Whole-genome CNV analysis: advances in computational approaches

Overexpression of SYNGAP1 suppresses the proliferation of rectal adenocarcinoma via Wnt/β-Catenin signaling pathway

Rectal adenocarcinoma (READ) is a common malignant tumor of the digestive tract. Growing studies have confirmed Ras GTPase-activating proteins are involved in the progression of several tumors. This study aimed to explore the expression and function of Ras GTPase-activating proteins in READ. In this study, we analyzed RNA sequencing data from 165 patients with READ and 789 normal tissue samples, identifying 5603 differentially expressed genes (DEGs), including 2937 upregulated genes and 2666 downregulated genes. Moreover, we also identified two dysregulated genes, RASA4 and SYNGAP1, among six Ras GTPase-activating proteins. High NF1 expression was associated with longer overall survival, while high SYNGAP1 expression showed a trend towards extended overall survival. Further analysis revealed the mutation frequency and copy number variations of Ras GTPase-activating proteins in various cancer samples. Additionally, DNA methylation analysis demonstrated a negative correlation between DNA methylation of Ras GTPase-activating proteins and their expression. Moreover, among Ras GTPase-activating proteins, we focused on SYNGAP1, and experimental validation confirmed that the overexpression of SYNGAP1 in READ significantly suppressed READ cell proliferation and increased apoptosis via regulating the Wnt/β-Catenin signaling pathway. These findings underscored the potential significance of SYNGAP1 in READ and provide new insights for further research and treatment.

Identify Candidate Genes Associated with the Weight and Egg Quality Traits in Wenshui Green Shell-Laying Chickens by the Copy Number Variation-Based Genome-Wide Association Study

Copy number variation (CNV), as an essential source of genetic variation, can have an impact on gene expression, genetic diversity, disease susceptibility, and species evolution in animals. To better understand the weight and egg quality traits of chickens, this paper aimed to detect CNVs in Wenshui green shell-laying chickens and conduct a copy number variation regions (CNVRs)-based genome-wide association study (GWAS) to identify variants and candidate genes associated with their weight and egg quality traits to support related breeding efforts. In our paper, we identified 11,035 CNVRs in Wenshui green shell-laying chickens, which collectively spanned a length of 13.1 Mb, representing approximately 1.4% of its autosomal genome. Out of these CNVRs, there were 10,446 loss types, 491 gain types, and 98 mixed types. Notably, two CNVRs showed significant correlations with egg quality, while four CNVRs exhibited significant associations with body weight. These significant CNVRs are located on chromosome 4. Further analysis identified potential candidate genes that influence weight and egg quality traits, including FAM184B, MED28, LAP3, ATOH8, ST3GAL5, LDB2, and SORCS2. In this paper, the CNV map of the Wenshui green shell-laying chicken genome was constructed for the first time through population genotyping. Additionally, CNVRs can be employed as molecular markers to genetically improve chickens' weight and egg quality traits.

Screening copy number variations in 35 unsolved inherited retinal disease families

The purpose of this study was to screen Copy Number Variations (CNVs) in 35 unsolved Inherited Retinal Dystrophy (IRD) families. Initially, next generation sequencing, including a specific Hereditary Eye Disease Enrichment Panel or Whole exome sequencing, was employed to screen (likely) pathogenic Single-nucleotide Variants (SNVs) and small Insertions and Deletions (indels) for these cases. All available SNVs and indels were further validated and co-segregation analyses were performed in available family members by Sanger sequencing. If not, after excluding deep intronic variants, Multiplex ligation-dependent probe amplification (MLPA), quantitative fluorescence PCR (QF-PCR) and Sanger sequencing were employed to screen CNVs. We determined that 18 probands who had heterozygous SNVs/indels or whose parents were not consanguineous but had homozygous SNVs/indels in autosomal recessive IRDs genes had CNVs in another allele of these genes, 11 families had disease-causing hemizygous CNVs in X-linked IRD genes, 6 families had (likely) pathogenic heterozygous CNVs in PRPF31 gene. Of 35 families, 33 different CNVs in 16 IRD-associated genes were detected, with PRPF31, EYS and USH2A the most common disease-causing gene in CNVs. Twenty-six and 7 of them were deletion and duplication CNVs, respectively. Among them, 14 CNVs were first reported in this study. Our research indicates that CNVs contribute a lot to IRDs, and screening of CNVs substantially increases the diagnostic rate of IRD. Our results emphasize that MLPA and QF-PCR are ideal methods to validate CNVs, and the novel CNVs reported herein expand the mutational spectrums of IRDs.

Next-generation sequencing and bioinformatics in rare movement disorders

The ability to sequence entire exomes and genomes has revolutionized molecular testing in rare movement disorders, and genomic sequencing is becoming an integral part of routine diagnostic workflows for these heterogeneous conditions. However, interpretation of the extensive genomic variant information that is being generated presents substantial challenges. In this Perspective, we outline multidimensional strategies for genetic diagnosis in patients with rare movement disorders. We examine bioinformatics tools and computational metrics that have been developed to facilitate accurate prioritization of disease-causing variants. Additionally, we highlight community-driven data-sharing and case-matchmaking platforms, which are designed to foster the discovery of new genotype-phenotype relationships. Finally, we consider how multiomic data integration might optimize diagnostic success by combining genomic, epigenetic, transcriptomic and/or proteomic profiling to enable a more holistic evaluation of variant effects. Together, the approaches that we discuss offer pathways to the improved understanding of the genetic basis of rare movement disorders.

Evidence from 2100 index cases supports genome sequencing as a first-tier genetic test

PURPOSE

Genome sequencing (GS) is one of the most comprehensive assays that interrogate single-nucleotide variants, copy number variants, mitochondrial variants, repeat expansions, and structural variants in a single assay. Despite the clear technical superiority, the full clinical utility of GS has yet to be determined.

METHODS

We systematically evaluated 2100 clinical GS index cases performed in our laboratory to explore the diagnostic yield of GS as first-tier and as follow-up testing.

RESULTS

The overall diagnostic yield was 28% (585/2100). The diagnostic yield for GS as the first-tier test was 26% (294/1146). Among cases with prior non-diagnostic genetic tests, GS provided a diagnosis for 27% (247/910) of cases, including 56 cases with prior exome sequencing (ES). Although re-analysis of previous ES might have resolved the diagnosis in 29 cases, diagnoses for 27 cases would have been missed because of the technical inferiority of ES. Moreover, GS further disclosed additional genetic etiology in 3 out of 44 cases with existing partial diagnosis.

CONCLUSION

We present the largest-to-date GS data set of a clinically heterogeneous cohort from a single clinical laboratory. Our data demonstrate that GS should be considered as the first-tier genetic test that has the potential to shorten the diagnostic odyssey.

