Population-scalable genotyping from low-coverage sequencing data using pangenome graphs

Ancient DNA provides a unique frame for directly studying human population genetics in time and space. Still, since most of the ancient genomic data is low coverage, analysis is confronted with a low number of SNPs, genotype uncertainties, and reference-bias. Here, we for the first time benchmark the two distinct versions of Glimpse tools on 120 ancient human genomes from Eurasia including those largely from previously under-evaluated regions and compare the performance of genotype imputation with de facto analysis approaches for low coverage genomic data analysis. We further investigate the impact of two distinct reference panels on imputation accuracy for low coverage genomic data. We compute accuracy statistics and perform PCA and f4-statistics to explore the behaviour of genotype imputation on low coverage data regarding (i)two versions of Glimpse, (ii)two reference panels, (iii)four post-imputation filters and coverages, as well as (iv)data type and geographical origin of the samples on the analyses. Our results reveal that even for 0.1X coverage ancient human genomes, genotype imputation using Glimpse-v2 is suitable. Additionally, using the 1000 Genomes merged with Human Genome Diversity Panel improves the accuracy of imputation for the rare variants with low MAF, which might be important not only for ancient genomics but also for modern human genomic studies based on low coverage data and for haplotype-based analysis. Most importantly, we reveal that genotype imputation of low coverage ancient human genomes reduces the genetic affinity of the samples towards human reference genome. Through solving one of the most challenging biases in data analysis, so-called reference bias, genotype imputation using Glimpse v2 is promising for low coverage ancient human genomic data analysis and for rare-variant-based and haplotype-based analysis.

Many species on Earth can carry up to two different versions of a given gene, with each of these ‘alleles’ having only a 50/50 chance of being transmitted to the next generation via sexual reproduction. Certain ‘selfish’ sequences, however, can hijack this process and increase their probability of being passed on to an offspring. Known as transmission distortion, this phenomenon may result in alleles spreading through the population even if they are detrimental to fertility. Transmission distortion has been detected in many species such as flies, mice and some plants. It can take place at various stages during reproduction; for example, the selfish alleles may become overrepresented among eggs or sperm. However, scientists need to study a large number of offspring or reproductive cells to be able to detect whether an allele is inherited more often than expected. This has made it difficult to determine whether transmission distortion also happens in humans, and research so far has resulted in conflicting conclusions. A recently published dataset of human sperm from 25 donors offered Carioscia, Weaver et al. the opportunity to examine this question. Every volunteer had produced between 969 and 3377 sperm cells, each with about 1% of their genome sequenced. Carioscia, Weaver et al. developed a computational method, which they named rhapsodi, that allowed them to ‘fill in the gaps’ and infer missing regions of the genome for each cell. To do so, they relied on the fact that sperm cells from a given individual are highly related to one another. With this more complete data at hand, it became possible to look for evidence of transmission distortion by searching for alleles that were overrepresented in sperm from a given donor. No selfish sequence could be detected in any of the 25 individuals, suggesting that human sperm may not be subject to pervasive transmission distortion. Signatures of selfish alleles detected in previous human studies may have therefore not resulted from this mechanism taking place at the sperm level. Instead, transmission distortion in humans could primarily target eggs or happen at later stages (for instance, if embryos carrying the selfish allele have better chances of survival). The ‘rhapsodi’ method developed by Carioscia, Weaver et al. should allow other scientists to work with datasets for which large portions of the genetic information is missing. It may therefore become easier for researchers to track selfish alleles which are difficult to detect, and to examine bigger, more diverse samples which also include individuals with known fertility challenges.

