Low-coverage Whole-genome Sequencing Research Articles

Detection and characterization of pancreatic and biliary tract cancers using cell-free DNA fragmentomics

BackgroundPlasma cell-free DNA (cfDNA) fragmentomics has demonstrated significant differentiation power between cancer patients and healthy individuals, but little is known in pancreatic and biliary tract cancers. The aim of this study is to characterize the cfDNA fragmentomics in biliopancreatic cancers and develop an accurate method for cancer detection.MethodsOne hundred forty-seven patients with biliopancreatic cancers and 71 non-cancer volunteers were enrolled, including 55 patients with cholangiocarcinoma, 30 with gallbladder cancer, and 62 with pancreatic cancer. Low-coverage whole-genome sequencing (median coverage: 2.9 ×) was performed on plasma cfDNA. Three cfDNA fragmentomic features, including fragment size, end motif and nucleosome footprint, were subjected to construct a stacked machine learning model for cancer detection. Integration of carbohydrate antigen 19–9 (CA19-9) was explored to improve model performance.ResultsThe stacked model presented robust performance for cancer detection (area under curve (AUC) of 0.978 in the training cohort, and AUC of 0.941 in the validation cohort), and remained consistent even when using extremely low-coverage sequencing depth of 0.5 × (AUC: 0.905). Besides, our method could also help differentiate biliopancreatic cancer subtypes. By integrating the stacked model and CA19-9 to generate the final detection model, a high accuracy in distinguishing biliopancreatic cancers from non-cancer samples with an AUC of 0.995 was achieved.ConclusionsOur model demonstrated ultrasensitivity of plasma cfDNA fragementomics in detecting biliopancreatic cancers, fulfilling the unmet accuracy of widely-used serum biomarker CA19-9, and provided an affordable way for accurate noninvasive biliopancreatic cancer screening in clinical practice.

Robustness in population-structure and demographic-inference results derived from the Aedes aegypti genotyping chip and whole-genome sequencing data

Abstract The mosquito Aedes aegypti is the primary vector of many human arboviruses such as dengue, yellow fever, chikungunya, and Zika, which affect millions of people worldwide. Population genetic studies on this mosquito have been important in understanding its invasion pathways and success as a vector of human disease. The Axiom aegypti1 SNP chip was developed from a sample of geographically diverse A. aegypti populations to facilitate genomic studies on this species. We evaluate the utility of the Axiom aegypti1 SNP chip for population genetics and compare it with a low-depth shotgun sequencing approach using mosquitoes from the native (Africa) and invasive ranges (outside Africa). These analyses indicate that results from the SNP chip are highly reproducible and have a higher sensitivity to capture alternative alleles than a low-coverage whole-genome sequencing approach. Although the SNP chip suffers from ascertainment bias, results from population structure, ancestry, demographic, and phylogenetic analyses using the SNP chip were congruent with those derived from low-coverage whole-genome sequencing, and consistent with previous reports on Africa and outside Africa populations using microsatellites. More importantly, we identified a subset of SNPs that can be reliably used to generate merged databases, opening the door to combined analyses. We conclude that the Axiom aegypti1 SNP chip is a convenient, more accurate, low-cost alternative to low-depth whole-genome sequencing for population genetic studies of A. aegypti that do not rely on full allelic frequency spectra. Whole-genome sequencing and SNP chip data can be easily merged, extending the usefulness of both approaches.

Mitogenomes do not substantially improve phylogenetic resolution in a young non-model adaptive radiation of freshwater gastropods

BackgroundSpecies flocks in ancient lakes, and particularly those arising from adaptive radiation, make up the bulk of overall taxonomic and morphological diversity in these insular ecosystems. For these mostly young species assemblages, classical mitochondrial barcoding markers have so far been key to disentangle interspecific relationships. However, with the rise and further development of next-generation sequencing (NGS) methods and mapping tools, genome-wide data have become an increasingly important source of information even for non-model groups.ResultsHere, we provide, for the first time, a comprehensive mitogenome dataset of freshwater gastropods endemic to Sulawesi and thus of an ancient lake invertebrate species flock in general. We applied low-coverage whole-genome sequencing for a total of 78 individuals including 27 out of the 28 Tylomelania morphospecies from the Malili lake system as well as selected representatives from Lake Poso and adjacent catchments. Our aim was to assess whether mitogenomes considerably contribute to the phylogenetic resolution within this young species flock. Interestingly, we identified a high number of variable and parsimony-informative sites across the other ‘non-traditional’ mitochondrial loci. However, although the overall support was very high, the topology obtained was largely congruent with previously published single-locus phylogenies. Several clades remained unresolved and a large number of species was recovered polyphyletic, indicative of both rapid diversification and mitochondrial introgression.ConclusionsThis once again illustrates that, despite the higher number of characters available, mitogenomes behave like a single locus and thus can only make a limited contribution to resolving species boundaries, particularly when introgression events are involved.

Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits.

The selection and breeding of good meat rabbit breeds are fundamental to their industrial development, and genomic selection (GS) can employ genomic information to make up for the shortcomings of traditional phenotype-based breeding methods. For the practical implementation of GS in meat rabbit breeding, it is necessary to assess different marker densities and GS models. Here, we obtained low-coverage whole-genome sequencing (lcWGS) data from 1515 meat rabbits (including parent herd and half-sibling offspring). The specific objectives were (1) to derive a baseline for heritability estimates and genomic predictions based on randomly selected marker densities and (2) to assess the accuracy of genomic predictions for single- and multiple-trait linear mixed models. We found that a marker density of 50 K can be used as a baseline for heritability estimation and genomic prediction. For GS, the multi-trait genomic best linear unbiased prediction (GBLUP) model results in more accurate predictions for virtually all traits compared to the single-trait model, with improvements greater than 15% for all of them, which may be attributed to the use of information on genetically related traits. In addition, we discovered a positive correlation between the performance of the multi-trait GBLUP and the genetic correlation between the traits. We anticipate that this approach will provide solutions for GS, as well as optimize breeding programs, in meat rabbits.

Abstract 6929: Inter- and intratumoral PIK3CA subclonal diversity in breast cancer contextualized by single-cell multiomics

Abstract Rare clonotypes within pre-cancerous tissues can drive progression to cancer. However, the evolution of rare clonotypes in tumors or normal tissue cannot be defined in the absence of single-cell resolution. At this single-cell level, multiomic interrogation across the Central Dogma of Biology provides enhanced power to reconstruct such evolutionary trajectories, defining the mutational profile, cell identify, and receptor expression within each subpopulation. Leveraging a multiomic approach, we aimed to define how different mutations in the same oncogenic driver observed in the same tumor resection associate with copy number variation (CNV) across the genome. We analyzed individual ductal carcinoma in situ/invasive ductal carcinoma cells using a unified whole-genome and full-transcript RNAseq workflow (ResolveOME™, BioSkryb Genomics) coupled with panel-level extracellular protein information through oligo-conjugated antibodies (BioLegend). We sequenced the exomes and transcriptomes of ResolveOME-amplified single cells from mastectomy samples from twelve patients. At the single nucleotide variant (SNV) level, we identified an allelic series of PIK3CA oncogenic driver mutations in the same tumor resection. A single amino acid, in-frame deletion of E109 dominated the sample, followed by H1047R and K111E in decreasing subclonal abundance. A fourth mutation, E345T, not present in the first sample, was detected as the sole PIK3CA variant in the second tumor sample. Intriguingly, each respective PIK3CA mutation class was associated with a distinct copy number alteration profile revealed by low-coverage whole-genome sequencing: Cells harboring the predominant ΔE109 mutation displayed chromosome 8p,16p, and 17 loss while the less abundant H1047R mutation was in single cells harboring 1q gain, 4q loss, and 22 loss in addition to the 8p and 16q loss present in the ΔE109 cells. PIK3CA K111E had a quiescent, 2n copy number profile. The transcriptomic arm of ResolveOME, containing an oligo-conjugated antibody readout of surface protein expression, jointly confirmed the epithelial identity for the cells harboring the oncogenic PIK3CA mutations. A subpopulation of cells harboring prototypical breast cancer CNV were typed as non-epithelial with increased stemness characteristics, indicative of the ability to resolve phenotypic cellular states. These results suggest a tight interrelationship between CNV and SNV influencing the relative rate of clonal expansion. They also provide the opportunity to explore CNV:SNV signature association with loci exclusive of PIK3CA, and to exploit the power of multiomic integration for lineage reconstruction and for defining common oncogenic signatures of the evolving tumors. Citation Format: Jon Zawistowski, Isai Salas-Gonzalez, Tia Tate, Tatiana Morozova, Katherine Kennedy, Durga Arvapalli, Jamie Remington, Jeffrey Marks, E. Shelley Hwang, Gary Harton, Victor Weigman, Jay A. West. Inter- and intratumoral PIK3CA subclonal diversity in breast cancer contextualized by single-cell multiomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6929.

