Identification Of Structural Variants Research Articles

Whole-genome sequencing analysis in fetal structural anomalies: novel phenotype-genotype discoveries.

The identification of structural variants and single-nucleotide variants is essential in finding molecular etiologies of monogenic genetic disorders. Whole-genome sequencing (WGS) is becoming more widespread in genetic disease diagnosis. However, data on its clinical utility remain limited in prenatal practice. We aimed to expand our understanding of implementing WGS in the genetic diagnosis of fetal structural anomalies. We employed trio WGS with a minimum coverage of 40× on the MGI DNBSEQ-T7 platform in a cohort of 17 fetuses presenting with aberrations detected by ultrasound, but uninformative findings of standard chromosomal microarray analysis (CMA) and exome sequencing (ES). Causative genetic variants were identified in two families, with an increased diagnostic yield of 11.8% (2/17). Both were exon-level copy-number variants of small size (3.03 kb and 5.16 kb) and beyond the detection thresholds of CMA and ES. Moreover, to the best of our knowledge, we have described the first prenatal instance of the association of FGF8 with holoprosencephaly and facial deformities. Our analysis demonstrates the clinical value of WGS in the diagnosis of the underlying etiology of fetuses with structural abnormalities, when routine genetic tests have failed to provide a diagnosis. Additionally, the novel variants and new fetal manifestations have expanded the mutational and phenotypic spectrums of BBS9 and FGF8. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data

Long-read sequencing offers long contiguous DNA fragments, facilitating diploid genome assembly and structural variant (SV) detection. Efficient and robust algorithms for SV identification are crucial with increasing data availability. Alignment-based methods, favored for their computational efficiency and lower coverage requirements, are prominent. Alternative approaches, relying solely on available reads for de novo genome assembly and employing assembly-based tools for SV detection via comparison to a reference genome, demand significantly more computational resources. However, the lack of comprehensive benchmarking constrains our comprehension and hampers further algorithm development. Here we systematically compare 14 read alignment-based SV calling methods (including 4 deep learning-based methods and 1 hybrid method), and 4 assembly-based SV calling methods, alongside 4 upstream aligners and 7 assemblers. Assembly-based tools excel in detecting large SVs, especially insertions, and exhibit robustness to evaluation parameter changes and coverage fluctuations. Conversely, alignment-based tools demonstrate superior genotyping accuracy at low sequencing coverage (5-10×) and excel in detecting complex SVs, like translocations, inversions, and duplications. Our evaluation provides performance insights, highlighting the absence of a universally superior tool. We furnish guidelines across 31 criteria combinations, aiding users in selecting the most suitable tools for diverse scenarios and offering directions for further method development.

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.

Haplotype information of large neuromuscular disease genes provided by linked-read sequencing has a potential to increase diagnostic yield.

Rare or novel missense variants in large genes such as TTN and NEB are frequent in the general population, which hampers the interpretation of putative disease-causing biallelic variants in patients with sporadic neuromuscular disorders. Often, when the first initial genetic analysis is performed, the reconstructed haplotype, i.e. phasing information of the variants is missing. Segregation analysis increases the diagnostic turnaround time and is not always possible if samples from family members are lacking. To overcome this difficulty, we investigated how well the linked-read technology succeeded to phase variants in these large genes, and whether it improved the identification of structural variants. Linked-read sequencing data of nemaline myopathy, distal myopathy, and proximal myopathy patients were analyzed for phasing, single nucleotide variants, and structural variants. Variant phasing was successful in the large muscle genes studied. The longest continuous phase blocks were gained using high-quality DNA samples with long DNA fragments. Homozygosity increased the number of phase blocks, especially in exome sequencing samples lacking intronic variation. In our cohort, linked-read sequencing added more information about the structural variation but did not lead to a molecular genetic diagnosis. The linked-read technology can support the clinical diagnosis of neuromuscular and other genetic disorders.

