Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing

What makes TMB an ambivalent biomarker for immunotherapy? A subtle mismatch between the sample-based design of variant callers and real clinical cohort

Tumor mutation burden (TMB) is a widely recognized biomarker for predicting the efficacy of immunotherapy. However, its use still remains highly controversial. In this study, we examine the underlying causes of this controversy based on clinical needs. By tracing the source of the TMB errors and analyzing the design philosophy behind variant callers, we identify the conflict between the incompleteness of biostatistics rules and the variety of clinical samples as the critical issue that renders TMB an ambivalent biomarker. A series of experiments were conducted to illustrate the challenges of mutation detection in clinical practice. Additionally, we also discuss potential strategies for overcoming these conflict issues to enable the application of TMB in guiding decision-making in real clinical settings.

Comparison and benchmark of structural variants detected from long read and long-read assembly

Structural variant (SV) detection is essential for genomic studies, and long-read sequencing technologies have advanced our capacity to detect SVs directly from read or de novo assembly, also known as read-based and assembly-based strategy. However, to date, no independent studies have compared and benchmarked the two strategies. Here, on the basis of SVs detected by 20 read-based and eight assembly-based detection pipelines from six datasets of HG002 genome, we investigated the factors that influence the two strategies and assessed their performance with well-curated SVs. We found that up to 80% of the SVs could be detected by both strategies among different long-read datasets, whereas variant type, size, and breakpoint detected by read-based strategy were greatly affected by aligners. For the high-confident insertions and deletions at non-tandem repeat regions, a remarkable subset of them (82% in assembly-based calls and 93% in read-based calls), accounting for around 4000 SVs, could be captured by both reads and assemblies. However, discordance between two strategies was largely caused by complex SVs and inversions, which resulted from inconsistent alignment of reads and assemblies at these loci. Finally, benchmarking with SVs at medically relevant genes, the recall of read-based strategy reached 77% on 5X coverage data, whereas assembly-based strategy required 20X coverage data to achieve similar performance. Therefore, integrating SVs from read and assembly is suggested for general-purpose detection because of inconsistently detected complex SVs and inversions, whereas assembly-based strategy is optional for applications with limited resources.

The Genome of a Pigeonpea Compatible Rhizobial Strain '10ap3' Appears to Lack Common Nodulation Genes

The symbiotic fixation of atmospheric nitrogen (N) in root nodules of tropical legumes such as pigeonpea (Cajanus cajan) is a complex process, which is regulated by multiple genetic factors at the host plant genotype microsymbiont interface. The process involves multiple genes with various modes of action and is accomplished only when both organisms are compatible. Therefore, it is necessary to develop tools for the genetic manipulation of the host or bacterium towards improving N fixation. In this study, we sequenced the genome of a robust rhizobial strain, Rhizobium tropici '10ap3' that was compatible with pigeonpea, and we determined its genome size. The genome consisted of a large circular chromosome (6,297,373 bp) and contained 6013 genes of which 99.13% were coding sequences. However only 5833 of the genes were associated with proteins that could be assigned to specific functions. The genes for nitrogen, phosphorus and iron metabolism, stress response and the adenosine monophosphate nucleoside for purine conversion were present in the genome. However, the genome contained no common nod genes, suggesting that an alternative pathway involving a purine derivative was involved in the symbiotic association with pigeonpea.

A comprehensive analysis of copy number variations in diverse apple populations

BACKGROUND

As an important source of genetic variation, copy number variation (CNV) can alter the dosage of DNA segments, which in turn may affect gene expression level and phenotype. However, our knowledge of CNV in apple is still limited. Here, we obtained high-confidence CNVs and investigated their functional impact based on genome resequencing data of two apple populations, cultivars and wild relatives.

RESULTS

In this study, we identified 914,610 CNVs comprising 14,839 CNV regions (CNVRs) from 346 apple accessions, including 289 cultivars and 57 wild relatives. CNVRs summed to 71.19 Mb, accounting for 10.03% of the apple genome. Under the low linkage disequilibrium (LD) with nearby SNPs, they could also accurately reflect the population structure of apple independent of SNPs. Furthermore, A total of 3,621 genes were covered by CNVRs and functionally involved in biological processes such as defense response, reproduction and metabolic processes. In addition, the population differentiation index ([Formula: see text]) analysis between cultivars and wild relatives revealed 127 CN-differentiated genes, which may contribute to trait differences in these two populations.

CONCLUSIONS

This study was based on identification of CNVs from 346 diverse apple accessions, which to our knowledge was the largest dataset for CNV analysis in apple. Our work presented the first comprehensive CNV map and provided valuable resources for understanding genomic variations in apple.

Structural genomic variants in thoracic aortic disease

PURPOSE OF REVIEW

Structural genomic variants have emerged as a relevant cause for several disorders, including intellectual disability, neuropsychiatric disorders, cancer and congenital heart disease. In this review, we will discuss the current knowledge about the involvement of structural genomic variants and, in particular, copy number variants in the development of thoracic aortic and aortic valve disease.

RECENT FINDINGS

There is a growing interest in the identification of structural variants in aortopathy. Copy number variants identified in thoracic aortic aneurysms and dissections, bicuspid aortic valve related aortopathy, Williams-Beuren syndrome and Turner syndrome are discussed in detail. Most recently, the first inversion disrupting FBN1 has been reported as a cause for Marfan syndrome.

SUMMARY

During the past 15 years, the knowledge on the role of copy number variants as a cause for aortopathy has grown significantly, which is partially due to the development of novel technologies including next-generation sequencing. Although copy number variants are now often investigated on a routine basis in diagnostic laboratories, more complex structural variants such as inversions, which require the use of whole genome sequencing, are still relatively new to the field of thoracic aortic and aortic valve disease.

High-resolution structural variants catalogue in a large-scale whole genome sequenced bovine family cohort data

BACKGROUND

Structural variants (SVs) are chromosomal segments that differ between genomes, such as deletions, duplications, insertions, inversions and translocations. The genomics revolution enabled the discovery of sub-microscopic SVs via array and whole-genome sequencing (WGS) data, paving the way to unravel the functional impact of SVs. Recent human expression QTL mapping studies demonstrated that SVs play a disproportionally large role in altering gene expression, underlining the importance of including SVs in genetic analyses. Therefore, this study aimed to generate and explore a high-quality bovine SV catalogue exploiting a unique cattle family cohort data (total 266 samples, forming 127 trios).

RESULTS

We curated 13,731 SVs segregating in the population, consisting of 12,201 deletions, 1,509 duplications, and 21 multi-allelic CNVs (> 50-bp). Of these, we validated a subset of copy number variants (CNVs) utilising a direct genotyping approach in an independent cohort, indicating that at least 62% of the CNVs are true variants, segregating in the population. Among gene-disrupting SVs, we prioritised two likely high impact duplications, encompassing ORM1 and POPDC3 genes, respectively. Liver expression QTL mapping results revealed that these duplications are likely causing altered gene expression, confirming the functional importance of SVs. Although most of the accurately genotyped CNVs are tagged by single nucleotide polymorphisms (SNPs) ascertained in WGS data, most CNVs were not captured by individual SNPs obtained from a 50K genotyping array.

