Fast computation and applications of genome mappability

Cell cycle gene alterations associate with a redistribution of mutation risk across chromosomal domains in human cancers

Mutations in human cells exhibit increased burden in heterochromatic, late DNA replication time (RT) chromosomal domains, with variation in mutation rates between tissues mirroring variation in heterochromatin and RT. We observed that regional mutation risk further varies between individual tumors in a manner independent of cell type, identifying three signatures of domain-scale mutagenesis in >4,000 tumor genomes. The major signature reflects remodeling of heterochromatin and of the RT program domains seen across tumors, tissues and cultured cells, and is robustly linked with higher expression of cell proliferation genes. Regional mutagenesis is associated with loss of activity of the tumor-suppressor genes RB1 and TP53, consistent with their roles in cell cycle control, with distinct mutational patterns generated by the two genes. Loss of regional heterogeneity in mutagenesis is associated with deficiencies in various DNA repair pathways. These mutation risk redistribution processes modify the mutation supply towards important genes, diverting the course of somatic evolution.

Genetic history of Cambridgeshire before and after the Black Death

The extent of the devastation of the Black Death pandemic (1346-1353) on European populations is known from documentary sources and its bacterial source illuminated by studies of ancient pathogen DNA. What has remained less understood is the effect of the pandemic on human mobility and genetic diversity at the local scale. Here, we report 275 ancient genomes, including 109 with coverage >0.1×, from later medieval and postmedieval Cambridgeshire of individuals buried before and after the Black Death. Consistent with the function of the institutions, we found a lack of close relatives among the friars and the inmates of the hospital in contrast to their abundance in general urban and rural parish communities. While we detect long-term shifts in local genetic ancestry in Cambridgeshire, we find no evidence of major changes in genetic ancestry nor higher differentiation of immune loci between cohorts living before and after the Black Death.

Recent insights into crosstalk between genetic parasites and their host genome

The bulk of higher order organismal genomes is comprised of transposable element (TE) copies, i.e. genetic parasites. The host-parasite relation is multi-faceted, varying across genomic region (genic versus intergenic), life-cycle stages, tissue-type and of course in health versus pathological state. The reach of functional genomics though, in investigating genotype-to-phenotype relations, has been limited when TEs are involved. The aim of this review is to highlight recent progress made in understanding how TE origin biochemical activity interacts with the central dogma stages of the host genome. Such interaction can also bring about modulation of the immune context and this could have important repercussions in disease state where immunity has a role to play. Thus, the review is to instigate ideas and action points around identifying evolutionary adaptations that the host genome and the genetic parasite have evolved and why they could be relevant.

Population genomics of the muskox' resilience in the near absence of genetic variation

Genomic studies of species threatened by extinction are providing crucial information about evolutionary mechanisms and genetic consequences of population declines and bottlenecks. However, to understand how species avoid the extinction vortex, insights can be drawn by studying species that thrive despite past declines. Here, we studied the population genomics of the muskox (Ovibos moschatus), an Ice Age relict that was at the brink of extinction for thousands of years at the end of the Pleistocene yet appears to be thriving today. We analysed 108 whole genomes, including present-day individuals representing the current native range of both muskox subspecies, the white-faced and the barren-ground muskox (O. moschatus wardi and O. moschatus moschatus) and a ~21,000-year-old ancient individual from Siberia. We found that the muskox' demographic history was profoundly shaped by past climate changes and post-glacial re-colonizations. In particular, the white-faced muskox has the lowest genome-wide heterozygosity recorded in an ungulate. Yet, there is no evidence of inbreeding depression in native muskox populations. We hypothesize that this can be explained by the effect of long-term gradual population declines that allowed for purging of strongly deleterious mutations. This study provides insights into how species with a history of population bottlenecks, small population sizes and low genetic diversity survive against all odds.

Systematic analysis of Mendelian disease-associated gene variants reveals new classes of cancer-predisposing genes

BACKGROUND

Despite the acceleration of somatic driver gene discovery facilitated by recent large-scale tumor sequencing data, the contribution of inherited variants remains largely unexplored, primarily focusing on previously known cancer predisposition genes (CPGs) due to the low statistical power associated with detecting rare pathogenic variant-phenotype associations.

METHODS

Here, we introduce a generalized log-regression model to measure the excess of pathogenic variants within genes in cancer patients compared to control samples. It aims to measure gene-level cancer risk enrichment by collapsing rare pathogenic variants after controlling the population differences across samples.

RESULTS

In this study, we investigate whether pathogenic variants in Mendelian disease-associated genes (OMIM genes) are enriched in cancer patients compared to controls. Utilizing data from PCAWG and the 1,000 Genomes Project, we identify 103 OMIM genes demonstrating significant enrichment of pathogenic variants in cancer samples (FDR 20%). Through an integrative approach considering three distinct properties, we classify these CPG-like OMIM genes into four clusters, indicating potential diverse mechanisms underlying tumor progression. Further, we explore the function of PAH (a key metabolic enzyme associated with Phenylketonuria), the gene exhibiting the highest prevalence of pathogenic variants in a pan-cancer (1.8%) compared to controls (0.6%).

CONCLUSIONS

Our findings suggest a possible cancer progression mechanism through metabolic profile alterations. Overall, our data indicates that pathogenic OMIM gene variants contribute to cancer progression and introduces new CPG classifications potentially underpinning diverse tumorigenesis mechanisms.

Using long-read CAGE sequencing to profile cryptic-promoter-derived transcripts and their contribution to the immunopeptidome

Recent studies have shown that the noncoding genome can produce unannotated proteins as antigens that induce immune response. One major source of this activity is the aberrant epigenetic reactivation of transposable elements (TEs). In tumors, TEs often provide cryptic or alternate promoters, which can generate transcripts that encode tumor-specific unannotated proteins. Thus, TE-derived transcripts (TE transcripts) have the potential to produce tumor-specific, but recurrent, antigens shared among many tumors. Identification of TE-derived tumor antigens holds the promise to improve cancer immunotherapy approaches; however, current genomics and computational tools are not optimized for their detection. Here we combined CAGE technology with full-length long-read transcriptome sequencing (long-read CAGE, or LRCAGE) and developed a suite of computational tools to significantly improve immunopeptidome detection by incorporating TE and other tumor transcripts into the proteome database. By applying our methods to human lung cancer cell line H1299 data, we show that long-read technology significantly improves mapping of promoters with low mappability scores and that LRCAGE guarantees accurate construction of uncharacterized 5' transcript structure. Augmenting a reference proteome database with newly characterized transcripts enabled us to detect noncanonical antigens from HLA-pulldown LC-MS/MS data. Lastly, we show that epigenetic treatment increased the number of noncanonical antigens, particularly those encoded by TE transcripts, which might expand the pool of targetable antigens for cancers with low mutational burden.

