Complex structural variants in Mendelian disorders: identification and breakpoint resolution using short- and long-read genome sequencing

Unraveling undiagnosed rare disease cases by HiFi long-read genome sequencing

Solve-RD is a pan-European rare disease (RD) research program that aims to identify disease-causing genetic variants in previously undiagnosed RD families. We utilized 10-fold coverage HiFi long-read sequencing (LRS) for detecting causative structural variants (SVs), single-nucleotide variants (SNVs), insertion-deletions (indels), and short tandem repeat (STR) expansions in previously studied RD families without a clear molecular diagnosis. Our cohort includes 293 individuals from 114 genetically undiagnosed RD families selected by European Reference Network (ERN) experts. Of these, 21 families were affected by so-called "unsolvable" syndromes for which genetic causes remain unknown and for which prior testing was not a prerequisite. The remaining 93 families had at least one individual affected by a rare neurological, neuromuscular, or epilepsy disorder without a genetic diagnosis despite extensive prior testing. Clinical interpretation and orthogonal validation of variants in known disease genes yielded 12 novel genetic diagnoses due to de novo and rare inherited SNVs, indels, SVs, and STR expansions. In an additional five families, we identified a candidate disease-causing variant, including an MCF2/FGF13 fusion and a PSMA3 deletion. However, no common genetic cause was identified in any of the "unsolvable" syndromes. Taken together, we found (likely) disease-causing genetic variants in 11.8% of previously unsolved families and additional candidate disease-causing SVs in another 5.4% of these families. In conclusion, our results demonstrate the potential added value of HiFi long-read genome sequencing in undiagnosed rare diseases.

The continued promise of genomic technologies and software in neurogenetics

The continued evolution of genomic technologies over the past few decades has revolutionized the field of neurogenetics, offering profound insights into the genetic underpinnings of neurological disorders. Identification of causal genes for numerous monogenic neurological conditions has informed key aspects of disease mechanisms and facilitated research into critical proteins and molecular pathways, laying the groundwork for therapeutic interventions. However, the question remains: has this transformative trend reached its zenith? In this review, we suggest that despite significant strides in genome sequencing and advanced computational analyses, there is still ample room for methodological refinement. We anticipate further major genetic breakthroughs corresponding with the increased use of long-read genomes, variant calling software, AI tools, and data aggregation databases. Genetic progress has historically been driven by technological advancements from the commercial sector, which are developed in response to academic research needs, creating a continuous cycle of innovation and discovery. This review explores the potential of genomic technologies to address the challenges of neurogenetic disorders. By outlining both established and modern resources, we aim to emphasize the importance of genetic technologies as we enter an era poised for discoveries.

OctopusV and TentacleSV: a one-stop toolkit for multi-sample, cross-platform structural variant comparison and analysis

Structural variants (SVs) significantly influence genomic variability and disease, but their accurate analysis across multiple samples and sequencing platforms remains challenging. We developed OctopusV, a tool that standardizes ambiguous breakend (BND) annotations into canonical SV types (inversions, duplications, translocations) and integrates variant calls using flexible set operations, such as union, intersection, difference, and complement, enabling cohort-specific variant identification. Together with TentacleSV, an automated pipeline, OctopusV provides an end-to-end solution from raw data to final callsets. Evaluations show improved precision, recall, and consistency, highlighting its value in cancer genomics and rare disease diagnostics. Both tools are available at https://github.com/ylab-hi/OctopusV and https://github.com/ylab-hi/TentacleSV.

Benchmarking long-read structural variant calling tools and combinations for detecting somatic variants in cancer genomes

Cancer genomes have a complicated landscape of mutations, including large-scale rearrangements known as structural variants (SVs). These SVs can disrupt genes or regulatory elements, playing a critical role in cancer development and progression. Despite their importance, accurate identification of somatic structural variants (SVs) remains a significant bottleneck in cancer genomics. Long-read sequencing technologies hold great promise in SV discovery, and there is an increasing number of efforts to develop new tools to detect them. In this study, we employ eight widely used SV callers on paired tumor and matched normal samples from both the NCI-H2009 lung cancer cell line and the COLO829 melanoma cell line, the latter of which has a well-established somatic SV truth set. Following separate variation detection in both tumor and normal DNA, the VCF merging procedure and a subtraction method were used to identify candidate somatic SVs. Additionally, we explored different combinations of the tools to enhance the accuracy of true somatic SV detection. Our analysis adopts a comprehensive approach, evaluating the performance of each SV caller across a spectrum of variant types and numbers in finding cancer-related somatic SVs. This study, by comparing eight different tools and their combinations, not only reveals the benefits and limitations of various techniques but also establishes a framework for developing more robust SV calling pipelines. Our findings highlight the strengths and weaknesses of current SV calling tools and suggest that combining multiple tools and testing different combinations can significantly enhance the validation of somatic alterations.

De novo and somatic structural variant discovery with SVision-pro

Long-read-based de novo and somatic structural variant (SV) discovery remains challenging, necessitating genomic comparison between samples. We developed SVision-pro, a neural-network-based instance segmentation framework that represents genome-to-genome-level sequencing differences visually and discovers SV comparatively between genomes without any prerequisite for inference models. SVision-pro outperforms state-of-the-art approaches, in particular, the resolving of complex SVs is improved, with low Mendelian error rates, high sensitivity of low-frequency SVs and reduced false-positive rates compared with SV merging approaches.

SUMMER: an integrated nanopore sequencing pipeline for variants detection and clinical annotation on the human genome

Long-read sequencing has emerged as a transformative technology in recent years, offering significant potential for the molecular diagnosis of unresolved genetic disorders. Despite its promise, the comprehensive detection and clinical annotation of genomic variants remain intricate and technically demanding. We present SUMMER, an integrated and structured workflow specifically designed to process raw Nanopore sequencing reads. SUMMER facilitates an in-depth analysis of multiple variant types, including SNV, SV, short tandem repeat and mobile element insertion. For clinical applications, SUMMER employs SvAnna to prioritize SV candidates based on phenotype relevance and utilizes Straglr to provide reference distributions of non-pathogenic unit counts for 55 known pathogenic short tandem repeats. By addressing critical challenges in variant detection and annotation, SUMMER seeks to advance the clinical utility of long-read sequencing in diagnostic genomics. SUMMER is available on the web at https://github.com/carolhuaxia/summer .

Diversity and consequences of structural variation in the human genome

The biomedical community is increasingly invested in capturing all genetic variants across human genomes, interpreting their functional consequences and translating these findings to the clinic. A crucial component of this endeavour is the discovery and characterization of structural variants (SVs), which are ubiquitous in the human population, heterogeneous in their mutational processes, key substrates for evolution and adaptation, and profound drivers of human disease. The recent emergence of new technologies and the remarkable scale of sequence-based population studies have begun to crystalize our understanding of SVs as a mutational class and their widespread influence across phenotypes. In this Review, we summarize recent discoveries and new insights into SVs in the human genome in terms of their mutational patterns, population genetics, functional consequences, and impact on human traits and disease. We conclude by outlining three frontiers to be explored by the field over the next decade.