Copy Number Variations in Pancreatic Cancer: From Biological Significance to Clinical Utility

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, characterized by high tumor heterogeneity and a poor prognosis. Inter- and intra-tumoral heterogeneity in PDAC is a major obstacle to effective PDAC treatment; therefore, it is highly desirable to explore the tumor heterogeneity and underlying mechanisms for the improvement of PDAC prognosis. Gene copy number variations (CNVs) are increasingly recognized as a common and heritable source of inter-individual variation in genomic sequence. In this review, we outline the origin, main characteristics, and pathological aspects of CNVs. We then describe the occurrence of CNVs in PDAC, including those that have been clearly shown to have a pathogenic role, and further highlight some key examples of their involvement in tumor development and progression. The ability to efficiently identify and analyze CNVs in tumor samples is important to support translational research and foster precision oncology, as copy number variants can be utilized to guide clinical decisions. We provide insights into understanding the CNV landscapes and the role of both somatic and germline CNVs in PDAC, which could lead to significant advances in diagnosis, prognosis, and treatment. Although there has been significant progress in this field, understanding the full contribution of CNVs to the genetic basis of PDAC will require further research, with more accurate CNV assays such as single-cell techniques and larger cohorts than have been performed to date.

Exploring quantitative traits-associated copy number deletions through reanalysis of UK10K consortium whole genome sequencing cohorts

OBJECTIVES

We performed comprehensive association analyses of common high-confidence gnomAD-reported copy number deletions (CNDs) with 60 quantitative traits from UK10K consortium WGS data.

METHODS

The study made use of data generated by the UK10K Consortium. UK10K consortium WGS data consist of TwinsUK (n = 1754, middle-aged females) and ALSPAC (n = 1867, birth to adolescence) cohorts. UK10K consortium called 18,739 CNDs (hg19) with GenomeSTRiP software. After filtering out variants with minor allele frequency < 0.05 or HWE P < 1.0 × 10- 6, 1222 (TwinsUK) and 1211 (ALSPAC) CNDs remained for association analyses with 60 normalized quantitative traits.

RESULTS

We identified 23 genome-wide significant associations at 13 loci, among which 2 associations reached experiment-wide significance. We found that two common deletions in chromosome 4, located between WDR1 and ZNF518B (23.3 kb, dbVar ID:nssv15888957, 4:10211262-10,234,569 and 9.8 kb, dbVar ID:nssv15888975, 4:10392422-10,402,191), were associated with uric acid levels (P = 5.23 × 10- 11 and 2.29 × 10- 8, respectively). We also discovered a novel deletion spanning chromosome 18 (823 bp, dbVar ID: nssv15841628, 8:74347187-74,348,010) associated with low HDL cholesterol levels (P = 4.15 × 10- 7). Additionally, we observed two red blood cell traits-associated loci with genome-wide significance, a 13.2 kb deletion in 7q22.1 (nssv15922542) and a 3.7 kb deletion in 12q24.12 (nssv15813226), both of which were located in regions previously reported to be associated with red blood cell traits. Two deletions in 11q11 (nssv15803200 and nssv15802240), where clusters of multiple olfactory receptor genes exist, and a deletion (nssv15929560) upstream to DOCK5 were associated with childhood obesity. Finally, when defining Trait-Associated copy number Deletions (TADs) as CNDs with phenotype associations at sub-threshold significance (P < 10- 3), we identified 157 (97.5%) out of 161 TADs in non-coding regions, with a mean size of 4 kb (range: 209 - 47,942 bp).

CONCLUSION

We conducted a reanalysis of the UK10K Whole Genome Sequencing cohort, which led to the identification of multiple high confidence copy number deletions associated with quantitative traits. These deletions have standard dbVar IDs and replicate previous findings, as well as reveal novel loci that require further replication studies.

Systematic evaluation of multiple NGS platforms for structural variants detection

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

Comparative analysis of genome-wide copy number variations between Tibetan sheep and White Suffolk sheep

The DNA copy number variations (CNVs) are widely involved in affecting various kinds of biological functions, such as environmental adaptation. Tibetan sheep and White Suffolk sheep are two representative indigenous and exotic breeds raised in Sichuan, China, and both of them have many contrasting biological characteristics. In this study, we employed high-throughput sequencing approach to investigate genome-wide CNVs between the two sheep breeds. A total of 11,135 CNV regions (CNVRs) consisting of 6,488 deletions and 4,647 duplications were detected, whose length ranged from 1,599 bp to 0.56 Mb with the mean of 4,658 bp. There were 281 CNVRs segregated between Tibetan sheep and White Suffolk sheep, and 18 of them have been fixed within both breeds. Functional analyses of candidate genes within the segregating CNVRs revealed the thyroid hormone signaling pathway and CTNNB1 gene that would be responsible for differential biological characteristics of breeds, such as energy metabolism, seasonal reproduction, and litter size. Furthermore, the segregating CNVRs identified in this study were overlapped with many known quantitative trait loci that are associated with growth, testis weight, and reproductive seasonality. In conclusion, these results help us better understanding differential biological characteristics between Tibetan sheep and White Suffolk sheep.

Read-depth based approach on whole genome resequencing data reveals important insights into the copy number variation (CNV) map of major global buffalo breeds

BACKGROUND

Elucidating genome-wide structural variants including copy number variations (CNVs) have gained increased significance in recent times owing to their contribution to genetic diversity and association with important pathophysiological states. The present study aimed to elucidate the high-resolution CNV map of six different global buffalo breeds using whole genome resequencing data at two coverages (10X and 30X). Post-quality control, the sequence reads were aligned to the latest draft release of the Bubaline genome. The genome-wide CNVs were elucidated using a read-depth approach in CNVnator with different bin sizes. Adjacent CNVs were concatenated into copy number variation regions (CNVRs) in different breeds and their genomic coverage was elucidated.

RESULTS

Overall, the average size of CNVR was lower at 30X coverage, providing finer details. Most of the CNVRs were either deletion or duplication type while the occurrence of mixed events was lesser in number on a comparative basis in all breeds. The average CNVR size was lower at 30X coverage (0.201 Mb) as compared to 10X (0.013 Mb) with the finest variants in Banni buffaloes. The maximum number of CNVs was observed in Murrah (2627) and Pandharpuri (25,688) at 10X and 30X coverages, respectively. Whereas the minimum number of CNVs were scored in Surti at both coverages (2092 and 17,373). On the other hand, the highest and lowest number of CNVRs were scored in Jaffarabadi (833 and 10,179 events) and Surti (783 and 7553 events) at both coverages. Deletion events overnumbered duplications in all breeds at both coverages. Gene profiling of common overlapped genes and longest CNVRs provided important insights into the evolutionary history of these breeds and indicate the genomic regions under selection in respective breeds.

CONCLUSION

The present study is the first of its kind to elucidate the high-resolution CNV map in major buffalo populations using a read-depth approach on whole genome resequencing data. The results revealed important insights into the divergence of major global buffalo breeds along the evolutionary timescale.