CONCLUSION

We generated a high-quality SV catalogue exploiting unique whole genome sequenced bovine family cohort data. Two high impact duplications upregulating the ORM1 and POPDC3 are putative candidates for postpartum feed intake and hoof health traits, thus warranting further investigation. Generally, CNVs were in low LD with SNPs on the 50K array. Hence, it remains crucial to incorporate CNVs via means other than tagging SNPs, such as investigation of tagging haplotypes, direct imputation of CNVs, or direct genotyping as done in the current study. The SV catalogue and the custom genotyping array generated in the current study will serve as valuable resources accelerating utilisation of full spectrum of genetic variants in bovine genomes.

The Application of Optical Genome Mapping (OGM) in Severe Short Stature Caused by Duplication of 15q14q21.3

(1) Background: Optical genome mapping (OGM) is a novel approach to identifying genomic structural variations with high accuracy and resolution. We report a proband with severe short stature caused by 46, XY, der (16) ins (16;15) (q23; q21.3q14) that was detected by OGM combined with other tests and review the clinical features of patients with duplication within 15q14q21.3; (2) Methods: OGM, whole exon sequencing (WES), copy number variation sequencing (CNV-seq), and karyotyping were used; (3) Results: The proband was a 10.7-year-old boy with a complaint of severe short stature (-3.41SDS) and abnormal gait. He had growth hormone deficiency, lumbar lordosis, and epiphyseal dysplasia of both femurs. WES and CNV-seq showed a 17.27 Mb duplication of chromosome 15, and there was an insertion in chromosome 16 found by karyotyping. Furthermore, OGM revealed that duplication of 15q14q21.3 was inversely inserted into 16q23.1, resulting in two fusion genes. A total of fourteen patients carried the duplication of 15q14q21.3, with thirteen previously reported and one from our center, 42.9% of which were de novo. In addition, neurologic symptoms (71.4%,10/14) were the most common phenotypes; (4) Conclusions: OGM combined with other genetic methods can reveal the genetic etiology of patients with the clinical syndrome, presenting great potential for use in properly diagnosing in the genetic cause of the clinical syndrome.

A collection of read depth profiles at structural variant breakpoints

SWaveform, a newly created open genome-wide resource for read depth signal in the vicinity of structural variant (SV) breakpoints, aims to boost development of computational tools and algorithms for discovery of genomic rearrangement events from sequencing data. SVs are a dominant force shaping genomes and substantially contributing to genetic diversity. Still, there are challenges in reliable and efficient genotyping of SVs from whole genome sequencing data, thus delaying translation into clinical applications and wasting valuable resources. SWaveform includes a database containing ~7 M of read depth profiles at SV breakpoints extracted from 911 sequencing samples generated by the Human Genome Diversity Project, generalised patterns of the signal at breakpoints, an interface for navigation and download, as well as a toolbox for local deployment with user's data. The dataset can be of immense value to bioinformatics and engineering communities as it empowers smooth application of intelligent signal processing and machine learning techniques for discovery of genomic rearrangement events and thus opens the floodgates for development of innovative algorithms and software.

Whole genome sequencing of simmental cattle for SNP and CNV discovery

BACKGROUD

The single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) are two major genomic variants, which play crucial roles in evolutionary and phenotypic diversity.

RESULTS

In this study, we performed a comprehensive analysis to explore the genetic variations (SNPs and CNVs) of high sperm motility (HSM) and poor sperm motility (PSM) Simmental bulls using the high-coverage (25×) short-read next generation sequencing and single-molecule long reads sequencing data. A total of ~ 15 million SNPs and 2,944 CNV regions (CNVRs) were detected in Simmental bulls, and a set of positive selected genes (PSGs) and CNVRs were found to be overlapped with quantitative trait loci (QTLs) involving immunity, muscle development, reproduction, etc. In addition, we detected two new variants in LEPR, which may be related to the artificial breeding to improve important economic traits. Moreover, a set of genes and pathways functionally related to male fertility were identified. Remarkably, a CNV on SPAG16 (chr2:101,427,468 - 101,429,883) was completely deleted in all poor sperm motility (PSM) bulls and half of the bulls in high sperm motility (HSM), which may play a crucial role in the bull-fertility.

CONCLUSIONS

In conclusion, this study provides a valuable genetic variation resource for the cattle breeding and selection programs.

Benchmarking of structural variant detection in the tetraploid potato genome using linked-read sequencing

It has recently been shown that structural variants (SV) can have a higher impact on gene expression variation compared to single nucleotide variants (SNV) in different plant species. Additionally, SV were associated with phenotypic variation in several crops. However, compared to the established SV detection based on short-read sequencing, less approaches were described for linked-read based SV calling. We therefore evaluated the performance of six linked-read SV callers compared to an established short-read SV caller based on simulated linked-reads in tetraploid potato. The objectives of our study were to i) compare the performance of SV callers based on linked-read sequencing to short-read sequencing, ii) examine the influence of SV type, SV length, haplotype incidence (HI), as well as sequencing coverage on the SV calling performance in the tetraploid potato genome, and iii) evaluate the accuracy of detecting insertions by linked-read compared to short-read sequencing. We observed high break point resolutions (BPR) detecting short SV and slightly lower BPR for large SV. Our observations highlighted the importance of short-read signals provided by Manta and LinkedSV to detect short SV. Manta and NAIBR performed well for detecting larger deletions, inversions, and duplications. Detected large SV were weakly influenced by the HI. Furthermore, we illustrated that large insertions can be assembled by Novel-X. Our results suggest the usage of the short-read and linked-read SV callers Manta, NAIBR, LinkedSV, and Novel-X based on at least 90x linked-read sequencing coverage to ensure the detection of a broad range of SV in the tetraploid potato genome.

Whole-genome sequencing of 1029 Indian individuals reveals unique and rare structural variants

Structural variants contribute to genetic variability in human genomes and they can be presented in population-specific patterns. We aimed to understand the landscape of structural variants in the genomes of healthy Indian individuals and explore their potential implications in genetic disease conditions. For the identification of structural variants, a whole genome sequencing dataset of 1029 self-declared healthy Indian individuals from the IndiGen project was analysed. Further, these variants were evaluated for potential pathogenicity and their associations with genetic diseases. We also compared our identified variations with the existing global datasets. We generated a compendium of total 38,560 high-confident structural variants, comprising 28,393 deletions, 5030 duplications, 5038 insertions, and 99 inversions. Particularly, we identified around 55% of all these variants were found to be unique to the studied population. Further analysis revealed 134 deletions with predicted pathogenic/likely pathogenic effects and their affected genes were majorly enriched for neurological disease conditions, such as intellectual disability and neurodegenerative diseases. The IndiGenomes dataset helped us to understand the unique spectrum of structural variants in the Indian population. More than half of identified variants were not present in the publicly available global dataset on structural variants. Clinically important deletions identified in IndiGenomes might aid in improving the diagnosis of unsolved genetic diseases, particularly in neurological conditions. Along with basal allele frequency data and clinically important deletions, IndiGenomes data might serve as a baseline resource for future studies on genomic structural variant analysis in the Indian population.