Abstract 1764: Long read sequencing allows comprehensive molecular profiling of complex karyotype acute myeloid leukemia (CKAML) with 5q deletions

Abstract Background Acute myeloid leukemia with complex karyotype (CKAML) does frequently show del(5q), deletion of chromosome 5q. Previous work in MDS and AML showed that CKAML form clusters distinguishing del(5q) cases with commonly retained regions (CRRs) in the telomeric (≥5q34) and centromeric (≤5q14.2) ends from cases with loss of the telomeric and/or centromeric regions. Interestingly, del(5q) patients with CRRs were shown to have less genomic lesions and were associated with a better prognosis than other 5q-deleted CKAML cases. Aim: Combining the advantages of two different long read sequencing technologies, Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), we aimed to more comprehensively characterize the molecular landscape of CKAML with del(5q). Our integrative workflow enabled an in-depth characterization of copy number variations (CNV), genomic rearrangements, transcriptomic and epigenomic changes. Methods: Low coverage whole genome sequencing (WGS) using ONT was performed on 209 patients with CKAML to identify del(5q) cases for further in-depth analysis. For 10 selected cases with known CRR status, genomic deep sequencing using PacBio Revio and ONT PromethION was performed, as well as PacBio Revio MAS-seq for fusion transcript detection. Results: A mean sequencing depth of 3-fold (range 0.4-17.3-fold) was obtained by low coverage WGS of the 209 CKAML cases. With a resolution of 0.1 Mbp n=134 del(5q) cases were identified in our CKAML cohort (64%) and commonly deleted regions on 5q could be resolved at 5q21.2 to 5q23.3 and 5q33.3 to 5q35 (Fisher test, FDR Benjamini-Hochberg, p<10−30). These regions separated del(5q) in n=52 cases showing CRR retention and n=82 showing partially no CRR, (either the centromeric or telomeric regions, n=74, or of both regions, n=8 involved). Subsequent co-occurrence analysis provided evidence of an association between the loss of telomeric 5q region with a 3p12-25 deletion. In-depth analysis (30-50 fold) of ten distinct del(5q) cases by PacBio and ONT revealed additional insights into the complex clonal composition of del(5q) cases. For example, one case showed two distinct subclones with 5q14.2-5q35.1 deletion and with translocation t(3;5)(p14;q14) respectively. Moreover, we could demonstrate that 5q-aberrations are far more complex than initially thought showing, e.g. inverted terminal duplications that resulted in fusion transcripts involving PDE4D and leading to its deregulated expression. Conclusion: Using our long read deep sequencing approach we could further delineate the structural complexity of del(5q) CKAML. While gene expression profiling, methylation analysis and correlation of findings with clinical outcome are ongoing and will be presented at the meeting, our results help to further pinpoint the biology underlying del(5q) and to further refine this heterogeneous CKAML subgroup. Citation Format: Felix Schick, Ekaterina Slonova, Eric Straeng, Sarah Huet, Maurizio Raso, Marlon Tilgner, Frederik Damm, Pierre Sujobert, Olga Blau, Lars Bullinger, Anna Dolnik. Long read sequencing allows comprehensive molecular profiling of complex karyotype acute myeloid leukemia (CKAML) with 5q deletions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1764.

Abstract 1266: Development and performance of a multi-cancer early detection test utilizing plasma cfDNA fragmentomics: A large-scale, prospective, multicenter study