Long-read sequencing and optical genome mapping identify causative gene disruptions in noncoding sequence in two patients with neurologic disease and known chromosome abnormalities

Despite advances in next generation sequencing (NGS), genetic diagnoses remain elusive for many patients with neurologic syndromes. Long-read sequencing (LRS) and optical genome mapping (OGM) technologies improve upon existing capabilities in the detection and interpretation of structural variation in repetitive DNA, on a single haplotype, while also providing enhanced breakpoint resolution. We performed LRS and OGM on two patients with known chromosomal rearrangements and inconclusive Sanger or NGS. The first patient, who had epilepsy and developmental delay, had a complex translocation between two chromosomes that included insertion and inversion events. The second patient, who had a movement disorder, had an inversion on a single chromosome disrupted by multiple smaller inversions and insertions. Sequence level resolution of the rearrangements identified pathogenic breaks in noncoding sequence in or near known disease-causing genes with relevant neurologic phenotypes (MBD5, NKX2-1). These specific variants have not been reported previously, but expected molecular consequences are consistent with previously reported cases. As the use of LRS and OGM technologies for clinical testing increases and data analyses become more standardized, these methods along with multiomic data to validate noncoding variation effects will improve diagnostic yield and increase the proportion of probands with detectable pathogenic variants for known genes implicated in neurogenetic disease.

Exploring the diverse clinical and variant spectrum of CEP78-associated syndrome: Novel pathogenic variants identified in a case series

Dual sensory impairment, commonly referred to as combined hearing and vision loss, can stem from a diverse spectrum of conditions, each presenting with its unique set of clinical characteristics. Our understanding of dual sensory impairment has expanded significantly in the past decade, broadening the scope of genetic differential diagnoses, including genes such as CEP250, ARSG, TUBB4B, CEP78, and ABHD12. A case series including three patients from two families with genetically diagnosed CEP78-associated cone-rod dystrophy was identified. We collected and reviewed their clinical records, imaging data, and genetic testing results. In addition, a comprehensive literature review was conducted on the phenotype and the genetic testing modality employed in all published CEP78 cases through a PubMed search using the keyword "CEP78." A retinal dystrophy panel detected a novel homozygous CEP78 pathogenic variant (c.1447C>T, p.Arg483*) in siblings-Cases 1 and 2-from Family 1. Both teenagers have a clinical diagnosis of cone-rod dystrophy with presumed normal hearing. Case 3 from Family 2, diagnosed with cone-rod dystrophy and early-onset hearing loss, was found to carry a CEP78 pathogenic variant (c.1206-2A>C) and a likely pathogenic variant (c.856_857del, p.Leu286Glyfs*12) also through panel-based genetic testing. Intriguingly, neither of these variants was reported in an affected sibling's clinical whole-exome sequencing (WES) report when performed in 2015. A review of CEP78-related literature unveiled that the initial report linking CEP78 to cone-rod dystrophy and hearing loss was published in September 2016. Any pathogenic variant found in CEP78 before 2016 would have been categorized as a "clearly disruptive variant in a gene of uncertain significance (GUS)" and might not have been reported in the WES report. It is important to acknowledge that our understanding of genotype-phenotype associations is undergoing rapid expansion. It is also crucial to recognize that repeat genetic testing may represent a fundamentally different approach, given the technological advancements not only in the coverage of the sequencing but also in the more comprehensive understanding of genotype-phenotype associations. This case series also enriches the existing CEP78 literature by providing phenotypic details of the youngest case of CEP78-associated retinopathy reported in the literature (Case 2), which expands our perspective on the natural history of disease in this disorder.

CNVoyant a machine learning framework for accurate and explainable copy number variant classification

The precise classification of copy number variants (CNVs) presents a significant challenge in genomic medicine, primarily due to the complex nature of CNVs and their diverse impact on rare genetic diseases (RGDs). This complexity is compounded by the limitations of existing methods in accurately distinguishing between benign, uncertain, and pathogenic CNVs. Addressing this gap, we introduce CNVoyant, a machine learning-based multi-class framework designed to enhance the clinical significance classification of CNVs. Trained on a comprehensive dataset of 52,176 ClinVar entries across pathogenic, uncertain, and benign classifications, CNVoyant incorporates a broad spectrum of genomic features, including genome position, disease-gene annotations, dosage sensitivity, and conservation scores. Models to predict the clinical significance of copy number gains and losses were trained independently. Final models were selected after testing 29 machine learning architectures and 10,000 hyperparameter combinations each for deletions and duplications via fivefold cross-validation. We validate the performance of CNVoyant by leveraging a comprehensive set of 21,574 CNVs from the DECIPHER database, a highly regarded resource known for its extensive catalog of chromosomal imbalances linked to clinical outcomes. Compared to alternative approaches, CNVoyant shows marked improvements in precision-recall and ROC AUC metrics for binary pathogenic classifications while going one step further, offering multi-classification of clinical significance and corresponding SHAP explainability plots. Additionally, when provided germline CNV calls from real-world RGD cases with diagnostic CNV(s), CNVoyant correctly classified all diagnostic CNVs as having pathogenic significance with high confidence. This large-scale validation demonstrates CNVoyant's superior accuracy and underscores its potential to aid genomic researchers and clinical geneticists in interpreting the clinical implications of real CNVs.

Impact and characterization of serial structural variations across humans and great apes

Modern sequencing technology enables the systematic detection of complex structural variation (SV) across genomes. However, extensive DNA rearrangements arising through a series of mutations, a phenomenon we refer to as serial SV (sSV), remain underexplored, posing a challenge for SV discovery. Here, we present NAHRwhals ( https://github.com/WHops/NAHRwhals ), a method to infer repeat-mediated series of SVs in long-read genomic assemblies. Applying NAHRwhals to haplotype-resolved human genomes from 28 individuals reveals 37 sSV loci of various length and complexity. These sSVs explain otherwise cryptic variation in medically relevant regions such as the TPSAB1 gene, 8p23.1, 22q11 and Sotos syndrome regions. Comparisons with great ape assemblies indicate that most human sSVs formed recently, after the human-ape split, and involved non-repeat-mediated processes in addition to non-allelic homologous recombination. NAHRwhals reliably discovers and characterizes sSVs at scale and independent of species, uncovering their genomic abundance and suggesting broader implications for disease.

GGTyper: genotyping complex structural variants using short-read sequencing data

MOTIVATION

Complex structural variants (SVs) are genomic rearrangements that involve multiple segments of DNA. They contribute to human diversity and have been shown to cause Mendelian disease. Nevertheless, our abilities to analyse complex SVs are very limited. As opposed to deletions and other canonical types of SVs, there are no established tools that have explicitly been designed for analysing complex SVs.

RESULTS

Here, we describe a new computational approach that we specifically designed for genotyping complex SVs in short-read sequenced genomes. Given a variant description, our approach computes genotype-specific probability distributions for observing aligned read pairs with a wide range of properties. Subsequently, these distributions can be used to efficiently determine the most likely genotype for any set of aligned read pairs observed in a sequenced genome. In addition, we use these distributions to compute a genotyping difficulty for a given variant, which predicts the amount of data needed to achieve a reliable call. Careful evaluation confirms that our approach outperforms other genotypers by making reliable genotype predictions across both simulated and real data. On up to 7829 human genomes, we achieve high concordance with population-genetic assumptions and expected inheritance patterns. On simulated data, we show that precision correlates well with our prediction of genotyping difficulty. This together with low memory and time requirements makes our approach well-suited for application in biomedical studies involving small to very large numbers of short-read sequenced genomes.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/kehrlab/Complex-SV-Genotyping.