ConanVarvar: a versatile tool for the detection of large syndromic copy number variation from whole-genome sequencing data

BACKGROUND

A wide range of tools are available for the detection of copy number variants (CNVs) from whole-genome sequencing (WGS) data. However, none of them focus on clinically-relevant CNVs, such as those that are associated with known genetic syndromes. Such variants are often large in size, typically 1-5 Mb, but currently available CNV callers have been developed and benchmarked for the discovery of smaller variants. Thus, the ability of these programs to detect tens of real syndromic CNVs remains largely unknown.

RESULTS

Here we present ConanVarvar, a tool which implements a complete workflow for the targeted analysis of large germline CNVs from WGS data. ConanVarvar comes with an intuitive R Shiny graphical user interface and annotates identified variants with information about 56 associated syndromic conditions. We benchmarked ConanVarvar and four other programs on a dataset containing real and simulated syndromic CNVs larger than 1 Mb. In comparison to other tools, ConanVarvar reports 10-30 times less false-positive variants without compromising sensitivity and is quicker to run, especially on large batches of samples.

CONCLUSIONS

ConanVarvar is a useful instrument for primary analysis in disease sequencing studies, where large CNVs could be the cause of disease.

Exome sequencing as first-tier genetic testing in infantile-onset pharmacoresistant epilepsy: diagnostic yield and treatment impact

Pharmacoresistant epilepsy presenting during infancy poses both diagnostic and therapeutic challenges. We aim to identify diagnostic yield and treatment implications of exome sequencing (ES) as first-tier genetic testing for infantile-onset pharmacoresistant epilepsy. From June 2016 to December 2020, we enrolled patients with infantile-onset (age ≤ 12 months) pharmacoresistant epilepsy. 103 unrelated patients underwent ES. Clinical characteristics and changes in management due to the molecular diagnosis were studied. 42% (43/103) had epilepsy onset within the first month of life. After ES as first-tier genetic testing, 62% (64/103) of the cases were solved. Two partially solved cases (2%; 2/103) with heterozygous variants identified in ALDH7A1 known to cause autosomal recessive pyridoxine dependent epilepsy underwent genome sequencing (GS). Two novel large deletions in ALDH7A1 were detected in both cases. ES identified 66 pathogenic and likely pathogenic single nucleotide variants (SNVs) in 27 genes. 19 variants have not been previously reported. GS identified two additional copy number variations (CNVs). The most common disease-causing genes are SCN1A (13%; 13/103) and KCNQ2 (8%; 8/103). Eight percent (8/103) of the patients had treatable disorders and specific treatments were provided resulting in seizure freedom. Pyridoxine dependent epilepsy was the most common treatable epilepsy (6%; 6/103). Furthermore, 35% (36/103) had genetic defects which guided gene-specific treatments. Altogether, the diagnostic yield is 64%. Molecular diagnoses change management in 43% of the cases. This study substantiates the use of next generation sequencing (NGS) as the first-tier genetic investigation in infantile-onset pharmacoresistant epilepsy.

A novel 1.38-kb deletion combined with a single nucleotide variant in KIAA0586 as a cause of Joubert syndrome

BACKGROUND

KIAA0586, also known as Talpid3, plays critical roles in primary cilia formation and hedgehog signaling in humans. Variants in KIAA0586 could cause some different ciliopathies, including Joubert syndrome (JBTS), which is a clinically and genetically heterogeneous group of autosomal recessive neurological disorders.

METHODS AND RESULTS

A 9-month-old girl was diagnosed as JBTS by the "molar tooth sign" of the mid-brain and global developmental delay. By whole-exome sequencing, we identified a single nucleotide variant c.3303G > A and a 1.38-kb deletion in KIAA0586 in the proband. These two variants of KIAA0586 were consistent with the mode of autosomal recessive inheritance in the family, which was verified using Sanger sequencing.

CONCLUSIONS

This finding of a compound heterozygote with a 1.38-kb deletion and c.3303G > A gave a precise genetic diagnosis for the patient, and the novel 1.38-kb deletion also expanded the pathogenic variation spectrum of JBTS caused by KIAA0586.

Is an SV caller compatible with sequencing data? An online recommendation tool to automatically recommend the optimal caller based on data features

A lot of bioinformatics tools were released to detect structural variants from the sequencing data during the past decade. For a data analyst, a natural question is about the selection of a tool fits for the data. Thus, this study presents an automatic tool recommendation method to facilitate data analysis. The optimal variant calling tool was recommended from a set of state-of-the-art bioinformatics tools by given a sequencing data. This recommendation method was implemented under a meta-learning framework, identifying the relationships between data features and the performance of tools. First, the meta-features were extracted to characterize the sequencing data and meta-targets were identified to pinpoint the optimal caller for the sequencing data. Second, a meta-model was constructed to bridge the meta-features and meta-targets. Finally, the recommendation was made according to the evaluation from the meta-model. A series of experiments were conducted to validate this recommendation method on both the simulated and real sequencing data. The results revealed that different SV callers often fit different sequencing data. The recommendation accuracy averaged more than 80% across all experimental configurations, outperforming the random- and fixed-pick strategy. To further facilitate the research community, we incorporated the recommendation method into an online cloud services for genomic data analysis, which is available at https://c.solargenomics.com/ via a simple registration. In addition, the source code and a pre-trained model is available at https://github.com/hello-json/CallerRecommendation for academic usages only.

Identification of copy number variations in the genome of Dairy Gir cattle

Studying structural variants that can control complex traits is relevant for dairy cattle production, especially for animals that are tolerant to breeding conditions in the tropics, such as the Dairy Gir cattle. This study identified and characterized high confidence copy number variation regions (CNVR) in the Gir breed genome. A total of 38 animals were whole-genome sequenced, and 566 individuals were genotyped with a high-density SNP panel, among which 36 animals had both sequencing and SNP genotyping data available. Two sets of high confidence CNVR were established: one based on common CNV identified in the studied population (CNVR_POP), and another with CNV identified in sires with both sequence and SNP genotyping data available (CNVR_ANI). We found 10 CNVR_POP and 45 CNVR_ANI, which covered 1.05 Mb and 4.4 Mb of the bovine genome, respectively. Merging these CNV sets for functional analysis resulted in 48 unique high confidence CNVR. The overlapping genes were previously related to embryonic mortality, environmental adaptation, evolutionary process, immune response, longevity, mammary gland, resistance to gastrointestinal parasites, and stimuli recognition, among others. Our results contribute to a better understanding of the Gir breed genome. Moreover, the CNV identified in this study can potentially affect genes related to complex traits, such as production, health, and reproduction.

