In order to provide a comprehensive resource for human structural variants (SVs), we generated long-read sequence data and analyzed SVs for fifteen human genomes. We sequence resolved 99,604 ...insertions, deletions, and inversions including 2,238 (1.6 Mbp) that are shared among all discovery genomes with an additional 13,053 (6.9 Mbp) present in the majority, indicating minor alleles or errors in the reference. Genotyping in 440 additional genomes confirms the most common SVs in unique euchromatin are now sequence resolved. We report a ninefold SV bias toward the last 5 Mbp of human chromosomes with nearly 55% of all VNTRs (variable number of tandem repeats) mapping to this portion of the genome. We identify SVs affecting coding and noncoding regulatory loci improving annotation and interpretation of functional variation. These data provide the framework to construct a canonical human reference and a resource for developing advanced representations capable of capturing allelic diversity.
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•We sequence resolve and annotate 99,604 common human structural variants•55% of VNTRs map to the end of chromosomes and correlate with double-strand breaks•Alternate alleles facilitate accurate genotyping with short reads and new associations•We patch the reference and add diversity needed for developing a pan human genome
Long-read sequencing allows generation of a large catalog of human structural variants and the development of an algorithm for genotyping SVs from short-read data, clarifying the spectrum and importance of structural variation in the human genome.
Human genomes are typically assembled as consensus sequences that lack information on parental haplotypes. Here we describe a reference-free workflow for diploid de novo genome assembly that combines ...the chromosome-wide phasing and scaffolding capabilities of single-cell strand sequencing
with continuous long-read or high-fidelity
sequencing data. Employing this strategy, we produced a completely phased de novo genome assembly for each haplotype of an individual of Puerto Rican descent (HG00733) in the absence of parental data. The assemblies are accurate (quality value > 40) and highly contiguous (contig N50 > 23 Mbp) with low switch error rates (0.17%), providing fully phased single-nucleotide variants, indels and structural variants. A comparison of Oxford Nanopore Technologies and Pacific Biosciences phased assemblies identified 154 regions that are preferential sites of contig breaks, irrespective of sequencing technology or phasing algorithms.
Oncoviral infection is responsible for 12%-15% of cancer in humans. Convergent evidence from epidemiology, pathology, and oncology suggests that new viral etiologies for cancers remain to be ...discovered. Oncoviral profiles can be obtained from cancer genome sequencing data; however, widespread viral sequence contamination and noncausal viruses complicate the process of identifying genuine oncoviruses. Here, we propose a novel strategy to address these challenges by performing virome-wide screening of early-stage clonal viral integrations. To implement this strategy, we developed VIcaller, a novel platform for identifying viral integrations that are derived from any characterized viruses and shared by a large proportion of tumor cells using whole-genome sequencing (WGS) data. The sensitivity and precision were confirmed with simulated and benchmark cancer data sets. By applying this platform to cancer WGS data sets with proven or speculated viral etiology, we newly identified or confirmed clonal integrations of hepatitis B virus (HBV), human papillomavirus (HPV), Epstein-Barr virus (EBV), and BK Virus (BKV), suggesting the involvement of these viruses in early stages of tumorigenesis in affected tumors, such as HBV in
and
(also known as
) gene loci in liver cancer, HPV and BKV in bladder cancer, and EBV in non-Hodgkin's lymphoma. We also showed the capacity of VIcaller to identify integrations from some uncharacterized viruses. This is the first study to systematically investigate the strategy and method of virome-wide screening of clonal integrations to identify oncoviruses. Searching clonal viral integrations with our platform has the capacity to identify virus-caused cancers and discover cancer viral etiologies.
Next-generation sequencing (NGS) is now more accessible to clinicians and researchers. As a result, our understanding of the genetics of neurodevelopmental disorders (NDDs) has rapidly advanced over ...the past few years. NGS has led to the discovery of new NDD genes with an excess of recurrent de novo mutations (DNMs) when compared to controls. Development of large-scale databases of normal and disease variation has given rise to metrics exploring the relative tolerance of individual genes to human mutation. Genetic etiology and diagnosis rates have improved, which have led to the discovery of new pathways and tissue types relevant to NDDs. In this review, we highlight several key findings based on the discovery of recurrent DNMs ranging from copy number variants to point mutations. We explore biases and patterns of DNM enrichment and the role of mosaicism and secondary mutations in variable expressivity. We discuss the benefit of whole-genome sequencing (WGS) over whole-exome sequencing (WES) to understand more complex, multifactorial cases of NDD and explain how this improved understanding aids diagnosis and management of these disorders. Comprehensive assessment of the DNM landscape across the genome using WGS and other technologies will lead to the development of novel functional and bioinformatics approaches to interpret DNMs and drive new insights into NDD biology.