The emergence of single-cell RNA-seq (scRNA-seq) technology has made it possible to measure gene expression variations at cellular level. This breakthrough enables the investigation of a wider range of problems including analysis of splicing heterogeneity among individual cells. However, compared to bulk RNA-seq, scRNA-seq data are much noisier due to high technical variability and low sequencing depth. Here we propose SCATS (Single-Cell Analysis of Transcript Splicing) for differential splicing analysis in scRNA-seq, which achieves high sensitivity at low coverage by accounting for technical noise. SCATS models scRNA-seq data either with or without Unique Molecular Identifiers (UMIs). For non-UMI data, SCATS explicitly models technical noise by accounting for capture efficiency and amplification bias through the use of external spike-ins; for UMI data, SCATS models capture efficiency and further accounts for transcriptional burstiness. A key aspect of SCATS lies in its ability to group “exons” that originate from the same isoform(s). Grouping exons is essential in splicing analysis of scRNA-seq data as it naturally aggregates spliced reads across different exons, making it possible to detect splicing events even when sequencing depth is low. To evaluate the performance of SCATS, we analyzed both simulated and real scRNA-seq datasets and compared with existing methods including Census and DEXSeq. We show that SCATS has well controlled type I error rate, and is more powerful than existing methods, especially when splicing difference is small. In contrast, Census suffers from severe type I error inflation, whereas DEXSeq is more conservative. When applied to mouse brain scRNA-seq datasets, SCATS identified more differential splicing events with subtle difference across cell types compared to Census and DEXSeq. With the increasing adoption of scRNA-seq, we believe SCATS will be well-suited for various splicing studies. The implementation of SCATS can be downloaded from https://github.com/huyustats/SCATS.

Background: Multi-Cancer Early Detection (MCED) represents a promising approach with the potential for high sensitivity in detecting rare tumor-derived signals in the blood. However, contamination from adjacent samples or from environmental sources poses significant challenges that can compromise the accuracy of these results. Current contamination detection methods primarily designed for tissue samples, leaving a notable gap in methodologies for assessing low-level contamination in cfDNA targeted methylation sequencing data in blood. The challenges include the absent of matched control samples, low sequencing depth, and probe capture bias. To address these limitations, we have developed a machine learning-based method aimed at improving contamination detection accuracy in MCED datasets. Methods: We first identified single nucleotide polymorphisms (SNPs) with high prevalence in the East-Asian population. We calculated the allele frequencies of SNPs using in-silico synthetic contaminated samples and fitted the distributions of allele frequencies at varying contamination levels to identify informative SNPs. To enhance signal-to-noise value, we constructed features based on reads containing alternative bases of these informative SNPs at each tier, which was categorized according to its coverage and assumed contamination levels. This comprehensive feature matrix was used to train an XGBoost algorithm on 185 normal samples and their corresponding in-silico synthetic contaminated samples. The performance of our approach was validated using two distinct datasets: the first set comprised in-silico simulated contamination samples with contamination levels ranging from 0.05% to 10%, while the second was built by titrating three healthy cfDNA samples into three other healthy cfDNA backgrounds, with titration levels ranging from 0.05% to 2%. Results: This method effectively identifies contamination, particularly in scenarios where coverage at SNPs is relatively low, ranging from 50X to 300X. It demonstrated the ability to detect contamination levels as low as 0.2% in the titrated samples, with an in-silico limit of detection (LoD) established at 0.1%. Conclusions: We have developed SCANs, a tool designed to address the challenges posed by low sequencing depth and capture bias-induced abnormal allele signals in methylation data. SCANs enables the detection of low-level contamination, thereby facilitating the precise analysis of cfDNA methylation data in MCED. Citation Format: Shaohua Ma, Chaoyang Liang, Shuliang Guo, Luyan Shen, XiaoSheng He, Zhihui Xu, Jing Zhao, Yonghui Li, Shiqing Chen, Baoliang Zhu, Xiaohui Wu. SCANs: SNP-based contamination assessment with nuanced signal in targeted methylation sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2493.