Optical Genome Mapping for Comprehensive Cytogenetic Analysis of Soft-Tissue and Bone Tumors for Diagnostic Purposes

Soft tissue and bone tumours represent a heterogeneous group of tumours encompassing more than 100 histological subtypes today. Identifying genetic aberrations is increasingly important in these tumours for accurate diagnosis. While gene mutations are typically detected by second generation sequencing, the identification of structural variants (SVs) and copy number alterations (CNAs) remains challenging and requires various cytogenetic techniques including karyotyping, fluorescent in situ hybridisation and arrays, each having important limitations.We applied Optical Genome Mapping (OGM), a non-sequencing-based technique for high-resolution detection of SVs and CNAs in a retrospective series of diagnostic soft tissue and bone tumour samples. Sample preparation was successful in 38 out of 53 cases, with the highest success rate in non-adipocytic soft tissue tumours (24/27 cases, 89%). In 32 out of 35 cases carrying a diagnostic SV or CNA, OGM identified the aberration (91%) including a POU2AF3::EWSR1 fusion in a round cell sarcoma and a translocation t(1;5)(p22;p15) in a myxoinflammatory fibroblastic sarcoma. Interestingly, OGM shed light on the genomic complexity underlying the various aberrations. In five samples OGM showed that chains of rearrangements generated the diagnostic fusion, three of which involved chromoplexy. Additionally, in nine samples chromothripsis was causal to the formation of giant marker/ring/double minute chromosomes. Finally, compared to standard-of-care cytogenetics, OGM revealed additional aberrations, requiring further investigation of their potential clinical relevance.

Kled: an ultra-fast and sensitive structural variant detection tool for long-read sequencing data.

Structural Variants (SVs) are a crucial type of genetic variant that can significantly impact phenotypes. Therefore, the identification of SVs is an essential part of modern genomic analysis. In this article, we present kled, an ultra-fast and sensitive SV caller for long-read sequencing data given the specially designed approach with a novel signature-merging algorithm, custom refinement strategies and a high-performance program structure. The evaluation results demonstrate that kled can achieve optimal SV calling compared to several state-of-the-art methods on simulated and real long-read data for different platforms and sequencing depths. Furthermore, kled excels at rapid SV calling and can efficiently utilize multiple Central Processing Unit (CPU) cores while maintaining low memory usage. The source code for kled can be obtained from https://github.com/CoREse/kled.

Investigation of chromosomal genetic characteristics and identification of structural variation in the offspring of hexaploid triticale×hexaploid wheat.

Hexaploid triticale is an important genetic resource for genetic improvement of common wheat, which can broaden the genetic basis of wheat. In order to lay a foundation for the subsequent research and utilization of triticale germplasm materials, the chromosomal genetic characteristics of cross and backcross offspring of hexaploid triticale×hexaploid wheat were investigated in the process of transferring rye chromatin from hexaploid triticale to hexaploid wheat. Hybrid and backcross combinations were prepared with hexaploid triticale 16yin171 as the maternal parent and hexaploid wheat Chuanmai62 as the paternal parent. The chromosomes in root tip cells of F1, BC1F1 and BC1F2 plants were traced and identified non-denaturing florescence in situ hybridization (ND-FISH). The results indicated that the backcross setting rate of hybrid F1 was 2.61%. The transmission frequency of 2R chromosome was the highest in BC1F1 plants while the transmissibility of rye chromosome in BC1F2 plant was 6R>4R>2R, and the 5B-7B wheat translocation in BC1F2 plants showed severe segregation. A total of 24 structural variant chromosomes were observed both in BC1F1 and BC1F2 plants, including chromosome fragments, isochromosomes, translocations, and dicentric chromosomes. In addition, the seed length and 1000-grain weight of some BC1F2 plants were better than that of the hexaploid wheat parent Chuanmai 62. Therefore, multiple backcrosses should be adopted as far as possible to make the rapid recovery of group D chromosomes, ensuring the recovery of fertility in offspring, when hexaploid tritriale is used as a bridge to introduce rye genetic material into common wheat. At the same time, the potential application value of chromosomal structural variation materials should be also concerned.

Whole genome sequencing enables new genetic diagnosis for inherited retinal diseases by identifying pathogenic variants

