Low-coverage Whole-genome Sequencing Research Articles

Unravelling mutational signatures with plasma circulating tumour DNA

The use of circulating tumour DNA (ctDNA) to profile mutational signatures represents a non-invasive opportunity for understanding cancer mutational processes. Here we present MisMatchFinder, a liquid biopsy approach for mutational signature detection using low-coverage whole-genome sequencing of ctDNA. Through analysis of 375 plasma samples across 9 cancers, we demonstrate that MisMatchFinder accurately infers single-base and doublet-base substitutions, as well as insertions and deletions to enhance the detection of ctDNA and clinically relevant mutational signatures.

Aridification and major geotectonic landscape change shaped an extraordinary species radiation across a world’s extreme elevational gradient

Understanding the profound influence of climatic and tectonic histories on adaptation and speciation is a crucial focus in biology research. While voyages like Humboldt’s expedition shaped our understanding of adaptation, the origin of current biodiversity remains unclear – whether it arose in situ or through dispersal from analogous habitats. Situated in the geologically complex Australopacific region, our study focuses on Limbodessus diving beetles (Dytiscidae), a diverse genus distributed from underground aquifers in Western Australia to alpine meadows in New Guinea. Using low-coverage whole-genome sequencing, we established a time-calibrated phylogenetic tree, elucidating Limbodessus’ origin in the mid-late Miocene, most likely in the Sahul continent (i.e., Australia and New Guinea) and western Pacific archipelagos. Our results provide evidence for parallel colonization and speciation at extreme altitudinal ends, driven by aridification in Australia, influencing subterranean colonization, and in situ diversification of alpine taxa by passive-uplifting of local biota in New Guinea. Furthermore, our findings highlight instances of subterranean speciation in isolated underground aquifers, marked by recurrent independent colonizations of this habitat.

Abstract B053: NucAE: Autoencoder-based enhancement of nucleosome occupancy signals from low-coverage cfDNA sequencing

Abstract Introduction: Cell-free DNA (cfDNA) is emerging as a valuable cancer biomarker, enabling both the detection and epigenetic characterization of malignancies through sequencing. cfDNA sequencing can reveal cancer-specific nucleosome occupancy patterns, which provide insights into tumor type and differentiation status, which define prognosis and therapy recommendations. Low-coverage whole-genome cfDNA sequencing is a cost-efficient approach to assay multiple cfDNA samples, however, the low coverage limits its sensitivity. We developed NucAE, a denoising autoencoder model, to enhance nucleosome occupancy signals from low-coverage cfDNA-sequencing. Methods: We designed NucAE to estimate nucleosome occupancy genome- wide from low-coverage sequencing data. To train the model, we undersampled high-coverage cfDNA-sequencing data and produced high- and low-coverage sample pairs at varying coverages (1x-90x). Inputs to NucAE include cfDNA fragment data and the corresponding genomic nucleotide sequence, with the model leveraging a symmetrical architecture comprising two convolutional layers in both the encoder and decoder, connected by ReLU activations. Mean squared error relative to the high-coverage signal was employed as the loss function. To evaluate the model independently, we performed peak detection from the nucleosome occupancy signals. We conducted a grid search to optimize hyperparameters. Results: We found that NucAE enhances the signal to noise ratio by detecting periodicities in nucleosome occupancy data. Denoising was improved by supplementing the cfDNA fragment signals with the genomic nucleotide sequences as input, which demonstrates that the model learned the nucleotide- sequence dependence of nucleosome positioning. Independent validation through peak detection on previously unseen samples demonstrated a significant improvement (p<0.001) in accuracy compared to raw signals across all tested coverages (1x-45x). Remarkably, at extremely low coverages (1x-2x), denoised signals allowed for more accurate peak detection than raw signals from 10-20x deeper sequencing. Conclusions: By using advanced machine-learning, we were able to enhance low-coverage cfDNA sequencing data to assay nucleosome positions at a nucleotide resolution. NucAE achieves an order of magnitude improvement over the low- coverage signals and greatly improves the utility of cfDNA-sequencing data to identify cancer- specific nucleosome occupancy, while preserving its low cost. Our model is the first to use autoencoders for genome-wide signal enhancement with potential use cases in many other areas of genomics. Citation Format: Zsolt Balázs, Jean Radig, Noah Wolford, Manuel Schürch, Michael Krauthammer. NucAE: Autoencoder-based enhancement of nucleosome occupancy signals from low-coverage cfDNA sequencing [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B053.

