROUND

Advances in genome technology have simplified a new comprehension of the genetic and historical processes crucial to rapid phenotypic evolution under domestication. To get new insight into the genetic basis of the dog domestication process, we conducted whole-genome sequence analysis of three wolves and three dogs from Iran which covers the eastern part of the Fertile Crescent located in Southwest Asia where the independent domestication of most of the plants and animals has been documented and also high haplotype sharing between wolves and dog breeds has been reported.

RESULTS

Higher diversity was found within the wolf genome compared with the dog genome. A total number of 12.45 million SNPs were detected in all individuals (10.45 and 7.82 million SNPs were identified for all the studied wolves and dogs, respectively) and a total number of 3.49 million small Indels were detected in all individuals (3.11 and 2.24 million small Indels were identified for all the studied wolves and dogs, respectively). A total of 10,571 copy number variation regions (CNVRs) were detected across the 6 individual genomes, covering 154.65 Mb, or 6.41%, of the reference genome (canFam3.1). Further analysis showed that the distribution of deleterious variants in the dog genome is higher than the wolf genome. Also, genomic annotation results from intron and intergenic regions showed that the proportion of variations in the wolf genome is higher than that in the dog genome, while the proportion of the coding sequences and 3'-UTR in the dog genome is higher than that in the wolf genome. The genes related to the olfactory and immune systems were enriched in the set of the structural variants (SVs) identified in this work.

CONCLUSIONS

Our results showed more deleterious mutations and coding sequence variants in the domestic dog genome than those in wolf genome. By providing the first Iranian dog and wolf variome map, our findings contribute to understanding the genetic architecture of the dog domestication.

Structural variant identification and characterization

Structural variant (SV) differences between human genomes can cause germline and mosaic disease as well as inter-individual variation. De-regulation of accurate DNA repair and genomic surveillance mechanisms results in a large number of SVs in cancer. Analysis of the DNA sequences at SV breakpoints can help identify pathways of mutagenesis and regions of the genome that are more susceptible to rearrangement. Large-scale SV analyses have been enabled by high-throughput genome-level sequencing on humans in the past decade. These studies have shed light on the mechanisms and prevalence of complex genomic rearrangements. Recent advancements in both sequencing and other mapping technologies as well as calling algorithms for detection of genomic rearrangements have helped propel SV detection into population-scale studies, and have begun to elucidate previously inaccessible regions of the genome. Here, we discuss the genomic organization of simple and complex SVs, the molecular mechanisms of their formation, and various ways to detect them. We also introduce methods for characterizing SVs and their consequences on human genomes.

MoMI-G: modular multi-scale integrated genome graph browser

Background

Genome graph is an emerging approach for representing structural variants on genomes with branches. For example, representing structural variants of cancer genomes as a genome graph is more natural than representing such genomes as differences from the linear reference genome. While more and more structural variants are being identified by long-read sequencing, many of them are difficult to visualize using existing structural variants visualization tools. To this end, visualization method for large genome graphs such as human cancer genome graphs is demanded.

Results

We developed MOdular Multi-scale Integrated Genome graph browser, MoMI-G, a web-based genome graph browser that can visualize genome graphs with structural variants and supporting evidences such as read alignments, read depth, and annotations. This browser allows more intuitive recognition of large, nested, and potentially more complex structural variations. MoMI-G has view modules for different scales, which allow users to view the whole genome down to nucleotide-level alignments of long reads. Alignments spanning reference alleles and those spanning alternative alleles are shown in the same view. Users can customize the view, if they are not satisfied with the preset views. In addition, MoMI-G has Interval Card Deck, a feature for rapid manual inspection of hundreds of structural variants. Herein, we describe the utility of MoMI-G by using representative examples of large and nested structural variations found in two cell lines, LC-2/ad and CHM1.

Conclusions

Users can inspect complex and large structural variations found by long-read analysis in large genomes such as human genomes more smoothly and more intuitively. In addition, users can easily filter out false positives by manually inspecting hundreds of identified structural variants with supporting long-read alignments and annotations in a short time.

Software availability

MoMI-G is freely available at https://github.com/MoMI-G/MoMI-G under the MIT license.

Implications of non-uniqueness in phylogenetic deconvolution of bulk DNA samples of tumors

Background

Tumors exhibit extensive intra-tumor heterogeneity, the presence of groups of cellular populations with distinct sets of somatic mutations. This heterogeneity is the result of an evolutionary process, described by a phylogenetic tree. In addition to enabling clinicians to devise patient-specific treatment plans, phylogenetic trees of tumors enable researchers to decipher the mechanisms of tumorigenesis and metastasis. However, the problem of reconstructing a phylogenetic tree T given bulk sequencing data from a tumor is more complicated than the classic phylogeny inference problem. Rather than observing the leaves of T directly, we are given mutation frequencies that are the result of mixtures of the leaves of T. The majority of current tumor phylogeny inference methods employ the perfect phylogeny evolutionary model. The underlying Perfect Phylogeny Mixture (PPM) combinatorial problem typically has multiple solutions.