Abstract Background: The implementation of the multi-cancer early detection (MCED) test offers a valuable adjunct to existing screening methods, enabling more efficient detection of cancer and potentially leading to improved treatment outcomes and prognoses for patients. Here, we report on the performance of an MCED test, which utilizes plasma cfDNA and leverages genome-wide fragmentomics-based characteristics to identify cancer signals and predict the signal origin across a diverse range of cancer types. Methods: Plasma cfDNA from evaluable blood samples was analyzed using an MCED blood test called MERCURY, a robust machine learning classifier leveraging the low-coverage whole-genome sequencing and a comprehensive set of genome-wide features derived from cfDNA fragmentomics. A carefully selected cohort of 3076 cancer patients representing 13 cancer types, in addition to 3477 healthy controls, were pre-specified into the training and internal validation sets to train and internally validate the models to assess cancer and tissue of origin (TOO). The classifier was trained to a target specificity of 99% and locked before analysis of the independent validation set. The independent validation was enrolled prospectively and consists of 1465 participants (cancer: n= 732; non-cancer: n= 733). Results: The performance metrics in the internal validation set demonstrated that the sensitivity and specificity for cancer detection were 0.865 (95% CI [0.840, 0.887]) and 0.989 (95% CI [0.980, 0.994]), respectively. These impressive results were further substantiated in the independent validation set, where the overall sensitivity and specificity were found to be 0.874 (95% CI [0.848, 0.897]) and 0.978 (95% CI [0.965, 0.987]) respectively. Notably, the sensitivity showed an incremental increase with the stage of cancer (Stage I: 0.769, 95% CI [0.708, 0.821]; Stage II: 0.840, 95% CI [0.784, 0.886]; Stage III: 0.923, 95% CI [0.874, 0.954]; Stage IV: 0.971, 95% CI [0.901, 0.995]. Regarding the TOO model, a total of 10 cancer types with more than 100 patients in the model construction cohort were considered. The TOO model achieved a prediction accuracy of 83.5% (95% CI [80.7%, 86.6%]) and 91.8% (95% CI [89.6%, 94.1%]) for the top predicted origin and the top two predicted origins, respectively, amongst the true positive cases in the independent validation set. Conclusions: In this pre-specified, large-scale study, the MCED test, utilizing cfDNA fragmentomics, demonstrates its remarkable ability to assess cancer signal with an elevated level of sensitivity and specificity across 13 distinct types of cancer. Moreover, it displays noteworthy accuracy in predicting the tissue of origin. The performances are to be further validated in a prospective cohort study (NCT06011694). Citation Format: Hua Bao, Xiaoxi Chen, Min Wu, Shiting Tang, Xuxiaochen Wu, Wanxiangfu Tang, Dongqin Zhu, Shanshan Yang, Shuang Chang, Peng He, Xiuxiu Xu, JinPeng Zhang, Yi Shen, Shuyu Wu, Ya Jiang, Sisi Liu, Xian Zhang, Xue Wu, Yang Shao. Development and performance of a multi-cancer early detection test utilizing plasma cfDNA fragmentomics: A large-scale, prospective, multicenter study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1266.

Abstract 6093: Evaluation of preanalytical and physiological variables affecting cfDNA-based multi-cancer early detection test

Abstract Background: The multi-cancer early detection (MCED) tests utilizing blood-based circulating cell-free DNA (cfDNA) have gained significant attention in the market as a promising approach for early cancer detection. However, potential effects of preanalytical and physiological variables, including extraction methods, preservation techniques, and physiological factors such as diet, on the accuracy and reliability of MCED results remains poorly investigated. In this study, we aimed to evaluate the impact of physiological variables during blood collection and preanalytical procedures on the outcomes of a MCED test. Methods: A total of 105 plasma samples from 19 healthy donors was collected and analyzed using a MCED test (MERCURY) which leverages the low-coverage whole-genome sequencing and a set of genome-wide features based on cfDNA fragmentomics. Samples were controlled for preanalytical procedures, including transportation condition and preservation time or physiological conditions (before/after meal or exercise) at blood collection. Results: Repeat blood collection (N=4) from the 5 healthy participants consistently yielded negative results for cancer (PPA: 100.0%; 95% CI [56.6%,100.0%]). Among the 5 healthy participants, plasma samples frozen within 1 year (7 days, 3 months, 9 months) showed the same agreement with the reference condition as non-frozen samples (PPA: 100.0%; 95% CI [56.6%,100.0%]), except for one sample frozen for 1 year, which showed a higher risk score and became a positive signal (PPA:80.0%; 95% CI [37.6%,96.4%]). Transportation conditions within 2 hours, 24 hours, 48 hours, and 96 hours at room temperature or 4℃ did not affect the test outcomes, as all samples remained in agreement with the reference condition as of 2 hours at 4℃ (PPA:100.0%; 95% CI [61.0%,100.0%]). Physiological conditions, including pre- and post-meal as well as pre- and post-exercise states, did not exert any influence on the test results, as all samples exhibited agreement with the reference condition (PPA:100.0%; 95% CI [43.9%,100.0%]). Conclusions: The results of this study suggest that variables such as time of blood collection and plasma freezing time may not significantly affect the accuracy of the MCED test in healthy participants. Transportation conditions and physiological conditions evaluated in this study did not have a notable influence on the test outcomes. Nevertheless, it is advised that the freezing duration of plasma samples should not exceed one year. Citation Format: Hua Bao, Xiaoxi Chen, Xuxiaochen Wu, Wanxiangfu Tang, Min Wu, Shiting Tang, Xue Wu, Yang Shao. Evaluation of preanalytical and physiological variables affecting cfDNA-based multi-cancer early detection test [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6093.

