Biology subjects

Jeffery, B.

Publications and source records attributed to Jeffery, B..

7 recordsLinked to original sources

bioRxiv2026

Tracing the evolutionary histories of ultra-rare variants using variational dating of large ancestral recombination graphs

Ultra-rare variants dominate whole-genome sequencing datasets, yet their interpretation is limited by allele frequency, which provides little information at very low counts and is highly sensitive to uneven ancestry representation. Allele age offers an ancestry-agnostic alternative but existing methods do not scale to biobank-sized cohorts. Here we present a scalable variational algorithm for dating Ancestral Recombination Graphs (ARGs), implemented in tsdate, together with new distributed methods enabling practical biobank-scale ARG inference using tsinfer. Applied to 47,535 genomes from the Genomics England 100,000 Genomes Project, we infer contiguous ARGs spanning 206 Mb and estimate ages for 23.2 million variants, including 11.8 million singletons. ARG-based allele ages remain accurate under extreme sampling imbalance and, in real data, reveal signatures of purifying selection and clinically relevant heterogeneity among variants with identical observed frequencies. Estimates for recent mutations are precise only at large sample sizes, highlighting the information accessible in the haplotype structure of large datasets. Biobank-scale ARGs therefore enable robust, ancestry-agnostic age estimation for ultra-rare variation with broad utility for statistical and clinical genomics.

genetics↗

bioRxiv2025

Tsbrowse: an interactive browser for Ancestral Recombination Graphs

Ancestral Recombination Graphs (ARGs) represent the interwoven paths of genetic ancestry for a set of recombining sequences. The ability to capture the evolutionary history of samples makes ARGs valuable in a wide range of applications in population and statistical genetics. ARG-based approaches are increasingly becoming a part of genetic data analysis pipelines due to breakthroughs enabling ARG inference at biobank-scale. However, there is a lack of visualisation tools, which are crucial for validating inferences and generating hypotheses. We present tsbrowse, an open-source Python web-app for the interactive visualisation of the fundamental building-blocks of ARGs, i.e., nodes, edges and mutations. We demonstrate the application of tsbrowse to various data sources and scenarios, and highlight its key features of browsability along the genome, user interactivity, and scalability to very large sample sizes. AvailabilityPython package: https://pypi.org/project/tsbrowse/, Development version: https://github.com/tskit.dev/tsbrowse, Documentation: https://tskit.dev/tsbrowse/docs/

genomics↗

bioRxiv2024

Analysis-ready VCF at Biobank scale using Zarr

BackgroundVariant Call Format (VCF) is the standard file format for interchanging genetic variation data and associated quality control metrics. The usual row-wise encoding of the VCF data model (either as text or packed binary) emphasises efficient retrieval of all data for a given variant, but accessing data on a field or sample basis is inefficient. Biobank scale datasets currently available consist of hundreds of thousands of whole genomes and hundreds of terabytes of compressed VCF. Row-wise data storage is fundamentally unsuitable and a more scalable approach is needed. ResultsZarr is a format for storing multi-dimensional data that is widely used across the sciences, and is ideally suited to massively parallel processing. We present the VCF Zarr specification, an encoding of the VCF data model using Zarr, along with fundamental software infrastructure for efficient and reliable conversion at scale. We show how this format is far more efficient than standard VCF based approaches, and competitive with specialised methods for storing genotype data in terms of compression ratios and single-threaded calculation performance. We present case studies on subsets of three large human datasets (Genomics England: n=78,195; Our Future Health: n=651,050; All of Us: n=245,394) along with whole genome datasets for Norway Spruce (n=1,063) and SARS-CoV-2 (n=4,484,157). We demonstrate the potential for VCF Zarr to enable a new generation of high-performance and cost-effective applications via illustrative examples using cloud computing and GPUs. ConclusionsLarge row-encoded VCF files are a major bottleneck for current research, and storing and processing these files incurs a substantial cost. The VCF Zarr specification, building on widely-used, open-source technologies has the potential to greatly reduce these costs, and may enable a diverse ecosystem of next-generation tools for analysing genetic variation data directly from cloud-based object stores, while maintaining compatibility with existing file-oriented workflows. Key PointsO_LIVCF is widely supported, and the underlying data model entrenched in bioinformatics pipelines. C_LIO_LIThe standard row-wise encoding as text (or binary) is inherently inefficient for large-scale data processing. C_LIO_LIThe Zarr format provides an efficient solution, by encoding fields in the VCF separately in chunk-compressed binary format. C_LI