Results

We prove that determining the exact number of solutions to the PPM problem is #P-complete and hard to approximate within a constant factor. Moreover, we show that sampling solutions uniformly at random is hard as well. On the positive side, we provide a polynomial-time computable upper bound on the number of solutions and introduce a simple rejection-sampling based scheme that works well for small instances. Using simulated and real data, we identify factors that contribute to and counteract non-uniqueness of solutions. In addition, we study the sampling performance of current methods, identifying significant biases.

Conclusions

Awareness of non-uniqueness of solutions to the PPM problem is key to drawing accurate conclusions in downstream analyses based on tumor phylogenies. This work provides the theoretical foundations for non-uniqueness of solutions in tumor phylogeny inference from bulk DNA samples.

Rapid detection of chromosomal translocation and precise breakpoint characterization in acute myeloid leukemia by nanopore long-read sequencing

Detection of chromosomal translocation is a key component in diagnosis and management of acute myeloid leukemia (AML). Targeted RNA next-generation sequencing (NGS) is emerging as a powerful and clinically practical tool, but it depends on expression of RNA transcript from the underlying DNA translocation. Here, we show the clinical utility of nanopore long-read sequencing in rapidly detecting DNA translocation with exact breakpoints. In a newly diagnosed patient with AML, conventional karyotyping showed translocation t(10;12)(q22;p13) but RNA NGS detected NUP98-NSD1 fusion transcripts from a known cryptic translocation t(5;11)(q35;p15). Rapid PCR-free nanopore whole-genome sequencing yielded a 26,194 bp sequencing read and revealed the t(10;12) breakpoint to be DUSP13 and GRIN2B in head-to-head configuration. This translocation was then classified as a passenger structural variant. The sequencing also yielded a 20,709 bp sequencing read and revealed the t(5;11) breakpoint of the driver NUP98-NSD1 fusion. The identified DNA breakpoints also served as markers for molecular monitoring, in addition to fusion transcript expression by digital PCR and sequence mutations by NGS. We illustrate that third-generation nanopore sequencing is a simple and low-cost workflow for DNA translocation detection.

Prevalence and properties of intragenic copy-number variation in Mendelian disease genes

Purpose

We investigated the frequencies and characteristics of intragenic copy-number variants (CNVs) in a deep sampling of disease genes associated with monogenic disorders.

Methods

Subsets of 1507 genes were tested using next-generation sequencing to simultaneously detect sequence variants and CNVs in >143,000 individuals referred for genetic testing. We analyzed CNVs in gene panels for hereditary cancer syndromes and cardiovascular, neurological, or pediatric disorders.

Results

Our analysis identified 2844 intragenic CNVs in 384 clinically tested genes. CNVs were observed in 1.9% of the entire cohort but in a disproportionately high fraction (9.8%) of individuals with a clinically significant result. CNVs accounted for 4.7–35% of pathogenic variants, depending on clinical specialty. Distinct patterns existed among CNVs in terms of copy number, location, exons affected, clinical classification, and genes affected. Separately, analysis of de-identified data for 599 genes unrelated to the clinical phenotype yielded 4054 CNVs. Most of these CNVs were novel rare events, present as duplications, and enriched in genes associated with recessive disorders or lacking loss-of-function mutational mechanisms.

Conclusion

Universal intragenic CNV analysis adds substantial clinical sensitivity to genetic testing. Clinically relevant CNVs have distinct properties that distinguish them from CNVs contributing to normal variation in human disease genes.

Detecting large deletions at base pair level by combining split read and paired read data

Background

Genomic structural variants (SV) play a significant role in the onset and progression of cancer. Genomic deletions can create oncogenic fusion genes or cause the loss of tumor suppressing gene function which can lead to tumorigenesis by downregulating these genes. Detecting these variants has clinical importance in the treatment of diseases. Furthermore, it is also clinically important to detect their breakpoint boundaries at high resolution. We have generalized the framework of a previously-published algorithm that located translocations, and we have applied that framework to develop a method to locate deletions at base pair level using next-generation sequencing data. Our method uses abnormally mapped read pairs, and then subsequently maps split reads to identify precise breakpoints.

Results

On a primary prostate cancer dataset and a simulated dataset, our method predicted the number, type, and breakpoints of biologically validated SVs at high accuracy. It also outperformed two existing algorithms on precise breakpoint prediction, which is clinically important.

Conclusion

Our algorithm, called Pegasus, accurately calls deletion breakpoints. However, the method must be extended to allow for germline variant filtering and heterozygous deletion detection.