Genome Sequencing Unveils the Role of Copy Number Variants in Hearing Loss and Identifies Novel Deletions With Founder Effect in the DFNB1 Locus

Sensorineural hearing loss is a prevalent disorder with significant genetic involvement, which is often challenging to diagnose due to genetic heterogeneity. Exome sequencing (ES) has been a standard diagnostic tool for sensorineural hearing loss, but its limitations in detecting copy number variants (CNVs) and intronic variants have prompted the exploration of genome sequencing (GS) for improved diagnostic yield. We conducted GS on 46 hearing loss families with previously negative ES results and an additional cohort of 36 patients with a monoallelic pathogenic variant in GJB2 (the most common deafness gene). Additionally, the impact of a previously unrecognized novel 125-kb deletion in the DFNB1 locus on GJB2 expression was assessed using quantitative polymerase chain reaction (qPCR), and haplotype analysis was performed to characterize the deletion. GS diagnosed eight cases (17%, 8/46) in the ES-negative cohort, primarily attributed to CNVs (6/8). Notably, a previously unrecognized 125 kb deletion in the DFNB1 region was identified, affecting GJB2 expression and characterizing it as a founder effect in East Asian. In 47 patients with a monoallelic GJB2 variant, 15% (95% CI, 7.4%-28%) were diagnosed with DFNB1 deletions. Analysis of the gnomAD database revealed the prevalence and ethnic diversity of DFNB1 deletions, with the novel 125 kb deletion emerging as a prominent pathogenic variant in East Asian, non-Finnish European, and admixed American populations. Our study highlights the utility of GS in diagnosing sensorineural hearing loss. The identification of DFNB1 deletions underscores their significant contribution to hearing loss etiology, advocating for their inclusion in routine diagnostic testing. We propose GS as a primary genetic testing approach for patients with hearing loss, offering comprehensive genomic analysis and the potential for improved diagnostic accuracy.

Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data

BACKGROUND

Structural variation (SV) detection methods using third-generation sequencing data are widely employed, yet accurately detecting SVs remains challenging. Different methods often yield inconsistent results for certain SV types, complicating tool selection and revealing biases in detection.

RESULTS

This study comprehensively evaluates 53 SV detection pipelines using simulated and real data from PacBio (CLR: Continuous Long Read, CCS: Circular Consensus Sequencing) and Nanopore (ONT) platforms. We assess their performance in detecting various sizes and types of SVs, breakpoint biases, and genotyping accuracy with various sequencing depths. Notably, pipelines such as Minimap2-cuteSV2, NGMLR-SVIM, PBMM2-pbsv, Winnowmap-Sniffles2, and Winnowmap-SVision exhibit comparatively higher recall and precision. Our findings also show that combining multiple pipelines with the same aligner, like pbmm2 or winnowmap, can significantly enhance performance. The individual pipelines' detailed ranking and performance metrics can be viewed in a dynamic table: http://pmglab.top/SVPipelinesRanking .

CONCLUSIONS

This study comprehensively characterizes the strengths and weaknesses of numerous pipelines, providing valuable insights that can improve SV detection in third-generation sequencing data and inform SV annotation and function prediction.

Altered chromatin topologies caused by balanced chromosomal translocation lead to central iris hypoplasia

Despite the advent of genomic sequencing, molecular diagnosis remains unsolved in approximately half of patients with Mendelian disorders, largely due to unclarified functions of noncoding regions and the difficulty in identifying complex structural variations. In this study, we map a unique form of central iris hypoplasia in a large family to 6q15-q23.3 and 18p11.31-q12.1 using a genome-wide linkage scan. Long-read sequencing reveals a balanced translocation t(6;18)(q22.31;p11.22) with intergenic breakpoints. By performing Hi-C on induced pluripotent stem cells from a patient, we identify two chromatin topologically associating domains spanning across the breakpoints. These alterations lead the ectopic chromatin interactions between APCDD1 on chromosome 18 and enhancers on chromosome 6, resulting in upregulation of APCDD1. Notably, APCDD1 is specifically localized in the iris of human eyes. Our findings demonstrate that noncoding structural variations can lead to Mendelian diseases by disrupting the 3D genome structure and resulting in altered gene expression.

Leaf: an ultrafast filter for population-scale long-read SV detection

Advances in sequencing technology have facilitated population-scale long-read structural variant (SV) detection. Arguably, one of the main challenges in population-scale analysis is developing effective computational pipelines. Here, we present a new filter-based pipeline for population-scale long-read SV detection. It better captures SV signals at an early stage than conventional assembly-based or alignment-based pipelines. Assessments in this work suggest that the filter-based pipeline helps better resolve intra-read rearrangements. Moreover, it is also more computationally efficient than conventional pipelines and thus may facilitate population-scale long-read applications.

Unravelling undiagnosed rare disease cases by HiFi long-read genome sequencing

Solve-RD is a pan-European rare disease (RD) research program that aims to identify disease-causing genetic variants in previously undiagnosed RD families. We utilised 10-fold coverage HiFi long-read sequencing (LRS) for detecting causative structural variants (SVs), single nucleotide variants (SNVs), insertion-deletions (InDels), and short tandem repeat (STR) expansions in extensively studied RD families without clear molecular diagnoses. Our cohort includes 293 individuals from 114 genetically undiagnosed RD families selected by European Rare Disease Network (ERN) experts. Of these, 21 families were affected by so-called 'unsolvable' syndromes for which genetic causes remain unknown, and 93 families with at least one individual affected by a rare neurological, neuromuscular, or epilepsy disorder without genetic diagnosis despite extensive prior testing. Clinical interpretation and orthogonal validation of variants in known disease genes yielded thirteen novel genetic diagnoses due to de novo and rare inherited SNVs, InDels, SVs, and STR expansions. In an additional four families, we identified a candidate disease-causing SV affecting several genes including an MCF2 / FGF13 fusion and PSMA3 deletion. However, no common genetic cause was identified in any of the 'unsolvable' syndromes. Taken together, we found (likely) disease-causing genetic variants in 13.0% of previously unsolved families and additional candidate disease-causing SVs in another 4.3% of these families. In conclusion, our results demonstrate the added value of HiFi long-read genome sequencing in undiagnosed rare diseases.

Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning

MOTIVATION

Whole-exome and genome sequencing have become common tools in diagnosing patients with rare diseases. Despite their success, this approach leaves many patients undiagnosed. A common argument is that more disease variants still await discovery, or the novelty of disease phenotypes results from a combination of variants in multiple disease-related genes. Interpreting the phenotypic consequences of genomic variants relies on information about gene functions, gene expression, physiology, and other genomic features. Phenotype-based methods to identify variants involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been successfully applied to prioritizing variants, such methods are based on known gene-disease or gene-phenotype associations as training data and are applicable to genes that have phenotypes associated, thereby limiting their scope. In addition, phenotypes are not assigned uniformly by different clinicians, and phenotype-based methods need to account for this variability.

RESULTS

We developed an Embedding-based Phenotype Variant Predictor (EmbedPVP), a computational method to prioritize variants involved in genetic diseases by combining genomic information and clinical phenotypes. EmbedPVP leverages a large amount of background knowledge from human and model organisms about molecular mechanisms through which abnormal phenotypes may arise. Specifically, EmbedPVP incorporates phenotypes linked to genes, functions of gene products, and the anatomical site of gene expression, and systematically relates them to their phenotypic effects through neuro-symbolic, knowledge-enhanced machine learning. We demonstrate EmbedPVP's efficacy on a large set of synthetic genomes and genomes matched with clinical information.

AVAILABILITY AND IMPLEMENTATION

EmbedPVP and all evaluation experiments are freely available at https://github.com/bio-ontology-research-group/EmbedPVP.