bioinformatics↗

bioRxiv2023

Towards Pandemic-Scale Ancestral Recombination Graphs of SARS-CoV-2

Millions of SARS-CoV-2 genome sequences were collected during the COVID-19 pandemic, forming a dataset of unprecedented richness. Estimated genealogies are fundamental to understanding this ocean of data and form the primary input to many downstream analyses. A basic assumption of methods to infer genealogies from viral genetic data is that recombination is negligible and the genealogy is a tree. However, recombinant lineages have risen to global prevalence, and simple tree representations are therefore incomplete and potentially misleading. We present sc2ts, a method to infer reticulate genealogies as an Ancestral Recombination Graph (ARG) in real time at pandemic scale. We infer an ARG for 2.48 million SARS-CoV-2 genomes, which leverages the widely used tskit software ecosystem to support further analyses and visualisation. This rich and validated resource clarifies the relationships among recombinant lineages, quantifies the rate of recombination over time, and provides a lower bound on detectable recombination.

evolutionary biology↗

bioRxiv2022

On the Genes, Genealogies, and Geographies of Quebec

Population genetic models only provide coarse representations of real-world ancestry. We use a pedigree compiled from four million parish records and genotype data from 2,276 French and 20,451 French Canadian (FC) individuals, to finely model and trace FC ancestry through space and time. The loss of ancestral French population structure and the appearance of spatial and regional structure highlights a wide range of population expansion models. Geographic features shaped migrations throughout, and we find enrichments for migration, genetic and genealogical relatedness patterns within river networks across Quebec regions. Finally, we provide a freely accessible simulated whole-genome sequence dataset with spatiotemporal metadata for 1,426,749 individuals reflecting intricate FC population structure. Such realistic populations-scale simulations provide new opportunities to investigate population genetics at an unprecedented resolution. Lay SummaryWe all share common ancestors ranging from a couple generations ago to hundreds of thousands of years ago. The genetic differences between individuals today mostly depends on how closely related they are. The only problem is that the actual genealogies that relate all of us are often forgotten over time. Some geneticists have tried to come up with simple models of our shared ancestry but they dont really explain the full, rich history of humanity. Our study uses a multi-institutional project in Quebec that has digitized parish records into a single unified genealogical database that dates back to the arrival of the first French settlers four hundred years ago. This genealogy traces the ancestry of millions of French-Canadian and we have used it to build a very high resolution genetic map. We used this genetic map to study in detail how certain historical events, and landscapes have influenced the genomes of French-Canadians today. One-Sentence SummaryWe present an accurate and high resolution spatiotemporal model of genetic variation in a founder population.

genomics↗

bioRxiv2021

Efficient ancestry and mutation simulation with msprime 1.0

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large number of specialised simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and tskit library. We summarise msprimes many features, and show that its performance is excellent, often many times faster and more memory efficient than specialised alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

genetics↗

bioRxiv2021

A unified genealogy of modern and ancient genomes

The sequencing of modern and ancient genomes from around the world has revolutionised our understanding of human history and evolution1,2. However, the general problem of how best to characterise the full complexity of ancestral relationships from the totality of human genomic variation remains unsolved. Patterns of variation in each data set are typically analysed independently, and often using parametric models or data reduction techniques that cannot capture the full complexity of human ancestry3,4. Moreover, variation in sequencing technology5,6, data quality7 and in silico processing8,9, coupled with complexities of data scale10, limit the ability to integrate data sources. Here, we introduce a non-parametric approach to inferring human genealogical history that overcomes many of these challenges and enables us to build the largest genealogy of both modern and ancient humans yet constructed. The genealogy provides a lossless and compact representation of multiple datasets, addresses the challenges of missing and erroneous data, and benefits from using ancient samples to constrain and date relationships. Using simulations and empirical analyses, we demonstrate the power of the method to recover relationships between individuals and populations, as well as to identify descendants of ancient samples. Finally, we show how applying a simple non-parametric estimator of ancestor geographical location to the inferred genealogy recapitulates key events in human history. Our results demonstrate that whole-genome genealogies are a powerful means of synthesising genetic data and provide rich insights into human evolution.

genomics↗