Abstract A062: Insightful analysis: Harnessing aqueous humor ctDNA for retinoblastoma stratification

Abstract Retinoblastoma (RB) the most common intraocular malignancy in children poses a significant challenge as traditional tissue biopsies are not feasible. Due to its critical location within the eye and the high risk of tumor seeding, clinicians are limited in performing molecular analysis-based risk stratification prior to enucleation. In this study, we explored the potential of aqueous humor (AH) as a source for minimally invasive liquid biopsies, aiming to establish a molecular profiling methodology for circulating tumor DNA (ctDNA) for risk stratification. We analyzed AH samples and corresponding tumor tissues from 19 enucleated eyes of patients diagnosed with RB. The extracted ctDNA from AH underwent low-coverage whole-genome sequencing (lcWGS) and methylation profiling. These data were then compared with the genomic and methylation landscapes of the matched tumor DNA. Additionally, we assessed the ability of AH- derived ctDNA to predict retinoblastoma methylation cluster. Our findings demonstrate a high concordance between ctDNA from AH and the primary tumor DNA in both methylation patterns and genomic alterations detected by lcWGS allowing the correct prediction of the retinoblastoma molecular group. The shared aberrations underscore the potential of AH as a reliable surrogate for tumor tissue in retinoblastoma. Additionally, the methylation profiles revealed consistent epigenetic modifications, further supporting the accuracy of AH-derived ctDNA in reflecting the tumor’s molecular characteristics. This study highlights the feasibility and effectiveness of using AH for molecular ctDNA genetic analysis in retinoblastoma. The overall high concordance between ctDNA profiles with primary tumor DNA and the ability to accurately predict RB methylation classes, highlights the superiority of the liquid biopsy approach overcoming the limitations of traditional biopsies and transforming the potential for early molecular analysis and risk stratification. This revolutionary technique could redefine pre-enucleation diagnostics, offering unprecedented insights and paving the way for tailored, precision medicine in retinoblastoma care. Citation Format: Sophia H Montigel, Tatsiana Ryl, Stefanie Volz, Elena Afanasyeva, Tatjana Wedig, Nathalie Schwarz, Stefan M Pfister, Kristian W Pajtler, Petra Ketteler, Kendra K Maaß. Insightful analysis: Harnessing aqueous humor ctDNA for retinoblastoma stratification [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A062.

Hypothesis-based investigation of known AD risk variants reveals the genetic underpinnings of neuropathological lesions observed in Alzheimer’s-type dementia

Alzheimer’s disease (AD) is the leading cause of dementia worldwide. Besides neurofibrillary tangles and amyloid beta (Aβ) plaques, a wide range of co-morbid neuropathological features can be observed in AD brains. Since AD has a very strong genetic background and displays a wide phenotypic heterogeneity, this study aims at investigating the genetic underpinnings of co-morbid and hallmark neuropathological lesions. This was realized by obtaining the genotypes for 75 AD risk variants from low-coverage whole-genome sequencing data for 325 individuals from the Leuven Brain Collection. Association testing with deeply characterized neuropathological lesions revealed a strong and likely direct effect of rs117618017, a SNP in exon 1 of APH1B, with tau-related pathology. Second, a relation between APOE and granulovacuolar degeneration, a proxy for necroptosis, was also discovered in addition to replication of the well-known association of APOE with AD hallmark neuropathological lesions. Additionally, several nominal associations with AD risk genes were detected for pTDP pathology, α-synuclein lesions and pTau-related pathology. These findings were confirmed in a meta-analysis with three independent cohorts. For example, we replicated a prior association between TPCN1 (rs6489896) and LATE-NC risk. Furthermore, we identified new putative LATE-NC-linked SNPs, including rs7068231, located upstream of ANK3. We found association between BIN1 (rs6733839) and α-synuclein pathology, and replicated a prior association between USP6NL (rs7912495) and Lewy body pathology. Additionally, we also found that UMAD1 (rs6943429) was nominally associated with Lewy body pathology. Overall, these results contribute to a broader general understanding of how AD risk variants discovered in large-scale clinical genome-wide association studies are involved in the pathological mechanisms of AD and indicate the importance of downstream elimination of phenotypic heterogeneity introduced in these studies.

