Biology subjects

Zoellner, S.

Publications and source records attributed to Zoellner, S..

8 recordsLinked to original sources

bioRxiv2026

Exploring the role of local sequence context on patterns of germline mutation

Germline mutations occur at a non-uniform rate across the genome. While the influence of neighboring nucleotides is well known and accounted for in mutation models, the effective range of these flanking influences and the extent of their higher-order interactions remain insufficiently understood. Here, we leverage millions of singleton variants from the 1000 Genomes Project as a proxy for \textit{de novo} variants to systematically map the contextual influence of local nucleotide sequence context on germline mutation rates. We show that while nucleotides immediately adjacent to the mutation site exert the strongest effect, the influence of nucleotides at flanking positions does not decay monotonically with increased distance from the focal position. Notably, significant associations between flanking nucleotides and mutation status persist up to hundreds of base pairs from the focal site. Furthermore, higher-order interactions among flanking positions significantly modulate mutation rates, though their effect size diminishes as the order of interaction increases. Additionally, these mutational signatures are highly consistent across diverse ancestral groups. Together, these findings provide a comprehensive map of how local genomic architecture shapes the mutational landscape.

evolutionary biology↗

bioRxiv2026

Scalable ARG-free Detection of Denisovan-mediated Superarchaic Introgression Reveals Heterogeneous Patterns across Populations

Ghost introgression from unsampled hominin lineages has emerged as an increasingly important component of human evolutionary history. Recent studies suggest that deeply divergent hominin lineages may have contributed ancestry either directly to modern humans or indirectly through Denisovan introgression, while inference remains difficult due to few reference genomes, weak signal, and uncertainty in reconstructing deep genealogies. Here we show analytically and through simulations that Denisovan-mediated superarchaic introgression produces predictable shifts in local coalescent depth that can be approximated by scalable summary statistics, particularly pairwise sequence divergence, suggesting that substantial information regarding deeply divergent ancestry is preserved in sequence variations without explicit reconstruction of genealogies. Leveraging this insight, we develop DEEP (Deep ancestry Estimation through Efficient Proxies), an ARG-free neural-network framework for identifying candidate regions of superarchaic ancestry. DEEP retains detectable power at low false positive rates across a broad range of demographic parameter space, remains scalable and recovers signals from small sample sizes. Applying DEEP to Oceanians, Tibetans, and Han Chinese, we identify approximately 0.4-0.6% of genomic windows with evidence of superarchaic ancestry. Candidate regions show both substantial overlap and notable heterogeneity across populations, with repeated enrichment near the HLA locus across all populations, suggesting immune-related regions recurrently retain deeply divergent ancestry.

evolutionary biology↗

bioRxiv2026

Optimal Reference Panel Design in Ancient DNA Imputation from Coalescent Theory, Simulation, and Real Data Application with an Ancient Reference Panel

Imputation is widely used in the ancient DNA (aDNA) field to determine which phenotypically important alleles ancient individuals carried, to study natural selection, and to detect segments of the genome that are shared between individuals identical by descent. However, rare variant imputation is less accurate, and rare variants tend to be excluded from downstream analyses. State-of-the-art imputation methods leverage large reference panels, improving rare variant accuracy in modern targets. However, it is unclear how to identify optimal panels for aDNA targets. It seems plausible that aDNA reference panels would improve imputation of aDNA, but no such panels have been assembled or tested. We leveraged analytical results from coalescent theory and complementary simulations to evaluate both performance of large modern panels, and ancient panels impact on aDNA imputation. For modern panels, sample sizes as small as 5,000 saturate imputation performance and model misspecifications in standard imputation algorithms increase imputation error for rare and intermediate frequency variants. For instance, for European hunter-gatherers, non-reference imputed variants with derived allele frequency less than at least 2% should be removed. Including ancient genomes in a modern reference panel substantially improved imputation accuracy in analytical modelling and simulations, particularly, for rare variants and older samples from groups with low effective population size. We assembled a joint reference panel with 1000 Genomes and 95 ancient samples and used it to impute 95 downsampled genomes, finding modest gains in imputation performance. This approach can rescue rare variants typically discarded from current imputation pipelines and may prove useful as the number of ancient samples increases.

genomics↗

bioRxiv2025

A Benchmark of Modern Statistical Phasing Methods

Modern statistical phasing methods efficiently and accurately infer haplotype phase in large genomic samples, and their performance is critical for downstream analyses. However, rigorously evaluating phasing accuracy remains challenging. Here we demonstrate the use of synthetic diploids generated from male X chromosome sequences to benchmark and compare three commonly-used phasing methods: Beagle 5.4, SHAPEIT 4, and Eagle v2.4.1. Our evaluation reveals highly correlated error rates across all methods, although we observe distinct variation among methods within error types. Specifically, Eagle v2.4.1 is characterized by a higher frequency of switch errors, whereas SHAPEIT 4 displays a higher rate of flip (double-switch) errors. These patterns are consistent across diverse populations. Additionally, we observe an enrichment of errors at both CpG sites and rare variant sites, particularly for flip errors. Validating these results in autosomal trio data, we observe similar trends between the methods and error patterns, though with an increased flip error rate, a discrepancy that may be biased by genotype error.

