RESEARCH ARTICLE Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes Jerome Kelleher1*, Alison M Etheridge2, Gilean McVean1,2,3 1 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom, 2 Department of Statistics, University of Oxford, Oxford, United Kingdom, 3 Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom *
[email protected] Abstract A central challenge in the analysis of genetic variation is to provide realistic genome simula- tion across millions of samples. Present day coalescent simulations do not scale well, or use approximations that fail to capture important long-range linkage properties. Analysing the results of simulations also presents a substantial challenge, as current methods to store OPEN ACCESS genealogies consume a great deal of space, are slow to parse and do not take advantage of Citation: Kelleher J, Etheridge AM, McVean G shared structure in correlated trees. We solve these problems by introducing sparse trees (2016) Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes. PLoS and coalescence records as the key units of genealogical analysis. Using these tools, exact Comput Biol 12(5): e1004842. doi:10.1371/journal. simulation of the coalescent with recombination for chromosome-sized regions over hun- pcbi.1004842 dreds of thousands of samples is possible, and substantially faster than present-day Editor: Yun S. Song, UC Berkeley, UNITED STATES approximate methods. We can also analyse the results orders of magnitude more quickly Received: December 10, 2015 than with existing methods. Accepted: March 2, 2016 Published: May 4, 2016 Copyright: © 2016 Kelleher et al.