A Novel Urine DNA Predictor for Noninvasive Early Diagnosis and Monitoring Minimal Residual Disease of Upper Tract Urothelial Carcinoma.

For early detection and postoperative monitoring of upper tract urothelial carcinoma (UTUC), the traditional detection method was limited to its invasiveness and insufficient sensitivity. We aim to use urine tumour DNA (utDNA) for detecting minimal residual disease (MRD), early diagnosis and perioperative monitoring in UTUC. We previously established a utDNA multidimensional bioinformatic valuation model, named utLIFE, using low-coverage whole-genome sequencing and targeted deep sequencing. This prospective cohort enrolled 93 patients diagnosed with UTUC without metastasis. We collected morning urine samples on the day of surgery and the discharge day after the operation for utLIFE testing. In addition, we also enrolled 80 healthy controls to further validate the specificity of the utLIFE model in the study. The utLIFE of preoperative samples could discriminate UTUC with high specificity (96.25%, 77/80), and high sensitivity (96.77%, 90/93) regardless of stage and grade. The sensitivity of utLIFE was significantly higher than urine cytology (p < 0.001) and fluorescence insitu hybridisation (FISH) (p < 0.001) (N = 19), especially in early-stage and low-grade UTUC. Postoperative utLIFE scores were significantly decreased compared with those of preoperative samples (79 vs. 36, p < 0.001), indicating its association with tumour burden. For special pathology types, utLIFE performed less well in sensitivity and perioperative alteration. In conclusion, we established a bioinformatic utDNA valuation model, utLIFE, which was validated to be a rapid and noninvasive approach with high sensitivity for early detection and MRD monitoring for UTUC.

Genome-wide association study of growth and reproductive traits based on low-coverage whole-genome sequencing in a Chubao black-head goat population

The Chubao black-head goat is a novel hybrid breed that combines the advantages of Macheng black goats, such as good reproductive performance, strong adaptability, and resistance to rough feeding, with the superior growth and meat characteristics of Boer goats. Given the substantial economic importance of growth (such as birth weight, body height, body length, and chest circumference across different growth stages) and reproductive traits (particularly average litter size after first parity), the aim of this study was to identify significant SNPs and candidate genes associated with these traits in Chubao black-head goats. Through whole-genome sequencing (with 34 goats at approximately 15× coverage and 466 goats at approximately 1× coverage), genotype imputation, and quality control, 22,665,331 SNPs were identified and subsequently used for genetic analyses. Heritability estimates indicated that growth traits exhibit moderate to high heritability (ranging from 0.297 ± 0.071 to 0.535 ± 0.118), while reproductive traits demonstrated low to moderate heritability (with a value of 0.220 ± 0.108). By performing FarmCPU-based genome-wide association studies, we identified 48 potentially significant SNPs associated with growth traits and 7 with reproductive traits. Additionally, 85 candidate genes (such as COL14A1, ZNF148, and TTC39C) linked to growth traits were identified and enriched in pathways associated with fundamental molecular biological activities such as protein deubiquitination, regulation of mRNA stability, and the MAPK signaling pathway. Furthermore, 10 candidate genes (such as SOHLH2, CCNA2, and SOX7) associated with reproductive traits were identified and enriched in pathways related to specific reproductive processes such as oocyte differentiation, endoderm formation, and progesterone-mediated oocyte maturation. Overall, these findings provide valuable preliminary insights into the molecular mechanisms underlying growth and reproductive traits in Chubao black-head goats. However, further functional validation is needed to effectively use these potential SNPs and candidate genes in improving the breeding of these traits in this breed.

Comparative Analysis of Two NGS-Based Platforms for Product-of-Conception Karyotyping.