genomics↗

bioRxiv2025

MetaGLIMPSE: Meta Imputation of Low Coverage Sequencing Data for Modern and Ancient Genomes

The advent of efficient and accurate imputation for low coverage sequencing offers an unbiased alternative to SNP array imputation, increasing the accuracy of rare variant imputation across all populations. Since imputation accuracy generally increases with larger reference panels and closer ancestry match between target and reference samples, leveraging imputation from multiple reference panels improves imputation accuracy; however, individual reference panel genotypes are often privacy protected. Meta-imputation bypasses individual level data by combining single-panel imputed genotypes through estimating panel and marker specific weights. We present a novel meta-imputation method, MetaGLIMPSE, that combines estimates from multiple reference panels for low coverage sequencing imputation. Across all our scenarios, for both modern and ancient DNA samples, MetaGLIMPSE consistently outperforms the best single panel imputation for coverages 0.1x - 8x and across all minor allele frequencies, equaling the combined panel imputation for some parameters. Finally, MetaGLIMPSE is computationally efficient, meta-imputing 500 whole genomes in 16% of the time of GLIMPSE2.

genomics↗

bioRxiv2023

FICTURE: Scalable segmentation-free analysis of sub-micron resolution spatial transcriptomics

Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. Analysis of high-resolution ST data relies heavily on image-based cell segmentation or gridding, which often fails in complex tissues due to diversity and irregularity of cell size and shape. Existing segmentation-free analysis methods scale only to small regions and a small number of genes, limiting their utility in high-throughput studies. Here we present FICTURE, a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron resolution spatial coordinates. FICTURE is orders of magnitude more efficient than existing methods and it is compatible with both sequencing- and imaging-based ST data. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular, and lipid-laden areas in real data where previous methods failed. FICTUREs cross-platform generality, scalability, and precision make it a powerful tool for exploring high-resolution ST.

bioinformatics↗

bioRxiv2023

Inferring CpG methylation signatures accumulated along human history from genetic variation catalogs

Understanding the DNA methylation patterns in the human genome is a key step to decipher gene regulatory mechanisms and model mutation rate heterogeneity in the human genome. We analyzed existing whole genome bisulfite sequencing (WGBS) data across tissues and large genetic variation catalogs and observed that 93.2% CpGs hyper-methylated in sperm are polymorphic. Moreover, methylation status of CpGs is spatially correlated, as 94% of CpG pairs within 1kb share the same methylation status. Leveraging only these properties, we infer germline CpG methylation in the human population using a new method, Methylation Hidden Markov Model (MHMM), and the polymorphism data from TOPMed. Our inference is orthogonal to WGBS-based experimental results; still we observed 90% concordance with human sperm WGBS while overcoming several challenges in that data: We inferred methylation status for [~] 721, 000 CpG sites that were missing from WGBS due to low coverage, and show that 42.2% of CpGs with allele frequency > 5% are hyper-methylated in the population but could not be captured in WGBS due to sample genetic variation. Our results provide a unique resource for CpG methylation levels in germline cells complementary to the existing WGBS-based measures, and can thus be leveraged to enhance analysis such as annotating regulatory and inactivated genomic regions in the germline.

genetics↗

bioRxiv2020

Why are rare variants hard to impute? Coalescent models reveal theoretical limits in existing algorithms.

Genotype imputation is an indispensable step in human genetic studies. Large reference panels with deeply sequenced genomes now allow interrogating variants with minor allele frequency < 1% without sequencing. While it is critical to consider limits of this approach, imputation methods for rare variants have only done so empirically; the theoretical basis of their imputation accuracy has not been explored. To provide theoretical consideration of imputation accuracy under the current imputation framework, we develop a coalescent model of imputing rare variants, leveraging the joint genealogy of the sample to be imputed and reference individuals. We show that broadly used imputation algorithms includes model misspecifications about this joint genealogy that limit the ability to correctly impute rare variants. We develop closed-form solutions for the probability distribution of this joint genealogy and quantify the inevitable error rate resulting from the model misspecification across a range of allele frequencies and reference sample sizes. We show that the probability of a falsely imputed minor allele decreases with reference sample size, but the proportion of falsely imputed minor alleles mostly depends on the allele count in the reference sample. We summarize the impact of this error on genotype imputation on association tests by calculating the r2 between imputed and true genotype and show that even when modeling other sources of error, the impact of the model misspecification have a significant impact on the r2 of rare variants. To evaluate these predictions in practice, we compare the imputation of the same dataset across imputation panels of different sizes. While this empirical imputation accuracy is substantially lower than our theoretical prediction, modeling misspecification seems to further decrease imputation accuracy for variants with low allele counts in the reference. These results provide a framework for developing new imputation algorithms and for interpreting rare variant association analyses.

genetics↗