Origin matters: Using a local reference genome improves measures in population genomics

Genome sequencing enables answering fundamental questions about the genetic basis of adaptation, population structure and epigenetic mechanisms. Yet, we usually need a suitable reference genome for mapping population-level resequencing data. In some model systems, multiple reference genomes are available, giving the challenging task of determining which reference genome best suits the data. Here, we compared the use of two different reference genomes for the three-spined stickleback (Gasterosteus aculeatus), one novel genome derived from a European gynogenetic individual and the published reference genome of a North American individual. Specifically, we investigated the impact of using a local reference versus one generated from a distinct lineage on several common population genomics analyses. Through mapping genome resequencing data of 60 sticklebacks from across Europe and North America, we demonstrate that genetic distance among samples and the reference genomes impacts downstream analyses. Using a local reference genome increased mapping efficiency and genotyping accuracy, effectively retaining more and better data. Despite comparable distributions of the metrics generated across the genome using SNP data (i.e. π, Tajima's D and FST ), window-based statistics using different references resulted in different outlier genes and enriched gene functions. A marker-based analysis of DNA methylation distributions had a comparably high overlap in outlier genes and functions, yet with distinct differences depending on the reference genome. Overall, our results highlight how using a local reference genome decreases reference bias to increase confidence in downstream analyses of the data. Such results have significant implications in all reference-genome-based population genomic analyses.

Identification of copy number variants with genome sequencing: Clinical experiences from the NYCKidSeq program

Copy number variations (CNVs) play a significant role in human disease. While chromosomal microarray has traditionally been the first-tier test for CNV detection, use of genome sequencing (GS) is increasing. We report the frequency of CNVs detected with GS in a diverse pediatric cohort from the NYCKidSeq program and highlight specific examples of its clinical impact. A total of 1052 children (0-21 years) with neurodevelopmental, cardiac, and/or immunodeficiency phenotypes received GS. Phenotype-driven analysis was used, resulting in 183 (17.4%) participants with a diagnostic result. CNVs accounted for 20.2% of participants with a diagnostic result (37/183) and ranged from 0.5 kb to 16 Mb. Of participants with a diagnostic result (n = 183) and phenotypes in more than one category, 5/17 (29.4%) were solved by a CNV finding, suggesting a high prevalence of diagnostic CNVs in participants with complex phenotypes. Thirteen participants with a diagnostic CNV (35.1%) had previously uninformative genetic testing, of which nine included a chromosomal microarray. This study demonstrates the benefits of GS for reliable detection of CNVs in a pediatric cohort with variable phenotypes.

Detection of trait-associated structural variations using short-read sequencing

Genomic structural variation (SV) affects genetic and phenotypic characteristics in diverse organisms, but the lack of reliable methods to detect SV has hindered genetic analysis. We developed a computational algorithm (MOPline) that includes missing call recovery combined with high-confidence SV call selection and genotyping using short-read whole-genome sequencing (WGS) data. Using 3,672 high-coverage WGS datasets, MOPline stably detected ∼16,000 SVs per individual, which is over ∼1.7-3.3-fold higher than previous large-scale projects while exhibiting a comparable level of statistical quality metrics. We imputed SVs from 181,622 Japanese individuals for 42 diseases and 60 quantitative traits. A genome-wide association study with the imputed SVs revealed 41 top-ranked or nearly top-ranked genome-wide significant SVs, including 8 exonic SVs with 5 novel associations and enriched mobile element insertions. This study demonstrates that short-read WGS data can be used to identify rare and common SVs associated with a variety of traits.

Best Practices in Microbial Experimental Evolution: Using Reporters and Long-Read Sequencing to Identify Copy Number Variation in Experimental Evolution

Copy number variants (CNVs), comprising gene amplifications and deletions, are a pervasive class of heritable variation. CNVs play a key role in rapid adaptation in both natural, and experimental, evolution. However, despite the advent of new DNA sequencing technologies, detection and quantification of CNVs in heterogeneous populations has remained challenging. Here, we summarize recent advances in the use of CNV reporters that provide a facile means of quantifying de novo CNVs at a specific locus in the genome, and nanopore sequencing, for resolving the often complex structures of CNVs. We provide guidance for the engineering and analysis of CNV reporters and practical guidelines for single-cell analysis of CNVs using flow cytometry. We summarize recent advances in nanopore sequencing, discuss the utility of this technology, and provide guidance for the bioinformatic analysis of these data to define the molecular structure of CNVs. The combination of reporter systems for tracking and isolating CNV lineages and long-read DNA sequencing for characterizing CNV structures enables unprecedented resolution of the mechanisms by which CNVs are generated and their evolutionary dynamics.

A comparison of algorithms for identifying copy number variants in family-based whole-exome sequencing data and its implications in inheritance pattern analysis

There remain challenges in accurately identifying constitutional or germline copy number variants (gCNVs) based on whole-exome sequencing data that have implications for genetic diagnosis for 'rare undiagnosed disease' in the clinical setting. Although multiple algorithms have been proposed, a systematic comparison of these algorithms for calling gCNVs and analyzing inherited pattern have yet to be fully conducted. Therefore, we empirically compared seven exome-based algorithms, including XHMM, CLAMMS, CODEX2, ExomeDepth, DECoN, CN.MOPS, and GATK gCNV, for calling gCNVs in 151 individuals from 44 pedigrees, together with the gold standard of genotyping-derived gCNVs in the same cohort for the performance assessment. These algorithms demonstrated varied powers in identifying gCNVs, although the distribution of gCNVs size was similar. The number of shared gCNVs across these algorithms was limited (e.g., only four gCNVs shared among seven algorithms); however, several algorithms showed varying degrees of consistency (e.g., 1,843 gCNVs shared between DECoN and ExomeDepth). CLAMMS and CODEX2 outperformed the remaining algorithms according to a relatively higher F-score (i.e., 0.145 and 0.152, respectively). In addition, these algorithms exhibited different Mendelian inconsistencies of gCNVs and significant challenges remained in inheritance pattern analysis. In conclusion, selecting good algorithms may have important implications in gCNVs-based inheritance pattern analysis for family-based studies.

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.

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.

Exome sequencing as a first-tier test for copy number variant detection: retrospective evaluation and prospective screening in 2418 cases

BACKGROUND

Despite the availability of whole exome (WES) and genome sequencing (WGS), chromosomal microarray (CMA) remains the first-line diagnostic test in most rare disorders diagnostic workup, looking for copy number variations (CNVs), with a diagnostic yield of 10%-20%. The question of the equivalence of CMA and WES in CNV calling is an organisational and economic question, especially when ordering a WGS after a negative CMA and/or WES.

METHODS

This study measures the equivalence between CMA and GATK4 exome sequencing depth of coverage method in detecting coding CNVs on a retrospective cohort of 615 unrelated individuals. A prospective detection of WES-CNV on a cohort of 2418 unrelated individuals, including the 615 individuals from the validation cohort, was performed.

RESULTS

On the retrospective validation cohort, every CNV detectable by the method (ie, a CNV with at least one exon not in a dark zone) was accurately called (64/64 events). In the prospective cohort, 32 diagnoses were performed among the 2418 individuals with CNVs ranging from 704 bp to aneuploidy. An incidental finding was reported. The overall increase in diagnostic yield was of 1.7%, varying from 1.2% in individuals with multiple congenital anomalies to 1.9% in individuals with chronic kidney failure.

CONCLUSION

Combining single-nucleotide variant (SNV) and CNV detection increases the suitability of exome sequencing as a first-tier diagnostic test for suspected rare Mendelian disorders. Before considering the prescription of a WGS after a negative WES, a careful reanalysis with updated CNV calling and SNV annotation should be considered.