Cytogenetic information about the product of conception (POC) is important to determine the presence of recurrent chromosomal abnormalities that are an indication for preimplantation genetic testing for aneuploidy or structural rearrangements. Although microscopic examination by G-staining has long been used for such an evaluation, detection failures are relatively common with this method, due to cell-culture-related issues. The utility of low-coverage whole-genome sequencing (lcWGS) using short-read next-generation sequencing (NGS) has been highlighted recently as an alternative cytogenomic approach for POC analysis. We, here, performed comparative analysis of two NGS-based protocols for this purpose based on different short-read sequencers (the Illumina VeriSeq system using a MiSeq sequencer and the Thermo Fisher ReproSeq system using an Ion S5 sequencer). The cytogenomic diagnosis obtained with each NGS method was equivalent in each of 20 POC samples analyzed. Notably, X chromosome sequence reads were reduced in some female samples with both systems. The possibility of low-level mosaicism for monosomy X as an explanation for this was excluded by FISH analysis. Additional data from samples with various degrees of X chromosome aneuploidy suggested that it was a technical artifact related to X chromosome inactivation. Indeed, subsequent nanopore sequencing indicated that the DNA in the samples showing the artifact was predominantly unmethylated. Our current findings indicate that although X chromosome data must be interpreted with caution, both the systems we tested for NGS-based lcWGS are useful alternatives for the karyotyping of POC samples.

Optimizing Strategy for Whole-Genome Genotype Imputation in Scallops

Advancements in sequencing technologies have facilitated low-coverage whole-genome sequencing (lcWGS) for detecting millions of single nucleotide polymorphisms (SNPs) in large populations at low cost. The effectiveness of this approach relies on genotype imputation following lcWGS. While several imputation methods have been applied in human and other terrestrial animals, their performance in aquaculture organisms, particularly mollusks, remains unassessed. Mollusks contribute 70% of mariculture production in China and are characterized by high genetic polymorphism. In this study, we aimed to optimize reference panel construction and evaluate imputation strategies for SNP detection in two scallops, Chlamys farreri and Patinopecten yessoensis, both important bivalve species. High-coverage whole-genome sequencing (hcWGS) data were used to construct haplotype reference panels, and three imputation methods were applied: Beagle5.4, GLIMPSE2, and QUILT. QUILT outperformed other methods, achieving imputation quality scores (IQS) exceeding 0.930 in C. farreri and 0.946 in P. yessoensis, and yielded the highest number of imputed loci in both species. Furthermore, panel construction was optimized by adjusting for genotype quality (GQ) and minor allele frequency (MAF); we determined that a GQ threshold > 20 and an MAF threshold > 0.01 were optimal. We observed that imputation quality improved with increased sequencing depth, plateauing at 0.5×, and varied with sample size depending on the imputation method. A negative correlation was observed between imputation quality and the ratio of non-synonymous to synonymous substitution rates (Ka/Ks) across chromosomal segments, highlighting the influence of selection pressure on imputation efficacy. Collectively, our findings provide an optimized framework for SNP genotyping using lcWGS in scallops, with implications for aquaculture breeding and genetic research.

Early detection of uterine corpus endometrial carcinoma utilizing plasma cfDNA fragmentomics

BackgroundUterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignancy with a favorable prognosis if detected early. However, there is a lack of accurate and reliable early detection tests for UCEC. This study aims to develop a precise and non-invasive diagnostic method for UCEC using circulating cell-free DNA (cfDNA) fragmentomics.MethodsPeripheral blood samples were collected from all participants, and cfDNA was extracted for analysis. Low-coverage whole-genome sequencing was performed to obtain cfDNA fragmentomics data. A robust machine learning model was developed using these features to differentiate between UCEC and healthy conditions.ResultsThe cfDNA fragmentomics-based model showed high predictive power for UCEC detection in training (n = 133; AUC 0.991) and validation cohorts (n = 89; AUC 0.994). The model manifested a specificity of 95.5% and a sensitivity of 98.5% in the training cohort, and a specificity of 95.5% and a sensitivity of 97.8% in the validation cohort. Physiological variables and preanalytical procedures had no significant impact on the classifier’s outcomes. In terms of clinical benefit, our model would identify 99% of Chinese UCEC patients at stage I, compared to 21% under standard care, potentially raising the 5-year survival rate from 84 to 95%.ConclusionThis study presents a novel approach for the early detection of UCEC using cfDNA fragmentomics and machine learning showing promising sensitivity and specificity. Using this model in clinical practice could significantly improve UCEC management and control, enabling early intervention and better patient outcomes. Further optimization and validation of this approach are warranted to establish its clinical utility.