Profiling of Copy Number Alterations Using Low-Coverage Whole-Genome Sequencing Informs Differential Diagnosis and Prognosis in Primary Cutaneous Follicle Center Lymphoma

Primary cutaneous follicle center lymphoma (PCFCL) has an excellent prognosis using local treatment, whereas nodal follicular lymphoma (nFL), occasionally presenting with cutaneous spread, often requires systemic therapy. Distinction of the 2 diseases based on histopathology alone might be challenging. Copy number alterations (CNAs) have scarcely been explored on a genome-wide scale in PCFCL; however, they might serve as potential biomarkers during differential diagnosis and risk stratification. Low-coverage whole-genome sequencing is a robust, high-throughput method for genome-wide copy number profiling. In this study, we analyzed 28 PCFCL samples from 20 patients and compared the copy number profiles with a cohort of diagnostic samples of 64 nFL patients. Although the copy number profile of PCFCL was similar to that of nFL, PCFCL lacked amplifications of 18q, with the frequency peaking at 18q21.33 in nFL cases involving the BCL2 locus (PCFCL: 5.0% vs nFL: 31.3%, P = .018, Fisher exact test). Development of distant cutaneous spread was significantly associated with higher genomic instability including the proportion of genome altered (0.02 vs 0.13, P = .033) and number of CNAs (2 vs 9 P = .017), as well as the enrichment of 2p22.2-p15 amplification involving REL and XPO1 (6.3% vs 60.0%, P = .005), 3q23-q24 amplification (0.0% vs 50.0%, P = .004), 6q16.1-q23.3 deletion (6.3% vs 50.0%, P = .018), and 9p21.3 deletion covering CDKN2A and CDKN2B loci (0.0% vs 40.0%, P = .014, all Fisher exact test) in PCFCL. Analysis of sequential tumor samples in 2 cases harboring an unfavorable clinical course pointed to the acquisition of 2p amplification in the earliest common progenitor underlining its pivotal role in malignant transformation. By performing genome-wide copy number profiling on the largest patient cohort to date, we identified distinctive CNA alterations conceivably facilitating the differential diagnosis of PCFCL and secondary cutaneous involvement of nFL and potentially aiding the risk stratification of patients with PCFCL in the future.

A genotyping array for the globally invasive vector mosquito, Aedes albopictus

BackgroundAlthough whole-genome sequencing (WGS) is the preferred genotyping method for most genomic analyses, limitations are often experienced when studying genomes characterized by a high percentage of repetitive elements, high linkage, and recombination deserts. The Asian tiger mosquito (Aedes albopictus), for example, has a genome comprising up to 72% repetitive elements, and therefore we set out to develop a single-nucleotide polymorphism (SNP) chip to be more cost-effective. Aedes albopictus is an invasive species originating from Southeast Asia that has recently spread around the world and is a vector for many human diseases. Developing an accessible genotyping platform is essential in advancing biological control methods and understanding the population dynamics of this pest species, with significant implications for public health.MethodsWe designed a SNP chip for Ae. albopictus (Aealbo chip) based on approximately 2.7 million SNPs identified using WGS data from 819 worldwide samples. We validated the chip using laboratory single-pair crosses, comparing technical replicates, and comparing genotypes of samples genotyped by WGS and the SNP chip. We then used the chip for a population genomic analysis of 237 samples from 28 sites in the native range to evaluate its usefulness in describing patterns of genomic variation and tracing the origins of invasions.ResultsProbes on the Aealbo chip targeted 175,396 SNPs in coding and non-coding regions across all three chromosomes, with a density of 102 SNPs per 1 Mb window, and at least one SNP in each of the 17,461 protein-coding genes. Overall, 70% of the probes captured the genetic variation. Segregation analysis found that 98% of the SNPs followed expectations of single-copy Mendelian genes. Comparisons with WGS indicated that sites with genotype disagreements were mostly heterozygotes at loci with WGS read depth < 20, while there was near complete agreement with WGS read depths > 20, indicating that the chip more accurately detects heterozygotes than low-coverage WGS. Sample sizes did not affect the accuracy of the SNP chip genotype calls. Ancestry analyses identified four to five genetic clusters in the native range with various levels of admixture.ConclusionsThe Aealbo chip is highly accurate, is concordant with genotypes from WGS with high sequence coverage, and may be more accurate than low-coverage WGS.Graphical

Integrated chromosomal instability and tumor microbiome redefined prognosis-related subtypes of pancreatic cancer