Inherited retinal diseases (IRDs) are a group of common primary retinal degenerative disorders. Conventional genetic testing strategies, such as panel-based sequencing and whole exome sequencing (WES), can only elucidate the genetic etiology in approximately 60% of IRD patients. Studies have suggested that unsolved IRD cases could be attributed to previously undetected structural variants (SVs) and intronic variants in IRD-related genes. The aim of our study was to obtain a definitive genetic diagnosis by employing whole genome sequencing (WGS) in IRD cases where the causative genes were inconclusive following an initial screening by panel sequencing. A total of 271 unresolved IRD patients and their available family members (n = 646) were screened using WGS to identify pathogenic SVs and intronic variants in 792 known ocular disease genes. Overall, 13% (34/271) of IRD patients received a confirmed genetic diagnosis, among which 7% were exclusively attributed to SVs, 4% to a combination of single nucleotide variants (SNVs) and SVs while another 2% were linked to intronic variants. 22 SVs, 3 deep-intronic variants, and 2 non-canonical splice-site variants across 14 IRD genes were identified in the entire cohort. Notably, all of these detected SVs and intronic variants were novel pathogenic variants. Among those, 74% (20/27) of variants were found in genes causally linked to Retinitis Pigmentosa (RP), with the gene EYS being the most frequently affected by SVs. The identification of SVs and intronic variants through WGS enhances the genetic diagnostic yield of IRDs and broadens the mutational spectrum of known IRD-associated genes.

Identification of the genetic characteristics of copy number variations in experimental specific pathogen-free ducks using whole-genome resequencing.

Specific pathogen-free ducks are a valuable laboratory resource for waterfowl disease research and poultry vaccine development. High throughput sequencing allows the systematic identification of structural variants in genomes. Copy number variation (CNV) can explain the variation of important duck genetic traits. Herein, the genome-wide CNVs of the three experimental duck species in China (Jinding ducks (JD), Shaoxing ducks (SX), and Fujian Shanma ducks (SM)) were characterized using resequencing to determine their genetic characteristics and selection signatures. We obtained 4,810 CNV regions (CNVRs) by merging 73,012 CNVs, covering 4.2% of the duck genome. Functional analysis revealed that the shared CNVR-harbored genes were significantly enriched for 31 gene ontology terms and 16 Kyoto Encyclopedia of Genes and Genomes pathways (e.g., olfactory transduction and immune system). Based on the genome-wide fixation index for each CNVR, growth (SPAG17 and PTH1R), disease resistance (CATHL3 and DMBT1), and thermoregulation (TRPC4 and SLIT3) candidate genes were identified in strongly selected signatures specific to JD, SM, and SX, respectively. In conclusion, we investigated the genome-wide distribution of experimental duck CNVs, providing a reference to establish the genetic basis of different phenotypic traits, thus contributing to the management of experimental animal genetic resources.

Chromosomal scale assembly reveals localized structural variants in avian caecal coccidian parasite Eimeria tenella

Eimeria tenella is a major cause of caecal coccidiosis in commercial poultry chickens worldwide. Here, we report chromosomal scale assembly of Eimeria tenella strain APU2, a strain isolated from commercial broiler chickens in the U.S. We obtained 100× sequencing Oxford Nanopore Technology (ONT) and more than 800× Coverage of Illumina Next-Seq. We created the assembly using the hybrid approach implemented in MaSuRCA, achieving a contiguous 51.34 Mb chromosomal-scale scaffolding enabling identification of structural variations. The AUGUSTUS pipeline predicted 8060 genes, and BUSCO deemed the genomes 99% complete; 6278 (78%) genes were annotated with Pfam domains, and 1395 genes were assigned GO-terms. Comparing E. tenella strains (APU2, US isolate and Houghton, UK isolate) derived Houghton strain of E. tenella revealed 62,905 high stringency differences, of which 45,322 are single nucleotide polymorphisms (SNPs) (0.088%). The rate of transitions/transversions among the SNPs are 1.63 ts/tv. The strains possess conserved gene order but have profound sequence heterogeneity in a several chromosomal segments (chr 2, 11 and 15). Genic and intergenic variation in defined gene families was evaluated between the two strains to possibly identify sequences under selection. The average genic nucleotide diversity of 2.8 with average 2 kb gene length (0.145%) at genic level. We examined population structure using available E. tenella sequences in NCBI, revealing that the two E. tenella isolates from the U.S. (E. tenella APU2 and Wisconsin, “ERR296879”) share a common maternal inheritance with the E. tenella Houghton. Our chromosomal level assembly promotes insight into Eimeria biology and evolution, hastening drug discovery and vaccine development.