Imputation strategies for low-coverage whole-genome sequencing data and their effects on genomic prediction and genome-wide association studies in pigs

The uncertainty resulting from missing genotypes in low-coverage whole-genome sequencing (LCWGS) data complicates genotype imputation. The aim of this study is to find out an optimal strategy for accurately imputing LCWGS data and assess its effectiveness for genomic prediction (GP) and genome-wide association study (GWAS) on economically important traits of Large White pigs. The LCWGS data of 1 423 Large White pigs were imputed using three different strategies: (1) using the high-coverage whole-genome sequencing (HCWGS) of 30 key progenitors as the reference panel (Ref_LG); (2) mixing HCWGS of key progenitors with LCWGS (Mix_HLG) and (3) self-imputation in LCWGS (Within_LG). Additionally, to compare the imputation effects of LCWGS, we also imputed SNP chip data of 1 423 Large White pigs to the whole-genome sequencing level using the reference panel consisting of key progenitors (Ref_SNP). To evaluate effects of the imputed sequencing data, we compared the accuracies of GP and statistical power of GWAS for four reproductive traits based on the chip data, sequencing data imputed from chip data and LCWGS data using an optimal strategy. The average imputation accuracies of the Within_LG, Ref_LG and Mix_HLG were 0.9893, 0.9899 and 0.9875, respectively, which were higher than that of the Ref_SNP (0.8522). Using the imputed sequencing data from LCWGS with the Ref_LG imputation strategy, the accuracies of GP for four traits improved by approximately 0.31–1.04% compared to the chip data, and by 0.7–1.05% compared to the imputed sequencing data from chip data. Furthermore, by using the sequence data imputed from LCWGS with the Ref_LG, 18 candidate genes were identified to be associated with the four reproductive traits of interest in Large White pigs: total number of piglets born - EPC2, MBD5, ORC4 and ACVR2A; number of piglets born healthy - IKBKE; total litter weight of piglets born alive - HSPA13 and CPA1; gestation length - GTF2H5, ITGAV, NFE2L2, CALCRL, ITGA4, STAT1, HOXD10, MSTN, COL5A2 and STAT4. With the exception of EPC2, ORC4, ACVR2A and MSTN, others represent novel candidates. Our findings can provide a reference for the application of LCWGS data in livestock and poultry.

Accurate genotype imputation from low-coverage whole-genome sequencing data of rainbow trout.

With the rapid and significant cost reduction of next-generation sequencing, low-coverage whole-genome sequencing (lcWGS), followed by genotype imputation, is becoming a cost-effective alternative to single-nucleotide polymorphism (SNP)-array genotyping. The objectives of this study were 2-fold: (1) construct a haplotype reference panel for genotype imputation from lcWGS data in rainbow trout (Oncorhynchus mykiss); and (2) evaluate the concordance between imputed genotypes and SNP-array genotypes in 2 breeding populations. Medium-coverage (12×) whole-genome sequences were obtained from a total of 410 fish representing 5 breeding populations with various spawning dates. The short-read sequences were mapped to the rainbow trout reference genome, and genetic variants were identified using GATK. After data filtering, 20,434,612 biallelic SNPs were retained. The reference panel was phased with SHAPEIT5 and was used as a reference to impute genotypes from lcWGS data employing GLIMPSE2. A total of 90 fish from the Troutlodge November breeding population were sequenced with an average coverage of 1.3×, and these fish were also genotyped with the Axiom 57K rainbow trout SNP array. The concordance between array-based genotypes and imputed genotypes was 99.1%. After downsampling the coverage to 0.5×, 0.2×, and 0.1×, the concordance between array-based genotypes and imputed genotypes was 98.7, 97.8, and 96.7%, respectively. In the USDA odd-year breeding population, the concordance between array-based genotypes and imputed genotypes was 97.8% for 109 fish downsampled to 0.5× coverage. Therefore, the reference haplotype panel reported in this study can be used to accurately impute genotypes from lcWGS data in rainbow trout breeding populations.