There are more than 55,000 variable number tandem repeats (VNTRs) in the human genome, notable for both their striking polymorphism and mutability. Despite their role in human evolution and genomic ...variation, they have yet to be studied collectively and in detail, partially owing to their large size, variability, and predominant location in noncoding regions. Here, we examine 467 VNTRs that are human-specific expansions, unique to one location in the genome, and not associated with retrotransposons. We leverage publicly available long-read genomes, including from the Human Genome Structural Variant Consortium, to ascertain the exact nucleotide composition of these VNTRs and compare their composition of alleles. We then confirm repeat unit composition in more than 3000 short-read samples from the 1000 Genomes Project. Our analysis reveals that these VNTRs contain highly structured repeat motif organization, modified by frequent deletion and duplication events. Although overall VNTR compositions tend to remain similar between 1000 Genomes Project superpopulations, we describe a notable exception with substantial differences in repeat composition (in
), as well as several VNTRs that are significantly different in length between superpopulations (in
, and
). We also observe that most of these VNTRs are expanded in archaic human genomes, yet remain stable in length between single generations. Collectively, our findings indicate that repeat motif variability, repeat composition, and repeat length are all informative modalities to consider when characterizing VNTRs and their contribution to genomic variation.
The sequence and assembly of human genomes using long‐read sequencing technologies has revolutionized our understanding of structural variation and genome organization. We compared the accuracy, ...continuity, and gene annotation of genome assemblies generated from either high‐fidelity (HiFi) or continuous long‐read (CLR) datasets from the same complete hydatidiform mole human genome. We find that the HiFi sequence data assemble an additional 10% of duplicated regions and more accurately represent the structure of tandem repeats, as validated with orthogonal analyses. As a result, an additional 5 Mbp of pericentromeric sequences are recovered in the HiFi assembly, resulting in a 2.5‐fold increase in the NG50 within 1 Mbp of the centromere (HiFi 480.6 kbp, CLR 191.5 kbp). Additionally, the HiFi genome assembly was generated in significantly less time with fewer computational resources than the CLR assembly. Although the HiFi assembly has significantly improved continuity and accuracy in many complex regions of the genome, it still falls short of the assembly of centromeric DNA and the largest regions of segmental duplication using existing assemblers. Despite these shortcomings, our results suggest that HiFi may be the most effective standalone technology for de novo assembly of human genomes.
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality. Almost half of HCC cases are associated with hepatitis B virus (HBV) infections, which often lead to HBV sequence ...integrations in the human genome. Accurate identification of HBV integration sites at a single nucleotide resolution is critical for developing a better understanding of the cancer genome landscape and of the disease itself. Here, we performed further analyses and characterization of HBV integrations identified by our recently reported VIcaller platform in recurrent or known HCC genes (such as
,
, and
) as well as non-recurrent cancer-related genes (such as
,
, and
). Our pathway enrichment analysis revealed multiple pathways involving the alcohol dehydrogenase 4 gene, such as the metabolism pathways of retinol, tyrosine, and fatty acid. Further analysis of the HBV integration sites revealed distinct patterns involving the integration upper breakpoints, integrated genome lengths, and integration allele fractions between tumor and normal tissues. Our analysis also implies that the VIcaller method has diagnostic potential through discovering novel clonal integrations in cancer-related genes. In conclusion, although VIcaller is a hypothesis free virome-wide approach, it can still be applied to accurately identify genome-wide integration events of a specific candidate virus and their integration allele fractions.
The complex interspersed pattern of segmental duplications in humans is responsible for rearrangements associated with neurodevelopmental disease, including the emergence of novel genes important in ...human brain evolution. We investigate the evolution of LCR16a, a putative driver of this phenomenon that encodes one of the most rapidly evolving human-ape gene families, nuclear pore interacting protein (NPIP).