Accessible high-throughput single-cell whole-genome sequencing with paired chromatin accessibility

Single-cell whole-genome sequencing (scWGS) enables the assessment of genome-level molecular differences between individual cells with particular relevance to genetically diverse systems like solid tumors. The application of scWGS was limited due to a dearth of accessible platforms capable of producing high-throughput profiles. We present a technique that leverages nucleosome disruption methodologies with the widely adopted 10× Genomics ATAC-seq workflow to produce scWGS profiles for high-throughput copy-number analysis without new equipment or custom reagents. We further demonstrate the use of commercially available indexed transposase complexes from ScaleBio for sample multiplexing, reducing the per-sample preparation costs. Finally, we demonstrate that sequential indexed tagmentation with an intervening nucleosome disruption step allows for the generation of both ATAC and WGS data from the same cell, producing comparable data to the unimodal assays. By exclusively utilizing accessible commercial reagents, we anticipate that these scWGS and scWGS+ATAC methods can be broadly adopted by the research community.

Genetic assessment of pathogenic germline alterations in lysosomal genes among Asian patients with pancreatic ductal adenocarcinoma

BACKGROUND

Lysosomes are closely linked to autophagic activity, which plays a vital role in pancreatic ductal adenocarcinoma (PDAC) biology. The survival of PDAC patients is still poor, and the identification of novel genetic factors for prognosis and treatment is highly required to prevent PDAC-related deaths. This study investigated the germline variants related to lysosomal dysfunction in patients with PDAC and to analyze whether they contribute to the development of PDAC.

METHODS

The germline putative pathogenic variants (PPV) in genes involved in lysosomal storage disease (LSD) was compared between patients with PDAC (n = 418) and healthy controls (n = 845) using targeted panel and whole-exome sequencing. Furthermore, pancreatic organoids from wild-type and KrasG12D mice were used to evaluate the effect of lysosomal dysfunction on PDAC development. RNA sequencing (RNA-seq) analysis was performed with established PDAC patient-derived organoids (PDOs) according to the PPV status.

RESULTS

The PPV in LSD-related genes was higher in patients with PDAC than in healthy controls (8.13 vs. 4.26%, Log2 OR = 1.65, P = 3.08 × 10-3). The PPV carriers of LSD-related genes with PDAC were significantly younger than the non-carriers (mean age 61.5 vs. 65.3 years, P = 0.031). We further studied a variant of the lysosomal enzyme, galactosylceramidase (GALC), which was the most frequently detected LSD variant in our cohort. Autophagolysosomal activity was hampered when GALC was downregulated, which was accompanied by paradoxically elevated autophagic flux. Furthermore, the number of proliferating Ki-67+ cells increased significantly in pancreatic organoids derived from Galc knockout KrasG12D mice. Moreover, GALC PPV carriers tended to show drug resistance in both PDAC cell line and PDAC PDO, and RNA-seq analysis revealed that various metabolism and gene repair pathways were upregulated in PDAC PDOs harboring a GALC variant.

CONCLUSIONS

Genetically defined lysosomal dysfunction is frequently observed in patients with young-onset PDAC. This might contribute to PDAC development by altering metabolism and impairing autophagolysosomal activity, which could be potentially implicated in therapeutic applications for PDAC.

Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under nonconvex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data although preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

Analyzing histone ChIP-seq data with a bin-based probability of being signal

Histone ChIP-seq is one of the primary methods for charting the cellular epigenomic landscape, the components of which play a critical regulatory role in gene expression. Analyzing the activity of regulatory elements across datasets and cell types can be challenging due to shifting peak positions and normalization artifacts resulting from, for example, differing read depths, ChIP efficiencies, and target sizes. Moreover, broad regions of enrichment seen in repressive histone marks often evade detection by commonly used peak callers. Here, we present a simple and versatile method for identifying enriched regions in ChIP-seq data that relies on estimating a gamma distribution fit to non-overlapping 5kB genomic bins to establish a global background. We use this distribution to assign a probability of being signal (PBS) between zero and one to each 5 kB bin. This approach, while lower in resolution than typical peak-calling methods, provides a straightforward way to identify enriched regions and compare enrichments among multiple datasets, by transforming the data to values that are universally normalized and can be readily visualized and integrated with downstream analysis methods. We demonstrate applications of PBS for both broad and narrow histone marks, and provide several illustrations of biological insights which can be gleaned by integrating PBS scores with downstream data types.

Interpretable single-cell transcription factor prediction based on deep learning with attention mechanism

Predicting the transcription factor binding site (TFBS) in the whole genome range is essential in exploring the rule of gene transcription control. Although many deep learning methods to predict TFBS have been proposed, predicting TFBS using single-cell ATAC-seq data and embedding attention mechanisms needs to be improved. To this end, we present IscPAM, an interpretable method based on deep learning with an attention mechanism to predict single-cell transcription factors. Our model adopts the convolution neural network to extract the data feature and optimize the pre-trained model. In particular, the model obtains faster training and prediction due to the embedded attention mechanism. For datasets, we take ATAC-seq, ChIP-seq, and DNA sequences data for the pre-trained model, and single-cell ATAC-seq data is used to predict the TF binding graph in the given cell. We verify the interpretability of the model through ablation experiments and sensitivity analysis. IscPAM can efficiently predict the combination of whole genome transcription factors in single cells and study cellular heterogeneity through chromatin accessibility of related diseases.

The clinical utility and costs of whole-genome sequencing to detect cancer susceptibility variants-a multi-site prospective cohort study

BACKGROUND

Many families and individuals do not meet criteria for a known hereditary cancer syndrome but display unusual clusters of cancers. These families may carry pathogenic variants in cancer predisposition genes and be at higher risk for developing cancer.

METHODS

This multi-centre prospective study recruited 195 cancer-affected participants suspected to have a hereditary cancer syndrome for whom previous clinical targeted genetic testing was either not informative or not available. To identify pathogenic disease-causing variants explaining participant presentation, germline whole-genome sequencing (WGS) and a comprehensive cancer virtual gene panel analysis were undertaken.

RESULTS

Pathogenic variants consistent with the presenting cancer(s) were identified in 5.1% (10/195) of participants and pathogenic variants considered secondary findings with potential risk management implications were identified in another 9.7% (19/195) of participants. Health economic analysis estimated the marginal cost per case with an actionable variant was significantly lower for upfront WGS with virtual panel ($8744AUD) compared to standard testing followed by WGS ($24,894AUD). Financial analysis suggests that national adoption of diagnostic WGS testing would require a ninefold increase in government annual expenditure compared to conventional testing.

CONCLUSIONS

These findings make a case for replacing conventional testing with WGS to deliver clinically important benefits for cancer patients and families. The uptake of such an approach will depend on the perspectives of different payers on affordability.