The release of 150,119 UK Biobank sequences represents an unprecedented opportunity as a reference panel to impute low-coverage whole-genome sequencing data with high accuracy but current methods cannot cope with the size of the data. Here we introduce GLIMPSE2, a low-coverage whole-genome sequencing imputation method that scales sublinearly in both the number of samples and markers, achieving efficient whole-genome imputation from the UK Biobank reference panel while retaining high accuracy for ancient and modern genomes, particularly at rare variants and for very low-coverage samples.

We here present two methods for inferring population structure and admixture proportions in low-depth next-generation sequencing (NGS) data. Inference of population structure is essential in both population genetics and association studies, and is often performed using principal component analysis (PCA) or clustering-based approaches. NGS methods provide large amounts of genetic data but are associated with statistical uncertainty, especially for low-depth sequencing data. Models can account for this uncertainty by working directly on genotype likelihoods of the unobserved genotypes. We propose a method for inferring population structure through PCA in an iterative heuristic approach of estimating individual allele frequencies, where we demonstrate improved accuracy in samples with low and variable sequencing depth for both simulated and real datasets. We also use the estimated individual allele frequencies in a fast non-negative matrix factorization method to estimate admixture proportions. Both methods have been implemented in the PCAngsd framework available at http://www.popgen.dk/software/.

Long-Read Whole-Transcriptome Sequencing and Selective Gene Panel Profiling Enable Sensitive Detection of Fusion Oncogenes in Pediatric B-Cell Acute Lymphoblastic Leukemia.

BackgroundExome sequencing is increasingly used to search for phenotypically-relevant sequence variants in the mouse genome. All of the current hybridization-based mouse exome capture systems are designed based on the genome reference sequences of the C57BL/6 J strain. Given that the substantial sequence divergence exists between C57BL/6 J and other distantly-related strains, the impact of sequence divergence on the efficiency of such capture systems needs to be systematically evaluated before they can be widely applied to the study of those strains.ResultsUsing the Agilent SureSelect mouse exome capture system, we performed exome sequencing on F1 generation hybrid mice that were derived by crossing two divergent strains, C57BL/6 J and SPRET/EiJ. Our results showed that the C57BL/6 J-based probes captured the sequences derived from C57BL/6 J alleles more efficiently and that the bias was higher for the target regions with greater sequence divergence. At low sequencing depths, the bias also affected the efficiency of variant detection. However, the effects became negligible when sufficient sequencing depth was achieved.ConclusionSufficient sequence depth needs to be planned to match the sequence divergence between C57BL/6 J and the strain to be studied, when the C57BL/6 J–based Agilent SureSelect exome capture system is to be used.

In recent years, there have been significant advances in various chromatin conformation capture techniques, and annotating the topological structure from Hi-C contact maps has become crucial for studying the three-dimensional structure of chromosomes. However, the structure and function of chromatin loops are highly dynamic and diverse, influenced by multiple factors. Therefore, obtaining the three-dimensional structure of the genome remains a challenging task. Among many chromatin loop prediction methods, it is difficult to fully extract features from the contact map and make accurate predictions at low sequencing depths. In this study, we put forward a deep learning framework based on the diffusion model called CD-Loop for predicting accurate chromatin loops. First, by pre-training the input data, we obtain prior probabilities for predicting the classification of the Hi-C contact map. Then, by combining the denoising process based on the diffusion model and the prior probability obtained by pre-training, candidate loops were predicted from the input Hi-C contact map. Finally, CD-Loop uses a density-based clustering algorithm to cluster the candidate chromatin loops and predict the final chromatin loops. We compared CD-Loop with the currently popular methods, such as Peakachu, Chromosight, and Mustache, and found that in different cell types, species, and sequencing depths, CD-Loop outperforms other methods in loop annotation. We conclude that CD-Loop can accurately predict chromatin loops and reveal cell-type specificity. The code is available at https://github.com/wangyang199897/CD-Loop.