Skimming the skaters: genome skimming improves phylogenetic resolution of Halobatinae (Hemiptera: Gerridae)

Abstract Gerromorpha Popov, 1971 is a fascinating and diverse insect lineage that evolved about 200 Mya to spend their entire life cycle on the air–water interface and have since colonized all types of aquatic habitats. The subfamily Halobatinae Bianchi, 1896 is particularly interesting because some species have adapted to life on the open ocean—a habitat where insects are very rarely found. Several attempts have been made to reconstruct the phylogenetic hypotheses of this subfamily, but the use of a few partial gene sequences recovered only a handful of well-supported relationships, thus limiting evolutionary inferences. Fortunately, the emergence of high-throughput sequencing technologies has enabled the recovery of more genetic markers for phylogenetic inference. We applied genome skimming to obtain mitochondrial and nuclear genes from low-coverage whole-genome sequencing of 85 specimens for reconstructing a well-supported phylogeny, with particular emphasis on Halobatinae. Our study confirmed that Metrocorini Matsuda, 1960, is paraphyletic, whereas Esakia Lundblad, 1933, and Ventidius Distant, 1910, are more closely related to Halobatini Bianchi, 1896, than Metrocoris Mayr, 1865, and Eurymetra Esaki, 1926. We also found that Ventidius is paraphyletic and in need of a taxonomic revision. Ancestral state reconstruction suggests that Halobatinae evolved progressively from limnic to coastal habitats, eventually attaining a marine lifestyle, especially in the genus Halobates Eschscholtz, 1822, where the oceanic lifestyle evolved thrice. Our results demonstrate that genome skimming is a powerful and straightforward approach to recover genetic loci for robust phylogenetic analysis in non-model insects.

Genome-wide association study for age-related hearing loss in CFW mice.

Age-related hearing impairment is the most common cause of hearing loss and is one of the most prevalent conditions affecting the elderly globally. It is influenced by a combination of environmental and genetic factors. The mouse and human inner ears are functionally and genetically homologous. Investigating the genetic basis of age-related hearing loss (ARHL) in an outbred mouse model may lead to a better understanding of the molecular mechanisms of this condition. We used Carworth Farms White (CFW) outbred mice, because they are genetically diverse and exhibit variation in the onset and severity of ARHL. The goal of this study was to identify genetic loci involved in regulating ARHL. Hearing at a range of frequencies was measured using Auditory Brainstem Response (ABR) thresholds in 946 male and female CFW mice at the age of 1, 6, and 10 months. We obtained genotypes at 4.18 million single nucleotide polymorphisms (SNP) using low-coverage (mean coverage 0.27x) whole-genome sequencing followed by imputation using STITCH. To determine the accuracy of the genotypes we sequenced 8 samples at >30x coverage and used calls from those samples to estimate the discordance rate, which was 0.45%. We performed genetic analysis for the ABR thresholds for each frequency at each age, and for the time of onset of deafness for each frequency. The SNP heritability ranged from 0 to 42% for different traits. Genome-wide association analysis identified several regions associated with ARHL that contained potential candidate genes, including Dnah11, Rapgef5, Cpne4, Prkag2, and Nek11. We confirmed, using functional study, that Prkag2 deficiency causes age-related hearing loss at high frequency in mice; this makes Prkag2 a candidate gene for further studies. This work helps to identify genetic risk factors for ARHL and to define novel therapeutic targets for the treatment and prevention of ARHL.

Cost-effective genomic prediction of critical economic traits in sturgeons through low-coverage sequencing

Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (∼13.68×). The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.

Longitudinal cell-free DNA characterization by low-coverage whole-genome sequencing in patients undergoing high-dose radiotherapy

Background and PurposeCurrent radiotherapy guidelines rely heavily on imaging-based monitoring. Liquid biopsy monitoring promises to complement imaging by providing frequent systemic information about the tumor. In particular, cell-free DNA (cfDNA) sequencing offers a tumor-agnostic approach, which lends itself to monitoring heterogeneous cohorts of cancer patients. MethodsWe collected plasma cfDNA from oligometastatic patients (OMD) and head-and-neck cancer patients (SCCHN) at six time points before, during, and after radiotherapy, and compared them to the plasma samples of healthy and polymetastatic volunteers. We performed low-pass (on average 7x) whole-genome sequencing on 93 plasma cfDNA samples and correlated copy number alterations and fragment length distributions to clinical and imaging findings. ResultsWe observed copy number alterations in 4/7 polymetastatic cancer patients, 1/7 OMD and 1/7 SCCHN patients, these patients’ imaging showed progression following radiotherapy. Using unsupervised learning, we identified cancer-specific fragment length features that showed a strong correlation with copy number-based tumor fraction estimates. In 4/4 HPV-positive SCCHN patient samples, we detected viral DNA that enabled the monitoring of very low tumor fraction samples. ConclusionsOur results indicate that an elevated tumor fraction is associated with tumor aggressiveness and systemic tumor spread. This information may be used to adapt treatment strategies. Further, we show that by detecting specific sequences such as viral DNA, the sensitivity of detecting cancer from cell-free DNA sequencing data can be greatly increased.