Allo: Accurate allocation of multi-mapped reads enables regulatory element analysis at repeats

Transposable elements (TEs) and other repetitive regions have been shown to contain gene regulatory elements, including transcription factor binding sites. Unfortunately, regulatory elements harbored by repeats have proven difficult to characterize using short-read sequencing assays such as ChIP-seq or ATAC-seq. Most regulatory genomics analysis pipelines discard "multi-mapped" reads that align equally well to multiple genomic locations. Since multi-mapped reads arise predominantly from repeats, current analysis pipelines fail to detect a substantial portion of regulatory events that occur in repetitive regions. To address this shortcoming, we developed Allo, a new approach to allocate multi-mapped reads in an efficient, accurate, and user-friendly manner. Allo combines probabilistic mapping of multi-mapped reads with a convolutional neural network that recognizes the read distribution features of potential peaks, offering enhanced accuracy in multi-mapping read assignment. Allo also provides read-level output in the form of a corrected alignment file, making it compatible with existing regulatory genomics analysis pipelines and downstream peak-finders. In a demonstration application on CTCF ChIP-seq data, we show that Allo results in the discovery of thousands of new CTCF peaks. Many of these peaks contain the expected cognate motif and/or serve as TAD boundaries. We additionally apply Allo to a diverse collection of ENCODE ChIP-seq datasets, resulting in multiple previously unidentified interactions between transcription factors and repetitive element families. Finally, we show that Allo may be particularly effective in identifying ChIP-seq peaks in younger TEs, which hold evolutionary significance due to their emergence during human evolution from primates.

Young LINE-1 transposon 5' UTRs marked by elongation factor ELL3 function as enhancers to regulate naïve pluripotency in embryonic stem cells

LINE-1s are the major clade of retrotransposons with autonomous retrotransposition activity. Despite the potential genotoxicity, LINE-1s are highly activated in early embryos. Here we show that a subset of young LINE-1s, L1Md_Ts, are marked by the RNA polymerase II elongation factor ELL3, and function as enhancers in mouse embryonic stem cells. ELL3 depletion dislodges the DNA hydroxymethylase TET1 and the co-repressor SIN3A from L1Md_Ts, but increases the enrichment of the Bromodomain protein BRD4, leading to loss of 5hmC, gain of H3K27ac, and upregulation of the L1Md_T nearby genes. Specifically, ELL3 occupies and represses the L1Md_T-based enhancer located within Akt3, which encodes a key regulator of AKT pathway. ELL3 is required for proper ERK activation and efficient shutdown of naïve pluripotency through inhibiting Akt3 during naïve-primed transition. Our study reveals that the enhancer function of a subset of young LINE-1s controlled by ELL3 in transcription regulation and mouse early embryo development.

Long-molecule scars of backup DNA repair in BRCA1- and BRCA2-deficient cancers

Homologous recombination (HR) deficiency is associated with DNA rearrangements and cytogenetic aberrations1. Paradoxically, the types of DNA rearrangements that are specifically associated with HR-deficient cancers only minimally affect chromosomal structure2. Here, to address this apparent contradiction, we combined genome-graph analysis of short-read whole-genome sequencing (WGS) profiles across thousands of tumours with deep linked-read WGS of 46 BRCA1- or BRCA2-mutant breast cancers. These data revealed a distinct class of HR-deficiency-enriched rearrangements called reciprocal pairs. Linked-read WGS showed that reciprocal pairs with identical rearrangement orientations gave rise to one of two distinct chromosomal outcomes, distinguishable only with long-molecule data. Whereas one (cis) outcome corresponded to the copying and pasting of a small segment to a distant site, a second (trans) outcome was a quasi-balanced translocation or multi-megabase inversion with substantial (10 kb) duplications at each junction. We propose an HR-independent replication-restart repair mechanism to explain the full spectrum of reciprocal pair outcomes. Linked-read WGS also identified single-strand annealing as a repair pathway that is specific to BRCA2 deficiency in human cancers. Integrating these features in a classifier improved discrimination between BRCA1- and BRCA2-deficient genomes. In conclusion, our data reveal classes of rearrangements that are specific to BRCA1 or BRCA2 deficiency as a source of cytogenetic aberrations in HR-deficient cells.

Personalised circulating tumour DNA assay with large-scale mutation coverage for sensitive minimal residual disease detection in colorectal cancer

BACKGROUND

Postoperative minimal residual disease (MRD) detection using circulating-tumour DNA (ctDNA) requires a highly sensitive analysis platform. We have developed a tumour-informed, hybrid-capture ctDNA sequencing MRD assay.

METHODS

Personalised target-capture panels for ctDNA detection were designed using individual variants identified in tumour whole-exome sequencing of each patient. MRD status was determined using ultra-high-depth sequencing data of plasma cell-free DNA. The MRD positivity and its association with clinical outcome were analysed in Stage II or III colorectal cancer (CRC).

RESULTS

In 98 CRC patients, personalised panels for ctDNA sequencing were built from tumour data, including a median of 185 variants per patient. In silico simulation showed that increasing the number of target variants increases MRD detection sensitivity in low fractions (<0.01%). At postoperative 3-week, 21.4% of patients were positive for MRD by ctDNA. Postoperative positive MRD was strongly associated with poor disease-free survival (DFS) (adjusted hazard ratio 8.40, 95% confidence interval 3.49-20.2). Patients with a negative conversion of MRD after adjuvant therapy showed significantly better DFS (P < 0.001).

CONCLUSION

Tumour-informed, hybrid-capture-based ctDNA assay monitoring a large number of patient-specific mutations is a sensitive strategy for MRD detection to predict recurrence in CRC.

Lessons from discovery of true ADAR RNA editing sites in a human cell line

BACKGROUND

Conversion or editing of adenosine (A) into inosine (I) catalyzed by specialized cellular enzymes represents one of the most common post-transcriptional RNA modifications with emerging connection to disease. A-to-I conversions can happen at specific sites and lead to increase in proteome diversity and changes in RNA stability, splicing, and regulation. Such sites can be detected as adenine-to-guanine sequence changes by next-generation RNA sequencing which resulted in millions reported sites from multiple genome-wide surveys. Nonetheless, the lack of extensive independent validation in such endeavors, which is critical considering the relatively high error rate of next-generation sequencing, leads to lingering questions about the validity of the current compendiums of the editing sites and conclusions based on them.

RESULTS

Strikingly, we found that the current analytical methods suffer from very high false positive rates and that a significant fraction of sites in the public databases cannot be validated. In this work, we present potential solutions to these problems and provide a comprehensive and extensively validated list of A-to-I editing sites in a human cancer cell line. Our findings demonstrate that most of true A-to-I editing sites in a human cancer cell line are located in the non-coding transcripts, the so-called RNA 'dark matter'. On the other hand, many ADAR editing events occurring in exons of human protein-coding mRNAs, including those that can recode the transcriptome, represent false positives and need to be interpreted with caution. Nonetheless, yet undiscovered authentic ADAR sites that increase the diversity of human proteome exist and warrant further identification.

CONCLUSIONS

Accurate identification of human ADAR sites remains a challenging problem, particularly for the sites in exons of protein-coding mRNAs. As a result, genome-wide surveys of ADAR editome must still be accompanied by extensive Sanger validation efforts. However, given the vast number of unknown human ADAR sites, there is a need for further developments of the analytical techniques, potentially those that are based on deep learning solutions, in order to provide a quick and reliable identification of the editome in any sample.