BackgroundPancreatic cancer is a common malignancy with poor prognosis and limited treatment. Here we aimed to investigate the role of host chromosomal instability (CIN) and tumor microbiome in the prognosis of pancreatic cancer patients. MethodsOne hundred formalin-fixed paraffin-embedded (FFPE) pancreatic cancer samples were collected. DNA extracted from FFPE samples were analyzed by low-coverage whole-genome sequencing (WGS) via a customized bioinformatics workflow named ultrasensitive chromosomal aneuploidy detector. ResultsSamples are tested according to the procedure of ultrasensitive chromosomal aneuploidy detector (UCAD). We excluded 2 samples with failed quality control, 1 patient lost to follow-up and 6 dead in the perioperative period. The final 91 patients were admitted for the following analyses. Thirteen (14.3%) patients with higher CIN score had worse overall survival (OS) than those with lower CIN score. The top 20 microbes in pancreatic cancer samples included 15 species of bacteria and 5 species of viruses. Patients with high human herpesvirus (HHV)-7 and HHV-5 DNA reads exhibited worse OS. Furthermore, we classified 91 patients into 3 subtypes. Patients with higher CIN score (n =13) had the worst prognosis (median OS 6.9 mon); patients with lower CIN score but with HHV-7/5 DNA load (n = 24) had worse prognosis (median OS 10.6 mon); while patients with lower CIN score and HHV-7/5 DNA negative (n = 54) had the best prognosis (median OS 21.1 mon). ConclusionsHigh CIN and HHV-7/5 DNA load were associated with worse survival of pancreatic cancer. The novel molecular subtypes of pancreatic cancer based on CIN and microbiome had prognostic value.

ViTAL: Vision TrAnsformer based Low coverage SARS-CoV-2 lineage assignment.

Rapid spread of viral diseases such as Coronavirus disease 2019 (COVID-19) highlights an urgent need for efficient surveillance of virus mutation and transmission dynamics, which requires fast, inexpensive and accurate viral lineage assignment. The first two goals might be achieved through low-coverage whole-genome sequencing (LC-WGS) which enables rapid genome sequencing at scale and at reduced costs. Unfortunately, LC-WGS significantly diminishes the genomic details, rendering accurate lineage assignment very challenging. We present ViTAL, a novel deep learning algorithm specifically designed to perform lineage assignment of low coverage-sequenced genomes. ViTAL utilizes a combination of MinHash for genomic feature extraction and Vision Transformer for fine-grain genome classification and lineage assignment. We show that ViTAL outperforms state-of-the-art tools across diverse coverage levels, reaching up to 87.7% lineage assignment accuracy at 1× coverage where state-of-the-art tools such as UShER and Kraken2 achieve the accuracy of 5.4% and 27.4% respectively. ViTAL achieves comparable accuracy results with up to 8× lower coverage than state-of-the-art tools. We explore ViTAL's ability to identify the lineages of novel genomes, i.e. genomes the Vision Transformer was not trained on. We show how ViTAL can be applied to preliminary phylogenetic placement of novel variants. The data underlying this article are available in https://github.com/zuherJahshan/vital and can be accessed with 10.5281/zenodo.10688110.

Genomic evidence for domestication selection in three hatchery populations of Chinook salmon, Oncorhynchus tshawytscha.

Fish hatcheries are widely used to enhance fisheries and supplement declining wild populations. However, substantial evidence suggests that hatchery fish are subject to differential selection pressures compared to their wild counterparts. Domestication selection, or adaptation to the hatchery environment, poses a risk to wild populations if traits specific to success in the hatchery environment have a genetic component and there is subsequent introgression between hatchery and wild fish. Few studies have investigated domestication selection in hatcheries on a genomic level, and even fewer have done so in parallel across multiple hatchery-wild population pairs. In this study, we used low-coverage whole-genome sequencing to investigate signals of domestication selection in three separate hatchery populations of Chinook salmon, Oncorhynchus tshawytscha, after approximately seven generations of divergence from their corresponding wild progenitor populations. We sequenced 192 individuals from populations across Southeast Alaska and estimated genotype likelihoods at over six million loci. We discovered a total of 14 outlier peaks displaying high genetic differentiation (F ST) between hatchery-wild pairs, although no peaks were shared across the three comparisons. Peaks were small (53 kb on average) and often displayed elevated absolute genetic divergence (D xy) and linkage disequilibrium, suggesting some level of domestication selection has occurred. Our study provides evidence that domestication selection can lead to genetic differences between hatchery and wild populations in only a few generations. Additionally, our data suggest that population-specific adaptation to hatchery environments likely occurs through different genetic pathways, even for populations with similar standing genetic variation. These results highlight the need to collect paired genotype-phenotype data to understand how domestication may be affecting fitness and to identify potential management practices that may mitigate genetic risks despite multiple pathways of domestication.