Non-invasive colorectal cancer detection using multimodal deep learning ensemble classifier.

3066 Background: The demand for alternative, non-invasive methods for colorectal cancer (CRC) screening is substantial. Cell-free DNA (cfDNA) whole genome sequencing (WGS) offers a promising avenue, utilizing diverse fragmentomic data. We aimed to develop a new approach: integrating fragment end motif by size (FEMS) with genomic coverage (COV) of cfDNA to enhance CRC screening. Methods: Participants were comprised of 1,506 colonoscopy verified normal samples, 130 advanced adenoma (AA) patients, 302 CRC patients (stage I: 28.5%, stage II: 25.5%, stage III: 31.1%, stage IV: 14.2%, unknown: 0.7%). We generated low depth cfDNA-WGS data (a minimum 40 million reads per sample) using the Novaseq 6000 sequencer. We minimized batch bias by normalizing with the same samples for each experimental batch. The development set comprised 1,332 samples (1,023 normal, 103 AA, and 206 CRC), while the validation set consisted of 606 samples (483 normal, 27 AA, and 96 CRC). Results: Our new algorithm achieved 84.0% sensitivity (95% CI: 81%-86%) for CRC and 69.9% (95% CI: 66%-81%) for AA in the development set, with a specificity of 90.0%. These findings were consistent in the external set with 84.9% specificity (95% CI: 82%-88%) and sensitivities of 80.2% (95% CI: 72%-88%) and 63.0% (95% CI: 44%-82%) for CRC and AA, respectively. The sensitivities across stages (Stage I: 80%, Stage II: 86.8%, Stage III: 78.8%, Stage IV: 96.7%) were similarly reflected in the validation sets (Stage I: 74%, Stage II: 83%, Stage III: 79%, Stage IV: 92%). Conclusions: Our multimodal deep learning model shows promise for accurate non-invasive colon cancer and AA detection using low-coverage cfDNA WGS. Its strong performance in detecting not only CRC but also AA lesions suggests its potential for early intervention and improved patient outcomes. The use of colonoscopy-verified normal samples strengthens the study's credibility and paves the way for future clinical translation.

Analysis of cell free DNA to predict outcome to bevacizumab therapy in colorectal cancer patients

To predict outcome to combination bevacizumab (BVZ) therapy, we employed cell-free DNA (cfDNA) to determine chromosomal instability (CIN), nucleosome footprints (NF) and methylation profiles in metastatic colorectal cancer (mCRC) patients. Low-coverage whole-genome sequencing (LC-WGS) was performed on matched tumor and plasma samples, collected from 74 mCRC patients from the AC-ANGIOPREDICT Phase II trial (NCT01822444), and analysed for CIN and NFs. A validation cohort of plasma samples from the University Medical Center Mannheim (UMM) was similarly profiled. 61 AC-ANGIOPREDICT plasma samples collected before and following BVZ treatment were selected for targeted methylation sequencing. Using cfDNA CIN profiles, AC-ANGIOPREDICT samples were subtyped with 92.3% accuracy into low and high CIN clusters, with good concordance observed between matched plasma and tumor. Improved survival was observed in CIN-high patients. Plasma-based CIN clustering was validated in the UMM cohort. Methylation profiling identified differences in CIN-low vs. CIN high (AUC = 0.87). Moreover, significant methylation score decreases following BVZ was associated with improved outcome (p = 0.013). Analysis of CIN, NFs and methylation profiles from cfDNA in plasma samples facilitates stratification into CIN clusters which inform patient response to treatment.

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.

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.