Impact of supraphysiologic MDM2 expression on chromatin networks and therapeutic responses in sarcoma

Amplification of MDM2 on supernumerary chromosomes is a common mechanism of P53 inactivation across tumors. Here, we investigated the impact of MDM2 overexpression on chromatin, gene expression, and cellular phenotypes in liposarcoma. Three independent regulatory circuits predominate in aggressive, dedifferentiated tumors. RUNX and AP-1 family transcription factors bind mesenchymal gene enhancers. P53 and MDM2 co-occupy enhancers and promoters associated with P53 signaling. When highly expressed, MDM2 also binds thousands of P53-independent growth and stress response genes, whose promoters engage in multi-way topological interactions. Overexpressed MDM2 concentrates within nuclear foci that co-localize with PML and YY1 and could also contribute to P53-independent phenotypes associated with supraphysiologic MDM2. Importantly, we observe striking cell-to-cell variability in MDM2 copy number and expression in tumors and models. Whereas liposarcoma cells are generally sensitive to MDM2 inhibitors and their combination with pro-apoptotic drugs, MDM2-high cells tolerate them and may underlie the poor clinical efficacy of these agents.

Ultrafast prediction of somatic structural variations by filtering out reads matched to pan-genome k-mer sets

Variant callers typically produce massive numbers of false positives for structural variations, such as cancer-relevant copy-number alterations and fusion genes resulting from genome rearrangements. Here we describe an ultrafast and accurate detector of somatic structural variations that reduces read-mapping costs by filtering out reads matched to pan-genome k-mer sets. The detector, which we named ETCHING (for efficient detection of chromosomal rearrangements and fusion genes), reduces the number of false positives by leveraging machine-learning classifiers trained with six breakend-related features (clipped-read count, split-reads count, supporting paired-end read count, average mapping quality, depth difference and total length of clipped bases). When benchmarked against six callers on reference cell-free DNA, validated biomarkers of structural variants, matched tumour and normal whole genomes, and tumour-only targeted sequencing datasets, ETCHING was 11-fold faster than the second-fastest structural-variant caller at comparable performance and memory use. The speed and accuracy of ETCHING may aid large-scale genome projects and facilitate practical implementations in precision medicine.

Mutation rates and adaptive variation among the clinically dominant clusters of Mycobacterium abscessus

Mycobacterium abscessus (Mab) is a multidrug-resistant pathogen increasingly responsible for severe pulmonary infections. Analysis of whole-genome sequences (WGS) of Mab demonstrates dense genetic clustering of clinical isolates collected from disparate geographic locations. This has been interpreted as supporting patient-to-patient transmission, but epidemiological studies have contradicted this interpretation. Here, we present evidence for a slowing of the Mab molecular clock rate coincident with the emergence of phylogenetic clusters. We performed phylogenetic inference using publicly available WGS from 483 Mab patient isolates. We implement a subsampling approach in combination with coalescent analysis to estimate the molecular clock rate along the long internal branches of the tree, indicating a faster long-term molecular clock rate compared to branches within phylogenetic clusters. We used ancestry simulation to predict the effects of clock rate variation on phylogenetic clustering and found that the degree of clustering in the observed phylogeny is more easily explained by a clock rate slowdown than by transmission. We also find that phylogenetic clusters are enriched in mutations affecting DNA repair machinery and report that clustered isolates have lower spontaneous mutation rates in vitro. We propose that Mab adaptation to the host environment through variation in DNA repair genes affects the organism's mutation rate and that this manifests as phylogenetic clustering. These results challenge the model that phylogenetic clustering in Mab is explained by person-to-person transmission and inform our understanding of transmission inference in emerging, facultative pathogens.

Variant calling and benchmarking in an era of complete human genome sequences

Genetic variant calling from DNA sequencing has enabled understanding of germline variation in hundreds of thousands of humans. Sequencing technologies and variant-calling methods have advanced rapidly, routinely providing reliable variant calls in most of the human genome. We describe how advances in long reads, deep learning, de novo assembly and pangenomes have expanded access to variant calls in increasingly challenging, repetitive genomic regions, including medically relevant regions, and how new benchmark sets and benchmarking methods illuminate their strengths and limitations. Finally, we explore the possible future of more complete characterization of human genome variation in light of the recent completion of a telomere-to-telomere human genome reference assembly and human pangenomes, and we consider the innovations needed to benchmark their newly accessible repetitive regions and complex variants.

Tensor decomposition based feature extraction and classification to detect natural selection from genomic data

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under non-convex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data while preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx , which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

A crowdsourcing database for the copy-number variation of the Spanish population

BACKGROUND

Despite being a very common type of genetic variation, the distribution of copy-number variations (CNVs) in the population is still poorly understood. The knowledge of the genetic variability, especially at the level of the local population, is a critical factor for distinguishing pathogenic from non-pathogenic variation in the discovery of new disease variants.

RESULTS

Here, we present the SPAnish Copy Number Alterations Collaborative Server (SPACNACS), which currently contains copy number variation profiles obtained from more than 400 genomes and exomes of unrelated Spanish individuals. By means of a collaborative crowdsourcing effort whole genome and whole exome sequencing data, produced by local genomic projects and for other purposes, is continuously collected. Once checked both, the Spanish ancestry and the lack of kinship with other individuals in the SPACNACS, the CNVs are inferred for these sequences and they are used to populate the database. A web interface allows querying the database with different filters that include ICD10 upper categories. This allows discarding samples from the disease under study and obtaining pseudo-control CNV profiles from the local population. We also show here additional studies on the local impact of CNVs in some phenotypes and on pharmacogenomic variants. SPACNACS can be accessed at: http://csvs.clinbioinfosspa.es/spacnacs/ .

CONCLUSION

SPACNACS facilitates disease gene discovery by providing detailed information of the local variability of the population and exemplifies how to reuse genomic data produced for other purposes to build a local reference database.

Comprehensive analysis of mutational signatures reveals distinct patterns and molecular processes across 27 pediatric cancers

Analysis of mutational signatures can reveal underlying molecular mechanisms of the processes that have imprinted the somatic mutations found in cancer genomes. Here, we analyze single base substitutions and small insertions and deletions in pediatric cancers encompassing 785 whole-genome sequenced tumors from 27 molecularly defined cancer subtypes. We identified only a small number of mutational signatures active in pediatric cancers, compared with previously analyzed adult cancers. Further, we report a significant difference in the proportion of pediatric tumors showing homologous recombination repair defect signatures compared with previous analyses. In pediatric leukemias, we identified an indel signature, not previously reported, characterized by long insertions in nonrepeat regions, affecting mainly intronic and intergenic regions, but also exons of known cancer genes. We provide a systematic overview of COSMIC v.3 mutational signatures active across pediatric cancers, which is highly relevant for understanding tumor biology and enabling future research in defining biomarkers of treatment response.

