Evaluation of genotype imputation using Glimpse tools on low coverage ancient DNA.

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.

Over the past decade, the continuous development of ancient genomic technology and research has significantly advanced our understanding of human history. Since 2017, large-scale studies of ancient human genomes in East Asia, particularly in China, have emerged, resulting in a wealth of ancient genomic data from various time periods and locations, which has provided new insights into the genetic history of East Asian populations over tens of thousands of years. Especially since 2022, there emerged a series of new research progresses in the genetic histories of the northern and southern Chinese populations within the past 10,000 years. However, there is currently no systematic review focused on these recent ancient genomic studies in East Asia. Therefore, this article emphasizes the study of ancient human genomes in China and systematically reviews the genetic patterns and migration history of populations in East Asia since the Late Paleolithic. Existing research indicates that by at least 19,000 years ago, there was a north-south differentiation among ancient East Asian populations, leading to different genetic lineages divided by the Qinling-Huaihe line. Gene flow and interactions between northern and southern East Asians began in the Early Neolithic and were further strengthened from the Mid-Neolithic. By the historical period, northern East Asian ancestry played a profound role in the genetic components of southern populations, shaping the genetic structure of present-day Chinese populations. Throughout this process, ancient populations in northern and southern China also engaged in extensive interactions through coastal and inland routes with populations from surrounding regions, including Siberia, Japan, Korea, Southeast Asia, and Pacific islands, playing a crucial role in the formation of different linguistic groups. These studies have charted the evolutionary and interaction history of East Asian populations over tens of thousands of years; yet, many unresolved mysteries remain. Further exploration is needed through ancient genomic data from additional time periods and broader geographic areas to facilitate a more comprehensive and detailed investigation, thereby advancing related scientific questions.

Genetic information discovered, characterized for and used in forensic case-works and anthropology has shown to be also highly useful and relevant in investigating human remains from archaeological findings. By technical means, forensic and aDNA (ancient Deoxyribonucleic acid) analyses are well suited to be done using the same laboratory infrastructures and scientific expertise referring to sampling, sample protection, sample processing, contamination control as well as requiring analogous technical know how and knowledge on reading and interpreting DNA encoded information. Forensic genetics has significantly profited from aDNA-related developments (and vice versa, of course!), especially, when it comes to the identification of unknown human remains referring to the detection limit. Additionally the tremendous developments of analyzing chemistry and kits as well as instruments in forensics opened the whole panel of reading human and nonhuman DNA for historians and archaeologists but also for anthropologists. Ancient DNA / molecular archaeology, however, is not limited to the comparatively restrictive set of information as usually employed in forensic case work analyses but can also be applied to phenotypical markers, ethno-related genotypes or pathological features.In this review the authors give a general overview on the field of ancient DNA analysis focussing of the potentials and limits, fields of application, requirements for samples, laboratory setup, reaction design and equipment as well as a brief outlook on current developments, future perspectives and potential cross links with associated scientific disciplines.Key words: Human DNA, Ancient DNA, Forensic DNA typing, Molecular archaeology, Application.

The methods presented here seek to maximize the chances for the recovery of human DNA from ancient archaeological remains while limiting input sample material. This was done by targeting anatomical sampling locations previously determined to yield the highest amounts of ancient DNA (aDNA) in a comparative analysis of DNA recovery across the skeleton. Prior research has suggested that these protocols maximize the chances for the successful recovery of ancient human and pathogen DNA from archaeological remains. DNA yields were previously assessed by Parker et al. 2020 in a broad survey of aDNA preservation across multiple skeletal elements from 11 individuals recovered from the medieval (radiocarbon dated to a period of circa (ca.) 1040-1400 CE, calibrated 2-sigma range) graveyard at Krakauer Berg, an abandoned medieval settlement near Peißen Germany. These eight sampling spots, which span five skeletal elements (pars petrosa, permanent molars, thoracic vertebra, distal phalanx, and talus) successfully yielded high-quality ancient human DNA, where yields were significantly greater than the overall average across all elements and individuals. Yields were adequate for use in most common downstream population genetic analyses. Our results support the preferential use of these anatomical sampling locations for most studies involving the analyses of ancient human DNA from archaeological remains. Implementation of these methods will help to minimize the destruction of precious archaeological specimens.

Pangenome-based genotyping of structurally complex loci remains challenging at low sequencing depths, particularly when samples are of low quality, such as in ancient DNA. We present COSIGT (COsine SImilarity-based GenoTyper), a method that infers structural genotypes by matching short-read coverage patterns to pangenome haplotypes using cosine similarity. COSIGT maintains robust accuracy at low coverage (1-2X), outperforming existing methods where depth-sensitive approaches degrade. We demonstrate scalability to thousands of modern and ancient genomes, enabling population-scale analyses of complex variation from low-coverage data.