First preimplantation genetic testing case of Meckel syndrome with a novel homozygous TXNDC15 variant in a non-consanguineous Chinese family.

Meckel-Gruber syndrome (MKS) is a perinatally lethal, genetically heterogeneous, autosomal recessive condition caused by defective primary cilium formation. So far, the association of TXNDC15-related MKS has been reported in only five independent families from diverse ethnic origins, including Saudi, Pakistani, Estonian, and Indian. Here, we report a fetus diagnosed with MKS at 12 weeks, exhibiting typical ultrasound findings. Low-coverage whole-genome sequencing was used to identify chromosomal abnormalities. Trio-base whole exome sequencing (trio-WES) was performed to investigate the potential pathogenic variants associated with MKS. Preimplantation genetic testing for monogenic disorders (PGT-M) was applied to prevent the transmission of the pathogenic variant. A novel homozygous pathogenic variant in the TXNDC15 gene was identified through trio-WES. The application of PGT-M successfully prevented the transmission of the pathogenic variant and resulted in an ongoing pregnancy. This is the first report of a TXNDC15 variant in the Chinese population and the first PGT case of TXNDC15-related MKS worldwide. The successful application of PGT-M in this family provides a potential approach for other monogenic diseases. Our case expands the variant spectrum of TXNDC15 and contributes to the molecular diagnosis and genetic counseling for MKS. This case underscores the importance of appropriate genetic testing methods and accurate genetic counseling in the diagnosis of rare monogenic diseases.

Patchwork: Alignment-Based Retrieval and Concatenation of Phylogenetic Markers from Genomic Data.

Low-coverage whole-genome sequencing (also known as "genome skimming") is becoming an increasingly affordable approach to large-scale phylogenetic analyses. While already routinely used to recover organellar genomes, genome skimming is rather rarely utilized for recovering single-copy nuclear markers. One reason might be that only few tools exist to work with this data type within a phylogenomic context, especially to deal with fragmented genome assemblies. We here present a new software tool called Patchwork for mining phylogenetic markers from highly fragmented short-read assemblies as well as directly from sequence reads. Patchwork is an alignment-based tool that utilizes the sequence aligner DIAMOND and is written in the programming language Julia. Homologous regions are obtained via a sequence similarity search, followed by a "hit stitching" phase, in which adjacent or overlapping regions are merged into a single unit. The novel sliding window algorithm trims away any noncoding regions from the resulting sequence. We demonstrate the utility of Patchwork by recovering near-universal single-copy orthologs within a benchmarking study, and we additionally assess the performance of Patchwork in comparison with other programs. We find that Patchwork allows for accurate retrieval of (putatively) single-copy genes from genome skimming data sets at different sequencing depths with high computational speed, outperforming existing software targeting similar tasks. Patchwork is released under the GNU General Public License version 3. Installation instructions, additional documentation, and the source code itself are all available via GitHub at https://github.com/fethalen/Patchwork.

Aberrant landscapes of maternal meiotic crossovers contribute to aneuploidies in human embryos.

Meiotic recombination is crucial for human genetic diversity and chromosome segregation accuracy. Understanding its variation across individuals and the processes by which it goes awry are long-standing goals in human genetics. Current approaches for inferring recombination landscapes rely either on population genetic patterns of linkage disequilibrium (LD)-capturing a time-averaged view-or on direct detection of crossovers in gametes or multigeneration pedigrees, which limits data set scale and availability. Here, we introduce an approach for inferring sex-specific recombination landscapes using data from preimplantation genetic testing for aneuploidy (PGT-A). This method relies on low-coverage (<0.05×) whole-genome sequencing of in vitro fertilized (IVF) embryo biopsies. To overcome the data sparsity, our method exploits its inherent relatedness structure, knowledge of haplotypes from external population reference panels, and the frequent occurrence of monosomies in embryos, whereby the remaining chromosome is phased by default. Extensive simulations show our method's high accuracy, even at coverages as low as 0.02×. Applying this method to PGT-A data from 18,967 embryos, we mapped 70,660 recombination events with ∼150 kbp resolution, replicating established sex-specific recombination patterns. We observed a reduced total length of the female genetic map in trisomies compared with disomies, as well as chromosome-specific alterations in crossover distributions. Based on haplotype configurations in pericentromeric regions, our data indicate chromosome-specific propensities for different mechanisms of meiotic error. Our results provide a comprehensive view of the role of aberrant meiotic recombination in the origins of human aneuploidies and offer a versatile tool for mapping crossovers in low-coverage sequencing data from multiple siblings.