CNVoyant: A Highly Performant and Explainable Multi-Classifier Machine Learning Approach for Determining the Clinical Significance of Copy Number Variants

The precise classification of copy number variants (CNVs) presents a significant challenge in genomic medicine, primarily due to the complex nature of CNVs and their diverse impact on genetic disorders. This complexity is compounded by the limitations of existing methods in accurately distinguishing between benign, uncertain, and pathogenic CNVs. Addressing this gap, we introduce CNVoyant, a machine learning-based multi-class framework designed to enhance the clinical significance classification of CNVs. Trained on a comprehensive dataset of 52,176 ClinVar entries across pathogenic, uncertain, and benign classifications, CNVoyant incorporates a broad spectrum of genomic features, including genome position, disease-gene annotations, dosage sensitivity, and conservation scores. Models to predict the clinical significance of copy number gains and losses were trained independently. Final models were selected after testing 29 machine learning architectures and 10,000 hyperparameter combinations each for deletions and duplications via 5-fold cross-validation. We validate the performance of the CNVoyant by leveraging a comprehensive set of 21,574 CNVs from the DECIPHER database, a highly regarded resource known for its extensive catalog of chromosomal imbalances linked to clinical outcomes. Compared to alternative approaches, CNVoyant shows marked improvements in precision-recall and ROC AUC metrics for binary pathogenic classifications while going one step further, offering multi-classification of clinical significance and corresponding SHAP explainability plots. This large-scale validation demonstrates CNVoyant's superior accuracy and underscores its potential to aid genomic researchers and clinical geneticists in interpreting the clinical implications of real CNVs.

Improved sensitivity for detection of pathogenic variants in familial NF2-related schwannomatosis

PURPOSE

To determine the impact of additional genetic screening techniques on the rate of detection of pathogenic variants leading to familial NF2-related schwannomatosis.

METHODS

We conducted genetic screening of a cohort of 168 second-generation individuals meeting the clinical criteria for NF2-related schwannomatosis. In addition to the current clinical screening techniques, targeted next-generation sequencing (NGS) and multiplex ligation-dependent probe amplification analysis, we applied additional genetic screening techniques, including karyotype and RNA analysis. For characterisation of a complex structural variant, we also performed long-read sequencing analysis.

RESULTS

Additional genetic analysis resulted in increased sensitivity of detection of pathogenic variants from 87% to 95% in our second-generation NF2-related schwannomatosis cohort. A number of pathogenic variants identified through extended analysis had been previously observed after NGS analysis but had been overlooked or classified as variants of uncertain significance.

CONCLUSION

Our study indicates there is added value in performing additional genetic analysis for detection of pathogenic variants that are difficult to identify with current clinical genetic screening methods. In particular, RNA analysis is valuable for accurate classification of non-canonical splicing variants. Karyotype analysis and whole genome sequencing analysis are of particular value for identification of large and/or complex structural variants, with additional advantages in the use of long-read sequencing techniques.

Long read sequencing on its way to the routine diagnostics of genetic diseases

The clinical application of technological progress in the identification of DNA alterations has always led to improvements of diagnostic yields in genetic medicine. At chromosome side, from cytogenetic techniques evaluating number and gross structural defects to genomic microarrays detecting cryptic copy number variants, and at molecular level, from Sanger method studying the nucleotide sequence of single genes to the high-throughput next-generation sequencing (NGS) technologies, resolution and sensitivity progressively increased expanding considerably the range of detectable DNA anomalies and alongside of Mendelian disorders with known genetic causes. However, particular genomic regions (i.e., repetitive and GC-rich sequences) are inefficiently analyzed by standard genetic tests, still relying on laborious, time-consuming and low-sensitive approaches (i.e., southern-blot for repeat expansion or long-PCR for genes with highly homologous pseudogenes), accounting for at least part of the patients with undiagnosed genetic disorders. Third generation sequencing, generating long reads with improved mappability, is more suitable for the detection of structural alterations and defects in hardly accessible genomic regions. Although recently implemented and not yet clinically available, long read sequencing (LRS) technologies have already shown their potential in genetic medicine research that might greatly impact on diagnostic yield and reporting times, through their translation to clinical settings. The main investigated LRS application concerns the identification of structural variants and repeat expansions, probably because techniques for their detection have not evolved as rapidly as those dedicated to single nucleotide variants (SNV) identification: gold standard analyses are karyotyping and microarrays for balanced and unbalanced chromosome rearrangements, respectively, and southern blot and repeat-primed PCR for the amplification and sizing of expanded alleles, impaired by limited resolution and sensitivity that have not been significantly improved by the advent of NGS. Nevertheless, more recently, with the increased accuracy provided by the latest product releases, LRS has been tested also for SNV detection, especially in genes with highly homologous pseudogenes and for haplotype reconstruction to assess the parental origin of alleles with de novo pathogenic variants. We provide a review of relevant recent scientific papers exploring LRS potential in the diagnosis of genetic diseases and its potential future applications in routine genetic testing.

GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM

BACKGROUND

Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the information in OMIM is textual, and heterogeneous (e.g. summarized by different experts), which complicates automated reading and understanding of the data. Here, we used Natural Language Processing (NLP) to make a tool (Gene-Phenotype Association Discovery (GPAD)) that could syntactically process OMIM text and extract the data of interest.

RESULTS

GPAD applies a series of language-based techniques to the text obtained from OMIM API to extract GDA discovery-related information. GPAD can inform when a particular gene was associated with a specific phenotype, as well as the type of validation-whether through model organisms or cohort-based patient-matching approaches-for such an association. GPAD extracted data was validated with published reports and was compared with large language model. Utilizing GPAD's extracted data, we analysed trends in GDA discoveries, noting a significant increase in their rate after the introduction of exome sequencing, rising from an average of about 150-250 discoveries each year. Contrary to hopes of resolving most GDAs for Mendelian disorders by now, our data indicate a substantial decline in discovery rates over the past five years (2017-2022). This decline appears to be linked to the increasing necessity for larger cohorts to substantiate GDAs. The rising use of zebrafish and Drosophila as model organisms in providing evidential support for GDAs is also observed.

CONCLUSIONS

GPAD's real-time analyzing capacity offers an up-to-date view of GDA discovery and could help in planning and managing the research strategies. In future, this solution can be extended or modified to capture other information in OMIM and scientific literature.

Genomics and conservation: Guidance from training to analyses and applications

Environmental change is intensifying the biodiversity crisis and threatening species across the tree of life. Conservation genomics can help inform conservation actions and slow biodiversity loss. However, more training, appropriate use of novel genomic methods and communication with managers are needed. Here, we review practical guidance to improve applied conservation genomics. We share insights aimed at ensuring effectiveness of conservation actions around three themes: (1) improving pedagogy and training in conservation genomics including for online global audiences, (2) conducting rigorous population genomic analyses properly considering theory, marker types and data interpretation and (3) facilitating communication and collaboration between managers and researchers. We aim to update students and professionals and expand their conservation toolkit with genomic principles and recent approaches for conserving and managing biodiversity. The biodiversity crisis is a global problem and, as such, requires international involvement, training, collaboration and frequent reviews of the literature and workshops as we do here.

Nanopore long-read sequencing analysis reveals ZIC1 dysregulation caused by a de novo 3q inversion with a breakpoint located 7 kb downstream of ZIC1

Zic family member 1 (ZIC1), a gene located on chromosome 3q24, encodes a transcription factor with zinc finger domains that is essential for the normal development of the cerebellum. Heterozygous loss-of-function of ZIC1 causes Dandy-Walker malformation, while heterozygous gain-of-function leads to a multiple congenital anomaly syndrome characterized by craniosynostosis, brain abnormalities, facial features, and learning disability. In this study, we present the results of genetic analysis of a male patient with clinically suspected Gomez-Lopez-Hernandez syndrome. The patient displayed multiple congenital abnormalities, including bicoronal craniosynostosis, characteristic facial features, cerebellar malformation with rhombencephalosynapsis, and temporal alopecia, and a de novo inversion of chromosome 3q. Breakpoint analysis using a Nanopore long-read sequencer revealed a breakpoint in the distal centromere of 3q24 located 7 kb downstream of the 3' untranslated region of ZIC1. On the basis of the clinical similarities, we concluded that the abnormalities in this patient were caused by the transcriptional dysregulation of ZIC1. We hypothesize the underlying molecular mechanisms of transcriptional dysregulation of ZIC1 such as the abnormalities in topologically associated domains encompassing ZIC1. This study highlights the usefulness of long-read sequencing in the analysis of de novo balanced chromosomal abnormalities.

