ArticleBioinformatics (Oxford, England)2024
tstrait: a quantitative trait simulator for ancestral recombination graphs.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- ARGscape: a modular, interactive tool for manipulation of spatiotemporal ancestral recombination graphs.Bioinformatics (Oxford, England) · 2026Article
- Genetic prediction with ARG-powered linear algebra.Genetics · 2026Article
- Fast phenotype simulation for genotype representation graphs.Bioinformatics advances · 2026Article
- A Pandemic-Scale Ancestral Recombination Graph for SARS-CoV-2.bioRxiv : the preprint server for biology · 2025Article
- ARGscape: A modular, interactive tool for manipulation of spatiotemporal ancestral recombination graphs.ArXiv · 2025Article
- Tsbrowse: an interactive browser for ancestral recombination graphs.Bioinformatics (Oxford, England) · 2025Article
- Admixed and single-continental genome segments of the same ancestry have distinct linkage disequilibrium patterns.Genome biology · 2025Article
- Analysis-ready VCF at Biobank scale using Zarr.bioRxiv : the preprint server for biology · 2025Article
- Analysis-ready VCF at Biobank scale using Zarr.GigaScience · 2025Article
- Inference and applications of ancestral recombination graphs.Nature reviews. Genetics · 2025Review
- Article
- A general and efficient representation of ancestral recombination graphs.bioRxiv : the preprint server for biology · 2024Article
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3 authors.
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Abstract
summaryAncestral recombination graphs (ARGs) encode the ensemble of correlated genealogical trees arising from recombination in a compact and efficient structure and are of fundamental importance in population and statistical genetics. Recent breakthroughs have made it possible to simulate and infer ARGs at biobank scale, and there is now intense interest in using ARG-based methods across a broad range of applications, particularly in genome-wide association studies (GWAS). Sophisticated methods exist to simulate ARGs using population genetics models, but there is currently no software to simulate quantitative traits directly from these ARGs. To apply existing quantitative trait simulators users must export genotype data, losing important information about ancestral processes and producing prohibitively large files when applied to the biobank-scale datasets currently of interest in GWAS. We present tstrait, an open-source Python library to simulate quantitative traits on ARGs, and show how this user-friendly software can quickly simulate phenotypes for biobank-scale datasets on a laptop computer. AVAILABILITY AND IMPLEMENTATION: tstrait is available for download on the Python Package Index. Full documentation with examples and workflow templates is available on https://tskit.dev/tstrait/docs/, and the development version is maintained on GitHub (https://github.com/tskit-dev/tstrait).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.