Selected Genomic Structural Variants Refine ELN-Based Prognostic Stratification of Patients with Acute Myeloid Leukemia and Normal Karyotype

Background. Normal karyotype Acute Myeloid Leukemia (nk-AML) accounts for 40% of all AML. Conventionally, nk-AML patients (pts) have been associated with the ELN favorable and intermediate risk category, characterized by extreme prognostic heterogeneity, representing an unmet therapeutic need especially as regards effective positioning of allogeneic transplantation (ASCT) as consolidation treatment. Aim. To improve risk stratification of nk-AML, on top of 2017 ELN criteria, by identification of structural variants (SV) with prognostic significance through the analysis of whole genome sequencing by long reads Nanopore platform. Patients and Methods. A total of 247 nk-AML pts receiving intensive therapy was included. The learning cohort included 95 pts from the prospective NILG 02/06 trial ( Bassan R Blood Adv 2019; 3:1103) comparing standard vs high-dose chemotherapy and 67 pts from the prospective GIMEMA AML1310 trial of risk-adapted, MRD-directed therapy ( Venditti A, Blood 2019;134:935). The validation cohort comprised 85 pts from Florence database. Whole genome long-read nanopore sequencing was performed on GridION platform to a median depth of 5x. After alignment to hgr38 genome, only those SV that had been concurrently identified by 4 independent callers were retained for analysis. Results. In the whole series (WS),197 pts (79.7%) obtained CR, 91 (46.2%) relapsed; 50 pts (20.2%) were primary refractory, 83 (33.6%) underwent ASCT. According to ELN criteria, 56.7%, 29.2% and 14.1% of the pts were in the favorable, intermediate and adverse risk category. Median survival (OS) was 16.9mo (9-24.7) in adverse risk category, 24.1mo (4.5-43.7) intermediate, and not reached in favorable risk category (P= 0.00007). However, adverse and intermediate category did not differ significantly (P=0.63), as expected considering the selection of nk-AML. Extensive filtering against 100 healthy donors and publicly available datasets of genomic variants to remove SV with >0.01% allele frequency resulted in 2,038 SV; after removal of private SV (ie, present in ≤2 pt), 122 SV were finally retained. Univariate regression analysis resulted in 10 SV associated with OS, of which 7 were validated with independent technology (Sanger and high-resolution melting, HRM). Multivariate analysis including all ELN-risk score variables showed that 5 of the 7 SV were independently associated with OS (hence, referred as high-risk variants, HRV). Three HRV affected the locus of coding genes on Chr4 q32.3 (inversion spanning 6,103bp), Chr5 p12 (333bp insertion) and Chr9 q34.3 (57bp deletion); Chr3 q26.1 (64bp insertion) in a Long Intergenic Non-Protein Coding RNA gene; Chr2 p13.2 (64bp deletion), in intergenic region. Presence of HRV impacted on OS with HR of 4.1 (95%CI,2.4-7; P=1.5e-7) in the learning cohort and HR 3.4 (95%CI, 1.5-7.6; P=0.003) in the validation cohort. Censoring at ASCT had no impact in either cohort. In total, 29 pts (11.7%) were HRV+, and their OS was 12.6mo (95%CI, 5.4-19.9) vs not reached (95%CI, 33.5-40.9; P=8.4183e-11) in HRV- pts (HR 3.9, 95%CI, 2.5-6; P=1.6261e-9) (Figure 1). DFS and EFS was 7mo (95%CI: 3.6-10.5) and 14.6mo (95%CI,2.1-27) in HRV+ pts vs not reached (95%CI, 31.7-40) and 30mo (95%CI: 29.9-30.1) (P<0.01 for both). Also, the rate of relapse was higher among HRV+, 58.6% vs 34.4% (P=0.01), with a trend to lower rate of CR attainment (72.4% vs 82.6%). There was no remarkable difference between HRV+ and HRV- pts as regarded frequency and type of ELN molecular variables. We finally evaluated whether integration of HRV within ELN risk score might improve the predictive performance of the latter. The resulting model enlisted 4 tiers, favorable (median OS not reached), intermediate (27.4mo, 95%CI: 3.6-51.1, HR:2.4; 95%CI,1.5-3.8), adverse (21.5mo, 95%CI: 5.4-37.6, HR:2.5; 95%CI,1.4-4.3) and HRV+ (12.6mo, 95%CI: 5.4-19.9, HR:5.9; 95%CI,3.6-9.8) categories (P=1.3395e-12) (Figure 2). Of note, the HRV+ category included 79.3% of pts otherwise categorized as ELN favorable or intermediate. Conclusions. We have identified, by long-read whole genome sequencing, a set of 5 structural variants that marks a subgroup of nk-AML pts with outmost dismal prognosis and may be integrated in ELN-risk score to help therapeutic decisions. Since HRV can be conveniently interrogated by routine approaches such as Sanger sequencing and HRM, further studies on larger cohorts might support these findings.