Beyond the exome: utility of long-read whole genome sequencing in exome-negative autosomal recessive diseases

BACKGROUND

Long-read whole genome sequencing (lrWGS) has the potential to address the technical limitations of exome sequencing in ways not possible by short-read WGS. However, its utility in autosomal recessive Mendelian diseases is largely unknown.

METHODS

In a cohort of 34 families in which the suspected autosomal recessive diseases remained undiagnosed by exome sequencing, lrWGS was performed on the Pacific Bioscience Sequel IIe platform.

RESULTS

Likely causal variants were identified in 13 (38%) of the cohort. These include (1) a homozygous splicing SV in TYMS as a novel candidate gene for lethal neonatal lactic acidosis, (2) a homozygous non-coding SV that we propose impacts STK25 expression and causes a novel neurodevelopmental disorder, (3) a compound heterozygous SV in RP1L1 with complex inheritance pattern in a family with inherited retinal disease, (4) homozygous deep intronic variants in LEMD2 and SNAP91 as novel candidate genes for neurodevelopmental disorders in two families, and (5) a promoter SNV in SLC4A4 causing non-syndromic band keratopathy. Surprisingly, we also encountered causal variants that could have been identified by short-read exome sequencing in 7 families. The latter highlight scenarios that are especially challenging at the interpretation level.

CONCLUSIONS

Our data highlight the continued need to address the interpretation challenges in parallel with efforts to improve the sequencing technology itself. We propose a path forward for the implementation of lrWGS sequencing in the setting of autosomal recessive diseases in a way that maximizes its utility.

Systematic evaluation of multiple NGS platforms for structural variants detection

Structural variations (SV) are critical genome changes affecting human diseases. Although many hybridization-based methods exist, evaluating SVs through next-generation sequencing (NGS) data is still necessary for broader research exploration. Here, we comprehensively compared the performance of 16 SV callers and multiple NGS platforms using NA12878 whole genome sequencing (WGS) datasets. The results indicated that several SV callers performed well relatively, such as Manta, GRIDSS, LUMPY, TARDIS, FermiKit, and Wham. Meanwhile, all NGS platforms have a similar performance using a single software. Additionally, we found that the source of undetected SVs was mostly from long reads datasets, therefore, the more appropriate strategy for accurate SV detection will be an integration of long and shorter reads in the future. At present, in the period of NGS as a mainstream method in bioinformatics, our study would provide helpful and comprehensive guidelines for specific categories of SV research.

AML with complex karyotype: extreme genomic complexity revealed by combined long-read sequencing and Hi-C technology

Acute myeloid leukemia with complex karyotype (CK-AML) is associated with poor prognosis, which is only in part explained by underlying TP53 mutations. Especially in the presence of complex chromosomal rearrangements, such as chromothripsis, the outcome of CK-AML is dismal. However, this degree of complexity of genomic rearrangements contributes to the leukemogenic phenotype and treatment resistance of CK-AML remains largely unknown. Applying an integrative workflow for the detection of structural variants (SVs) based on Oxford Nanopore (ONT) genomic DNA long-read sequencing (gDNA-LRS) and high-throughput chromosome confirmation capture (Hi-C) in a well-defined cohort of CK-AML identified regions with an extreme density of SVs. These rearrangements consisted to a large degree of focal amplifications enriched in the proximity of mammalian-wide interspersed repeat elements, which often result in oncogenic fusion transcripts, such as USP7::MVD, or the deregulation of oncogenic driver genes as confirmed by RNA-seq and ONT direct complementary DNA sequencing. We termed this novel phenomenon chromocataclysm. Thus, our integrative SV detection workflow combing gDNA-LRS and Hi-C enables to unravel complex genomic rearrangements at a very high resolution in regions hard to analyze by conventional sequencing technology, thereby providing an important tool to identify novel important drivers underlying cancer with complex karyotypic changes.

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.

The application of long-read sequencing in clinical settings

Long-read DNA sequencing technologies have been rapidly evolving in recent years, and their ability to assess large and complex regions of the genome makes them ideal for clinical applications in molecular diagnosis and therapy selection, thereby providing a valuable tool for precision medicine. In the third-generation sequencing duopoly, Oxford Nanopore Technologies and Pacific Biosciences work towards increasing the accuracy, throughput, and portability of long-read sequencing methods while trying to keep costs low. These trades have made long-read sequencing an attractive tool for use in research and clinical settings. This article provides an overview of current clinical applications and limitations of long-read sequencing and explores its potential for point-of-care testing and health care in remote settings.

Genome sequencing and comprehensive rare-variant analysis of 465 families with neurodevelopmental disorders

Despite significant progress in unraveling the genetic causes of neurodevelopmental disorders (NDDs), a substantial proportion of individuals with NDDs remain without a genetic diagnosis after microarray and/or exome sequencing. Here, we aimed to assess the power of short-read genome sequencing (GS), complemented with long-read GS, to identify causal variants in participants with NDD from the National Institute for Health and Care Research (NIHR) BioResource project. Short-read GS was conducted on 692 individuals (489 affected and 203 unaffected relatives) from 465 families. Additionally, long-read GS was performed on five affected individuals who had structural variants (SVs) in technically challenging regions, had complex SVs, or required distal variant phasing. Causal variants were identified in 36% of affected individuals (177/489), and a further 23% (112/489) had a variant of uncertain significance after multiple rounds of re-analysis. Among all reported variants, 88% (333/380) were coding nuclear SNVs or insertions and deletions (indels), and the remainder were SVs, non-coding variants, and mitochondrial variants. Furthermore, long-read GS facilitated the resolution of challenging SVs and invalidated variants of difficult interpretation from short-read GS. This study demonstrates the value of short-read GS, complemented with long-read GS, in investigating the genetic causes of NDDs. GS provides a comprehensive and unbiased method of identifying all types of variants throughout the nuclear and mitochondrial genomes in individuals with NDD.

Technological and computational approaches to detect somatic mosaicism in epilepsy

Lesional epilepsy is a common and severe disease commonly associated with malformations of cortical development, including focal cortical dysplasia and hemimegalencephaly. Recent advances in sequencing and variant calling technologies have identified several genetic causes, including both short/single nucleotide and structural somatic variation. In this review, we aim to provide a comprehensive overview of the methodological advancements in this field while highlighting the unresolved technological and computational challenges that persist, including ultra-low variant allele fractions in bulk tissue, low availability of paired control samples, spatial variability of mutational burden within the lesion, and the issue of false-positive calls and validation procedures. Information from genetic testing in focal epilepsy may be integrated into clinical care to inform histopathological diagnosis, postoperative prognosis, and candidate precision therapies.

Implementation of Nanopore sequencing as a pragmatic workflow for copy number variant confirmation in the clinic

BACKGROUND

Diagnosis of rare genetic diseases can be a long, expensive and complex process, involving an array of tests in the hope of obtaining an actionable result. Long-read sequencing platforms offer the opportunity to make definitive molecular diagnoses using a single assay capable of detecting variants, characterizing methylation patterns, resolving complex rearrangements, and assigning findings to long-range haplotypes. Here, we demonstrate the clinical utility of Nanopore long-read sequencing by validating a confirmatory test for copy number variants (CNVs) in neurodevelopmental disorders and illustrate the broader applications of this platform to assess genomic features with significant clinical implications.