Regulation of endogenous retrovirus-derived regulatory elements by GATA2/3 and MSX2 in human trophoblast stem cells

The placenta is an organ with extraordinary phenotypic diversity in eutherian mammals. Recent evidence suggests that numerous human placental enhancers are evolved from lineage-specific insertions of endogenous retroviruses (ERVs), yet the transcription factors (TFs) underlying their regulation remain largely elusive. Here, by first focusing on MER41, a primate-specific ERV family previously linked to placenta and innate immunity, we uncover the binding motifs of multiple crucial trophoblast TFs (GATA2/3, MSX2, GRHL2) in addition to innate immunity TFs STAT1 and IRF1. Integration of ChIP-seq data confirms the binding of GATA2/3, MSX2, and their related factors on the majority of MER41-derived enhancers in human trophoblast stem cells (TSCs). MER41-derived enhancers that are constitutively active in human TSCs are distinct from those activated upon interferon stimulation, which is determined by the binding of relevant TFs and their subfamily compositions. We further demonstrate that GATA2/3 and MSX2 have prevalent binding to numerous other ERV families - indicating their broad impact on ERV-derived enhancers. Functionally, the derepression of many syncytiotrophoblast genes after MSX2 knockdown is likely to be mediated by regulatory elements derived from ERVs - suggesting ERVs are also important for mediating transcriptional repression. Overall, this study characterizes the regulation of ERV-derived regulatory elements by GATA2/3, MSX2, and their cofactors in human TSCs, and provides mechanistic insights into the importance of ERVs in human trophoblast regulatory network.

CONGA: Copy number variation genotyping in ancient genomes and low-coverage sequencing data

To date, ancient genome analyses have been largely confined to the study of single nucleotide polymorphisms (SNPs). Copy number variants (CNVs) are a major contributor of disease and of evolutionary adaptation, but identifying CNVs in ancient shotgun-sequenced genomes is hampered by typical low genome coverage (<1×) and short fragments (<80 bps), precluding standard CNV detection software to be effectively applied to ancient genomes. Here we present CONGA, tailored for genotyping CNVs at low coverage. Simulations and down-sampling experiments suggest that CONGA can genotype deletions >1 kbps with F-scores >0.75 at ≥1×, and distinguish between heterozygous and homozygous states. We used CONGA to genotype 10,002 outgroup-ascertained deletions across a heterogenous set of 71 ancient human genomes spanning the last 50,000 years, produced using variable experimental protocols. A fraction of these (21/71) display divergent deletion profiles unrelated to their population origin, but attributable to technical factors such as coverage and read length. The majority of the sample (50/71), despite originating from nine different laboratories and having coverages ranging from 0.44×-26× (median 4×) and average read lengths 52-121 bps (median 69), exhibit coherent deletion frequencies. Across these 50 genomes, inter-individual genetic diversity measured using SNPs and CONGA-genotyped deletions are highly correlated. CONGA-genotyped deletions also display purifying selection signatures, as expected. CONGA thus paves the way for systematic CNV analyses in ancient genomes, despite the technical challenges posed by low and variable genome coverage.

CRAG: de novo characterization of cell-free DNA fragmentation hotspots in plasma whole-genome sequencing

The fine-scale cell-free DNA fragmentation patterns in early-stage cancers are poorly understood. We developed a de novo approach to characterize the cell-free DNA fragmentation hotspots from plasma whole-genome sequencing. Hotspots are enriched in open chromatin regions, and, interestingly, 3'end of transposons. Hotspots showed global hypo-fragmentation in early-stage liver cancers and are associated with genes involved in the initiation of hepatocellular carcinoma and associated with cancer stem cells. The hotspots varied across multiple early-stage cancers and demonstrated high performance for the diagnosis and identification of tissue-of-origin in early-stage cancers. We further validated the performance with a small number of independent case-control-matched early-stage cancer samples.

Accurate somatic variant detection using weakly supervised deep learning

Identification of somatic mutations in tumor samples is commonly based on statistical methods in combination with heuristic filters. Here we develop VarNet, an end-to-end deep learning approach for identification of somatic variants from aligned tumor and matched normal DNA reads. VarNet is trained using image representations of 4.6 million high-confidence somatic variants annotated in 356 tumor whole genomes. We benchmark VarNet across a range of publicly available datasets, demonstrating performance often exceeding current state-of-the-art methods. Overall, our results demonstrate how a scalable deep learning approach could augment and potentially supplant human engineered features and heuristic filters in somatic variant calling.

Deep learning-assisted genome-wide characterization of massively parallel reporter assays

Massively parallel reporter assay (MPRA) is a high-throughput method that enables the study of the regulatory activities of tens of thousands of DNA oligonucleotides in a single experiment. While MPRA experiments have grown in popularity, their small sample sizes compared to the scale of the human genome limits our understanding of the regulatory effects they detect. To address this, we develop a deep learning model, MpraNet, to distinguish potential MPRA targets from the background genome. This model achieves high discriminative performance (AUROC = 0.85) at differentiating MPRA positives from a set of control variants that mimic the background genome when applied to the lymphoblastoid cell line. We observe that existing functional scores represent very distinct functional effects, and most of them fail to characterize the regulatory effect that MPRA detects. Using MpraNet, we predict potential MPRA functional variants across the genome and identify the distributions of MPRA effect relative to other characteristics of genetic variation, including allele frequency, alternative functional annotations specified by FAVOR, and phenome-wide associations. We also observed that the predicted MPRA positives are not uniformly distributed across the genome; instead, they are clumped together in active regions comprising 9.95% of the genome and inactive regions comprising 89.07% of the genome. Furthermore, we propose our model as a screen to filter MPRA experiment candidates at genome-wide scale, enabling future experiments to be more cost-efficient by increasing precision relative to that observed from previous MPRAs.

Multiregional Sequencing Analysis Reveals Extensive Genetic Heterogeneity in Gastric Tumors from Latinos

Gastric cancer is a leading cause of cancer mortality and health disparities in Latinos. We evaluated gastric intratumoral heterogeneity using multiregional sequencing of >700 cancer genes in 115 tumor biopsies from 32 patients, 29 who were Latinos. Analyses focused on comparisons with The Cancer Genome Atlas (TCGA) and on mutation clonality, druggability, and signatures. We found that only approximately 30% of all mutations were clonal and that only 61% of the known TCGA gastric cancer drivers harbored clonal mutations. Multiple clonal mutations were found in new candidate gastric cancer drivers such as EYS, FAT4, PCDHA1, RAD50, EXO1, RECQL4, and FSIP2. The genomically stable (GS) molecular subtype, which has the worse prognosis, was identified in 48% of our Latino patients, a fraction that was >2.3-fold higher than in TCGA Asian and White patients. Only a third of all tumors harbored clonal pathogenic mutations in druggable genes, with most (93%) GS tumors lacking actionable clonal mutations. Mutation signature analyses revealed that, in microsatellite-stable (MSS) tumors, DNA repair mutations were common for both tumor initiation and progression, while tobacco, POLE, and inflammation signatures likely initiate carcinogenesis. MSS tumor progression was likely driven by aging- and aflatoxin-associated mutations, as these latter changes were usually nonclonal. In microsatellite-unstable tumors, nonclonal tobacco-associated mutations were common. Our study, therefore, contributed to advancing gastric cancer molecular diagnostics and suggests clonal status is important to understanding gastric tumorigenesis. Our findings of a higher frequency of a poor prognosis associated molecular subtype in Latinos and a possible new aflatoxin gastric cancer etiology also advance cancer disparities research.

SIGNIFICANCE

Our study contributes to advancing our knowledge of gastric carcinogenesis, diagnostics, and cancer health disparities.