Accurate identification of structural variations from cancer samples.

Structural variations (SVs) are commonly found in cancer genomes. They can cause gene amplification, deletion and fusion, among other functional consequences. With an average read length of hundreds of kilobases, nano-channel-based optical DNA mapping is powerful in detecting large SVs. However, existing SV calling methods are not tailored for cancer samples, which have special properties such as mixed cell types and sub-clones. Here we propose the Cancer Optical Mapping for detecting Structural Variations (COMSV) method that is specifically designed for cancer samples. It shows high sensitivity and specificity in benchmark comparisons. Applying to cancer cell lines and patient samples, COMSV identifies hundreds of novel SVs per sample.

Searching for sequencing signal anomalies associated with genome structural variations

Genomic structural variations (SVs) are one of the main sources of genetic diversity. Structural variants as mutagens may have a significant impact on human health and lead to hereditary diseases and cancers. Existing methods of finding structural variants are based on analysis of high-throughput sequencing data and despite significant progress in the development of the detection methods, there is still a need for improving the identification of structural variations with accuracy appropriate for use in a diagnostic procedure. Analysis of the signal of sequencing coverage (i.e., the number of sequencing fragments that aligned to every point of a genome) holds new potential for the design of approaches for structural variations discovery, and can be used as time-series analysis. Here, we present an approach for identification of patterns in the coverage signal. The method has been developed based on algorithms used for analysis of time series data, namely KNN (K-nearest neighbour) search algorithm and the SAX (Symbolic Aggregation Approximation) method. Using the rich dataset encompassing full genomes of 911 individuals with different ethnic backgrounds generated by the Human Genome Diversity Project initiative, we constructed generalized patterns of signal coverage in the vicinity of breakpoints corresponding to various structural variant types. Also, with the benefit of the SAX models of the motifs we developed a software package for fast detection of anomalies in the coverage signal.

Pangenome graphs in infectious disease: a comprehensive genetic variation analysis of Neisseria meningitidis leveraging Oxford Nanopore long reads.

Whole genome sequencing has revolutionized infectious disease surveillance for tracking and monitoring the spread and evolution of pathogens. However, using a linear reference genome for genomic analyses may introduce biases, especially when studies are conducted on highly variable bacterial genomes of the same species. Pangenome graphs provide an efficient model for representing and analyzing multiple genomes and their variants as a graph structure that includes all types of variations. In this study, we present a practical bioinformatics pipeline that employs the PanGenome Graph Builder and the Variation Graph toolkit to build pangenomes from assembled genomes, align whole genome sequencing data and call variants against a graph reference. The pangenome graph enables the identification of structural variants, rearrangements, and small variants (e.g., single nucleotide polymorphisms and insertions/deletions) simultaneously. We demonstrate that using a pangenome graph, instead of a single linear reference genome, improves mapping rates and variant calling for both simulated and real datasets of the pathogen Neisseria meningitidis. Overall, pangenome graphs offer a promising approach for comparative genomics and comprehensive genetic variation analysis in infectious disease. Moreover, this innovative pipeline, leveraging pangenome graphs, can bridge variant analysis, genome assembly, population genetics, and evolutionary biology, expanding the reach of genomic understanding and applications.

A powerful random forest-based pipeline for accurately identifying the structural variants on panel sequencing data.