Community-led risk analysis of direct-to-consumer whole-genome sequencing

Direct-to-consumer (DTC) genetic testing is cheaper and more accessible than ever before; however, the intention to combine, reuse, and resell this genetic information as powerful data sets is generally hidden from the consumer. This financial gain is creating a competitive DTC market, reducing the price of whole-genome sequencing (WGS) to under 300 USD. Entering this transition from single-nucleotide polymorphism-based DTC testing to WGS DTC testing, individuals looking for access to their whole-genomic information face new privacy and security risks. Differences between WGS and other methods of consumer genetic tests are left unexplored by regulation, leading to the application of legal data anonymization methods on whole-genome data, and questionable consent methods. Large representative genomic data sets are important for research and improve the standard of medicine and personalized care. However, these data can also be used by market players, law enforcement, and governments for surveillance, population analyses, marketing purposes, and discrimination. Here, we present a summary of the state of WGS DTC genetic testing and its current regulation, through a community-based lens to expose dual-use risks in consumer-facing biotechnologies.

DNA methylation episignatures: insight into copy number variation

In this review we discuss epigenetic disorders that result from aberrations in genes linked to epigenetic regulation. We describe current testing methods for the detection of copy number variants (CNVs) in Mendelian disorders, dosage sensitivity, reciprocal phenotypes and the challenges of test selection and overlapping clinical features in genetic diagnosis. We discuss aberrations of DNA methylation and propose a role for episignatures as a novel clinical testing method in CNV disorders. Finally, we postulate that episignature mapping in CNV disorders may provide novel insights into the molecular mechanisms of disease and unlock key findings of the genome-wide impact on disease gene networks.

iPSCs derived from infertile men carrying complex genetic abnormalities can generate primordial germ-like cells

Despite increasing insight into the genetics of infertility, the developmental disease processes remain unclear due to the lack of adequate experimental models. The advent of induced pluripotent stem cell (iPSC) technology has provided a unique tool for in vitro disease modeling enabling major advances in our understanding of developmental disease processes. We report the full characterization of complex genetic abnormalities in two infertile patients with either azoospermia or XX male syndrome and we identify genes of potential interest implicated in their infertility. Using the erythroblasts of both patients, we generated primed iPSCs and converted them into a naive-like pluripotent state. Naive-iPSCs were then differentiated into primordial germ-like cells (PGC-LCs). The expression of early PGC marker genes SOX17, CD-38, NANOS3, c-KIT, TFAP2C, and D2-40, confirmed progression towards the early germline stage. Our results demonstrate that iPSCs from two infertile patients with significant genetic abnormalities are capable of efficient production of PGCs. Such in vitro model of infertility will certainly help identifying causative factors leading to early germ cells development failure and provide a valuable tool to explore novel therapeutic strategies.

Application of Droplet Digital PCR Technology in Muscular Dystrophies Research

Although they are considered rare disorders, muscular dystrophies have a strong impact on people’s health. Increased disease severity with age, frequently accompanied by the loss of ability to walk in some people, and the lack of treatment, have directed the researchers towards the development of more effective therapeutic strategies aimed to improve the quality of life and life expectancy, slow down the progression, and delay the onset or convert a severe phenotype into a milder one. Improved understanding of the complex pathology of these diseases together with the tremendous advances in molecular biology technologies has led to personalized therapeutic procedures. Different approaches that are currently under extensive investigation require more efficient, sensitive, and less invasive methods. Due to its remarkable analytical sensitivity, droplet digital PCR has become a promising tool for accurate measurement of biomarkers that monitor disease progression and quantification of various therapeutic efficiency and can be considered a tool for non-invasive prenatal diagnosis and newborn screening. Here, we summarize the recent applications of droplet digital PCR in muscular dystrophy research and discuss the factors that should be considered to get the best performance with this technology.

Genomic characterization of the world's longest selection experiment in mouse reveals the complexity of polygenic traits

BACKGROUND

Long-term selection experiments are a powerful tool to understand the genetic background of complex traits. The longest of such experiments has been conducted in the Research Institute for Farm Animal Biology (FBN), generating extreme mouse lines with increased fertility, body mass, protein mass and endurance. For >140 generations, these lines have been maintained alongside an unselected control line, representing a valuable resource for understanding the genetic basis of polygenic traits. However, their history and genomes have not been reported in a comprehensive manner yet. Therefore, the aim of this study is to provide a summary of the breeding history and phenotypic traits of these lines along with their genomic characteristics. We further attempt to decipher the effects of the observed line-specific patterns of genetic variation on each of the selected traits.

RESULTS

Over the course of >140 generations, selection on the control line has given rise to two extremely fertile lines (>20 pups per litter each), two giant growth lines (one lean, one obese) and one long-distance running line. Whole genome sequencing analysis on 25 animals per line revealed line-specific patterns of genetic variation among lines, as well as high levels of homozygosity within lines. This high degree of distinctiveness results from the combined effects of long-term continuous selection, genetic drift, population bottleneck and isolation. Detection of line-specific patterns of genetic differentiation and structural variation revealed multiple candidate genes behind the improvement of the selected traits.

CONCLUSIONS

The genomes of the Dummerstorf trait-selected mouse lines display distinct patterns of genomic variation harbouring multiple trait-relevant genes. Low levels of within-line genetic diversity indicate that many of the beneficial alleles have arrived to fixation alongside with neutral alleles. This study represents the first step in deciphering the influence of selection and neutral evolutionary forces on the genomes of these extreme mouse lines and depicts the genetic complexity underlying polygenic traits.

Discovering Copy Number Variation in Dual-Purpose XinJiang Brown Cattle

Copy number variants (CNVs), which are a class of structural variant, can be important in relating genomic variation to phenotype. The primary aims of this study were to discover the common CNV regions (CNVRs) in the dual-purpose XinJiang-Brown cattle population and to detect differences between CNVs inferred using the ARS-UCD 1.2 (ARS) or the UMD 3.1 (UMD) genome assemblies based on the 150K SNP (Single Nucleotide Polymorphisms) Chip. PennCNV and CNVPartition methods were applied to calculate the deviation of the standardized signal intensity of SNPs markers to detect CNV status. Following the discovery of CNVs, we used the R package HandyCNV to generate and visualize CNVRs, compare CNVs and CNVRs between genome assemblies, and identify consensus genes using annotation resources. We identified 38 consensus CNVRs using the ARS assembly with 1.95% whole genome coverage, and 33 consensus CNVRs using the UMD assembly with 1.46% whole genome coverage using PennCNV and CNVPartition. We identified 37 genes that intersected 13 common CNVs (>5% frequency), these included functionally interesting genes such as GBP4 for which an increased copy number has been negatively associated with cattle stature, and the BoLA gene family which has been linked to the immune response and adaption of cattle. The ARS map file of the GGP Bovine 150K Bead Chip maps the genomic position of more SNPs with increased accuracy compared to the UMD map file. Comparison of the CNVRs identified between the two reference assemblies suggests the newly released ARS reference assembly is better for CNV detection. In spite of this, different CNV detection methods can complement each other to generate a larger number of CNVRs than using a single approach and can highlight more genes of interest.

