@article {Baumdicker2021.08.31.457499, author = {Franz Baumdicker and Gertjan Bisschop and Daniel Goldstein and Graham Gower and Aaron P. Ragsdale and Georgia Tsambos and Sha Zhu and Bjarki Eldon and Castedo E. Ellerman and Jared G. Galloway and Ariella L. Gladstein and Gregor Gorjanc and Bing Guo and Ben Jeffery and Warren W. Kretzschmar and Konrad Lohse and Michael Matschiner and Dominic Nelson and Nathaniel S. Pope and Consuelo D. Quinto-Cort{\'e}s and Murillo F. Rodrigues and Kumar Saunack and Thibaut Sellinger and Kevin Thornton and Hugo van Kemenade and Anthony W. Wohns and H. Yan Wong and Simon Gravel and Andrew D. Kern and Jere Koskela and Peter L. Ralph and Jerome Kelleher}, title = {Efficient ancestry and mutation simulation with msprime 1.0}, elocation-id = {2021.08.31.457499}, year = {2021}, doi = {10.1101/2021.08.31.457499}, publisher = {Cold Spring Harbor Laboratory}, abstract = {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 msprime{\textquoteright}s 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.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2021/09/21/2021.08.31.457499}, eprint = {https://www.biorxiv.org/content/early/2021/09/21/2021.08.31.457499.full.pdf}, journal = {bioRxiv} }