e18898 Background: Numerous studies have reported that the identification of structural variants (SVs) can provide ideal targets for cancer targeting therapy. However, on the one hand, the current algorithms exhibited different performances in detecting different types and sizes of SVs; on the other hand, the whole-genome/exome sequencing for detecting SVs is costly, and only a portion of SVs can be used to guide the clinical treatment of cancer patients. Therefore, developing a powerful, highly accurate, and cost-effective pipeline to detect SVs for clinical application is urgently required. Methods: Based on simulated sequencing data, four common SV detection tools (Delly, Lumpy, SvABA, and Manta) were used to select the best performance one. This tool will be used for detecting SVs in panel sequencing data. Then, Integrative Genomics Viewer (IGV) was used to annotate these SVs as true positive (TP) and false positive (FP) SVs. These annotated TP and FP SVs were used as input in the random forest classifier (RFC) to train a model for predicting the true or false positive SVs. Two independent testing cohorts and standards were used to validate this constructed pipeline. In addition, IHC/FISH experiments were performed to further validate these predicted TP SVs. Results: In the simulation data, the detection tool with the highest SVs identification sensitivity was Delly. By Delly, a total of 1,303 SVs in 384 tumor samples were detected. Based on annotated TP and FP SVs, an RFC model was constructed in the training cohort, which predicted 334 and 560 TP and FP SVs, respectively. The accuracy rates (AR) were 99.85% and 100% of TP and FP SVs, respectively. The predicted TP and FP SVs by constructed pipeline also showed a high accuracy rate in cohort 1 (98.96% and 99.24% of TP and FP SVs, respectively), cohort 2 (98.48% and 100% of TP and FP SVs, respectively), and standards (the mean accuracy rate was 90%). Finally, based on these TP SVs predicted by the constructed pipeline, FISH/IHC experiment results further verified the robustness of this pipeline (the man accuracy rate was 95.83%). Conclusions: The constructed random forest-based pipeline can robustly and accurately identify SVs based on panel sequencing data, which will be helpful for clinical decision-making.

Abstract 222: High frequency of structural variants in FFPE colorectal cancer tissue detected by targeting selected common fragile site genes and LINE transposable elements

Abstract Introduction: Structural variants (SVs) caused by chromosomal rearrangements or LINE retrotranspositions are a highly prevalent type of somatic DNA alterations in colorectal cancer (CRC). Studies about the function of SVs in tumor biology and their putative application as biomarkers are currently hampered by the need for fresh frozen tumor material to detect SVs by long-read or deep whole genome sequencing. This impedes the identification of SVs in retrospective cohorts and implementation in routine diagnostics (where only formalin-fixed paraffin-embedded (FFPE) tissue is available). In this study, we explored the applicability of FFPE-Targeted Locus Capture (FFPE-TLC) as a novel technology for targeted detection of SVs in CRC. Materials and methods: We defined the regions most frequently presenting SVs in CRC and included these in a customized capture panel. FFPE-TLC followed by targeted sequencing was performed on primary tumors of 29 metastatic CRC patients and eight patient-matched normal colon tissues. SV-specific droplet digital PCR (ddPCR) assays were designed for orthogonal validation of SVs in tumor tissue and detection of SVs in cell-free plasma circulating tumor DNA (ctDNA). Results: The regions associated with most SVs in metastatic CRC were four common fragile site (CFS) genes; MACROD2, PARK2, FHIT, WWOX, and three LINE source elements, and were included in a 2.7 Mb targeted panel. After filtering for germline variation, we identified 168 somatic SVs in 26/29 patients (90%), median SVs per patient: 3, range: 1-22. 110 SVs were found in CFS genes in 19/29 patients (66%), 12 of them presenting SVs in MACROD2. 58 SVs were caused by LINE integrations in 19/29 patients (66%), with different LINE behaviors observed: one source LINE generated 19 SVs in 13 patients, whilst another LINE caused 33 SVs in 5 patients. For selected cases, the tumor-specific origin of SVs was verified and its applicability as a plasma ctDNA biomarker demonstrated. Discussion: This study demonstrates, for the first time, the feasibility of targeted detection of SVs in CFS genes and SVs caused by LINE retrotransposition in routinely obtained FFPE tissue without prior knowledge of the breakpoint location. The high prevalence of SVs offers opportunities to detect many somatic alterations by targeting few genes and LINEs, e.g. for translation into ctDNA biomarker assays. We conclude that FFPE-TLC enables the investigation of SV mutations in retrospective cohorts and opens new avenues for mutation analysis in routine diagnostics. Citation Format: Carmen Rubio-Alarcon, Ellen Stelloo, Daan C. Vessies, Iris van ‘t Erve, Nienke Mekkes, Joost Swennenhuis, Soufyan Lakbir, Elise van Bree, Marianne Tijssen, Pien Delis-van Diemen, Mirthe Lanfermeijer, Theodora C. Linders, Daan van den Broek, Cornelis J. Punt, Jaap Heringa, Gerrit A. Meijer, Sanne Abeln, Harma Feitsma, Remond J. Fijneman. High frequency of structural variants in FFPE colorectal cancer tissue detected by targeting selected common fragile site genes and LINE transposable elements [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 222.