Copy number variants calling from WES data through eXome hidden Markov model (XHMM) identifies additional 2.5% pathogenic genomic imbalances smaller than 30 kb undetected by array-CGH

It has been estimated that Copy Number Variants (CNVs) account for 10%-20% of patients affected by Developmental Disorder (DD)/Intellectual Disability (ID). Although array comparative genomic hybridization (array-CGH) represents the gold-standard for the detection of genomic imbalances, common Agilent array-CGH 4 × 180 kb arrays fail to detect CNVs smaller than 30 kb. Whole Exome sequencing (WES) is becoming the reference application for the detection of gene variants and makes it possible also to infer genomic imbalances at single exon resolution. However, the contribution of small CNVs in DD/ID is still underinvestigated. We made use of the eXome Hidden Markov Model (XHMM) software, a tool utilized by the ExAC consortium, to detect CNVs from whole exome sequencing data, in a cohort of 200 unsolved DD/DI patients after array-CGH and WES-based single nucleotide/indel variant analyses. In five out of 200 patients (2.5%), we identified pathogenic CNV(s) smaller than 30 kb, ranging from one to six exons. They included two heterozygous deletions in TCF4 and STXBP1 and three homozygous deletions in PPT1, CLCN2, and PIGN. After reverse phenotyping, all variants were reported as causative. This study shows the interest in applying sequencing-based CNV detection, from available WES data, to reduce the diagnostic odyssey of additional patients unsolved DD/DI patients and compare the CNV-detection yield of Agilent array-CGH 4 × 180kb versus whole exome sequencing.

Analyzing Association between Expression Quantitative Trait and CNV for Breast Cancer Based on Gene Interaction Network Clustering and Group Sparse Learning

Aims:

The occurrence and development of tumor is accompanied by the change of pathogenic gene expression. Tumor cells avoid the damage of immune cells by regulating the expression of immune related genes.

Background:

Tracing the causes of gene expression variation is helpful to understand tumor evolution and metastasis.

Objective:

Current gene expression variation explanation methods are confronted with several main challenges: low explanation power, insufficient prediction accuracy, and lack of biological meaning.

Method:

In this study, we propose a novel method to analyze the mRNA expression variations of breast cancers risk genes. Firstly, we collected some high-confidence risk genes related to breast cancer and then designed a rank-based method to preprocess the breast cancers copy number variation (CNV) and mRNA data. Secondly, to elevate the biological meaning and narrow down the combinatorial space, we introduced a prior gene interaction network and applied a network clustering algorithm to generate high density subnetworks. Lastly, to describe the interlinked structure within and between subnetworks and target genes mRNA expression, we proposed a group sparse learning model to identify CNVs for pathogenic genes expression variations.

Result:

The performance of the proposed method is evaluated by both significantly improved predication accuracy and biological meaning of pathway enrichment analysis.

Conclusion:

The experimental results show that our method has practical significance

Combining callers improves the detection of copy number variants from whole-genome sequencing

Copy Number Variants (CNVs) are deletions, duplications or insertions larger than 50 base pairs. They account for a large percentage of the normal genome variation and play major roles in human pathology. While array-based approaches have long been used to detect them in clinical practice, whole-genome sequencing (WGS) bears the promise to allow concomitant exploration of CNVs and smaller variants. However, accurately calling CNVs from WGS remains a difficult computational task, for which a consensus is still lacking. In this paper, we explore practical calling options to reach the best compromise between sensitivity and sensibility. We show that callers based on different signal (paired-end reads, split reads, coverage depth) yield complementary results. We suggest approaches combining four selected callers (Manta, Delly, ERDS, CNVnator) and a regenotyping tool (SV2), and show that this is applicable in everyday practice in terms of computation time and further interpretation. We demonstrate the superiority of these approaches over array-based Comparative Genomic Hybridization (aCGH), specifically regarding the lack of resolution in breakpoint definition and the detection of potentially relevant CNVs. Finally, we confirm our results on the NA12878 benchmark genome, as well as one clinically validated sample. In conclusion, we suggest that WGS constitutes a timely and economically valid alternative to the combination of aCGH and whole-exome sequencing.

CNV-P: a machine-learning framework for predicting high confident copy number variations

Background

Copy-number variants (CNVs) have been recognized as one of the major causes of genetic disorders. Reliable detection of CNVs from genome sequencing data has been a strong demand for disease research. However, current software for detecting CNVs has high false-positive rates, which needs further improvement.

Methods

Here, we proposed a novel and post-processing approach for CNVs prediction (CNV-P), a machine-learning framework that could efficiently remove false-positive fragments from results of CNVs detecting tools. A series of CNVs signals such as read depth (RD), split reads (SR) and read pair (RP) around the putative CNV fragments were defined as features to train a classifier.

Results

The prediction results on several real biological datasets showed that our models could accurately classify the CNVs at over 90% precision rate and 85% recall rate, which greatly improves the performance of state-of-the-art algorithms. Furthermore, our results indicate that CNV-P is robust to different sizes of CNVs and the platforms of sequencing.

Conclusions

Our framework for classifying high-confident CNVs could improve both basic research and clinical diagnosis of genetic diseases.

A Comparison of Tools for Copy-Number Variation Detection in Germline Whole Exome and Whole Genome Sequencing Data

Copy-number variations (CNVs) have important clinical implications for several diseases and cancers. Relevant CNVs are hard to detect because common structural variations define large parts of the human genome. CNV calling from short-read sequencing would allow single protocol full genomic profiling. We reviewed 50 popular CNV calling tools and included 11 tools for benchmarking in a reference cohort encompassing 39 whole genome sequencing (WGS) samples paired current clinical standard-SNP-array based CNV calling. Additionally, for nine samples we also performed whole exome sequencing (WES), to address the effect of sequencing protocol on CNV calling. Furthermore, we included Gold Standard reference sample NA12878, and tested 12 samples with CNVs confirmed by multiplex ligation-dependent probe amplification (MLPA). Tool performance varied greatly in the number of called CNVs and bias for CNV lengths. Some tools had near-perfect recall of CNVs from arrays for some samples, but poor precision. Several tools had better performance for NA12878, which could be a result of overfitting. We suggest combining the best tools also based on different methodologies: GATK gCNV, Lumpy, DELLY, and cn.MOPS. Reducing the total number of called variants could potentially be assisted by the use of background panels for filtering of frequently called variants.

Comprehensive characterization of copy number variation (CNV) called from array, long- and short-read data

BACKGROUND

SNP arrays, short- and long-read genome sequencing are genome-wide high-throughput technologies that may be used to assay copy number variants (CNVs) in a personal genome. Each of these technologies comes with its own limitations and biases, many of which are well-known, but not all of them are thoroughly quantified.

RESULTS

We assembled an ensemble of public datasets of published CNV calls and raw data for the well-studied Genome in a Bottle individual NA12878. This assembly represents a variety of methods and pipelines used for CNV calling from array, short- and long-read technologies. We then performed cross-technology comparisons regarding their ability to call CNVs. Different from other studies, we refrained from using the golden standard. Instead, we attempted to validate the CNV calls by the raw data of each technology.