A multi-tissue atlas of regulatory variants in cattle

Characterization of genetic regulatory variants acting on livestock gene expression is essential for interpreting the molecular mechanisms underlying traits of economic value and for increasing the rate of genetic gain through artificial selection. Here we build a Cattle Genotype-Tissue Expression atlas (CattleGTEx) as part of the pilot phase of the Farm animal GTEx (FarmGTEx) project for the research community based on 7,180 publicly available RNA-sequencing (RNA-seq) samples. We describe the transcriptomic landscape of more than 100 tissues/cell types and report hundreds of thousands of genetic associations with gene expression and alternative splicing for 23 distinct tissues. We evaluate the tissue-sharing patterns of these genetic regulatory effects, and functionally annotate them using multiomics data. Finally, we link gene expression in different tissues to 43 economically important traits using both transcriptome-wide association and colocalization analyses to decipher the molecular regulatory mechanisms underpinning such agronomic traits in cattle.

Ongoing transposition in cell culture reveals the phylogeny of diverse Drosophila S2 sublines

Cultured cells are widely used in molecular biology despite poor understanding of how cell line genomes change in vitro over time. Previous work has shown that Drosophila cultured cells have a higher transposable element content than whole flies, but whether this increase in transposable element content resulted from an initial burst of transposition during cell line establishment or ongoing transposition in cell culture remains unclear. Here, we sequenced the genomes of 25 sublines of Drosophila S2 cells and show that transposable element insertions provide abundant markers for the phylogenetic reconstruction of diverse sublines in a model animal cell culture system. DNA copy number evolution across S2 sublines revealed dramatically different patterns of genome organization that support the overall evolutionary history reconstructed using transposable element insertions. Analysis of transposable element insertion site occupancy and ancestral states support a model of ongoing transposition dominated by episodic activity of a small number of retrotransposon families. Our work demonstrates that substantial genome evolution occurs during long-term Drosophila cell culture, which may impact the reproducibility of experiments that do not control for subline identity.

A protocol for applying a population-specific reference genome assembly to population genetics and medical studies

With a growing number of available de novo sequenced genomes, protocols for their applications to population genetics will benefit our understanding of the human genome. Here we detail analytic steps to apply an example de novo reference genome to map and detect variants of short-read sequences from corresponding populations and to discover variants of disease-relevant genes. Using this protocol, we can improve variant discovery, better investigate population-specific genome properties, and evaluate the potential of sequenced genomes in medical studies.

For complete details on the use and execution of this protocol, please refer to Lou et al. (2022).

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Protocol for mapping and variants detection of short-read sequences

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Advantages of using a population-specific reference genome in population genomic studies

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Analytic steps to discover potential variants of disease-relevant genes

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function

Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, generating a cross-tissue atlas of 209,126 nuclei profiles, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder. Using the resulting cross-tissue atlas, we highlight shared and tissue-specific features of tissue-resident cell populations; identify cell types that might contribute to neuromuscular, metabolic, and immune components of monogenic diseases and the biological processes involved in their pathology; and determine cell types and gene modules that might underlie disease mechanisms for complex traits analyzed by genome-wide association studies.

PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions

The precisionFDA Truth Challenge V2 aimed to assess the state of the art of variant calling in challenging genomic regions. Starting with FASTQs, 20 challenge participants applied their variant-calling pipelines and submitted 64 variant call sets for one or more sequencing technologies (Illumina, PacBio HiFi, and Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with updated Genome in a Bottle benchmark sets and genome stratifications. Challenge submissions included numerous innovative methods, with graph-based and machine learning methods scoring best for short-read and long-read datasets, respectively. With machine learning approaches, combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

Small and intermediate size structural RNAs in the unicellular parasite Cryptosporidium parvum as revealed by sRNA-seq and comparative genomics

Small and intermediate-size noncoding RNAs (sRNAs and is-ncRNAs) have been shown to play important regulatory roles in the development of several eukaryotic organisms. However, they have not been thoroughly explored in Cryptosporidium parvum, an obligate zoonotic protist parasite responsible for the diarrhoeal disease cryptosporidiosis. Using Illumina sequencing of a small RNA library, a systematic identification of novel small and is-ncRNAs was performed in C. parvum excysted sporozoites. A total of 79 novel is-ncRNA candidates, including antisense, intergenic and intronic is-ncRNAs, were identified, including 7 new small nucleolar RNAs (snoRNAs). Expression of select novel is-ncRNAs was confirmed by RT-PCR. Phylogenetic conservation was analysed using covariance models (CMs) in related Cryptosporidium and apicomplexan parasite genome sequences. A potential new type of small ncRNA derived from tRNA fragments was observed. Overall, a deep profiling analysis of novel is-ncRNAs in C. parvum and related species revealed structural features and conservation of these novel is-ncRNAs. Covariance models can be used to detect is-ncRNA genes in other closely related parasites. These findings provide important new sequences for additional functional characterization of novel is-ncRNAs in the protist pathogen C. parvum.

Combining familial hypercholesterolemia and statin genetic studies as a strategy for the implementation of pharmacogenomics. A multidisciplinary approach

The diagnostic process of familial hypercholesterolemia frequently involves the use of genetic studies. Patients are treated with lipid-lowering drugs, frequently statins. Although pharmacogenomic clinical practice guidelines focusing on genotype-based statin prescription have been published, their use in routine clinical practice remains very modest.We have implemented a new NGS strategy that combines a panel of genes related to familial hypercholesterolemia with genomic regions related to the pharmacogenomics of lipid-lowering drugs described in clinical practice guidelines and in EMA and FDA drug labels. A multidisciplinary team of doctors, biologists, and pharmacists creates a clinical report that provides diagnostic and therapeutic findings using a knowledge management and clinical decision support system, as well as an algorithm for treatment selection.For 12 months, a total of 483 genetic diagnostic studies for familial hypercholesterolemia were carried out, of which 221 (45.8%) requested a complementary pharmacogenomic test. Of these 221 patients, 66.5% were carriers of actionable variants in any of the studied pharmacogenomic pathways: 46.6% of patients in one pathway, 19.0% in two pathways, and 0.9% in three pathways. 45.7% of patients could have a response to atorvastatin different from that of the reference population, 45.7% for simvastatin and lovastatin, 29.0% for fluvastatin, and 6.7% patients for pitavastatin.This implementation approach facilitates the incorporation of pharmacogenomic studies in clinical care practice, it does not add complexity nor additional steps to laboratory processes, and improves the pharmacotherapeutic process of patients.

Genome-wide analysis of somatic noncoding mutation patterns in cancer

We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6, FGFR2, RAD51B, SMC6, TERT, and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene's promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.