METHODS

We used adaptive sampling on the Oxford Nanopore platform to sequence 25 genomic DNA samples and 5 blood samples collected from patients with known or false-positive copy number changes originally detected using short-read sequencing. Across the 30 samples (a total of 50 with replicates), we assayed 35 known unique CNVs (a total of 55 with replicates) and one false-positive CNV, ranging in size from 40 kb to 155 Mb, and assessed the presence or absence of suspected CNVs using normalized read depth.

RESULTS

Across 50 samples (including replicates) sequenced on individual MinION flow cells, we achieved an average on-target mean depth of 9.5X and an average on-target read length of 4805 bp. Using a custom read depth-based analysis, we successfully confirmed the presence of all 55 known CNVs (including replicates) and the absence of one false-positive CNV. Using the same CNV-targeted data, we compared genotypes of single nucleotide variant loci to verify that no sample mix-ups occurred between assays. For one case, we also used methylation detection and phasing to investigate the parental origin of a 15q11.2-q13 duplication with implications for clinical prognosis.

CONCLUSIONS

We present an assay that efficiently targets genomic regions to confirm clinically relevant CNVs with a concordance rate of 100%. Furthermore, we demonstrate how integration of genotype, methylation, and phasing data from the Nanopore sequencing platform can potentially simplify and shorten the diagnostic odyssey.

Multiplexed long-read plasmid validation and analysis using OnRamp

Recombinant plasmid vectors are versatile tools that have facilitated discoveries in molecular biology, genetics, proteomics, and many other fields. As the enzymatic and bacterial processes used to create recombinant DNA can introduce errors, sequence validation is an essential step in plasmid assembly. Sanger sequencing is the current standard for plasmid validation; however, this method is limited by an inability to sequence through complex secondary structure and lacks scalability when applied to full-plasmid sequencing of multiple plasmids owing to read-length limits. Although high-throughput sequencing does provide full-plasmid sequencing at scale, it is impractical and costly when used outside of library-scale validation. Here, we present Oxford nanopore-based rapid analysis of multiplexed plasmids (OnRamp), an alternative method for routine plasmid validation that combines the advantages of high-throughput sequencing's full-plasmid coverage and scalability with Sanger's affordability and accessibility by leveraging nanopore's long-read sequencing technology. We include customized wet-laboratory protocols for plasmid preparation along with a pipeline designed for analysis of read data obtained using these protocols. This analysis pipeline is deployed on the OnRamp web app, which generates alignments between actual and predicted plasmid sequences, quality scores, and read-level views. OnRamp is designed to be broadly accessible regardless of programming experience to facilitate more widespread adoption of long-read sequencing for routine plasmid validation. Here we describe the OnRamp protocols and pipeline and show our ability to obtain full sequences from pooled plasmids while detecting sequence variation even in regions of high secondary structure at less than half the cost of equivalent Sanger sequencing.

Identification and Targeting of Mutant Neoantigens in Multiple Myeloma Treatment

Multiple myeloma (MM) is malignant disease characterized by the clonal proliferation of plasma cells in the bone marrow, leading to anemia, immunosuppression, and other symptoms, that is generally hard to treat. In MM, the immune system is likely exposed to neoplasia-associated neoantigens for several years before the tumor onset. Different types of neoantigens have been identified. Public or shared neoantigens derive from tumor-specific modifications often reported in several patients or across diverse tumors. They are intriguing therapeutic targets because they are frequently observed, and they have an oncogenic effect. Only a small number of public neoantigens have been recognized. Most of the neoantigens that have been identified are patient-specific or "private", necessitating a personalized approach for adaptive cell treatment. It was demonstrated that the targeting of a single greatly immunogenic neoantigen may be appropriate for tumor control. The purpose of this review was to analyze the neoantigens present in patients with MM, and to evaluate the possibility of using their presence as a prognostic factor or as a therapeutic target. We reviewed the most recent literature on neoantigen treatment strategies and on the use of bispecific, trispecific, and conjugated antibodies for the treatment of MM. Finally, a section was dedicated to the use of CAR-T in relapsed and refractory patients.

A Complex Intrachromosomal Rearrangement Disrupting IRF6 in a Family with Popliteal Pterygium and Van der Woude Syndromes

Clefts of the lip and/or palate (CL/P) are considered the most common form of congenital anomalies occurring either in isolation or in association with other clinical features. Van der woude syndrome (VWS) is associated with about 2% of all CL/P cases and is further characterized by having lower lip pits. Popliteal pterygium syndrome (PPS) is a more severe form of VWS, normally characterized by orofacial clefts, lower lip pits, skin webbing, skeletal anomalies and syndactyly of toes and fingers. Both syndromes are inherited in an autosomal dominant manner, usually caused by heterozygous mutations in the Interferon Regulatory Factor 6 (IRF6) gene. Here we report the case of a two-generation family where the index presented with popliteal pterygium syndrome while both the father and sister had clinical features of van der woude syndrome, but without any point mutations detected by re-sequencing of known gene panels or microarray testing. Using whole genome sequencing (WGS) followed by local de novo assembly, we discover and validate a copy-neutral, 429 kb complex intra-chromosomal rearrangement in the long arm of chromosome 1, disrupting the IRF6 gene. This variant is copy-neutral, novel against publicly available databases, and segregates in the family in an autosomal dominant pattern. This finding suggests that missing heritability in rare diseases may be due to complex genomic rearrangements that can be resolved by WGS and de novo assembly, helping deliver answers to patients where no genetic etiology was identified by other means.

Genome-wide polygenic risk score for major osteoporotic fractures in postmenopausal women using associated single nucleotide polymorphisms

BACKGROUND

Osteoporosis is highly polygenic and heritable, with heritability ranging from 50 to 80%; most inherited susceptibility is associated with the cumulative effect of many common genetic variants. However, existing genetic risk scores (GRS) only provide a few percent predictive power for osteoporotic fracture.

METHODS

We derived and validated a novel genome-wide polygenic score (GPS) comprised of 103,155 common genetic variants to quantify this susceptibility and tested this GPS prediction ability in an independent dataset (n = 15,776).

RESULTS

Among postmenopausal women, we found a fivefold gradient in the risk of major osteoporotic fracture (MOF) (p < 0.001) and a 15.25-fold increased risk of severe osteoporosis (p < 0.001) across the GPS deciles. Compared with the remainder of the GPS distribution, the top GPS decile was associated with a 3.59-, 2.48-, 1.92-, and 1.58-fold increased risk of any fracture, MOF, hip fracture, and spine fracture, respectively. The top GPS decile also identified nearly twofold more high-risk osteoporotic patients than the top decile of conventional GRS based on 1103 conditionally independent genome-wide significant SNPs. Although the relative risk of severe osteoporosis for postmenopausal women at around 50 is relatively similar, the cumulative incident at 20-year follow-up is significantly different between the top GPS decile (13.7%) and the bottom decile (< 1%). In the subgroup analysis, the GPS transferability in non-Hispanic White is better than in other racial/ethnic groups.

CONCLUSIONS

This new method to quantify inherited susceptibility to osteoporosis and osteoporotic fracture affords new opportunities for clinical prevention and risk assessment.