Svante Pääbo: pushing ancient DNA to the limit

Insights from the Analysis of Ancient and Modern DNA with Population-Specific Single-Nucleotide Polymorphisms

Multi-ethnic Imputation System (MI-System): A genotype imputation server for high-dimensional data

Projection analysis is a tool that extracts information from the joint allele frequency spectrum to better understand the relationship between two populations. In projection analysis, a test genome is compared to a set of genomes from a reference population. The projection’s shape depends on the historical relationship of the test genome’s population to the reference population. Here, we explore in greater depth the effects on the projection when ancient samples are included in the analysis. First, we conduct a series of simulations in which the ancient sample is directly ancestral to a present-day population (one-population model), or the ancient sample is ancestral to a sister population that diverged before the time of sampling (two-population model). We find that there are characteristic differences between the projections for the one-population and two-population models, which indicate that the projection can be used to determine whether a test genome is directly ancestral to a present-day population or not. Second, we compute projections for several published ancient genomes. We compare two Neanderthals and three ancient human genomes to European, Han Chinese and Yoruba reference panels. We use a previously constructed demographic model and insert these five ancient genomes to assess how well the observed projections are recovered.

BackgroundThe formation of the Han Chinese is deeply rooted in the Neolithic cultures of the Yellow River basin, particularly the pivotal Longshan cultural sphere which bridged prehistoric societies and early dynastic civilization. However, the genetic impact of Longshan-era populations on subsequent historical groups remains largely unexplored due to a critical lack of ancient genomic data from this key transitional period. This gap hinders a clear understanding of how early cultural integration in the heartland shaped the genetic structure of later Chinese populations.ResultsThis study reports 28 newly sequenced ancient human genomes from the Han Dynasty Xujiacundong and Zhouhe archaeological sites in Shandong Province, which are integrated with previously published regional datasets to investigate the genetic legacy of Neolithic Longshan populations in the formation of Han Chinese ancestry. Our analyses reveal pronounced genetic differentiation between Longshan populations from the Central Plain and lower Yellow River basin during the Late Neolithic period. Most individuals from the Xujiacundong site exhibit mixed ancestry, predominantly derived from Central Plain Longshan-related ancestry (93.8%) with a minor contribution from southeastern coastal China-related ancestry (6.2%). In contrast, all individuals from the Zhouhe site exhibit genetic homogeneity with Central Plain Longshan-related ancestry. These results indicate substantial genetic heterogeneity within the lower Yellow River basin during the Han Dynasty. Moreover, we found a high degree of genetic homogeneity between ancient Han Dynasty populations and modern Han Chinese from Shandong. Admixture modeling and f-statistics further demonstrate that Longshan-related ancestries—particularly those associated with the Central Plain—played a dominant role in shaping the genetic structure of historical populations across a wide geographic range, including the Upper Yellow River, the West Liao River Basin, and Southwest China, etc.ConclusionsThese findings underscore the profound and pervasive genetic influence of the Central Plain Longshan populations on surrounding regions, driving the demographic expansion and genetic homogenization of the Han Chinese. This interplay of population movements and cultural diffusion highlights the central role of Longshan-era demic expansion in shaping the genetic landscape and cohesion of the Han people.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12915-026-02541-7.

Cave burial is a funerary practice believed to be associated with modern Kra-Dai (KD) and Hmong-Mien (HM) speakers for thousands of years. However, the extent to which these ancient cave burial practitioners contributed to the formation of modern ethnic minority groups remains poorly understood due to the limited ancient genomic data. We generated 14 newly sequenced ancient human genomes from cave burial sites in Guangxi. The findings reveal continuous gene flow from northern lineages into ancient cave burial populations, shaping their genetic profiles over time. We observed a significant genetic distinction in HM populations: Southeast Asian HM groups derive 74.8% to 100% of their ancestry from cave burials, preserving a robust ancient southern genetic signature, while Chinese HM populations exhibit only 11.1% to 37.2% ancient cave burial ancestry, but heavily admixed with Yellow River-related populations (14.7% to 52.1%), reflecting differential historical interactions with northern migrants. In contrast, most KD speakers maintain tight genetic clustering with Guangxi ancestors (28.5% to 100% contribution from cave burials). The HM formation involved admixture between ancient cave burials, northern farmers, and local KD-related groups, which is evident in the genetic cline of She and Miao populations.