A spatially aware likelihood test to detect sweeps from haplotype distributions

The inference of positive selection in genomes is a problem of great interest in evolutionary genomics. By identifying putative regions of the genome that contain adaptive mutations, we are able to learn about the biology of organisms and their evolutionary history. Here we introduce a composite likelihood method that identifies recently completed or ongoing positive selection by searching for extreme distortions in the spatial distribution of the haplotype frequency spectrum along the genome relative to the genome-wide expectation taken as neutrality. Furthermore, the method simultaneously infers two parameters of the sweep: the number of sweeping haplotypes and the “width” of the sweep, which is related to the strength and timing of selection. We demonstrate that this method outperforms the leading haplotype-based selection statistics, though strong signals in low-recombination regions merit extra scrutiny. As a positive control, we apply it to two well-studied human populations from the 1000 Genomes Project and examine haplotype frequency spectrum patterns at the LCT and MHC loci. We also apply it to a data set of brown rats sampled in NYC and identify genes related to olfactory perception. To facilitate use of this method, we have implemented it in user-friendly open source software.

Identifying regions of the genome that contain adaptive variation is of fundamental interest in evolutionary biology, providing insight into an organism’s history and biology. When positive selection is recent or ongoing, we expect to find genomic patterns such as high frequency haplotypes and low genetic diversity in the vicinity of the adaptive locus. Here we develop a statistic to identify these regions based on distortions of the haplotype frequency spectrum from a background distribution. We evaluate the performance of this statistic under numerous realistic settings of interest to empiricists and demonstrate its superior performance relative to other haplotype-based selection statistics. We also apply this statistic to real population-genetic data. As a positive control, we explore two well-studied loci, LCT and MHC, in a European and an African human population that show strong evidence for selection. We also apply this statistic to the genomes of an urban brown rat population, where we uncover evidence for adaptation in olfactory perception genes. We release user-friendly software implementing this statistic.

SCONCE: a method for profiling copy number alterations in cancer evolution using single-cell whole genome sequencing

MOTIVATION

Copy number alterations (CNAs) are a significant driver in cancer growth and development, but remain poorly characterized on the single cell level. Although genome evolution in cancer cells is Markovian through evolutionary time, CNAs are not Markovian along the genome. However, existing methods call copy number profiles with Hidden Markov Models or change point detection algorithms based on changes in observed read depth, corrected by genome content and do not account for the stochastic evolutionary process.

RESULTS

We present a theoretical framework to use tumor evolutionary history to accurately call CNAs in a principled manner. To model the tumor evolutionary process and account for technical noise from low coverage single-cell whole genome sequencing data, we developed SCONCE, a method based on a Hidden Markov Model to analyze read depth data from tumor cells using matched normal cells as negative controls. Using a combination of public data sets and simulations, we show SCONCE accurately decodes copy number profiles, and provides a useful tool for understanding tumor evolution.

AVAILABILITYAND IMPLEMENTATION

SCONCE is implemented in C++11 and is freely available from https://github.com/NielsenBerkeleyLab/sconce.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Quality control and evaluation of plant epigenomics data

Epigenomics is the study of molecular signatures associated with discrete regions within genomes, many of which are important for a wide range of nuclear processes. The ability to profile the epigenomic landscape associated with genes, repetitive regions, transposons, transcription, differential expression, cis-regulatory elements, and 3D chromatin interactions has vastly improved our understanding of plant genomes. However, many epigenomic and single-cell genomic assays are challenging to perform in plants, leading to a wide range of data quality issues; thus, the data require rigorous evaluation prior to downstream analyses and interpretation. In this commentary, we provide considerations for the evaluation of plant epigenomics and single-cell genomics data quality with the aim of improving the quality and utility of studies using those data across diverse plant species.

Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed

Genetic studies on telomere length are important for understanding age-related diseases. Prior GWAS for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally-diverse individuals (European, African, Asian and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n=109,122 individuals. We identified 59 sentinel variants (p-value <5×10-9) in 36 loci associated with telomere length, including 20 newly associated loci (13 were replicated in external datasets). There was little evidence of effect size heterogeneity across populations. Fine-mapping at OBFC1 indicated the independent signals colocalized with cell-type specific eQTLs for OBFC1 (STN1). Using a multi-variant gene-based approach, we identified two genes newly implicated in telomere length, DCLRE1B (SNM1B) and PARN. In PheWAS, we demonstrated our TL polygenic trait scores (PTS) were associated with increased risk of cancer-related phenotypes.

Zebrafish transposable elements show extensive diversification in age, genomic distribution, and developmental expression

There is considerable interest in understanding the effect of transposable elements (TEs) on embryonic development. Studies in humans and mice are limited by the difficulty of working with mammalian embryos and by the relative scarcity of active TEs in these organisms. The zebrafish is an outstanding model for the study of vertebrate development, and over half of its genome consists of diverse TEs. However, zebrafish TEs remain poorly characterized. Here we describe the demography and genomic distribution of zebrafish TEs and their expression throughout embryogenesis using bulk and single-cell RNA sequencing data. These results reveal a highly dynamic genomic ecosystem comprising nearly 2000 distinct TE families, which vary in copy number by four orders of magnitude and span a wide range of ages. Longer retroelements tend to be retained in intergenic regions, whereas short interspersed nuclear elements (SINEs) and DNA transposons are more frequently found nearby or within genes. Locus-specific mapping of TE expression reveals extensive TE transcription during development. Although two-thirds of TE transcripts are likely driven by nearby gene promoters, we still observe stage- and tissue-specific expression patterns in self-regulated TEs. Long terminal repeat (LTR) retroelements are most transcriptionally active immediately following zygotic genome activation, whereas DNA transposons are enriched among transcripts expressed in later stages of development. Single-cell analysis reveals several endogenous retroviruses expressed in specific somatic cell lineages. Overall, our study provides a valuable resource for using zebrafish as a model to study the impact of TEs on vertebrate development.

Sequence Variant in the TRIM39-RPP21 Gene Readthrough is Shared Across a Cohort of Arabian Foals Diagnosed with Juvenile Idiopathic Epilepsy

Juvenile idiopathic epilepsy (JIE) is a self-limiting neurological disorder with a suspected genetic predisposition affecting young Arabian foals of the Egyptian lineage. The condition is characterized by tonic-clonic seizures with intermittent post-ictal blindness, in which most incidents are sporadic and unrecognized. This study aimed to identify genetic components shared across a local cohort of Arabian foals diagnosed with JIE via a combined whole genome and targeted resequencing approach: Initial whole genome comparisons between a small cohort of nine diagnosed foals (cases) and 27 controls from other horse breeds identified variants uniquely shared amongst the case cohort. Further validation via targeted resequencing of these variants, that pertain to non-intergenic regions, on additional eleven case individuals revealed a single 19bp deletion coupled with a triple-C insertion (Δ19InsCCC) within the TRIM39-RPP21 gene readthrough that was uniquely shared across all case individuals, and absent from three additional Arabian controls. Furthermore, we have confirmed recent findings refuting potential linkage between JIE and other inherited diseases in the Arabian lineage, and refuted the potential linkage between JIE and genes predisposing a similar disorder in human newborns. This is the first study to report a genetic variant to be shared in a sub-population cohort of Arabian foals diagnosed with JIE. Further evaluation of the sensitivity and specificity of the Δ19InsCCC allele within additional cohorts of the Arabian horse is warranted in order to validate its credibility as a marker for JIE, and to ascertain whether it has been introduced into other horse breeds by Arabian ancestry.