Absence of CEP78 causes photoreceptor and sperm flagella impairments in mice and a human individual

Cone-rod dystrophy (CRD) is a genetically inherited retinal disease that can be associated with male infertility, while the specific genetic mechanisms are not well known. Here, we report CEP78 as a causative gene of a particular syndrome including CRD and male infertility with multiple morphological abnormalities of sperm flagella (MMAF) both in human and mouse. Cep78 knockout mice exhibited impaired function and morphology of photoreceptors, typified by reduced ERG amplitudes, disrupted translocation of cone arrestin, attenuated and disorganized photoreceptor outer segments (OS) disks and widen OS bases, as well as interrupted connecting cilia elongation and abnormal structures. Cep78 deletion also caused male infertility and MMAF, with disordered '9+2' structure and triplet microtubules in sperm flagella. Intraflagellar transport (IFT) proteins IFT20 and TTC21A are identified as interacting proteins of CEP78. Furthermore, CEP78 regulated the interaction, stability, and centriolar localization of its interacting protein. Insufficiency of CEP78 or its interacting protein causes abnormal centriole elongation and cilia shortening. Absence of CEP78 protein in human caused similar phenotypes in vision and MMAF as Cep78^-/- mice. Collectively, our study supports the important roles of CEP78 defects in centriole and ciliary dysfunctions and molecular pathogenesis of such multi-system syndrome.

Deciphering the exact breakpoints of structural variations using long sequencing reads with DeBreak

Long-read sequencing has demonstrated great potential for characterizing all types of structural variations (SVs). However, existing algorithms have insufficient sensitivity and precision. To address these limitations, we present DeBreak, a computational method for comprehensive and accurate SV discovery. Based on alignment results, DeBreak employs a density-based approach for clustering SV candidates together with a local de novo assembly approach for reconstructing long insertions. A partial order alignment algorithm ensures precise SV breakpoints with single base-pair resolution, and a k-means clustering method can report multi-allele SV events. DeBreak outperforms existing tools on both simulated and real long-read sequencing data from both PacBio and Nanopore platforms. An important application of DeBreak is analyzing cancer genomes for potentially tumor-driving SVs. DeBreak can also be used for supplementing whole-genome assembly-based SV discovery.

Stepwise use of genomics and transcriptomics technologies increases diagnostic yield in Mendelian disorders

Purpose: Multi-omics offer worthwhile and increasingly accessible technologies to diagnostic laboratories seeking potential second-tier strategies to help patients with unresolved rare diseases, especially patients clinically diagnosed with a rare OMIM (Online Mendelian Inheritance in Man) disease. However, no consensus exists regarding the optimal diagnostic care pathway to adopt after negative results with standard approaches. Methods: In 15 unsolved individuals clinically diagnosed with recognizable OMIM diseases but with negative or inconclusive first-line genetic results, we explored the utility of a multi-step approach using several novel omics technologies to establish a molecular diagnosis. Inclusion criteria included a clinical autosomal recessive disease diagnosis and single heterozygous pathogenic variant in the gene of interest identified by first-line analysis (60%-9/15) or a clinical diagnosis of an X-linked recessive or autosomal dominant disease with no causative variant identified (40%-6/15). We performed a multi-step analysis involving short-read genome sequencing (srGS) and complementary approaches such as mRNA sequencing (mRNA-seq), long-read genome sequencing (lrG), or optical genome mapping (oGM) selected according to the outcome of the GS analysis. Results: SrGS alone or in combination with additional genomic and/or transcriptomic technologies allowed us to resolve 87% of individuals by identifying single nucleotide variants/indels missed by first-line targeted tests, identifying variants affecting transcription, or structural variants sometimes requiring lrGS or oGM for their characterization. Conclusion: Hypothesis-driven implementation of combined omics technologies is particularly effective in identifying molecular etiologies. In this study, we detail our experience of the implementation of genomics and transcriptomics technologies in a pilot cohort of previously investigated patients with a typical clinical diagnosis without molecular etiology.

The diagnostic yield, candidate genes, and pitfalls for a genetic study of intellectual disability in 118 middle eastern families

Global Developmental Delay/Intellectual disability (ID) is the term used to describe various disorders caused by abnormal brain development and characterized by impairments in cognition, communication, behavior, or motor skills. In the past few years, whole-exome sequencing (WES) has been proven to be a powerful, robust, and scalable approach for candidate gene discoveries in consanguineous populations. In this study, we recruited 215 patients affected with ID from 118 Middle Eastern families. Whole-exome sequencing was completed for 188 individuals. The average age at which WES was completed was 8.5 years. Pathogenic or likely pathogenic variants were detected in 32/118 families (27%). Variants of uncertain significance were seen in 33/118 families (28%). The candidate genes with a possible association with ID were detected in 32/118 (27%) with a total number of 64 affected individuals. These genes are novel, were previously reported in a single family, or cause strikingly different phenotypes with a different mode of inheritance. These genes included: AATK, AP1G2, CAMSAP1, CCDC9B, CNTROB, DNAH14, DNAJB4, DRG1, DTNBP1, EDRF1, EEF1D, EXOC8, EXOSC4, FARSB, FBXO22, FILIP1, INPP4A, P2RX7, PRDM13, PTRHD1, SCN10A, SCYL2, SMG8, SUPV3L1, TACC2, THUMPD1, XPR1, ZFYVE28. During the 5 years of the study and through gene matching databases, several of these genes have now been confirmed as causative of ID. In conclusion, understanding the causes of ID will help understand biological mechanisms, provide precise counseling for affected families, and aid in primary prevention.

DBFE: distribution-based feature extraction from structural variants in whole-genome data

MOTIVATION

Whole-genome sequencing has revolutionized biosciences by providing tools for constructing complete DNA sequences of individuals. With entire genomes at hand, scientists can pinpoint DNA fragments responsible for oncogenesis and predict patient responses to cancer treatments. Machine learning plays a paramount role in this process. However, the sheer volume of whole-genome data makes it difficult to encode the characteristics of genomic variants as features for learning algorithms.

RESULTS

In this article, we propose three feature extraction methods that facilitate classifier learning from sets of genomic variants. The core contributions of this work include: (i) strategies for determining features using variant length binning, clustering and density estimation; (ii) a programing library for automating distribution-based feature extraction in machine learning pipelines. The proposed methods have been validated on five real-world datasets using four different classification algorithms and a clustering approach. Experiments on genomes of 219 ovarian, 61 lung and 929 breast cancer patients show that the proposed approaches automatically identify genomic biomarkers associated with cancer subtypes and clinical response to oncological treatment. Finally, we show that the extracted features can be used alongside unsupervised learning methods to analyze genomic samples.

AVAILABILITY AND IMPLEMENTATION

The source code of the presented algorithms and reproducible experimental scripts are available on Github at https://github.com/MNMdiagnostics/dbfe.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A companion to the preclinical common data elements for genomics, transcriptomics, and epigenomics data in rodent epilepsy models. A report of the TASK3-WG4 omics working group of the ILAE/AES joint translational TASK force

The International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force established the TASK3 working groups to create common data elements (CDEs) for various preclinical epilepsy research disciplines. The aim of the CDEs is to improve the standardization of experimental designs across a range of epilepsy research-related methods. Here, we have generated CDE tables with key parameters and case report forms (CRFs) containing the essential contents of the study protocols for genomics, transcriptomics, and epigenomics in rodent models of epilepsy, with a specific focus on adult rats and mice. We discuss the important elements that need to be considered for genomics, transcriptomics, and epigenomics methodologies, providing a rationale for the parameters that should be collected. This is the first in a two-part series of omics papers with the second installment to cover proteomics, lipidomics, and metabolomics in adult rodents.