The source code that implements Pegasus can be downloaded from the following URL: http://github.com/mhayes20/Pegasus.

Clinico-Hematological Profile of Hb Q India: An Uncommon Hemoglobin Variant

Inherited hemoglobin disorders include thalassemias and structural variants like HbS, HbE, and HbD, Hb Lepore, HbD-Iran, Hb-H disease and HbQ India. HbQ India is an uncommon alpha-chain structural hemoglobin variant seen in North and West India. Patients are mostly asymptomatic and often present in the heterozygous state or co-inherited with beta-thalassaemia. This study was done in a tertiary care teaching hospital in North India over a period of 7 years among patients referred from antenatal and other clinics for screening of hemoglobin disorders. Complete blood count, peripheral blood smear examination and cation exchange high performance liquid chromatography (HPLC) was done to quantify various hemoglobins. HbQ India was diagnosed if the unknown variant hemoglobin was detected within the characteristic retention window. Of a total of 7530 patients screened, 31 (0.4%) were detected to have HbQ India. Of these, 25 (0.3%) patients had HbQ India trait and 6 (0.1%) patients had compound heterozygosity for HbQ India and Beta Thalassemia trait (HbQ India-BTT). All patients were clinically asymptomatic and were detected as part of the screening for hemoglobin disorders. Only two patients with HbQ India-BTT had hemoglobin less than 10 g/dL. In 25 patients with HbQ India trait, HbQ ranged from 13.6 to 24.4% and in 6 patients with HbQ India-BTT, HbQ India ranged from 7.4 to 9.0%. HbQ India is an uncommon structural hemoglobin variant. Although asymptomatic, it may cause diagnostic difficulty in the compound heterozygous state with beta thalassemia. HPLC provides a rapid, accurate and reproducible method for screening of this condition to identify and counsel individuals.

Improvements and impacts of GRCh38 human reference on high throughput sequencing data analysis

Analyses of high throughput sequencing data starts with alignment against a reference genome, which is the foundation for all re-sequencing data analyses. Each new release of the human reference genome has been augmented with improved accuracy and completeness. It is presumed that the latest release of human reference genome, GRCh38 will contribute more to high throughput sequencing data analysis by providing more accuracy. But the amount of improvement has not yet been quantified. We conducted a study to compare the genomic analysis results between the GRCh38 reference and its predecessor GRCh37. Through analyses of alignment, single nucleotide polymorphisms, small insertion/deletions, copy number and structural variants, we show that GRCh38 offers overall more accurate analysis of human sequencing data. More importantly, GRCh38 produced fewer false positive structural variants. In conclusion, GRCh38 is an improvement over GRCh37 not only from the genome assembly aspect, but also yields more reliable genomic analysis results.

Fitness consequences of polymorphic inversions in the zebra finch genome

Background

Inversion polymorphisms constitute an evolutionary puzzle: they should increase embryo mortality in heterokaryotypic individuals but still they are widespread in some taxa. Some insect species have evolved mechanisms to reduce the cost of embryo mortality but humans have not. In birds, a detailed analysis is missing although intraspecific inversion polymorphisms are regarded as common. In Australian zebra finches (Taeniopygia guttata), two polymorphic inversions are known cytogenetically and we set out to detect these two and potentially additional inversions using genomic tools and study their effects on embryo mortality and other fitness-related and morphological traits.

Results

Using whole-genome SNP data, we screened 948 wild zebra finches for polymorphic inversions and describe four large (12–63 Mb) intraspecific inversion polymorphisms with allele frequencies close to 50 %. Using additional data from 5229 birds and 9764 eggs from wild and three captive zebra finch populations, we show that only the largest inversions increase embryo mortality in heterokaryotypic males, with surprisingly small effect sizes. We test for a heterozygote advantage on other fitness components but find no evidence for heterosis for any of the inversions. Yet, we find strong additive effects on several morphological traits.

Conclusions

The mechanism that has carried the derived inversion haplotypes to such high allele frequencies remains elusive. It appears that selection has effectively minimized the costs associated with inversions in zebra finches. The highly skewed distribution of recombination events towards the chromosome ends in zebra finches and other estrildid species may function to minimize crossovers in the inverted regions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-016-1056-3) contains supplementary material, which is available to authorized users.

Short Read Alignment Using SOAP2

Next-generation sequencing (NGS) technologies have rapidly evolved in the last 5 years, leading to the generation of millions of short reads in a single run. Consequently, various sequence alignment algorithms have been developed to compare these reads to an appropriate reference in order to perform important downstream analysis. SOAP2 from the SOAP series is one of the most commonly used alignment programs to handle NGS data, and it efficiently does so using low computer memory usage and fast alignment speed. This chapter describes the protocol used to align short reads to a reference genome using SOAP2, and highlights the significance of using the in-built command-line options to tune the behavior of the algorithm according to the inputs and the desired results.