A 39 kb structural variant causing Lynch Syndrome detected by optical genome mapping and nanopore sequencing

Lynch Syndrome (LS) is a hereditary cancer syndrome caused by pathogenic germline variants in one of the four mismatch repair (MMR) genes MLH1, MSH2, MSH6 and PMS2. It is characterized by a significantly increased risk of multiple cancer types, particularly colorectal and endometrial cancer, with autosomal dominant inheritance. Access to precise and sensitive methods for genetic testing is important, as early detection and prevention of cancer is possible when the variant is known. We present here two unrelated Norwegian families with family histories strongly suggestive of LS, where immunohistochemical and microsatellite instability analyses indicated presence of a pathogenic variant in MSH2, but targeted exon sequencing and multiplex ligation-dependent probe amplification (MLPA) were negative. Using Bionano optical genome mapping, we detected a 39 kb insertion in the MSH2 gene. Precise mapping of the insertion breakpoints and inserted sequence was performed by low-coverage whole-genome sequencing with an Oxford Nanopore MinION. The same variant was present in both families, and later found in other families from the same region of Norway, indicative of a founder event. To our knowledge, this is the first diagnosis of LS caused by a structural variant using these technologies. We suggest that structural variant detection be performed when LS is suspected but not confirmed with first-tier standard genetic testing.

Panmixia in the American eel extends to its tropical range of distribution: Biological implications and policymaking challenges.

The American eel (Anguilla rostrata) has long been regarded as a panmictic fish and has been confirmed as such in the northern part of its range. In this paper, we tested for the first time whether panmixia extends to the tropical range of the species. To do so, we first assembled a reference genome (975 Mbp, 19 chromosomes) combining long (PacBio and Nanopore and short (Illumina paired-end) reads technologies to support both this study and future research. To test for population structure, we estimated genotype likelihoods from low-coverage whole-genome sequencing of 460 American eels, collected at 21 sampling sites (in seven geographic regions) ranging from Canada to Trinidad and Tobago. We estimated genetic distance between regions, performed ADMIXTURE-like clustering analysis and multivariate analysis, and found no evidence of population structure, thus confirming that panmixia extends to the tropical range of the species. In addition, two genomic regions with putative inversions were observed, both geographically widespread and present at similar frequencies in all regions. We discuss the implications of lack of genetic population structure for the species. Our results are key for the future genomic research in the American eel and the implementation of conservation measures throughout its geographic range. Additionally, our results can be applied to fisheries management and aquaculture of the species.

Risk Prediction of Metachronous Colorectal Cancer from Molecular Features of Adenomas: A Nested Case-Control Study.

Identifying new biomarkers may improve prediction of me-CRC for individuals with adenomas and optimize surveillance intervals to reduce risk of colorectal cancer and reduce oversurveillance of patients with low risk of colorectal cancer. Use of DNA CNAs alone does not improve prediction of me-CRC. Further research to improve risk classification is required.

AGIDB: a versatile database for genotype imputation and variant decoding across species.

The high cost of large-scale, high-coverage whole-genome sequencing has limited its application in genomics and genetics research. The common approach has been to impute whole-genome sequence variants obtained from a few individuals for a larger population of interest individually genotyped using SNP chip. An alternative involves low-coverage whole-genome sequencing (lcWGS) of all individuals in the larger population, followed by imputation to sequence resolution. To overcome limitations of processing lcWGS data and meeting specific genotype imputation requirements, we developed AGIDB (https://agidb.pro), a website comprising tools and database with an unprecedented sample size and comprehensive variant decoding for animals. AGIDB integrates whole-genome sequencing and chip data from 17360 and 174945 individuals, respectively, across 89 species to identify over one billion variants, totaling a massive 688.57 TB of processed data. AGIDB focuses on integrating multiple genotype imputation scenarios. It also provides user-friendly searching and data analysis modules that enable comprehensive annotation of genetic variants for specific populations. To meet a wide range of research requirements, AGIDB offers downloadable reference panels for each species in addition to its extensive dataset, variant decoding and utility tools. We hope that AGIDB will become a key foundational resource in genetics and breeding, providing robust support to researchers.