Comparative analysis of seed and seedling irradiation with gamma rays and carbon ions for mutation induction in Arabidopsis

The molecular nature of mutations induced by ionizing radiation and chemical mutagens in plants is becoming clearer owing to the availability of high-throughput DNA sequencing technology. However, few studies have compared the induced mutations between different radiation qualities and between different irradiated materials with the same analysis method. To compare mutation induction between dry-seeds and seedlings irradiated with carbon ions and gamma rays in Arabidopsis, in this study we detected the mutations induced by seedling irradiation with gamma rays and analyzed the data together with data previously obtained for the other irradiation treatments. Mutation frequency at the equivalent dose for survival reduction was higher with gamma rays than with carbon ions, and was higher with dry-seed irradiation than with seedling irradiation. Carbon ions induced a higher frequency of deletions (2-99 bp) than gamma rays in the case of dry-seed irradiation, but this difference was less evident in the case of seedling irradiation. This result supported the inference that dry-seed irradiation under a lower water content more clearly reflects the difference in radiation quality. However, the ratio of rearrangements (inversions, translocations, and deletions larger than 100 bp), which are considered to be derived from the rejoining of two distantly located DNA breaks, was significantly higher with carbon ions than gamma rays irrespective of the irradiated material. This finding suggested that high-linear energy transfer radiation induced closely located DNA damage, irrespective of the water content of the material, that could lead to the generation of rearrangements. Taken together, the results provide an overall picture of radiation-induced mutation in Arabidopsis and will be useful for selection of a suitable radiation treatment for mutagenesis.

Different structural variant prediction tools yield considerably different results in Caenorhabditis elegans

The accurate characterization of structural variation is crucial for our understanding of how large chromosomal alterations affect phenotypic differences and contribute to genome evolution. Whole-genome sequencing is a popular approach for identifying structural variants, but the accuracy of popular tools remains unclear due to the limitations of existing benchmarks. Moreover, the performance of these tools for predicting variants in non-human genomes is less certain, as most tools were developed and benchmarked using data from the human genome. To evaluate the use of long-read data for the validation of short-read structural variant calls, the agreement between predictions from a short-read ensemble learning method and long-read tools were compared using real and simulated data from Caenorhabditis elegans. The results obtained from simulated data indicate that the best performing tool is contingent on the type and size of the variant, as well as the sequencing depth of coverage. These results also highlight the need for reference datasets generated from real data that can be used as 'ground truth' in benchmarks.

Recent advances and current challenges in population genomics of structural variation in animals and plants

The field of population genomics has seen a surge of studies on genomic structural variation over the past two decades. These studies witnessed that structural variation is taxonomically ubiquitous and represent a dominant form of genetic variation within species. Recent advances in technology, especially the development of long-read sequencing platforms, have enabled the discovery of structural variants (SVs) in previously inaccessible genomic regions which unlocked additional structural variation for population studies and revealed that more SVs contribute to evolution than previously perceived. An increasing number of studies suggest that SVs of all types and sizes may have a large effect on phenotype and consequently major impact on rapid adaptation, population divergence, and speciation. However, the functional effect of the vast majority of SVs is unknown and the field generally lacks evidence on the phenotypic consequences of most SVs that are suggested to have adaptive potential. Non-human genomes are heavily under-represented in population-scale studies of SVs. We argue that more research on other species is needed to objectively estimate the contribution of SVs to evolution. We discuss technical challenges associated with SV detection and outline the most recent advances towards more representative reference genomes, which opens a new era in population-scale studies of structural variation.

Genomic architecture of autism from comprehensive whole-genome sequence annotation

Fully understanding autism spectrum disorder (ASD) genetics requires whole-genome sequencing (WGS). We present the latest release of the Autism Speaks MSSNG resource, which includes WGS data from 5,100 individuals with ASD and 6,212 non-ASD parents and siblings (total n = 11,312). Examining a wide variety of genetic variants in MSSNG and the Simons Simplex Collection (SSC; n = 9,205), we identified ASD-associated rare variants in 718/5,100 individuals with ASD from MSSNG (14.1%) and 350/2,419 from SSC (14.5%). Considering genomic architecture, 52% were nuclear sequence-level variants, 46% were nuclear structural variants (including copy-number variants, inversions, large insertions, uniparental isodisomies, and tandem repeat expansions), and 2% were mitochondrial variants. Our study provides a guidebook for exploring genotype-phenotype correlations in families who carry ASD-associated rare variants and serves as an entry point to the expanded studies required to dissect the etiology in the ∼85% of the ASD population that remain idiopathic.

Population Structure and Selection Signatures Underlying Domestication Inferred from Genome-Wide Copy Number Variations in Chinese Indigenous Pigs

Single nucleotide polymorphism was widely used to perform genetic and evolution research in pigs. However, little is known about the effect of copy number variation (CNV) on characteristics in pigs. This study performed a genome-wide comparison of CNVs between Wannan black pigs (WBP) and Asian wild boars (AWB), using whole genome resequencing data. By using Manta, we detected in total 28,720 CNVs that covered approximately 1.98% of the pig genome length. We identified 288 selected CNVs (top 1%) by performing Fst statistics. Functional enrichment analyses for genes located in selected CNVs were found to be muscle related (NDN, TMOD4, SFRP1, and SMYD3), reproduction related (GJA1, CYP26B1, WNT5A, SRD5A2, PTPN11, SPEF2, and CCNB1), residual feed intake (RFI) related (MAP3K5), and ear size related (WIF1). This study provides essential information on selected CNVs in Wannan black pigs for further research on the genetic basis of the complex phenotypic and provides essential information for direction in the protection and utilization of Wannan black pig.

Long-read sequencing for molecular diagnostics in constitutional genetic disorders

Long-read sequencing (LRS) has been around for more than a decade, but widespread adoption of the technology has been slow due to the perceived high error rates and high sequencing cost. This is changing due to the recent advancements to produce highly accurate sequences and the reducing costs. LRS promises significant improvement over short read sequencing in four major areas: (1) better detection of structural variation (2) better resolution of highly repetitive or nonunique regions (3) accurate long-range haplotype phasing and (4) the detection of base modifications natively from the sequencing data. Several successful applications of LRS have demonstrated its ability to resolve molecular diagnoses where short-read sequencing fails to identify a cause. However, the argument for increased diagnostic yield from LRS remains to be validated. Larger cohort studies may be required to establish the realistic boundaries of LRS's clinical utility and analytical validity, as well as the development of standards for clinical applications. We discuss the limitations of the current standard of care, and contrast with the applications and advantages of two major LRS platforms, PacBio and Oxford Nanopore, for molecular diagnostics of constitutional disorders, and present a critical argument about the potential of LRS in diagnostic settings.