Differentiating between Hyperdiploidy and Pseudo-Hyperdiploidy in B-Lymphoblastic Leukemia Utilizing Low-Coverage Mate-Pair Sequencing

Context The peopling of Europe by modern humans is a widely debated topic in the field of modern and ancient genomics. While several recent syntheses have focussed on this topic, little has been discussed about the genetic history of populations in the continent’s surrounding regions. Objective We explore genetic transformations in three key areas that played an essential role in the formation of the European genetic landscape through time, focussing on the periods spanning from the Epipalaeolithic/Mesolithic and up until the Iron Age. Methods We review published ancient genomic studies and integrate the associated data to provide a quantification and visualisation of major trends in the population histories of the Near East, the western Eurasian Steppe and North East Europe. Results We describe cross-regional as well as localised prehistoric demographic shifts and discuss potential research directions while highlighting geo-temporal gaps in the data. Conclusion In recent years, archaeogenetic studies have contributed to the understanding of human genetic diversity through time in regions located at the doorstep of Europe. Further studies focussing on these areas will allow for a better characterisation of genetic shifts and regionally-specific patterns of admixture across western Eurasia.

BackgroundEstimation of allele frequency is of fundamental importance in population genetic analyses and in association mapping. In most studies using next-generation sequencing, a cost effective approach is to use medium or low-coverage data (e.g., < 15X). However, SNP calling and allele frequency estimation in such studies is associated with substantial statistical uncertainty because of varying coverage and high error rates.ResultsWe evaluate a new maximum likelihood method for estimating allele frequencies in low and medium coverage next-generation sequencing data. The method is based on integrating over uncertainty in the data for each individual rather than first calling genotypes. This method can be applied to directly test for associations in case/control studies. We use simulations to compare the likelihood method to methods based on genotype calling, and show that the likelihood method outperforms the genotype calling methods in terms of: (1) accuracy of allele frequency estimation, (2) accuracy of the estimation of the distribution of allele frequencies across neutrally evolving sites, and (3) statistical power in association mapping studies. Using real re-sequencing data from 200 individuals obtained from an exon-capture experiment, we show that the patterns observed in the simulations are also found in real data.ConclusionsOverall, our results suggest that association mapping and estimation of allele frequencies should not be based on genotype calling in low to medium coverage data. Furthermore, if genotype calling methods are used, it is usually better not to filter genotypes based on the call confidence score.

Anchored-fusion enables targeted fusion search in bulk and single-cell RNA sequencing data

Several pipelines exist for analysis of single-cell or single-nucleus RNA-sequencing (sxRNA-seq) data in multiple programming languages. Although filtering and quality control (QC) of data as preprocessing steps are included in several analytical pipelines, visual assessment of filtered cells is often lacking. Here, we present a streamlined pipeline for filtering of sxRNA-seq data and visualisation of every filtering step using existing software packages. This includes methods to remove ambient RNA, doublets, cells with a high fraction of mitochondrial genes expressed, and cells with low sequencing depth that are difficult to cluster and annotate. All steps are accompanied by plotting functions to ease the selection of thresholds for these crucial steps in data preprocessing.

Existing targeted approaches either rely on per-gene barcoded probes and transcript tiling, which limit scalability, or forgo reverse transcription, precluding capture of highly variable transcripts. Here, we present targeted reverse transcription-linker (TRTL), a modular method that can be integrated into existing single-cell transcriptomic workflows to enable targeted readout of user-defined transcript panels, with minimal changes in protocol when scaling from tens to thousands of genes. Because TRTL retains reverse transcription, it also enables the capture of variable transcripts, such as TCR and BCR sequences. Applying TRTL and combinatorial indexing RNA-seq to the mouse brain, we show that carefully designed panels support robust alignment to existing reference atlases, enabling accurate cell type annotation and detection of cellular populations at low sequencing depths. Lastly, we combine TRTL with nuclear hashing-based multiplexing for a targeted-sci-Plex protocol and further demonstrate that targeted-sci-Plex can resolve dynamic T-cell fate trajectories following diverse activating exposures while concurrently profiling T-cell clonotypes.