Identification of structural variations related to drought tolerance in wheat (Triticum aestivum L.).

Structural variations are common in plant genomes, affecting meiotic recombination and distorted segregation in wheat. And presence/absence variations can significantly affect drought tolerance in wheat. Drought is a major abiotic stress limiting wheat production. Common wheat has a complex genome with three sub-genomes, which host large numbers of structural variations (SVs). SVs play critical roles in understanding the genetic contributions of plant domestication and phenotypic plasticity, but little is known about their genomic characteristics and their effects on drought tolerance. In the present study, high-resolution karyotypes of 180 doubled haploids (DHs) were developed. Signal polymorphisms between the parents involved with 8 presence-absence variations (PAVs) of tandem repeats (TR) distributed on the 7 (2A, 4A, 5A, 7A, 3B, 7B, and 2D) of 21 chromosomes. Among them, PAV on chromosome 2D showed distorted segregation, others transmit normal conforming to a 1:1 segregation ration in the population; and a PAVs recombination occurred on chromosome 2A. Association analysis of PAV and phenotypic traits under different water regimes, we found PAVs on chromosomes 4A, 5A, and 7B showed negative effect on grain length (GL) and grain width (GW); PAV.7A had opposite effect on grain thickness (GT) and spike length (SL), with the effect on traits differing under different water regimes. PAVs on linkage group 2A, 4A, 7A, 2D, and 7B associated with the drought tolerance coefficients (DTCs), and significant negative effect on drought resistance values (D values) were detected in PAV.7B. Additionally, quantitative trait loci (QTL) associated with phenotypic traits using the 90K SNP array showed QTL for DTCs and grain-related traits in chromosomes 4A, and 5A, 3B were co-localized in differential regions of PAVs. These PAVs can cause the differentiation of the target region of SNP and could be used for genetic improvement of agronomic traits under drought stress via marker-assisted selection (MAS) breeding.

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.

HiSV: A control-free method for structural variation detection from Hi-C data.

Structural variations (SVs) play an essential role in the evolution of human genomes and are associated with cancer genetics and rare disease. High-throughput chromosome capture (Hi-C) technology probed all genome-wide crosslinked chromatin to study the spatial architecture of chromosomes. Hi-C read pairs can span megabases, making the technology useful for detecting large-scale SVs. So far, the identification of SVs from Hi-C data is still in the early stages with only a few methods available. Especially, no algorithm has been developed that can detect SVs without control samples. Therefore, we developed HiSV (Hi-C for Structural Variation), a control-free method for identifying large-scale SVs from a Hi-C sample. Inspired by the single image saliency detection model, HiSV constructed a saliency map of interaction frequencies and extracted saliency segments as large-scale SVs. By evaluating both simulated and real data, HiSV not only detected all variant types, but also achieved a higher level of accuracy and sensitivity than existing methods. Moreover, our results on cancer cell lines showed that HiSV effectively detected eight complex SV events and identified two novel SVs of key factors associated with cancer development. Finally, we found that integrating the result of HiSV helped the WGS method to identify a total number of 94 novel SVs in two cancer cell lines.

An efficient genotyper and star-allele caller for pharmacogenomics.

High-throughput sequencing provides sufficient means for determining genotypes of clinically important pharmacogenes that can be used to tailor medical decisions to individual patients. However, pharmacogene genotyping, also known as star-allele calling, is a challenging problem that requires accurate copy number calling, structural variation identification, variant calling, and phasing within each pharmacogene copy present in the sample. Here we introduce Aldy 4, a fast and efficient tool for genotyping pharmacogenes that uses combinatorial optimization for accurate star-allele calling across different sequencing technologies. Aldy 4 adds support for long reads and uses a novel phasing model and improved copy number and variant calling models. We compare Aldy 4 against the current state-of-the-art star-allele callers on a large and diverse set of samples and genes sequenced by various sequencing technologies, such as whole-genome and targeted Illumina sequencing, barcoded 10x Genomics, and Pacific Biosciences (PacBio) HiFi. We show that Aldy 4 is the most accurate star-allele caller with near-perfect accuracy in all evaluated contexts, and hope that Aldy remains an invaluable tool in the clinical toolbox even with the advent of long-read sequencing technologies.