Comprehensive analysis of structural variants in chickens using PacBio sequencing

Structural variants (SVs) are one of the main sources of genetic variants and have a greater impact on phenotype evolution, disease susceptibility, and environmental adaptations than single nucleotide polymorphisms (SNPs). However, SVs remain challenging to accurately type, with several detection methods showing different limitations. Here, we explored SVs from 10 different chickens using PacBio technology and detected 49,501 high-confidence SVs. The results showed that the PacBio long-read detected more SVs than Illumina short-read technology genomes owing to some SV sites on chromosomes, which are related to chicken growth and development. During chicken domestication, some SVs beneficial to the breed or without any effect on the genomic function of the breed were retained, whereas deleterious SVs were generally eliminated. This study could facilitate the analysis of the genetic characteristics of different chickens and provide a better understanding of their phenotypic characteristics at the SV level, based on the long-read sequencing method. This study enriches our knowledge of SVs in chickens and improves our understanding of chicken genomic diversity.

PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform

Structural variations (SVs) play important roles in human evolution and diseases, but there is a lack of data resources concerning representative samples, especially for East Asians. Taking advantage of both next-generation sequencing and third-generation sequencing data at the whole-genome level, we developed the database PGG.SV to provide a practical platform for both regionally and globally representative structural variants. In its current version, PGG.SV archives 584 277 SVs obtained from whole-genome sequencing data of 6048 samples, including 1030 long-read sequencing genomes representing 177 global populations. PGG.SV provides (i) high-quality SVs with fine-scale and precise genomic locations in both GRCh37 and GRCh38, covering underrepresented SVs in existing sequencing and microarray data; (ii) hierarchical estimation of SV prevalence in geographical populations; (iii) informative annotations of SV-related genes, potential functions and clinical effects; (iv) an analysis platform to facilitate SV-based case-control association studies and (v) various visualization tools for understanding the SV structures in the human genome. Taken together, PGG.SV provides a user-friendly online interface, easy-to-use analysis tools and a detailed presentation of results. PGG.SV is freely accessible via https://www.biosino.org/pggsv.

Analyses of rare predisposing variants of lung cancer in 6,004 whole genomes in Chinese

We present the largest whole-genome sequencing (WGS) study of non-small cell lung cancer (NSCLC) to date among 6,004 individuals of Chinese ancestry, coupled with 23,049 individuals genotyped by SNP array. We construct a high-quality haplotype reference panel for imputation and identify 20 common and low-frequency loci (minor allele frequency [MAF] ≥ 0.5%), including five loci that have never been reported before. For rare loss-of-function (LoF) variants (MAF < 0.5%), we identify BRCA2 and 18 other cancer predisposition genes that affect 5.29% of individuals with NSCLC, and 98.91% (181 of 183) of LoF variants have not been linked previously to NSCLC risk. Promoter variants of BRCA2 also have a substantial effect on NSCLC risk, and their prevalence is comparable with BRCA2 LoF variants. The associations are validated in an independent case-control study including 4,410 individuals and a prospective cohort study including 23,826 individuals. Our findings not only provide a high-quality reference panel for future array-based association studies but depict the whole picture of rare pathogenic variants for NSCLC.

From genome structure to function: insights into structural variation in microbiology

Structural variation in bacterial genomes is an important evolutionary driver. Genomic rearrangements, such as inversions, duplications, and insertions, can regulate gene expression and promote niche adaptation. Importantly, many of these variations are reversible and preprogrammed to generate heterogeneity. While many tools have been developed to detect structural variation in eukaryotic genomes, variation in bacterial genomes and metagenomes remains understudied. However, recent advances in genome sequencing technology and the development of new bioinformatic pipelines hold promise in further understanding microbial genomics.

Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

Structural variants (SV) of 23 barley inbreds, detected by the best combination of SV callers based on short-read sequencing, were associated with genome-wide and gene-specific gene expression and, thus, were evaluated to predict agronomic traits. In human genetics, several studies have shown that phenotypic variation is more likely to be caused by structural variants (SV) than by single nucleotide variants. However, accurate while cost-efficient discovery of SV in complex genomes remains challenging. The objectives of our study were to (i) facilitate SV discovery studies by benchmarking SV callers and their combinations with respect to their sensitivity and precision to detect SV in the barley genome, (ii) characterize the occurrence and distribution of SV clusters in the genomes of 23 barley inbreds that are the parents of a unique resource for mapping quantitative traits, the double round robin population, (iii) quantify the association of SV clusters with transcript abundance, and (iv) evaluate the use of SV clusters for the prediction of phenotypic traits. In our computer simulations based on a sequencing coverage of 25x, a sensitivity > 70% and precision > 95% was observed for all combinations of SV types and SV length categories if the best combination of SV callers was used. We observed a significant (P < 0.05) association of gene-associated SV clusters with global gene-specific gene expression. Furthermore, about 9% of all SV clusters that were within 5 kb of a gene were significantly (P < 0.05) associated with the gene expression of the corresponding gene. The prediction ability of SV clusters was higher compared to that of single-nucleotide polymorphisms from an array across the seven studied phenotypic traits. These findings suggest the usefulness of exploiting SV information when fine mapping and cloning the causal genes underlying quantitative traits as well as the high potential of using SV clusters for the prediction of phenotypes in diverse germplasm sets.

Local assembly of long reads enables phylogenomics of transposable elements in a polyploid cell line

Animal cell lines often undergo extreme genome restructuring events, including polyploidy and segmental aneuploidy that can impede de novo whole-genome assembly (WGA). In some species like Drosophila, cell lines also exhibit massive proliferation of transposable elements (TEs). To better understand the role of transposition during animal cell culture, we sequenced the genome of the tetraploid Drosophila S2R+ cell line using long-read and linked-read technologies. WGAs for S2R+ were highly fragmented and generated variable estimates of TE content across sequencing and assembly technologies. We therefore developed a novel WGA-independent bioinformatics method called TELR that identifies, locally assembles, and estimates allele frequency of TEs from long-read sequence data (https://github.com/bergmanlab/telr). Application of TELR to a ∼130x PacBio dataset for S2R+ revealed many haplotype-specific TE insertions that arose by transposition after initial cell line establishment and subsequent tetraploidization. Local assemblies from TELR also allowed phylogenetic analysis of paralogous TEs, which revealed that proliferation of TE families in vitro can be driven by single or multiple source lineages. Our work provides a model for the analysis of TEs in complex heterozygous or polyploid genomes that are recalcitrant to WGA and yields new insights into the mechanisms of genome evolution in animal cell culture.