CONCLUSIONS

Our study confirms that long-read platforms enable recalling CNVs in genomic regions inaccessible to arrays or short reads. We also found that the reproducibility of a CNV by different pipelines within each technology is strongly linked to other CNV evidence measures. Importantly, the three technologies show distinct public database frequency profiles, which differ depending on what technology the database was built on.

From DNA human sequence to the chromatin higher order organisation and its biological meaning: Using biomolecular interaction networks to understand the influence of structural variation on spatial genome organisation and its functional effect

The three-dimensional structure of the human genome has been proven to have a significant functional impact on gene expression. The high-order spatial chromatin is organised first by looping mediated by multiple protein factors, and then it is further formed into larger structures of topologically associated domains (TADs) or chromatin contact domains (CCDs), followed by A/B compartments and finally the chromosomal territories (CTs). The genetic variation observed in human population influences the multi-scale structures, posing a question regarding the functional impact of structural variants reflected by the variability of the genes expression patterns. The current methods of evaluating the functional effect include eQTLs analysis which uses statistical testing of influence of variants on spatially close genes. Rarely, non-coding DNA sequence changes are evaluated by their impact on the biomolecular interaction network (BIN) reflecting the cellular interactome that can be analysed by the classical graph-theoretic algorithms. Therefore, in the second part of the review, we introduce the concept of BIN, i.e. a meta-network model of the complete molecular interactome developed by integrating various biological networks. The BIN meta-network model includes DNA-protein binding by the plethora of protein factors as well as chromatin interactions, therefore allowing connection of genomics with the downstream biomolecular processes present in a cell. As an illustration, we scrutinise the chromatin interactions mediated by the CTCF protein detected in a ChIA-PET experiment in the human lymphoblastoid cell line GM12878. In the corresponding BIN meta-network the DNA spatial proximity is represented as a graph model, combined with the Proteins-Interaction Network (PIN) of human proteome using the Gene Association Network (GAN). Furthermore, we enriched the BIN with the signalling and metabolic pathways and Gene Ontology (GO) terms to assert its functional context. Finally, we mapped the Single Nucleotide Polymorphisms (SNPs) from the GWAS studies and identified the chromatin mutational hot-spots associated with a significant enrichment of SNPs related to autoimmune diseases. Afterwards, we mapped Structural Variants (SVs) from healthy individuals of 1000 Genomes Project and identified an interesting example of the missing protein complex associated with protein Q6GYQ0 due to a deletion on chromosome 14. Such an analysis using the meta-network BIN model is therefore helpful in evaluating the influence of genetic variation on spatial organisation of the genome and its functional effect in a cell.

The Emerging Genetic Landscape of Hirschsprung Disease and Its Potential Clinical Applications

Hirschsprung disease (HSCR) is the leading cause of neonatal functional intestinal obstruction. It is a rare congenital disease with an incidence of one in 3,500-5,000 live births. HSCR is characterized by the absence of enteric ganglia in the distal colon, plausibly due to genetic defects perturbing the normal migration, proliferation, differentiation, and/or survival of the enteric neural crest cells as well as impaired interaction with the enteric progenitor cell niche. Early linkage analyses in Mendelian and syndromic forms of HSCR uncovered variants with large effects in major HSCR genes including RET, EDNRB, and their interacting partners in the same biological pathways. With the advances in genome-wide genotyping and next-generation sequencing technologies, there has been a remarkable progress in understanding of the genetic basis of HSCR in the past few years, with common and rare variants with small to moderate effects being uncovered. The discovery of new HSCR genes such as neuregulin and BACE2 as well as the deeper understanding of the roles and mechanisms of known HSCR genes provided solid evidence that many HSCR cases are in the form of complex polygenic/oligogenic disorder where rare variants act in the sensitized background of HSCR-associated common variants. This review summarizes the roadmap of genetic discoveries of HSCR from the earlier family-based linkage analyses to the recent population-based genome-wide analyses coupled with functional genomics, and how these discoveries facilitated our understanding of the genetic architecture of this complex disease and provide the foundation of clinical translation for precision and stratified medicine.

Clinical application of an NGS-based method in the preimplantation genetic testing for Duchenne muscular dystrophy

PURPOSE

To determine whether next-generation sequencing (NGS) could be used to directly detect different mutations of Duchenne muscular dystrophy (DMD) during preimplantation genetic testing (PGT).

METHODS

From Sep. 2016 to Aug. 2018, a total of six couples participated in this study. Four cases carried DMD exon deletions and two carried exon duplications. Trophectoderm cells were biopsied at day 5 or 6 and NGS was used in the genetic testing of the biopsied cells after whole-genome amplification. We developed a new method-DIRected Embryonic Cell Testing of Exon Deletion/Duplication (DIRECTED) to directly detect the single-gene mutation by NGS. Linage analysis based on single-nucleotide polymorphism (SNP) was used to validate the results from DIRECTED.

RESULTS

In the four deletion cases, DIRECTED was used to detect DMD exon deletion in 16 biopsied embryos. All DIRECTED results were consistent with linkage analysis, indicating this method was reliable in detecting deletions around 1 Mb. In the two cases carrying exon duplications, no blastocyst was obtained for biopsy. Nonetheless, preliminary experiment results suggested that DIRECTED could also be used for direct detection of exon duplications in embryos.

CONCLUSIONS

Exon deletions or duplications in DMD of preimplantation embryos could be detected directly by NGS-based methods during PGT.

Introgression contributes to distribution of structural variations in cattle

Structural variations (SVs) are an important source of phenotypic diversity in cattle. Here, 72 whole genome sequences representing taurine and zebu cattle were used to identify SVs. Applying multiple approaches, 16,738 SVs were identified. A comparison against the Database of Genomic Variants archives revealed that 1575 SVs were novel in our data. A novel duplication covering the entire GALNT15 gene, was observed only in N'Dama. A duplication, which was previously reported only in zebu and associated with navel length, was also observed in N'Dama. Investigation of a novel deletion located upstream of CAST13 gene and identified only in Italian cattle and zebu, revealed its introgressed origin in the former. Overall, our data highlights how the SVs distribution in cattle is also shaped by forces such as demographical differences and gene flow. The cattle SVs of this study and its meta-data can be visualized on an interactive genome browser at https://tinyurl.com/svCowArs.

Detection of copy number variants in African goats using whole genome sequence data

Background

Copy number variations (CNV) are a significant source of variation in the genome and are therefore essential to the understanding of genetic characterization. The aim of this study was to develop a fine-scaled copy number variation map for African goats. We used sequence data from multiple breeds and from multiple African countries.

Results

A total of 253,553 CNV (244,876 deletions and 8677 duplications) were identified, corresponding to an overall average of 1393 CNV per animal. The mean CNV length was 3.3 kb, with a median of 1.3 kb. There was substantial differentiation between the populations for some CNV, suggestive of the effect of population-specific selective pressures. A total of 6231 global CNV regions (CNVR) were found across all animals, representing 59.2 Mb (2.4%) of the goat genome. About 1.6% of the CNVR were present in all 34 breeds and 28.7% were present in all 5 geographical areas across Africa, where animals had been sampled. The CNVR had genes that were highly enriched in important biological functions, molecular functions, and cellular components including retrograde endocannabinoid signaling, glutamatergic synapse and circadian entrainment.