Comparative analysis shows that LCR16a has independently expanded in five primate lineages over the last 35 million years of primate evolution. The expansions are associated with independent lineage-specific segmental duplications flanking LCR16a leading to the emergence of large interspersed duplication blocks at non-orthologous chromosomal locations in each primate lineage. The intron-exon structure of the NPIP gene family has changed dramatically throughout primate evolution with different branches showing characteristic gene models yet maintaining an open reading frame. In the African ape lineage, we detect signatures of positive selection that occurred after a transition to more ubiquitous expression among great ape tissues when compared to Old World and New World monkeys. Mouse transgenic experiments from baboon and human genomic loci confirm these expression differences and suggest that the broader ape expression pattern arose due to mutational changes that emerged in cis.
LCR16a promotes serial interspersed duplications and creates hotspots of genomic instability that appear to be an ancient property of primate genomes. Dramatic changes to NPIP gene structure and altered tissue expression preceded major bouts of positive selection in the African ape lineage, suggestive of a gene undergoing strong adaptive evolution.
Viral sequence integrations in the human genome have been implicated in various human diseases. Viral integrations remain among the most challenging-to-detect structural changes of the human genome. ...No studies have systematically analyzed how molecular and bioinformatics factors affect the power (sensitivity) to detect viral integrations using high-throughput sequencing (HTS). We selected a wide-range of molecular and bioinformatics factors covering genome sequence characteristics, HTS features, and viral integration detection. We designed a fast simulation-based framework to model the process of detecting variable viral integration events in the human genome. We then examined the associations of selected factors with viral integration detection power. We identified six factors that significantly affected viral integration detection power (P < 2 × 10−16). The strongest factors associated with detection power included proportion of sample cells with clonal viral integrations (Pearson's ρ = 0.64), sequencing depth (ρ = 0.37), length of viral integration (ρ = 0.37), paired-end read insert size (ρ = 0.23), user-defined threshold (number of supporting reads) to claim successful identification of integrations (ρ = −0.19), and read length (when sequence volume was fixed) (ρ = −0.09). As the first tool of its kind, VIpower incorporates all these factors, which can be manipulated in concert with each other to optimize the detection power. This tool may be used to estimate viral integration detection power for various combinations of sequencing or analytic parameters. It may also be used to estimate the parameters required to achieve a specific power when designing new sequencing experiments.
•A fast simulation tool was designed to model the process of detecting viral integrations from the human genome.•A wide range of molecular and bioinformatics factors relevant to viral integration detection power were collected.•At least six factors were found to be significantly associated with integration detection power.•VIpower may be used to calculate viral integration detection power and to estimate parameters to achieve a specific power.
Genome-wide association studies (GWAS) have successfully identified genes associated with complex human diseases. Although much of the heritability remains unexplained, combining single nucleotide ...polymorphism (SNP) genotypes from multiple studies for meta-analysis will increase the statistical power to identify new disease-associated variants. Meta-analysis requires same allele definition (nomenclature) and genome build among individual studies. Similarly, imputation, commonly-used prior to meta-analysis, requires the same consistency. However, the genotypes from various GWAS are generated using different genotyping platforms, arrays or SNP-calling approaches, resulting in use of different genome builds and allele definitions. Incorrect assumptions of identical allele definition among combined GWAS lead to a large portion of discarded genotypes or incorrect association findings. There is no published tool that predicts and converts among all major allele definitions.
In this study, we have developed a tool, GACT, which stands for Genome build and Allele definition Conversion Tool, that predicts and inter-converts between any of the common SNP allele definitions and between the major genome builds. In addition, we assessed several factors that may affect imputation quality, and our results indicated that inclusion of singletons in the reference had detrimental effects while ambiguous SNPs had no measurable effect. Unexpectedly, exclusion of genotypes with missing rate > 0.001 (40% of study SNPs) showed no significant decrease of imputation quality (even significantly higher when compared to the imputation with singletons in the reference), especially for rare SNPs.
GACT is a new, powerful, and user-friendly tool with both command-line and interactive online versions that can accurately predict, and convert between any of the common allele definitions and between genome builds for genome-wide meta-analysis and imputation of genotypes from SNP-arrays or deep-sequencing, particularly for data from the dbGaP and other public databases.
http://www.uvm.edu/genomics/software/gact.
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