Human-associated metagenomic data often contain human nucleic acid information, which can affect the accuracy of microbial classification or raise ethical concerns. These reads are typically removed through alignment to the human genome using various metagenomic mapping tools or human reference genomes, followed by filtration before metagenomic analysis. In this study, we conducted a comprehensive analysis to identify the optimal combination of alignment software and human reference genomes using benchmarking data. Our findings show that the combination of bwa-mem and the telomere-to-telomere human genome (CHM13-T2T) is the most effective in removing human reads in simulated data. We also analyzed CHM13-T2T-derived sequences in RefSeq to understand how CHM13-T2T reduces false positive results. Finally, we assessed clinical samples and found that CHM13-T2T effectively reduces host-derived contamination, particularly in low microbial biomass samples. This study provides a thorough overview of the application of CHM13-T2T in metagenomic analysis and highlights its significance in improving microbial classification accuracy.IMPORTANCEHuman gene sequences account for a large proportion of metagenomic sequences. To gain accurate and precise microbiome information, effective host-derived contamination removal methods are required. Both the alignment algorithm and the reference genome could influence the effectiveness of this process. The telomere-to-telomere human genome (CHM13-T2T) is a state-of-the-art human genome with 216 Mbp of additional new sequences compared with the commonly used GRCh38.p14. Our findings show the optimal dehosting effect of CHM13-T2T combined with the bwa-mem software in metagenomic analysis. We also investigate the reasons for the superiority of CHM13-T2T. Our study provides insights into optimal strategies for host sequence removal from metagenomic data. A standard reference is proposed for future metagenomic analysis, which can improve the accuracy of microbial identification.

Inexpensive genotyping methods are essential for genetic studies requiring large sample sizes. In human studies, array-based microarrays and high-density haplotype reference panels allow efficient genotype imputation for this purpose. However, these resources are typically unavailable in non-human settings. Here we describe a method (STITCH) for imputation based only on sequencing read data, without requiring additional reference panels or array data. We demonstrate its applicability even in settings of extremely low sequencing coverage, by accurately imputing 5.7 million SNPs at a mean r2 of 0.98 in 2,073 outbred laboratory mice (0.15X sequencing coverage). In a sample of 11,670 Han Chinese (1.7X), we achieve accuracy similar to alternative approaches that require a reference panel, demonstrating that this approach can work for genetically diverse populations. Our method enables straightforward progression from low-coverage sequence to imputed genotypes, overcoming barriers that at present restrict the application of genome-wide association study technology outside humans.

Pathogen genomic data analysis can be extremely bespoke and diverse. This paper presents our plan and progress towards creating a Scalable Pathogen Pipeline Platform (SP 3 ) providing an efficient and unified process of collecting, analysing and comparing genomic data analysis with the benefit of elastic cloud computing. SP 3 enables container-centric bioinformatic workflows run on personal computers, High-performance computing (HPC) clusters and cloud platforms. We have deployed and tested SP 3 on local HPC, Google Cloud Platform (GCP), Microsoft Azure and OpenStack Platforms. SP 3 allows users to fetch genomic sequencing data from European Nucleotide Archive (ENA) and conduct analysis with open-source bioinformatic pipelines. We believe SP 3 will promote common standards around pathogen genomic data quality, data processing and data analysis, helping answer the challenges of tools divergence and leveraging a pool of public genomic data repository and cloud resources.

Proof of authenticity is the greatest challenge in palaeogenetic research, and many safeguards have become standard routine in laboratories specialized on ancient DNA research. Here we describe an as-yet unknown source of artifacts that will require special attention in the future. We show that ancient DNA extracts on their own can have an inhibitory and mutagenic effect under PCR. We have spiked PCR reactions including known human test DNA with 14 selected ancient DNA extracts from human and nonhuman sources. We find that the ancient DNA extracts inhibit the amplification of large fragments to different degrees, suggesting that the usual control against contaminations, i.e., the absence of long amplifiable fragments, is not sufficient. But even more important, we find that the extracts induce mutations in a nonrandom fashion. We have amplified a 148-bp stretch of the mitochondrial HVRI from contemporary human template DNA in spiked PCR reactions. Subsequent analysis of 547 sequences from cloned amplicons revealed that the vast majority (76.97%) differed from the correct sequence by single nucleotide substitutions and/or indels. In total, 34 positions of a 103-bp alignment are affected, and most mutations occur repeatedly in independent PCR amplifications. Several of the induced mutations occur at positions that have previously been detected in studies of ancient hominid sequences, including the Neandertal sequences. Our data imply that PCR-induced mutations are likely to be an intrinsic and general problem of PCR amplifications of ancient templates. Therefore, ancient DNA sequences should be considered with caution, at least as long as the molecular basis for the extract-induced mutations is not understood.