Conclusions

This study presents the first fine CNV map of African goat based on WGS data and adds to the growing body of knowledge on the genetic characterization of goats.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07703-1.

Evaluation of tools for identifying large copy number variations from ultra-low-coverage whole-genome sequencing data

BACKGROUND

Detection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years. However, only a little is known about the applicability of the developed algorithms to ultra-low-coverage (0.0005-0.8×) data that is used in various research and clinical applications, such as digital karyotyping and single-cell CNV detection.

RESULT

Here, the performance of six popular read-depth based CNV detection algorithms (BIC-seq2, Canvas, CNVnator, FREEC, HMMcopy, and QDNAseq) was studied using ultra-low-coverage WGS data. Real-world array- and karyotyping kit-based validation were used as a benchmark in the evaluation. Additionally, ultra-low-coverage WGS data was simulated to investigate the ability of the algorithms to identify CNVs in the sex chromosomes and the theoretical minimum coverage at which these tools can accurately function. Our results suggest that while all the methods were able to detect large CNVs, many methods were susceptible to producing false positives when smaller CNVs (< 2 Mbp) were detected. There was also significant variability in their ability to identify CNVs in the sex chromosomes. Overall, BIC-seq2 was found to be the best method in terms of statistical performance. However, its significant drawback was by far the slowest runtime among the methods (> 3 h) compared with FREEC (~ 3 min), which we considered the second-best method.

CONCLUSIONS

Our comparative analysis demonstrates that CNV detection from ultra-low-coverage WGS data can be a highly accurate method for the detection of large copy number variations when their length is in millions of base pairs. These findings facilitate applications that utilize ultra-low-coverage CNV detection.

CNVfilteR: an R/bioconductor package to identify false positives produced by germline NGS CNV detection tools

 

Germline copy-number variants (CNVs) are relevant mutations for multiple genetics fields, such as the study of hereditary diseases. However, available benchmarks show that all next-generation sequencing (NGS) CNV calling tools produce false positives. We developed CNVfilteR, an R package that uses the single nucleotide variant calls usually obtained in germline NGS pipelines to identify those false positives. The package can detect both false deletions and false duplications. We evaluated CNVfilteR performance on callsets generated by 13 CNV calling tools on 3 whole-genome sequencing and 541 panel samples, showing a decrease of up to 44.8% in false positives and consistent F1-score increase. Using CNVfilteR to detect false-positive calls can improve the overall performance of existing CNV calling pipelines.

AVAILABILITY

CNVfilteR is released under Artistic-2.0 License. Source code and documentation are freely available at Bioconductor (http://www.bioconductor.org/packages/CNVfilteR).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell sequencing of the small and AT-skewed genome of malaria parasites

Single-cell genomics is a rapidly advancing field; however, most techniques are designed for mammalian cells. We present a single-cell sequencing pipeline for an intracellular parasite, Plasmodium falciparum, with a small genome of extreme base content. Through optimization of a quasi-linear amplification method, we target the parasite genome over contaminants and generate coverage levels allowing detection of minor genetic variants. This work, as well as efforts that build on these findings, will enable detection of parasite heterogeneity contributing to P. falciparum adaptation. Furthermore, this study provides a framework for optimizing single-cell amplification and variant analysis in challenging genomes.

Genome-wide association study between copy number variants and hoof health traits in Holstein dairy cattle

Genome-wide association studies based on SNP have been completed for multiple traits in dairy cattle; however, copy number variants (CNV) could add genomic information that has yet to be harnessed. The objectives of this study were to identify CNV in genotyped Holstein animals and assess their association with hoof health traits using deregressed estimated breeding values as pseudophenotypes. A total of 23,256 CNV comprising 1,645 genomic regions were identified in 5,845 animals. Fourteen genomic regions harboring structural variations, including 9 deletions and 5 duplications, were associated with at least 1 of the studied hoof health traits. This group of traits included digital dermatitis, interdigital dermatitis, heel horn erosion, sole ulcer, white line lesion, sole hemorrhage, and interdigital hyperplasia; no regions were associated with toe ulcer. Twenty candidate genes overlapped with the regions associated with these traits including SCART1, NRXN2, KIF26A, GPHN, and OR7A17. In this study, an effect on infectious hoof lesions could be attributed to the PRAME (Preferentially Expressed Antigen in Melanoma) gene. Almost all genes detected in association with noninfectious hoof lesions could be linked to known metabolic disorders. The knowledge obtained considering information of associated CNV to the traits of interest in this study could improve the accuracy of estimated breeding values. This may further increase the genetic gain for these traits in the Canadian Holstein population, thus reducing the involuntary animal losses due to lameness.

Next Generation Sequencing Technology in the Clinic and Its Challenges

Simple Summary

Precise identification and annotation of mutations are of utmost importance in clinical oncology. Insights of the DNA sequence can provide meaningful knowledge to unravel the underlying genetics of disease. Hence, tailoring of personalized medicine often relies on specific genomic alteration for treatment efficacy. The aim of this review is to highlight that sequencing harbors much more than just four nucleotides. Moreover, the gradual transition from first to second generation sequencing technologies has led to awareness for choosing the most appropriate bioinformatic analytic tools based on the aim, quality and demand for a specific purpose. Thus, the same raw data can lead to various results reflecting the intrinsic features of different datamining pipelines.

Abstract

Data analysis has become a crucial aspect in clinical oncology to interpret output from next-generation sequencing-based testing. NGS being able to resolve billions of sequencing reactions in a few days has consequently increased the demand for tools to handle and analyze such large data sets. Many tools have been developed since the advent of NGS, featuring their own peculiarities. Increased awareness when interpreting alterations in the genome is therefore of utmost importance, as the same data using different tools can provide diverse outcomes. Hence, it is crucial to evaluate and validate bioinformatic pipelines in clinical settings. Moreover, personalized medicine implies treatment targeting efficacy of biological drugs for specific genomic alterations. Here, we focused on different sequencing technologies, features underlying the genome complexity, and bioinformatic tools that can impact the final annotation. Additionally, we discuss the clinical demand and design for implementing NGS.