Copy Number Variation Detection by Single-Cell DNA Sequencing with SCOPE

Whole-genome single-cell DNA sequencing (scDNA-seq) enables the characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we describe SCOPE, a normalization and copy number estimation method for scDNA-seq data. We give an overview of the methodology and illustrate SCOPE with step-by-step demonstrations.

Population structure, genomic diversity and demographic history of Komodo dragons inferred from whole-genome sequencing

Population and conservation genetics studies have greatly benefited from the development of new techniques and bioinformatic tools associated with next-generation sequencing. Analysis of extensive data sets from whole-genome sequencing of even a few individuals allows the detection of patterns of fine-scale population structure and detailed reconstruction of demographic dynamics through time. In this study, we investigated the population structure, genomic diversity and demographic history of the Komodo dragon (Varanus komodoensis), the world's largest lizard, by sequencing the whole genomes of 24 individuals from the five main Indonesian islands comprising the entire range of the species. Three main genomic groups were observed. The populations of the Island of Komodo and the northern coast of Flores, in particular, were identified as two distinct conservation units. Degrees of genomic divergence among island populations were interpreted as a result of changes in sea level affecting connectivity across islands. Demographic inference suggested that Komodo dragons probably experienced a relatively steep population decline over the last million years, reaching a relatively stable N_e during the Saalian glacial cycle (400-150 thousand years ago) followed by a rapid N_e decrease. Genomic diversity of Komodo dragons was similar to that found in endangered or already extinct reptile species. Overall, this study provides an example of how whole-genome analysis of a few individuals per population can help define population structure and intraspecific demographic dynamics. This is particularly important when applying population genomics data to conservation of rare or elusive endangered species.

Subpopulations of swine γδ T cells defined by TCRγ and WC1 gene expression

γδ T cells constitute a major portion of lymphocytes in the blood of both ruminants and swine. Subpopulations of swine γδ T cells have been distinguished by CD2 and CD8α expression. However, it was not clear if they have distinct expression profiles of their T-cell receptor (TCR) or WC1 genes. Identifying receptor expression will contribute to understanding the functional differences between these subpopulations and their contributions to immune protection. Here, we annotated three genomic assemblies of the swine TCRγ gene locus finding four gene cassettes containing C, J and V genes, although some haplotypes carried a null TRGC gene (TRGC4). Genes in the TRGC1 cassette were homologs of bovine TRGC5 cassette while the others were not homologous to bovine genes. Here we evaluated three principal populations of γδ T cells (CD2⁺/SWC5^-, CD2^-/SWC5⁺, and CD2^-/SWC5^-). Both CD2^- subpopulations transcribed WC1 co-receptor genes, albeit with different patterns of gene expression but CD2⁺ cells did not. All subpopulations transcribed TCR genes from all four cassettes, although there were differences in expression levels. Finally, the CD2⁺ and CD2^- γδ T-cell populations differed in their representation in various organs and tissues, presumably at least partially reflective of different ligand specificities for their receptors.

Enhancer Hijacking Drives Oncogenic BCL11B Expression in Lineage-Ambiguous Stem Cell Leukemia

Lineage-ambiguous leukemias are high-risk malignancies of poorly understood genetic basis. Here, we describe a distinct subgroup of acute leukemia with expression of myeloid, T lymphoid, and stem cell markers driven by aberrant allele-specific deregulation of BCL11B, a master transcription factor responsible for thymic T-lineage commitment and specification. Mechanistically, this deregulation was driven by chromosomal rearrangements that juxtapose BCL11B to superenhancers active in hematopoietic progenitors, or focal amplifications that generate a superenhancer from a noncoding element distal to BCL11B. Chromatin conformation analyses demonstrated long-range interactions of rearranged enhancers with the expressed BCL11B allele and association of BCL11B with activated hematopoietic progenitor cell cis-regulatory elements, suggesting BCL11B is aberrantly co-opted into a gene regulatory network that drives transformation by maintaining a progenitor state. These data support a role for ectopic BCL11B expression in primitive hematopoietic cells mediated by enhancer hijacking as an oncogenic driver of human lineage-ambiguous leukemia. SIGNIFICANCE: Lineage-ambiguous leukemias pose significant diagnostic and therapeutic challenges due to a poorly understood molecular and cellular basis. We identify oncogenic deregulation of BCL11B driven by diverse structural alterations, including de novo superenhancer generation, as the driving feature of a subset of lineage-ambiguous leukemias that transcend current diagnostic boundaries.This article is highlighted in the In This Issue feature, p. 2659.

Grafting vigour is associated with DNA de-methylation in eggplant

In horticulture, grafting is a popular technique used to combine positive traits from two different plants. This is achieved by joining the plant top part (scion) onto a rootstock which contains the stem and roots. Rootstocks can provide resistance to stress and increase plant production, but despite their wide use, the biological mechanisms driving rootstock-induced alterations of the scion phenotype remain largely unknown. Given that epigenetics plays a relevant role during distance signalling in plants, we studied the genome-wide DNA methylation changes induced in eggplant (Solanum melongena) scion using two interspecific rootstocks to increase vigour. We found that vigour was associated with a change in scion gene expression and a genome-wide hypomethylation in the CHH context. Interestingly, this hypomethylation correlated with the downregulation of younger and potentially more active long terminal repeat retrotransposable elements (LTR-TEs), suggesting that graft-induced epigenetic modifications are associated with both physiological and molecular phenotypes in grafted plants. Our results indicate that the enhanced vigour induced by heterografting in eggplant is associated with epigenetic modifications, as also observed in some heterotic hybrids.

Patterns of Genomic Instability in Interspecific Yeast Hybrids With Diverse Ancestries

The genomes of hybrids often show substantial deviations from the features of the parent genomes, including genomic instabilities characterized by chromosomal rearrangements, gains, and losses. This plastic genomic architecture generates phenotypic diversity, potentially giving hybrids access to new ecological niches. It is however unclear if there are any generalizable patterns and predictability in the type and prevalence of genomic variation and instability across hybrids with different genetic and ecological backgrounds. Here, we analyzed the genomic architecture of 204 interspecific Saccharomyces yeast hybrids isolated from natural, industrial fermentation, clinical, and laboratory environments. Synchronous mapping to all eight putative parental species showed significant variation in read depth indicating frequent aneuploidy, affecting 44% of all hybrid genomes and particularly smaller chromosomes. Early generation hybrids with largely equal genomic content from both parent species were more likely to contain aneuploidies than introgressed genomes with an older hybridization history, which presumably stabilized the genome. Shared k-mer analysis showed that the degree of genomic diversity and variability varied among hybrids with different parent species. Interestingly, more genetically distant crosses produced more similar hybrid genomes, which may be a result of stronger negative epistasis at larger genomic divergence, putting constraints on hybridization outcomes. Mitochondrial genomes were typically inherited from the species also contributing the majority nuclear genome, but there were clear exceptions to this rule. Together, we find reliable genomic predictors of instability in hybrids, but also report interesting cross- and environment-specific idiosyncrasies. Our results are an important step in understanding the factors shaping divergent hybrid genomes and their role in adaptive evolution.