Construction of a trio-based structural variation panel utilizing activated T lymphocytes and long-read sequencing technology

Long-read sequencing technology enable better characterization of structural variants (SVs). To adapt the technology to population-scale analyses, one critical issue is to obtain sufficient amount of high-molecular-weight genomic DNA. Here, we propose utilizing activated T lymphocytes, which can be established efficiently in a biobank to stably supply high-grade genomic DNA sufficiently. We conducted nanopore sequencing of 333 individuals constituting 111 trios with high-coverage long-read sequencing data (depth 22.2x, N50 of 25.8 kb) and identified 74,201 SVs. Our trio-based analysis revealed that more than 95% of the SVs were concordant with Mendelian inheritance. We also identified SVs associated with clinical phenotypes, all of which appear to be stably transmitted from parents to offspring. Our data provide a catalog of SVs in the general Japanese population, and the applied approach using the activated T-lymphocyte resource will contribute to biobank-based human genetic studies focusing on SVs at the population scale.

Long-read sequencing on activated T-cells from a sample of 333 Japanese individuals (representing 111 parent-offspring trios) provides a useful reference dataset of structural variation in the Japanese population.

Deleterious mutations and the rare allele burden on rice gene expression

Deleterious genetic variation is maintained in populations at low frequencies. Under a model of stabilizing selection, rare (and presumably deleterious) genetic variants are associated with increase or decrease in gene expression from some intermediate optimum. We investigate this phenomenon in a population of largely Oryza sativa ssp. indica rice landraces under normal unstressed wet and stressful drought field conditions. We include single nucleotide polymorphisms, insertion/deletion mutations, and structural variants in our analysis and find a stronger association between rare variants and gene expression outliers under the stress condition. We also show an association of the strength of this rare variant effect with linkage, gene expression levels, network connectivity, local recombination rate, and fitness consequence scores, consistent with the stabilizing selection model of gene expression.

A survey of current methods to detect and genotype inversions

Polymorphic inversions are ubiquitous in humans, and they have been linked to both adaptation and disease. Following their discovery in Drosophila more than a century ago, inversions have proved to be more elusive than other structural variants. A wide variety of methods for the detection and genotyping of inversions have recently been developed: multiple techniques based on selective amplification by PCR, short- and long-read sequencing approaches, principal component analysis of small variant haplotypes, template strand sequencing, optical mapping, and various genome assembly methods. Many methods apply complex wet lab protocols or increasingly refined bioinformatic analyses. This review is an attempt to provide a practical summary and comparison of the methods that are in current use, with a focus on metrics such as the maximum size of segmental duplications at inversion breakpoints that each method can tolerate, the size range of inversions that they recover, their throughput, and whether the locations of putative inversions must be known beforehand. This article is protected by copyright. All rights reserved.

Automated filtering of genome-wide large deletions through an ensemble deep learning framework

Computational methods based on whole genome linked-reads and short-reads have been successful in genome assembly and detection of structural variants (SVs). Numerous variant callers that rely on linked-reads and short reads can detect genetic variations, including SVs. A shortcoming of existing tools is a propensity for overestimating SVs, especially for deletions. Optimizing the advantages of linked-read and short-read sequencing technologies would thus benefit from an additional step to effectively identify and eliminate false positive large deletions. Here, we introduce a novel tool, AquilaDeepFilter, aiming to automatically filter genome-wide false positive large deletions. Our approach relies on transforming sequencing data into an image and then relying on convolutional neural networks to improve classification of candidate deletions as such. Input data take into account multiple alignment signals including read depth, split reads and discordant read pairs. We tested the performance of AquilaDeepFilter on five linked-reads and short-read libraries sequenced from the well-studied NA24385 sample, validated against the Genome in a Bottle benchmark. To demonstrate the filtering ability of AquilaDeepFilter, we utilized the SV calls from three upstream SV detection tools including Aquila, Aquila_stLFR and Delly as the baseline. We showed that AquilaDeepFilter increased precision while preserving the recall rate of all three tools. The overall F1-score improved by an average 20% on linked-reads and by an average of 15% on short-read data. AquilaDeepFilter also compared favorably to existing deep learning based methods for SV filtering, such as DeepSVFilter. AquilaDeepFilter is thus an effective SV refinement framework that can improve SV calling for both linked-reads and short-read data.

Multi-Omic Investigations of a 17-19 Translocation Links MINK1 Disruption to Autism, Epilepsy and Osteoporosis

Balanced structural variants, such as reciprocal translocations, are sometimes hard to detect with sequencing, especially when the breakpoints are located in repetitive or insufficiently mapped regions of the genome. In such cases, long-range information is required to resolve the rearrangement, identify disrupted genes and, in symptomatic carriers, pinpoint the disease-causing mechanisms. Here, we report an individual with autism, epilepsy and osteoporosis and a de novo balanced reciprocal translocation: t(17;19) (p13;p11). The genomic DNA was analyzed by short-, linked- and long-read genome sequencing, as well as optical mapping. Transcriptional consequences were assessed by transcriptome sequencing of patient-specific neuroepithelial stem cells derived from induced pluripotent stem cells (iPSC). The translocation breakpoints were only detected by long-read sequencing, the first on 17p13, located between exon 1 and exon 2 of MINK1 (Misshapen-like kinase 1), and the second in the chromosome 19 centromere. Functional validation in induced neural cells showed that MINK1 expression was reduced by >50% in the patient’s cells compared to healthy control cells. Furthermore, pathway analysis revealed an enrichment of changed neural pathways in the patient’s cells. Altogether, our multi-omics experiments highlight MINK1 as a candidate monogenic disease gene and show the advantages of long-read genome sequencing in capturing centromeric translocations.

PerSVade: personalized structural variant detection in any species of interest

Structural variants (SVs) underlie genomic variation but are often overlooked due to difficult detection from short reads. Most algorithms have been tested on humans, and it remains unclear how applicable they are in other organisms. To solve this, we develop perSVade (personalized structural variation detection), a sample-tailored pipeline that provides optimally called SVs and their inferred accuracy, as well as small and copy number variants. PerSVade increases SV calling accuracy on a benchmark of six eukaryotes. We find no universal set of optimal parameters, underscoring the need for sample-specific parameter optimization. PerSVade will facilitate SV detection and study across diverse organisms.

Phenotype-driven approaches to enhance variant prioritization and diagnosis of rare disease

Rare disease diagnostics and disease gene discovery have been revolutionized by whole-exome and genome sequencing but identifying the causative variant(s) from the millions in each individual remains challenging. The use of deep phenotyping of patients and reference genotype-phenotype knowledge, alongside variant data such as allele frequency, segregation, and predicted pathogenicity, has proved an effective strategy to tackle this issue. Here we review the numerous tools that have been developed to automate this approach and demonstrate the power of such an approach on several thousand diagnosed cases from the 100,000 Genomes Project. Finally, we discuss the challenges that need to be overcome if we are going to improve detection rates and help the majority of patients that still remain without a molecular diagnosis after state-of-the-art genomic interpretation.

Accurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens

Cancer-associated gene fusions are a potential source for highly immunogenic neoantigens, but the lack of computational tools for accurate, sensitive identification of personal gene fusions has limited their targeting in personalized cancer immunotherapy. Here we present EasyFuse, a machine learning computational pipeline for detecting cancer-specific gene fusions in transcriptome data obtained from human cancer samples. EasyFuse predicts personal gene fusions with high precision and sensitivity, outperforming previously described tools. By testing immunogenicity with autologous blood lymphocytes from patients with cancer, we detected pre-established CD4⁺ and CD8⁺ T cell responses for 10 of 21 (48%) and for 1 of 30 (3%) identified gene fusions, respectively. The high frequency of T cell responses detected in patients with cancer supports the relevance of individual gene fusions as neoantigens that might be targeted in personalized immunotherapies, especially for tumors with low mutation burden.

No evidence of increased mutations in the germline of a group of British nuclear test veterans

The potential germline effects of radiation exposure to military veterans present at British nuclear tests in Australia and the South Pacific is of considerable interest. We analyzed germline mutations in 60 families of UK military personnel comprising 30 control and 30 nuclear test veterans (NTV). Using whole-genome sequencing we studied the frequency and spectra of de novo mutations to investigate the transgenerational effect of veterans' (potential) exposure to radiation at nuclear bomb test sites. We find no elevation in total de novo single nucleotide variants, small insertion-deletions, structural variants or clustered mutations among the offspring of nuclear test veterans compared to those of control personnel. We did observe an elevated occurrence of single base substitution mutations within mutation signature SBS16, due to a subset of NTV offspring. The relevance of this elevation to potential exposure of veteran fathers and, future health risks, require further investigation. Overall, we find no evidence of increased mutations in the germline of a group of British nuclear test veterans. ISRCTN Registry 17461668.

A comprehensive benchmarking of WGS-based deletion structural variant callers

Advances in whole-genome sequencing (WGS) promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from WGS data presents a substantial number of challenges and a plethora of SV detection methods have been developed. Currently, evidence that investigators can use to select appropriate SV detection tools is lacking. In this article, we have evaluated the performance of SV detection tools on mouse and human WGS data using a comprehensive polymerase chain reaction-confirmed gold standard set of SVs and the genome-in-a-bottle variant set, respectively. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of the SV detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance as the SV detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low- and ultralow-pass sequencing data as well as for different deletion length categories.

Advancing designer crops for climate resilience through an integrated genomics approach

Climate change and exponential population growth are exposing an immediate need for developing future crops that are highly resilient and adaptable to changing environments to maintain global food security in the next decade. Rigorous selection from long domestication history has rendered cultivated crops genetically disadvantaged, raising concerns in their ability to adapt to these new challenges and limiting their usefulness in breeding programmes. As a result, future crop improvement efforts must rely on integrating various genomic strategies ranging from high-throughput sequencing to machine learning, in order to exploit germplasm diversity and overcome bottlenecks created by domestication, expansive multi-dimensional phenotypes, arduous breeding processes, complex traits and big data.

Dysgu: efficient structural variant calling using short or long reads

Structural variation (SV) plays a fundamental role in genome evolution and can underlie inherited or acquired diseases such as cancer. Long-read sequencing technologies have led to improvements in the characterization of structural variants (SVs), although paired-end sequencing offers better scalability. Here, we present dysgu, which calls SVs or indels using paired-end or long reads. Dysgu detects signals from alignment gaps, discordant and supplementary mappings, and generates consensus contigs, before classifying events using machine learning. Additional SVs are identified by remapping of anomalous sequences. Dysgu outperforms existing state-of-the-art tools using paired-end or long-reads, offering high sensitivity and precision whilst being among the fastest tools to run. We find that combining low coverage paired-end and long-reads is competitive in terms of performance with long-reads at higher coverage values.

MAMnet: detecting and genotyping deletions and insertions based on long reads and a deep learning approach

Structural variations (SVs) play important roles in human genetic diversity; deletions and insertions are two common types of SVs that have been proven to be associated with genetic diseases. Hence, accurately detecting and genotyping SVs is significant for disease research. Despite the fact that long-read sequencing technologies have improved the field of SV detection and genotyping, there are still some challenges that prevent satisfactory results from being obtained. In this paper, we propose MAMnet, a fast and scalable SV detection and genotyping method based on long reads and a combination of convolutional neural network and long short-term network. MAMnet uses a deep neural network to implement sensitive SV detection with a novel prediction strategy. On real long-read sequencing datasets, we demonstrate that MAMnet outperforms Sniffles, SVIM, cuteSV and PBSV in terms of their F1 scores while achieving better scaling performance. The source code is available from https://github.com/micahvista/MAMnet.

Emerging technologies for prenatal diagnosis: The application of whole genome and RNA sequencing

DNA sequencing technologies for clinical genetic testing have been rapidly evolving in recent years, and steadily become more important within the field of prenatal diagnostics. This review aims to give an overview of recent developments and to describe how they have the potential to fill the gaps of the currently clinically implemented methods for prenatal diagnosis of various genetic disorders. It has been shown for postnatal testing that whole genome sequencing provides a set of added benefits compared to exome sequencing, and it is to be expected that this will be the case for prenatal testing as well. RNA-sequencing, already used postnatally, can provide valuable complementary data to DNA-based testing, and aid in variant interpretation. While not ready for clinical implementation, emerging technologies such as long-read and Hi-C sequencing analyses might add to the toolbox for interpreting the expanding genetic data sets generated by genome-wide sequencing. Lastly, we also discuss some more practical implications of introducing these emerging technologies, which generate larger and larger genomic data sets, in the prenatal field.

Genome Analysis in Sick Neonates and Infants: High-yield Phenotypes and Contribution of Small Copy Number Variations

OBJECTIVE

To delineate the diagnostic efficacy of medical exome, whole exome, and whole genome sequencing according to primary symptoms, the contribution of small copy number variations, and the impact of molecular diagnosis on clinical management.

STUDY DESIGN

This was a prospective study of 17 tertiary care centers in Japan, conducted between April 2019 and March 2021. Critically ill neonates and infants less than 6 months of age were recruited in neonatal intensive care units and in outpatient clinics. The patients underwent medical exome, whole exome, or whole genome sequencing as the first tier of testing. Patients with negative results after medical exome or whole exome sequencing subsequently underwent whole genome sequencing. The impact of molecular diagnosis on clinical management was evaluated through contacting primary care physicians.

RESULTS

Of the 85 patients, 41 (48%) had positive results. Based on the primary symptoms, patients with metabolic phenotypes had the highest diagnostic yield (67%, 4/6 patients), followed by renal (60%, 3/5 patients), and neurologic phenotypes (58%, 14/24 patients). Among them, 4 patients had pathogenic small copy number variations identified using whole genome sequencing. In the 41 patients with a molecular diagnosis, 20 (49%) had changes in clinical management.