Thermal adaptation rather than demographic history drives genetic structure inferred by copy number variants in a marine fish

Increasing evidence shows that structural variants represent an overlooked aspect of genetic variation with consequential evolutionary roles. Among those, copy number variants (CNVs), including duplicated genomic regions and transposable elements (TEs), may contribute to local adaptation and/or reproductive isolation among divergent populations. Those mechanisms suppose that CNVs could be used to infer neutral and/or adaptive population genetic structure, whose study has been restricted to microsatellites, mitochondrial DNA and Amplified fragment length polymorphism markers in the past and more recently the use of single nucleotide polymorphisms (SNPs). Taking advantage of recent developments allowing CNV analysis from RAD-seq data, we investigated how variation in fitness-related traits, local environmental conditions and demographic history are associated with CNVs, and how subsequent copy number variation drives population genetic structure in a marine fish, the capelin (Mallotus villosus). We collected 1538 DNA samples from 35 sampling sites in the north Atlantic Ocean and identified 6620 putative CNVs. We found associations between CNVs and the gonadosomatic index, suggesting that six duplicated regions could affect female fitness by modulating oocyte production. We also detected 105 CNV candidates associated with water temperature, among which 20% corresponded to genomic regions located within the sequence of protein-coding genes, suggesting local adaptation to cold water by means of gene sequence amplification. We also identified 175 CNVs associated with the divergence of three previously defined parapatric glacial lineages, of which 24% were located within protein-coding genes, making those loci potential candidates for reproductive isolation. Lastly, our analyses unveiled a hierarchical, complex CNV population structure determined by temperature and local geography, which was in stark contrast to that inferred based on SNPs in a previous study. Our findings underline the complementarity of those two types of genomic variation in population genomics studies.

ClinSV: clinical grade structural and copy number variant detection from whole genome sequencing data

Whole genome sequencing (WGS) has the potential to outperform clinical microarrays for the detection of structural variants (SV) including copy number variants (CNVs), but has been challenged by high false positive rates. Here we present ClinSV, a WGS based SV integration, annotation, prioritization, and visualization framework, which identified 99.8% of simulated pathogenic ClinVar CNVs > 10 kb and 11/11 pathogenic variants from matched microarrays. The false positive rate was low (1.5-4.5%) and reproducibility high (95-99%). In clinical practice, ClinSV identified reportable variants in 22 of 485 patients (4.7%) of which 35-63% were not detectable by current clinical microarray designs. ClinSV is available at https://github.com/KCCG/ClinSV .

Performance of copy number variants detection based on whole-genome sequencing by DNBSEQ platforms

BACKGROUND

DNBSEQ™ platforms are new massively parallel sequencing (MPS) platforms that use DNA nanoball technology. Use of data generated from DNBSEQ™ platforms to detect single nucleotide variants (SNVs) and small insertions and deletions (indels) has proven to be quite effective, while the feasibility of copy number variants (CNVs) detection is unclear.

RESULTS

Here, we first benchmarked different CNV detection tools based on Illumina whole-genome sequencing (WGS) data of NA12878 and then assessed these tools in CNV detection based on DNBSEQ™ sequencing data from the same sample. When the same tool was used, the CNVs detected based on DNBSEQ™ and Illumina data were similar in quantity, length and distribution, while great differences existed within results from different tools and even based on data from a single platform. We further estimated the CNV detection power based on available CNV benchmarks of NA12878 and found similar precision and sensitivity between the DNBSEQ™ and Illumina platforms. We also found higher precision of CNVs shorter than 1 kbp based on DNBSEQ™ platforms than those based on Illumina platforms by using Pindel, DELLY and LUMPY. We carefully compared these two available benchmarks and found a large proportion of specific CNVs between them. Thus, we constructed a more complete CNV benchmark of NA12878 containing 3512 CNV regions.

CONCLUSIONS

We assessed and benchmarked CNV detections based on WGS with DNBSEQ™ platforms and provide guidelines for future studies.

A random forest-based framework for genotyping and accuracy assessment of copy number variations

Detection of copy number variations (CNVs) is essential for uncovering genetic factors underlying human diseases. However, CNV detection by current methods is prone to error, and precisely identifying CNVs from paired-end whole genome sequencing (WGS) data is still challenging. Here, we present a framework, CNV-JACG, for Judging the Accuracy of CNVs and Genotyping using paired-end WGS data. CNV-JACG is based on a random forest model trained on 21 distinctive features characterizing the CNV region and its breakpoints. Using the data from the 1000 Genomes Project, Genome in a Bottle Consortium, the Human Genome Structural Variation Consortium and in-house technical replicates, we show that CNV-JACG has superior sensitivity over the latest genotyping method, SV², particularly for the small CNVs (≤1 kb). We also demonstrate that CNV-JACG outperforms SV² in terms of Mendelian inconsistency in trios and concordance between technical replicates. Our study suggests that CNV-JACG would be a useful tool in assessing the accuracy of CNVs to meet the ever-growing needs for uncovering the missing heritability linked to CNVs.

A Practical Guide for Structural Variation Detection in the Human Genome

Profiling genetic variants-including single nucleotide variants, small insertions and deletions, copy number variations, and structural variations (SVs)-from both healthy individuals and individuals with disease is a key component of genetic and biomedical research. SVs are large-scale changes in the genome and involve breakage and rejoining of DNA fragments. They may affect thousands to millions of nucleotides and can lead to loss, gain, and reshuffling of genes and regulatory elements. SVs are known to impact gene expression and potentially result in altered phenotypes and diseases. Therefore, identifying SVs from the human genomes is particularly important. In this review, I describe advantages and disadvantages of the available high-throughput assays for the discovery of SVs, which are the most challenging genetic alterations to detect. A practical guide is offered to suggest the most suitable strategies for discovering different types of SVs including common germline, rare, somatic, and complex variants. I also discuss factors to be considered, such as cost and performance, for different strategies when designing experiments. Last, I present several approaches to identify potential SV artifacts caused by samples, experimental procedures, and computational analysis. © 2020 Wiley Periodicals LLC.

Genome-wide characterization of copy number variations in the host genome in genetic resistance to Marek's disease using next generation sequencing

Background

Marek’s disease (MD) is a highly neoplastic disease primarily affecting chickens, and remains as a chronic infectious disease that threatens the poultry industry. Copy number variation (CNV) has been examined in many species and is recognized as a major source of genetic variation that directly contributes to phenotypic variation such as resistance to infectious diseases. Two highly inbred chicken lines, 6₃ (MD-resistant) and 7₂ (MD-susceptible), as well as their F₁ generation and six recombinant congenic strains (RCSs) with varied susceptibility to MD, are considered as ideal models to identify the complex mechanisms of genetic and molecular resistance to MD.

Results

In the present study, to unravel the potential genetic mechanisms underlying resistance to MD, we performed a genome-wide CNV detection using next generation sequencing on the inbred chicken lines with the assistance of CNVnator. As a result, a total of 1649 CNV regions (CNVRs) were successfully identified after merging all the nine datasets, of which 90 CNVRs were overlapped across all the chicken lines. Within these shared regions, 1360 harbored genes were identified. In addition, 55 and 44 CNVRs with 62 and 57 harbored genes were specifically identified in line 6₃ and 7₂, respectively. Bioinformatics analysis showed that the nearby genes were significantly enriched in 36 GO terms and 6 KEGG pathways including JAK/STAT signaling pathway. Ten CNVRs (nine deletions and one duplication) involved in 10 disease-related genes were selected for validation by using quantitative real-time PCR (qPCR), all of which were successfully confirmed. Finally, qPCR was also used to validate two deletion events in line 7₂ that were definitely normal in line 6₃. One high-confidence gene, IRF2 was identified as the most promising candidate gene underlying resistance and susceptibility to MD in view of its function and overlaps with data from previous study.

Conclusions

Our findings provide valuable insights for understanding the genetic mechanism of resistance to MD and the identified gene and pathway could be considered as the subject of further functional characterization.