Detection of Structural Variants by NGS: Revealing Missing Alleles in Lysosomal Storage Diseases

Lysosomal storage diseases (LSDs) are a heterogeneous group of rare multisystem metabolic disorders occurring mostly in infancy and childhood, characterized by a gradual accumulation of non-degraded substrates inside the cells. Although biochemical enzymatic assays are considered the gold standard for diagnosis of symptomatic patients, genotyping is a requirement for inclusion in enzyme replacement programs and is a prerequisite for carrier tests in relatives and DNA-based prenatal diagnosis. The emerging next-generation sequencing (NGS) technologies are now offering a powerful diagnostic tool for genotyping LSDs patients by providing faster, cheaper, and higher-resolution testing options, and are allowing to unravel, in a single integrated workflow SNVs, small insertions and deletions (indels), as well as major structural variations (SVs) responsible for the pathology. Here, we summarize the current knowledge about the most recurrent and private SVs involving LSDs-related genes, review advantages and drawbacks related to the use of the NGS in the SVs detection, and discuss the challenges to bring this type of analysis in clinical diagnostics.

Genetic etiology and clinical challenges of phenylketonuria

This review discusses the epidemiology, pathophysiology, genetic etiology, and management of phenylketonuria (PKU). PKU, an autosomal recessive disease, is an inborn error of phenylalanine (Phe) metabolism caused by pathogenic variants in the phenylalanine hydroxylase (PAH) gene. The prevalence of PKU varies widely among ethnicities and geographic regions, affecting approximately 1 in 24,000 individuals worldwide. Deficiency in the PAH enzyme or, in rare cases, the cofactor tetrahydrobiopterin results in high blood Phe concentrations, causing brain dysfunction. Untreated PKU, also known as PAH deficiency, results in severe and irreversible intellectual disability, epilepsy, behavioral disorders, and clinical features such as acquired microcephaly, seizures, psychological signs, and generalized hypopigmentation of skin (including hair and eyes). Severe phenotypes are classic PKU, and less severe forms of PAH deficiency are moderate PKU, mild PKU, mild hyperphenylalaninaemia (HPA), or benign HPA. Early diagnosis and intervention must start shortly after birth to prevent major cognitive and neurological effects. Dietary treatment, including natural protein restriction and Phe-free supplements, must be used to maintain blood Phe concentrations of 120-360 μmol/L throughout the life span. Additional treatments include the casein glycomacropeptide (GMP), which contains very limited aromatic amino acids and may improve immunological function, and large neutral amino acid (LNAA) supplementation to prevent plasma Phe transport into the brain. The synthetic BH4 analog, sapropterin hydrochloride (i.e., Kuvan®, BioMarin), is another potential treatment that activates residual PAH, thus decreasing Phe concentrations in the blood of PKU patients. Moreover, daily subcutaneous injection of pegylated Phe ammonia-lyase (i.e., pegvaliase; PALYNZIQ®, BioMarin) has promised gene therapy in recent clinical trials, and mRNA approaches are also being studied.

Long-Read Sequencing Identifies the First Retrotransposon Insertion and Resolves Structural Variants Causing Antithrombin Deficiency

The identification of inherited antithrombin deficiency (ATD) is critical to prevent potentially life-threatening thrombotic events. Causal variants in SERPINC1 are identified for up to 70% of cases, the majority being single-nucleotide variants and indels. The detection and characterization of structural variants (SVs) in ATD remain challenging due to the high number of repetitive elements in SERPINC1. Here, we performed long-read whole-genome sequencing on 10 familial and 9 singleton cases with type I ATD proven by functional and antigen assays, who were selected from a cohort of 340 patients with this rare disorder because genetic analyses were either negative, ambiguous, or not fully characterized. We developed an analysis workflow to identify disease-associated SVs. This approach resolved, independently of its size or type, all eight SVs detected by multiple ligation-dependent probe amplification, and identified for the first time a complex rearrangement previously misclassified as a deletion. Remarkably, we identified the mechanism explaining ATD in 2 out of 11 cases with previous unknown defect: the insertion of a novel 2.4 kb SINE-VNTR-Alu retroelement, which was characterized by de novo assembly and verified by specific polymerase chain reaction amplification and sequencing in the probands and affected relatives. The nucleotide-level resolution achieved for all SVs allowed breakpoint analysis, which revealed repetitive elements and microhomologies supporting a common replication-based mechanism for all the SVs. Our study underscores the utility of long-read sequencing technology as a complementary method to identify, characterize, and unveil the molecular mechanism of disease-causing SVs involved in ATD, and enlarges the catalogue of genetic disorders caused by retrotransposon insertions.

Detection of rare thalassemia mutations using long-read single-molecule real-time sequencing

Gap- polymerase chain reaction (PCR), reverse dot-blot assay (RDB), real-time PCR based multicolor melting curve analysis (MMCA assay), multiplex ligation-dependent probe amplification (MLPA) and Sanger sequencing are conventional methods to diagnose thalassemia but all of them have limitations. In this study, we applied single-molecule real-time (SMRT) sequencing following multiplex long-range PCR to uncover rare mutations in nine patients and their family members. The patients with different results between Gap-PCR and MMCA assay or with phenotype not matching genotype were included. Using SMRT sequencing, we first identified the carriers with ααα^anti3.7/HKαα, -α^762bpα/αα (chr16:172,648-173,409), αα^fusion/α^QSα (in a trans configuration), two cases with novel gene rearrangements and another case with a novel 341 bp insertion in α-globin gene cluster, respectively. One carrier with --^SEA/ααα^anti4.2, and two carriers with the coexistence of globin variant and an α-globin gene duplication were also found. Most importantly, we could determine two defects in α-globin gene cluster being a cis or trans configuration in a single test. Our results showed that SMRT has great advantages in detection of α-globin gene triplications, rare deletions and determination of a cis or trans configuration. SMRT is a comprehensive and one-step method for thalassemia screening and diagnosis, especially for detection of rare thalassemia mutations.

Population-scale genotyping of structural variation in the era of long-read sequencing

Population-scale studies of structural variation (SV) are growing rapidly worldwide with the development of long-read sequencing technology, yielding a considerable number of novel SVs and complete gap-closed genome assemblies. Herein, we highlight recent studies using a hybrid sequencing strategy and present the challenges toward large-scale genotyping for SVs due to the reference bias. Genotyping SVs at a population scale remains challenging, which severely impacts genotype-based population genetic studies or genome-wide association studies of complex diseases. We summarize academic efforts to improve genotype quality through linear or graph representations of reference and alternative alleles. Graph-based genotypers capable of integrating diverse genetic information are effectively applied to large and diverse cohorts, contributing to unbiased downstream analysis. Meanwhile, there is still an urgent need in this field for efficient tools to construct complex graphs and perform sequence-to-graph alignments.

The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms

In October 2021, 59 scientists from 14 countries and 13 U.S. states collaborated virtually in the Third Annual Baylor College of Medicine & DNANexus Structural Variation hackathon. The goal of the hackathon was to advance research on structural variants (SVs) by prototyping and iterating on open-source software. This led to nine hackathon projects focused on diverse genomics research interests, including various SV discovery and genotyping methods, SV sequence reconstruction, and clinically relevant structural variation, including SARS-CoV-2 variants. Repositories for the projects that participated in the hackathon are available at https://github.com/collaborativebioinformatics.

TT-Mars: structural variants assessment based on haplotype-resolved assemblies

Variant benchmarking is often performed by comparing a test callset to a gold standard set of variants. In repetitive regions of the genome, it may be difficult to establish what is the truth for a call, for example, when different alignment scoring metrics provide equally supported but different variant calls on the same data. Here, we provide an alternative approach, TT-Mars, that takes advantage of the recent production of high-quality haplotype-resolved genome assemblies by providing false discovery rates for variant calls based on how well their call reflects the content of the assembly, rather than comparing calls themselves.