CONCLUSIONS

Genome analysis for critically ill neonates and infants had a high diagnostic yield for metabolic, renal, and neurologic phenotypes. Small copy number variations detected using whole genome sequencing contributed to the overall molecular diagnosis in 5% of all the patients. The resulting molecular diagnoses had a significant impact on clinical management.

Computational analysis of cancer genome sequencing data

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

Comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data

Background

Structural variants (SVs) play a crucial role in gene regulation, trait association, and disease in humans. SV genotyping has been extensively applied in genomics research and clinical diagnosis. Although a growing number of SV genotyping methods for long reads have been developed, a comprehensive performance assessment of these methods has yet to be done.

Results

Based on one simulated and three real SV datasets, we performed an in-depth evaluation of five SV genotyping methods, including cuteSV, LRcaller, Sniffles, SVJedi, and VaPoR. The results show that for insertions and deletions, cuteSV and LRcaller have similar F1 scores (cuteSV, insertions: 0.69–0.90, deletions: 0.77–0.90 and LRcaller, insertions: 0.67–0.87, deletions: 0.74–0.91) and are superior to other methods. For duplications, inversions, and translocations, LRcaller yields the most accurate genotyping results (0.84, 0.68, and 0.47, respectively). When genotyping SVs located in tandem repeat region or with imprecise breakpoints, cuteSV (insertions and deletions) and LRcaller (duplications, inversions, and translocations) are better than other methods. In addition, we observed a decrease in F1 scores when the SV size increased. Finally, our analyses suggest that the F1 scores of these methods reach the point of diminishing returns at 20× depth of coverage.

Conclusions

We present an in-depth benchmark study of long-read SV genotyping methods. Our results highlight the advantages and disadvantages of each genotyping method, which provide practical guidance for optimal application selection and prospective directions for tool improvement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08548-y.

Bioinformatic Challenges Detecting Genetic Variation in Precision Medicine Programs

Precision medicine programs to identify clinically relevant genetic variation have been revolutionized by access to increasingly affordable high-throughput sequencing technologies. A decade of continual drops in per-base sequencing costs means it is now feasible to sequence an individual patient genome and interrogate all classes of genetic variation for < $1,000 USD. However, while advances in these technologies have greatly simplified the ability to obtain patient sequence information, the timely analysis and interpretation of variant information remains a challenge for the rollout of large-scale precision medicine programs. This review will examine the challenges and potential solutions that exist in identifying predictive genetic biomarkers and pharmacogenetic variants in a patient and discuss the larger bioinformatic challenges likely to emerge in the future. It will examine how both software and hardware development are aiming to overcome issues in short read mapping, variant detection and variant interpretation. It will discuss the current state of the art for genetic disease and the remaining challenges to overcome for complex disease. Success across all types of disease will require novel statistical models and software in order to ensure precision medicine programs realize their full potential now and into the future.

Unravelling the tumour genome: the evolutionary and clinical impacts of structural variants in tumourigenesis

Structural variants (SVs) represent a major source of aberration in tumour genomes. Given the diversity in the size and type of SVs present in tumours, the accurate detection and interpretation of SVs in tumours is challenging. New classes of complex structural events in tumours are discovered frequently, and the definitions of the genomic consequences of complex events are constantly being refined. Detailed analyses of short‐read whole‐genome sequencing (WGS) data from large tumour cohorts facilitate the interrogation of SVs at orders of magnitude greater scale and depth. However, the inherent technical limitations of short‐read WGS prevent us from accurately detecting and investigating the impact of all the SVs present in tumours. The expanded use of long‐read WGS will be critical for improving the accuracy of SV detection, and in fully resolving complex SV events, both of which are crucial for determining the impact of SVs on tumour progression and clinical outcome. Despite the present limitations, we demonstrate that SVs play an important role in tumourigenesis. In particular, SVs contribute significantly to late‐stage tumour development and to intratumoural heterogeneity. The evolutionary trajectories of SVs represent a window into the clonal dynamics in tumours, a comprehensive understanding of which will be vital for influencing patient outcomes in the future. Recent findings have highlighted many clinical applications of SVs in cancer, from early detection to biomarkers for treatment response and prognosis. As the methods to detect and interpret SVs improve, elucidating the full breadth of the complex SV landscape and determining how these events modulate tumour evolution will improve our understanding of cancer biology and our ability to capitalise on the utility of SVs in the clinical management of cancer patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Screening and diagnosis of inherited platelet disorders

Inherited platelet disorders are important conditions that often manifest with bleeding. These disorders have heterogeneous underlying pathologies. Some are syndromic disorders with non-blood phenotypic features, and others are associated with an increased predisposition to developing myelodysplasia and leukemia. Platelet disorders can present with thrombocytopenia, defects in platelet function, or both. As the underlying pathogenesis of inherited thrombocytopenias and platelet function disorders are quite diverse, their evaluation requires a thorough clinical assessment and specialized diagnostic tests, that often challenge diagnostic laboratories. At present, many of the commonly encountered, non-syndromic platelet disorders do not have a defined molecular cause. Nonetheless, significant progress has been made over the past few decades to improve the diagnostic evaluation of inherited platelet disorders, from the assessment of the bleeding history to improved standardization of light transmission aggregometry, which remains a "gold standard" test of platelet function. Some platelet disorder test findings are highly predictive of a bleeding disorder and some show association to symptoms of prolonged bleeding, surgical bleeding, and wound healing problems. Multiple assays can be required to diagnose common and rare platelet disorders, each requiring control of preanalytical, analytical, and post-analytical variables. The laboratory investigations of platelet disorders include evaluations of platelet counts, size, and morphology by light microscopy; assessments for aggregation defects; tests for dense granule deficiency; analyses of granule constituents and their release; platelet protein analysis by immunofluorescent staining or flow cytometry; tests of platelet procoagulant function; evaluations of platelet ultrastructure; high-throughput sequencing and other molecular diagnostic tests. The focus of this article is to review current methods for the diagnostic assessment of platelet function, with a focus on contemporary, best diagnostic laboratory practices, and relationships between clinical and laboratory findings.

Towards accurate and reliable resolution of structural variants for clinical diagnosis

Structural variants (SVs) are a major source of human genetic diversity and have been associated with different diseases and phenotypes. The detection of SVs is difficult, and a diverse range of detection methods and data analysis protocols has been developed. This difficulty and diversity make the detection of SVs for clinical applications challenging and requires a framework to ensure accuracy and reproducibility. Here, we discuss current developments in the diagnosis of SVs and propose a roadmap for the accurate and reproducible detection of SVs that includes case studies provided from the FDA-led SEquencing Quality Control Phase II (SEQC-II) and other consortium efforts.